https://pardee.du.edu/w/api.php?action=feedcontributions&user=Wikiadmin&feedformat=atomWiki - User contributions [en]2020-01-29T04:23:16ZUser contributionsMediaWiki 1.23.13//pardee.du.edu/wiki/Version_notes_7.37_(October_2018)Version notes 7.37 (October 2018)2018-10-12T15:35:39Z<p>Wikiadmin: </p>
<hr />
<div>= Recent model updates =<br />
<br />
*New education quality variables&nbsp;within education model<br />
**Note: this is a recent model update still in development. Please do not use for formal analysis or publication.<br />
**See the flow chart overview of education quality&nbsp;[https://pardee.du.edu/wiki/Education#Education:_Learning_Quality_Scores here]<br />
**See the equations for education quality&nbsp;[https://pardee.du.edu/wiki/Education#Education_Equations:_Learning_Quality.C2.A0 here]<br />
*New labor model - detailed documentation&nbsp;[https://pardee.du.edu/wiki/Labor here]<br />
**Note: this is a recent model update still in development. Please do not use for formal analysis or publication.<br />
**Note: this model is turned ''off'' in the IFs Base Case<br />
*New drug demand module&nbsp;within the Socio-Political model<br />
**Note: this is a recent model update still in development. Please do not use for formal analysis or publication.<br />
**See the drug demand flow chart&nbsp;[https://pardee.du.edu/wiki/Socio-Political#Drug_Demand here]<br />
**See the drug demand equations&nbsp;[https://pardee.du.edu/wiki/Socio-Political#Drug_Model_Equations here]<br />
*New societal violence module&nbsp;within the Socio-Political model<br />
**Note: this is a recent model update still in development. Please do not use for formal analysis or publication.<br />
**See the violence&nbsp;flow chart&nbsp;[https://pardee.du.edu/wiki/Socio-Political#Violence here]<br />
**See the violence&nbsp;equations&nbsp;[https://pardee.du.edu/wiki/Socio-Political#Violence_Model_Equations here]<br />
<br />
= Recent data updates (since January 2018) =<br />
<br />
{| border="1" cellpadding="0" cellspacing="0" width="471"<br />
|-<br />
| height="20" width="347" | '''Source'''<br />
| width="124" | '''Number of series&nbsp;'''<br />
|-<br />
| height="20" | AQU (AQUASTAT) BATCH PULL<br />
| align="right" | 51<br />
|-<br />
| height="20" | Barro-Lee<br />
| align="right" | 22<br />
|-<br />
| height="20" | BP’s Statistical Review of World Energy 2016<br />
| align="right" | 6<br />
|-<br />
| height="20" | Carbon Dioxide Information Analysis Center<br />
| align="right" | 1<br />
|-<br />
| height="20" | FAO<br />
| align="right" | 38<br />
|-<br />
| height="20" | Freedom House<br />
| align="right" | 1<br />
|-<br />
| height="20" | IFs calculations (drugs, education quality, Minerva)<br />
| align="right" | 6<br />
|-<br />
| height="20" | IHME<br />
| align="right" | 51<br />
|-<br />
| height="20" | IMF GFS<br />
| align="right" | 8<br />
|-<br />
| height="20" | IMF World Economic Outlook 2017<br />
| align="right" | 2<br />
|-<br />
| height="20" | JMP<br />
| align="right" | 5<br />
|-<br />
| height="20" | PovCalNet<br />
| align="right" | 1<br />
|-<br />
| height="20" | UNAIDS<br />
| align="right" | 6<br />
|-<br />
| height="20" | UNESCO Institute for Statistics (UIS)<br />
| align="right" | 97<br />
|-<br />
| height="20" | UNODC<br />
| align="right" | 4<br />
|-<br />
| height="20" | UNPD<br />
| align="right" | 3<br />
|-<br />
| height="20" | WDI<br />
| align="right" | 392<br />
|}<br />
<br />
= Bug fixes/updates since 7.36 (internal version of IFs) =<br />
<br />
*Changes&nbsp;in&nbsp;educational attainment model<br />
**Updated regression for estimating&nbsp;tertiary graduation rate - rises more slowly in many countries<br />
*Fixed bugs associated with sub-regionalizing&nbsp;countries<br />
*Sub-regional files for Brazil included<br />
*Updated informal labor variable (LABINFORMALSHR)&nbsp;initialization to take ILO/WEIGO data first, then World Bank data to fill holes<br />
*7.37 includes scenario files for current UNDP projects<br />
*Calculation of the homicide index (HOMICIDEINDEX)&nbsp;now on the basis of total number of deaths as opposed to the death rate. This fixes the problem of the index value looking inflated for Honduras<br />
*Display issues: bug in the way population variables are displayed in the flexible display was fixed, radial graph bug fixed, development priorities display fixed<br />
<br />
&nbsp;</div>Wikiadmin//pardee.du.edu/wiki/Version_notes_7.37_(October_2018)Version notes 7.37 (October 2018)2018-10-12T15:34:26Z<p>Wikiadmin: </p>
<hr />
<div>= Recent model updates =<br />
<br />
*New education quality variables&nbsp;within education model<br />
**[https://pardee.du.edu/wiki/Labor ]Note: this is a recent model update still in development. Please do not use for formal analysis or publication.<br />
**See the flow chart overview of education quality&nbsp;[https://pardee.du.edu/wiki/Education#Education:_Learning_Quality_Scores here]<br />
**See the equations for education quality&nbsp;[https://pardee.du.edu/wiki/Education#Education_Equations:_Learning_Quality.C2.A0 here]<br />
*New labor model - detailed documentation&nbsp;[https://pardee.du.edu/wiki/Labor here]<br />
**[https://pardee.du.edu/wiki/Labor ]Note: this is a recent model update still in development. Please do not use for formal analysis or publication.<br />
**Note: this model is turned ''off'' in the IFs Base Case<br />
*New drug demand module&nbsp;within the Socio-Political model<br />
**Note: this is a recent model update still in development. Please do not use for formal analysis or publication.<br />
**See the drug demand flow chart&nbsp;[https://pardee.du.edu/wiki/Socio-Political#Drug_Demand here]<br />
**See the drug demand equations&nbsp;[https://pardee.du.edu/wiki/Socio-Political#Drug_Model_Equations here]<br />
*New societal violence module&nbsp;within the Socio-Political model<br />
**Note: this is a recent model update still in development. Please do not use for formal analysis or publication.<br />
**See the violence&nbsp;flow chart&nbsp;[https://pardee.du.edu/wiki/Socio-Political#Violence here]<br />
**See the violence&nbsp;equations&nbsp;[https://pardee.du.edu/wiki/Socio-Political#Violence_Model_Equations here]<br />
<br />
= Recent data updates (since January 2018) =<br />
<br />
{| border="1" cellpadding="0" cellspacing="0" width="471"<br />
|-<br />
| height="20" width="347" | '''Source'''<br />
| width="124" | '''Number of series&nbsp;'''<br />
|-<br />
| height="20" | AQU (AQUASTAT) BATCH PULL<br />
| align="right" | 51<br />
|-<br />
| height="20" | Barro-Lee<br />
| align="right" | 22<br />
|-<br />
| height="20" | BP’s Statistical Review of World Energy 2016<br />
| align="right" | 6<br />
|-<br />
| height="20" | Carbon Dioxide Information Analysis Center<br />
| align="right" | 1<br />
|-<br />
| height="20" | FAO<br />
| align="right" | 38<br />
|-<br />
| height="20" | Freedom House<br />
| align="right" | 1<br />
|-<br />
| height="20" | IFs calculations (drugs, education quality, Minerva)<br />
| align="right" | 6<br />
|-<br />
| height="20" | IHME<br />
| align="right" | 51<br />
|-<br />
| height="20" | IMF GFS<br />
| align="right" | 8<br />
|-<br />
| height="20" | IMF World Economic Outlook 2017<br />
| align="right" | 2<br />
|-<br />
| height="20" | JMP<br />
| align="right" | 5<br />
|-<br />
| height="20" | PovCalNet<br />
| align="right" | 1<br />
|-<br />
| height="20" | UNAIDS<br />
| align="right" | 6<br />
|-<br />
| height="20" | UNESCO Institute for Statistics (UIS)<br />
| align="right" | 97<br />
|-<br />
| height="20" | UNODC<br />
| align="right" | 4<br />
|-<br />
| height="20" | UNPD<br />
| align="right" | 3<br />
|-<br />
| height="20" | WDI<br />
| align="right" | 392<br />
|}<br />
<br />
= Bug fixes/updates since 7.36 (internal version of IFs) =<br />
<br />
*Changes&nbsp;in&nbsp;educational attainment model<br />
**Updated regression for estimating&nbsp;tertiary graduation rate - rises more slowly in many countries<br />
*Fixed bugs associated with sub-regionalizing&nbsp;countries<br />
*Sub-regional files for Brazil included<br />
*Updated informal labor variable (LABINFORMALSHR)&nbsp;initialization to take ILO/WEIGO data first, then World Bank data to fill holes<br />
*7.37 includes scenario files for current UNDP projects<br />
*Calculation of the homicide index (HOMICIDEINDEX)&nbsp;now on the basis of total number of deaths as opposed to the death rate. This fixes the problem of the index value looking inflated for Honduras<br />
*Display issues: bug in the way population variables are displayed in the flexible display was fixed, radial graph bug fixed, development priorities display fixed<br />
<br />
&nbsp;</div>Wikiadmin//pardee.du.edu/wiki/Version_notes_7.37_(October_2018)Version notes 7.37 (October 2018)2018-10-11T16:40:02Z<p>Wikiadmin: </p>
<hr />
<div>= Recent model updates =<br />
<br />
*New education quality variables&nbsp;within education model<br />
**See the flow chart overview of education quality&nbsp;[https://pardee.du.edu/wiki/Education#Education:_Learning_Quality_Scores here]<br />
**See the equations for education quality&nbsp;[https://pardee.du.edu/wiki/Education#Education_Equations:_Learning_Quality.C2.A0 here]<br />
*New labor model - detailed documentation&nbsp;[https://pardee.du.edu/wiki/Labor here]<br />
**Note: this model is turned ''off'' in the IFs Base Case<br />
*New drug demand module&nbsp;within the Socio-Political model<br />
**See the drug demand flow chart&nbsp;[https://pardee.du.edu/wiki/Socio-Political#Drug_Demand here]<br />
**See the drug demand equations&nbsp;[https://pardee.du.edu/wiki/Socio-Political#Drug_Model_Equations here]<br />
*New societal violence module&nbsp;within the Socio-Political model<br />
**See the violence&nbsp;flow chart&nbsp;[https://pardee.du.edu/wiki/Socio-Political#Violence here]<br />
**See the violence&nbsp;equations&nbsp;[https://pardee.du.edu/wiki/Socio-Political#Violence_Model_Equations here]<br />
<br />
= Recent data updates (since January 2018) =<br />
<br />
{| border="1" cellpadding="0" cellspacing="0" width="471"<br />
|-<br />
| height="20" width="347" | '''Source'''<br />
| width="124" | '''Number of series&nbsp;'''<br />
|-<br />
| height="20" | AQU (AQUASTAT) BATCH PULL<br />
| align="right" | 51<br />
|-<br />
| height="20" | Barro-Lee<br />
| align="right" | 22<br />
|-<br />
| height="20" | BP’s Statistical Review of World Energy 2016<br />
| align="right" | 6<br />
|-<br />
| height="20" | Carbon Dioxide Information Analysis Center<br />
| align="right" | 1<br />
|-<br />
| height="20" | FAO<br />
| align="right" | 38<br />
|-<br />
| height="20" | Freedom House<br />
| align="right" | 1<br />
|-<br />
| height="20" | IFs calculations (drugs, education quality, Minerva)<br />
| align="right" | 6<br />
|-<br />
| height="20" | IHME<br />
| align="right" | 51<br />
|-<br />
| height="20" | IMF GFS<br />
| align="right" | 8<br />
|-<br />
| height="20" | IMF World Economic Outlook 2017<br />
| align="right" | 2<br />
|-<br />
| height="20" | JMP<br />
| align="right" | 5<br />
|-<br />
| height="20" | PovCalNet<br />
| align="right" | 1<br />
|-<br />
| height="20" | UNAIDS<br />
| align="right" | 6<br />
|-<br />
| height="20" | UNESCO Institute for Statistics (UIS)<br />
| align="right" | 97<br />
|-<br />
| height="20" | UNODC<br />
| align="right" | 4<br />
|-<br />
| height="20" | UNPD<br />
| align="right" | 3<br />
|-<br />
| height="20" | WDI<br />
| align="right" | 392<br />
|}<br />
<br />
= Bug fixes/updates since 7.36 (internal version of IFs) =<br />
<br />
*Changes&nbsp;in&nbsp;educational attainment model<br />
**Updated regression for estimating&nbsp;tertiary graduation rate - rises more slowly in many countries<br />
*Fixed bugs associated with sub-regionalizing&nbsp;countries<br />
*Sub-regional files for Brazil included<br />
*Updated informal labor variable (LABINFORMALSHR)&nbsp;initialization to take ILO/WEIGO data first, then World Bank data to fill holes<br />
*7.37 includes scenario files for current UNDP projects<br />
*Calculation of the homicide index (HOMICIDEINDEX)&nbsp;now on the basis of total number of deaths as opposed to the death rate. This fixes the problem of the index value looking inflated for Honduras<br />
*Display issues: bug in the way population variables are displayed in the flexible display was fixed, radial graph bug fixed, development priorities display fixed<br />
<br />
&nbsp;</div>Wikiadmin//pardee.du.edu/wiki/Version_notes_7.37_(October_2018)Version notes 7.37 (October 2018)2018-10-11T16:35:42Z<p>Wikiadmin: </p>
<hr />
<div>= Recent model updates =<br />
<br />
*New education quality variables&nbsp;within education model<br />
**See the flow chart overview of education quality&nbsp;[https://pardee.du.edu/wiki/Education#Education:_Learning_Quality_Scores here]<br />
**See the equations for education quality&nbsp;[https://pardee.du.edu/wiki/Education#Education_Equations:_Learning_Quality.C2.A0 here]<br />
*New labor model - detailed documentation&nbsp;[https://pardee.du.edu/wiki/Labor here]<br />
*New drug demand module&nbsp;within the Socio-Political model<br />
**See the drug demand flow chart&nbsp;[https://pardee.du.edu/wiki/Socio-Political#Drug_Demand here]<br />
**See the drug demand equations&nbsp;[https://pardee.du.edu/wiki/Socio-Political#Drug_Model_Equations here]<br />
*New societal violence module&nbsp;within the Socio-Political model<br />
**See the violence&nbsp;flow chart&nbsp;[https://pardee.du.edu/wiki/Socio-Political#Violence here]<br />
**See the violence&nbsp;equations&nbsp;[https://pardee.du.edu/wiki/Socio-Political#Violence_Model_Equations here]<br />
<br />
= Recent data updates (since January 2018) =<br />
<br />
{| border="1" cellpadding="0" cellspacing="0" width="471"<br />
|-<br />
| height="20" width="347" | '''Source'''<br />
| width="124" | '''Number of series&nbsp;'''<br />
|-<br />
| height="20" | AQU (AQUASTAT) BATCH PULL<br />
| align="right" | 51<br />
|-<br />
| height="20" | Barro-Lee<br />
| align="right" | 22<br />
|-<br />
| height="20" | BP’s Statistical Review of World Energy 2016<br />
| align="right" | 6<br />
|-<br />
| height="20" | Carbon Dioxide Information Analysis Center<br />
| align="right" | 1<br />
|-<br />
| height="20" | FAO<br />
| align="right" | 38<br />
|-<br />
| height="20" | Freedom House<br />
| align="right" | 1<br />
|-<br />
| height="20" | IFs calculations (drugs, education quality, Minerva)<br />
| align="right" | 6<br />
|-<br />
| height="20" | IHME<br />
| align="right" | 51<br />
|-<br />
| height="20" | IMF GFS<br />
| align="right" | 8<br />
|-<br />
| height="20" | IMF World Economic Outlook 2017<br />
| align="right" | 2<br />
|-<br />
| height="20" | JMP<br />
| align="right" | 5<br />
|-<br />
| height="20" | PovCalNet<br />
| align="right" | 1<br />
|-<br />
| height="20" | UNAIDS<br />
| align="right" | 6<br />
|-<br />
| height="20" | UNESCO Institute for Statistics (UIS)<br />
| align="right" | 97<br />
|-<br />
| height="20" | UNODC<br />
| align="right" | 4<br />
|-<br />
| height="20" | UNPD<br />
| align="right" | 3<br />
|-<br />
| height="20" | WDI<br />
| align="right" | 392<br />
|}<br />
<br />
= Bug fixes/updates since 7.36 (internal version of IFs) =<br />
<br />
*Changes&nbsp;in&nbsp;educational attainment model<br />
**Updated regression for estimating&nbsp;tertiary graduation rate - rises more slowly in many countries<br />
*Fixed bugs associated with sub-regionalizing&nbsp;countries<br />
*Sub-regional files for Brazil included<br />
*Updated informal labor variable (LABINFORMALSHR)&nbsp;initialization to take ILO/WEIGO data first, then World Bank data to fill holes<br />
*7.37 includes scenario files for current UNDP projects<br />
*Calculation of the homicide index (HOMICIDEINDEX)&nbsp;now on the basis of total number of deaths as opposed to the death rate. This fixes the problem of the index value looking inflated for Honduras<br />
*Display issues: bug in the way population variables are displayed in the flexible display was fixed, radial graph bug fixed, development priorities display fixed<br />
<br />
&nbsp;</div>Wikiadmin//pardee.du.edu/wiki/Version_notes_7.37_(October_2018)Version notes 7.37 (October 2018)2018-10-11T15:49:02Z<p>Wikiadmin: </p>
<hr />
<div>= Recent model updates =<br />
<br />
*New education quality variables&nbsp;within education model<br />
**See the flow chart overview of education quality&nbsp;[https://pardee.du.edu/wiki/Education#Education:_Learning_Quality_Scores here]<br />
**See the equations for education quality&nbsp;[https://pardee.du.edu/wiki/Education#Education_Equations:_Learning_Quality.C2.A0 here]<br />
*New labor model - detailed documentation&nbsp;[https://pardee.du.edu/wiki/Labor here]<br />
*New drug demand module&nbsp;within the Socio-Political model<br />
**See the drug demand flow chart&nbsp;[https://pardee.du.edu/wiki/Socio-Political#Drug_Demand here]<br />
**See the drug demand equations&nbsp;[https://pardee.du.edu/wiki/Socio-Political#Drug_Model_Equations here]<br />
*New societal violence module&nbsp;within the Socio-Political model<br />
**See the violence&nbsp;flow chart&nbsp;[https://pardee.du.edu/wiki/Socio-Political#Violence here]<br />
**See the violence&nbsp;equations&nbsp;[https://pardee.du.edu/wiki/Socio-Political#Violence_Model_Equations here]<br />
<br />
= Recent data updates (since January 2018) =<br />
<br />
{| border="1" cellpadding="0" cellspacing="0" width="471"<br />
|-<br />
| height="20" width="347" | '''Source'''<br />
| width="124" | '''Number of series&nbsp;'''<br />
|-<br />
| height="20" | AQU (AQUASTAT) BATCH PULL<br />
| align="right" | 51<br />
|-<br />
| height="20" | Barro-Lee<br />
| align="right" | 22<br />
|-<br />
| height="20" | BP’s Statistical Review of World Energy 2016<br />
| align="right" | 6<br />
|-<br />
| height="20" | Carbon Dioxide Information Analysis Center<br />
| align="right" | 1<br />
|-<br />
| height="20" | FAO<br />
| align="right" | 38<br />
|-<br />
| height="20" | Freedom House<br />
| align="right" | 1<br />
|-<br />
| height="20" | IFs calculations (drugs, education quality, Minerva)<br />
| align="right" | 6<br />
|-<br />
| height="20" | IHME<br />
| align="right" | 51<br />
|-<br />
| height="20" | IMF GFS<br />
| align="right" | 8<br />
|-<br />
| height="20" | IMF World Economic Outlook 2017<br />
| align="right" | 2<br />
|-<br />
| height="20" | JMP<br />
| align="right" | 5<br />
|-<br />
| height="20" | PovCalNet<br />
| align="right" | 1<br />
|-<br />
| height="20" | UNAIDS<br />
| align="right" | 6<br />
|-<br />
| height="20" | UNESCO Institute for Statistics (UIS)<br />
| align="right" | 97<br />
|-<br />
| height="20" | UNODC<br />
| align="right" | 4<br />
|-<br />
| height="20" | UNPD<br />
| align="right" | 3<br />
|-<br />
| height="20" | WDI<br />
| align="right" | 392<br />
|}</div>Wikiadmin//pardee.du.edu/wiki/Version_notes_7.37_(October_2018)Version notes 7.37 (October 2018)2018-10-11T15:48:26Z<p>Wikiadmin: Created page with "= Version notes 7.36 (September 2018) = = Recent model updateshttps://pardee.du.edu/w/index.php?title=Version_notes_7.36_(September_2018)&action=edit&section=1 edit = *N..."</p>
<hr />
<div>= Version notes 7.36 (September 2018) =<br />
<br />
= Recent model updates[[https://pardee.du.edu/w/index.php?title=Version_notes_7.36_(September_2018)&action=edit&section=1 edit]] =<br />
<br />
*New education quality variables&nbsp;within education model<br />
**See the flow chart overview of education quality&nbsp;[https://pardee.du.edu/wiki/Education#Education:_Learning_Quality_Scores here]<br />
**See the equations for education quality&nbsp;[https://pardee.du.edu/wiki/Education#Education_Equations:_Learning_Quality.C2.A0 here]<br />
*New labor model - detailed documentation&nbsp;[https://pardee.du.edu/wiki/Labor here]<br />
*New drug demand module&nbsp;within the Socio-Political model<br />
**See the drug demand flow chart&nbsp;[https://pardee.du.edu/wiki/Socio-Political#Drug_Demand here]<br />
**See the drug demand equations&nbsp;[https://pardee.du.edu/wiki/Socio-Political#Drug_Model_Equations here]<br />
*New societal violence module&nbsp;within the Socio-Political model<br />
**See the violence&nbsp;flow chart&nbsp;[https://pardee.du.edu/wiki/Socio-Political#Violence here]<br />
**See the violence&nbsp;equations&nbsp;[https://pardee.du.edu/wiki/Socio-Political#Violence_Model_Equations here]<br />
<br />
= Recent data updates (since January 2018)[[https://pardee.du.edu/w/index.php?title=Version_notes_7.36_(September_2018)&action=edit&section=2 edit]] =<br />
<br />
{| border="1" cellpadding="0" cellspacing="0" width="471"<br />
|-<br />
| height="20" width="347" | '''Source'''<br />
| width="124" | '''Number of series&nbsp;'''<br />
|-<br />
| height="20" | AQU (AQUASTAT) BATCH PULL<br />
| align="right" | 51<br />
|-<br />
| height="20" | Barro-Lee<br />
| align="right" | 22<br />
|-<br />
| height="20" | BP’s Statistical Review of World Energy 2016<br />
| align="right" | 6<br />
|-<br />
| height="20" | Carbon Dioxide Information Analysis Center<br />
| align="right" | 1<br />
|-<br />
| height="20" | FAO<br />
| align="right" | 38<br />
|-<br />
| height="20" | Freedom House<br />
| align="right" | 1<br />
|-<br />
| height="20" | IFs calculations (drugs, education quality, Minerva)<br />
| align="right" | 6<br />
|-<br />
| height="20" | IHME<br />
| align="right" | 51<br />
|-<br />
| height="20" | IMF GFS<br />
| align="right" | 8<br />
|-<br />
| height="20" | IMF World Economic Outlook 2017<br />
| align="right" | 2<br />
|-<br />
| height="20" | JMP<br />
| align="right" | 5<br />
|-<br />
| height="20" | PovCalNet<br />
| align="right" | 1<br />
|-<br />
| height="20" | UNAIDS<br />
| align="right" | 6<br />
|-<br />
| height="20" | UNESCO Institute for Statistics (UIS)<br />
| align="right" | 97<br />
|-<br />
| height="20" | UNODC<br />
| align="right" | 4<br />
|-<br />
| height="20" | UNPD<br />
| align="right" | 3<br />
|-<br />
| height="20" | WDI<br />
| align="right" | 392<br />
|}</div>Wikiadmin//pardee.du.edu/wiki/Version_notesVersion notes2018-10-11T15:48:07Z<p>Wikiadmin: </p>
<hr />
<div>[[Version_notes_7.36_(September_2018)|Version notes 7.36 (September 2018)]]<br />
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<div>The <span class="scayt-misspell-word" data-scayt-word="EconDash" data-scayt-lang="en_US">EconDash</span> is a set of interactive data visualizations created by the [http://pardee.du.edu/ Frederick S. <span class="scayt-misspell-word" data-scayt-word="Pardee" data-scayt-lang="en_US">Pardee</span> Center for International Futures]. The purpose of these visualizations is to allow the user to explore and better understand relevant indicators of financial and economic instability and resilience. <span class="scayt-misspell-word" data-scayt-word="EconDash" data-scayt-lang="en_US">EconDash</span> uses both [http://pardee.du.edu/wiki/EconDash#Data_Structure monadic and dyadic data] across time, and includes some forecasted variables from the [[International_Futures_(IFs)|International Futures (IFs)]] system. There is currently one public user interface available from <span class="scayt-misspell-word" data-scayt-word="EconDash" data-scayt-lang="en_US">EconDash that explores [http://pardee.du.edu/wiki/EconDash#EconDash:_Trade_Networks_Interface Trade Networks]</span>. A new Economic Vulnerability interface will be available by&nbsp;late summer 2017.<br />
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= <span style="font-size:x-large;"><span class="scayt-misspell-word" data-scayt-word="EconDash" data-scayt-lang="en_US">EconDash</span>: Trade Networks Interface</span> =<br />
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This&nbsp;dashboard&nbsp;focuses on trade networks from 1960 to 2014 and the centrality of countries in these networks. It also contains data on financial crises over the same time period. To access the dashboard [https://pardee.du.edu/econdash/ click here].<br />
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== Navigating the Interface ==<br />
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The <span class="scayt-misspell-word" data-scayt-word="EconDash" data-scayt-lang="en_US">EconDash</span> interface allows users to display and explore financial and economic crises and global trade networks along a variety of dimensions. [http://pardee.du.edu/wiki/images/b/bb/EconDashTrade_Figure_1.jpg Figure 1] is an [[File:EconDashTrade Figure 1.jpg|frame|right|600x400px|Figure 1: EconDash Annotated Main Display Page]]annotated view of the&nbsp;[http://54.149.184.118/econdash/ main display page]&nbsp;with its default settings. It provides definitions and instructions on each of the page's functions. On this page, the user can select the [http://pardee.du.edu/wiki/index.php?title=EconDash#Independent_Variable:.C2.A0Drivers_of.C2.A0Crises independent&nbsp;variables]&nbsp;that determine: 1) the size of the bubbles that represent each country ("Select country size"); 2) the bubbles' color scheme ("Select country color"); 3) the network that is represented by the links (grey lines) between countries ("Select network"); and 4) the value threshold over which network links should be displayed for the selected network variable ("Show connections"). One can also select the year of the data that will be displayed ("Select year"). Currently, data is available from 1960 to 2014.<br />
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In addition, the interface provides information about the selected independent variables in the textual display on the right and in graphs at the bottom of the screen. One can access country-specific information about [[File:EconDashTrade Figure 2.jpg|frame|right|600x400px|Figure 2: Mouse-over Country Display Example]]selected variables by <span class="scayt-misspell-word" data-scayt-word="mousing" data-scayt-lang="en_US">mousing</span> over each country bubble (see&nbsp;[http://pardee.du.edu/wiki/images/5/51/EconDashTrade_Figure_2.jpg Figure 2]). Information about the network variable&nbsp;for two countries ([http://pardee.du.edu/wiki/index.php?title=EconDash#Data_Structure country dyads]) can be viewed by <span class="scayt-misspell-word" data-scayt-word="mousing" data-scayt-lang="en_US">mousing</span> over the grey line linking them (see [http://pardee.du.edu/wiki/images/9/9e/EconDashTrade_Figure_3.jpg Figure 3]). Generally, the most meaningful stories emerge when two or more of the selected variables represents the same category of information.&nbsp;For example, one could gain a better understanding of the world's energy trade networks and how they&nbsp;relate&nbsp;to economic <span class="scayt-misspell-word" data-scayt-word="sophisitaction" data-scayt-lang="en_US">sophisitaction</span> (as measured by GDP per capita) by selecting "<span class="scayt-misspell-word" data-scayt-word="GDPPCP" data-scayt-lang="en_US">GDPPCP</span>" for country size,&nbsp;"centrality score energy" for country color, "total energy trade" for network, and experimenting with connection thresholds.<br />
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== Example Exploration:&nbsp;Financial Crises Across Time ==<br />
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Say you want to better understand the occurrence and movement of financial crises across time. Select&nbsp;"GDP at MER" for the country [[File:EconDashTrade Figure 3.jpg|frame|right|600x400px|Figure 3: Mouse-over Network Link Display Example]]size, "Financial Crisis (Binary)" for the country color and "Total Trade" for the network. Then, select 1960 for the year, and (without clicking again) begin to scroll down though subsequent years using the down arrow key&nbsp;on your keyboard. In this way, you can quickly see which countries experienced a financial crisis in each year. Some patterns you may notice are:<br />
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- Between 1960 and 1980 financial crises were limited to the Global South<br />
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- After&nbsp;1980, more countries in the Global North began to experience crisis, and the US had its first post-1960 crisis in 1988<br />
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- Crises occur in geographically contiguous country clusters relatively often (e.g. western South America 1981, <span class="scayt-misspell-word" data-scayt-word="Scandanavia" data-scayt-lang="en_US">Scandanavia</span>&nbsp;1991, Eastern Europe 1992, east and southeast Asia 1997 and&nbsp;1998)<br />
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- When countries with large economies are involved in a crisis, it can affect a region and/or&nbsp;trading partners in subsequent years; this cascading effect can be seen in the map view and in the bar graph displayed at the bottom of the page&nbsp;(e.g. Asian financial crisis with Japan as the epicenter in 1996 and 1997 and the Global Financial Crisis with the US and UK as epicenters in 2007-2009)<br />
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== <span style="font-size:xx-large;">Defining the Variables</span> ==<br />
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The different categories of relevant indicators are listed below, with a justification for their inclusion in the <span class="scayt-misspell-word" data-scayt-word="EconDash" data-scayt-lang="en_US">EconDash</span> visualization.<br />
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=== <span style="font-size:x-large;">Dependent Variable:Types of Crises</span> ===<br />
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The dependent variable is defined as an economic crisis that occurs as a result of strictly economic phenomena. This excludes economic instability resulting from&nbsp;political instability or&nbsp;natural disasters. Economic crises are classified according to the following IMF data.<br />
<br />
The IMF [https://www.imf.org/en/Publications/WP/Issues/2016/12/31/Systemic-Banking-Crises-Database-An-Update-26015 Systemic Banking Crises Database] was originally published in 2008 by Luc <span class="scayt-misspell-word" data-scayt-word="Laeven" data-scayt-lang="en_US">Laeven</span> and <span class="scayt-misspell-word" data-scayt-word="Fabián" data-scayt-lang="en_US">Fabián</span> Valencia, and updated in 2012. The IMF Systemic Banking Crises Database covers 431 crisis events identified from 1970 to 2011, of which 134 are identified as systemic banking crises, 13 borderline systemic banking crises, 218 currency crises, and 66 sovereign debt crises. For the 147 systemic or borderline systemic banking crises, the database also tracks the mixture of policy responses to each of these systemic banking crises. The authors of the database classify each of the crisis events per the following criteria:<br />
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==== Financial Crises&nbsp; ====<br />
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Financial crises are analyzed as&nbsp;binary variables from the IMF's&nbsp;banking crises database.&nbsp;They&nbsp;observe&nbsp;the <span class="scayt-misspell-word" data-scayt-word="occurence" data-scayt-lang="en_US">occurence</span> of any one of the following types of financial crises:<br />
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#Systemic Banking Crisis<br />
#Currency Crisis<br />
#Sovereign Debt Crisis<br />
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==== Systemic Banking Crisis ====<br />
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Systemic banking crises are contingent upon satisfying the following two conditions:<br />
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1) Significant signs of financial distress in the banking system (as indicated by significant bank runs, losses in the banking system, and/or bank liquidations)<br />
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2) Significant banking policy intervention measures in response to significant losses in the banking system.&nbsp;The first year that both conditions are satisfied is considered the onset year.<br />
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The second condition can be met when three of the following six policy intervention measures have been implemented:<br />
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#''Extensive liquidity support&nbsp;''- Liquidity support is extensive when the ratio of central bank claims on the financial sector to deposits and foreign liabilities exceeds five percent and more than doubles relative to its pre-crisis level. The authors also included any liquidity support extended directly from the treasury. But liquidity support to subsidiaries of foreign banks is not included in the ratio of the foreign country, only the domestic ratio.<br />
#''Bank restructuring gross costs&nbsp;''- Bank restructuring costs are defined as gross fiscal outlays directed to the restructuring of the financial sector. The authors exclude liquidity assistance from the treasury captured by the first intervention to avoid potentially double counting. Bank restructuring costs are considered significant if they compose at least 3% of GDP<br />
#''Significant bank nationalizations -&nbsp;''Significant nationalizations are takeovers by the government of systemically important financial institutions and include cases where the government takes a majority stake in the capital of those financial institutions.<br />
#''Significant guarantees put in place&nbsp;''- Significant guarantee on bank liabilities indicate that either a full protection of liabilities has been issued or that guarantees have been extended to non-deposit liabilities of banks. However, policy interventions that only target the level of deposit insurance coverage are excluded.<br />
#''Significant asset purchases&nbsp;''- Significant asset purchases from&nbsp;financial institutions by the central bank or the treasury exceeding five percent of GDP.<br />
#''Deposit freezes and/or bank holidays - ''Government halts acccount activity or require bank closure; this action is taken more frequently by emerging economies.<br />
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<br />
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Outside of these criteria, a crisis can be deemed systemic if&nbsp;1) a country’s banking system exhibits significant losses resulting in a share of <span class="scayt-misspell-word" data-scayt-word="nonperforming" data-scayt-lang="en_US">nonperforming</span> loans above 20 percent, or bank closures of at least 20 percent of banking system assets; or 2) fiscal restructuring costs of the banking sector are sufficiently high exceeding 5 percent of GDP.<br />
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==== Currency Crisis ====<br />
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Currency crises occur when the national currency experiences a nominal depreciation of the currency against the U.S. dollar of at least 30 percent and is also at least 10 percentage points greater than the rate of depreciation in the year before. The authors use the bilateral dollar exchange rate from the World Economic Outlook database from the IMF. In cases where countries meet the currency criteria for several continuous years, the authors use the first year of each 5-year window to identify the crisis. Using this approach the authors identify 218 currency crises from 1970 to 2011, of which, 10 occur from 2008 to 2011.<br />
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==== Sovereign Debt Crisis and Debt Restructuring Years ====<br />
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Sovereign debt crises occur when countries default on their sovereign debt to private creditors. The authors identify 66 sovereign debt crises using data taken from a <span class="scayt-misspell-word" data-scayt-word="Beim" data-scayt-lang="en_US">Beim</span> and <span class="scayt-misspell-word" data-scayt-word="Calomiris" data-scayt-lang="en_US">Calomiris</span> 2001 paper, the World Bank, a <span class="scayt-misspell-word" data-scayt-word="Sturzenegger" data-scayt-lang="en_US">Sturzenegger</span> and <span class="scayt-misspell-word" data-scayt-word="Zettelmeyer" data-scayt-lang="en_US">Zettelmeyer</span> 2006 paper, IMF staff reports, and reports from rating agencies. Similarly, the year of debt restructuring is the year a country restructures their debt. It is possible to have multiple crises and debt <span class="scayt-misspell-word" data-scayt-word="restructurings" data-scayt-lang="en_US">restructurings</span> in a single year, see Greece 2012. <ref>Luc Laeven and Fabian Valencia. "Systemic Banking Crises Database: An Update," IMF Working Paper 12 (2012): 1-32. Accessed July 6, 2017. https://www.imf.org/~/media/Websites/IMF/imported-full-text-pdf/external/pubs/ft/wp/2012/_wp12163.ashx.</ref><br />
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=== <span style="font-size:x-large;">Independent Variable:&nbsp;Drivers of&nbsp;Crises</span> ===<br />
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The independent variables in this dataset describe countries' internal economic conditions and their networked relationships, i.e. [http://pardee.du.edu/wiki/index.php?title=EconDash#Centrality_Scores centrality scores]. [http://pardee.du.edu/wiki/index.php?title=EconDash#Table_1:_Variable_List Table 1] lists each independent variable and provides its category, source, and definition. See additional information on data sources [http://pardee.du.edu/wiki/index.php?title=EconDash#Data_Sources below].<br />
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==== Table 1: Variable List ====<br />
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{| border="1" cellspacing="1" cellpadding="1" style="width: 668px;"<br />
|-<br />
| style="text-align: center; width: 142px;" | <span style="font-size:smaller;">'''Variable Name'''</span><br />
| style="width: 126px; text-align: center;" | <span style="font-size:smaller;">'''Source Institution(s)'''</span><br />
| style="width: 118px; text-align: center;" | <span style="font-size:smaller;">'''Source Database(s)'''</span><br />
| style="width: 260px; text-align: center;" | <span style="font-size:smaller;">'''Definition'''</span><br />
|-<br />
| colspan="4" style="text-align: center; width: 662px;" | <span style="font-size:smaller;">''Structural Variables''</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">GDP Growth Rate</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center & International Monetary Fund (IMF)</span><br />
| style="width: 118px;" | <span style="font-size:smaller;">[[International_Futures_(IFs)|International Futures]] (IFs) & IMF's&nbsp;[https://www.imf.org/external/pubs/ft/weo/2017/01/weodata/index.aspx World Economic Outlook] (WEO)</span><br />
| style="width: 260px;" | <span style="font-size:smaller;">Gross domestic product (GDP) growth rate, percent</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">GDP at MER</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center & IMF</span><br />
| style="width: 118px;" | <span style="font-size:smaller;">IFs & WEO</span><br />
| style="width: 260px;" | <span style="font-size:smaller;">GDP at Market Exchange Rates (billion USD), 2011 constant prices</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;"><span class="scayt-misspell-word" data-scayt-word="GDPPCP" data-scayt-lang="en_US">GDPPCP</span></span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center & IMF</span><br />
| style="width: 118px;" | <span style="font-size:smaller;">IFs & WEO</span><br />
| style="width: 260px;" | <span style="font-size:smaller;">GDP per capita at Purchasing Power Parity (PPP) (thousand USD), 2011 constant prices</span><br/><br />
|-<br />
| colspan="4" style="text-align: center; width: 662px;" | <span style="font-size:smaller;">''Financial Variables''</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Centrality Score Ag</span><br />
| style="width: 126px;" | <span style="font-size:smaller;"><span class="scayt-misspell-word" data-scayt-word="Pardee" data-scayt-lang="en_US">Pardee</span> Center, [https://unstats.un.org/unsd/trade/default.asp United Nations&nbsp;Trade Statistics] (UNTS) &&nbsp;[http://www.cepii.fr/CEPII/en/welcome.asp CEPii]</span><br />
| style="width: 118px;" | <span style="font-size:smaller;">[https://comtrade.un.org/ UN&nbsp;][https://comtrade.un.org/ Comtrade Database]&nbsp;(Comtrade) & CEPii's [http://www.cepii.fr/CEPII/en/bdd_modele/presentation.asp?id=1 BACI]</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Centrality of country in the agricultural trade network meausured as aggregate trade in millions of USD</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Centrality Score Ag (Percent)</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br />
| style="width: 260px;" | <span style="font-size:smaller;">Centrality of country in the agricultural trade network measured as a percent of a country's GDP</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Centrality Score Energy</span><br/><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br />
| style="width: 260px;" | <span style="font-size:smaller;">Centrality of country in the energy trade networkmeasured as aggregate trade in millions of USD</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Centrality Score Energy (Percent)</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br />
| style="width: 260px;" | <span style="font-size:smaller;">Centrality of country in the energy trade network measured as a percent of a country's GDP</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Centrality Score ICT</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br />
| style="width: 260px;" | <span style="font-size:smaller;">Centrality of country in the ICT trade network measured as aggregate trade in millions of USD</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Centrality Score ICT (Percent)</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br />
| style="width: 260px;" | <span style="font-size:smaller;">Centrality of country in the energy trade network measured as a percent of a country's GDP</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Centrality Score Manufacturing</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Centrality of country in the manufacturing trade network measured as aggregate trade in millions of USD</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Centrality Score Manufacturing (Percent)</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br />
| style="width: 260px;" | <span style="font-size:smaller;">Centrality of country in the manufacturing trade network measured as a percent of a country's GDP</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Centrality Score Materials</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Centrality of country in the materials trade network measured as aggregate trade in millions of USD</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Centrality Score Materials (Percent)</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Centrality of country in the materials trade network measured as a percent of a country's GDP</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Centrality Score Services</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center & UNTS</span><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & [https://unstats.un.org/unsd/servicetrade/ UN Service Trade Statistics Database]</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Centrality of country in the services trade network measured as aggregate trade in millions of USD</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Centrality Score Services (Percent)</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center & UNTS</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & UN Service Trade Statistics Database</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Centrality of country in the services trade network measured as a percent of a country's GDP</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Centrality Score Total</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade, BACI, & UN Service Trade Statistics Database</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Centrality of country in the total trade network measured as aggregate trade in millions of USD</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Centrality Score Total (Percent)</span><br/><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade, BACI, & UN Service Trade Statistics Database</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Centrality of country in the total trade network measured as a percent of a country's GDP</span><br/><br />
|-<br />
| style="width: 111px; text-align: center;" colspan="4" | <span style="font-size:smaller;">''Network Variables''</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Total Trade as a Percent of GDP</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade, BACI, & UN Service Trade Statistics Database</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Total trade as a percent of the partner country's GDP</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Total Energy Trade</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Total bilateral energy trade in millions of USD</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Total ICT Trade</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Total bilateral ICT trade in millions of USD</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Total Manufacturing Trade</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Total bilateral manufacturing trade in millions of USD</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Total Materials Trade</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Total bilateral materials trade in millions of USD</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Total Services Trade</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center & UNTS</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & UN Service Trade Statistics Database</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Total bilateral services trade in millions of USD</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Total Agricultural Trade</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Total bilateral agricultural trade in millions of USD</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Total Trade</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Total bilateral trade in millions of USD</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Total Energy Trade as a Percent of GDP</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Total energy trade as a percent of the partner country's GDP</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Total ICT Trade as a Percent of GDP</span><br/><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Total ICT trade as a percent of the partner country's GDP</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Total Manufacturing Trade as a Percent of GDP</span><br/><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Total manufacturing trade as a percent of the partner country's GDP</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Total Materials Trade as a Percent of GDP</span><br/><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Total materials trade as a percent of the partner country's GDP</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Total Services Trade as a Percent of GDP</span><br/><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center & UNTS</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & UN Service Trade Statistics Database</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Total services trade as a percent of the partner country's GDP</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Total Agricultural Trade as a Percent of GDP</span><br/><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Total agricultural trade as a percent of the partner country's GDP</span><br />
|}<br />
<br />
=== Data Sources ===<br />
<br />
Most dyadic trade data comes from the [https://comtrade.un.org/ UN Comtrade Database], which houses the world's "official international trade statistics." CEPii&nbsp;cleans the Comtrade data, so data has been pulled from its [http://www.cepii.fr/CEPII/en/bdd_modele/presentation.asp?id=1 BACI Database] for ease of use. However, CEPii&nbsp;does not have dyadic trade data for the services sector, so data from the [https://unstats.un.org/unsd/servicetrade/default.aspx UN Service Trade Statistics Database]&nbsp;is blended with the CEPii&nbsp;data to get a complete trade balance. Both Comtrade and the Service Trade Statistics databases are managed by the [https://unstats.un.org/unsd/trade/default.asp UN Trade Statistics] branch of the&nbsp;[https://unstats.un.org/home/ United Nations Statistics Division].<br />
<br />
== Centrality Scores ==<br />
<br />
Network analysis can be used to determine a country's centrality within a global network. In network analysis, centrality&nbsp;has been defined along the following dimensions:<br />
<br />
#''Reach&nbsp;''- ability of an entity&nbsp;to reach other vertices<br />
#''Flow&nbsp;''- quantity/weight of &nbsp;passing through entity<br />
#''Vitality&nbsp;''- Effect of removing entity from the network<br />
#''Feedback&nbsp;''- A recursive function of alter centralities<ref>Peter Hoff. "Centrality: Statistical Analysis of social networks." (n.d). Retrieved July 6, 2017, from http://www.stat.washington.edu/people/pdhoff/courses/567/Notes/l6_centrality_paused.pdf.</ref><br />
<br />
EconDash uses eigenvector centrality to determine the centrality of each country, or "node," in&nbsp;a global&nbsp;network. Eigenvector centrality''&nbsp;''assigns each node a relative score based on the centrality of its connections. Connections to higher-scoring nodes contribute more to a node's centrality score than connections to lower-scoring nodes. It uses a matrix calculation to iteratively determine each node's centrality score. The basic idea behind eigenvector centrality is that a central actor is connected to other central actors. It is distinct from the simpler degree centrality in that it weights connections rather than assigning a score based on the number of connections alone.<ref>"Eigenvector Centrality." (n.d.). Retrieved July 6, 2017, from https://www.sci.unich.it/~francesc/teaching/network/eigenvector.html.</ref> In <span class="scayt-misspell-word" data-scayt-word="EconDash" data-scayt-lang="en_US">Trade Networks visualization</span>, eigenvector centrality is used to analyze centrality of a country in a trade network in a particular year.&nbsp;<br />
<br />
== Data Structure ==<br />
<br />
=== Monadic Data ===<br />
<br />
Monadic data are those that describe one&nbsp;country in a single year with a structure of country-year. For example, Senegal's GDP per capita at PPP in 2012. In the EconDash's Trade Networks visualization, monadic variables include all Dependent Variables (i.e. crises),&nbsp;Structural Variables (i.e. economic statistics) and Financial Variables (i.e.centrality scores). While centrality scores are calculated based on a country's trade relationships with other countries (nodes) in the global network, countries receive a single, annual centrality score for each trade&nbsp;sector.<br />
<br />
=== Dyadic Data ===<br />
<br />
Dyadic data are those that describe the relationship between two countries in a single year with a structure of country-country-year. For example, total ICT trade between the US and China in 2015. In the EconDash's Trade Networks visualization, dyadic variables include all Network Variables (i.e. abosolute and relative levels of trade). The dyadic trade data is used to analyze bilateral trade levels between countries in the following sectors.&nbsp;Each sector is analyzed as percent of partner country's GDP as well as&nbsp;total intrasector trade in millions of US dollars:<br />
<br />
#Energy<br />
#Manufacturing<br />
#Information and Communication Technology (ICT)<br />
#Materials<br />
#Services<br />
#Agriculture<br />
#Total Trade<br />
<br />
= EconDash: Economic Vulnerabilities =<br />
<br />
This interface focuses on economic vulnerabilities across countries across time. This interface&nbsp;is based on monadic independent variables from 1960 to 2015 and a binary dependent variable namely the occurrence of economic crises. This visualization also includes groups of the independent variables along with groups of countries developed on the basis of specific criteria. To access the dashboard click [https://pardee.du.edu/econdash2/ here].&nbsp;<br />
<br />
== Navigating the interface ==<br />
<br />
[[File:EconDash viz 2.JPG|frame|right|sub|upright|Figure 4: Economic Vulnerabilities Interface (with description of all features)]]<br />
<br />
The interface enables the user to view and analyze various independent variables across countries across time. The dashboard presents a world map populated with a relevant variable in a particular year. The variable is color scaled and a “valence” has been defined for each variable. For example, the demographic dividend moves from red to green (lowest to highest), whereas infant mortality moves from green to red (lowest value to highest value). There are three filters at the top right of the visualization that allow the user to select a relevant year, a relevant variable or indicator and a particular country group. The dashboard allows the user to play a particular variable over time so that the unfolding trend can be analyzed visually across countries.<br />
<br />
Below the map visualization, the user can see a line/bar graph describing the trend of the variable for the world as a whole over time. When a user hovers over a particular country, this line/bar graph describes the trend for the country instead of the world as a whole over time. Also note that this graph will show the trend over the entire time horizon even when the filter above is set to a particular year. This enables the user to understand the overall trend before selecting a particular country.<br />
<br />
Under the line graph, the user can see what group a particular variable belongs to. For example, the variable demographic dividend belongs to the group ‘Demographic Vulnerabilities’.<br />
<br />
Finally, in order to better understand vulnerability to crises, the dashboard helps the user analyze the same not just across countries but also across “groups” of countries. The basis of these groups include factors such as income levels, development levels, geographic region, exchange rate regime etc. The filter above the map visualization has an option for selection of country groups. This enables the user to see a cluster of countries and the variables for the same.<br />
<br />
[[Media:Figure_4|Figure 4]]&nbsp;shows the interface along with all of its basic features. The country grouping function has been described in detail in the sections below.<br />
<br />
== Defining variables and groups ==<br />
<br />
=== Dependent variable: Occurence of economic crises ===<br />
<br />
The dependent variable (DV) was for the purpose of the second visualization was calculated on the basis of the percent change in the GDP at MER. The following steps were followed in the computation of the DV,<br />
<br />
First, the percent change in GDP at MER was calculated from 1960 onwards using historical data and forecasts from IFs. A threshold&nbsp;was&nbsp;set for the DV, namely, where the change in the growth rate was lesser than -4%. A binary variable&nbsp;was&nbsp;computed i.e. the value in a particular year for a particular country was set to 1 where an economic crisis was said to occur, if the&nbsp;threshold&nbsp;was&nbsp;met.<br />
<br />
The variable “Occurrence of Economic Crisis” that appears in the visualization is a combination of these three dependent variables.&nbsp;<br />
<br />
=== Independent variables ===<br />
<br />
This display allows the user to view 44&nbsp;independent variables in addition to the dependent variable (described above) across countries and across time. All the variables have been grouped into seven main categories, namely,<br />
<br />
#'''The Dependent variable''' (This is the occurrence of economic crisis that has been described above)<br />
#'''Economic input dependencies and vulnerabilities''' (This group includes variables such as Raw material imports, Food imports etc.)<br />
#'''Financial vulnerabilities''' (This group includes variables such as the average exchange rate, the balance of payments, capital account balance etc.)<br />
#'''Environmental vulnerabilities''' (This group includes variables such as the number of displacements on account of natural disasters, carbon emissions, precipitation change etc.)<br />
#'''Political vulnerabilities''' (This group includes variables such as polity scores, the occurrence of events of political instability etc.)<br />
#'''Demographic vulnerabilities''' (This group includes variables such as the population, youth bulge, dependency ratios etc.)<br />
#'''Economic output dependencies and vulnerabilities''' (This group includes variables such as Exports, export diversification, GDP, GDP per capita etc.)<br />
<br />
=== Country grouping function ===<br />
<br />
[[File:EconDash viz2 groups.JPG|frame|right|upright|Figure 5: Display of the grouping function in the interface along with description of all components]]<br />
<br />
To better understand what drives economic crises, the visualization also gives the user the option to view the occurrence of crises across groups of countries in addition to individual countries. These groups were developed using specific criteria such as fuel imports, exchange rate regimes, levels of development etc. The user can currently select from up to 8 groups of countries with various sub-groups. The main groups are,<br />
<br />
#Levels of corruption<br />
#Ease of doing business<br />
#Fuel exports<br />
#Income levels<br />
#Currency regimes<br />
#Anchor currency in the economy<br />
#Credit rating for the country<br />
#Development levels.<br />
<br />
Figure 5&nbsp;shows the country grouping function&nbsp;in the interface.<br />
<br />
== List of variables presented in the interface ==<br />
<br />
{| border="1" cellpadding="1" cellspacing="1" width="899"<br />
|-<br />
| height="20" width="321" | '''Variable name'''<br />
| width="387" | '''Description'''<br />
| width="191" | '''Variable Group'''<br />
|-<br />
| height="60" width="321" | Occurrence Of Economic Crisis<br />
| width="387" | Binary variable that describes the occurrence of economic crisis.&nbsp; Derived using change in the GDP growth rates of a country over time.<br />
| width="191" | Dependent variable<br />
|-<br />
| height="60" width="321" | Raw Materials Import<br />
| width="387" | Raw materials imports<br />
| width="191" | Economic input dependencies and vulnerabilities<br />
|-<br />
| height="60" width="321" | Agricultural Import Dependence<br />
| width="387" | Agricultural imports as a percentage of food demand<br />
| width="191" | Economic input dependencies and vulnerabilities<br />
|-<br />
| height="40" width="321" | Average Exchange Rate (National Currency To USD)<br />
| width="387" | Avg. Exchange Rate, NC/US$, Rate<br />
| width="191" | Financial vulnerabilities<br />
|-<br />
| height="20" width="321" | Capital Account Balance<br />
| width="387" | Balance of payments: Capital account (net)<br />
| width="191" | Financial vulnerabilities<br />
|-<br />
| height="40" width="321" | Climate Vulnerability Index<br />
| width="387" | Index of climate change vulnerability from Notre Dame Global Adaptation Initiative (ND-GAIN)<br />
| width="191" | Environmental vulnerabilities<br />
|-<br />
| height="40" width="321" | Carbon Emissions<br />
| width="387" | Annual carbon emissions<br />
| width="191" | Environmental vulnerabilities<br />
|-<br />
| height="20" width="321" | Corruption Perception Index<br />
| width="387" | Corruption scores from transparency international<br />
| width="191" | Political Vulnerabilities<br />
|-<br />
| height="20" width="321" | Current Account (As A Percent Of GDP)<br />
| width="387" | Current account balance as a percent of GDP<br />
| width="191" | Financial Vulnerabilities<br />
|-<br />
| height="40" width="321" | Demographic Dividend<br />
| width="387" | Ratio of the working population to that of non-working population<br />
| width="191" | Demographic Vulnerabilities<br />
|-<br />
| height="20" width="321" | Discount Rate<br />
| width="387" | Discount Rate, Percent per annum<br />
| width="191" | Financial vulnerabilities<br />
|-<br />
| height="60" width="321" | Diversification Index&nbsp;&nbsp; (Exports)<br />
| width="387" | Diversification index&nbsp;&nbsp; Exports<br />
| width="191" | Economic output dependencies and vulnerabilities<br />
|-<br />
| height="60" width="321" | Diversification Index&nbsp;&nbsp; (Imports)<br />
| width="387" | Diversification index&nbsp;&nbsp; Imports<br />
| width="191" | Economic input dependencies and vulnerabilities<br />
|-<br />
| height="20" width="321" | Economic Freedom Score<br />
| width="387" | Economic freedom scores from fraser international<br />
| width="191" | Political Vulnerabilities<br />
|-<br />
| height="20" width="321" | Education (Years Of Schooling)<br />
| width="387" | Average years of schooling<br />
| width="191" | Demographic Vulnerabilities<br />
|-<br />
| height="40" width="321" | Educational Attainment<br />
| width="387" | Education- Average years of Education between ages 15 to 24<br />
| width="191" | Demographic Vulnerabilities<br />
|-<br />
| height="60" width="321" | Electricity Access<br />
| width="387" | Percent of population with electricity access<br />
| width="191" | Economic Input Dependencies and Vulnerabilities<br />
|-<br />
| height="60" width="321" | Exports As A Percent Of GDP<br />
| width="387" | Exports as a percent of GDP<br />
| width="191" | Economic Output Dependencies and Vulnerabilities<br />
|-<br />
| height="60" width="321" | FDI Inflows As A Percent Of GDP<br />
| width="387" | FDI Inflows as a percent of GDP<br />
| width="191" | Economic Input Dependencies and Vulnerabilities<br />
|-<br />
| height="20" width="321" | Foreign Exchange Reserves (Including Gold)<br />
| width="387" | Foreign Exchange Reserves (Including Gold)<br />
| width="191" | Financial vulnerabilities<br />
|-<br />
| height="20" width="321" | Sociopolitical Freedom Score<br />
| width="387" | Socio-Political Freedom<br />
| width="191" | Political Vulnerabilities<br />
|-<br />
| height="60" width="321" | GDP Growth Rate<br />
| width="387" | Growth rate of GDP at MER<br />
| width="191" | Economic Output Dependencies and Vulnerabilities<br />
|-<br />
| height="60" width="321" | GDP Per Capita At PPP<br />
| width="387" | GDP per capita at Purchasing Power Parity<br />
| width="191" | Economic Output Dependencies and Vulnerabilities<br />
|-<br />
| height="20" width="321" | Government Effectiveness<br />
| width="387" | Government Effectiveness<br />
| width="191" | Political Vulnerabilities<br />
|-<br />
| height="20" width="321" | Government Expenditure As A Percent Of GDP<br />
| width="387" | Government expenditure as a percent of GDP<br />
| width="191" | Financial vulnerabilities<br />
|-<br />
| height="20" width="321" | Gross Savings (% Of GDP)<br />
| width="387" | Gross savings (% of GDP)<br />
| width="191" | Financial vulnerabilities<br />
|-<br />
| height="20" width="321" | IGO Membership<br />
| width="387" | Membership in international organizations<br />
| width="191" | Political Vulnerabilities<br />
|-<br />
| height="20" width="321" | Index&nbsp; Inflation (End Of Period Consumer Prices)<br />
| width="387" | Index&nbsp; Inflation, end of period consumer prices<br />
| width="191" | Financial vulnerabilities<br />
|-<br />
| height="20" width="321" | Infant Mortality<br />
| width="387" | Deaths per 1000 infants born<br />
| width="191" | Demographic Vulnerabilities<br />
|-<br />
| height="40" width="321" | Internal War Magnitude<br />
| width="387" | Magnitude defined by PITF on the basis of number of casualties<br />
| width="191" | Political Vulnerabilities<br />
|-<br />
| height="20" width="321" | Internal War Occurrence<br />
| width="387" | Occurrence of internal war (binary variable)<br />
| width="191" | Political Vulnerabilities<br />
|-<br />
| height="20" width="321" | Lending Rate<br />
| width="387" | Lending Rate, Percent per annum<br />
| width="191" | Financial vulnerabilities<br />
|-<br />
| height="20" width="321" | Life Expectancy<br />
| width="387" | Average life expectancy at birth<br />
| width="191" | Demographic Vulnerabilities<br />
|-<br />
| height="20" width="321" | Middle Income Trap (Binary)<br />
| width="387" | Middle income trap (binary)<br />
| width="191" | Financial vulnerabilities<br />
|-<br />
| height="20" width="321" | National Currency Per SDR<br />
| width="387" | National Currency per SDR, Period average, Rate<br />
| width="191" | Financial vulnerabilities<br />
|-<br />
| height="40" width="321" | Percent Change&nbsp; Inflation ( End Of Period Consumer Prices)<br />
| width="387" | Percent change&nbsp; Inflation, end of period consumer prices<br />
| width="191" | Financial vulnerabilities<br />
|-<br />
| height="20" width="321" | Polity Score<br />
| width="387" | Polity scores from 0-20<br />
| width="191" | Political Vulnerabilities<br />
|-<br />
| height="20" width="321" | Population<br />
| width="387" | Population in millions<br />
| width="191" | Demographic Vulnerabilities<br />
|-<br />
| height="40" width="321" | Precipitation Change<br />
| width="387" | Percent change in precipitation since 1990<br />
| width="191" | Environmental vulnerabilities<br />
|-<br />
| height="20" width="321" | Ratio Of Gdp Growth Rate To That Of The US<br />
| width="387" | Ratio of GDP growth rate to that of the US<br />
| width="191" | Financial vulnerabilities<br />
|-<br />
| height="20" width="321" | National Currency Per SDR<br />
| width="387" | SDR, National Currency per SDR, Rate<br />
| width="191" | Financial vulnerabilities<br />
|-<br />
| height="20" width="321" | Treasury Bill Rate<br />
| width="387" | Treasury Bill Rate, Percent per annum<br />
| width="191" | Financial vulnerabilities<br />
|-<br />
| height="20" width="321" | Urban Population (Percent Of Total Population)<br />
| width="387" | Urban population as a percent of total population<br />
| width="191" | Demographic Vulnerabilities<br />
|-<br />
| height="40" width="321" | Water Demand As A Percent Of Freshwater Resources<br />
| width="387" | Annual water demand as a proportion of exploitable water resources<br />
| width="191" | Environmental vulnerabilities<br />
|-<br />
| height="20" width="321" | GDP At MER<br />
| width="387" | GDP at Market Exchange Rates<br />
| width="191" | Financial vulnerabilities<br />
|}<br />
<br />
== List of country groups presented in the interface&nbsp; ==<br />
<br />
{| border="1" cellpadding="1" cellspacing="1" width="988"<br />
|-<br />
| height="20" width="380" | '''Group name'''<br />
| width="608" | '''Description'''<br />
|-<br />
| height="100" | Corruption grouping<br />
| width="608" | Countries with a TI index score of greater than 5 are defined as "more transparent" and those with a score of less than 5 are defined as "less transparent". From 2012 onwards with a revision in the way the index is structured, countries with an index score of higher than 50 were defined as "more transparent" and those with lower than 50 were defined as "less transparent"<br />
|-<br />
| height="140" | Ease of doing business<br />
| width="608" | Countries are grouped into four quartiles on the basis of ranks on the ease of doing business scores from the World Bank,<br/>1. First quartile- Least ease of doing business (Ranked less than 48 on the index)<br/>2. Second quartile- Ranked between 47 and 97<br/>3. Third quartile- Ranked between 97 and 144<br/>4. Fourth quartile- Most ease in doing business Ranked 145 and higher<br/>2. Second quartile-&nbsp;&nbsp;<br />
|-<br />
| height="60" | Fuel exports<br />
| width="608" | Where , less than 35 percent of exports are made up of fuels, countries are classified as "Low percentage" and where more than 35 percent of exports are made up of fuel, they are classified as "High percentage"<br />
|-<br />
| height="20" | Income levels<br />
| width="608" | Based on income level definition from the world bank<br />
|-<br />
| height="20" | Currency regime<br />
| width="608" | Currency regime definitions from the IMF<br />
|-<br />
| height="20" | Anchor currency<br />
| width="608" | Anchor currency in the economy as identified by the IMF<br />
|-<br />
| height="20" | Credit rating<br />
| width="608" | Credit ratings in 2016 from Fitch<br />
|-<br />
| height="20" | Development levels<br />
| width="608" | Development levels defined by the IMF<br />
|}<br />
<br />
= References =<br />
<br />
<references /></div>Wikiadmin//pardee.du.edu/wiki/EconDashEconDash2018-09-11T00:59:39Z<p>Wikiadmin: </p>
<hr />
<div>The <span class="scayt-misspell-word" data-scayt-word="EconDash" data-scayt-lang="en_US">EconDash</span> is a set of interactive data visualizations created by the [http://pardee.du.edu/ Frederick S. <span class="scayt-misspell-word" data-scayt-word="Pardee" data-scayt-lang="en_US">Pardee</span> Center for International Futures]. The purpose of these visualizations is to allow the user to explore and better understand relevant indicators of financial and economic instability and resilience. <span class="scayt-misspell-word" data-scayt-word="EconDash" data-scayt-lang="en_US">EconDash</span> uses both [http://pardee.du.edu/wiki/EconDash#Data_Structure monadic and dyadic data] across time, and includes some forecasted variables from the [[International_Futures_(IFs)|International Futures (IFs)]] system. There is currently one public user interface available from <span class="scayt-misspell-word" data-scayt-word="EconDash" data-scayt-lang="en_US">EconDash that explores [http://pardee.du.edu/wiki/EconDash#EconDash:_Trade_Networks_Interface Trade Networks]</span>. A new Economic Vulnerability interface will be available by&nbsp;late summer 2017.<br />
<br />
= <span style="font-size:x-large;"><span class="scayt-misspell-word" data-scayt-word="EconDash" data-scayt-lang="en_US">EconDash</span>: Trade Networks Interface</span> =<br />
<br />
This&nbsp;dashboard&nbsp;focuses on trade networks from 1960 to 2014 and the centrality of countries in these networks. It also contains data on financial crises over the same time period. To access the dashboard [https://pardee.du.edu/econdash/ click here].<br />
<br />
== Navigating the Interface ==<br />
<br />
The <span class="scayt-misspell-word" data-scayt-word="EconDash" data-scayt-lang="en_US">EconDash</span> interface allows users to display and explore financial and economic crises and global trade networks along a variety of dimensions. [http://pardee.du.edu/wiki/images/b/bb/EconDashTrade_Figure_1.jpg Figure 1] is an [[File:EconDashTrade Figure 1.jpg|frame|right|600x400px|Figure 1: EconDash Annotated Main Display Page]]annotated view of the&nbsp;[http://54.149.184.118/econdash/ main display page]&nbsp;with its default settings. It provides definitions and instructions on each of the page's functions. On this page, the user can select the [http://pardee.du.edu/wiki/index.php?title=EconDash#Independent_Variable:.C2.A0Drivers_of.C2.A0Crises independent&nbsp;variables]&nbsp;that determine: 1) the size of the bubbles that represent each country ("Select country size"); 2) the bubbles' color scheme ("Select country color"); 3) the network that is represented by the links (grey lines) between countries ("Select network"); and 4) the value threshold over which network links should be displayed for the selected network variable ("Show connections"). One can also select the year of the data that will be displayed ("Select year"). Currently, data is available from 1960 to 2014.<br />
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In addition, the interface provides information about the selected independent variables in the textual display on the right and in graphs at the bottom of the screen. One can access country-specific information about [[File:EconDashTrade Figure 2.jpg|frame|right|600x400px|Figure 2: Mouse-over Country Display Example]]selected variables by <span class="scayt-misspell-word" data-scayt-word="mousing" data-scayt-lang="en_US">mousing</span> over each country bubble (see&nbsp;[http://pardee.du.edu/wiki/images/5/51/EconDashTrade_Figure_2.jpg Figure 2]). Information about the network variable&nbsp;for two countries ([http://pardee.du.edu/wiki/index.php?title=EconDash#Data_Structure country dyads]) can be viewed by <span class="scayt-misspell-word" data-scayt-word="mousing" data-scayt-lang="en_US">mousing</span> over the grey line linking them (see [http://pardee.du.edu/wiki/images/9/9e/EconDashTrade_Figure_3.jpg Figure 3]). Generally, the most meaningful stories emerge when two or more of the selected variables represents the same category of information.&nbsp;For example, one could gain a better understanding of the world's energy trade networks and how they&nbsp;relate&nbsp;to economic <span class="scayt-misspell-word" data-scayt-word="sophisitaction" data-scayt-lang="en_US">sophisitaction</span> (as measured by GDP per capita) by selecting "<span class="scayt-misspell-word" data-scayt-word="GDPPCP" data-scayt-lang="en_US">GDPPCP</span>" for country size,&nbsp;"centrality score energy" for country color, "total energy trade" for network, and experimenting with connection thresholds.<br />
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== Example Exploration:&nbsp;Financial Crises Across Time ==<br />
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Say you want to better understand the occurrence and movement of financial crises across time. Select&nbsp;"GDP at MER" for the country [[File:EconDashTrade Figure 3.jpg|frame|right|600x400px|Figure 3: Mouse-over Network Link Display Example]]size, "Financial Crisis (Binary)" for the country color and "Total Trade" for the network. Then, select 1960 for the year, and (without clicking again) begin to scroll down though subsequent years using the down arrow key&nbsp;on your keyboard. In this way, you can quickly see which countries experienced a financial crisis in each year. Some patterns you may notice are:<br />
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- Between 1960 and 1980 financial crises were limited to the Global South<br />
<br />
- After&nbsp;1980, more countries in the Global North began to experience crisis, and the US had its first post-1960 crisis in 1988<br />
<br />
- Crises occur in geographically contiguous country clusters relatively often (e.g. western South America 1981, <span class="scayt-misspell-word" data-scayt-word="Scandanavia" data-scayt-lang="en_US">Scandanavia</span>&nbsp;1991, Eastern Europe 1992, east and southeast Asia 1997 and&nbsp;1998)<br />
<br />
- When countries with large economies are involved in a crisis, it can affect a region and/or&nbsp;trading partners in subsequent years; this cascading effect can be seen in the map view and in the bar graph displayed at the bottom of the page&nbsp;(e.g. Asian financial crisis with Japan as the epicenter in 1996 and 1997 and the Global Financial Crisis with the US and UK as epicenters in 2007-2009)<br />
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== <span style="font-size:xx-large;">Defining the Variables</span> ==<br />
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The different categories of relevant indicators are listed below, with a justification for their inclusion in the <span class="scayt-misspell-word" data-scayt-word="EconDash" data-scayt-lang="en_US">EconDash</span> visualization.<br />
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=== <span style="font-size:x-large;">Dependent Variable:Types of Crises</span> ===<br />
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The dependent variable is defined as an economic crisis that occurs as a result of strictly economic phenomena. This excludes economic instability resulting from&nbsp;political instability or&nbsp;natural disasters. Economic crises are classified according to the following IMF data.<br />
<br />
The IMF [https://www.imf.org/en/Publications/WP/Issues/2016/12/31/Systemic-Banking-Crises-Database-An-Update-26015 Systemic Banking Crises Database] was originally published in 2008 by Luc <span class="scayt-misspell-word" data-scayt-word="Laeven" data-scayt-lang="en_US">Laeven</span> and <span class="scayt-misspell-word" data-scayt-word="Fabián" data-scayt-lang="en_US">Fabián</span> Valencia, and updated in 2012. The IMF Systemic Banking Crises Database covers 431 crisis events identified from 1970 to 2011, of which 134 are identified as systemic banking crises, 13 borderline systemic banking crises, 218 currency crises, and 66 sovereign debt crises. For the 147 systemic or borderline systemic banking crises, the database also tracks the mixture of policy responses to each of these systemic banking crises. The authors of the database classify each of the crisis events per the following criteria:<br />
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==== Financial Crises&nbsp; ====<br />
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Financial crises are analyzed as&nbsp;binary variables from the IMF's&nbsp;banking crises database.&nbsp;They&nbsp;observe&nbsp;the <span class="scayt-misspell-word" data-scayt-word="occurence" data-scayt-lang="en_US">occurence</span> of any one of the following types of financial crises:<br />
<br />
#Systemic Banking Crisis<br />
#Currency Crisis<br />
#Sovereign Debt Crisis<br />
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==== Systemic Banking Crisis ====<br />
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Systemic banking crises are contingent upon satisfying the following two conditions:<br />
<br />
1) Significant signs of financial distress in the banking system (as indicated by significant bank runs, losses in the banking system, and/or bank liquidations)<br />
<br />
2) Significant banking policy intervention measures in response to significant losses in the banking system.&nbsp;The first year that both conditions are satisfied is considered the onset year.<br />
<br />
The second condition can be met when three of the following six policy intervention measures have been implemented:<br />
<br />
<br />
<br />
#''Extensive liquidity support&nbsp;''- Liquidity support is extensive when the ratio of central bank claims on the financial sector to deposits and foreign liabilities exceeds five percent and more than doubles relative to its pre-crisis level. The authors also included any liquidity support extended directly from the treasury. But liquidity support to subsidiaries of foreign banks is not included in the ratio of the foreign country, only the domestic ratio.<br />
#''Bank restructuring gross costs&nbsp;''- Bank restructuring costs are defined as gross fiscal outlays directed to the restructuring of the financial sector. The authors exclude liquidity assistance from the treasury captured by the first intervention to avoid potentially double counting. Bank restructuring costs are considered significant if they compose at least 3% of GDP<br />
#''Significant bank nationalizations -&nbsp;''Significant nationalizations are takeovers by the government of systemically important financial institutions and include cases where the government takes a majority stake in the capital of those financial institutions.<br />
#''Significant guarantees put in place&nbsp;''- Significant guarantee on bank liabilities indicate that either a full protection of liabilities has been issued or that guarantees have been extended to non-deposit liabilities of banks. However, policy interventions that only target the level of deposit insurance coverage are excluded.<br />
#''Significant asset purchases&nbsp;''- Significant asset purchases from&nbsp;financial institutions by the central bank or the treasury exceeding five percent of GDP.<br />
#''Deposit freezes and/or bank holidays - ''Government halts acccount activity or require bank closure; this action is taken more frequently by emerging economies.<br />
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<br />
<br />
Outside of these criteria, a crisis can be deemed systemic if&nbsp;1) a country’s banking system exhibits significant losses resulting in a share of <span class="scayt-misspell-word" data-scayt-word="nonperforming" data-scayt-lang="en_US">nonperforming</span> loans above 20 percent, or bank closures of at least 20 percent of banking system assets; or 2) fiscal restructuring costs of the banking sector are sufficiently high exceeding 5 percent of GDP.<br />
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==== Currency Crisis ====<br />
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Currency crises occur when the national currency experiences a nominal depreciation of the currency against the U.S. dollar of at least 30 percent and is also at least 10 percentage points greater than the rate of depreciation in the year before. The authors use the bilateral dollar exchange rate from the World Economic Outlook database from the IMF. In cases where countries meet the currency criteria for several continuous years, the authors use the first year of each 5-year window to identify the crisis. Using this approach the authors identify 218 currency crises from 1970 to 2011, of which, 10 occur from 2008 to 2011.<br />
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==== Sovereign Debt Crisis and Debt Restructuring Years ====<br />
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Sovereign debt crises occur when countries default on their sovereign debt to private creditors. The authors identify 66 sovereign debt crises using data taken from a <span class="scayt-misspell-word" data-scayt-word="Beim" data-scayt-lang="en_US">Beim</span> and <span class="scayt-misspell-word" data-scayt-word="Calomiris" data-scayt-lang="en_US">Calomiris</span> 2001 paper, the World Bank, a <span class="scayt-misspell-word" data-scayt-word="Sturzenegger" data-scayt-lang="en_US">Sturzenegger</span> and <span class="scayt-misspell-word" data-scayt-word="Zettelmeyer" data-scayt-lang="en_US">Zettelmeyer</span> 2006 paper, IMF staff reports, and reports from rating agencies. Similarly, the year of debt restructuring is the year a country restructures their debt. It is possible to have multiple crises and debt <span class="scayt-misspell-word" data-scayt-word="restructurings" data-scayt-lang="en_US">restructurings</span> in a single year, see Greece 2012. <ref>Luc Laeven and Fabian Valencia. "Systemic Banking Crises Database: An Update," IMF Working Paper 12 (2012): 1-32. Accessed July 6, 2017. https://www.imf.org/~/media/Websites/IMF/imported-full-text-pdf/external/pubs/ft/wp/2012/_wp12163.ashx.</ref><br />
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=== <span style="font-size:x-large;">Independent Variable:&nbsp;Drivers of&nbsp;Crises</span> ===<br />
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The independent variables in this dataset describe countries' internal economic conditions and their networked relationships, i.e. [http://pardee.du.edu/wiki/index.php?title=EconDash#Centrality_Scores centrality scores]. [http://pardee.du.edu/wiki/index.php?title=EconDash#Table_1:_Variable_List Table 1] lists each independent variable and provides its category, source, and definition. See additional information on data sources [http://pardee.du.edu/wiki/index.php?title=EconDash#Data_Sources below].<br />
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==== Table 1: Variable List ====<br />
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{| border="1" cellspacing="1" cellpadding="1" style="width: 668px;"<br />
|-<br />
| style="text-align: center; width: 142px;" | <span style="font-size:smaller;">'''Variable Name'''</span><br />
| style="width: 126px; text-align: center;" | <span style="font-size:smaller;">'''Source Institution(s)'''</span><br />
| style="width: 118px; text-align: center;" | <span style="font-size:smaller;">'''Source Database(s)'''</span><br />
| style="width: 260px; text-align: center;" | <span style="font-size:smaller;">'''Definition'''</span><br />
|-<br />
| colspan="4" style="text-align: center; width: 662px;" | <span style="font-size:smaller;">''Structural Variables''</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">GDP Growth Rate</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center & International Monetary Fund (IMF)</span><br />
| style="width: 118px;" | <span style="font-size:smaller;">[[International_Futures_(IFs)|International Futures]] (IFs) & IMF's&nbsp;[https://www.imf.org/external/pubs/ft/weo/2017/01/weodata/index.aspx World Economic Outlook] (WEO)</span><br />
| style="width: 260px;" | <span style="font-size:smaller;">Gross domestic product (GDP) growth rate, percent</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">GDP at MER</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center & IMF</span><br />
| style="width: 118px;" | <span style="font-size:smaller;">IFs & WEO</span><br />
| style="width: 260px;" | <span style="font-size:smaller;">GDP at Market Exchange Rates (billion USD), 2011 constant prices</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;"><span class="scayt-misspell-word" data-scayt-word="GDPPCP" data-scayt-lang="en_US">GDPPCP</span></span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center & IMF</span><br />
| style="width: 118px;" | <span style="font-size:smaller;">IFs & WEO</span><br />
| style="width: 260px;" | <span style="font-size:smaller;">GDP per capita at Purchasing Power Parity (PPP) (thousand USD), 2011 constant prices</span><br/><br />
|-<br />
| colspan="4" style="text-align: center; width: 662px;" | <span style="font-size:smaller;">''Financial Variables''</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Centrality Score Ag</span><br />
| style="width: 126px;" | <span style="font-size:smaller;"><span class="scayt-misspell-word" data-scayt-word="Pardee" data-scayt-lang="en_US">Pardee</span> Center, [https://unstats.un.org/unsd/trade/default.asp United Nations&nbsp;Trade Statistics] (UNTS) &&nbsp;[http://www.cepii.fr/CEPII/en/welcome.asp CEPii]</span><br />
| style="width: 118px;" | <span style="font-size:smaller;">[https://comtrade.un.org/ UN&nbsp;][https://comtrade.un.org/ Comtrade Database]&nbsp;(Comtrade) & CEPii's [http://www.cepii.fr/CEPII/en/bdd_modele/presentation.asp?id=1 BACI]</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Centrality of country in the agricultural trade network meausured as aggregate trade in millions of USD</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Centrality Score Ag (Percent)</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br />
| style="width: 260px;" | <span style="font-size:smaller;">Centrality of country in the agricultural trade network measured as a percent of a country's GDP</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Centrality Score Energy</span><br/><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br />
| style="width: 260px;" | <span style="font-size:smaller;">Centrality of country in the energy trade networkmeasured as aggregate trade in millions of USD</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Centrality Score Energy (Percent)</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br />
| style="width: 260px;" | <span style="font-size:smaller;">Centrality of country in the energy trade network measured as a percent of a country's GDP</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Centrality Score ICT</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br />
| style="width: 260px;" | <span style="font-size:smaller;">Centrality of country in the ICT trade network measured as aggregate trade in millions of USD</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Centrality Score ICT (Percent)</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br />
| style="width: 260px;" | <span style="font-size:smaller;">Centrality of country in the energy trade network measured as a percent of a country's GDP</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Centrality Score Manufacturing</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Centrality of country in the manufacturing trade network measured as aggregate trade in millions of USD</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Centrality Score Manufacturing (Percent)</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br />
| style="width: 260px;" | <span style="font-size:smaller;">Centrality of country in the manufacturing trade network measured as a percent of a country's GDP</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Centrality Score Materials</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Centrality of country in the materials trade network measured as aggregate trade in millions of USD</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Centrality Score Materials (Percent)</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Centrality of country in the materials trade network measured as a percent of a country's GDP</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Centrality Score Services</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center & UNTS</span><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & [https://unstats.un.org/unsd/servicetrade/ UN Service Trade Statistics Database]</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Centrality of country in the services trade network measured as aggregate trade in millions of USD</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Centrality Score Services (Percent)</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center & UNTS</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & UN Service Trade Statistics Database</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Centrality of country in the services trade network measured as a percent of a country's GDP</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Centrality Score Total</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade, BACI, & UN Service Trade Statistics Database</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Centrality of country in the total trade network measured as aggregate trade in millions of USD</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Centrality Score Total (Percent)</span><br/><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade, BACI, & UN Service Trade Statistics Database</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Centrality of country in the total trade network measured as a percent of a country's GDP</span><br/><br />
|-<br />
| style="width: 111px; text-align: center;" colspan="4" | <span style="font-size:smaller;">''Network Variables''</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Total Trade as a Percent of GDP</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade, BACI, & UN Service Trade Statistics Database</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Total trade as a percent of the partner country's GDP</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Total Energy Trade</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Total bilateral energy trade in millions of USD</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Total ICT Trade</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Total bilateral ICT trade in millions of USD</span><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Total Manufacturing Trade</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Total bilateral manufacturing trade in millions of USD</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Total Materials Trade</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Total bilateral materials trade in millions of USD</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Total Services Trade</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center & UNTS</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & UN Service Trade Statistics Database</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Total bilateral services trade in millions of USD</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Total Agricultural Trade</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Total bilateral agricultural trade in millions of USD</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Total Trade</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Total bilateral trade in millions of USD</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Total Energy Trade as a Percent of GDP</span><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Total energy trade as a percent of the partner country's GDP</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Total ICT Trade as a Percent of GDP</span><br/><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Total ICT trade as a percent of the partner country's GDP</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Total Manufacturing Trade as a Percent of GDP</span><br/><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Total manufacturing trade as a percent of the partner country's GDP</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Total Materials Trade as a Percent of GDP</span><br/><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Total materials trade as a percent of the partner country's GDP</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Total Services Trade as a Percent of GDP</span><br/><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center & UNTS</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & UN Service Trade Statistics Database</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Total services trade as a percent of the partner country's GDP</span><br/><br />
|-<br />
| style="width: 142px;" | <span style="font-size:smaller;">Total Agricultural Trade as a Percent of GDP</span><br/><br />
| style="width: 126px;" | <span style="font-size:smaller;">Pardee Center, UNTS, & CEPii</span><br/><br />
| style="width: 118px;" | <span style="font-size:smaller;">Comtrade & BACI</span><br/><br />
| style="width: 260px;" | <span style="font-size:smaller;">Total agricultural trade as a percent of the partner country's GDP</span><br />
|}<br />
<br />
=== Data Sources ===<br />
<br />
Most dyadic trade data comes from the [https://comtrade.un.org/ UN Comtrade Database], which houses the world's "official international trade statistics." CEPii&nbsp;cleans the Comtrade data, so data has been pulled from its [http://www.cepii.fr/CEPII/en/bdd_modele/presentation.asp?id=1 BACI Database] for ease of use. However, CEPii&nbsp;does not have dyadic trade data for the services sector, so data from the [https://unstats.un.org/unsd/servicetrade/default.aspx UN Service Trade Statistics Database]&nbsp;is blended with the CEPii&nbsp;data to get a complete trade balance. Both Comtrade and the Service Trade Statistics databases are managed by the [https://unstats.un.org/unsd/trade/default.asp UN Trade Statistics] branch of the&nbsp;[https://unstats.un.org/home/ United Nations Statistics Division].<br />
<br />
== Centrality Scores ==<br />
<br />
Network analysis can be used to determine a country's centrality within a global network. In network analysis, centrality&nbsp;has been defined along the following dimensions:<br />
<br />
#''Reach&nbsp;''- ability of an entity&nbsp;to reach other vertices<br />
#''Flow&nbsp;''- quantity/weight of &nbsp;passing through entity<br />
#''Vitality&nbsp;''- Effect of removing entity from the network<br />
#''Feedback&nbsp;''- A recursive function of alter centralities<ref>Peter Hoff. "Centrality: Statistical Analysis of social networks." (n.d). Retrieved July 6, 2017, from http://www.stat.washington.edu/people/pdhoff/courses/567/Notes/l6_centrality_paused.pdf.</ref><br />
<br />
EconDash uses eigenvector centrality to determine the centrality of each country, or "node," in&nbsp;a global&nbsp;network. Eigenvector centrality''&nbsp;''assigns each node a relative score based on the centrality of its connections. Connections to higher-scoring nodes contribute more to a node's centrality score than connections to lower-scoring nodes. It uses a matrix calculation to iteratively determine each node's centrality score. The basic idea behind eigenvector centrality is that a central actor is connected to other central actors. It is distinct from the simpler degree centrality in that it weights connections rather than assigning a score based on the number of connections alone.<ref>"Eigenvector Centrality." (n.d.). Retrieved July 6, 2017, from https://www.sci.unich.it/~francesc/teaching/network/eigenvector.html.</ref> In <span class="scayt-misspell-word" data-scayt-word="EconDash" data-scayt-lang="en_US">Trade Networks visualization</span>, eigenvector centrality is used to analyze centrality of a country in a trade network in a particular year.&nbsp;<br />
<br />
== Data Structure ==<br />
<br />
=== Monadic Data ===<br />
<br />
Monadic data are those that describe one&nbsp;country in a single year with a structure of country-year. For example, Senegal's GDP per capita at PPP in 2012. In the EconDash's Trade Networks visualization, monadic variables include all Dependent Variables (i.e. crises),&nbsp;Structural Variables (i.e. economic statistics) and Financial Variables (i.e.centrality scores). While centrality scores are calculated based on a country's trade relationships with other countries (nodes) in the global network, countries receive a single, annual centrality score for each trade&nbsp;sector.<br />
<br />
=== Dyadic Data ===<br />
<br />
Dyadic data are those that describe the relationship between two countries in a single year with a structure of country-country-year. For example, total ICT trade between the US and China in 2015. In the EconDash's Trade Networks visualization, dyadic variables include all Network Variables (i.e. abosolute and relative levels of trade). The dyadic trade data is used to analyze bilateral trade levels between countries in the following sectors.&nbsp;Each sector is analyzed as percent of partner country's GDP as well as&nbsp;total intrasector trade in millions of US dollars:<br />
<br />
#Energy<br />
#Manufacturing<br />
#Information and Communication Technology (ICT)<br />
#Materials<br />
#Services<br />
#Agriculture<br />
#Total Trade<br />
<br />
= EconDash: Economic Vulnerabilities =<br />
<br />
This interface focuses on economic vulnerabilities across countries across time. This interface&nbsp;is based on monadic independent variables from 1960 to 2015 and a binary dependent variable namely the occurrence of economic crises. This visualization also includes groups of the independent variables along with groups of countries developed on the basis of specific criteria. To access the dashboard click [[Here|here]].&nbsp;<br />
<br />
== Navigating the interface ==<br />
<br />
[[File:EconDash viz 2.JPG|frame|right|sub|upright|Figure 4: Economic Vulnerabilities Interface (with description of all features)]]<br />
<br />
The interface enables the user to view and analyze various independent variables across countries across time. The dashboard presents a world map populated with a relevant variable in a particular year. The variable is color scaled and a “valence” has been defined for each variable. For example, the demographic dividend moves from red to green (lowest to highest), whereas infant mortality moves from green to red (lowest value to highest value). There are three filters at the top right of the visualization that allow the user to select a relevant year, a relevant variable or indicator and a particular country group. The dashboard allows the user to play a particular variable over time so that the unfolding trend can be analyzed visually across countries.<br />
<br />
Below the map visualization, the user can see a line/bar graph describing the trend of the variable for the world as a whole over time. When a user hovers over a particular country, this line/bar graph describes the trend for the country instead of the world as a whole over time. Also note that this graph will show the trend over the entire time horizon even when the filter above is set to a particular year. This enables the user to understand the overall trend before selecting a particular country.<br />
<br />
Under the line graph, the user can see what group a particular variable belongs to. For example, the variable demographic dividend belongs to the group ‘Demographic Vulnerabilities’.<br />
<br />
Finally, in order to better understand vulnerability to crises, the dashboard helps the user analyze the same not just across countries but also across “groups” of countries. The basis of these groups include factors such as income levels, development levels, geographic region, exchange rate regime etc. The filter above the map visualization has an option for selection of country groups. This enables the user to see a cluster of countries and the variables for the same.<br />
<br />
[[Media:Figure_4|Figure 4]]&nbsp;shows the interface along with all of its basic features. The country grouping function has been described in detail in the sections below.<br />
<br />
== Defining variables and groups ==<br />
<br />
=== Dependent variable: Occurence of economic crises ===<br />
<br />
The dependent variable (DV) was for the purpose of the second visualization was calculated on the basis of the percent change in the GDP at MER. The following steps were followed in the computation of the DV,<br />
<br />
First, the percent change in GDP at MER was calculated from 1960 onwards using historical data and forecasts from IFs. A threshold&nbsp;was&nbsp;set for the DV, namely, where the change in the growth rate was lesser than -4%. A binary variable&nbsp;was&nbsp;computed i.e. the value in a particular year for a particular country was set to 1 where an economic crisis was said to occur, if the&nbsp;threshold&nbsp;was&nbsp;met.<br />
<br />
The variable “Occurrence of Economic Crisis” that appears in the visualization is a combination of these three dependent variables.&nbsp;<br />
<br />
=== Independent variables ===<br />
<br />
This display allows the user to view 44&nbsp;independent variables in addition to the dependent variable (described above) across countries and across time. All the variables have been grouped into seven main categories, namely,<br />
<br />
#'''The Dependent variable''' (This is the occurrence of economic crisis that has been described above)<br />
#'''Economic input dependencies and vulnerabilities''' (This group includes variables such as Raw material imports, Food imports etc.)<br />
#'''Financial vulnerabilities''' (This group includes variables such as the average exchange rate, the balance of payments, capital account balance etc.)<br />
#'''Environmental vulnerabilities''' (This group includes variables such as the number of displacements on account of natural disasters, carbon emissions, precipitation change etc.)<br />
#'''Political vulnerabilities''' (This group includes variables such as polity scores, the occurrence of events of political instability etc.)<br />
#'''Demographic vulnerabilities''' (This group includes variables such as the population, youth bulge, dependency ratios etc.)<br />
#'''Economic output dependencies and vulnerabilities''' (This group includes variables such as Exports, export diversification, GDP, GDP per capita etc.)<br />
<br />
=== Country grouping function ===<br />
<br />
[[File:EconDash viz2 groups.JPG|frame|right|upright|Figure 5: Display of the grouping function in the interface along with description of all components]]<br />
<br />
To better understand what drives economic crises, the visualization also gives the user the option to view the occurrence of crises across groups of countries in addition to individual countries. These groups were developed using specific criteria such as fuel imports, exchange rate regimes, levels of development etc. The user can currently select from up to 8 groups of countries with various sub-groups. The main groups are,<br />
<br />
#Levels of corruption<br />
#Ease of doing business<br />
#Fuel exports<br />
#Income levels<br />
#Currency regimes<br />
#Anchor currency in the economy<br />
#Credit rating for the country<br />
#Development levels.<br />
<br />
Figure 5&nbsp;shows the country grouping function&nbsp;in the interface.<br />
<br />
== List of variables presented in the interface ==<br />
<br />
{| border="1" cellpadding="1" cellspacing="1" width="899"<br />
|-<br />
| height="20" width="321" | '''Variable name'''<br />
| width="387" | '''Description'''<br />
| width="191" | '''Variable Group'''<br />
|-<br />
| height="60" width="321" | Occurrence Of Economic Crisis<br />
| width="387" | Binary variable that describes the occurrence of economic crisis.&nbsp; Derived using change in the GDP growth rates of a country over time.<br />
| width="191" | Dependent variable<br />
|-<br />
| height="60" width="321" | Raw Materials Import<br />
| width="387" | Raw materials imports<br />
| width="191" | Economic input dependencies and vulnerabilities<br />
|-<br />
| height="60" width="321" | Agricultural Import Dependence<br />
| width="387" | Agricultural imports as a percentage of food demand<br />
| width="191" | Economic input dependencies and vulnerabilities<br />
|-<br />
| height="40" width="321" | Average Exchange Rate (National Currency To USD)<br />
| width="387" | Avg. Exchange Rate, NC/US$, Rate<br />
| width="191" | Financial vulnerabilities<br />
|-<br />
| height="20" width="321" | Capital Account Balance<br />
| width="387" | Balance of payments: Capital account (net)<br />
| width="191" | Financial vulnerabilities<br />
|-<br />
| height="40" width="321" | Climate Vulnerability Index<br />
| width="387" | Index of climate change vulnerability from Notre Dame Global Adaptation Initiative (ND-GAIN)<br />
| width="191" | Environmental vulnerabilities<br />
|-<br />
| height="40" width="321" | Carbon Emissions<br />
| width="387" | Annual carbon emissions<br />
| width="191" | Environmental vulnerabilities<br />
|-<br />
| height="20" width="321" | Corruption Perception Index<br />
| width="387" | Corruption scores from transparency international<br />
| width="191" | Political Vulnerabilities<br />
|-<br />
| height="20" width="321" | Current Account (As A Percent Of GDP)<br />
| width="387" | Current account balance as a percent of GDP<br />
| width="191" | Financial Vulnerabilities<br />
|-<br />
| height="40" width="321" | Demographic Dividend<br />
| width="387" | Ratio of the working population to that of non-working population<br />
| width="191" | Demographic Vulnerabilities<br />
|-<br />
| height="20" width="321" | Discount Rate<br />
| width="387" | Discount Rate, Percent per annum<br />
| width="191" | Financial vulnerabilities<br />
|-<br />
| height="60" width="321" | Diversification Index&nbsp;&nbsp; (Exports)<br />
| width="387" | Diversification index&nbsp;&nbsp; Exports<br />
| width="191" | Economic output dependencies and vulnerabilities<br />
|-<br />
| height="60" width="321" | Diversification Index&nbsp;&nbsp; (Imports)<br />
| width="387" | Diversification index&nbsp;&nbsp; Imports<br />
| width="191" | Economic input dependencies and vulnerabilities<br />
|-<br />
| height="20" width="321" | Economic Freedom Score<br />
| width="387" | Economic freedom scores from fraser international<br />
| width="191" | Political Vulnerabilities<br />
|-<br />
| height="20" width="321" | Education (Years Of Schooling)<br />
| width="387" | Average years of schooling<br />
| width="191" | Demographic Vulnerabilities<br />
|-<br />
| height="40" width="321" | Educational Attainment<br />
| width="387" | Education- Average years of Education between ages 15 to 24<br />
| width="191" | Demographic Vulnerabilities<br />
|-<br />
| height="60" width="321" | Electricity Access<br />
| width="387" | Percent of population with electricity access<br />
| width="191" | Economic Input Dependencies and Vulnerabilities<br />
|-<br />
| height="60" width="321" | Exports As A Percent Of GDP<br />
| width="387" | Exports as a percent of GDP<br />
| width="191" | Economic Output Dependencies and Vulnerabilities<br />
|-<br />
| height="60" width="321" | FDI Inflows As A Percent Of GDP<br />
| width="387" | FDI Inflows as a percent of GDP<br />
| width="191" | Economic Input Dependencies and Vulnerabilities<br />
|-<br />
| height="20" width="321" | Foreign Exchange Reserves (Including Gold)<br />
| width="387" | Foreign Exchange Reserves (Including Gold)<br />
| width="191" | Financial vulnerabilities<br />
|-<br />
| height="20" width="321" | Sociopolitical Freedom Score<br />
| width="387" | Socio-Political Freedom<br />
| width="191" | Political Vulnerabilities<br />
|-<br />
| height="60" width="321" | GDP Growth Rate<br />
| width="387" | Growth rate of GDP at MER<br />
| width="191" | Economic Output Dependencies and Vulnerabilities<br />
|-<br />
| height="60" width="321" | GDP Per Capita At PPP<br />
| width="387" | GDP per capita at Purchasing Power Parity<br />
| width="191" | Economic Output Dependencies and Vulnerabilities<br />
|-<br />
| height="20" width="321" | Government Effectiveness<br />
| width="387" | Government Effectiveness<br />
| width="191" | Political Vulnerabilities<br />
|-<br />
| height="20" width="321" | Government Expenditure As A Percent Of GDP<br />
| width="387" | Government expenditure as a percent of GDP<br />
| width="191" | Financial vulnerabilities<br />
|-<br />
| height="20" width="321" | Gross Savings (% Of GDP)<br />
| width="387" | Gross savings (% of GDP)<br />
| width="191" | Financial vulnerabilities<br />
|-<br />
| height="20" width="321" | IGO Membership<br />
| width="387" | Membership in international organizations<br />
| width="191" | Political Vulnerabilities<br />
|-<br />
| height="20" width="321" | Index&nbsp; Inflation (End Of Period Consumer Prices)<br />
| width="387" | Index&nbsp; Inflation, end of period consumer prices<br />
| width="191" | Financial vulnerabilities<br />
|-<br />
| height="20" width="321" | Infant Mortality<br />
| width="387" | Deaths per 1000 infants born<br />
| width="191" | Demographic Vulnerabilities<br />
|-<br />
| height="40" width="321" | Internal War Magnitude<br />
| width="387" | Magnitude defined by PITF on the basis of number of casualties<br />
| width="191" | Political Vulnerabilities<br />
|-<br />
| height="20" width="321" | Internal War Occurrence<br />
| width="387" | Occurrence of internal war (binary variable)<br />
| width="191" | Political Vulnerabilities<br />
|-<br />
| height="20" width="321" | Lending Rate<br />
| width="387" | Lending Rate, Percent per annum<br />
| width="191" | Financial vulnerabilities<br />
|-<br />
| height="20" width="321" | Life Expectancy<br />
| width="387" | Average life expectancy at birth<br />
| width="191" | Demographic Vulnerabilities<br />
|-<br />
| height="20" width="321" | Middle Income Trap (Binary)<br />
| width="387" | Middle income trap (binary)<br />
| width="191" | Financial vulnerabilities<br />
|-<br />
| height="20" width="321" | National Currency Per SDR<br />
| width="387" | National Currency per SDR, Period average, Rate<br />
| width="191" | Financial vulnerabilities<br />
|-<br />
| height="40" width="321" | Percent Change&nbsp; Inflation ( End Of Period Consumer Prices)<br />
| width="387" | Percent change&nbsp; Inflation, end of period consumer prices<br />
| width="191" | Financial vulnerabilities<br />
|-<br />
| height="20" width="321" | Polity Score<br />
| width="387" | Polity scores from 0-20<br />
| width="191" | Political Vulnerabilities<br />
|-<br />
| height="20" width="321" | Population<br />
| width="387" | Population in millions<br />
| width="191" | Demographic Vulnerabilities<br />
|-<br />
| height="40" width="321" | Precipitation Change<br />
| width="387" | Percent change in precipitation since 1990<br />
| width="191" | Environmental vulnerabilities<br />
|-<br />
| height="20" width="321" | Ratio Of Gdp Growth Rate To That Of The US<br />
| width="387" | Ratio of GDP growth rate to that of the US<br />
| width="191" | Financial vulnerabilities<br />
|-<br />
| height="20" width="321" | National Currency Per SDR<br />
| width="387" | SDR, National Currency per SDR, Rate<br />
| width="191" | Financial vulnerabilities<br />
|-<br />
| height="20" width="321" | Treasury Bill Rate<br />
| width="387" | Treasury Bill Rate, Percent per annum<br />
| width="191" | Financial vulnerabilities<br />
|-<br />
| height="20" width="321" | Urban Population (Percent Of Total Population)<br />
| width="387" | Urban population as a percent of total population<br />
| width="191" | Demographic Vulnerabilities<br />
|-<br />
| height="40" width="321" | Water Demand As A Percent Of Freshwater Resources<br />
| width="387" | Annual water demand as a proportion of exploitable water resources<br />
| width="191" | Environmental vulnerabilities<br />
|-<br />
| height="20" width="321" | GDP At MER<br />
| width="387" | GDP at Market Exchange Rates<br />
| width="191" | Financial vulnerabilities<br />
|}<br />
<br />
== List of country groups presented in the interface&nbsp; ==<br />
<br />
{| border="1" cellpadding="1" cellspacing="1" width="988"<br />
|-<br />
| height="20" width="380" | '''Group name'''<br />
| width="608" | '''Description'''<br />
|-<br />
| height="100" | Corruption grouping<br />
| width="608" | Countries with a TI index score of greater than 5 are defined as "more transparent" and those with a score of less than 5 are defined as "less transparent". From 2012 onwards with a revision in the way the index is structured, countries with an index score of higher than 50 were defined as "more transparent" and those with lower than 50 were defined as "less transparent"<br />
|-<br />
| height="140" | Ease of doing business<br />
| width="608" | Countries are grouped into four quartiles on the basis of ranks on the ease of doing business scores from the World Bank,<br/>1. First quartile- Least ease of doing business (Ranked less than 48 on the index)<br/>2. Second quartile- Ranked between 47 and 97<br/>3. Third quartile- Ranked between 97 and 144<br/>4. Fourth quartile- Most ease in doing business Ranked 145 and higher<br/>2. Second quartile-&nbsp;&nbsp;<br />
|-<br />
| height="60" | Fuel exports<br />
| width="608" | Where , less than 35 percent of exports are made up of fuels, countries are classified as "Low percentage" and where more than 35 percent of exports are made up of fuel, they are classified as "High percentage"<br />
|-<br />
| height="20" | Income levels<br />
| width="608" | Based on income level definition from the world bank<br />
|-<br />
| height="20" | Currency regime<br />
| width="608" | Currency regime definitions from the IMF<br />
|-<br />
| height="20" | Anchor currency<br />
| width="608" | Anchor currency in the economy as identified by the IMF<br />
|-<br />
| height="20" | Credit rating<br />
| width="608" | Credit ratings in 2016 from Fitch<br />
|-<br />
| height="20" | Development levels<br />
| width="608" | Development levels defined by the IMF<br />
|}<br />
<br />
= References =<br />
<br />
<references /></div>Wikiadmin//pardee.du.edu/wiki/International_Energy_Agency_(IEA)International Energy Agency (IEA)2018-09-10T23:54:27Z<p>Wikiadmin: </p>
<hr />
<div>The '''International Energy Agency commenced''' its operation in 1974 under the auspices of the Organization for Economic Co-operation and Development (OECD). The IEA is the energy forum for 26 Member countries, all from the OECD, to improve the world’s energy supply and to promote reliable databases for energy-related information. IEA member governments are committed to sharing energy information, to co-ordinating their energy policies and to co-operating in the development of rational energy programs. IEA publishes monthly reports on electricity, natural gas, prices, and the oil market. The&nbsp;[http://www.worldenergyoutlook.org/publications/ ''World Energy Outlook'']&nbsp;is the IEA's most comprehensive publication, and is considerd " the world’s most authoritative source of energy market analysis and projections."<br />
<br />
The main IEA sources used by IFs are the '''''World Energy Balances (WEB)&nbsp;'''''<b>and </b>'''''World Energy Statistics (WES)''''','''&nbsp;'''databases assocated with the ''World Energy Outlook.'' They contain variables such as the production, trade, and consumption of coal, oil, gas, electricity, heat, renewables, and waste for OECD countries and over 100 non-OECD countries.<br />
<br />
= Data Acquisition =<br />
<br />
Unlike most data used in IFs, IEA data from the WEB and WES are not open source. The data must be purchased from the IEA and is delivered on two CD-ROMs.[http://www.iea.org/bookshop/730-World_Energy_Statistics_and_Balances_2016 [1]]&nbsp;Each disc runs on a database management software program called Beyond 20/20 that comes loaded on the discs, along with the data. Financial support for the purchase of the IEA data is available from a University Library Association grant. Pardee successfully applied for grant funding through this program for the 2017 update with the help of staff at&nbsp;the Anderson Academic Commons at the University of Denver.<br />
<br />
= Documentation =<br />
<br />
Full documentation is available for each dataset detailing its contents, structure, definitions, geographical coverage, etc.&nbsp;<br />
<br />
[http://wds.iea.org/wds/pdf/worldbal_documentation.pdf World Energy Balances 2016 Database Documentation]<br />
<br />
[http://wds.iea.org/wds/pdf/WORLDBES_Documentation.pdf World&nbsp;Energy Statistics 2016 Database Documentation]<br />
<br />
= Batch Pull =<br />
<br />
The IEA update is performed as a batch pull that includes '''138 series''' using the Batch Data Update feature in IFs. In the display, "IEA" is the Source and the&nbsp;"IEA Countries" country list is used for country concordance. For the Code Location in Source Data portion of the update form, the source Excel must be formatted so that each Code in Source term is in a different column. For example, SeriesEnImportsOilProductsIEA's Code in Source is&nbsp;"Imports.Oil products," so there should be two columns with "Imports" in one and "Oil products" in the next.<br />
<br />
= Series Codes =<br />
<br />
Because this is a batch pull, each series needs a Code in addition to a variable name to be imported. The Codes are listed in the DataDict, and should match&nbsp;the series name in the IEA source database. For the 2017 update, series on the WEB and WES discs were organized by two parameters, FLOW and PRODUCT, where FLOW is the first term of the Code and PRODUCT is the second term of the Code. [NOTE: Drag and drop FLOW, PRODUCT, UNIT,&nbsp;and COUNTRY to column and row headers to reconfigure the display in Beyond 20/20.]<br />
<br />
'''Example'''<br />
<br />
Variable:&nbsp;EnImportsOilProductsIEA<br />
<br />
Code:&nbsp;Imports.Oil products<br />
<br />
FLOW: Imports<br />
<br />
PRODUCT: Oil products<br />
<br />
[[File:IEA Beyond 2020 screenshot.png|800px|IEA Beyond 2020 screenshot.png]]<br />
<br />
= Pulling and Formatting the Data =<br />
<br />
== Pulling ==<br />
<br />
Ideally, the IEA series are pulled from the discs in bulk. As of the 2017 update, Beyond 20/20 limits the number of records that can be exported at one time, preventing all the data from being exported at once; however, it is possible to pull all records by PRODUCT&nbsp;or FLOW. For example, it is possible to pull all natural gas or oil products series&nbsp;at once from the WEB disc. Pull the data by assembling the correct configuration in Beyond 20/20, then going to File>Save As and saving it as a .xls file. It is also possible to copy and past individual (or a few) series into an Excel by highlighting them, but not a large number of series at once. [NOTE: The directly exported Excel file did not work on Pardee computers due to a policy setting issue, so the data&nbsp;had to be copied and pasted into a new sheet to be manipulated]<br />
<br />
[[File:IEA Beyond 2020 screenshot 2.png|800px|IEA Beyond 2020 screenshot 2.png]]<br />
<br />
== Formatting ==<br />
<br />
Once the series have been pulled by PRODUCT or FLOW, they need to be cleaned. Missing data are marked with a "..", "x", or "n," so remove these with a Find+Replace. The data can then be imported with the Batch Import tool using the Codes assocated with the FLOW and PRODUCT, along with the country name. It may be necessary to also clean the Codes, for example if there is a unit listed parenthetically after the name of the product (i.e. "Solar thermal (TJ-net)").<br />
<br />
[[File:IEA Excel ex.png|800px|IEA Excel ex.png]]<br />
<br />
= Conversions =<br />
<br />
Most of the necessary conversion for these series are done automatically through the Batch Import form. However, 22 series had to be converted manually for the the 2017 update from terajoules (TJ) to kilotones of energy equivalent (ktoe). The conversion factor used for these series is from the&nbsp;[https://www.iea.org/statistics/resources/unitconverter/ IEA Unit Converter]:&nbsp;TJ value*0.0238845897. These series include:<br />
<br />
{| border="0" cellpadding="0" cellspacing="0" width="320"<br />
|-<br />
| height="20" width="320" | EnConBiogasIndustrialIEA<br />
|-<br />
| height="20" | EnConBiogasOtherIEA<br />
|-<br />
| height="20" | EnConBiogasTotIEA<br />
|-<br />
| height="20" | EnConBiomassIndustrialIEA<br />
|-<br />
| height="20" | EnConBiomassOtherIEA<br />
|-<br />
| height="20" | EnConBiomassResidentialIEA<br />
|-<br />
| height="20" | EnConBiomassTotIEA<br />
|-<br />
| height="20" | EnConBiomassTransportIEA<br />
|-<br />
| height="20" | EnConSolarThermalTotIEA<br />
|-<br />
| height="20" | EnConSolarThermIndustrialIEA<br />
|-<br />
| height="20" | EnConSolarThermOtherIEA<br />
|-<br />
| height="20" | EnConSolarThermResidentialIEA<br />
|-<br />
| height="20" | EnExportsBiomassIEA<br />
|-<br />
| height="20" | EnExpProdBiogasCDIEA<br />
|-<br />
| height="20" | EnExpProdIndustrialWasteCDIEA<br />
|-<br />
| height="20" | EnExpProdMunicipalWasteNonRenewableCDIEA<br />
|-<br />
| height="20" | EnExpProdMunicipalWasteRenewableCDIEA<br />
|-<br />
| height="20" | EnExpProdnonspecPrimaryBiomassWasteCDIEA<br />
|-<br />
| height="20" | EnExpProdSolarThermalCDIEA<br />
|-<br />
| height="20" | EnImportsBiomassIEA<br />
|-<br />
| height="20" | EnProdBiomassIEA<br />
|-<br />
| height="20" | EnExpProdnonspecPrimaryBiomassWasteCDIEA<br />
|}<br />
<br />
= DataDict =<br />
<br />
'''Variable: '''<span style="font-size: 13px;">Variable names were not changed from previous years this data was pulled, and no additional variables were added for the 2017 pull.</span><br />
<br />
'''Table:&nbsp;'''These were not changed from previous years this data was pulled.&nbsp;<br />
<br />
'''Code in Source:&nbsp;'''These were not changed from previous years this data was pulled.<br />
<br />
'''Groups:&nbsp;'''These were not changed from previous years this data was pulled.&nbsp;<br />
<br />
'''Subgroups:&nbsp;'''These were not changed form previoys years this data was pulled.<br />
<br />
'''Definitions and Units:'''&nbsp;These&nbsp;were not changed from previous years this data was pulled.<br />
<br />
'''Extended Source Defn: '''All were marked as "No Extended Source" for 2017 pull.&nbsp;<br />
<br />
'''Units:'''&nbsp;These were not changed from previous years; the units used in this dataset are BBOE and GwHr.<br />
<br />
'''Years:&nbsp;'''Years for every series were changed to available data provided through the database. Most series begin&nbsp;in 1960 with the exception of EnExportsOilIEA, EnImportsOiliIEA, and EnProdOilIEA, which begin in 1971. All extend&nbsp;through either 2014 or 2015 as of the 2017 update.<br />
<br />
'''Source:'''&nbsp;The source name used in the 2017 update for all batch pull IEA series is "IEA (International Energy Agency) Batch Pull."<br />
<br />
'''Original Source:'''&nbsp;The original source used in the 2017 update for all series is the "World Energy Outlook."<br />
<br />
'''Notes: '''Notes were updated to reflect the source disc for the series (WEB or WES), any conversion factors used, and the appropriate initials.<br />
<br />
'''Aggregation:&nbsp;'''Aggregations were not changed from pervious updates.<br />
<br />
'''Disaggregation:&nbsp;'''Disaggregations were not changed from previous updates.<br />
<br />
'''Name in source:'''&nbsp;Names were updated based on the name of each variable as it is displayed in the Beyond 20/20 database format; generally matches the Code in Source.<br />
<br />
'''Decimal places:&nbsp;'''These&nbsp;were not changed from previous years this data was pulled.<br />
<br />
'''Country Concordance:&nbsp;'''IEA Countries were used (and updated for the 2017 pull, see below).<br />
<br />
'''Formula:&nbsp;'''These&nbsp;were not changed from previous years this data was pulled, and are either blank or convert data to BBOE or GwHr.<br />
<br />
= Preprocessor Series =<br />
<br />
Of the 138&nbsp;IEA Batch Pull series, 24 are preprocessor. These series should be first priority in any IEA batch update. They include:<br />
<br />
{| border="0" cellpadding="0" cellspacing="0" width="175"<br />
|-<br />
| height="20" width="175" | EnExportsCoalIEA<br />
|-<br />
| height="20" | EnExportsNatGasIEA<br />
|-<br />
| height="20" | EnExportsOilIEA<br />
|-<br />
| height="20" | EnExportsOilProductsIEA<br />
|-<br />
| height="20" | EnExportsPeatIEA<br />
|-<br />
| height="20" | EnExportsTotalIEA<br />
|-<br />
| height="20" | EnImportsCoalIEA<br />
|-<br />
| height="20" | EnImportsNatGasIEA<br />
|-<br />
| height="20" | EnImportsOilIEA<br />
|-<br />
| height="20" | EnImportsOilProductsIEA<br />
|-<br />
| height="20" | EnImportsPeatIEA<br />
|-<br />
| height="20" | EnImportsTotalIEA<br />
|-<br />
| height="20" | EnProdBiodieselIEA<br />
|-<br />
| height="20" | EnProdBiogasIEA<br />
|-<br />
| height="20" | EnProdCoalIEA<br />
|-<br />
| height="20" | EnProdGeothermIEA<br />
|-<br />
| height="20" | EnProdHydroIEA<br />
|-<br />
| height="20" | EnProdNatGasIEA<br />
|-<br />
| height="20" | EnProdNuclearIEA<br />
|-<br />
| height="20" | EnProdOilIEA<br />
|-<br />
| height="20" | EnProdSolarPhotoIEA<br />
|-<br />
| height="20" | EnProdSolarThermIEA<br />
|-<br />
| height="20" | EnProdTideWaveOceanIEA<br />
|-<br />
| height="20" | EnProdWindIEA<br />
|}<br />
<br />
= Non-Preprocessor Series =<br />
<br />
There are 114 nonpreprocessor series included in the IEA update. They include:<br />
<br />
{| border="0" cellpadding="0" cellspacing="0" width="64"<br />
|-<br />
| height="20" width="64" | EnConBiodieselTotIEA<br />
|-<br />
| height="20" | EnConBiodieselTransportIEA<br />
|-<br />
| height="20" | EnConBiogasIndustrialIEA<br />
|-<br />
| height="20" | EnConBiogasolineTotIEA<br />
|-<br />
| height="20" | EnConBiogasolineTransportIEA<br />
|-<br />
| height="20" | EnConBiogasOtherIEA<br />
|-<br />
| height="20" | EnConBiogasTotIEA<br />
|-<br />
| height="20" | EnConBiomassIndustrialIEA<br />
|-<br />
| height="20" | EnConBiomassOtherIEA<br />
|-<br />
| height="20" | EnConBiomassResidentialIEA<br />
|-<br />
| height="20" | EnConBiomassTotIEA<br />
|-<br />
| height="20" | EnConBiomassTransportIEA<br />
|-<br />
| height="20" | EnConCoalIndustrialIEA<br />
|-<br />
| height="20" | EnConCoalOtherIEA<br />
|-<br />
| height="20" | EnConCoalResidentialIEA<br />
|-<br />
| height="20" | EnConCoalTotIEA<br />
|-<br />
| height="20" | EnConCoalTransportIEA<br />
|-<br />
| height="20" | EnConCombustRenewWasteIndustrialIEA<br />
|-<br />
| height="20" | EnConCombustRenewWasteOtherIEA<br />
|-<br />
| height="20" | EnConCombustRenewWasteResidentialIEA<br />
|-<br />
| height="20" | EnConCombustRenewWasteTotIEA<br />
|-<br />
| height="20" | EnConCombustRenewWasteTransportIEA<br />
|-<br />
| height="20" | EnConElecIndustrialIEA<br />
|-<br />
| height="20" | EnConElecOtherIEA<br />
|-<br />
| height="20" | EnConElecResidentIEA<br />
|-<br />
| height="20" | EnConElecTotIEA<br />
|-<br />
| height="20" | EnConElecTransportIEA<br />
|-<br />
| height="20" | EnConGeothermIndustrialIEA<br />
|-<br />
| height="20" | EnConGeothermOtherIEA<br />
|-<br />
| height="20" | EnConGeothermResidentialIEA<br />
|-<br />
| height="20" | EnConGeothermTotIEA<br />
|-<br />
| height="20" | EnConNatGasIndustrialIEA<br />
|-<br />
| height="20" | EnConNatGasOtherIEA<br />
|-<br />
| height="20" | EnConNatGasResidentialIEA<br />
|-<br />
| height="20" | EnConNatGasTotIEA<br />
|-<br />
| height="20" | EnConNatGasTransportIEA<br />
|-<br />
| height="20" | EnConOtherBiofuelsIndustrialIEA<br />
|-<br />
| height="20" | EnConOtherBiofuelsTotIEA<br />
|-<br />
| height="20" | EnConOtherBiofuelsTransportIEA<br />
|-<br />
| height="20" | EnConSolarThermalTotIEA<br />
|-<br />
| height="20" | EnConSolarThermIndustrialIEA<br />
|-<br />
| height="20" | EnConSolarThermOtherIEA<br />
|-<br />
| height="20" | EnConSolarThermResidentialIEA<br />
|-<br />
| height="20" | EnExportsBiodieselIEA<br />
|-<br />
| height="20" | EnExportsBiogasolineIEA<br />
|-<br />
| height="20" | EnExportsBiomassIEA<br />
|-<br />
| height="20" | EnExportsCombustRenewWasteIEA<br />
|-<br />
| height="20" | EnExportsElecGwHrIEA<br />
|-<br />
| height="20" | EnExportsElecIEA<br />
|-<br />
| height="20" | EnExportsOtherBiofuelsIEA<br />
|-<br />
| height="20" | EnExpProdBioDieselsCDIEA<br />
|-<br />
| height="20" | EnExpProdBiogasCDIEA<br />
|-<br />
| height="20" | EnExpProdBiogasolineCDIEA<br />
|-<br />
| height="20" | EnExpProdCharcoalCDIEA<br />
|-<br />
| height="20" | EnExpProdIndustrialWasteCDIEA<br />
|-<br />
| height="20" | EnExpProdMunicipalWasteNonRenewableCDIEA<br />
|-<br />
| height="20" | EnExpProdMunicipalWasteRenewableCDIEA<br />
|-<br />
| height="20" | EnExpProdnonspecPrimaryBiomassWasteCDIEA<br />
|-<br />
| height="20" | EnExpProdOtherLiquidbiofuelsCDIEA<br />
|-<br />
| height="20" | EnExpProdOtherSourcesCDIEA<br />
|-<br />
| height="20" | EnExpProdPrimarySolidGasCDIEA<br />
|-<br />
| height="20" | EnExpProdSolarThermalCDIEA<br />
|-<br />
| height="20" | EnExpProdSPVCDIEA<br />
|-<br />
| height="20" | EnExpProdTideWaveOCeanCDIEA<br />
|-<br />
| height="20" | EnExpProdWindCDIEA<br />
|-<br />
| height="20" | EnImportsBiodieselIEA<br />
|-<br />
| height="20" | EnImportsBiogasolineIEA<br />
|-<br />
| height="20" | EnImportsBiomassIEA<br />
|-<br />
| height="20" | EnImportsElecGwHrIEA<br />
|-<br />
| height="20" | EnImportsElecIEA<br />
|-<br />
| height="20" | EnImportsOtherBiofuelsIEA<br />
|-<br />
| height="20" | EnOutputElecCoalCDIEA<br />
|-<br />
| height="20" | EnOutputElecCombustibleRenewableWasteCDIEA<br />
|-<br />
| height="20" | EnOutputElecCrudeNGLFeedstocksCDIEA<br />
|-<br />
| height="20" | EnOutputElecElectricityCDIEA<br />
|-<br />
| height="20" | EnOutputElecGasCDIEA<br />
|-<br />
| height="20" | EnOutputElecGeothermalCDIEA<br />
|-<br />
| height="20" | EnOutputElecHeatCDIEA<br />
|-<br />
| height="20" | EnOutputElecHydroCDIEA<br />
|-<br />
| height="20" | EnOutputElecNuclearCDIEA<br />
|-<br />
| height="20" | EnOutputElecOilProductsCDIEA<br />
|-<br />
| height="20" | EnOutputElecPeatCDIEA<br />
|-<br />
| height="20" | EnOutputElecSolarWindOtherCDIEA<br />
|-<br />
| height="20" | EnOutputElecTotalCDIEA<br />
|-<br />
| height="20" | EnProdBiogasolineIEA<br />
|-<br />
| height="20" | EnProdBiomassIEA<br />
|-<br />
| height="20" | EnProdCoalCDIEA<br />
|-<br />
| height="20" | EnProdCombustibleRenewableWasteCDIEA<br />
|-<br />
| height="20" | EnProdCombustRenewWasteIEA<br />
|-<br />
| height="20" | EnProdCrudeNGLFeedstocksCDIEA<br />
|-<br />
| height="20" | EnProdElectricityCDIEA<br />
|-<br />
| height="20" | EnProdGasCDIEA<br />
|-<br />
| height="20" | EnProdGeothermalCDIEA<br />
|-<br />
| height="20" | EnProdHeatCDIEA<br />
|-<br />
| height="20" | EnProdHydroCDIEA<br />
|-<br />
| height="20" | EnProdNuclearCDIEA<br />
|-<br />
| height="20" | EnProdOilProductsCDIEA<br />
|-<br />
| height="20" | EnProdOtherBiofuelsIEA<br />
|-<br />
| height="20" | EnProdPeatCDIEA<br />
|-<br />
| height="20" | EnProdSolarWindOtherCDIEA<br />
|-<br />
| height="20" | EnProdTotalCDIEA<br />
|-<br />
| height="20" | EnTPESCoalCDIEA<br />
|-<br />
| height="20" | EnTPESCombustibleRenewableWasteCDIEA<br />
|-<br />
| height="20" | EnTPESCrudeNGLFeedstocksCDIEA<br />
|-<br />
| height="20" | EnTPESElectricityCDIEA<br />
|-<br />
| height="20" | EnTPESGasCDIEA<br />
|-<br />
| height="20" | EnTPESGeothermalCDIEA<br />
|-<br />
| height="20" | EnTPESHeatCDIEA<br />
|-<br />
| height="20" | EnTPESHydroCDIEA<br />
|-<br />
| height="20" | EnTPESNuclearCDIEA<br />
|-<br />
| height="20" | EnTPESOilProductsCDIEA<br />
|-<br />
| height="20" | EnTPESPeatCDIEA<br />
|-<br />
| height="20" | EnTPESSolarWindOtherCDIEA<br />
|-<br />
| height="20" | EnTPESTotalCDIEA<br />
|}<br />
<br />
= Country Concordance =<br />
<br />
There is a unique country concordance table for the IEA series called "IEA Countries." This table should be checked for accuracy at the time of each update. For the 2017 update, the following countries had to be updated (to the form listed):<br />
<br />
{| border="0" cellpadding="0" cellspacing="0" width="279"<br />
|-<br />
| height="20" width="279" | Bosnia and Herzegovina<br />
|-<br />
| height="20" | Democratic Republic of the Congo<br />
|-<br />
| height="20" | Hong Kong (China)<br />
|-<br />
| height="20" | Democratic People's Republic of Korea<br />
|-<br />
| height="20" | Libya<br />
|-<br />
| height="20" | Moldova<br />
|-<br />
| height="20" width="279" | Mauritius<br />
|-<br />
| height="20" | Niger<br />
|-<br />
| height="20" | Suriname<br />
|-<br />
| height="20" | South Sudan<br />
|-<br />
| height="20" | Tanzania<br />
|-<br />
| height="20" | Viet Nam<br />
|}</div>Wikiadmin//pardee.du.edu/wiki/Version_notes_7.36_(September_2018)Version notes 7.36 (September 2018)2018-09-07T22:59:46Z<p>Wikiadmin: </p>
<hr />
<div>= Recent model updates =<br />
<br />
*New education quality variables&nbsp;within education model<br />
**See the flow chart overview of education quality [https://pardee.du.edu/wiki/Education#Education:_Learning_Quality_Scores here]<br />
**See the equations for education quality [https://pardee.du.edu/wiki/Education#Education_Equations:_Learning_Quality.C2.A0 here]<br />
*New labor model - detailed documentation [https://pardee.du.edu/wiki/Labor here]<br />
*New drug demand module&nbsp;within the Socio-Political model<br />
**See the drug demand flow chart [https://pardee.du.edu/wiki/Socio-Political#Drug_Demand here]<br />
**See the drug demand equations [https://pardee.du.edu/wiki/Socio-Political#Drug_Model_Equations here]<br />
*New societal violence module&nbsp;within the Socio-Political model<br />
**See the violence&nbsp;flow chart [https://pardee.du.edu/wiki/Socio-Political#Violence here]<br />
**See the violence&nbsp;equations [https://pardee.du.edu/wiki/Socio-Political#Violence_Model_Equations here]<br />
<br />
= Recent data updates (since January 2018) =<br />
<br />
{| border="1" cellpadding="0" cellspacing="0" width="471"<br />
|-<br />
| height="20" width="347" | '''Source'''<br />
| width="124" | '''Number of series&nbsp;'''<br />
|-<br />
| height="20" | AQU (AQUASTAT) BATCH PULL<br />
| align="right" | 51<br />
|-<br />
| height="20" | Barro-Lee<br />
| align="right" | 22<br />
|-<br />
| height="20" | BP’s Statistical Review of World Energy 2016<br />
| align="right" | 6<br />
|-<br />
| height="20" | Carbon Dioxide Information Analysis Center<br />
| align="right" | 1<br />
|-<br />
| height="20" | FAO<br />
| align="right" | 38<br />
|-<br />
| height="20" | Freedom House<br />
| align="right" | 1<br />
|-<br />
| height="20" | IFs calculations (drugs, education quality, Minerva)<br />
| align="right" | 6<br />
|-<br />
| height="20" | IHME<br />
| align="right" | 51<br />
|-<br />
| height="20" | IMF GFS<br />
| align="right" | 8<br />
|-<br />
| height="20" | IMF World Economic Outlook 2017<br />
| align="right" | 2<br />
|-<br />
| height="20" | JMP<br />
| align="right" | 5<br />
|-<br />
| height="20" | PovCalNet<br />
| align="right" | 1<br />
|-<br />
| height="20" | UNAIDS<br />
| align="right" | 6<br />
|-<br />
| height="20" | UNESCO Institute for Statistics (UIS)<br />
| align="right" | 97<br />
|-<br />
| height="20" | UNODC<br />
| align="right" | 4<br />
|-<br />
| height="20" | UNPD<br />
| align="right" | 3<br />
|-<br />
| height="20" | WDI<br />
| align="right" | 392<br />
|}</div>Wikiadmin//pardee.du.edu/wiki/LaborLabor2018-09-07T22:59:03Z<p>Wikiadmin: </p>
<hr />
<div>Please cite as:&nbsp;Irfan, T. Mohammod.&nbsp;2018.&nbsp;''"IFs Labor&nbsp;Model Documentation."''&nbsp;Pardee Center for International Futures, Josef Korbel School of International Studies, University of Denver, Denver, CO. Accessed DD Month YYYY &lt;https://pardee.du.edu/wiki/Labor&gt;<br />
<br />
Workers in an economy supply the expertise and the efforts needed to produce goods and services. In return the labor receives wages that they use to meet their current and future consumption needs. On one hand, shortage of labor with required skills prevents economies from realizing their growth potential. On the other hand, individuals falling short of the right qualifications might remain unemployed or underemployed failing to secure income needed for a decent living. The ongoing adjustments to find the best match between skills, jobs and wages can only be studied through a dynamic model of the labor market.&nbsp; &nbsp;<br />
<br />
Such a model should go beyond providing a reasonable answer to the obvious question of why employment and wages go up and down. An aggregate labor market must deal with issues that have strong interconnections with various other dynamic changes in the greater society. What kind of dividend of deficit can a society expect from its labor force given the phase of demographic transition in which it is situated? How severely would aging affect the pool of working age adults? Might increasing female participation rates offset some of the losses from aging? What is the level of skills and educational attainment in a society? These supply phenomena move relatively slowly unless there are huge disruptions, like a war or famine, or an aggressive policy push. The demand side, in contrast, needs to be more responsive in adjusting wages and employment given the investment and technology in the various sectors of the broader economy. In general, though, the labor market demonstrates some sluggishness compared to the goods and services markets as it involves moving human beings with various limitations. Consumption of goods and services depend on the income earned by the labor. Uneven distribution of employment and wages among labors of various types or between labor and capital for a long period of time can give rise to persistent inequality in a society. &nbsp;&nbsp;&nbsp;&nbsp;<br />
<br />
== Conceptual Framework ==<br />
<br />
Labor markets are markets for workers and jobs. In a labor market, employers meet their demand for labor with the supply of people willing to work at the wage the employers can offer. The employers raise the wage when there is a shortage of workers. Workers agree to take a lower wage when there are more of them than the firms need. In the real-world labor markets do not always clear at perfect equilibrium. Frinctional unemployment results for various reasons, for example, the search time between jobs. Structural unemployment can result from technology induced disruptions. Some unemployment could thus persist in the labor market even when there aren’t any short-term fluctuations. There is also the phenomenon of informal employment that consists of less sophisticated workers and entrepreneurs engaged in unregulated economic activities. &nbsp;In a dynamic model that covers the entire economy, the real wage earned by the labor drives the income and social mobility.&nbsp;&nbsp;&nbsp;<br />
<br />
To understand the long-term dynamics of the labor market, we need also examine the deeper determinants of labor demand and supply, the determinants that can shift the curves. Labor demand changes over time with the changes in demand for goods and services and the labor input needed to produce those. Labor productivity itself improves with technological progress. Long term transitions in the supply of labor are mostly demographic. &nbsp;<br />
<br />
Labor supply is determined by the working age population and the share of that population who are available for participation in the workforce. The labor supply is relatively stable as the demographic changes are slow in pace. As the share of elderly in the population increases, a recent trend in many societies, the rate of participation declines. Some of the aging impacts will be offset by the greater female participation rates, a second trend that surfaces as economies develop and women attain more education. Educational attainment also drives the general skill level of workers, male and female. Specific skills are obtained through training and experience that augment the knowledge obtained through general and specialized education. &nbsp;&nbsp;&nbsp;&nbsp;<br />
<br />
It is the demand side that causes most of the short-term imbalances in the labor market. &nbsp;In the long term, as said earlier, the important driver of demand for labor and their skills is technological progress. Labor requirement drops with advances in technology, more so for less skilled labor. Labor composition changes accordingly both within and across sectors. Rapid advances in technology can also cause disruption in the system when there is not much opening in the other sectors. Labor displacement is offset to some extent by the growth in the economy and the resulting increase in total demand. &nbsp;&nbsp;&nbsp;<br />
<br />
As we have already mentioned, employees maximize income and the firms minimize labor costs. When there are more laborers than the firms can hire, there is unemployment. Shifts in the rates of unemployment impacts wage, the price of labor. For example, wages drop in the event of rising unemployment as there are more people to hire from. Wage adjustments feed back to the demand for labor seeking to bring the market back to equilibrium.<br />
<br />
The challenges around the conceptual distinction between unemployment and employment is further complicated by the phenomenon of informal employment. In many developing countries there is a large urban non-agricultural informal sector where low-skilled workers work for wages typically lower than a formal employment.<br />
<br />
[[File:LMFlowchart1.png|frame|center|Description of the labor model]]<br />
<br />
== Dominant Relations ==<br />
<br />
The labor model in the International Futures system (IFs) balances the total supply of labor with the total labor demanded by all economic sectors. Total labor (LAB) is computed from the working age population and the labor participation rate. Population forecasts are obtained from the IFs demographic model. Participation rates (LABPARR) are computed by sex with a catchup algorithm for the female participation towards that for the male. Labor is also disaggregated by skill level, as determined by educational attainment, in a separate labor supply variable (LABSUP) which is used to distribute labor earnings by skill level. [** LABSUP do not affect the demand/supply balance now]<br />
<br />
Labor demands (LABDEMS) are driven by sectoral technology functions used to compute the labor requirement by skill level for each unit of potential valued added in the sector. These labor coefficients (LABCOEFFS) are multiplied with the projected value added for the sector to compute the needed manpower. The balancing mechanisms determines the labor employed in each of the sectors (LABS).&nbsp;&nbsp;<br />
<br />
The balancing, in the current version of the model, can be done in one of the two ways. In the first method, total needs combined from all economic sectors is normalized to the available pool of labor computed by subtracting the unemployed from those who are at or looking for work. The rate of unemployment is kept at its natural rate for which we use the base year rate of unemployment. (** This might need to be changed for countries where the market is undergoing some abrupt transition.)<br />
<br />
In the second balancing method, added in a recent revision of the model, total demand is equilibrated to supply through a CGE like market equilibrium model. An indexed wage (LABWAGEIND) and the rate of unemployment (LABUNEMPR) work as the equilibrating variables. As unemployment deviates from the target, PID algorithms send a signal for the wage to adjust. Wage adjustments cause adjustments in the “base” labor demands by sector computed from the labor-coefficient functions as described earlier. Wage signals also affects the labor participation rate. The magnitude of impact on the supply side is much lower than that on the demand side.<br />
<br />
Wage and unemployment rate are aggregated for the total labor market. The wage index starts with a base year value of 1 and the unemployment rates start with the historical data for the base year. Initial year unemployment rate works as the target for long term unemployment.<br />
<br />
== Key Dynamics ==<br />
<br />
The following key dynamics are directly related to the dominant relations:<br />
<br />
*Labor supply is determined from population of appropriate age in the population model (see its dominant relations and dynamics) and endogenous labor force participation rates, influenced exogenously by the growth of female participation.<br />
*Labor demand is driven by sectoral demand functions driven by technological progress<br />
<br />
== Structure and Agent System ==<br />
<br />
{| border="1" cellspacing="0" cellpadding="0" width="0" style="width:502px;"<br />
|-<br />
| style="width:242px;height:49px;" | <br />
'''System/Subsystem'''<br />
<br />
| style="height:49px;" | <br />
Labor market<br />
<br />
|-<br />
| <br />
'''Organizing Structure'''<br />
<br />
| <br />
Labor supply by skill level and labor demand by sector for each skill category represented within an equilibrium-seeking model with wage and unemployment rate as the equilibrating variables<br />
<br />
|-<br />
| <br />
'''Stocks'''<br />
<br />
| <br />
Population, labor, education, &nbsp;accumulated technology<br />
<br />
|-<br />
| <br />
'''Flows'''<br />
<br />
| <br />
Participation rate; Coefficients of labor demand; Employment (unemployment); Wage<br />
<br />
|-<br />
| <br />
'''Key Aggregate&nbsp;''''''Relationships&nbsp;'''<br />
<br />
'''(illustrative, not comprehensive)'''<br />
<br />
| <br />
Labor supply is driven by demographic changes; Participation of female change over time; Labor requirement changes with technological development; Unemployment rate drives wage; Wage movements affect labor demand and participation rate<br />
<br />
|-<br />
| <br />
'''Key Agent-Class Behavior&nbsp;''''''Relationships'''<br />
<br />
'''(illustrative, not comprehensive)'''<br />
<br />
| <br />
Households and work/leisure, and female participation patterns;<br />
<br />
&nbsp;<br />
<br />
Firms and hiring;<br />
<br />
&nbsp;<br />
<br />
<br />
<br />
<br />
<br />
|}<br />
<br />
= Labor Model Data =<br />
<br />
The labor supply and unemployment data that we use in our model is from International Labor Organization (ILO). For data on the demand side, we used data from the Global Trade Analysis Project. Wage variable used in the equilibration algorithm&nbsp;is an index anchored to the base year of the model.<ref>GTAP database helped us compute wage rates by sector and skill.</ref> IFs preprocessor prepared these data for model use using various estimation, conversion and reconciliation processes.&nbsp; &nbsp; &nbsp;<br />
<br />
== Definitional Issues ==<br />
<br />
There are ambiguities in the way some of the labor market variables are defined. Labor participation rates and the rate of unemployment are two that need special attention.<br />
<br />
The size of the labor supply available for economic activities is expressed with the labor force participation rate. ILO defines this as a “measure of the proportion of country’s working-age population that engages actively in the labor market, either by working or looking for work.”<ref>http://www.ilo.org/ilostat-files/Documents/description_LFPR_EN.pdf</ref>&nbsp;National labor force surveys and census data are used to estimate this rate. The definition of labor force here includes both employed and unemployed and the rate is expressed as a percentage of working-age population. Working-age population is defined here as the population above legal working-age. For international comparability, ILO adopts a convenient minimum threshold of fifteen years as working age and avoids putting any upper age limit. In practice, both the minimum and the upper-age limits can vary by country. For example, the working-age in the USA is sixteen years. In the Netherlands the upper age limit is seventy-five years, whereas South African data uses an upper age limit of 64.<ref>https://www.bls.gov/fls/flscomparelf/technical_notes.pdf</ref><br />
<br />
Ambiguities are more abundant in the definition of unemployment. ILO came up with a guideline on this as well. Per the ILO guideline, the unemployed are those among the working-age population who are not employed, are available for work and are actively looking for jobs<ref>The definitions around employed and unemployed were agreed upon by nations through the ‘Resolution concerning statistics of work, employment and labor underutilization’ adopted by the 19th International Conference of Labor Statisticians (ICLS) in 2013. (Bourmpoula et al, 2017: 6).</ref>; the unemployment rate is expressed as a percentage of those who are in the labor force. The availability and job-seeker status could be defined in different ways giving rise to incompatibility in data. &nbsp;While there seems to be little room for disagreement on whether someone is at work or not, whether that work should be considered as employment is contested at many times.<br />
<br />
The debates around the nature and type of employment can range from gainfulness to workplace setting. For example, a large number of workers in the low-income low-regulation developing countries work outside the purview of formal enterprises. According to an ILO estimate, more than half of the global labor force and more than 90% of Micro and Small Enterprises (MSEs) worldwide are in the so called informal economy.<ref>http://www.ilo.org/global/topics/employment-promotion/informal-economy/lang--en/index.htm</ref> This might explain the apparently counterintuitive pattern of low unemployment rate in some low-income countries (e.g., 2.2% for Guatemala) and relatively higher numbers for some of the developed nations. The low numbers in the poorer countries hide the prevalence of extremely low wage jobs in the informal sectors in these countries, the only options for the vulnerable people in the absence of any kind of social safety net. &nbsp;Contrastingly, in the developed countries the so called ‘gig-economy’ is attracting more and more workers who choose to work on their own rather than in a formal enterprise. ILO conceptualization makes the informal work part of total employment. The stacked Venn diagram below presents the relationship among the labor force metric including informal employment. IFs also models informal economy both in terms of GDP share and employment share of informal in the total economy and employment.<br />
<br />
[[File:LaborSubsets.png|frame|right|Relationship among various labor measurement]]<br />
<br />
Incompatibility can arise in the treatment of various population groups for the computation of the denominator for participation and unemployment rates.<ref>For example, the USA excludes people in the defense services and those in the prisons or mental asylums in their computation of the civilian non-institutional working-age population. There are also variations in the treatments of students, those recently laid-off, and family workers. Please see https://stats.bls.gov/opub/mlr/2000/06/art1full.pdf for a discussion </ref> ILO makes their best efforts to make adjustments in the data for the sake of international comparison. For example, ILO asks countries that deviate from ILO guidelines to collect data needed to convert national figures to ILO figures. It is likely that some differences might have slipped past the adjustment process. We use ILO data and continue to update our database&nbsp;&nbsp;<br />
<div><div id="ftn4"><div id="ftn1"><br />
The GTAP data that we use for the demand side of the labor model is taken as labor headcounts and is thus immune from ambiguities around rate computation. As far as we could gather<ref>Please see the webpage for documentation on GTAP labor data statistic: https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=3248</ref>, the data includes both the formal and informal employment. We also need mention here that the GTAP database reconciles the labor data to calibrate the general equilibrium modeling that they do for the trade analyses. The data could thus be somewhat different from data collected through direct surveys. As a CGE model IFs is benefited by using calibrated data.<br />
<br />
== Sources of Labor Data ==<br />
<br />
IFs model uses ILO data for labor participation rates and for the unemployment rate. The data in IFs are collected from World Bank’s World Development Indicators (WDI) database. According to their documentation, WDI obtained the data from the ILO.<br />
<br />
Unemployment rate data in IFs is also collected from WDI. Like the participation rates WDI also obtains their unemployment data from ILO.<ref>The name of the IFs table is SeriesLaborUnemploy%</ref><br />
<br />
For employment and labor demand data IFs uses Purdue University’s Global Trade Analysis Project (GTAP) database. GTAP collects and compiles factor payments, imports, and intersectoral flow data to calibrate CGE models of national economies for trade and other analyses. In their ninth release in 2016, GTAP published data for 140 countries and regions for the year 2011. The earlier GTAP releases, which the IFs model used for its previous versions, compiled data for the years 2004 and 2007. GTAP data release aggregates economic activities into 57 commodities and activities following International Standard Industrial Classification (ISIC). The IFs model maps the 57 GTAP sectors into six economic sectors of IFs – agriculture, energy, material and mining, manufacture, services and ICT. Appendix 2 presents two tables listing the sectors mapping between IFs and GTAP, and GTAP and ISIC. GTAP further disaggregates labor in each of the commodities/activities into five occupation and skill categories following the nine category International Standard Classification of Occupations (ISCO-88). The IFs model collapses five GTAP occupation categories into the simple IFs dichotomy of skilled and unskilled. The mapping of occupations and skills are presented in the third appendix of this document. &nbsp;<br />
<br />
The data in the main GTAP database, prepared for CGE modeling, are all in dollar unit and thus do not include labor headcounts. We have used a ‘satellite’ GTAP database<ref>See Weingarden and Tsigas, 2010 for the details on the preparation of this database.</ref>&nbsp;for labor headcounts by skill and sector. The labor counts were also used to plot labor requirement functions for each of the IFs economic sectors and skill categories. The wage share of skilled and unskilled labor in each sector was computed using the labor headcounts and labor payments.<br />
<br />
== Scope of IFs Labor Model ==<br />
<br />
The IFs labor model simulates labor market at the national level. Each national labor market forecasts labor demand and employment by six sectors - agriculture, energy, mining, manufacture, services and ICT- and two skill levels - skilled and unskilled. The supply side do not have sectoral representation. IFs forecasts total labor force and labor supply by the two skill levels. Labor participation rate is computed in IFs by gender. Wage and unemployment rate is forecast for the overall labor market only.&nbsp;&nbsp;<br />
<br />
== Labor Model Pre-processor ==<br />
<br />
IFs system has a data preprocessor that prepares the initial conditions for the model using historical databases and various assumptions and estimated relationships to fill in the missing data and make data adjustments as needed.<ref>For more details, please see ‘The Data Pre-Processor of International Futures (IFs)” by Barry B. Hughes (with Mohammod Irfan) at http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf</ref> Pre-processing of labor data takes place in two IFs pre-processing modules. Labor participation rate data, which is closely related to demography, is processed in the population pre-processor. Unemployment rate and labor demand data are processed in the economic pre-processor.&nbsp;<br />
<div><div id="ftn1"><br />
=== Pre-processing Labor participation rate and unemployment ===<br />
<br />
For initializing labor participation rates by sex (LABPARR) the model uses the historical values from the base year or the most recent year with data.<ref>The data tables that the IFs model pre-processor use for initializing labor participation rates are: SeriesLaborParRate15PlusFemale%, SeriesLaborParRate15PlusMale%.</ref> For countries with no data we use regression relationships of the participation rates, for men and for women, with income per capita. The relationships, shown in the next figure, are not great. However, the functions affect only five countries for which we do not have any data at all: Grenada, Kosovo, Micronesia, Seychelles and South Sudan.<ref>We should try to collect participation rate for these countries from country sources.</ref><br />
<div><div id="ftn2"><br />
IFs data series SeriesLaborUnemploy% is used for the initialization of unemployment rates. That series has annual unemployment rates for one or more years between 1980 and 2016, for 181 of the 186 IFs countries. For five countries (Grenada, Kosovo, Micronesia, Taiwan and South Sudan<ref>These are pretty much the same countries for which we do not have any participation rate data. This indicates ILO might have some administrative limitation in reporting data for these countries (notice Kosovo, Seychelles etc in the list)</ref>) there is no data at all. To fill in the missing data we use a regression function of unemployment rate against GDP per capita. Like the participation rate functions, this function does also not have much of an explanatory power.<br />
<div><div id="ftn1"><br />
=== Pre-processing labor demand and unemployment from GTAP ===<br />
<br />
The IFs economic pre-processor reads labor headcount and labor payment data from the GTAP database. In addition to performing sector and occupation/skill mapping between GTAP and IFs, pre-processor also use the labor headcount data to compute labor coefficient functions, the principal driver of labor demand in the IFs model.<br />
<br />
Labor coefficients are defined as the amount of labor needed to produce one unit of value added in a certain sector of the economy. The coefficients depend on the level of technology. The model uses GDP per capita as an indicator of the level of technological development. IFs pre-processor estimates labor coefficient functions for labor of different skill levels for the different sectors of the economy.<br />
<br />
The functions are derived from GTAP data we described earlier. The model pre-processor reads data on factor payments and aggregates data from 57 GTAP sectors to six IFs sectors. Shares of payment going to skilled and less-skilled workers in each of the sectors are then computed. Countries are grouped according to their level of technological development as represented by per capita income. For each group labor coefficients are obtained by taking an average of the country coefficients. &nbsp;We also convert labor payments data to labor headcount data using per capita income as a proxy for average wage. Labor coefficients and income are then plotted into a power function relationship. The figure below plots some of those labor functions.&nbsp;The functions fit quite well with a power law formulation.<ref>This is interesting given the prevalence of power law in all sorts of scale-up activities (West 2017).</ref><br />
<div><div id="ftn1"></div></div></div></div></div></div></div></div><br />
= Labor Model Flowcharts =<br />
<br />
The diagram below shows an outline of the IFs labor model. On the supply side, the total labor pool (LAB) is computed from the labor force participation rates, by sex, (LABPARR) and the population (POP) in their working age, i.e., population over 15 (POP15TO65 + POPGT65). Participation rates are driven by the demographic changes with an additional negative impact from aging and a catch-up in female participation rate. Skill level of the labor supply (LABSUP) is driven by the level of development (GDPPCP) and the demand for labor is driven by labor-coefficients (LABCOEFFS) computed from coefficient function representing shifts in demand with technological progress as proxied by the level of development (GDPPCP). Coefficients computed by sector and skill gives the labor requirement by skill type for each unit of value added (VADD) in the sector. Multiplying these coefficients with projected value added in each sector gives an estimate of the labor demand. &nbsp;<br />
<br />
Any surplus or shortage between total labor demand and supply is used to compute the rate of unemployment. Deviations in the unemployment rate (LABUNEMPR) signal wage changes through an equilibrium seeking algorithm. Both demand and supply respond to the wage variable (LABWAGEIND) indexed to the base year. The supply responses are much slower than the demand responses.<br />
<br />
[[File:FLOCHART2.png|frame|center|Labor Model Flowchart]]<br />
<br />
= Labor Model Equations =<br />
<br />
== Overview ==<br />
<br />
The labor model is a part of the IFs economic model that uses labor model output as an input to a Cobb-Douglas production function in a multi-sector general equilibrium model. IFs is a very long-run dynamic model. Instead of computing fixed short-run equilibria that clear the relevant markets IFs uses an equilibrium seeking algorithm to balance the various systems over the longer run. The algorithm is known as the PID (proportion-integral-derivative) controller algorithm and is used widely in industrial control systems. It makes equilibrium seeking variables in IFs move towards a set target. The algorithm works by computing a multiplier based on the movement of the variable towards the target, as obtained by an integral (I) of the path traversed, and the rate of movement towards the target, the derivative term. The multiplier is applied on the process variable (the P term), or a response variable, in the subsequent time period. In the labor model, unemployment rate (LABUNEMPR) is used as the process variable and the PID multiplier is used on the wage rate (LABWAGEIND). Job availability (LABDEMS) and participation rate (LABPARR) get affected by changes in wage. &nbsp;&nbsp;<br />
<br />
Throughout this section we use subscripts and notations common to other modules of IFs. For example, we use t for time period. Subscripts p and r represent sex and country/region, respectively, c is the cohort number, with cohort 1 representing the newborns, cohort1 the the one-year to four-year-olds, cohort two five-year to nine-year-olds etc. Values for p are 1 for male, 2 for female and 3 for both sexes combined. For economic sectors we use s and for skill levels sk.<br />
<br />
== Labor Supply: Equations ==<br />
<br />
The total pool of labor is computed by multiplying the population of working age with the labor force participation rate (LABPARR). &nbsp;Population forecasts come from IFs demographic model which computes both five-year and single-year age-sex cohorts (''agedst'', ''fagedst''). &nbsp;<br />
<br />
The labor model forecasts participation rates by country/region&nbsp; and gender. Participation rates in the model move with the changes in the demographic composition. Female participation rates, which have historically been lower than the same for the male in all societies, but has moved up in modern and affluent societies, get a catch-up boost in the model. Participation rates can also change when there is labor shortage or surplus and the employers try to incentivize or discourage workers by changing wage. This last impact is much less slow than similar wage impacts on the demand side.<br />
<br />
== Labor Participation Rate ==<br />
<br />
Labor participation rates (''LABPARR'') for male and female are first initialized with historical data.<br />
<br />
:<math>LABPARR_{r,p}= LABPARR_{r,p,t=1} </math><br />
<br />
A ‘catch-up’ boost is added to the female participation rate. The boost added (FemParLabMul) starts at a third of a percentage point and withers away following a non-linear path as the female rates approaches the catch-up target (FemParTar), The maximum catch-up that can occur over the horizon of the model is thirty percent.<br />
<br />
:<math>FemParTar_{r}=Amin(LabParRI_{r,p=1},LabParRI_{r,p=2}+30)</math><br />
<br />
:<math>FemParLabMul_{r}=(FemParTar_{r}-LABPARR_{r,p=2,t-1})/(FemParTar_{r}-LABPARR_{r,p=2,t-1})</math><br />
<br />
:<math>LABPARR_{r,p=2,t}=LABPARR_{r,p=2,t-1}+FemParLabMul_{r}*0.3</math><br />
<br />
Next, we compute and apply the aging impact on the participation rate. As the relative share of people over the retirement age increases, the participation rate declines. The model keeps track of the changes in the demographic ratio (PopAgingRatio) of the population who are in their prime working age of 15 to 64 (POPWORKING) to those at a common retirement age of sixty-five or older (POPGT65). This ratio declines as countries age. The percentage drop in the ratio comparative to the base year is scaled appropriately to compute the aging impact (aging_impact). This impact is added to the male and female labor participation rates, with the impact on the female participation rate being slightly lower than that on male rates.<br />
<br />
:<math>POPAgingRatio_{r,t}=POPWORKING_{r,t}/POPGT65_{r,t}</math><br />
<br />
:<math>aging_impact_{r,t}=100*((POPAgingRatio_{r,t}/POPAgingRatio_{r,t=1})-1)*0.2</math><br />
<br />
:<math>LABPARR_{r,p=1,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t} </math><br />
<br />
:<math>LABPARR_{r,p=2,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t}*0.95 </math><br />
<br />
Participation rates respond slowly to changes in wage and unemployment rate. The impact is implemented through a wage impact factor computed from annual changes in the wage index (labwageimpact). The base participation rates can be changed by model user through two model parameters: a direct multiplier on the participation rate (labparm), or one that changes participation by moving the retirement age (labretagem)<br />
<br />
<br />
<br />
:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact*0.05)*labparm_{r,p,t}*labretagem_{r,p,t}</math><br />
<br />
Total participation rate (LABPARRr,p=3,t) is computed by an weighted average of male and female participation rates.<br />
<br />
:<math>LABPARR_{r,p=3,t}= (∑_{p=1 to 2}∑_{c=4 to 21}(agedst{r,c,p,t}*LABPARR_{r,p,t}))/(∑_{p=1 to 2}∑_{c=4 to 21}agedst{r,c,p,t})</math><br />
<br />
== Total Labor ==<br />
<br />
Finally, the total number of labor available for work (LAB) is computed by multiplying the total participation rate with the population of fifteen-year-olds or older.<br />
<br />
:<math>LAB_{r,t}= LABPARR_{r,p=3,t}*∑_{p=1 to 2,c=4 to 21}agedst_{r,c,p,t}</math><br />
<br />
== Labor by skill level ==<br />
<br />
The labor model forecasts labor supply (LABSUP) by two skill categories. The variable (''LABSUP'') is initialized in the pre-processor by reading the employment by skill/occupation (''LABEMPS'') data from GTAP<ref>We collapse GTAP’s 57 sectors into the six economic sectors of IFs. GTAP collapses the nine occupation categories of ISCO-88 into five. In IFs those five categories are collapsed into a binary – skilled and unskilled. The sectoral and skill mappings are described in two appendices of these document.</ref>&nbsp; and adding the unemployment numbers. We assume same unemployment rate (''LABUMEMPR'') for skilled and unskilled labor.<br />
<br />
:<math>LABSUP_{r,t=1,sk}=∑_{s=1 to 6}(LABEMPS_{r,s,t=1}/(1-(LABUNEMPR_{r,t=1}/100))</math><br />
<br />
The model forecasts labor by skill through a model of the skilled share of the labor. Education, training, exposure, and experience of the employees all improve with the level of development. The model captures this with an analytic function of the skilled share (perskilled) driven by GDP per capita at PPP (GDPPCP) -<br />
<br />
:<math>perskilled_{r}=f(GDPPCP_{r})</math><br />
<br />
Among the causal drivers of skill, education is considered to be the most proximate. Education is strongly correlated with the level of development, the deeper driver of skill in the model. However, the recent increase in education and/or a policy driven educational expansion might add to the impact of education on skill. Additional impacts from education on skill, when there is any, is computed through an expected function formulation. For example, in a society where an average adult has more (or less) education than the adults in other societies at that level of development, the skill share is given a slight upward push (or downward pull). The expectation function is a logarithmic function of educational attainment of working age population (EDYRSAG15) driven by GDP per capita at PPP. Attainment above (or below) the expected level (YearsEdExp) is computed by the function output (YearsEd) adjusted for country situation (yearseddiff). The percentage adjustment to the skilled share (LabSupSkiAdj) is computed using additional (limited) education, i.e., the difference between actual (EDYRSAG15) and expected values of educational attainment, expressed as a percentage of the expected value. The adjustment is scaled appropriately and peters off over time.<br />
<br />
:<math>YearsEd_{r,t}= f(GDPPCP_{r,t})</math><br />
<br />
:<math>yearsdeddiff_{r}= EDYRSAG15_{r,p=3,t=2}-YearsEd_{r,t=2}</math><br />
<br />
:<math>YearsEdExp_{r,t}=YearsEd_{r,t}+yearsdeddiff_{r}</math><br />
<br />
:<math>LabSupSkiAdj_{r,t}=0.3*(EDYRSAG15_{r,p=3,t=2}*YearsEdExp_{r,t})/YearsEd_{r,t}</math><br />
<br />
:<math>LabSupSkiAdj_{r,t}=ConvergeOverTime(0,LabSupSkiAdj_{r,t},70)</math><br />
<br />
:<math>perskilled_{r,t}= perskilled_{r,t}*(1+LabSupSkiAdj_{r,t})</math><br />
<br />
The skilled share (perskilled) is multiplied with the total labor supply (LAB) to obtain the number of labors who are skilled (LABSUPskilled)<br />
<br />
:<math>LABSUP_{r,skilled,t}=LAB_{r,p,t}*perskilledI_{r,t} </math><br />
<br />
As a last step, the model adjusts for the country specific variations in the skilled labor count not captured by the deeper and the proximate models. This is done by saving a ratio (LABSUPSkilledRI) of the actual historical data and the model computed value in the initial year. In the subsequent years this ratio is used to adjust the skilled labor forecast gradually.<br />
<br />
:<math>LABSUPCompSkilled_{r}=LAB_{r}*perskilled_{r,t=1}/100 </math><br />
<br />
:<math>LABSUPSkilledRI_{r}=LABSUP_{r,skilled,t=1}/LABSUPCompSkilled_{r}</math><br />
<br />
:<math>LABSUP_{r,skilled,t}= LABSUP_{r,skilled,t}*ConvergeOverTime(LABSUPSkilledRI_{r},1,85)</math><br />
<br />
Number of unskilled labor is obtained by subtracting the skilled labor from the total pool.<br />
<br />
<br />
<br />
:<math>LABSUP_{r,unskilled,t}= LAB_{r,p,t}- LABSUP_{r,skilled,t}</math><br />
<br />
== Labor Demand: Equations ==<br />
<br />
IFs economic model forecasts production in six economic sectors. IFs labor model computes the longer-term and shorter-term determinants of demand for skilled and unskilled labor (LABDEMS) for the production processes. The long-term drivers of labor requirement are technological progress or the lack of it. In the shorter-term wage affects the labor demand most. Wage in turn is affected by labor supply or skill shortage.<br />
<br />
The IFs model divides economic activities into six economic sectors – agriculture, energy, materials, manufacture, services and information, and communication technologies. Workers in the IFs labor model are disaggregated into two skill types. While the skill composition varies by the technology used in the sector and starts tilting towards the more skilled with the progress in technology, absolute number of labors needed to produce the same output goes down with technological development for both skilled and unskilled labor. This is illustrated in the next figure which plots the changes in labor requirement against GDP per capita at PPP, a proxy for level of development. Agriculture is a much less skill-intensive process than the manufacture, however, with technological progress skill requirement improves rapidly in both sectors. The IFs labor model computes these labor requirement functions in the model pre-processor. As we have already described in the pre-processor section, the computation of these functions use GTAP data on employment by occupation and economic activity. Appendices 3 and 4 lists sector and occupation mapping between GTAP and IFs.<br />
<br />
[[File:LaborCoefficientFunctions.png|frame|center|665x445px|Labor coefficient functions by skill type for the agriculture and the manufacturing sector]]<br />
<br />
These functions are used to compute the labor coefficients (LABCOEFFS), i.e., number of skilled and unskilled labor needed to produce unit amount of output with the technology available, for which we use GDP per capita at PPP as a proxy.<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= f(GDPPCP_{r})</math><br />
<br />
manufacture, services and ICTech) and the subscrip sk stands for skill categories with 1 denoting unskilled and 2 skilled. The labor coefficients obtained from the analytical functions require some adjustments to incorporate country deviations from the functions for various factors not captured in the regression relationship. The first of these adjustments is a gradual removal of impacts of short-run fluctuations in output and labor from the computation of labor coefficient. This adjustment is applied on the coefficients computed from the function. The equation below shows a simplified form of these computations.<br />
<br />
:<math>LabCoeffAdjFac_{r,k,s,t}=f(igdpr_{r,t=2},(LAB_{r,t=2}/LAB_{r,t=1}),(LABCOEFFS_{r,t}/LABCOEFFS_{r,t-1}))</math><br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}=LABCOEFFS_{r,sk,s,t}(1-LabCoeffAdjFac_{r,k,s,t})</math><br />
<br />
Model users can use a global parameter (labcoeffsm) to change the labor coefficients by skill level for any or all of the six sectors –<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= LABCOEFFS_{r,sk,s,t}*'''labcoeffsm_{s,sk}'''</math><br />
<br />
To forecast the total labor demand, the labor coefficients (LABCOEFFS) are multiplied to the total projected output for each of the economic sectors. The forecast is adjusted for any discrepancy between data and model. The adjustment factor (LABDemsAdjFac) is computed as the initial ratio between the actual and computed employment. Actual employment is obtained from historical data (LABEMPS) processed using the GTAP database. The computed employment is obtained by multiplying the labor coefficients (LABCOEFFS) with the final output of the sector (VADD).<br />
<br />
:<math>LabDemsAdjFac_{r,s,sk}= LABEMPS_{r,s,sk,t=1}/(VADD_{r,s,t=1}*LABCOEFFS_{r,sk,s,t})</math><br />
<br />
The projected output is obtained by applying the growth rate (IGDPRCOR) on the sectoral value added from the previous year (VADD). The total labor demand is given by the product of the labor coefficients, projected output, demand adjustments and wage impacts (labwageimpactmul) and the number 1000 which adjusts the units for the equation. Wage impact comes from the level of unemployment and is computed in an equilibration process described in the next section. Model users can use a multiplicative parameter (labdemsm) to slide the demand upward or downward.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}=1000*VADD_{r,s,t-1}*(1+IGDPRCOR_{r})*LABCOEFFS_{r,sk,s,t}*LabDemsAdjFac_{r,s,sk}*labwageimpactmul_{r,s,sk}*'''labdemsm_{r,s}'''</math><br />
<br />
== Unemployment and Wage: Labor Market Equilibration ==<br />
<br />
The IFs labor model balances the labor market through an equilibrium seeking algorithm rather than computing an exact equilibrium at each time step. We use an algorithm borrowed from the control systems engineering. This PID controller algorithm, described also in the IFs economic model documentation, works by computing corrective signals for equilibrating variables using the deviations of a buffer variable, for example unemployment rate (LABUNEMPR), from a target value. The signal is computed from two quantities, the distance of the buffer from the target and the current rate of change of the buffer. The computation is tuned with PID elasticities to avoid oscillations. The computed signal is applied on the variable/s which need to be balanced, for example, demand and supply in the event of a market equilibration, thus getting closer to a balance at each step of simulation. The target value for the buffer variable and the tuning parameters of the control algorithm are obtained through rules-of-thumb and model calibration. The IFs labor model uses unemployment rate (LABUNEMPR) as the buffer variable for the market equilibration of labor demand and labor supply. The multiplier (i.e., corrective signal) obtained from the PID is applied on the wage index (LABWAGEIND). Changes in wage indices comparative to the base year, moderated through a second PID controller, is used to compute the final signal (labwageimpactmul) that drives labor demand and labor supply. Even though the model forecasts labor demand by sector and skill, and computes labor supply for both skill types, the equilibration algorithm works over the entire pool of labor. In other words, we assume that the skills are replaceable across sectors and the lack (or abundance) of jobs affects skilled and unskilled persons equally.<br />
<br />
At each annual timestep, the model computes the unemployment rate (LABUNEMPR) as the gap in between the total supply of labor (LAB) and the total demand. The gap (EmplGap) is expressed as a share of the total labor, the standard way to express unemployment rate.<br />
<br />
:<math>sumld=∑_{s,sk}LADEMS_{r,s,sk,t}</math><br />
<br />
:<math>EmplGap= LAB_{r,t}*sumld</math><br />
<br />
:<math>LABUMENPR_{r,t}= (EmplGap/LAB_{r,t})*100</math><br />
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As the target value (LabUnEmpRateTar) for the PID controller that modulates unemployment rate we use either the historical unemployment rate or a ten percent unemployment rate when the historical rate is higher than ten. Model users can override the historical target through a model parameter (labunemprtrgtval).<br />
<br />
:<math>LABUMENPRi_{r,t}= LABUMENPR_{r,t}</math><br />
<br />
:<math>LabUnempRateTarget_{r}=labunemptargetval_{r}</math><br />
:<math>If LabUnempRateTarget_{r}=0,<br />
LabUnempRateTarget_{r}= AMIN(LABUMENPRi_{r,t},10) </math><br />
<br />
Unemployment rate target, when it is different from the base year value, is reached gradually with a convergence period of forty years . The target rate is converted to count (LabUnEmplTar) to make it equivalent to the employment gap (EmplGap) computed earlier.<br />
<br />
:<math>LabUnEmplTar_{r}= LAB_{r,t}*ConvergeOverTime(LABUMENPRi_{r,t},0,100)</math><br />
<br />
The first order difference (Diffl1) between the target unemployment and the demand-supply gap is used to compute a second order difference (Diffl2) accounting for changes in the rate of movement. The two differences and the PID multipliers (elwageunemp1, elwageunemp2) are provided to the PID function (ADJSTR). Working age population (POP15TO65r,t) works as the scaling base of the PID controller. The controller algorithm gives a multiplier (mullw) that is used in the subsequent year to adjust wage.<br />
<br />
:<math>Diffl1_{t}=LabUnEmplTar_{r}-EmplGap</math><br />
<br />
:<math>Diffl2_{t}=Diffl1_{t}-Diffl1_{t-1}</math><br />
<br />
:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},elwageunemp1_{r},elwageunemp2_{r})</math><br />
<br />
Wage adjustments affect demand and supply with an increase in wage drawing demand downward and supply upward. The opposite affects occur with a downward movement of wage. The wage variable affected by the PID multiplier (LABWAGEIND) is an index initialized at one. We use an indexed rather than a dollar wage in the equilibration process to avoid affecting the process from other economic phenomena that affects wage, for example, a rise in real wage as GDP or the labor share of income grows.<br />
<br />
:<math>LABWAGEIND_{r,t=1}=1</math><br />
<br />
In the subsequent years of the model run, the wage index is first adjusted with the equilibration signal obtained from the unemployment rate PID controller in the previous period<br />
<br />
:<math>LABWAGEIND_{r,t=1}= LABWAGEIND_{r,t=1}* mullw_{r,t-1} </math><br />
<br />
A wage impact (labwageimpact) is then computed using the changes in the wage index relative to the base value. The impact is smoothed with a moving average algorithm.<br />
<br />
:<math>labwageimpact_{r}= labwageimpact_{r,t-1}*0.9+ (1-LABWAGEIND_{r,t})*0.1</math><br />
<br />
The smoothed impact is used as the equilibration signal for labor supply. As we have already described in the section on labor supply, a small fraction of the impact (labwageimpact) is applied to the labor participation rate. The impact is scaled down to account for the slow pace of changes on the supply side.<br />
<br />
:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact_{r,t}*0.05)</math><br />
<br />
For the impacts of wage on labor demand we use a second PID multiplier as opposed to using the changes in wage index that we have done on the supply side. The second PID uses the wage index itself as the process variable and uses the base year value of 1 as the target. The reason we had to use this second PID is to control the pace at which wage disequilibrium can affect demand, especially in the event of an abrupt shock. The smoothing and scaling down that works on the supply side is not enough to control oscillations on the demand side.<br />
<br />
:<math>Diffl1_{t}=LABWAGEIND_{r,t=1}-1</math><br />
<br />
:<math>Diffl2_{t}=LABWAGEIND_{r,t}-LABWAGEIND_{r,t-1} </math><br />
<br />
:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},ellabwage1_{r},ellabwage1_{r})</math><br />
<br />
A second impact factor (labwageimpactmul) is computed using the correction signal from this second multiplier:<br />
<br />
:<math>labwageimpactmul_{r,t}= labwageimpactmul_{r,t-1}*mullw_{r,t}</math><br />
<br />
This impact factor is applied on the labor demand as described in the section on labor demand.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}= LABDEMS_{r,s,sk,t}* labwageimpactmul_{r,t}</math><br />
<br />
== Informal Labor ==<br />
<br />
IFs forecast labor and GDP share of the informal sector. Informal labor forecast is not explicitly endogenized in the labor market though. They are rather driven by development, skill and regulatory factors.<ref>IFs economic model documentation has a detail description of the informal economy model.</ref>&nbsp;However, the productivity and revenue impacts of changes in informality affects output and thus labor demand implicitly as a very distal driver.<br />
<br />
= References =<br />
<references /></div></div></div></div>Wikiadmin//pardee.du.edu/wiki/LaborLabor2018-09-07T22:56:20Z<p>Wikiadmin: </p>
<hr />
<div>Workers in an economy supply the expertise and the efforts needed to produce goods and services. In return the labor receives wages that they use to meet their current and future consumption needs. On one hand, shortage of labor with required skills prevents economies from realizing their growth potential. On the other hand, individuals falling short of the right qualifications might remain unemployed or underemployed failing to secure income needed for a decent living. The ongoing adjustments to find the best match between skills, jobs and wages can only be studied through a dynamic model of the labor market.&nbsp; &nbsp;<br />
<br />
Such a model should go beyond providing a reasonable answer to the obvious question of why employment and wages go up and down. An aggregate labor market must deal with issues that have strong interconnections with various other dynamic changes in the greater society. What kind of dividend of deficit can a society expect from its labor force given the phase of demographic transition in which it is situated? How severely would aging affect the pool of working age adults? Might increasing female participation rates offset some of the losses from aging? What is the level of skills and educational attainment in a society? These supply phenomena move relatively slowly unless there are huge disruptions, like a war or famine, or an aggressive policy push. The demand side, in contrast, needs to be more responsive in adjusting wages and employment given the investment and technology in the various sectors of the broader economy. In general, though, the labor market demonstrates some sluggishness compared to the goods and services markets as it involves moving human beings with various limitations. Consumption of goods and services depend on the income earned by the labor. Uneven distribution of employment and wages among labors of various types or between labor and capital for a long period of time can give rise to persistent inequality in a society. &nbsp;&nbsp;&nbsp;&nbsp;<br />
<br />
== Conceptual Framework ==<br />
<br />
Labor markets are markets for workers and jobs. In a labor market, employers meet their demand for labor with the supply of people willing to work at the wage the employers can offer. The employers raise the wage when there is a shortage of workers. Workers agree to take a lower wage when there are more of them than the firms need. In the real-world labor markets do not always clear at perfect equilibrium. Frinctional unemployment results for various reasons, for example, the search time between jobs. Structural unemployment can result from technology induced disruptions. Some unemployment could thus persist in the labor market even when there aren’t any short-term fluctuations. There is also the phenomenon of informal employment that consists of less sophisticated workers and entrepreneurs engaged in unregulated economic activities. &nbsp;In a dynamic model that covers the entire economy, the real wage earned by the labor drives the income and social mobility.&nbsp;&nbsp;&nbsp;<br />
<br />
To understand the long-term dynamics of the labor market, we need also examine the deeper determinants of labor demand and supply, the determinants that can shift the curves. Labor demand changes over time with the changes in demand for goods and services and the labor input needed to produce those. Labor productivity itself improves with technological progress. Long term transitions in the supply of labor are mostly demographic. &nbsp;<br />
<br />
Labor supply is determined by the working age population and the share of that population who are available for participation in the workforce. The labor supply is relatively stable as the demographic changes are slow in pace. As the share of elderly in the population increases, a recent trend in many societies, the rate of participation declines. Some of the aging impacts will be offset by the greater female participation rates, a second trend that surfaces as economies develop and women attain more education. Educational attainment also drives the general skill level of workers, male and female. Specific skills are obtained through training and experience that augment the knowledge obtained through general and specialized education. &nbsp;&nbsp;&nbsp;&nbsp;<br />
<br />
It is the demand side that causes most of the short-term imbalances in the labor market. &nbsp;In the long term, as said earlier, the important driver of demand for labor and their skills is technological progress. Labor requirement drops with advances in technology, more so for less skilled labor. Labor composition changes accordingly both within and across sectors. Rapid advances in technology can also cause disruption in the system when there is not much opening in the other sectors. Labor displacement is offset to some extent by the growth in the economy and the resulting increase in total demand. &nbsp;&nbsp;&nbsp;<br />
<br />
As we have already mentioned, employees maximize income and the firms minimize labor costs. When there are more laborers than the firms can hire, there is unemployment. Shifts in the rates of unemployment impacts wage, the price of labor. For example, wages drop in the event of rising unemployment as there are more people to hire from. Wage adjustments feed back to the demand for labor seeking to bring the market back to equilibrium.<br />
<br />
The challenges around the conceptual distinction between unemployment and employment is further complicated by the phenomenon of informal employment. In many developing countries there is a large urban non-agricultural informal sector where low-skilled workers work for wages typically lower than a formal employment.<br />
<br />
[[File:LMFlowchart1.png|frame|center|Description of the labor model]]<br />
<br />
== Dominant Relations ==<br />
<br />
The labor model in the International Futures system (IFs) balances the total supply of labor with the total labor demanded by all economic sectors. Total labor (LAB) is computed from the working age population and the labor participation rate. Population forecasts are obtained from the IFs demographic model. Participation rates (LABPARR) are computed by sex with a catchup algorithm for the female participation towards that for the male. Labor is also disaggregated by skill level, as determined by educational attainment, in a separate labor supply variable (LABSUP) which is used to distribute labor earnings by skill level. [** LABSUP do not affect the demand/supply balance now]<br />
<br />
Labor demands (LABDEMS) are driven by sectoral technology functions used to compute the labor requirement by skill level for each unit of potential valued added in the sector. These labor coefficients (LABCOEFFS) are multiplied with the projected value added for the sector to compute the needed manpower. The balancing mechanisms determines the labor employed in each of the sectors (LABS).&nbsp;&nbsp;<br />
<br />
The balancing, in the current version of the model, can be done in one of the two ways. In the first method, total needs combined from all economic sectors is normalized to the available pool of labor computed by subtracting the unemployed from those who are at or looking for work. The rate of unemployment is kept at its natural rate for which we use the base year rate of unemployment. (** This might need to be changed for countries where the market is undergoing some abrupt transition.)<br />
<br />
In the second balancing method, added in a recent revision of the model, total demand is equilibrated to supply through a CGE like market equilibrium model. An indexed wage (LABWAGEIND) and the rate of unemployment (LABUNEMPR) work as the equilibrating variables. As unemployment deviates from the target, PID algorithms send a signal for the wage to adjust. Wage adjustments cause adjustments in the “base” labor demands by sector computed from the labor-coefficient functions as described earlier. Wage signals also affects the labor participation rate. The magnitude of impact on the supply side is much lower than that on the demand side.<br />
<br />
Wage and unemployment rate are aggregated for the total labor market. The wage index starts with a base year value of 1 and the unemployment rates start with the historical data for the base year. Initial year unemployment rate works as the target for long term unemployment.<br />
<br />
== Key Dynamics ==<br />
<br />
The following key dynamics are directly related to the dominant relations:<br />
<br />
*Labor supply is determined from population of appropriate age in the population model (see its dominant relations and dynamics) and endogenous labor force participation rates, influenced exogenously by the growth of female participation.<br />
*Labor demand is driven by sectoral demand functions driven by technological progress<br />
<br />
== Structure and Agent System ==<br />
<br />
{| border="1" cellspacing="0" cellpadding="0" width="0" style="width:502px;"<br />
|-<br />
| style="width:242px;height:49px;" | <br />
'''System/Subsystem'''<br />
<br />
| style="height:49px;" | <br />
Labor market<br />
<br />
|-<br />
| <br />
'''Organizing Structure'''<br />
<br />
| <br />
Labor supply by skill level and labor demand by sector for each skill category represented within an equilibrium-seeking model with wage and unemployment rate as the equilibrating variables<br />
<br />
|-<br />
| <br />
'''Stocks'''<br />
<br />
| <br />
Population, labor, education, &nbsp;accumulated technology<br />
<br />
|-<br />
| <br />
'''Flows'''<br />
<br />
| <br />
Participation rate; Coefficients of labor demand; Employment (unemployment); Wage<br />
<br />
|-<br />
| <br />
'''Key Aggregate&nbsp;''''''Relationships&nbsp;'''<br />
<br />
'''(illustrative, not comprehensive)'''<br />
<br />
| <br />
Labor supply is driven by demographic changes; Participation of female change over time; Labor requirement changes with technological development; Unemployment rate drives wage; Wage movements affect labor demand and participation rate<br />
<br />
|-<br />
| <br />
'''Key Agent-Class Behavior&nbsp;''''''Relationships'''<br />
<br />
'''(illustrative, not comprehensive)'''<br />
<br />
| <br />
Households and work/leisure, and female participation patterns;<br />
<br />
&nbsp;<br />
<br />
Firms and hiring;<br />
<br />
&nbsp;<br />
<br />
<br />
<br />
<br />
<br />
|}<br />
<br />
= Labor Model Data =<br />
<br />
The labor supply and unemployment data that we use in our model is from International Labor Organization (ILO). For data on the demand side, we used data from the Global Trade Analysis Project. Wage variable used in the equilibration algorithm&nbsp;is an index anchored to the base year of the model.<ref>GTAP database helped us compute wage rates by sector and skill.</ref> IFs preprocessor prepared these data for model use using various estimation, conversion and reconciliation processes.&nbsp; &nbsp; &nbsp;<br />
<br />
== Definitional Issues ==<br />
<br />
There are ambiguities in the way some of the labor market variables are defined. Labor participation rates and the rate of unemployment are two that need special attention.<br />
<br />
The size of the labor supply available for economic activities is expressed with the labor force participation rate. ILO defines this as a “measure of the proportion of country’s working-age population that engages actively in the labor market, either by working or looking for work.”<ref>http://www.ilo.org/ilostat-files/Documents/description_LFPR_EN.pdf</ref>&nbsp;National labor force surveys and census data are used to estimate this rate. The definition of labor force here includes both employed and unemployed and the rate is expressed as a percentage of working-age population. Working-age population is defined here as the population above legal working-age. For international comparability, ILO adopts a convenient minimum threshold of fifteen years as working age and avoids putting any upper age limit. In practice, both the minimum and the upper-age limits can vary by country. For example, the working-age in the USA is sixteen years. In the Netherlands the upper age limit is seventy-five years, whereas South African data uses an upper age limit of 64.<ref>https://www.bls.gov/fls/flscomparelf/technical_notes.pdf</ref><br />
<br />
Ambiguities are more abundant in the definition of unemployment. ILO came up with a guideline on this as well. Per the ILO guideline, the unemployed are those among the working-age population who are not employed, are available for work and are actively looking for jobs<ref>The definitions around employed and unemployed were agreed upon by nations through the ‘Resolution concerning statistics of work, employment and labor underutilization’ adopted by the 19th International Conference of Labor Statisticians (ICLS) in 2013. (Bourmpoula et al, 2017: 6).</ref>; the unemployment rate is expressed as a percentage of those who are in the labor force. The availability and job-seeker status could be defined in different ways giving rise to incompatibility in data. &nbsp;While there seems to be little room for disagreement on whether someone is at work or not, whether that work should be considered as employment is contested at many times.<br />
<br />
The debates around the nature and type of employment can range from gainfulness to workplace setting. For example, a large number of workers in the low-income low-regulation developing countries work outside the purview of formal enterprises. According to an ILO estimate, more than half of the global labor force and more than 90% of Micro and Small Enterprises (MSEs) worldwide are in the so called informal economy.<ref>http://www.ilo.org/global/topics/employment-promotion/informal-economy/lang--en/index.htm</ref> This might explain the apparently counterintuitive pattern of low unemployment rate in some low-income countries (e.g., 2.2% for Guatemala) and relatively higher numbers for some of the developed nations. The low numbers in the poorer countries hide the prevalence of extremely low wage jobs in the informal sectors in these countries, the only options for the vulnerable people in the absence of any kind of social safety net. &nbsp;Contrastingly, in the developed countries the so called ‘gig-economy’ is attracting more and more workers who choose to work on their own rather than in a formal enterprise. ILO conceptualization makes the informal work part of total employment. The stacked Venn diagram below presents the relationship among the labor force metric including informal employment. IFs also models informal economy both in terms of GDP share and employment share of informal in the total economy and employment.<br />
<br />
[[File:LaborSubsets.png|frame|right|Relationship among various labor measurement]]<br />
<br />
Incompatibility can arise in the treatment of various population groups for the computation of the denominator for participation and unemployment rates.<ref>For example, the USA excludes people in the defense services and those in the prisons or mental asylums in their computation of the civilian non-institutional working-age population. There are also variations in the treatments of students, those recently laid-off, and family workers. Please see https://stats.bls.gov/opub/mlr/2000/06/art1full.pdf for a discussion </ref> ILO makes their best efforts to make adjustments in the data for the sake of international comparison. For example, ILO asks countries that deviate from ILO guidelines to collect data needed to convert national figures to ILO figures. It is likely that some differences might have slipped past the adjustment process. We use ILO data and continue to update our database&nbsp;&nbsp;<br />
<div><div id="ftn4"><div id="ftn1"><br />
The GTAP data that we use for the demand side of the labor model is taken as labor headcounts and is thus immune from ambiguities around rate computation. As far as we could gather<ref>Please see the webpage for documentation on GTAP labor data statistic: https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=3248</ref>, the data includes both the formal and informal employment. We also need mention here that the GTAP database reconciles the labor data to calibrate the general equilibrium modeling that they do for the trade analyses. The data could thus be somewhat different from data collected through direct surveys. As a CGE model IFs is benefited by using calibrated data.<br />
<br />
== Sources of Labor Data ==<br />
<br />
IFs model uses ILO data for labor participation rates and for the unemployment rate. The data in IFs are collected from World Bank’s World Development Indicators (WDI) database. According to their documentation, WDI obtained the data from the ILO.<br />
<br />
Unemployment rate data in IFs is also collected from WDI. Like the participation rates WDI also obtains their unemployment data from ILO.<ref>The name of the IFs table is SeriesLaborUnemploy%</ref><br />
<br />
For employment and labor demand data IFs uses Purdue University’s Global Trade Analysis Project (GTAP) database. GTAP collects and compiles factor payments, imports, and intersectoral flow data to calibrate CGE models of national economies for trade and other analyses. In their ninth release in 2016, GTAP published data for 140 countries and regions for the year 2011. The earlier GTAP releases, which the IFs model used for its previous versions, compiled data for the years 2004 and 2007. GTAP data release aggregates economic activities into 57 commodities and activities following International Standard Industrial Classification (ISIC). The IFs model maps the 57 GTAP sectors into six economic sectors of IFs – agriculture, energy, material and mining, manufacture, services and ICT. Appendix 2 presents two tables listing the sectors mapping between IFs and GTAP, and GTAP and ISIC. GTAP further disaggregates labor in each of the commodities/activities into five occupation and skill categories following the nine category International Standard Classification of Occupations (ISCO-88). The IFs model collapses five GTAP occupation categories into the simple IFs dichotomy of skilled and unskilled. The mapping of occupations and skills are presented in the third appendix of this document. &nbsp;<br />
<br />
The data in the main GTAP database, prepared for CGE modeling, are all in dollar unit and thus do not include labor headcounts. We have used a ‘satellite’ GTAP database<ref>See Weingarden and Tsigas, 2010 for the details on the preparation of this database.</ref>&nbsp;for labor headcounts by skill and sector. The labor counts were also used to plot labor requirement functions for each of the IFs economic sectors and skill categories. The wage share of skilled and unskilled labor in each sector was computed using the labor headcounts and labor payments.<br />
<br />
== Scope of IFs Labor Model ==<br />
<br />
The IFs labor model simulates labor market at the national level. Each national labor market forecasts labor demand and employment by six sectors - agriculture, energy, mining, manufacture, services and ICT- and two skill levels - skilled and unskilled. The supply side do not have sectoral representation. IFs forecasts total labor force and labor supply by the two skill levels. Labor participation rate is computed in IFs by gender. Wage and unemployment rate is forecast for the overall labor market only.&nbsp;&nbsp;<br />
<br />
== Labor Model Pre-processor ==<br />
<br />
IFs system has a data preprocessor that prepares the initial conditions for the model using historical databases and various assumptions and estimated relationships to fill in the missing data and make data adjustments as needed.<ref>For more details, please see ‘The Data Pre-Processor of International Futures (IFs)” by Barry B. Hughes (with Mohammod Irfan) at http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf</ref> Pre-processing of labor data takes place in two IFs pre-processing modules. Labor participation rate data, which is closely related to demography, is processed in the population pre-processor. Unemployment rate and labor demand data are processed in the economic pre-processor.&nbsp;<br />
<div><div id="ftn1"><br />
=== Pre-processing Labor participation rate and unemployment ===<br />
<br />
For initializing labor participation rates by sex (LABPARR) the model uses the historical values from the base year or the most recent year with data.<ref>The data tables that the IFs model pre-processor use for initializing labor participation rates are: SeriesLaborParRate15PlusFemale%, SeriesLaborParRate15PlusMale%.</ref> For countries with no data we use regression relationships of the participation rates, for men and for women, with income per capita. The relationships, shown in the next figure, are not great. However, the functions affect only five countries for which we do not have any data at all: Grenada, Kosovo, Micronesia, Seychelles and South Sudan.<ref>We should try to collect participation rate for these countries from country sources.</ref><br />
<div><div id="ftn2"><br />
IFs data series SeriesLaborUnemploy% is used for the initialization of unemployment rates. That series has annual unemployment rates for one or more years between 1980 and 2016, for 181 of the 186 IFs countries. For five countries (Grenada, Kosovo, Micronesia, Taiwan and South Sudan<ref>These are pretty much the same countries for which we do not have any participation rate data. This indicates ILO might have some administrative limitation in reporting data for these countries (notice Kosovo, Seychelles etc in the list)</ref>) there is no data at all. To fill in the missing data we use a regression function of unemployment rate against GDP per capita. Like the participation rate functions, this function does also not have much of an explanatory power.<br />
<div><div id="ftn1"><br />
=== Pre-processing labor demand and unemployment from GTAP ===<br />
<br />
The IFs economic pre-processor reads labor headcount and labor payment data from the GTAP database. In addition to performing sector and occupation/skill mapping between GTAP and IFs, pre-processor also use the labor headcount data to compute labor coefficient functions, the principal driver of labor demand in the IFs model.<br />
<br />
Labor coefficients are defined as the amount of labor needed to produce one unit of value added in a certain sector of the economy. The coefficients depend on the level of technology. The model uses GDP per capita as an indicator of the level of technological development. IFs pre-processor estimates labor coefficient functions for labor of different skill levels for the different sectors of the economy.<br />
<br />
The functions are derived from GTAP data we described earlier. The model pre-processor reads data on factor payments and aggregates data from 57 GTAP sectors to six IFs sectors. Shares of payment going to skilled and less-skilled workers in each of the sectors are then computed. Countries are grouped according to their level of technological development as represented by per capita income. For each group labor coefficients are obtained by taking an average of the country coefficients. &nbsp;We also convert labor payments data to labor headcount data using per capita income as a proxy for average wage. Labor coefficients and income are then plotted into a power function relationship. The figure below plots some of those labor functions.&nbsp;The functions fit quite well with a power law formulation.<ref>This is interesting given the prevalence of power law in all sorts of scale-up activities (West 2017).</ref><br />
<div><div id="ftn1"></div></div></div></div></div></div></div></div><br />
= Labor Model Flowcharts =<br />
<br />
The diagram below shows an outline of the IFs labor model. On the supply side, the total labor pool (LAB) is computed from the labor force participation rates, by sex, (LABPARR) and the population (POP) in their working age, i.e., population over 15 (POP15TO65 + POPGT65). Participation rates are driven by the demographic changes with an additional negative impact from aging and a catch-up in female participation rate. Skill level of the labor supply (LABSUP) is driven by the level of development (GDPPCP) and the demand for labor is driven by labor-coefficients (LABCOEFFS) computed from coefficient function representing shifts in demand with technological progress as proxied by the level of development (GDPPCP). Coefficients computed by sector and skill gives the labor requirement by skill type for each unit of value added (VADD) in the sector. Multiplying these coefficients with projected value added in each sector gives an estimate of the labor demand. &nbsp;<br />
<br />
Any surplus or shortage between total labor demand and supply is used to compute the rate of unemployment. Deviations in the unemployment rate (LABUNEMPR) signal wage changes through an equilibrium seeking algorithm. Both demand and supply respond to the wage variable (LABWAGEIND) indexed to the base year. The supply responses are much slower than the demand responses.<br />
<br />
[[File:FLOCHART2.png|frame|center|Labor Model Flowchart]]<br />
<br />
= Labor Model Equations =<br />
<br />
== Overview ==<br />
<br />
The labor model is a part of the IFs economic model that uses labor model output as an input to a Cobb-Douglas production function in a multi-sector general equilibrium model. IFs is a very long-run dynamic model. Instead of computing fixed short-run equilibria that clear the relevant markets IFs uses an equilibrium seeking algorithm to balance the various systems over the longer run. The algorithm is known as the PID (proportion-integral-derivative) controller algorithm and is used widely in industrial control systems. It makes equilibrium seeking variables in IFs move towards a set target. The algorithm works by computing a multiplier based on the movement of the variable towards the target, as obtained by an integral (I) of the path traversed, and the rate of movement towards the target, the derivative term. The multiplier is applied on the process variable (the P term), or a response variable, in the subsequent time period. In the labor model, unemployment rate (LABUNEMPR) is used as the process variable and the PID multiplier is used on the wage rate (LABWAGEIND). Job availability (LABDEMS) and participation rate (LABPARR) get affected by changes in wage. &nbsp;&nbsp;<br />
<br />
Throughout this section we use subscripts and notations common to other modules of IFs. For example, we use t for time period. Subscripts p and r represent sex and country/region, respectively, c is the cohort number, with cohort 1 representing the newborns, cohort1 the the one-year to four-year-olds, cohort two five-year to nine-year-olds etc. Values for p are 1 for male, 2 for female and 3 for both sexes combined. For economic sectors we use s and for skill levels sk.<br />
<br />
== Labor Supply: Equations ==<br />
<br />
The total pool of labor is computed by multiplying the population of working age with the labor force participation rate (LABPARR). &nbsp;Population forecasts come from IFs demographic model which computes both five-year and single-year age-sex cohorts (''agedst'', ''fagedst''). &nbsp;<br />
<br />
The labor model forecasts participation rates by country/region&nbsp; and gender. Participation rates in the model move with the changes in the demographic composition. Female participation rates, which have historically been lower than the same for the male in all societies, but has moved up in modern and affluent societies, get a catch-up boost in the model. Participation rates can also change when there is labor shortage or surplus and the employers try to incentivize or discourage workers by changing wage. This last impact is much less slow than similar wage impacts on the demand side.<br />
<br />
== Labor Participation Rate ==<br />
<br />
Labor participation rates (''LABPARR'') for male and female are first initialized with historical data.<br />
<br />
:<math>LABPARR_{r,p}= LABPARR_{r,p,t=1} </math><br />
<br />
A ‘catch-up’ boost is added to the female participation rate. The boost added (FemParLabMul) starts at a third of a percentage point and withers away following a non-linear path as the female rates approaches the catch-up target (FemParTar), The maximum catch-up that can occur over the horizon of the model is thirty percent.<br />
<br />
:<math>FemParTar_{r}=Amin(LabParRI_{r,p=1},LabParRI_{r,p=2}+30)</math><br />
<br />
:<math>FemParLabMul_{r}=(FemParTar_{r}-LABPARR_{r,p=2,t-1})/(FemParTar_{r}-LABPARR_{r,p=2,t-1})</math><br />
<br />
:<math>LABPARR_{r,p=2,t}=LABPARR_{r,p=2,t-1}+FemParLabMul_{r}*0.3</math><br />
<br />
Next, we compute and apply the aging impact on the participation rate. As the relative share of people over the retirement age increases, the participation rate declines. The model keeps track of the changes in the demographic ratio (PopAgingRatio) of the population who are in their prime working age of 15 to 64 (POPWORKING) to those at a common retirement age of sixty-five or older (POPGT65). This ratio declines as countries age. The percentage drop in the ratio comparative to the base year is scaled appropriately to compute the aging impact (aging_impact). This impact is added to the male and female labor participation rates, with the impact on the female participation rate being slightly lower than that on male rates.<br />
<br />
:<math>POPAgingRatio_{r,t}=POPWORKING_{r,t}/POPGT65_{r,t}</math><br />
<br />
:<math>aging_impact_{r,t}=100*((POPAgingRatio_{r,t}/POPAgingRatio_{r,t=1})-1)*0.2</math><br />
<br />
:<math>LABPARR_{r,p=1,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t} </math><br />
<br />
:<math>LABPARR_{r,p=2,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t}*0.95 </math><br />
<br />
Participation rates respond slowly to changes in wage and unemployment rate. The impact is implemented through a wage impact factor computed from annual changes in the wage index (labwageimpact). The base participation rates can be changed by model user through two model parameters: a direct multiplier on the participation rate (labparm), or one that changes participation by moving the retirement age (labretagem)<br />
<br />
<br />
<br />
:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact*0.05)*labparm_{r,p,t}*labretagem_{r,p,t}</math><br />
<br />
Total participation rate (LABPARRr,p=3,t) is computed by an weighted average of male and female participation rates.<br />
<br />
:<math>LABPARR_{r,p=3,t}= (∑_{p=1 to 2}∑_{c=4 to 21}(agedst{r,c,p,t}*LABPARR_{r,p,t}))/(∑_{p=1 to 2}∑_{c=4 to 21}agedst{r,c,p,t})</math><br />
<br />
== Total Labor ==<br />
<br />
Finally, the total number of labor available for work (LAB) is computed by multiplying the total participation rate with the population of fifteen-year-olds or older.<br />
<br />
:<math>LAB_{r,t}= LABPARR_{r,p=3,t}*∑_{p=1 to 2,c=4 to 21}agedst_{r,c,p,t}</math><br />
<br />
== Labor by skill level ==<br />
<br />
The labor model forecasts labor supply (LABSUP) by two skill categories. The variable (''LABSUP'') is initialized in the pre-processor by reading the employment by skill/occupation (''LABEMPS'') data from GTAP<ref>We collapse GTAP’s 57 sectors into the six economic sectors of IFs. GTAP collapses the nine occupation categories of ISCO-88 into five. In IFs those five categories are collapsed into a binary – skilled and unskilled. The sectoral and skill mappings are described in two appendices of these document.</ref>&nbsp; and adding the unemployment numbers. We assume same unemployment rate (''LABUMEMPR'') for skilled and unskilled labor.<br />
<br />
:<math>LABSUP_{r,t=1,sk}=∑_{s=1 to 6}(LABEMPS_{r,s,t=1}/(1-(LABUNEMPR_{r,t=1}/100))</math><br />
<br />
The model forecasts labor by skill through a model of the skilled share of the labor. Education, training, exposure, and experience of the employees all improve with the level of development. The model captures this with an analytic function of the skilled share (perskilled) driven by GDP per capita at PPP (GDPPCP) -<br />
<br />
:<math>perskilled_{r}=f(GDPPCP_{r})</math><br />
<br />
Among the causal drivers of skill, education is considered to be the most proximate. Education is strongly correlated with the level of development, the deeper driver of skill in the model. However, the recent increase in education and/or a policy driven educational expansion might add to the impact of education on skill. Additional impacts from education on skill, when there is any, is computed through an expected function formulation. For example, in a society where an average adult has more (or less) education than the adults in other societies at that level of development, the skill share is given a slight upward push (or downward pull). The expectation function is a logarithmic function of educational attainment of working age population (EDYRSAG15) driven by GDP per capita at PPP. Attainment above (or below) the expected level (YearsEdExp) is computed by the function output (YearsEd) adjusted for country situation (yearseddiff). The percentage adjustment to the skilled share (LabSupSkiAdj) is computed using additional (limited) education, i.e., the difference between actual (EDYRSAG15) and expected values of educational attainment, expressed as a percentage of the expected value. The adjustment is scaled appropriately and peters off over time.<br />
<br />
:<math>YearsEd_{r,t}= f(GDPPCP_{r,t})</math><br />
<br />
:<math>yearsdeddiff_{r}= EDYRSAG15_{r,p=3,t=2}-YearsEd_{r,t=2}</math><br />
<br />
:<math>YearsEdExp_{r,t}=YearsEd_{r,t}+yearsdeddiff_{r}</math><br />
<br />
:<math>LabSupSkiAdj_{r,t}=0.3*(EDYRSAG15_{r,p=3,t=2}*YearsEdExp_{r,t})/YearsEd_{r,t}</math><br />
<br />
:<math>LabSupSkiAdj_{r,t}=ConvergeOverTime(0,LabSupSkiAdj_{r,t},70)</math><br />
<br />
:<math>perskilled_{r,t}= perskilled_{r,t}*(1+LabSupSkiAdj_{r,t})</math><br />
<br />
The skilled share (perskilled) is multiplied with the total labor supply (LAB) to obtain the number of labors who are skilled (LABSUPskilled)<br />
<br />
:<math>LABSUP_{r,skilled,t}=LAB_{r,p,t}*perskilledI_{r,t} </math><br />
<br />
As a last step, the model adjusts for the country specific variations in the skilled labor count not captured by the deeper and the proximate models. This is done by saving a ratio (LABSUPSkilledRI) of the actual historical data and the model computed value in the initial year. In the subsequent years this ratio is used to adjust the skilled labor forecast gradually.<br />
<br />
:<math>LABSUPCompSkilled_{r}=LAB_{r}*perskilled_{r,t=1}/100 </math><br />
<br />
:<math>LABSUPSkilledRI_{r}=LABSUP_{r,skilled,t=1}/LABSUPCompSkilled_{r}</math><br />
<br />
:<math>LABSUP_{r,skilled,t}= LABSUP_{r,skilled,t}*ConvergeOverTime(LABSUPSkilledRI_{r},1,85)</math><br />
<br />
Number of unskilled labor is obtained by subtracting the skilled labor from the total pool.<br />
<br />
<br />
<br />
:<math>LABSUP_{r,unskilled,t}= LAB_{r,p,t}- LABSUP_{r,skilled,t}</math><br />
<br />
== Labor Demand: Equations ==<br />
<br />
IFs economic model forecasts production in six economic sectors. IFs labor model computes the longer-term and shorter-term determinants of demand for skilled and unskilled labor (LABDEMS) for the production processes. The long-term drivers of labor requirement are technological progress or the lack of it. In the shorter-term wage affects the labor demand most. Wage in turn is affected by labor supply or skill shortage.<br />
<br />
The IFs model divides economic activities into six economic sectors – agriculture, energy, materials, manufacture, services and information, and communication technologies. Workers in the IFs labor model are disaggregated into two skill types. While the skill composition varies by the technology used in the sector and starts tilting towards the more skilled with the progress in technology, absolute number of labors needed to produce the same output goes down with technological development for both skilled and unskilled labor. This is illustrated in the next figure which plots the changes in labor requirement against GDP per capita at PPP, a proxy for level of development. Agriculture is a much less skill-intensive process than the manufacture, however, with technological progress skill requirement improves rapidly in both sectors. The IFs labor model computes these labor requirement functions in the model pre-processor. As we have already described in the pre-processor section, the computation of these functions use GTAP data on employment by occupation and economic activity. Appendices 3 and 4 lists sector and occupation mapping between GTAP and IFs.<br />
<br />
[[File:LaborCoefficientFunctions.png|frame|center|665x445px|Labor coefficient functions by skill type for the agriculture and the manufacturing sector]]<br />
<br />
These functions are used to compute the labor coefficients (LABCOEFFS), i.e., number of skilled and unskilled labor needed to produce unit amount of output with the technology available, for which we use GDP per capita at PPP as a proxy.<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= f(GDPPCP_{r})</math><br />
<br />
manufacture, services and ICTech) and the subscrip sk stands for skill categories with 1 denoting unskilled and 2 skilled. The labor coefficients obtained from the analytical functions require some adjustments to incorporate country deviations from the functions for various factors not captured in the regression relationship. The first of these adjustments is a gradual removal of impacts of short-run fluctuations in output and labor from the computation of labor coefficient. This adjustment is applied on the coefficients computed from the function. The equation below shows a simplified form of these computations.<br />
<br />
:<math>LabCoeffAdjFac_{r,k,s,t}=f(igdpr_{r,t=2},(LAB_{r,t=2}/LAB_{r,t=1}),(LABCOEFFS_{r,t}/LABCOEFFS_{r,t-1}))</math><br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}=LABCOEFFS_{r,sk,s,t}(1-LabCoeffAdjFac_{r,k,s,t})</math><br />
<br />
Model users can use a global parameter (labcoeffsm) to change the labor coefficients by skill level for any or all of the six sectors –<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= LABCOEFFS_{r,sk,s,t}*'''labcoeffsm_{s,sk}'''</math><br />
<br />
To forecast the total labor demand, the labor coefficients (LABCOEFFS) are multiplied to the total projected output for each of the economic sectors. The forecast is adjusted for any discrepancy between data and model. The adjustment factor (LABDemsAdjFac) is computed as the initial ratio between the actual and computed employment. Actual employment is obtained from historical data (LABEMPS) processed using the GTAP database. The computed employment is obtained by multiplying the labor coefficients (LABCOEFFS) with the final output of the sector (VADD).<br />
<br />
:<math>LabDemsAdjFac_{r,s,sk}= LABEMPS_{r,s,sk,t=1}/(VADD_{r,s,t=1}*LABCOEFFS_{r,sk,s,t})</math><br />
<br />
The projected output is obtained by applying the growth rate (IGDPRCOR) on the sectoral value added from the previous year (VADD). The total labor demand is given by the product of the labor coefficients, projected output, demand adjustments and wage impacts (labwageimpactmul) and the number 1000 which adjusts the units for the equation. Wage impact comes from the level of unemployment and is computed in an equilibration process described in the next section. Model users can use a multiplicative parameter (labdemsm) to slide the demand upward or downward.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}=1000*VADD_{r,s,t-1}*(1+IGDPRCOR_{r})*LABCOEFFS_{r,sk,s,t}*LabDemsAdjFac_{r,s,sk}*labwageimpactmul_{r,s,sk}*'''labdemsm_{r,s}'''</math><br />
<br />
== Unemployment and Wage: Labor Market Equilibration ==<br />
<br />
The IFs labor model balances the labor market through an equilibrium seeking algorithm rather than computing an exact equilibrium at each time step. We use an algorithm borrowed from the control systems engineering. This PID controller algorithm, described also in the IFs economic model documentation, works by computing corrective signals for equilibrating variables using the deviations of a buffer variable, for example unemployment rate (LABUNEMPR), from a target value. The signal is computed from two quantities, the distance of the buffer from the target and the current rate of change of the buffer. The computation is tuned with PID elasticities to avoid oscillations. The computed signal is applied on the variable/s which need to be balanced, for example, demand and supply in the event of a market equilibration, thus getting closer to a balance at each step of simulation. The target value for the buffer variable and the tuning parameters of the control algorithm are obtained through rules-of-thumb and model calibration. The IFs labor model uses unemployment rate (LABUNEMPR) as the buffer variable for the market equilibration of labor demand and labor supply. The multiplier (i.e., corrective signal) obtained from the PID is applied on the wage index (LABWAGEIND). Changes in wage indices comparative to the base year, moderated through a second PID controller, is used to compute the final signal (labwageimpactmul) that drives labor demand and labor supply. Even though the model forecasts labor demand by sector and skill, and computes labor supply for both skill types, the equilibration algorithm works over the entire pool of labor. In other words, we assume that the skills are replaceable across sectors and the lack (or abundance) of jobs affects skilled and unskilled persons equally.<br />
<br />
At each annual timestep, the model computes the unemployment rate (LABUNEMPR) as the gap in between the total supply of labor (LAB) and the total demand. The gap (EmplGap) is expressed as a share of the total labor, the standard way to express unemployment rate.<br />
<br />
:<math>sumld=∑_{s,sk}LADEMS_{r,s,sk,t}</math><br />
<br />
:<math>EmplGap= LAB_{r,t}*sumld</math><br />
<br />
:<math>LABUMENPR_{r,t}= (EmplGap/LAB_{r,t})*100</math><br />
<br />
As the target value (LabUnEmpRateTar) for the PID controller that modulates unemployment rate we use either the historical unemployment rate or a ten percent unemployment rate when the historical rate is higher than ten. Model users can override the historical target through a model parameter (labunemprtrgtval).<br />
<br />
:<math>LABUMENPRi_{r,t}= LABUMENPR_{r,t}</math><br />
<br />
:<math>LabUnempRateTarget_{r}=labunemptargetval_{r}</math><br />
:<math>If LabUnempRateTarget_{r}=0,<br />
LabUnempRateTarget_{r}= AMIN(LABUMENPRi_{r,t},10) </math><br />
<br />
Unemployment rate target, when it is different from the base year value, is reached gradually with a convergence period of forty years . The target rate is converted to count (LabUnEmplTar) to make it equivalent to the employment gap (EmplGap) computed earlier.<br />
<br />
:<math>LabUnEmplTar_{r}= LAB_{r,t}*ConvergeOverTime(LABUMENPRi_{r,t},0,100)</math><br />
<br />
The first order difference (Diffl1) between the target unemployment and the demand-supply gap is used to compute a second order difference (Diffl2) accounting for changes in the rate of movement. The two differences and the PID multipliers (elwageunemp1, elwageunemp2) are provided to the PID function (ADJSTR). Working age population (POP15TO65r,t) works as the scaling base of the PID controller. The controller algorithm gives a multiplier (mullw) that is used in the subsequent year to adjust wage.<br />
<br />
:<math>Diffl1_{t}=LabUnEmplTar_{r}-EmplGap</math><br />
<br />
:<math>Diffl2_{t}=Diffl1_{t}-Diffl1_{t-1}</math><br />
<br />
:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},elwageunemp1_{r},elwageunemp2_{r})</math><br />
<br />
Wage adjustments affect demand and supply with an increase in wage drawing demand downward and supply upward. The opposite affects occur with a downward movement of wage. The wage variable affected by the PID multiplier (LABWAGEIND) is an index initialized at one. We use an indexed rather than a dollar wage in the equilibration process to avoid affecting the process from other economic phenomena that affects wage, for example, a rise in real wage as GDP or the labor share of income grows.<br />
<br />
:<math>LABWAGEIND_{r,t=1}=1</math><br />
<br />
In the subsequent years of the model run, the wage index is first adjusted with the equilibration signal obtained from the unemployment rate PID controller in the previous period<br />
<br />
:<math>LABWAGEIND_{r,t=1}= LABWAGEIND_{r,t=1}* mullw_{r,t-1} </math><br />
<br />
A wage impact (labwageimpact) is then computed using the changes in the wage index relative to the base value. The impact is smoothed with a moving average algorithm.<br />
<br />
:<math>labwageimpact_{r}= labwageimpact_{r,t-1}*0.9+ (1-LABWAGEIND_{r,t})*0.1</math><br />
<br />
The smoothed impact is used as the equilibration signal for labor supply. As we have already described in the section on labor supply, a small fraction of the impact (labwageimpact) is applied to the labor participation rate. The impact is scaled down to account for the slow pace of changes on the supply side.<br />
<br />
:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact_{r,t}*0.05)</math><br />
<br />
For the impacts of wage on labor demand we use a second PID multiplier as opposed to using the changes in wage index that we have done on the supply side. The second PID uses the wage index itself as the process variable and uses the base year value of 1 as the target. The reason we had to use this second PID is to control the pace at which wage disequilibrium can affect demand, especially in the event of an abrupt shock. The smoothing and scaling down that works on the supply side is not enough to control oscillations on the demand side.<br />
<br />
:<math>Diffl1_{t}=LABWAGEIND_{r,t=1}-1</math><br />
<br />
:<math>Diffl2_{t}=LABWAGEIND_{r,t}-LABWAGEIND_{r,t-1} </math><br />
<br />
:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},ellabwage1_{r},ellabwage1_{r})</math><br />
<br />
A second impact factor (labwageimpactmul) is computed using the correction signal from this second multiplier:<br />
<br />
:<math>labwageimpactmul_{r,t}= labwageimpactmul_{r,t-1}*mullw_{r,t}</math><br />
<br />
This impact factor is applied on the labor demand as described in the section on labor demand.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}= LABDEMS_{r,s,sk,t}* labwageimpactmul_{r,t}</math><br />
<br />
== Informal Labor ==<br />
<br />
IFs forecast labor and GDP share of the informal sector. Informal labor forecast is not explicitly endogenized in the labor market though. They are rather driven by development, skill and regulatory factors.<ref>IFs economic model documentation has a detail description of the informal economy model.</ref>&nbsp;However, the productivity and revenue impacts of changes in informality affects output and thus labor demand implicitly as a very distal driver.<br />
<br />
= References =<br />
<br />
<references /></div>Wikiadmin//pardee.du.edu/wiki/LaborLabor2018-09-07T22:55:52Z<p>Wikiadmin: </p>
<hr />
<div>Workers in an economy supply the expertise and the efforts needed to produce goods and services. In return the labor receives wages that they use to meet their current and future consumption needs. On one hand, shortage of labor with required skills prevents economies from realizing their growth potential. On the other hand, individuals falling short of the right qualifications might remain unemployed or underemployed failing to secure income needed for a decent living. The ongoing adjustments to find the best match between skills, jobs and wages can only be studied through a dynamic model of the labor market.&nbsp; &nbsp;<br />
<br />
Such a model should go beyond providing a reasonable answer to the obvious question of why employment and wages go up and down. An aggregate labor market must deal with issues that have strong interconnections with various other dynamic changes in the greater society. What kind of dividend of deficit can a society expect from its labor force given the phase of demographic transition in which it is situated? How severely would aging affect the pool of working age adults? Might increasing female participation rates offset some of the losses from aging? What is the level of skills and educational attainment in a society? These supply phenomena move relatively slowly unless there are huge disruptions, like a war or famine, or an aggressive policy push. The demand side, in contrast, needs to be more responsive in adjusting wages and employment given the investment and technology in the various sectors of the broader economy. In general, though, the labor market demonstrates some sluggishness compared to the goods and services markets as it involves moving human beings with various limitations. Consumption of goods and services depend on the income earned by the labor. Uneven distribution of employment and wages among labors of various types or between labor and capital for a long period of time can give rise to persistent inequality in a society. &nbsp;&nbsp;&nbsp;&nbsp;<br />
<br />
== Conceptual Framework ==<br />
<br />
Labor markets are markets for workers and jobs. In a labor market, employers meet their demand for labor with the supply of people willing to work at the wage the employers can offer. The employers raise the wage when there is a shortage of workers. Workers agree to take a lower wage when there are more of them than the firms need. In the real-world labor markets do not always clear at perfect equilibrium. Frinctional unemployment results for various reasons, for example, the search time between jobs. Structural unemployment can result from technology induced disruptions. Some unemployment could thus persist in the labor market even when there aren’t any short-term fluctuations. There is also the phenomenon of informal employment that consists of less sophisticated workers and entrepreneurs engaged in unregulated economic activities. &nbsp;In a dynamic model that covers the entire economy, the real wage earned by the labor drives the income and social mobility.&nbsp;&nbsp;&nbsp;<br />
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To understand the long-term dynamics of the labor market, we need also examine the deeper determinants of labor demand and supply, the determinants that can shift the curves. Labor demand changes over time with the changes in demand for goods and services and the labor input needed to produce those. Labor productivity itself improves with technological progress. Long term transitions in the supply of labor are mostly demographic. &nbsp;<br />
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Labor supply is determined by the working age population and the share of that population who are available for participation in the workforce. The labor supply is relatively stable as the demographic changes are slow in pace. As the share of elderly in the population increases, a recent trend in many societies, the rate of participation declines. Some of the aging impacts will be offset by the greater female participation rates, a second trend that surfaces as economies develop and women attain more education. Educational attainment also drives the general skill level of workers, male and female. Specific skills are obtained through training and experience that augment the knowledge obtained through general and specialized education. &nbsp;&nbsp;&nbsp;&nbsp;<br />
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It is the demand side that causes most of the short-term imbalances in the labor market. &nbsp;In the long term, as said earlier, the important driver of demand for labor and their skills is technological progress. Labor requirement drops with advances in technology, more so for less skilled labor. Labor composition changes accordingly both within and across sectors. Rapid advances in technology can also cause disruption in the system when there is not much opening in the other sectors. Labor displacement is offset to some extent by the growth in the economy and the resulting increase in total demand. &nbsp;&nbsp;&nbsp;<br />
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As we have already mentioned, employees maximize income and the firms minimize labor costs. When there are more laborers than the firms can hire, there is unemployment. Shifts in the rates of unemployment impacts wage, the price of labor. For example, wages drop in the event of rising unemployment as there are more people to hire from. Wage adjustments feed back to the demand for labor seeking to bring the market back to equilibrium.<br />
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The challenges around the conceptual distinction between unemployment and employment is further complicated by the phenomenon of informal employment. In many developing countries there is a large urban non-agricultural informal sector where low-skilled workers work for wages typically lower than a formal employment.<br />
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[[File:LMFlowchart1.png|frame|center|Description of the labor model]]<br />
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== Dominant Relations ==<br />
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The labor model in the International Futures system (IFs) balances the total supply of labor with the total labor demanded by all economic sectors. Total labor (LAB) is computed from the working age population and the labor participation rate. Population forecasts are obtained from the IFs demographic model. Participation rates (LABPARR) are computed by sex with a catchup algorithm for the female participation towards that for the male. Labor is also disaggregated by skill level, as determined by educational attainment, in a separate labor supply variable (LABSUP) which is used to distribute labor earnings by skill level. [** LABSUP do not affect the demand/supply balance now]<br />
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Labor demands (LABDEMS) are driven by sectoral technology functions used to compute the labor requirement by skill level for each unit of potential valued added in the sector. These labor coefficients (LABCOEFFS) are multiplied with the projected value added for the sector to compute the needed manpower. The balancing mechanisms determines the labor employed in each of the sectors (LABS).&nbsp;&nbsp;<br />
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The balancing, in the current version of the model, can be done in one of the two ways. In the first method, total needs combined from all economic sectors is normalized to the available pool of labor computed by subtracting the unemployed from those who are at or looking for work. The rate of unemployment is kept at its natural rate for which we use the base year rate of unemployment. (** This might need to be changed for countries where the market is undergoing some abrupt transition.)<br />
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In the second balancing method, added in a recent revision of the model, total demand is equilibrated to supply through a CGE like market equilibrium model. An indexed wage (LABWAGEIND) and the rate of unemployment (LABUNEMPR) work as the equilibrating variables. As unemployment deviates from the target, PID algorithms send a signal for the wage to adjust. Wage adjustments cause adjustments in the “base” labor demands by sector computed from the labor-coefficient functions as described earlier. Wage signals also affects the labor participation rate. The magnitude of impact on the supply side is much lower than that on the demand side.<br />
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Wage and unemployment rate are aggregated for the total labor market. The wage index starts with a base year value of 1 and the unemployment rates start with the historical data for the base year. Initial year unemployment rate works as the target for long term unemployment.<br />
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== Key Dynamics ==<br />
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The following key dynamics are directly related to the dominant relations:<br />
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*Labor supply is determined from population of appropriate age in the population model (see its dominant relations and dynamics) and endogenous labor force participation rates, influenced exogenously by the growth of female participation.<br />
*Labor demand is driven by sectoral demand functions driven by technological progress<br />
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== Structure and Agent System ==<br />
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{| border="1" cellspacing="0" cellpadding="0" width="0" style="width:502px;"<br />
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'''System/Subsystem'''<br />
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Labor market<br />
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'''Organizing Structure'''<br />
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Labor supply by skill level and labor demand by sector for each skill category represented within an equilibrium-seeking model with wage and unemployment rate as the equilibrating variables<br />
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'''Stocks'''<br />
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Population, labor, education, &nbsp;accumulated technology<br />
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'''Flows'''<br />
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Participation rate; Coefficients of labor demand; Employment (unemployment); Wage<br />
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'''Key Aggregate&nbsp;''''''Relationships&nbsp;'''<br />
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'''(illustrative, not comprehensive)'''<br />
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Labor supply is driven by demographic changes; Participation of female change over time; Labor requirement changes with technological development; Unemployment rate drives wage; Wage movements affect labor demand and participation rate<br />
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'''Key Agent-Class Behavior&nbsp;''''''Relationships'''<br />
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'''(illustrative, not comprehensive)'''<br />
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Households and work/leisure, and female participation patterns;<br />
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Firms and hiring;<br />
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&nbsp;<br />
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|}<br />
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= Labor Model Data =<br />
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The labor supply and unemployment data that we use in our model is from International Labor Organization (ILO). For data on the demand side, we used data from the Global Trade Analysis Project. Wage variable used in the equilibration algorithm&nbsp;is an index anchored to the base year of the model.<ref>GTAP database helped us compute wage rates by sector and skill.</ref> IFs preprocessor prepared these data for model use using various estimation, conversion and reconciliation processes.&nbsp; &nbsp; &nbsp;<br />
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== Definitional Issues ==<br />
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There are ambiguities in the way some of the labor market variables are defined. Labor participation rates and the rate of unemployment are two that need special attention.<br />
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The size of the labor supply available for economic activities is expressed with the labor force participation rate. ILO defines this as a “measure of the proportion of country’s working-age population that engages actively in the labor market, either by working or looking for work.”<ref>http://www.ilo.org/ilostat-files/Documents/description_LFPR_EN.pdf</ref>&nbsp;National labor force surveys and census data are used to estimate this rate. The definition of labor force here includes both employed and unemployed and the rate is expressed as a percentage of working-age population. Working-age population is defined here as the population above legal working-age. For international comparability, ILO adopts a convenient minimum threshold of fifteen years as working age and avoids putting any upper age limit. In practice, both the minimum and the upper-age limits can vary by country. For example, the working-age in the USA is sixteen years. In the Netherlands the upper age limit is seventy-five years, whereas South African data uses an upper age limit of 64.<ref>https://www.bls.gov/fls/flscomparelf/technical_notes.pdf</ref><br />
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Ambiguities are more abundant in the definition of unemployment. ILO came up with a guideline on this as well. Per the ILO guideline, the unemployed are those among the working-age population who are not employed, are available for work and are actively looking for jobs<ref>The definitions around employed and unemployed were agreed upon by nations through the ‘Resolution concerning statistics of work, employment and labor underutilization’ adopted by the 19th International Conference of Labor Statisticians (ICLS) in 2013. (Bourmpoula et al, 2017: 6).</ref>; the unemployment rate is expressed as a percentage of those who are in the labor force. The availability and job-seeker status could be defined in different ways giving rise to incompatibility in data. &nbsp;While there seems to be little room for disagreement on whether someone is at work or not, whether that work should be considered as employment is contested at many times.<br />
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The debates around the nature and type of employment can range from gainfulness to workplace setting. For example, a large number of workers in the low-income low-regulation developing countries work outside the purview of formal enterprises. According to an ILO estimate, more than half of the global labor force and more than 90% of Micro and Small Enterprises (MSEs) worldwide are in the so called informal economy.<ref>http://www.ilo.org/global/topics/employment-promotion/informal-economy/lang--en/index.htm</ref> This might explain the apparently counterintuitive pattern of low unemployment rate in some low-income countries (e.g., 2.2% for Guatemala) and relatively higher numbers for some of the developed nations. The low numbers in the poorer countries hide the prevalence of extremely low wage jobs in the informal sectors in these countries, the only options for the vulnerable people in the absence of any kind of social safety net. &nbsp;Contrastingly, in the developed countries the so called ‘gig-economy’ is attracting more and more workers who choose to work on their own rather than in a formal enterprise. ILO conceptualization makes the informal work part of total employment. The stacked Venn diagram below presents the relationship among the labor force metric including informal employment. IFs also models informal economy both in terms of GDP share and employment share of informal in the total economy and employment.<br />
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[[File:LaborSubsets.png|frame|right|Relationship among various labor measurement]]<br />
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Incompatibility can arise in the treatment of various population groups for the computation of the denominator for participation and unemployment rates.<ref>For example, the USA excludes people in the defense services and those in the prisons or mental asylums in their computation of the civilian non-institutional working-age population. There are also variations in the treatments of students, those recently laid-off, and family workers. Please see https://stats.bls.gov/opub/mlr/2000/06/art1full.pdf for a discussion </ref> ILO makes their best efforts to make adjustments in the data for the sake of international comparison. For example, ILO asks countries that deviate from ILO guidelines to collect data needed to convert national figures to ILO figures. It is likely that some differences might have slipped past the adjustment process. We use ILO data and continue to update our database&nbsp;&nbsp;<br />
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The GTAP data that we use for the demand side of the labor model is taken as labor headcounts and is thus immune from ambiguities around rate computation. As far as we could gather<ref>Please see the webpage for documentation on GTAP labor data statistic: https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=3248</ref>, the data includes both the formal and informal employment. We also need mention here that the GTAP database reconciles the labor data to calibrate the general equilibrium modeling that they do for the trade analyses. The data could thus be somewhat different from data collected through direct surveys. As a CGE model IFs is benefited by using calibrated data.<br />
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== Sources of Labor Data ==<br />
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IFs model uses ILO data for labor participation rates and for the unemployment rate. The data in IFs are collected from World Bank’s World Development Indicators (WDI) database. According to their documentation, WDI obtained the data from the ILO.<br />
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Unemployment rate data in IFs is also collected from WDI. Like the participation rates WDI also obtains their unemployment data from ILO.<ref>The name of the IFs table is SeriesLaborUnemploy%</ref><br />
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For employment and labor demand data IFs uses Purdue University’s Global Trade Analysis Project (GTAP) database. GTAP collects and compiles factor payments, imports, and intersectoral flow data to calibrate CGE models of national economies for trade and other analyses. In their ninth release in 2016, GTAP published data for 140 countries and regions for the year 2011. The earlier GTAP releases, which the IFs model used for its previous versions, compiled data for the years 2004 and 2007. GTAP data release aggregates economic activities into 57 commodities and activities following International Standard Industrial Classification (ISIC). The IFs model maps the 57 GTAP sectors into six economic sectors of IFs – agriculture, energy, material and mining, manufacture, services and ICT. Appendix 2 presents two tables listing the sectors mapping between IFs and GTAP, and GTAP and ISIC. GTAP further disaggregates labor in each of the commodities/activities into five occupation and skill categories following the nine category International Standard Classification of Occupations (ISCO-88). The IFs model collapses five GTAP occupation categories into the simple IFs dichotomy of skilled and unskilled. The mapping of occupations and skills are presented in the third appendix of this document. &nbsp;<br />
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The data in the main GTAP database, prepared for CGE modeling, are all in dollar unit and thus do not include labor headcounts. We have used a ‘satellite’ GTAP database<ref>See Weingarden and Tsigas, 2010 for the details on the preparation of this database.</ref>&nbsp;for labor headcounts by skill and sector. The labor counts were also used to plot labor requirement functions for each of the IFs economic sectors and skill categories. The wage share of skilled and unskilled labor in each sector was computed using the labor headcounts and labor payments.<br />
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== Scope of IFs Labor Model ==<br />
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The IFs labor model simulates labor market at the national level. Each national labor market forecasts labor demand and employment by six sectors - agriculture, energy, mining, manufacture, services and ICT- and two skill levels - skilled and unskilled. The supply side do not have sectoral representation. IFs forecasts total labor force and labor supply by the two skill levels. Labor participation rate is computed in IFs by gender. Wage and unemployment rate is forecast for the overall labor market only.&nbsp;&nbsp;<br />
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== Labor Model Pre-processor ==<br />
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IFs system has a data preprocessor that prepares the initial conditions for the model using historical databases and various assumptions and estimated relationships to fill in the missing data and make data adjustments as needed.<ref>For more details, please see ‘The Data Pre-Processor of International Futures (IFs)” by Barry B. Hughes (with Mohammod Irfan) at http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf</ref> Pre-processing of labor data takes place in two IFs pre-processing modules. Labor participation rate data, which is closely related to demography, is processed in the population pre-processor. Unemployment rate and labor demand data are processed in the economic pre-processor.&nbsp;<br />
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=== Pre-processing Labor participation rate and unemployment ===<br />
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For initializing labor participation rates by sex (LABPARR) the model uses the historical values from the base year or the most recent year with data.<ref>The data tables that the IFs model pre-processor use for initializing labor participation rates are: SeriesLaborParRate15PlusFemale%, SeriesLaborParRate15PlusMale%.</ref> For countries with no data we use regression relationships of the participation rates, for men and for women, with income per capita. The relationships, shown in the next figure, are not great. However, the functions affect only five countries for which we do not have any data at all: Grenada, Kosovo, Micronesia, Seychelles and South Sudan.<ref>We should try to collect participation rate for these countries from country sources.</ref><br />
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IFs data series SeriesLaborUnemploy% is used for the initialization of unemployment rates. That series has annual unemployment rates for one or more years between 1980 and 2016, for 181 of the 186 IFs countries. For five countries (Grenada, Kosovo, Micronesia, Taiwan and South Sudan<ref>These are pretty much the same countries for which we do not have any participation rate data. This indicates ILO might have some administrative limitation in reporting data for these countries (notice Kosovo, Seychelles etc in the list)</ref>) there is no data at all. To fill in the missing data we use a regression function of unemployment rate against GDP per capita. Like the participation rate functions, this function does also not have much of an explanatory power.<br />
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=== Pre-processing labor demand and unemployment from GTAP ===<br />
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The IFs economic pre-processor reads labor headcount and labor payment data from the GTAP database. In addition to performing sector and occupation/skill mapping between GTAP and IFs, pre-processor also use the labor headcount data to compute labor coefficient functions, the principal driver of labor demand in the IFs model.<br />
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Labor coefficients are defined as the amount of labor needed to produce one unit of value added in a certain sector of the economy. The coefficients depend on the level of technology. The model uses GDP per capita as an indicator of the level of technological development. IFs pre-processor estimates labor coefficient functions for labor of different skill levels for the different sectors of the economy.<br />
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The functions are derived from GTAP data we described earlier. The model pre-processor reads data on factor payments and aggregates data from 57 GTAP sectors to six IFs sectors. Shares of payment going to skilled and less-skilled workers in each of the sectors are then computed. Countries are grouped according to their level of technological development as represented by per capita income. For each group labor coefficients are obtained by taking an average of the country coefficients. &nbsp;We also convert labor payments data to labor headcount data using per capita income as a proxy for average wage. Labor coefficients and income are then plotted into a power function relationship. The figure below plots some of those labor functions.&nbsp;The functions fit quite well with a power law formulation.<ref>This is interesting given the prevalence of power law in all sorts of scale-up activities (West 2017).</ref><br />
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= Labor Model Flowcharts =<br />
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The diagram below shows an outline of the IFs labor model. On the supply side, the total labor pool (LAB) is computed from the labor force participation rates, by sex, (LABPARR) and the population (POP) in their working age, i.e., population over 15 (POP15TO65 + POPGT65). Participation rates are driven by the demographic changes with an additional negative impact from aging and a catch-up in female participation rate. Skill level of the labor supply (LABSUP) is driven by the level of development (GDPPCP) and the demand for labor is driven by labor-coefficients (LABCOEFFS) computed from coefficient function representing shifts in demand with technological progress as proxied by the level of development (GDPPCP). Coefficients computed by sector and skill gives the labor requirement by skill type for each unit of value added (VADD) in the sector. Multiplying these coefficients with projected value added in each sector gives an estimate of the labor demand. &nbsp;<br />
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Any surplus or shortage between total labor demand and supply is used to compute the rate of unemployment. Deviations in the unemployment rate (LABUNEMPR) signal wage changes through an equilibrium seeking algorithm. Both demand and supply respond to the wage variable (LABWAGEIND) indexed to the base year. The supply responses are much slower than the demand responses.<br />
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[[File:FLOCHART2.png|frame|center|Labor Model Flowchart]]<br />
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= Labor Model Equations =<br />
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== Overview ==<br />
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The labor model is a part of the IFs economic model that uses labor model output as an input to a Cobb-Douglas production function in a multi-sector general equilibrium model. IFs is a very long-run dynamic model. Instead of computing fixed short-run equilibria that clear the relevant markets IFs uses an equilibrium seeking algorithm to balance the various systems over the longer run. The algorithm is known as the PID (proportion-integral-derivative) controller algorithm and is used widely in industrial control systems. It makes equilibrium seeking variables in IFs move towards a set target. The algorithm works by computing a multiplier based on the movement of the variable towards the target, as obtained by an integral (I) of the path traversed, and the rate of movement towards the target, the derivative term. The multiplier is applied on the process variable (the P term), or a response variable, in the subsequent time period. In the labor model, unemployment rate (LABUNEMPR) is used as the process variable and the PID multiplier is used on the wage rate (LABWAGEIND). Job availability (LABDEMS) and participation rate (LABPARR) get affected by changes in wage. &nbsp;&nbsp;<br />
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Throughout this section we use subscripts and notations common to other modules of IFs. For example, we use t for time period. Subscripts p and r represent sex and country/region, respectively, c is the cohort number, with cohort 1 representing the newborns, cohort1 the the one-year to four-year-olds, cohort two five-year to nine-year-olds etc. Values for p are 1 for male, 2 for female and 3 for both sexes combined. For economic sectors we use s and for skill levels sk.<br />
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== Labor Supply: Equations ==<br />
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The total pool of labor is computed by multiplying the population of working age with the labor force participation rate (LABPARR). &nbsp;Population forecasts come from IFs demographic model which computes both five-year and single-year age-sex cohorts (''agedst'', ''fagedst''). &nbsp;<br />
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The labor model forecasts participation rates by country/region&nbsp; and gender. Participation rates in the model move with the changes in the demographic composition. Female participation rates, which have historically been lower than the same for the male in all societies, but has moved up in modern and affluent societies, get a catch-up boost in the model. Participation rates can also change when there is labor shortage or surplus and the employers try to incentivize or discourage workers by changing wage. This last impact is much less slow than similar wage impacts on the demand side.<br />
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== Labor Participation Rate ==<br />
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Labor participation rates (''LABPARR'') for male and female are first initialized with historical data.<br />
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:<math>LABPARR_{r,p}= LABPARR_{r,p,t=1} </math><br />
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A ‘catch-up’ boost is added to the female participation rate. The boost added (FemParLabMul) starts at a third of a percentage point and withers away following a non-linear path as the female rates approaches the catch-up target (FemParTar), The maximum catch-up that can occur over the horizon of the model is thirty percent.<br />
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:<math>FemParTar_{r}=Amin(LabParRI_{r,p=1},LabParRI_{r,p=2}+30)</math><br />
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:<math>FemParLabMul_{r}=(FemParTar_{r}-LABPARR_{r,p=2,t-1})/(FemParTar_{r}-LABPARR_{r,p=2,t-1})</math><br />
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:<math>LABPARR_{r,p=2,t}=LABPARR_{r,p=2,t-1}+FemParLabMul_{r}*0.3</math><br />
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Next, we compute and apply the aging impact on the participation rate. As the relative share of people over the retirement age increases, the participation rate declines. The model keeps track of the changes in the demographic ratio (PopAgingRatio) of the population who are in their prime working age of 15 to 64 (POPWORKING) to those at a common retirement age of sixty-five or older (POPGT65). This ratio declines as countries age. The percentage drop in the ratio comparative to the base year is scaled appropriately to compute the aging impact (aging_impact). This impact is added to the male and female labor participation rates, with the impact on the female participation rate being slightly lower than that on male rates.<br />
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:<math>POPAgingRatio_{r,t}=POPWORKING_{r,t}/POPGT65_{r,t}</math><br />
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:<math>aging_impact_{r,t}=100*((POPAgingRatio_{r,t}/POPAgingRatio_{r,t=1})-1)*0.2</math><br />
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:<math>LABPARR_{r,p=1,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t} </math><br />
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:<math>LABPARR_{r,p=2,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t}*0.95 </math><br />
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Participation rates respond slowly to changes in wage and unemployment rate. The impact is implemented through a wage impact factor computed from annual changes in the wage index (labwageimpact). The base participation rates can be changed by model user through two model parameters: a direct multiplier on the participation rate (labparm), or one that changes participation by moving the retirement age (labretagem)<br />
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:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact*0.05)*labparm_{r,p,t}*labretagem_{r,p,t}</math><br />
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Total participation rate (LABPARRr,p=3,t) is computed by an weighted average of male and female participation rates.<br />
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:<math>LABPARR_{r,p=3,t}= (∑_{p=1 to 2}∑_{c=4 to 21}(agedst{r,c,p,t}*LABPARR_{r,p,t}))/(∑_{p=1 to 2}∑_{c=4 to 21}agedst{r,c,p,t})</math><br />
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== Total Labor ==<br />
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Finally, the total number of labor available for work (LAB) is computed by multiplying the total participation rate with the population of fifteen-year-olds or older.<br />
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:<math>LAB_{r,t}= LABPARR_{r,p=3,t}*∑_{p=1 to 2,c=4 to 21}agedst_{r,c,p,t}</math><br />
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== Labor by skill level ==<br />
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The labor model forecasts labor supply (LABSUP) by two skill categories. The variable (''LABSUP'') is initialized in the pre-processor by reading the employment by skill/occupation (''LABEMPS'') data from GTAP<ref>We collapse GTAP’s 57 sectors into the six economic sectors of IFs. GTAP collapses the nine occupation categories of ISCO-88 into five. In IFs those five categories are collapsed into a binary – skilled and unskilled. The sectoral and skill mappings are described in two appendices of these document.</ref>&nbsp; and adding the unemployment numbers. We assume same unemployment rate (''LABUMEMPR'') for skilled and unskilled labor.<br />
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:<math>LABSUP_{r,t=1,sk}=∑_{s=1 to 6}(LABEMPS_{r,s,t=1}/(1-(LABUNEMPR_{r,t=1}/100))</math><br />
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The model forecasts labor by skill through a model of the skilled share of the labor. Education, training, exposure, and experience of the employees all improve with the level of development. The model captures this with an analytic function of the skilled share (perskilled) driven by GDP per capita at PPP (GDPPCP) -<br />
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:<math>perskilled_{r}=f(GDPPCP_{r})</math><br />
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Among the causal drivers of skill, education is considered to be the most proximate. Education is strongly correlated with the level of development, the deeper driver of skill in the model. However, the recent increase in education and/or a policy driven educational expansion might add to the impact of education on skill. Additional impacts from education on skill, when there is any, is computed through an expected function formulation. For example, in a society where an average adult has more (or less) education than the adults in other societies at that level of development, the skill share is given a slight upward push (or downward pull). The expectation function is a logarithmic function of educational attainment of working age population (EDYRSAG15) driven by GDP per capita at PPP. Attainment above (or below) the expected level (YearsEdExp) is computed by the function output (YearsEd) adjusted for country situation (yearseddiff). The percentage adjustment to the skilled share (LabSupSkiAdj) is computed using additional (limited) education, i.e., the difference between actual (EDYRSAG15) and expected values of educational attainment, expressed as a percentage of the expected value. The adjustment is scaled appropriately and peters off over time.<br />
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:<math>YearsEd_{r,t}= f(GDPPCP_{r,t})</math><br />
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:<math>yearsdeddiff_{r}= EDYRSAG15_{r,p=3,t=2}-YearsEd_{r,t=2}</math><br />
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:<math>YearsEdExp_{r,t}=YearsEd_{r,t}+yearsdeddiff_{r}</math><br />
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:<math>LabSupSkiAdj_{r,t}=0.3*(EDYRSAG15_{r,p=3,t=2}*YearsEdExp_{r,t})/YearsEd_{r,t}</math><br />
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:<math>LabSupSkiAdj_{r,t}=ConvergeOverTime(0,LabSupSkiAdj_{r,t},70)</math><br />
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:<math>perskilled_{r,t}= perskilled_{r,t}*(1+LabSupSkiAdj_{r,t})</math><br />
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The skilled share (perskilled) is multiplied with the total labor supply (LAB) to obtain the number of labors who are skilled (LABSUPskilled)<br />
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:<math>LABSUP_{r,skilled,t}=LAB_{r,p,t}*perskilledI_{r,t} </math><br />
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As a last step, the model adjusts for the country specific variations in the skilled labor count not captured by the deeper and the proximate models. This is done by saving a ratio (LABSUPSkilledRI) of the actual historical data and the model computed value in the initial year. In the subsequent years this ratio is used to adjust the skilled labor forecast gradually.<br />
<br />
:<math>LABSUPCompSkilled_{r}=LAB_{r}*perskilled_{r,t=1}/100 </math><br />
<br />
:<math>LABSUPSkilledRI_{r}=LABSUP_{r,skilled,t=1}/LABSUPCompSkilled_{r}</math><br />
<br />
:<math>LABSUP_{r,skilled,t}= LABSUP_{r,skilled,t}*ConvergeOverTime(LABSUPSkilledRI_{r},1,85)</math><br />
<br />
Number of unskilled labor is obtained by subtracting the skilled labor from the total pool.<br />
<br />
<br />
<br />
:<math>LABSUP_{r,unskilled,t}= LAB_{r,p,t}- LABSUP_{r,skilled,t}</math><br />
<br />
== Labor Demand: Equations ==<br />
<br />
IFs economic model forecasts production in six economic sectors. IFs labor model computes the longer-term and shorter-term determinants of demand for skilled and unskilled labor (LABDEMS) for the production processes. The long-term drivers of labor requirement are technological progress or the lack of it. In the shorter-term wage affects the labor demand most. Wage in turn is affected by labor supply or skill shortage.<br />
<br />
The IFs model divides economic activities into six economic sectors – agriculture, energy, materials, manufacture, services and information, and communication technologies. Workers in the IFs labor model are disaggregated into two skill types. While the skill composition varies by the technology used in the sector and starts tilting towards the more skilled with the progress in technology, absolute number of labors needed to produce the same output goes down with technological development for both skilled and unskilled labor. This is illustrated in the next figure which plots the changes in labor requirement against GDP per capita at PPP, a proxy for level of development. Agriculture is a much less skill-intensive process than the manufacture, however, with technological progress skill requirement improves rapidly in both sectors. The IFs labor model computes these labor requirement functions in the model pre-processor. As we have already described in the pre-processor section, the computation of these functions use GTAP data on employment by occupation and economic activity. Appendices 3 and 4 lists sector and occupation mapping between GTAP and IFs.<br />
<br />
[[File:LaborCoefficientFunctions.png|frame|center|665x445px|Labor coefficient functions by skill type for the agriculture and the manufacturing sector]]<br />
<br />
These functions are used to compute the labor coefficients (LABCOEFFS), i.e., number of skilled and unskilled labor needed to produce unit amount of output with the technology available, for which we use GDP per capita at PPP as a proxy.<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= f(GDPPCP_{r})</math><br />
<br />
manufacture, services and ICTech) and the subscrip sk stands for skill categories with 1 denoting unskilled and 2 skilled. The labor coefficients obtained from the analytical functions require some adjustments to incorporate country deviations from the functions for various factors not captured in the regression relationship. The first of these adjustments is a gradual removal of impacts of short-run fluctuations in output and labor from the computation of labor coefficient. This adjustment is applied on the coefficients computed from the function. The equation below shows a simplified form of these computations.<br />
<br />
:<math>LabCoeffAdjFac_{r,k,s,t}=f(igdpr_{r,t=2},(LAB_{r,t=2}/LAB_{r,t=1}),(LABCOEFFS_{r,t}/LABCOEFFS_{r,t-1}))</math><br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}=LABCOEFFS_{r,sk,s,t}(1-LabCoeffAdjFac_{r,k,s,t})</math><br />
<br />
Model users can use a global parameter (labcoeffsm) to change the labor coefficients by skill level for any or all of the six sectors –<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= LABCOEFFS_{r,sk,s,t}*'''labcoeffsm_{s,sk}'''</math><br />
<br />
To forecast the total labor demand, the labor coefficients (LABCOEFFS) are multiplied to the total projected output for each of the economic sectors. The forecast is adjusted for any discrepancy between data and model. The adjustment factor (LABDemsAdjFac) is computed as the initial ratio between the actual and computed employment. Actual employment is obtained from historical data (LABEMPS) processed using the GTAP database. The computed employment is obtained by multiplying the labor coefficients (LABCOEFFS) with the final output of the sector (VADD).<br />
<br />
:<math>LabDemsAdjFac_{r,s,sk}= LABEMPS_{r,s,sk,t=1}/(VADD_{r,s,t=1}*LABCOEFFS_{r,sk,s,t})</math><br />
<br />
The projected output is obtained by applying the growth rate (IGDPRCOR) on the sectoral value added from the previous year (VADD). The total labor demand is given by the product of the labor coefficients, projected output, demand adjustments and wage impacts (labwageimpactmul) and the number 1000 which adjusts the units for the equation. Wage impact comes from the level of unemployment and is computed in an equilibration process described in the next section. Model users can use a multiplicative parameter (labdemsm) to slide the demand upward or downward.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}=1000*VADD_{r,s,t-1}*(1+IGDPRCOR_{r})*LABCOEFFS_{r,sk,s,t}*LabDemsAdjFac_{r,s,sk}*labwageimpactmul_{r,s,sk}*'''labdemsm_{r,s}'''</math><br />
<br />
== Unemployment and Wage: Labor Market Equilibration ==<br />
<br />
The IFs labor model balances the labor market through an equilibrium seeking algorithm rather than computing an exact equilibrium at each time step. We use an algorithm borrowed from the control systems engineering. This PID controller algorithm, described also in the IFs economic model documentation, works by computing corrective signals for equilibrating variables using the deviations of a buffer variable, for example unemployment rate (LABUNEMPR), from a target value. The signal is computed from two quantities, the distance of the buffer from the target and the current rate of change of the buffer. The computation is tuned with PID elasticities to avoid oscillations. The computed signal is applied on the variable/s which need to be balanced, for example, demand and supply in the event of a market equilibration, thus getting closer to a balance at each step of simulation. The target value for the buffer variable and the tuning parameters of the control algorithm are obtained through rules-of-thumb and model calibration. The IFs labor model uses unemployment rate (LABUNEMPR) as the buffer variable for the market equilibration of labor demand and labor supply. The multiplier (i.e., corrective signal) obtained from the PID is applied on the wage index (LABWAGEIND). Changes in wage indices comparative to the base year, moderated through a second PID controller, is used to compute the final signal (labwageimpactmul) that drives labor demand and labor supply. Even though the model forecasts labor demand by sector and skill, and computes labor supply for both skill types, the equilibration algorithm works over the entire pool of labor. In other words, we assume that the skills are replaceable across sectors and the lack (or abundance) of jobs affects skilled and unskilled persons equally.<br />
<br />
At each annual timestep, the model computes the unemployment rate (LABUNEMPR) as the gap in between the total supply of labor (LAB) and the total demand. The gap (EmplGap) is expressed as a share of the total labor, the standard way to express unemployment rate.<br />
<br />
:<math>sumld=∑_{s,sk}LADEMS_{r,s,sk,t}</math><br />
<br />
:<math>EmplGap= LAB_{r,t}*sumld</math><br />
<br />
:<math>LABUMENPR_{r,t}= (EmplGap/LAB_{r,t})*100</math><br />
<br />
As the target value (LabUnEmpRateTar) for the PID controller that modulates unemployment rate we use either the historical unemployment rate or a ten percent unemployment rate when the historical rate is higher than ten. Model users can override the historical target through a model parameter (labunemprtrgtval).<br />
<br />
:<math>LABUMENPRi_{r,t}= LABUMENPR_{r,t}</math><br />
<br />
:<math>LabUnempRateTarget_{r}=labunemptargetval_{r}</math><br />
:<math>If LabUnempRateTarget_{r}=0,<br />
LabUnempRateTarget_{r}= AMIN(LABUMENPRi_{r,t},10) </math><br />
<br />
Unemployment rate target, when it is different from the base year value, is reached gradually with a convergence period of forty years . The target rate is converted to count (LabUnEmplTar) to make it equivalent to the employment gap (EmplGap) computed earlier.<br />
<br />
:<math>LabUnEmplTar_{r}= LAB_{r,t}*ConvergeOverTime(LABUMENPRi_{r,t},0,100)</math><br />
<br />
The first order difference (Diffl1) between the target unemployment and the demand-supply gap is used to compute a second order difference (Diffl2) accounting for changes in the rate of movement. The two differences and the PID multipliers (elwageunemp1, elwageunemp2) are provided to the PID function (ADJSTR). Working age population (POP15TO65r,t) works as the scaling base of the PID controller. The controller algorithm gives a multiplier (mullw) that is used in the subsequent year to adjust wage.<br />
<br />
:<math>Diffl1_{t}=LabUnEmplTar_{r}-EmplGap</math><br />
<br />
:<math>Diffl2_{t}=Diffl1_{t}-Diffl1_{t-1}</math><br />
<br />
:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},elwageunemp1_{r},elwageunemp2_{r})</math><br />
<br />
Wage adjustments affect demand and supply with an increase in wage drawing demand downward and supply upward. The opposite affects occur with a downward movement of wage. The wage variable affected by the PID multiplier (LABWAGEIND) is an index initialized at one. We use an indexed rather than a dollar wage in the equilibration process to avoid affecting the process from other economic phenomena that affects wage, for example, a rise in real wage as GDP or the labor share of income grows.<br />
<br />
:<math>LABWAGEIND_{r,t=1}=1</math><br />
<br />
In the subsequent years of the model run, the wage index is first adjusted with the equilibration signal obtained from the unemployment rate PID controller in the previous period<br />
<br />
:<math>LABWAGEIND_{r,t=1}= LABWAGEIND_{r,t=1}* mullw_{r,t-1} </math><br />
<br />
A wage impact (labwageimpact) is then computed using the changes in the wage index relative to the base value. The impact is smoothed with a moving average algorithm.<br />
<br />
:<math>labwageimpact_{r}= labwageimpact_{r,t-1}*0.9+ (1-LABWAGEIND_{r,t})*0.1</math><br />
<br />
The smoothed impact is used as the equilibration signal for labor supply. As we have already described in the section on labor supply, a small fraction of the impact (labwageimpact) is applied to the labor participation rate. The impact is scaled down to account for the slow pace of changes on the supply side.<br />
<br />
:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact_{r,t}*0.05)</math><br />
<br />
For the impacts of wage on labor demand we use a second PID multiplier as opposed to using the changes in wage index that we have done on the supply side. The second PID uses the wage index itself as the process variable and uses the base year value of 1 as the target. The reason we had to use this second PID is to control the pace at which wage disequilibrium can affect demand, especially in the event of an abrupt shock. The smoothing and scaling down that works on the supply side is not enough to control oscillations on the demand side.<br />
<br />
:<math>Diffl1_{t}=LABWAGEIND_{r,t=1}-1</math><br />
<br />
:<math>Diffl2_{t}=LABWAGEIND_{r,t}-LABWAGEIND_{r,t-1} </math><br />
<br />
:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},ellabwage1_{r},ellabwage1_{r})</math><br />
<br />
A second impact factor (labwageimpactmul) is computed using the correction signal from this second multiplier:<br />
<br />
:<math>labwageimpactmul_{r,t}= labwageimpactmul_{r,t-1}*mullw_{r,t}</math><br />
<br />
This impact factor is applied on the labor demand as described in the section on labor demand.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}= LABDEMS_{r,s,sk,t}* labwageimpactmul_{r,t}</math><br />
<br />
== Informal Labor ==<br />
<br />
IFs forecast labor and GDP share of the informal sector. Informal labor forecast is not explicitly endogenized in the labor market though. They are rather driven by development, skill and regulatory factors.<ref>IFs economic model documentation has a detail description of the informal economy model.</ref>&nbsp;However, the productivity and revenue impacts of changes in informality affects output and thus labor demand implicitly as a very distal driver.<br />
<br />
= References =<br />
<br />
<references /><br />
</div></div></div></div></div></div></div></div></div></div></div>Wikiadmin//pardee.du.edu/wiki/LaborLabor2018-09-07T22:54:39Z<p>Wikiadmin: </p>
<hr />
<div>Workers in an economy supply the expertise and the efforts needed to produce goods and services. In return the labor receives wages that they use to meet their current and future consumption needs. On one hand, shortage of labor with required skills prevents economies from realizing their growth potential. On the other hand, individuals falling short of the right qualifications might remain unemployed or underemployed failing to secure income needed for a decent living. The ongoing adjustments to find the best match between skills, jobs and wages can only be studied through a dynamic model of the labor market.&nbsp; &nbsp;<br />
<br />
Such a model should go beyond providing a reasonable answer to the obvious question of why employment and wages go up and down. An aggregate labor market must deal with issues that have strong interconnections with various other dynamic changes in the greater society. What kind of dividend of deficit can a society expect from its labor force given the phase of demographic transition in which it is situated? How severely would aging affect the pool of working age adults? Might increasing female participation rates offset some of the losses from aging? What is the level of skills and educational attainment in a society? These supply phenomena move relatively slowly unless there are huge disruptions, like a war or famine, or an aggressive policy push. The demand side, in contrast, needs to be more responsive in adjusting wages and employment given the investment and technology in the various sectors of the broader economy. In general, though, the labor market demonstrates some sluggishness compared to the goods and services markets as it involves moving human beings with various limitations. Consumption of goods and services depend on the income earned by the labor. Uneven distribution of employment and wages among labors of various types or between labor and capital for a long period of time can give rise to persistent inequality in a society. &nbsp;&nbsp;&nbsp;&nbsp;<br />
<br />
== Conceptual Framework ==<br />
<br />
Labor markets are markets for workers and jobs. In a labor market, employers meet their demand for labor with the supply of people willing to work at the wage the employers can offer. The employers raise the wage when there is a shortage of workers. Workers agree to take a lower wage when there are more of them than the firms need. In the real-world labor markets do not always clear at perfect equilibrium. Frinctional unemployment results for various reasons, for example, the search time between jobs. Structural unemployment can result from technology induced disruptions. Some unemployment could thus persist in the labor market even when there aren’t any short-term fluctuations. There is also the phenomenon of informal employment that consists of less sophisticated workers and entrepreneurs engaged in unregulated economic activities. &nbsp;In a dynamic model that covers the entire economy, the real wage earned by the labor drives the income and social mobility.&nbsp;&nbsp;&nbsp;<br />
<br />
To understand the long-term dynamics of the labor market, we need also examine the deeper determinants of labor demand and supply, the determinants that can shift the curves. Labor demand changes over time with the changes in demand for goods and services and the labor input needed to produce those. Labor productivity itself improves with technological progress. Long term transitions in the supply of labor are mostly demographic. &nbsp;<br />
<br />
Labor supply is determined by the working age population and the share of that population who are available for participation in the workforce. The labor supply is relatively stable as the demographic changes are slow in pace. As the share of elderly in the population increases, a recent trend in many societies, the rate of participation declines. Some of the aging impacts will be offset by the greater female participation rates, a second trend that surfaces as economies develop and women attain more education. Educational attainment also drives the general skill level of workers, male and female. Specific skills are obtained through training and experience that augment the knowledge obtained through general and specialized education. &nbsp;&nbsp;&nbsp;&nbsp;<br />
<br />
It is the demand side that causes most of the short-term imbalances in the labor market. &nbsp;In the long term, as said earlier, the important driver of demand for labor and their skills is technological progress. Labor requirement drops with advances in technology, more so for less skilled labor. Labor composition changes accordingly both within and across sectors. Rapid advances in technology can also cause disruption in the system when there is not much opening in the other sectors. Labor displacement is offset to some extent by the growth in the economy and the resulting increase in total demand. &nbsp;&nbsp;&nbsp;<br />
<br />
As we have already mentioned, employees maximize income and the firms minimize labor costs. When there are more laborers than the firms can hire, there is unemployment. Shifts in the rates of unemployment impacts wage, the price of labor. For example, wages drop in the event of rising unemployment as there are more people to hire from. Wage adjustments feed back to the demand for labor seeking to bring the market back to equilibrium.<br />
<br />
The challenges around the conceptual distinction between unemployment and employment is further complicated by the phenomenon of informal employment. In many developing countries there is a large urban non-agricultural informal sector where low-skilled workers work for wages typically lower than a formal employment.<br />
<br />
[[File:LMFlowchart1.png|frame|center|Description of the labor model]]<br />
<br />
== Dominant Relations ==<br />
<br />
The labor model in the International Futures system (IFs) balances the total supply of labor with the total labor demanded by all economic sectors. Total labor (LAB) is computed from the working age population and the labor participation rate. Population forecasts are obtained from the IFs demographic model. Participation rates (LABPARR) are computed by sex with a catchup algorithm for the female participation towards that for the male. Labor is also disaggregated by skill level, as determined by educational attainment, in a separate labor supply variable (LABSUP) which is used to distribute labor earnings by skill level. [** LABSUP do not affect the demand/supply balance now]<br />
<br />
Labor demands (LABDEMS) are driven by sectoral technology functions used to compute the labor requirement by skill level for each unit of potential valued added in the sector. These labor coefficients (LABCOEFFS) are multiplied with the projected value added for the sector to compute the needed manpower. The balancing mechanisms determines the labor employed in each of the sectors (LABS).&nbsp;&nbsp;<br />
<br />
The balancing, in the current version of the model, can be done in one of the two ways. In the first method, total needs combined from all economic sectors is normalized to the available pool of labor computed by subtracting the unemployed from those who are at or looking for work. The rate of unemployment is kept at its natural rate for which we use the base year rate of unemployment. (** This might need to be changed for countries where the market is undergoing some abrupt transition.)<br />
<br />
In the second balancing method, added in a recent revision of the model, total demand is equilibrated to supply through a CGE like market equilibrium model. An indexed wage (LABWAGEIND) and the rate of unemployment (LABUNEMPR) work as the equilibrating variables. As unemployment deviates from the target, PID algorithms send a signal for the wage to adjust. Wage adjustments cause adjustments in the “base” labor demands by sector computed from the labor-coefficient functions as described earlier. Wage signals also affects the labor participation rate. The magnitude of impact on the supply side is much lower than that on the demand side.<br />
<br />
Wage and unemployment rate are aggregated for the total labor market. The wage index starts with a base year value of 1 and the unemployment rates start with the historical data for the base year. Initial year unemployment rate works as the target for long term unemployment.<br />
<br />
== Key Dynamics ==<br />
<br />
The following key dynamics are directly related to the dominant relations:<br />
<br />
*Labor supply is determined from population of appropriate age in the population model (see its dominant relations and dynamics) and endogenous labor force participation rates, influenced exogenously by the growth of female participation.<br />
*Labor demand is driven by sectoral demand functions driven by technological progress<br />
<br />
== Structure and Agent System ==<br />
<br />
{| border="1" cellspacing="0" cellpadding="0" width="0" style="width:502px;"<br />
|-<br />
| style="width:242px;height:49px;" | <br />
'''System/Subsystem'''<br />
<br />
| style="height:49px;" | <br />
Labor market<br />
<br />
|-<br />
| <br />
'''Organizing Structure'''<br />
<br />
| <br />
Labor supply by skill level and labor demand by sector for each skill category represented within an equilibrium-seeking model with wage and unemployment rate as the equilibrating variables<br />
<br />
|-<br />
| <br />
'''Stocks'''<br />
<br />
| <br />
Population, labor, education, &nbsp;accumulated technology<br />
<br />
|-<br />
| <br />
'''Flows'''<br />
<br />
| <br />
Participation rate; Coefficients of labor demand; Employment (unemployment); Wage<br />
<br />
|-<br />
| <br />
'''Key Aggregate&nbsp;''''''Relationships&nbsp;'''<br />
<br />
'''(illustrative, not comprehensive)'''<br />
<br />
| <br />
Labor supply is driven by demographic changes; Participation of female change over time; Labor requirement changes with technological development; Unemployment rate drives wage; Wage movements affect labor demand and participation rate<br />
<br />
|-<br />
| <br />
'''Key Agent-Class Behavior&nbsp;''''''Relationships'''<br />
<br />
'''(illustrative, not comprehensive)'''<br />
<br />
| <br />
Households and work/leisure, and female participation patterns;<br />
<br />
&nbsp;<br />
<br />
Firms and hiring;<br />
<br />
&nbsp;<br />
<br />
<br />
<br />
<br />
<br />
|}<br />
<br />
= Labor Model Data =<br />
<br />
The labor supply and unemployment data that we use in our model is from International Labor Organization (ILO). For data on the demand side, we used data from the Global Trade Analysis Project. Wage variable used in the equilibration algorithm&nbsp;is an index anchored to the base year of the model.<ref>GTAP database helped us compute wage rates by sector and skill.</ref> IFs preprocessor prepared these data for model use using various estimation, conversion and reconciliation processes.&nbsp; &nbsp; &nbsp;<br />
<br />
== Definitional Issues ==<br />
<br />
There are ambiguities in the way some of the labor market variables are defined. Labor participation rates and the rate of unemployment are two that need special attention.<br />
<br />
The size of the labor supply available for economic activities is expressed with the labor force participation rate. ILO defines this as a “measure of the proportion of country’s working-age population that engages actively in the labor market, either by working or looking for work.”<ref>http://www.ilo.org/ilostat-files/Documents/description_LFPR_EN.pdf</ref>&nbsp;National labor force surveys and census data are used to estimate this rate. The definition of labor force here includes both employed and unemployed and the rate is expressed as a percentage of working-age population. Working-age population is defined here as the population above legal working-age. For international comparability, ILO adopts a convenient minimum threshold of fifteen years as working age and avoids putting any upper age limit. In practice, both the minimum and the upper-age limits can vary by country. For example, the working-age in the USA is sixteen years. In the Netherlands the upper age limit is seventy-five years, whereas South African data uses an upper age limit of 64.<ref>https://www.bls.gov/fls/flscomparelf/technical_notes.pdf</ref><br />
<br />
Ambiguities are more abundant in the definition of unemployment. ILO came up with a guideline on this as well. Per the ILO guideline, the unemployed are those among the working-age population who are not employed, are available for work and are actively looking for jobs<ref>The definitions around employed and unemployed were agreed upon by nations through the ‘Resolution concerning statistics of work, employment and labor underutilization’ adopted by the 19th International Conference of Labor Statisticians (ICLS) in 2013. (Bourmpoula et al, 2017: 6).</ref>; the unemployment rate is expressed as a percentage of those who are in the labor force. The availability and job-seeker status could be defined in different ways giving rise to incompatibility in data. &nbsp;While there seems to be little room for disagreement on whether someone is at work or not, whether that work should be considered as employment is contested at many times.<br />
<br />
The debates around the nature and type of employment can range from gainfulness to workplace setting. For example, a large number of workers in the low-income low-regulation developing countries work outside the purview of formal enterprises. According to an ILO estimate, more than half of the global labor force and more than 90% of Micro and Small Enterprises (MSEs) worldwide are in the so called informal economy.<ref>http://www.ilo.org/global/topics/employment-promotion/informal-economy/lang--en/index.htm</ref> This might explain the apparently counterintuitive pattern of low unemployment rate in some low-income countries (e.g., 2.2% for Guatemala) and relatively higher numbers for some of the developed nations. The low numbers in the poorer countries hide the prevalence of extremely low wage jobs in the informal sectors in these countries, the only options for the vulnerable people in the absence of any kind of social safety net. &nbsp;Contrastingly, in the developed countries the so called ‘gig-economy’ is attracting more and more workers who choose to work on their own rather than in a formal enterprise. ILO conceptualization makes the informal work part of total employment. The stacked Venn diagram below presents the relationship among the labor force metric including informal employment. IFs also models informal economy both in terms of GDP share and employment share of informal in the total economy and employment.<br />
<br />
[[File:LaborSubsets.png|frame|right|Relationship among various labor measurement]]<br />
<br />
Incompatibility can arise in the treatment of various population groups for the computation of the denominator for participation and unemployment rates.<ref>For example, the USA excludes people in the defense services and those in the prisons or mental asylums in their computation of the civilian non-institutional working-age population. There are also variations in the treatments of students, those recently laid-off, and family workers. Please see https://stats.bls.gov/opub/mlr/2000/06/art1full.pdf for a discussion </ref> ILO makes their best efforts to make adjustments in the data for the sake of international comparison. For example, ILO asks countries that deviate from ILO guidelines to collect data needed to convert national figures to ILO figures. It is likely that some differences might have slipped past the adjustment process. We use ILO data and continue to update our database&nbsp;&nbsp;<br />
<div><div id="ftn4"><div id="ftn1"><br />
The GTAP data that we use for the demand side of the labor model is taken as labor headcounts and is thus immune from ambiguities around rate computation. As far as we could gather<ref>Please see the webpage for documentation on GTAP labor data statistic: https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=3248</ref>, the data includes both the formal and informal employment. We also need mention here that the GTAP database reconciles the labor data to calibrate the general equilibrium modeling that they do for the trade analyses. The data could thus be somewhat different from data collected through direct surveys. As a CGE model IFs is benefited by using calibrated data.<br />
<br />
== Sources of Labor Data ==<br />
<br />
IFs model uses ILO data for labor participation rates and for the unemployment rate. The data in IFs are collected from World Bank’s World Development Indicators (WDI) database. According to their documentation, WDI obtained the data from the ILO.<br />
<br />
Unemployment rate data in IFs is also collected from WDI. Like the participation rates WDI also obtains their unemployment data from ILO.<ref>The name of the IFs table is SeriesLaborUnemploy%</ref><br />
<br />
For employment and labor demand data IFs uses Purdue University’s Global Trade Analysis Project (GTAP) database. GTAP collects and compiles factor payments, imports, and intersectoral flow data to calibrate CGE models of national economies for trade and other analyses. In their ninth release in 2016, GTAP published data for 140 countries and regions for the year 2011. The earlier GTAP releases, which the IFs model used for its previous versions, compiled data for the years 2004 and 2007. GTAP data release aggregates economic activities into 57 commodities and activities following International Standard Industrial Classification (ISIC). The IFs model maps the 57 GTAP sectors into six economic sectors of IFs – agriculture, energy, material and mining, manufacture, services and ICT. Appendix 2 presents two tables listing the sectors mapping between IFs and GTAP, and GTAP and ISIC. GTAP further disaggregates labor in each of the commodities/activities into five occupation and skill categories following the nine category International Standard Classification of Occupations (ISCO-88). The IFs model collapses five GTAP occupation categories into the simple IFs dichotomy of skilled and unskilled. The mapping of occupations and skills are presented in the third appendix of this document. &nbsp;<br />
<br />
The data in the main GTAP database, prepared for CGE modeling, are all in dollar unit and thus do not include labor headcounts. We have used a ‘satellite’ GTAP database<ref>See Weingarden and Tsigas, 2010 for the details on the preparation of this database.</ref>&nbsp;for labor headcounts by skill and sector. The labor counts were also used to plot labor requirement functions for each of the IFs economic sectors and skill categories. The wage share of skilled and unskilled labor in each sector was computed using the labor headcounts and labor payments.<br />
<br />
== Scope of IFs Labor Model ==<br />
<br />
The IFs labor model simulates labor market at the national level. Each national labor market forecasts labor demand and employment by six sectors - agriculture, energy, mining, manufacture, services and ICT- and two skill levels - skilled and unskilled. The supply side do not have sectoral representation. IFs forecasts total labor force and labor supply by the two skill levels. Labor participation rate is computed in IFs by gender. Wage and unemployment rate is forecast for the overall labor market only.&nbsp;&nbsp;<br />
<br />
== Labor Model Pre-processor ==<br />
<br />
IFs system has a data preprocessor that prepares the initial conditions for the model using historical databases and various assumptions and estimated relationships to fill in the missing data and make data adjustments as needed.<ref>For more details, please see ‘The Data Pre-Processor of International Futures (IFs)” by Barry B. Hughes (with Mohammod Irfan) at http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf</ref> Pre-processing of labor data takes place in two IFs pre-processing modules. Labor participation rate data, which is closely related to demography, is processed in the population pre-processor. Unemployment rate and labor demand data are processed in the economic pre-processor.&nbsp;<br />
<div><div id="ftn1"><br />
=== Pre-processing Labor participation rate and unemployment ===<br />
<br />
For initializing labor participation rates by sex (LABPARR) the model uses the historical values from the base year or the most recent year with data.<ref>The data tables that the IFs model pre-processor use for initializing labor participation rates are: SeriesLaborParRate15PlusFemale%, SeriesLaborParRate15PlusMale%.</ref> For countries with no data we use regression relationships of the participation rates, for men and for women, with income per capita. The relationships, shown in the next figure, are not great. However, the functions affect only five countries for which we do not have any data at all: Grenada, Kosovo, Micronesia, Seychelles and South Sudan.<ref>We should try to collect participation rate for these countries from country sources.</ref><br />
<div><div id="ftn2"><br />
IFs data series SeriesLaborUnemploy% is used for the initialization of unemployment rates. That series has annual unemployment rates for one or more years between 1980 and 2016, for 181 of the 186 IFs countries. For five countries (Grenada, Kosovo, Micronesia, Taiwan and South Sudan<ref>These are pretty much the same countries for which we do not have any participation rate data. This indicates ILO might have some administrative limitation in reporting data for these countries (notice Kosovo, Seychelles etc in the list)</ref>) there is no data at all. To fill in the missing data we use a regression function of unemployment rate against GDP per capita. Like the participation rate functions, this function does also not have much of an explanatory power.<br />
<div><div id="ftn1"><br />
=== Pre-processing labor demand and unemployment from GTAP ===<br />
<br />
The IFs economic pre-processor reads labor headcount and labor payment data from the GTAP database. In addition to performing sector and occupation/skill mapping between GTAP and IFs, pre-processor also use the labor headcount data to compute labor coefficient functions, the principal driver of labor demand in the IFs model.<br />
<br />
Labor coefficients are defined as the amount of labor needed to produce one unit of value added in a certain sector of the economy. The coefficients depend on the level of technology. The model uses GDP per capita as an indicator of the level of technological development. IFs pre-processor estimates labor coefficient functions for labor of different skill levels for the different sectors of the economy.<br />
<br />
The functions are derived from GTAP data we described earlier. The model pre-processor reads data on factor payments and aggregates data from 57 GTAP sectors to six IFs sectors. Shares of payment going to skilled and less-skilled workers in each of the sectors are then computed. Countries are grouped according to their level of technological development as represented by per capita income. For each group labor coefficients are obtained by taking an average of the country coefficients. &nbsp;We also convert labor payments data to labor headcount data using per capita income as a proxy for average wage. Labor coefficients and income are then plotted into a power function relationship. The figure below plots some of those labor functions.&nbsp;The functions fit quite well with a power law formulation.<ref>This is interesting given the prevalence of power law in all sorts of scale-up activities (West 2017).</ref><br />
<div><div id="ftn1"></div></div></div></div></div></div></div></div><br />
<br />
= Labor Model Flowcharts =<br />
<br />
The diagram below shows an outline of the IFs labor model. On the supply side, the total labor pool (LAB) is computed from the labor force participation rates, by sex, (LABPARR) and the population (POP) in their working age, i.e., population over 15 (POP15TO65 + POPGT65). Participation rates are driven by the demographic changes with an additional negative impact from aging and a catch-up in female participation rate. Skill level of the labor supply (LABSUP) is driven by the level of development (GDPPCP) and the demand for labor is driven by labor-coefficients (LABCOEFFS) computed from coefficient function representing shifts in demand with technological progress as proxied by the level of development (GDPPCP). Coefficients computed by sector and skill gives the labor requirement by skill type for each unit of value added (VADD) in the sector. Multiplying these coefficients with projected value added in each sector gives an estimate of the labor demand. &nbsp;<br />
<br />
Any surplus or shortage between total labor demand and supply is used to compute the rate of unemployment. Deviations in the unemployment rate (LABUNEMPR) signal wage changes through an equilibrium seeking algorithm. Both demand and supply respond to the wage variable (LABWAGEIND) indexed to the base year. The supply responses are much slower than the demand responses.<br />
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[[File:FLOCHART2.png|frame|center|Labor Model Flowchart]]<br />
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= Labor Model Equations =<br />
<br />
== Overview ==<br />
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The labor model is a part of the IFs economic model that uses labor model output as an input to a Cobb-Douglas production function in a multi-sector general equilibrium model. IFs is a very long-run dynamic model. Instead of computing fixed short-run equilibria that clear the relevant markets IFs uses an equilibrium seeking algorithm to balance the various systems over the longer run. The algorithm is known as the PID (proportion-integral-derivative) controller algorithm and is used widely in industrial control systems. It makes equilibrium seeking variables in IFs move towards a set target. The algorithm works by computing a multiplier based on the movement of the variable towards the target, as obtained by an integral (I) of the path traversed, and the rate of movement towards the target, the derivative term. The multiplier is applied on the process variable (the P term), or a response variable, in the subsequent time period. In the labor model, unemployment rate (LABUNEMPR) is used as the process variable and the PID multiplier is used on the wage rate (LABWAGEIND). Job availability (LABDEMS) and participation rate (LABPARR) get affected by changes in wage. &nbsp;&nbsp;<br />
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Throughout this section we use subscripts and notations common to other modules of IFs. For example, we use t for time period. Subscripts p and r represent sex and country/region, respectively, c is the cohort number, with cohort 1 representing the newborns, cohort1 the the one-year to four-year-olds, cohort two five-year to nine-year-olds etc. Values for p are 1 for male, 2 for female and 3 for both sexes combined. For economic sectors we use s and for skill levels sk.<br />
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== Labor Supply: Equations ==<br />
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The total pool of labor is computed by multiplying the population of working age with the labor force participation rate (LABPARR). &nbsp;Population forecasts come from IFs demographic model which computes both five-year and single-year age-sex cohorts (''agedst'', ''fagedst''). &nbsp;<br />
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The labor model forecasts participation rates by country/region&nbsp; and gender. Participation rates in the model move with the changes in the demographic composition. Female participation rates, which have historically been lower than the same for the male in all societies, but has moved up in modern and affluent societies, get a catch-up boost in the model. Participation rates can also change when there is labor shortage or surplus and the employers try to incentivize or discourage workers by changing wage. This last impact is much less slow than similar wage impacts on the demand side.<br />
<br />
== Labor Participation Rate ==<br />
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Labor participation rates (''LABPARR'') for male and female are first initialized with historical data.<br />
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:<math>LABPARR_{r,p}= LABPARR_{r,p,t=1} </math><br />
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A ‘catch-up’ boost is added to the female participation rate. The boost added (FemParLabMul) starts at a third of a percentage point and withers away following a non-linear path as the female rates approaches the catch-up target (FemParTar), The maximum catch-up that can occur over the horizon of the model is thirty percent.<br />
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:<math>FemParTar_{r}=Amin(LabParRI_{r,p=1},LabParRI_{r,p=2}+30)</math><br />
<br />
:<math>FemParLabMul_{r}=(FemParTar_{r}-LABPARR_{r,p=2,t-1})/(FemParTar_{r}-LABPARR_{r,p=2,t-1})</math><br />
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:<math>LABPARR_{r,p=2,t}=LABPARR_{r,p=2,t-1}+FemParLabMul_{r}*0.3</math><br />
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Next, we compute and apply the aging impact on the participation rate. As the relative share of people over the retirement age increases, the participation rate declines. The model keeps track of the changes in the demographic ratio (PopAgingRatio) of the population who are in their prime working age of 15 to 64 (POPWORKING) to those at a common retirement age of sixty-five or older (POPGT65). This ratio declines as countries age. The percentage drop in the ratio comparative to the base year is scaled appropriately to compute the aging impact (aging_impact). This impact is added to the male and female labor participation rates, with the impact on the female participation rate being slightly lower than that on male rates.<br />
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:<math>POPAgingRatio_{r,t}=POPWORKING_{r,t}/POPGT65_{r,t}</math><br />
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:<math>aging_impact_{r,t}=100*((POPAgingRatio_{r,t}/POPAgingRatio_{r,t=1})-1)*0.2</math><br />
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:<math>LABPARR_{r,p=1,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t} </math><br />
<br />
:<math>LABPARR_{r,p=2,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t}*0.95 </math><br />
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Participation rates respond slowly to changes in wage and unemployment rate. The impact is implemented through a wage impact factor computed from annual changes in the wage index (labwageimpact). The base participation rates can be changed by model user through two model parameters: a direct multiplier on the participation rate (labparm), or one that changes participation by moving the retirement age (labretagem)<br />
<br />
<br />
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:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact*0.05)*labparm_{r,p,t}*labretagem_{r,p,t}</math><br />
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Total participation rate (LABPARRr,p=3,t) is computed by an weighted average of male and female participation rates.<br />
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:<math>LABPARR_{r,p=3,t}= (∑_{p=1 to 2}∑_{c=4 to 21}(agedst{r,c,p,t}*LABPARR_{r,p,t}))/(∑_{p=1 to 2}∑_{c=4 to 21}agedst{r,c,p,t})</math><br />
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== Total Labor ==<br />
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Finally, the total number of labor available for work (LAB) is computed by multiplying the total participation rate with the population of fifteen-year-olds or older.<br />
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:<math>LAB_{r,t}= LABPARR_{r,p=3,t}*∑_{p=1 to 2,c=4 to 21}agedst_{r,c,p,t}</math><br />
<br />
== Labor by skill level ==<br />
<br />
The labor model forecasts labor supply (LABSUP) by two skill categories. The variable (''LABSUP'') is initialized in the pre-processor by reading the employment by skill/occupation (''LABEMPS'') data from GTAP<ref>We collapse GTAP’s 57 sectors into the six economic sectors of IFs. GTAP collapses the nine occupation categories of ISCO-88 into five. In IFs those five categories are collapsed into a binary – skilled and unskilled. The sectoral and skill mappings are described in two appendices of these document.</ref>&nbsp; and adding the unemployment numbers. We assume same unemployment rate (''LABUMEMPR'') for skilled and unskilled labor.<br />
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:<math>LABSUP_{r,t=1,sk}=∑_{s=1 to 6}(LABEMPS_{r,s,t=1}/(1-(LABUNEMPR_{r,t=1}/100))</math><br />
<br />
The model forecasts labor by skill through a model of the skilled share of the labor. Education, training, exposure, and experience of the employees all improve with the level of development. The model captures this with an analytic function of the skilled share (perskilled) driven by GDP per capita at PPP (GDPPCP) -<br />
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:<math>perskilled_{r}=f(GDPPCP_{r})</math><br />
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Among the causal drivers of skill, education is considered to be the most proximate. Education is strongly correlated with the level of development, the deeper driver of skill in the model. However, the recent increase in education and/or a policy driven educational expansion might add to the impact of education on skill. Additional impacts from education on skill, when there is any, is computed through an expected function formulation. For example, in a society where an average adult has more (or less) education than the adults in other societies at that level of development, the skill share is given a slight upward push (or downward pull). The expectation function is a logarithmic function of educational attainment of working age population (EDYRSAG15) driven by GDP per capita at PPP. Attainment above (or below) the expected level (YearsEdExp) is computed by the function output (YearsEd) adjusted for country situation (yearseddiff). The percentage adjustment to the skilled share (LabSupSkiAdj) is computed using additional (limited) education, i.e., the difference between actual (EDYRSAG15) and expected values of educational attainment, expressed as a percentage of the expected value. The adjustment is scaled appropriately and peters off over time.<br />
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:<math>YearsEd_{r,t}= f(GDPPCP_{r,t})</math><br />
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:<math>yearsdeddiff_{r}= EDYRSAG15_{r,p=3,t=2}-YearsEd_{r,t=2}</math><br />
<br />
:<math>YearsEdExp_{r,t}=YearsEd_{r,t}+yearsdeddiff_{r}</math><br />
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:<math>LabSupSkiAdj_{r,t}=0.3*(EDYRSAG15_{r,p=3,t=2}*YearsEdExp_{r,t})/YearsEd_{r,t}</math><br />
<br />
:<math>LabSupSkiAdj_{r,t}=ConvergeOverTime(0,LabSupSkiAdj_{r,t},70)</math><br />
<br />
:<math>perskilled_{r,t}= perskilled_{r,t}*(1+LabSupSkiAdj_{r,t})</math><br />
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The skilled share (perskilled) is multiplied with the total labor supply (LAB) to obtain the number of labors who are skilled (LABSUPskilled)<br />
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:<math>LABSUP_{r,skilled,t}=LAB_{r,p,t}*perskilledI_{r,t} </math><br />
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As a last step, the model adjusts for the country specific variations in the skilled labor count not captured by the deeper and the proximate models. This is done by saving a ratio (LABSUPSkilledRI) of the actual historical data and the model computed value in the initial year. In the subsequent years this ratio is used to adjust the skilled labor forecast gradually.<br />
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:<math>LABSUPCompSkilled_{r}=LAB_{r}*perskilled_{r,t=1}/100 </math><br />
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:<math>LABSUPSkilledRI_{r}=LABSUP_{r,skilled,t=1}/LABSUPCompSkilled_{r}</math><br />
<br />
:<math>LABSUP_{r,skilled,t}= LABSUP_{r,skilled,t}*ConvergeOverTime(LABSUPSkilledRI_{r},1,85)</math><br />
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Number of unskilled labor is obtained by subtracting the skilled labor from the total pool.<br />
<br />
<br />
<br />
:<math>LABSUP_{r,unskilled,t}= LAB_{r,p,t}- LABSUP_{r,skilled,t}</math><br />
<br />
== Labor Demand: Equations ==<br />
<br />
IFs economic model forecasts production in six economic sectors. IFs labor model computes the longer-term and shorter-term determinants of demand for skilled and unskilled labor (LABDEMS) for the production processes. The long-term drivers of labor requirement are technological progress or the lack of it. In the shorter-term wage affects the labor demand most. Wage in turn is affected by labor supply or skill shortage.<br />
<br />
The IFs model divides economic activities into six economic sectors – agriculture, energy, materials, manufacture, services and information, and communication technologies. Workers in the IFs labor model are disaggregated into two skill types. While the skill composition varies by the technology used in the sector and starts tilting towards the more skilled with the progress in technology, absolute number of labors needed to produce the same output goes down with technological development for both skilled and unskilled labor. This is illustrated in the next figure which plots the changes in labor requirement against GDP per capita at PPP, a proxy for level of development. Agriculture is a much less skill-intensive process than the manufacture, however, with technological progress skill requirement improves rapidly in both sectors. The IFs labor model computes these labor requirement functions in the model pre-processor. As we have already described in the pre-processor section, the computation of these functions use GTAP data on employment by occupation and economic activity. Appendices 3 and 4 lists sector and occupation mapping between GTAP and IFs.<br />
<br />
[[File:LaborCoefficientFunctions.png|frame|center|665x445px|Labor coefficient functions by skill type for the agriculture and the manufacturing sector]]<br />
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These functions are used to compute the labor coefficients (LABCOEFFS), i.e., number of skilled and unskilled labor needed to produce unit amount of output with the technology available, for which we use GDP per capita at PPP as a proxy.<br />
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:<math>LABCOEFFS_{r,sk,s,t}= f(GDPPCP_{r})</math><br />
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manufacture, services and ICTech) and the subscrip sk stands for skill categories with 1 denoting unskilled and 2 skilled. The labor coefficients obtained from the analytical functions require some adjustments to incorporate country deviations from the functions for various factors not captured in the regression relationship. The first of these adjustments is a gradual removal of impacts of short-run fluctuations in output and labor from the computation of labor coefficient. This adjustment is applied on the coefficients computed from the function. The equation below shows a simplified form of these computations.<br />
<br />
:<math>LabCoeffAdjFac_{r,k,s,t}=f(igdpr_{r,t=2},(LAB_{r,t=2}/LAB_{r,t=1}),(LABCOEFFS_{r,t}/LABCOEFFS_{r,t-1}))</math><br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}=LABCOEFFS_{r,sk,s,t}(1-LabCoeffAdjFac_{r,k,s,t})</math><br />
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Model users can use a global parameter (labcoeffsm) to change the labor coefficients by skill level for any or all of the six sectors –<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= LABCOEFFS_{r,sk,s,t}*'''labcoeffsm_{s,sk}'''</math><br />
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To forecast the total labor demand, the labor coefficients (LABCOEFFS) are multiplied to the total projected output for each of the economic sectors. The forecast is adjusted for any discrepancy between data and model. The adjustment factor (LABDemsAdjFac) is computed as the initial ratio between the actual and computed employment. Actual employment is obtained from historical data (LABEMPS) processed using the GTAP database. The computed employment is obtained by multiplying the labor coefficients (LABCOEFFS) with the final output of the sector (VADD).<br />
<br />
:<math>LabDemsAdjFac_{r,s,sk}= LABEMPS_{r,s,sk,t=1}/(VADD_{r,s,t=1}*LABCOEFFS_{r,sk,s,t})</math><br />
<br />
The projected output is obtained by applying the growth rate (IGDPRCOR) on the sectoral value added from the previous year (VADD). The total labor demand is given by the product of the labor coefficients, projected output, demand adjustments and wage impacts (labwageimpactmul) and the number 1000 which adjusts the units for the equation. Wage impact comes from the level of unemployment and is computed in an equilibration process described in the next section. Model users can use a multiplicative parameter (labdemsm) to slide the demand upward or downward.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}=1000*VADD_{r,s,t-1}*(1+IGDPRCOR_{r})*LABCOEFFS_{r,sk,s,t}*LabDemsAdjFac_{r,s,sk}*labwageimpactmul_{r,s,sk}*'''labdemsm_{r,s}'''</math><br />
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== Unemployment and Wage: Labor Market Equilibration ==<br />
<br />
The IFs labor model balances the labor market through an equilibrium seeking algorithm rather than computing an exact equilibrium at each time step. We use an algorithm borrowed from the control systems engineering. This PID controller algorithm, described also in the IFs economic model documentation, works by computing corrective signals for equilibrating variables using the deviations of a buffer variable, for example unemployment rate (LABUNEMPR), from a target value. The signal is computed from two quantities, the distance of the buffer from the target and the current rate of change of the buffer. The computation is tuned with PID elasticities to avoid oscillations. The computed signal is applied on the variable/s which need to be balanced, for example, demand and supply in the event of a market equilibration, thus getting closer to a balance at each step of simulation. The target value for the buffer variable and the tuning parameters of the control algorithm are obtained through rules-of-thumb and model calibration. The IFs labor model uses unemployment rate (LABUNEMPR) as the buffer variable for the market equilibration of labor demand and labor supply. The multiplier (i.e., corrective signal) obtained from the PID is applied on the wage index (LABWAGEIND). Changes in wage indices comparative to the base year, moderated through a second PID controller, is used to compute the final signal (labwageimpactmul) that drives labor demand and labor supply. Even though the model forecasts labor demand by sector and skill, and computes labor supply for both skill types, the equilibration algorithm works over the entire pool of labor. In other words, we assume that the skills are replaceable across sectors and the lack (or abundance) of jobs affects skilled and unskilled persons equally.<br />
<br />
At each annual timestep, the model computes the unemployment rate (LABUNEMPR) as the gap in between the total supply of labor (LAB) and the total demand. The gap (EmplGap) is expressed as a share of the total labor, the standard way to express unemployment rate.<br />
<br />
:<math>sumld=∑_{s,sk}LADEMS_{r,s,sk,t}</math><br />
<br />
:<math>EmplGap= LAB_{r,t}*sumld</math><br />
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:<math>LABUMENPR_{r,t}= (EmplGap/LAB_{r,t})*100</math><br />
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As the target value (LabUnEmpRateTar) for the PID controller that modulates unemployment rate we use either the historical unemployment rate or a ten percent unemployment rate when the historical rate is higher than ten. Model users can override the historical target through a model parameter (labunemprtrgtval).<br />
<br />
:<math>LABUMENPRi_{r,t}= LABUMENPR_{r,t}</math><br />
<br />
:<math>LabUnempRateTarget_{r}=labunemptargetval_{r}</math><br />
:<math>If LabUnempRateTarget_{r}=0,<br />
LabUnempRateTarget_{r}= AMIN(LABUMENPRi_{r,t},10) </math><br />
<br />
Unemployment rate target, when it is different from the base year value, is reached gradually with a convergence period of forty years . The target rate is converted to count (LabUnEmplTar) to make it equivalent to the employment gap (EmplGap) computed earlier.<br />
<br />
:<math>LabUnEmplTar_{r}= LAB_{r,t}*ConvergeOverTime(LABUMENPRi_{r,t},0,100)</math><br />
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The first order difference (Diffl1) between the target unemployment and the demand-supply gap is used to compute a second order difference (Diffl2) accounting for changes in the rate of movement. The two differences and the PID multipliers (elwageunemp1, elwageunemp2) are provided to the PID function (ADJSTR). Working age population (POP15TO65r,t) works as the scaling base of the PID controller. The controller algorithm gives a multiplier (mullw) that is used in the subsequent year to adjust wage.<br />
<br />
:<math>Diffl1_{t}=LabUnEmplTar_{r}-EmplGap</math><br />
<br />
:<math>Diffl2_{t}=Diffl1_{t}-Diffl1_{t-1}</math><br />
<br />
:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},elwageunemp1_{r},elwageunemp2_{r})</math><br />
<br />
Wage adjustments affect demand and supply with an increase in wage drawing demand downward and supply upward. The opposite affects occur with a downward movement of wage. The wage variable affected by the PID multiplier (LABWAGEIND) is an index initialized at one. We use an indexed rather than a dollar wage in the equilibration process to avoid affecting the process from other economic phenomena that affects wage, for example, a rise in real wage as GDP or the labor share of income grows.<br />
<br />
:<math>LABWAGEIND_{r,t=1}=1</math><br />
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In the subsequent years of the model run, the wage index is first adjusted with the equilibration signal obtained from the unemployment rate PID controller in the previous period<br />
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:<math>LABWAGEIND_{r,t=1}= LABWAGEIND_{r,t=1}* mullw_{r,t-1} </math><br />
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A wage impact (labwageimpact) is then computed using the changes in the wage index relative to the base value. The impact is smoothed with a moving average algorithm.<br />
<br />
:<math>labwageimpact_{r}= labwageimpact_{r,t-1}*0.9+ (1-LABWAGEIND_{r,t})*0.1</math><br />
<br />
The smoothed impact is used as the equilibration signal for labor supply. As we have already described in the section on labor supply, a small fraction of the impact (labwageimpact) is applied to the labor participation rate. The impact is scaled down to account for the slow pace of changes on the supply side.<br />
<br />
:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact_{r,t}*0.05)</math><br />
<br />
For the impacts of wage on labor demand we use a second PID multiplier as opposed to using the changes in wage index that we have done on the supply side. The second PID uses the wage index itself as the process variable and uses the base year value of 1 as the target. The reason we had to use this second PID is to control the pace at which wage disequilibrium can affect demand, especially in the event of an abrupt shock. The smoothing and scaling down that works on the supply side is not enough to control oscillations on the demand side.<br />
<br />
:<math>Diffl1_{t}=LABWAGEIND_{r,t=1}-1</math><br />
<br />
:<math>Diffl2_{t}=LABWAGEIND_{r,t}-LABWAGEIND_{r,t-1} </math><br />
<br />
:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},ellabwage1_{r},ellabwage1_{r})</math><br />
<br />
A second impact factor (labwageimpactmul) is computed using the correction signal from this second multiplier:<br />
<br />
:<math>labwageimpactmul_{r,t}= labwageimpactmul_{r,t-1}*mullw_{r,t}</math><br />
<br />
This impact factor is applied on the labor demand as described in the section on labor demand.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}= LABDEMS_{r,s,sk,t}* labwageimpactmul_{r,t}</math><br />
<br />
== Informal Labor ==<br />
<br />
IFs forecast labor and GDP share of the informal sector. Informal labor forecast is not explicitly endogenized in the labor market though. They are rather driven by development, skill and regulatory factors[[#_ftn1|[1]]]. However, the productivity and revenue impacts of changes in informality affects output and thus labor demand implicitly as a very distal driver.<br />
<br />
= References =<br />
<br />
<references /><br />
</div></div></div></div></div></div></div></div></div></div></div>Wikiadmin//pardee.du.edu/wiki/LaborLabor2018-09-07T22:54:11Z<p>Wikiadmin: </p>
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<div>Workers in an economy supply the expertise and the efforts needed to produce goods and services. In return the labor receives wages that they use to meet their current and future consumption needs. On one hand, shortage of labor with required skills prevents economies from realizing their growth potential. On the other hand, individuals falling short of the right qualifications might remain unemployed or underemployed failing to secure income needed for a decent living. The ongoing adjustments to find the best match between skills, jobs and wages can only be studied through a dynamic model of the labor market.&nbsp; &nbsp;<br />
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Such a model should go beyond providing a reasonable answer to the obvious question of why employment and wages go up and down. An aggregate labor market must deal with issues that have strong interconnections with various other dynamic changes in the greater society. What kind of dividend of deficit can a society expect from its labor force given the phase of demographic transition in which it is situated? How severely would aging affect the pool of working age adults? Might increasing female participation rates offset some of the losses from aging? What is the level of skills and educational attainment in a society? These supply phenomena move relatively slowly unless there are huge disruptions, like a war or famine, or an aggressive policy push. The demand side, in contrast, needs to be more responsive in adjusting wages and employment given the investment and technology in the various sectors of the broader economy. In general, though, the labor market demonstrates some sluggishness compared to the goods and services markets as it involves moving human beings with various limitations. Consumption of goods and services depend on the income earned by the labor. Uneven distribution of employment and wages among labors of various types or between labor and capital for a long period of time can give rise to persistent inequality in a society. &nbsp;&nbsp;&nbsp;&nbsp;<br />
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== Conceptual Framework ==<br />
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Labor markets are markets for workers and jobs. In a labor market, employers meet their demand for labor with the supply of people willing to work at the wage the employers can offer. The employers raise the wage when there is a shortage of workers. Workers agree to take a lower wage when there are more of them than the firms need. In the real-world labor markets do not always clear at perfect equilibrium. Frinctional unemployment results for various reasons, for example, the search time between jobs. Structural unemployment can result from technology induced disruptions. Some unemployment could thus persist in the labor market even when there aren’t any short-term fluctuations. There is also the phenomenon of informal employment that consists of less sophisticated workers and entrepreneurs engaged in unregulated economic activities. &nbsp;In a dynamic model that covers the entire economy, the real wage earned by the labor drives the income and social mobility.&nbsp;&nbsp;&nbsp;<br />
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To understand the long-term dynamics of the labor market, we need also examine the deeper determinants of labor demand and supply, the determinants that can shift the curves. Labor demand changes over time with the changes in demand for goods and services and the labor input needed to produce those. Labor productivity itself improves with technological progress. Long term transitions in the supply of labor are mostly demographic. &nbsp;<br />
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Labor supply is determined by the working age population and the share of that population who are available for participation in the workforce. The labor supply is relatively stable as the demographic changes are slow in pace. As the share of elderly in the population increases, a recent trend in many societies, the rate of participation declines. Some of the aging impacts will be offset by the greater female participation rates, a second trend that surfaces as economies develop and women attain more education. Educational attainment also drives the general skill level of workers, male and female. Specific skills are obtained through training and experience that augment the knowledge obtained through general and specialized education. &nbsp;&nbsp;&nbsp;&nbsp;<br />
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It is the demand side that causes most of the short-term imbalances in the labor market. &nbsp;In the long term, as said earlier, the important driver of demand for labor and their skills is technological progress. Labor requirement drops with advances in technology, more so for less skilled labor. Labor composition changes accordingly both within and across sectors. Rapid advances in technology can also cause disruption in the system when there is not much opening in the other sectors. Labor displacement is offset to some extent by the growth in the economy and the resulting increase in total demand. &nbsp;&nbsp;&nbsp;<br />
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As we have already mentioned, employees maximize income and the firms minimize labor costs. When there are more laborers than the firms can hire, there is unemployment. Shifts in the rates of unemployment impacts wage, the price of labor. For example, wages drop in the event of rising unemployment as there are more people to hire from. Wage adjustments feed back to the demand for labor seeking to bring the market back to equilibrium.<br />
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The challenges around the conceptual distinction between unemployment and employment is further complicated by the phenomenon of informal employment. In many developing countries there is a large urban non-agricultural informal sector where low-skilled workers work for wages typically lower than a formal employment.<br />
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[[File:LMFlowchart1.png|frame|center|Description of the labor model]]<br />
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== Dominant Relations ==<br />
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The labor model in the International Futures system (IFs) balances the total supply of labor with the total labor demanded by all economic sectors. Total labor (LAB) is computed from the working age population and the labor participation rate. Population forecasts are obtained from the IFs demographic model. Participation rates (LABPARR) are computed by sex with a catchup algorithm for the female participation towards that for the male. Labor is also disaggregated by skill level, as determined by educational attainment, in a separate labor supply variable (LABSUP) which is used to distribute labor earnings by skill level. [** LABSUP do not affect the demand/supply balance now]<br />
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Labor demands (LABDEMS) are driven by sectoral technology functions used to compute the labor requirement by skill level for each unit of potential valued added in the sector. These labor coefficients (LABCOEFFS) are multiplied with the projected value added for the sector to compute the needed manpower. The balancing mechanisms determines the labor employed in each of the sectors (LABS).&nbsp;&nbsp;<br />
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The balancing, in the current version of the model, can be done in one of the two ways. In the first method, total needs combined from all economic sectors is normalized to the available pool of labor computed by subtracting the unemployed from those who are at or looking for work. The rate of unemployment is kept at its natural rate for which we use the base year rate of unemployment. (** This might need to be changed for countries where the market is undergoing some abrupt transition.)<br />
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In the second balancing method, added in a recent revision of the model, total demand is equilibrated to supply through a CGE like market equilibrium model. An indexed wage (LABWAGEIND) and the rate of unemployment (LABUNEMPR) work as the equilibrating variables. As unemployment deviates from the target, PID algorithms send a signal for the wage to adjust. Wage adjustments cause adjustments in the “base” labor demands by sector computed from the labor-coefficient functions as described earlier. Wage signals also affects the labor participation rate. The magnitude of impact on the supply side is much lower than that on the demand side.<br />
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Wage and unemployment rate are aggregated for the total labor market. The wage index starts with a base year value of 1 and the unemployment rates start with the historical data for the base year. Initial year unemployment rate works as the target for long term unemployment.<br />
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== Key Dynamics ==<br />
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The following key dynamics are directly related to the dominant relations:<br />
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*Labor supply is determined from population of appropriate age in the population model (see its dominant relations and dynamics) and endogenous labor force participation rates, influenced exogenously by the growth of female participation.<br />
*Labor demand is driven by sectoral demand functions driven by technological progress<br />
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== Structure and Agent System ==<br />
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{| border="1" cellspacing="0" cellpadding="0" width="0" style="width:502px;"<br />
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'''System/Subsystem'''<br />
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Labor market<br />
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'''Organizing Structure'''<br />
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Labor supply by skill level and labor demand by sector for each skill category represented within an equilibrium-seeking model with wage and unemployment rate as the equilibrating variables<br />
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'''Stocks'''<br />
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Population, labor, education, &nbsp;accumulated technology<br />
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'''Flows'''<br />
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Participation rate; Coefficients of labor demand; Employment (unemployment); Wage<br />
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'''Key Aggregate&nbsp;''''''Relationships&nbsp;'''<br />
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'''(illustrative, not comprehensive)'''<br />
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Labor supply is driven by demographic changes; Participation of female change over time; Labor requirement changes with technological development; Unemployment rate drives wage; Wage movements affect labor demand and participation rate<br />
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'''Key Agent-Class Behavior&nbsp;''''''Relationships'''<br />
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'''(illustrative, not comprehensive)'''<br />
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Households and work/leisure, and female participation patterns;<br />
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Firms and hiring;<br />
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= Labor Model Data =<br />
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The labor supply and unemployment data that we use in our model is from International Labor Organization (ILO). For data on the demand side, we used data from the Global Trade Analysis Project. Wage variable used in the equilibration algorithm&nbsp;is an index anchored to the base year of the model.<ref>GTAP database helped us compute wage rates by sector and skill.</ref> IFs preprocessor prepared these data for model use using various estimation, conversion and reconciliation processes.&nbsp; &nbsp; &nbsp;<br />
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== Definitional Issues ==<br />
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There are ambiguities in the way some of the labor market variables are defined. Labor participation rates and the rate of unemployment are two that need special attention.<br />
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The size of the labor supply available for economic activities is expressed with the labor force participation rate. ILO defines this as a “measure of the proportion of country’s working-age population that engages actively in the labor market, either by working or looking for work.”<ref>http://www.ilo.org/ilostat-files/Documents/description_LFPR_EN.pdf</ref>&nbsp;National labor force surveys and census data are used to estimate this rate. The definition of labor force here includes both employed and unemployed and the rate is expressed as a percentage of working-age population. Working-age population is defined here as the population above legal working-age. For international comparability, ILO adopts a convenient minimum threshold of fifteen years as working age and avoids putting any upper age limit. In practice, both the minimum and the upper-age limits can vary by country. For example, the working-age in the USA is sixteen years. In the Netherlands the upper age limit is seventy-five years, whereas South African data uses an upper age limit of 64.<ref>https://www.bls.gov/fls/flscomparelf/technical_notes.pdf</ref><br />
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Ambiguities are more abundant in the definition of unemployment. ILO came up with a guideline on this as well. Per the ILO guideline, the unemployed are those among the working-age population who are not employed, are available for work and are actively looking for jobs<ref>The definitions around employed and unemployed were agreed upon by nations through the ‘Resolution concerning statistics of work, employment and labor underutilization’ adopted by the 19th International Conference of Labor Statisticians (ICLS) in 2013. (Bourmpoula et al, 2017: 6).</ref>; the unemployment rate is expressed as a percentage of those who are in the labor force. The availability and job-seeker status could be defined in different ways giving rise to incompatibility in data. &nbsp;While there seems to be little room for disagreement on whether someone is at work or not, whether that work should be considered as employment is contested at many times.<br />
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The debates around the nature and type of employment can range from gainfulness to workplace setting. For example, a large number of workers in the low-income low-regulation developing countries work outside the purview of formal enterprises. According to an ILO estimate, more than half of the global labor force and more than 90% of Micro and Small Enterprises (MSEs) worldwide are in the so called informal economy.<ref>http://www.ilo.org/global/topics/employment-promotion/informal-economy/lang--en/index.htm</ref> This might explain the apparently counterintuitive pattern of low unemployment rate in some low-income countries (e.g., 2.2% for Guatemala) and relatively higher numbers for some of the developed nations. The low numbers in the poorer countries hide the prevalence of extremely low wage jobs in the informal sectors in these countries, the only options for the vulnerable people in the absence of any kind of social safety net. &nbsp;Contrastingly, in the developed countries the so called ‘gig-economy’ is attracting more and more workers who choose to work on their own rather than in a formal enterprise. ILO conceptualization makes the informal work part of total employment. The stacked Venn diagram below presents the relationship among the labor force metric including informal employment. IFs also models informal economy both in terms of GDP share and employment share of informal in the total economy and employment.<br />
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[[File:LaborSubsets.png|frame|right|Relationship among various labor measurement]]<br />
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Incompatibility can arise in the treatment of various population groups for the computation of the denominator for participation and unemployment rates.<ref>For example, the USA excludes people in the defense services and those in the prisons or mental asylums in their computation of the civilian non-institutional working-age population. There are also variations in the treatments of students, those recently laid-off, and family workers. Please see https://stats.bls.gov/opub/mlr/2000/06/art1full.pdf for a discussion </ref> ILO makes their best efforts to make adjustments in the data for the sake of international comparison. For example, ILO asks countries that deviate from ILO guidelines to collect data needed to convert national figures to ILO figures. It is likely that some differences might have slipped past the adjustment process. We use ILO data and continue to update our database&nbsp;&nbsp;<br />
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The GTAP data that we use for the demand side of the labor model is taken as labor headcounts and is thus immune from ambiguities around rate computation. As far as we could gather<ref>Please see the webpage for documentation on GTAP labor data statistic: https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=3248</ref>, the data includes both the formal and informal employment. We also need mention here that the GTAP database reconciles the labor data to calibrate the general equilibrium modeling that they do for the trade analyses. The data could thus be somewhat different from data collected through direct surveys. As a CGE model IFs is benefited by using calibrated data.<br />
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== Sources of Labor Data ==<br />
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IFs model uses ILO data for labor participation rates and for the unemployment rate. The data in IFs are collected from World Bank’s World Development Indicators (WDI) database. According to their documentation, WDI obtained the data from the ILO.<br />
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Unemployment rate data in IFs is also collected from WDI. Like the participation rates WDI also obtains their unemployment data from ILO.<ref>The name of the IFs table is SeriesLaborUnemploy%</ref><br />
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For employment and labor demand data IFs uses Purdue University’s Global Trade Analysis Project (GTAP) database. GTAP collects and compiles factor payments, imports, and intersectoral flow data to calibrate CGE models of national economies for trade and other analyses. In their ninth release in 2016, GTAP published data for 140 countries and regions for the year 2011. The earlier GTAP releases, which the IFs model used for its previous versions, compiled data for the years 2004 and 2007. GTAP data release aggregates economic activities into 57 commodities and activities following International Standard Industrial Classification (ISIC). The IFs model maps the 57 GTAP sectors into six economic sectors of IFs – agriculture, energy, material and mining, manufacture, services and ICT. Appendix 2 presents two tables listing the sectors mapping between IFs and GTAP, and GTAP and ISIC. GTAP further disaggregates labor in each of the commodities/activities into five occupation and skill categories following the nine category International Standard Classification of Occupations (ISCO-88). The IFs model collapses five GTAP occupation categories into the simple IFs dichotomy of skilled and unskilled. The mapping of occupations and skills are presented in the third appendix of this document. &nbsp;<br />
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The data in the main GTAP database, prepared for CGE modeling, are all in dollar unit and thus do not include labor headcounts. We have used a ‘satellite’ GTAP database<ref>See Weingarden and Tsigas, 2010 for the details on the preparation of this database.</ref>&nbsp;for labor headcounts by skill and sector. The labor counts were also used to plot labor requirement functions for each of the IFs economic sectors and skill categories. The wage share of skilled and unskilled labor in each sector was computed using the labor headcounts and labor payments.<br />
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== Scope of IFs Labor Model ==<br />
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The IFs labor model simulates labor market at the national level. Each national labor market forecasts labor demand and employment by six sectors - agriculture, energy, mining, manufacture, services and ICT- and two skill levels - skilled and unskilled. The supply side do not have sectoral representation. IFs forecasts total labor force and labor supply by the two skill levels. Labor participation rate is computed in IFs by gender. Wage and unemployment rate is forecast for the overall labor market only.&nbsp;&nbsp;<br />
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== Labor Model Pre-processor ==<br />
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IFs system has a data preprocessor that prepares the initial conditions for the model using historical databases and various assumptions and estimated relationships to fill in the missing data and make data adjustments as needed.<ref>For more details, please see ‘The Data Pre-Processor of International Futures (IFs)” by Barry B. Hughes (with Mohammod Irfan) at http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf</ref> Pre-processing of labor data takes place in two IFs pre-processing modules. Labor participation rate data, which is closely related to demography, is processed in the population pre-processor. Unemployment rate and labor demand data are processed in the economic pre-processor.&nbsp;<br />
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=== Pre-processing Labor participation rate and unemployment ===<br />
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For initializing labor participation rates by sex (LABPARR) the model uses the historical values from the base year or the most recent year with data.<ref>The data tables that the IFs model pre-processor use for initializing labor participation rates are: SeriesLaborParRate15PlusFemale%, SeriesLaborParRate15PlusMale%.</ref> For countries with no data we use regression relationships of the participation rates, for men and for women, with income per capita. The relationships, shown in the next figure, are not great. However, the functions affect only five countries for which we do not have any data at all: Grenada, Kosovo, Micronesia, Seychelles and South Sudan.<ref>We should try to collect participation rate for these countries from country sources.</ref><br />
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IFs data series SeriesLaborUnemploy% is used for the initialization of unemployment rates. That series has annual unemployment rates for one or more years between 1980 and 2016, for 181 of the 186 IFs countries. For five countries (Grenada, Kosovo, Micronesia, Taiwan and South Sudan<ref>These are pretty much the same countries for which we do not have any participation rate data. This indicates ILO might have some administrative limitation in reporting data for these countries (notice Kosovo, Seychelles etc in the list)</ref>) there is no data at all. To fill in the missing data we use a regression function of unemployment rate against GDP per capita. Like the participation rate functions, this function does also not have much of an explanatory power.<br />
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=== Pre-processing labor demand and unemployment from GTAP ===<br />
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The IFs economic pre-processor reads labor headcount and labor payment data from the GTAP database. In addition to performing sector and occupation/skill mapping between GTAP and IFs, pre-processor also use the labor headcount data to compute labor coefficient functions, the principal driver of labor demand in the IFs model.<br />
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Labor coefficients are defined as the amount of labor needed to produce one unit of value added in a certain sector of the economy. The coefficients depend on the level of technology. The model uses GDP per capita as an indicator of the level of technological development. IFs pre-processor estimates labor coefficient functions for labor of different skill levels for the different sectors of the economy.<br />
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The functions are derived from GTAP data we described earlier. The model pre-processor reads data on factor payments and aggregates data from 57 GTAP sectors to six IFs sectors. Shares of payment going to skilled and less-skilled workers in each of the sectors are then computed. Countries are grouped according to their level of technological development as represented by per capita income. For each group labor coefficients are obtained by taking an average of the country coefficients. &nbsp;We also convert labor payments data to labor headcount data using per capita income as a proxy for average wage. Labor coefficients and income are then plotted into a power function relationship. The figure below plots some of those labor functions.&nbsp;The functions fit quite well with a power law formulation.<ref>This is interesting given the prevalence of power law in all sorts of scale-up activities (West 2017).</ref><br />
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= Labor Model Flowcharts =<br />
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The diagram below shows an outline of the IFs labor model. On the supply side, the total labor pool (LAB) is computed from the labor force participation rates, by sex, (LABPARR) and the population (POP) in their working age, i.e., population over 15 (POP15TO65 + POPGT65). Participation rates are driven by the demographic changes with an additional negative impact from aging and a catch-up in female participation rate. Skill level of the labor supply (LABSUP) is driven by the level of development (GDPPCP) and the demand for labor is driven by labor-coefficients (LABCOEFFS) computed from coefficient function representing shifts in demand with technological progress as proxied by the level of development (GDPPCP). Coefficients computed by sector and skill gives the labor requirement by skill type for each unit of value added (VADD) in the sector. Multiplying these coefficients with projected value added in each sector gives an estimate of the labor demand. &nbsp;<br />
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Any surplus or shortage between total labor demand and supply is used to compute the rate of unemployment. Deviations in the unemployment rate (LABUNEMPR) signal wage changes through an equilibrium seeking algorithm. Both demand and supply respond to the wage variable (LABWAGEIND) indexed to the base year. The supply responses are much slower than the demand responses.<br />
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[[File:FLOCHART2.png|frame|center|Labor Model Flowchart]]<br />
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= Labor Model Equations =<br />
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== Overview ==<br />
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The labor model is a part of the IFs economic model that uses labor model output as an input to a Cobb-Douglas production function in a multi-sector general equilibrium model. IFs is a very long-run dynamic model. Instead of computing fixed short-run equilibria that clear the relevant markets IFs uses an equilibrium seeking algorithm to balance the various systems over the longer run. The algorithm is known as the PID (proportion-integral-derivative) controller algorithm and is used widely in industrial control systems. It makes equilibrium seeking variables in IFs move towards a set target. The algorithm works by computing a multiplier based on the movement of the variable towards the target, as obtained by an integral (I) of the path traversed, and the rate of movement towards the target, the derivative term. The multiplier is applied on the process variable (the P term), or a response variable, in the subsequent time period. In the labor model, unemployment rate (LABUNEMPR) is used as the process variable and the PID multiplier is used on the wage rate (LABWAGEIND). Job availability (LABDEMS) and participation rate (LABPARR) get affected by changes in wage. &nbsp;&nbsp;<br />
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Throughout this section we use subscripts and notations common to other modules of IFs. For example, we use t for time period. Subscripts p and r represent sex and country/region, respectively, c is the cohort number, with cohort 1 representing the newborns, cohort1 the the one-year to four-year-olds, cohort two five-year to nine-year-olds etc. Values for p are 1 for male, 2 for female and 3 for both sexes combined. For economic sectors we use s and for skill levels sk.<br />
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== Labor Supply: Equations ==<br />
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The total pool of labor is computed by multiplying the population of working age with the labor force participation rate (LABPARR). &nbsp;Population forecasts come from IFs demographic model which computes both five-year and single-year age-sex cohorts (''agedst'', ''fagedst''). &nbsp;<br />
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The labor model forecasts participation rates by country/region&nbsp; and gender. Participation rates in the model move with the changes in the demographic composition. Female participation rates, which have historically been lower than the same for the male in all societies, but has moved up in modern and affluent societies, get a catch-up boost in the model. Participation rates can also change when there is labor shortage or surplus and the employers try to incentivize or discourage workers by changing wage. This last impact is much less slow than similar wage impacts on the demand side.<br />
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== Labor Participation Rate ==<br />
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Labor participation rates (''LABPARR'') for male and female are first initialized with historical data.<br />
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:<math>LABPARR_{r,p}= LABPARR_{r,p,t=1} </math><br />
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A ‘catch-up’ boost is added to the female participation rate. The boost added (FemParLabMul) starts at a third of a percentage point and withers away following a non-linear path as the female rates approaches the catch-up target (FemParTar), The maximum catch-up that can occur over the horizon of the model is thirty percent.<br />
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:<math>FemParTar_{r}=Amin(LabParRI_{r,p=1},LabParRI_{r,p=2}+30)</math><br />
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:<math>FemParLabMul_{r}=(FemParTar_{r}-LABPARR_{r,p=2,t-1})/(FemParTar_{r}-LABPARR_{r,p=2,t-1})</math><br />
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:<math>LABPARR_{r,p=2,t}=LABPARR_{r,p=2,t-1}+FemParLabMul_{r}*0.3</math><br />
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Next, we compute and apply the aging impact on the participation rate. As the relative share of people over the retirement age increases, the participation rate declines. The model keeps track of the changes in the demographic ratio (PopAgingRatio) of the population who are in their prime working age of 15 to 64 (POPWORKING) to those at a common retirement age of sixty-five or older (POPGT65). This ratio declines as countries age. The percentage drop in the ratio comparative to the base year is scaled appropriately to compute the aging impact (aging_impact). This impact is added to the male and female labor participation rates, with the impact on the female participation rate being slightly lower than that on male rates.<br />
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:<math>POPAgingRatio_{r,t}=POPWORKING_{r,t}/POPGT65_{r,t}</math><br />
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:<math>aging_impact_{r,t}=100*((POPAgingRatio_{r,t}/POPAgingRatio_{r,t=1})-1)*0.2</math><br />
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:<math>LABPARR_{r,p=1,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t} </math><br />
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:<math>LABPARR_{r,p=2,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t}*0.95 </math><br />
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Participation rates respond slowly to changes in wage and unemployment rate. The impact is implemented through a wage impact factor computed from annual changes in the wage index (labwageimpact). The base participation rates can be changed by model user through two model parameters: a direct multiplier on the participation rate (labparm), or one that changes participation by moving the retirement age (labretagem)<br />
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:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact*0.05)*labparm_{r,p,t}*labretagem_{r,p,t}</math><br />
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Total participation rate (LABPARRr,p=3,t) is computed by an weighted average of male and female participation rates.<br />
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:<math>LABPARR_{r,p=3,t}= (∑_{p=1 to 2}∑_{c=4 to 21}(agedst{r,c,p,t}*LABPARR_{r,p,t}))/(∑_{p=1 to 2}∑_{c=4 to 21}agedst{r,c,p,t})</math><br />
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== Total Labor ==<br />
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Finally, the total number of labor available for work (LAB) is computed by multiplying the total participation rate with the population of fifteen-year-olds or older.<br />
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:<math>LAB_{r,t}= LABPARR_{r,p=3,t}*∑_{p=1 to 2,c=4 to 21}agedst_{r,c,p,t}</math><br />
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== Labor by skill level ==<br />
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The labor model forecasts labor supply (LABSUP) by two skill categories. The variable (''LABSUP'') is initialized in the pre-processor by reading the employment by skill/occupation (''LABEMPS'') data from GTAP<ref>We collapse GTAP’s 57 sectors into the six economic sectors of IFs. GTAP collapses the nine occupation categories of ISCO-88 into five. In IFs those five categories are collapsed into a binary – skilled and unskilled. The sectoral and skill mappings are described in two appendices of these document.</ref>&nbsp; and adding the unemployment numbers. We assume same unemployment rate (''LABUMEMPR'') for skilled and unskilled labor.<br />
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:<math>LABSUP_{r,t=1,sk}=∑_{s=1 to 6}(LABEMPS_{r,s,t=1}/(1-(LABUNEMPR_{r,t=1}/100))</math><br />
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The model forecasts labor by skill through a model of the skilled share of the labor. Education, training, exposure, and experience of the employees all improve with the level of development. The model captures this with an analytic function of the skilled share (perskilled) driven by GDP per capita at PPP (GDPPCP) -<br />
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:<math>perskilled_{r}=f(GDPPCP_{r})</math><br />
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Among the causal drivers of skill, education is considered to be the most proximate. Education is strongly correlated with the level of development, the deeper driver of skill in the model. However, the recent increase in education and/or a policy driven educational expansion might add to the impact of education on skill. Additional impacts from education on skill, when there is any, is computed through an expected function formulation. For example, in a society where an average adult has more (or less) education than the adults in other societies at that level of development, the skill share is given a slight upward push (or downward pull). The expectation function is a logarithmic function of educational attainment of working age population (EDYRSAG15) driven by GDP per capita at PPP. Attainment above (or below) the expected level (YearsEdExp) is computed by the function output (YearsEd) adjusted for country situation (yearseddiff). The percentage adjustment to the skilled share (LabSupSkiAdj) is computed using additional (limited) education, i.e., the difference between actual (EDYRSAG15) and expected values of educational attainment, expressed as a percentage of the expected value. The adjustment is scaled appropriately and peters off over time.<br />
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:<math>YearsEd_{r,t}= f(GDPPCP_{r,t})</math><br />
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:<math>yearsdeddiff_{r}= EDYRSAG15_{r,p=3,t=2}-YearsEd_{r,t=2}</math><br />
<br />
:<math>YearsEdExp_{r,t}=YearsEd_{r,t}+yearsdeddiff_{r}</math><br />
<br />
:<math>LabSupSkiAdj_{r,t}=0.3*(EDYRSAG15_{r,p=3,t=2}*YearsEdExp_{r,t})/YearsEd_{r,t}</math><br />
<br />
:<math>LabSupSkiAdj_{r,t}=ConvergeOverTime(0,LabSupSkiAdj_{r,t},70)</math><br />
<br />
:<math>perskilled_{r,t}= perskilled_{r,t}*(1+LabSupSkiAdj_{r,t})</math><br />
<br />
The skilled share (perskilled) is multiplied with the total labor supply (LAB) to obtain the number of labors who are skilled (LABSUPskilled)<br />
<br />
:<math>LABSUP_{r,skilled,t}=LAB_{r,p,t}*perskilledI_{r,t} </math><br />
<br />
As a last step, the model adjusts for the country specific variations in the skilled labor count not captured by the deeper and the proximate models. This is done by saving a ratio (LABSUPSkilledRI) of the actual historical data and the model computed value in the initial year. In the subsequent years this ratio is used to adjust the skilled labor forecast gradually.<br />
<br />
:<math>LABSUPCompSkilled_{r}=LAB_{r}*perskilled_{r,t=1}/100 </math><br />
<br />
:<math>LABSUPSkilledRI_{r}=LABSUP_{r,skilled,t=1}/LABSUPCompSkilled_{r}</math><br />
<br />
:<math>LABSUP_{r,skilled,t}= LABSUP_{r,skilled,t}*ConvergeOverTime(LABSUPSkilledRI_{r},1,85)</math><br />
<br />
Number of unskilled labor is obtained by subtracting the skilled labor from the total pool.<br />
<br />
<br />
<br />
:<math>LABSUP_{r,unskilled,t}= LAB_{r,p,t}- LABSUP_{r,skilled,t}</math><br />
<br />
== Labor Demand: Equations ==<br />
<br />
IFs economic model forecasts production in six economic sectors. IFs labor model computes the longer-term and shorter-term determinants of demand for skilled and unskilled labor (LABDEMS) for the production processes. The long-term drivers of labor requirement are technological progress or the lack of it. In the shorter-term wage affects the labor demand most. Wage in turn is affected by labor supply or skill shortage.<br />
<br />
The IFs model divides economic activities into six economic sectors – agriculture, energy, materials, manufacture, services and information, and communication technologies. Workers in the IFs labor model are disaggregated into two skill types. While the skill composition varies by the technology used in the sector and starts tilting towards the more skilled with the progress in technology, absolute number of labors needed to produce the same output goes down with technological development for both skilled and unskilled labor. This is illustrated in the next figure which plots the changes in labor requirement against GDP per capita at PPP, a proxy for level of development. Agriculture is a much less skill-intensive process than the manufacture, however, with technological progress skill requirement improves rapidly in both sectors. The IFs labor model computes these labor requirement functions in the model pre-processor. As we have already described in the pre-processor section, the computation of these functions use GTAP data on employment by occupation and economic activity. Appendices 3 and 4 lists sector and occupation mapping between GTAP and IFs.<br />
<br />
[[File:LaborCoefficientFunctions.png|frame|center|665x225px|Labor coefficient functions by skill type for the agriculture and the manufacturing sector ]]<br />
<br />
These functions are used to compute the labor coefficients (LABCOEFFS), i.e., number of skilled and unskilled labor needed to produce unit amount of output with the technology available, for which we use GDP per capita at PPP as a proxy.<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= f(GDPPCP_{r})</math><br />
<br />
manufacture, services and ICTech) and the subscrip sk stands for skill categories with 1 denoting unskilled and 2 skilled. The labor coefficients obtained from the analytical functions require some adjustments to incorporate country deviations from the functions for various factors not captured in the regression relationship. The first of these adjustments is a gradual removal of impacts of short-run fluctuations in output and labor from the computation of labor coefficient. This adjustment is applied on the coefficients computed from the function. The equation below shows a simplified form of these computations.<br />
<br />
:<math>LabCoeffAdjFac_{r,k,s,t}=f(igdpr_{r,t=2},(LAB_{r,t=2}/LAB_{r,t=1}),(LABCOEFFS_{r,t}/LABCOEFFS_{r,t-1}))</math><br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}=LABCOEFFS_{r,sk,s,t}(1-LabCoeffAdjFac_{r,k,s,t})</math><br />
<br />
Model users can use a global parameter (labcoeffsm) to change the labor coefficients by skill level for any or all of the six sectors –<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= LABCOEFFS_{r,sk,s,t}*'''labcoeffsm_{s,sk}'''</math><br />
<br />
To forecast the total labor demand, the labor coefficients (LABCOEFFS) are multiplied to the total projected output for each of the economic sectors. The forecast is adjusted for any discrepancy between data and model. The adjustment factor (LABDemsAdjFac) is computed as the initial ratio between the actual and computed employment. Actual employment is obtained from historical data (LABEMPS) processed using the GTAP database. The computed employment is obtained by multiplying the labor coefficients (LABCOEFFS) with the final output of the sector (VADD).<br />
<br />
:<math>LabDemsAdjFac_{r,s,sk}= LABEMPS_{r,s,sk,t=1}/(VADD_{r,s,t=1}*LABCOEFFS_{r,sk,s,t})</math><br />
<br />
The projected output is obtained by applying the growth rate (IGDPRCOR) on the sectoral value added from the previous year (VADD). The total labor demand is given by the product of the labor coefficients, projected output, demand adjustments and wage impacts (labwageimpactmul) and the number 1000 which adjusts the units for the equation. Wage impact comes from the level of unemployment and is computed in an equilibration process described in the next section. Model users can use a multiplicative parameter (labdemsm) to slide the demand upward or downward.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}=1000*VADD_{r,s,t-1}*(1+IGDPRCOR_{r})*LABCOEFFS_{r,sk,s,t}*LabDemsAdjFac_{r,s,sk}*labwageimpactmul_{r,s,sk}*'''labdemsm_{r,s}'''</math><br />
<br />
== Unemployment and Wage: Labor Market Equilibration ==<br />
<br />
The IFs labor model balances the labor market through an equilibrium seeking algorithm rather than computing an exact equilibrium at each time step. We use an algorithm borrowed from the control systems engineering. This PID controller algorithm, described also in the IFs economic model documentation, works by computing corrective signals for equilibrating variables using the deviations of a buffer variable, for example unemployment rate (LABUNEMPR), from a target value. The signal is computed from two quantities, the distance of the buffer from the target and the current rate of change of the buffer. The computation is tuned with PID elasticities to avoid oscillations. The computed signal is applied on the variable/s which need to be balanced, for example, demand and supply in the event of a market equilibration, thus getting closer to a balance at each step of simulation. The target value for the buffer variable and the tuning parameters of the control algorithm are obtained through rules-of-thumb and model calibration. The IFs labor model uses unemployment rate (LABUNEMPR) as the buffer variable for the market equilibration of labor demand and labor supply. The multiplier (i.e., corrective signal) obtained from the PID is applied on the wage index (LABWAGEIND). Changes in wage indices comparative to the base year, moderated through a second PID controller, is used to compute the final signal (labwageimpactmul) that drives labor demand and labor supply. Even though the model forecasts labor demand by sector and skill, and computes labor supply for both skill types, the equilibration algorithm works over the entire pool of labor. In other words, we assume that the skills are replaceable across sectors and the lack (or abundance) of jobs affects skilled and unskilled persons equally.<br />
<br />
At each annual timestep, the model computes the unemployment rate (LABUNEMPR) as the gap in between the total supply of labor (LAB) and the total demand. The gap (EmplGap) is expressed as a share of the total labor, the standard way to express unemployment rate.<br />
<br />
:<math>sumld=∑_{s,sk}LADEMS_{r,s,sk,t}</math><br />
<br />
:<math>EmplGap= LAB_{r,t}*sumld</math><br />
<br />
:<math>LABUMENPR_{r,t}= (EmplGap/LAB_{r,t})*100</math><br />
<br />
As the target value (LabUnEmpRateTar) for the PID controller that modulates unemployment rate we use either the historical unemployment rate or a ten percent unemployment rate when the historical rate is higher than ten. Model users can override the historical target through a model parameter (labunemprtrgtval).<br />
<br />
:<math>LABUMENPRi_{r,t}= LABUMENPR_{r,t}</math><br />
<br />
:<math>LabUnempRateTarget_{r}=labunemptargetval_{r}</math><br />
:<math>If LabUnempRateTarget_{r}=0,<br />
LabUnempRateTarget_{r}= AMIN(LABUMENPRi_{r,t},10) </math><br />
<br />
Unemployment rate target, when it is different from the base year value, is reached gradually with a convergence period of forty years . The target rate is converted to count (LabUnEmplTar) to make it equivalent to the employment gap (EmplGap) computed earlier.<br />
<br />
:<math>LabUnEmplTar_{r}= LAB_{r,t}*ConvergeOverTime(LABUMENPRi_{r,t},0,100)</math><br />
<br />
The first order difference (Diffl1) between the target unemployment and the demand-supply gap is used to compute a second order difference (Diffl2) accounting for changes in the rate of movement. The two differences and the PID multipliers (elwageunemp1, elwageunemp2) are provided to the PID function (ADJSTR). Working age population (POP15TO65r,t) works as the scaling base of the PID controller. The controller algorithm gives a multiplier (mullw) that is used in the subsequent year to adjust wage.<br />
<br />
:<math>Diffl1_{t}=LabUnEmplTar_{r}-EmplGap</math><br />
<br />
:<math>Diffl2_{t}=Diffl1_{t}-Diffl1_{t-1}</math><br />
<br />
:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},elwageunemp1_{r},elwageunemp2_{r})</math><br />
<br />
Wage adjustments affect demand and supply with an increase in wage drawing demand downward and supply upward. The opposite affects occur with a downward movement of wage. The wage variable affected by the PID multiplier (LABWAGEIND) is an index initialized at one. We use an indexed rather than a dollar wage in the equilibration process to avoid affecting the process from other economic phenomena that affects wage, for example, a rise in real wage as GDP or the labor share of income grows.<br />
<br />
:<math>LABWAGEIND_{r,t=1}=1</math><br />
<br />
In the subsequent years of the model run, the wage index is first adjusted with the equilibration signal obtained from the unemployment rate PID controller in the previous period<br />
<br />
:<math>LABWAGEIND_{r,t=1}= LABWAGEIND_{r,t=1}* mullw_{r,t-1} </math><br />
<br />
A wage impact (labwageimpact) is then computed using the changes in the wage index relative to the base value. The impact is smoothed with a moving average algorithm.<br />
<br />
:<math>labwageimpact_{r}= labwageimpact_{r,t-1}*0.9+ (1-LABWAGEIND_{r,t})*0.1</math><br />
<br />
The smoothed impact is used as the equilibration signal for labor supply. As we have already described in the section on labor supply, a small fraction of the impact (labwageimpact) is applied to the labor participation rate. The impact is scaled down to account for the slow pace of changes on the supply side.<br />
<br />
:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact_{r,t}*0.05)</math><br />
<br />
For the impacts of wage on labor demand we use a second PID multiplier as opposed to using the changes in wage index that we have done on the supply side. The second PID uses the wage index itself as the process variable and uses the base year value of 1 as the target. The reason we had to use this second PID is to control the pace at which wage disequilibrium can affect demand, especially in the event of an abrupt shock. The smoothing and scaling down that works on the supply side is not enough to control oscillations on the demand side.<br />
<br />
:<math>Diffl1_{t}=LABWAGEIND_{r,t=1}-1</math><br />
<br />
:<math>Diffl2_{t}=LABWAGEIND_{r,t}-LABWAGEIND_{r,t-1} </math><br />
<br />
:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},ellabwage1_{r},ellabwage1_{r})</math><br />
<br />
A second impact factor (labwageimpactmul) is computed using the correction signal from this second multiplier:<br />
<br />
:<math>labwageimpactmul_{r,t}= labwageimpactmul_{r,t-1}*mullw_{r,t}</math><br />
<br />
This impact factor is applied on the labor demand as described in the section on labor demand.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}= LABDEMS_{r,s,sk,t}* labwageimpactmul_{r,t}</math><br />
<br />
== Informal Labor ==<br />
<br />
IFs forecast labor and GDP share of the informal sector. Informal labor forecast is not explicitly endogenized in the labor market though. They are rather driven by development, skill and regulatory factors[[#_ftn1|[1]]]. However, the productivity and revenue impacts of changes in informality affects output and thus labor demand implicitly as a very distal driver.<br />
<br />
= References =<br />
<br />
<references /><br />
</div></div></div></div></div></div></div></div></div></div></div>Wikiadmin//pardee.du.edu/wiki/File:LaborCoefficientFunctions.pngFile:LaborCoefficientFunctions.png2018-09-07T22:52:50Z<p>Wikiadmin: </p>
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<div></div>Wikiadmin//pardee.du.edu/wiki/LaborLabor2018-09-07T22:48:57Z<p>Wikiadmin: </p>
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<div>Workers in an economy supply the expertise and the efforts needed to produce goods and services. In return the labor receives wages that they use to meet their current and future consumption needs. On one hand, shortage of labor with required skills prevents economies from realizing their growth potential. On the other hand, individuals falling short of the right qualifications might remain unemployed or underemployed failing to secure income needed for a decent living. The ongoing adjustments to find the best match between skills, jobs and wages can only be studied through a dynamic model of the labor market.&nbsp; &nbsp;<br />
<br />
Such a model should go beyond providing a reasonable answer to the obvious question of why employment and wages go up and down. An aggregate labor market must deal with issues that have strong interconnections with various other dynamic changes in the greater society. What kind of dividend of deficit can a society expect from its labor force given the phase of demographic transition in which it is situated? How severely would aging affect the pool of working age adults? Might increasing female participation rates offset some of the losses from aging? What is the level of skills and educational attainment in a society? These supply phenomena move relatively slowly unless there are huge disruptions, like a war or famine, or an aggressive policy push. The demand side, in contrast, needs to be more responsive in adjusting wages and employment given the investment and technology in the various sectors of the broader economy. In general, though, the labor market demonstrates some sluggishness compared to the goods and services markets as it involves moving human beings with various limitations. Consumption of goods and services depend on the income earned by the labor. Uneven distribution of employment and wages among labors of various types or between labor and capital for a long period of time can give rise to persistent inequality in a society. &nbsp;&nbsp;&nbsp;&nbsp;<br />
<br />
== Conceptual Framework ==<br />
<br />
Labor markets are markets for workers and jobs. In a labor market, employers meet their demand for labor with the supply of people willing to work at the wage the employers can offer. The employers raise the wage when there is a shortage of workers. Workers agree to take a lower wage when there are more of them than the firms need. In the real-world labor markets do not always clear at perfect equilibrium. Frinctional unemployment results for various reasons, for example, the search time between jobs. Structural unemployment can result from technology induced disruptions. Some unemployment could thus persist in the labor market even when there aren’t any short-term fluctuations. There is also the phenomenon of informal employment that consists of less sophisticated workers and entrepreneurs engaged in unregulated economic activities. &nbsp;In a dynamic model that covers the entire economy, the real wage earned by the labor drives the income and social mobility.&nbsp;&nbsp;&nbsp;<br />
<br />
To understand the long-term dynamics of the labor market, we need also examine the deeper determinants of labor demand and supply, the determinants that can shift the curves. Labor demand changes over time with the changes in demand for goods and services and the labor input needed to produce those. Labor productivity itself improves with technological progress. Long term transitions in the supply of labor are mostly demographic. &nbsp;<br />
<br />
Labor supply is determined by the working age population and the share of that population who are available for participation in the workforce. The labor supply is relatively stable as the demographic changes are slow in pace. As the share of elderly in the population increases, a recent trend in many societies, the rate of participation declines. Some of the aging impacts will be offset by the greater female participation rates, a second trend that surfaces as economies develop and women attain more education. Educational attainment also drives the general skill level of workers, male and female. Specific skills are obtained through training and experience that augment the knowledge obtained through general and specialized education. &nbsp;&nbsp;&nbsp;&nbsp;<br />
<br />
It is the demand side that causes most of the short-term imbalances in the labor market. &nbsp;In the long term, as said earlier, the important driver of demand for labor and their skills is technological progress. Labor requirement drops with advances in technology, more so for less skilled labor. Labor composition changes accordingly both within and across sectors. Rapid advances in technology can also cause disruption in the system when there is not much opening in the other sectors. Labor displacement is offset to some extent by the growth in the economy and the resulting increase in total demand. &nbsp;&nbsp;&nbsp;<br />
<br />
As we have already mentioned, employees maximize income and the firms minimize labor costs. When there are more laborers than the firms can hire, there is unemployment. Shifts in the rates of unemployment impacts wage, the price of labor. For example, wages drop in the event of rising unemployment as there are more people to hire from. Wage adjustments feed back to the demand for labor seeking to bring the market back to equilibrium.<br />
<br />
The challenges around the conceptual distinction between unemployment and employment is further complicated by the phenomenon of informal employment. In many developing countries there is a large urban non-agricultural informal sector where low-skilled workers work for wages typically lower than a formal employment.<br />
<br />
[[File:LMFlowchart1.png|frame|center|Description of the labor model]]<br />
<br />
== Dominant Relations ==<br />
<br />
The labor model in the International Futures system (IFs) balances the total supply of labor with the total labor demanded by all economic sectors. Total labor (LAB) is computed from the working age population and the labor participation rate. Population forecasts are obtained from the IFs demographic model. Participation rates (LABPARR) are computed by sex with a catchup algorithm for the female participation towards that for the male. Labor is also disaggregated by skill level, as determined by educational attainment, in a separate labor supply variable (LABSUP) which is used to distribute labor earnings by skill level. [** LABSUP do not affect the demand/supply balance now]<br />
<br />
Labor demands (LABDEMS) are driven by sectoral technology functions used to compute the labor requirement by skill level for each unit of potential valued added in the sector. These labor coefficients (LABCOEFFS) are multiplied with the projected value added for the sector to compute the needed manpower. The balancing mechanisms determines the labor employed in each of the sectors (LABS).&nbsp;&nbsp;<br />
<br />
The balancing, in the current version of the model, can be done in one of the two ways. In the first method, total needs combined from all economic sectors is normalized to the available pool of labor computed by subtracting the unemployed from those who are at or looking for work. The rate of unemployment is kept at its natural rate for which we use the base year rate of unemployment. (** This might need to be changed for countries where the market is undergoing some abrupt transition.)<br />
<br />
In the second balancing method, added in a recent revision of the model, total demand is equilibrated to supply through a CGE like market equilibrium model. An indexed wage (LABWAGEIND) and the rate of unemployment (LABUNEMPR) work as the equilibrating variables. As unemployment deviates from the target, PID algorithms send a signal for the wage to adjust. Wage adjustments cause adjustments in the “base” labor demands by sector computed from the labor-coefficient functions as described earlier. Wage signals also affects the labor participation rate. The magnitude of impact on the supply side is much lower than that on the demand side.<br />
<br />
Wage and unemployment rate are aggregated for the total labor market. The wage index starts with a base year value of 1 and the unemployment rates start with the historical data for the base year. Initial year unemployment rate works as the target for long term unemployment.<br />
<br />
== Key Dynamics ==<br />
<br />
The following key dynamics are directly related to the dominant relations:<br />
<br />
*Labor supply is determined from population of appropriate age in the population model (see its dominant relations and dynamics) and endogenous labor force participation rates, influenced exogenously by the growth of female participation.<br />
*Labor demand is driven by sectoral demand functions driven by technological progress<br />
<br />
== Structure and Agent System ==<br />
<br />
{| border="1" cellspacing="0" cellpadding="0" width="0" style="width:502px;"<br />
|-<br />
| style="width:242px;height:49px;" | <br />
'''System/Subsystem'''<br />
<br />
| style="height:49px;" | <br />
Labor market<br />
<br />
|-<br />
| <br />
'''Organizing Structure'''<br />
<br />
| <br />
Labor supply by skill level and labor demand by sector for each skill category represented within an equilibrium-seeking model with wage and unemployment rate as the equilibrating variables<br />
<br />
|-<br />
| <br />
'''Stocks'''<br />
<br />
| <br />
Population, labor, education, &nbsp;accumulated technology<br />
<br />
|-<br />
| <br />
'''Flows'''<br />
<br />
| <br />
Participation rate; Coefficients of labor demand; Employment (unemployment); Wage<br />
<br />
|-<br />
| <br />
'''Key Aggregate&nbsp;''''''Relationships&nbsp;'''<br />
<br />
'''(illustrative, not comprehensive)'''<br />
<br />
| <br />
Labor supply is driven by demographic changes; Participation of female change over time; Labor requirement changes with technological development; Unemployment rate drives wage; Wage movements affect labor demand and participation rate<br />
<br />
|-<br />
| <br />
'''Key Agent-Class Behavior&nbsp;''''''Relationships'''<br />
<br />
'''(illustrative, not comprehensive)'''<br />
<br />
| <br />
Households and work/leisure, and female participation patterns;<br />
<br />
&nbsp;<br />
<br />
Firms and hiring;<br />
<br />
&nbsp;<br />
<br />
<br />
<br />
<br />
<br />
|}<br />
<br />
= Labor Model Data =<br />
<br />
The labor supply and unemployment data that we use in our model is from International Labor Organization (ILO). For data on the demand side, we used data from the Global Trade Analysis Project. Wage variable used in the equilibration algorithm&nbsp;is an index anchored to the base year of the model.<ref>GTAP database helped us compute wage rates by sector and skill.</ref> IFs preprocessor prepared these data for model use using various estimation, conversion and reconciliation processes.&nbsp; &nbsp; &nbsp;<br />
<br />
== Definitional Issues ==<br />
<br />
There are ambiguities in the way some of the labor market variables are defined. Labor participation rates and the rate of unemployment are two that need special attention.<br />
<br />
The size of the labor supply available for economic activities is expressed with the labor force participation rate. ILO defines this as a “measure of the proportion of country’s working-age population that engages actively in the labor market, either by working or looking for work.”<ref>http://www.ilo.org/ilostat-files/Documents/description_LFPR_EN.pdf</ref>&nbsp;National labor force surveys and census data are used to estimate this rate. The definition of labor force here includes both employed and unemployed and the rate is expressed as a percentage of working-age population. Working-age population is defined here as the population above legal working-age. For international comparability, ILO adopts a convenient minimum threshold of fifteen years as working age and avoids putting any upper age limit. In practice, both the minimum and the upper-age limits can vary by country. For example, the working-age in the USA is sixteen years. In the Netherlands the upper age limit is seventy-five years, whereas South African data uses an upper age limit of 64.<ref>https://www.bls.gov/fls/flscomparelf/technical_notes.pdf</ref><br />
<br />
Ambiguities are more abundant in the definition of unemployment. ILO came up with a guideline on this as well. Per the ILO guideline, the unemployed are those among the working-age population who are not employed, are available for work and are actively looking for jobs<ref>The definitions around employed and unemployed were agreed upon by nations through the ‘Resolution concerning statistics of work, employment and labor underutilization’ adopted by the 19th International Conference of Labor Statisticians (ICLS) in 2013. (Bourmpoula et al, 2017: 6).</ref>; the unemployment rate is expressed as a percentage of those who are in the labor force. The availability and job-seeker status could be defined in different ways giving rise to incompatibility in data. &nbsp;While there seems to be little room for disagreement on whether someone is at work or not, whether that work should be considered as employment is contested at many times.<br />
<br />
The debates around the nature and type of employment can range from gainfulness to workplace setting. For example, a large number of workers in the low-income low-regulation developing countries work outside the purview of formal enterprises. According to an ILO estimate, more than half of the global labor force and more than 90% of Micro and Small Enterprises (MSEs) worldwide are in the so called informal economy.<ref>http://www.ilo.org/global/topics/employment-promotion/informal-economy/lang--en/index.htm</ref> This might explain the apparently counterintuitive pattern of low unemployment rate in some low-income countries (e.g., 2.2% for Guatemala) and relatively higher numbers for some of the developed nations. The low numbers in the poorer countries hide the prevalence of extremely low wage jobs in the informal sectors in these countries, the only options for the vulnerable people in the absence of any kind of social safety net. &nbsp;Contrastingly, in the developed countries the so called ‘gig-economy’ is attracting more and more workers who choose to work on their own rather than in a formal enterprise. ILO conceptualization makes the informal work part of total employment. The stacked Venn diagram below presents the relationship among the labor force metric including informal employment. IFs also models informal economy both in terms of GDP share and employment share of informal in the total economy and employment.<br />
<br />
[[File:LaborSubsets.png|frame|right|Relationship among various labor measurement]]<br />
<br />
Incompatibility can arise in the treatment of various population groups for the computation of the denominator for participation and unemployment rates.<ref>For example, the USA excludes people in the defense services and those in the prisons or mental asylums in their computation of the civilian non-institutional working-age population. There are also variations in the treatments of students, those recently laid-off, and family workers. Please see https://stats.bls.gov/opub/mlr/2000/06/art1full.pdf for a discussion </ref> ILO makes their best efforts to make adjustments in the data for the sake of international comparison. For example, ILO asks countries that deviate from ILO guidelines to collect data needed to convert national figures to ILO figures. It is likely that some differences might have slipped past the adjustment process. We use ILO data and continue to update our database&nbsp;&nbsp;<br />
<div><div id="ftn4"><div id="ftn1"><br />
The GTAP data that we use for the demand side of the labor model is taken as labor headcounts and is thus immune from ambiguities around rate computation. As far as we could gather<ref>Please see the webpage for documentation on GTAP labor data statistic: https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=3248</ref>, the data includes both the formal and informal employment. We also need mention here that the GTAP database reconciles the labor data to calibrate the general equilibrium modeling that they do for the trade analyses. The data could thus be somewhat different from data collected through direct surveys. As a CGE model IFs is benefited by using calibrated data.<br />
<br />
== Sources of Labor Data ==<br />
<br />
IFs model uses ILO data for labor participation rates and for the unemployment rate. The data in IFs are collected from World Bank’s World Development Indicators (WDI) database. According to their documentation, WDI obtained the data from the ILO.<br />
<br />
Unemployment rate data in IFs is also collected from WDI. Like the participation rates WDI also obtains their unemployment data from ILO.<ref>The name of the IFs table is SeriesLaborUnemploy%</ref><br />
<br />
For employment and labor demand data IFs uses Purdue University’s Global Trade Analysis Project (GTAP) database. GTAP collects and compiles factor payments, imports, and intersectoral flow data to calibrate CGE models of national economies for trade and other analyses. In their ninth release in 2016, GTAP published data for 140 countries and regions for the year 2011. The earlier GTAP releases, which the IFs model used for its previous versions, compiled data for the years 2004 and 2007. GTAP data release aggregates economic activities into 57 commodities and activities following International Standard Industrial Classification (ISIC). The IFs model maps the 57 GTAP sectors into six economic sectors of IFs – agriculture, energy, material and mining, manufacture, services and ICT. Appendix 2 presents two tables listing the sectors mapping between IFs and GTAP, and GTAP and ISIC. GTAP further disaggregates labor in each of the commodities/activities into five occupation and skill categories following the nine category International Standard Classification of Occupations (ISCO-88). The IFs model collapses five GTAP occupation categories into the simple IFs dichotomy of skilled and unskilled. The mapping of occupations and skills are presented in the third appendix of this document. &nbsp;<br />
<br />
The data in the main GTAP database, prepared for CGE modeling, are all in dollar unit and thus do not include labor headcounts. We have used a ‘satellite’ GTAP database<ref>See Weingarden and Tsigas, 2010 for the details on the preparation of this database.</ref>&nbsp;for labor headcounts by skill and sector. The labor counts were also used to plot labor requirement functions for each of the IFs economic sectors and skill categories. The wage share of skilled and unskilled labor in each sector was computed using the labor headcounts and labor payments.<br />
<br />
== Scope of IFs Labor Model ==<br />
<br />
The IFs labor model simulates labor market at the national level. Each national labor market forecasts labor demand and employment by six sectors - agriculture, energy, mining, manufacture, services and ICT- and two skill levels - skilled and unskilled. The supply side do not have sectoral representation. IFs forecasts total labor force and labor supply by the two skill levels. Labor participation rate is computed in IFs by gender. Wage and unemployment rate is forecast for the overall labor market only.&nbsp;&nbsp;<br />
<br />
== Labor Model Pre-processor ==<br />
<br />
IFs system has a data preprocessor that prepares the initial conditions for the model using historical databases and various assumptions and estimated relationships to fill in the missing data and make data adjustments as needed.<ref>For more details, please see ‘The Data Pre-Processor of International Futures (IFs)” by Barry B. Hughes (with Mohammod Irfan) at http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf</ref> Pre-processing of labor data takes place in two IFs pre-processing modules. Labor participation rate data, which is closely related to demography, is processed in the population pre-processor. Unemployment rate and labor demand data are processed in the economic pre-processor.&nbsp;<br />
<div><div id="ftn1"><br />
=== Pre-processing Labor participation rate and unemployment ===<br />
<br />
For initializing labor participation rates by sex (LABPARR) the model uses the historical values from the base year or the most recent year with data.<ref>The data tables that the IFs model pre-processor use for initializing labor participation rates are: SeriesLaborParRate15PlusFemale%, SeriesLaborParRate15PlusMale%.</ref> For countries with no data we use regression relationships of the participation rates, for men and for women, with income per capita. The relationships, shown in the next figure, are not great. However, the functions affect only five countries for which we do not have any data at all: Grenada, Kosovo, Micronesia, Seychelles and South Sudan.<ref>We should try to collect participation rate for these countries from country sources.</ref><br />
<div><div id="ftn2"><br />
IFs data series SeriesLaborUnemploy% is used for the initialization of unemployment rates. That series has annual unemployment rates for one or more years between 1980 and 2016, for 181 of the 186 IFs countries. For five countries (Grenada, Kosovo, Micronesia, Taiwan and South Sudan<ref>These are pretty much the same countries for which we do not have any participation rate data. This indicates ILO might have some administrative limitation in reporting data for these countries (notice Kosovo, Seychelles etc in the list)</ref>) there is no data at all. To fill in the missing data we use a regression function of unemployment rate against GDP per capita. Like the participation rate functions, this function does also not have much of an explanatory power.<br />
<div><div id="ftn1"><br />
=== Pre-processing labor demand and unemployment from GTAP ===<br />
<br />
The IFs economic pre-processor reads labor headcount and labor payment data from the GTAP database. In addition to performing sector and occupation/skill mapping between GTAP and IFs, pre-processor also use the labor headcount data to compute labor coefficient functions, the principal driver of labor demand in the IFs model.<br />
<br />
Labor coefficients are defined as the amount of labor needed to produce one unit of value added in a certain sector of the economy. The coefficients depend on the level of technology. The model uses GDP per capita as an indicator of the level of technological development. IFs pre-processor estimates labor coefficient functions for labor of different skill levels for the different sectors of the economy.<br />
<br />
The functions are derived from GTAP data we described earlier. The model pre-processor reads data on factor payments and aggregates data from 57 GTAP sectors to six IFs sectors. Shares of payment going to skilled and less-skilled workers in each of the sectors are then computed. Countries are grouped according to their level of technological development as represented by per capita income. For each group labor coefficients are obtained by taking an average of the country coefficients. &nbsp;We also convert labor payments data to labor headcount data using per capita income as a proxy for average wage. Labor coefficients and income are then plotted into a power function relationship. The figure below plots some of those labor functions.&nbsp;The functions fit quite well with a power law formulation.<ref>This is interesting given the prevalence of power law in all sorts of scale-up activities (West 2017).</ref><br />
<div><div id="ftn1"></div></div></div></div></div></div></div></div><br />
<br />
= Labor Model Flowcharts =<br />
<br />
The diagram below shows an outline of the IFs labor model. On the supply side, the total labor pool (LAB) is computed from the labor force participation rates, by sex, (LABPARR) and the population (POP) in their working age, i.e., population over 15 (POP15TO65 + POPGT65). Participation rates are driven by the demographic changes with an additional negative impact from aging and a catch-up in female participation rate. Skill level of the labor supply (LABSUP) is driven by the level of development (GDPPCP) and the demand for labor is driven by labor-coefficients (LABCOEFFS) computed from coefficient function representing shifts in demand with technological progress as proxied by the level of development (GDPPCP). Coefficients computed by sector and skill gives the labor requirement by skill type for each unit of value added (VADD) in the sector. Multiplying these coefficients with projected value added in each sector gives an estimate of the labor demand. &nbsp;<br />
<br />
Any surplus or shortage between total labor demand and supply is used to compute the rate of unemployment. Deviations in the unemployment rate (LABUNEMPR) signal wage changes through an equilibrium seeking algorithm. Both demand and supply respond to the wage variable (LABWAGEIND) indexed to the base year. The supply responses are much slower than the demand responses.<br />
<br />
[[File:FLOCHART2.png|frame|center|Labor Model Flowchart]]<br />
<br />
= Labor Model Equations =<br />
<br />
== Overview ==<br />
<br />
The labor model is a part of the IFs economic model that uses labor model output as an input to a Cobb-Douglas production function in a multi-sector general equilibrium model. IFs is a very long-run dynamic model. Instead of computing fixed short-run equilibria that clear the relevant markets IFs uses an equilibrium seeking algorithm to balance the various systems over the longer run. The algorithm is known as the PID (proportion-integral-derivative) controller algorithm and is used widely in industrial control systems. It makes equilibrium seeking variables in IFs move towards a set target. The algorithm works by computing a multiplier based on the movement of the variable towards the target, as obtained by an integral (I) of the path traversed, and the rate of movement towards the target, the derivative term. The multiplier is applied on the process variable (the P term), or a response variable, in the subsequent time period. In the labor model, unemployment rate (LABUNEMPR) is used as the process variable and the PID multiplier is used on the wage rate (LABWAGEIND). Job availability (LABDEMS) and participation rate (LABPARR) get affected by changes in wage. &nbsp;&nbsp;<br />
<br />
Throughout this section we use subscripts and notations common to other modules of IFs. For example, we use t for time period. Subscripts p and r represent sex and country/region, respectively, c is the cohort number, with cohort 1 representing the newborns, cohort1 the the one-year to four-year-olds, cohort two five-year to nine-year-olds etc. Values for p are 1 for male, 2 for female and 3 for both sexes combined. For economic sectors we use s and for skill levels sk.<br />
<br />
== Labor Supply: Equations ==<br />
<br />
The total pool of labor is computed by multiplying the population of working age with the labor force participation rate (LABPARR). &nbsp;Population forecasts come from IFs demographic model which computes both five-year and single-year age-sex cohorts (''agedst'', ''fagedst''). &nbsp;<br />
<br />
The labor model forecasts participation rates by country/region&nbsp; and gender. Participation rates in the model move with the changes in the demographic composition. Female participation rates, which have historically been lower than the same for the male in all societies, but has moved up in modern and affluent societies, get a catch-up boost in the model. Participation rates can also change when there is labor shortage or surplus and the employers try to incentivize or discourage workers by changing wage. This last impact is much less slow than similar wage impacts on the demand side.<br />
<br />
== Labor Participation Rate ==<br />
<br />
Labor participation rates (''LABPARR'') for male and female are first initialized with historical data.<br />
<br />
:<math>LABPARR_{r,p}= LABPARR_{r,p,t=1} </math><br />
<br />
A ‘catch-up’ boost is added to the female participation rate. The boost added (FemParLabMul) starts at a third of a percentage point and withers away following a non-linear path as the female rates approaches the catch-up target (FemParTar), The maximum catch-up that can occur over the horizon of the model is thirty percent.<br />
<br />
:<math>FemParTar_{r}=Amin(LabParRI_{r,p=1},LabParRI_{r,p=2}+30)</math><br />
<br />
:<math>FemParLabMul_{r}=(FemParTar_{r}-LABPARR_{r,p=2,t-1})/(FemParTar_{r}-LABPARR_{r,p=2,t-1})</math><br />
<br />
:<math>LABPARR_{r,p=2,t}=LABPARR_{r,p=2,t-1}+FemParLabMul_{r}*0.3</math><br />
<br />
Next, we compute and apply the aging impact on the participation rate. As the relative share of people over the retirement age increases, the participation rate declines. The model keeps track of the changes in the demographic ratio (PopAgingRatio) of the population who are in their prime working age of 15 to 64 (POPWORKING) to those at a common retirement age of sixty-five or older (POPGT65). This ratio declines as countries age. The percentage drop in the ratio comparative to the base year is scaled appropriately to compute the aging impact (aging_impact). This impact is added to the male and female labor participation rates, with the impact on the female participation rate being slightly lower than that on male rates.<br />
<br />
:<math>POPAgingRatio_{r,t}=POPWORKING_{r,t}/POPGT65_{r,t}</math><br />
<br />
:<math>aging_impact_{r,t}=100*((POPAgingRatio_{r,t}/POPAgingRatio_{r,t=1})-1)*0.2</math><br />
<br />
:<math>LABPARR_{r,p=1,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t} </math><br />
<br />
:<math>LABPARR_{r,p=2,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t}*0.95 </math><br />
<br />
Participation rates respond slowly to changes in wage and unemployment rate. The impact is implemented through a wage impact factor computed from annual changes in the wage index (labwageimpact). The base participation rates can be changed by model user through two model parameters: a direct multiplier on the participation rate (labparm), or one that changes participation by moving the retirement age (labretagem)<br />
<br />
<br />
<br />
:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact*0.05)*labparm_{r,p,t}*labretagem_{r,p,t}</math><br />
<br />
Total participation rate (LABPARRr,p=3,t) is computed by an weighted average of male and female participation rates.<br />
<br />
:<math>LABPARR_{r,p=3,t}= (∑_{p=1 to 2}∑_{c=4 to 21}(agedst{r,c,p,t}*LABPARR_{r,p,t}))/(∑_{p=1 to 2}∑_{c=4 to 21}agedst{r,c,p,t})</math><br />
<br />
== Total Labor ==<br />
<br />
Finally, the total number of labor available for work (LAB) is computed by multiplying the total participation rate with the population of fifteen-year-olds or older.<br />
<br />
:<math>LAB_{r,t}= LABPARR_{r,p=3,t}*∑_{p=1 to 2,c=4 to 21}agedst_{r,c,p,t}</math><br />
<br />
== Labor by skill level ==<br />
<br />
The labor model forecasts labor supply (LABSUP) by two skill categories. The variable (''LABSUP'') is initialized in the pre-processor by reading the employment by skill/occupation (''LABEMPS'') data from GTAP<ref>We collapse GTAP’s 57 sectors into the six economic sectors of IFs. GTAP collapses the nine occupation categories of ISCO-88 into five. In IFs those five categories are collapsed into a binary – skilled and unskilled. The sectoral and skill mappings are described in two appendices of these document.</ref>&nbsp; and adding the unemployment numbers. We assume same unemployment rate (''LABUMEMPR'') for skilled and unskilled labor.<br />
<br />
:<math>LABSUP_{r,t=1,sk}=∑_{s=1 to 6}(LABEMPS_{r,s,t=1}/(1-(LABUNEMPR_{r,t=1}/100))</math><br />
<br />
The model forecasts labor by skill through a model of the skilled share of the labor. Education, training, exposure, and experience of the employees all improve with the level of development. The model captures this with an analytic function of the skilled share (perskilled) driven by GDP per capita at PPP (GDPPCP) -<br />
<br />
:<math>perskilled_{r}=f(GDPPCP_{r})</math><br />
<br />
Among the causal drivers of skill, education is considered to be the most proximate. Education is strongly correlated with the level of development, the deeper driver of skill in the model. However, the recent increase in education and/or a policy driven educational expansion might add to the impact of education on skill. Additional impacts from education on skill, when there is any, is computed through an expected function formulation. For example, in a society where an average adult has more (or less) education than the adults in other societies at that level of development, the skill share is given a slight upward push (or downward pull). The expectation function is a logarithmic function of educational attainment of working age population (EDYRSAG15) driven by GDP per capita at PPP. Attainment above (or below) the expected level (YearsEdExp) is computed by the function output (YearsEd) adjusted for country situation (yearseddiff). The percentage adjustment to the skilled share (LabSupSkiAdj) is computed using additional (limited) education, i.e., the difference between actual (EDYRSAG15) and expected values of educational attainment, expressed as a percentage of the expected value. The adjustment is scaled appropriately and peters off over time.<br />
<br />
:<math>YearsEd_{r,t}= f(GDPPCP_{r,t})</math><br />
<br />
:<math>yearsdeddiff_{r}= EDYRSAG15_{r,p=3,t=2}-YearsEd_{r,t=2}</math><br />
<br />
:<math>YearsEdExp_{r,t}=YearsEd_{r,t}+yearsdeddiff_{r}</math><br />
<br />
:<math>LabSupSkiAdj_{r,t}=0.3*(EDYRSAG15_{r,p=3,t=2}*YearsEdExp_{r,t})/YearsEd_{r,t}</math><br />
<br />
:<math>LabSupSkiAdj_{r,t}=ConvergeOverTime(0,LabSupSkiAdj_{r,t},70)</math><br />
<br />
:<math>perskilled_{r,t}= perskilled_{r,t}*(1+LabSupSkiAdj_{r,t})</math><br />
<br />
The skilled share (perskilled) is multiplied with the total labor supply (LAB) to obtain the number of labors who are skilled (LABSUPskilled)<br />
<br />
:<math>LABSUP_{r,skilled,t}=LAB_{r,p,t}*perskilledI_{r,t} </math><br />
<br />
As a last step, the model adjusts for the country specific variations in the skilled labor count not captured by the deeper and the proximate models. This is done by saving a ratio (LABSUPSkilledRI) of the actual historical data and the model computed value in the initial year. In the subsequent years this ratio is used to adjust the skilled labor forecast gradually.<br />
<br />
:<math>LABSUPCompSkilled_{r}=LAB_{r}*perskilled_{r,t=1}/100 </math><br />
<br />
:<math>LABSUPSkilledRI_{r}=LABSUP_{r,skilled,t=1}/LABSUPCompSkilled_{r}</math><br />
<br />
:<math>LABSUP_{r,skilled,t}= LABSUP_{r,skilled,t}*ConvergeOverTime(LABSUPSkilledRI_{r},1,85)</math><br />
<br />
Number of unskilled labor is obtained by subtracting the skilled labor from the total pool.<br />
<br />
<br />
<br />
:<math>LABSUP_{r,unskilled,t}= LAB_{r,p,t}- LABSUP_{r,skilled,t}</math><br />
<br />
== Labor Demand: Equations ==<br />
<br />
IFs economic model forecasts production in six economic sectors. IFs labor model computes the longer-term and shorter-term determinants of demand for skilled and unskilled labor (LABDEMS) for the production processes. The long-term drivers of labor requirement are technological progress or the lack of it. In the shorter-term wage affects the labor demand most. Wage in turn is affected by labor supply or skill shortage.<br />
<br />
The IFs model divides economic activities into six economic sectors – agriculture, energy, materials, manufacture, services and information, and communication technologies. Workers in the IFs labor model are disaggregated into two skill types. While the skill composition varies by the technology used in the sector and starts tilting towards the more skilled with the progress in technology, absolute number of labors needed to produce the same output goes down with technological development for both skilled and unskilled labor. This is illustrated in the next figure which plots the changes in labor requirement against GDP per capita at PPP, a proxy for level of development. Agriculture is a much less skill-intensive process than the manufacture, however, with technological progress skill requirement improves rapidly in both sectors. The IFs labor model computes these labor requirement functions in the model pre-processor. As we have already described in the pre-processor section, the computation of these functions use GTAP data on employment by occupation and economic activity. Appendices 3 and 4 lists sector and occupation mapping between GTAP and IFs.<br />
<br />
These functions are used to compute the labor coefficients (LABCOEFFS), i.e., number of skilled and unskilled labor needed to produce unit amount of output with the technology available, for which we use GDP per capita at PPP as a proxy.<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= f(GDPPCP_{r})</math><br />
<br />
manufacture, services and ICTech) and the subscrip sk stands for skill categories with 1 denoting unskilled and 2 skilled. The labor coefficients obtained from the analytical functions require some adjustments to incorporate country deviations from the functions for various factors not captured in the regression relationship. The first of these adjustments is a gradual removal of impacts of short-run fluctuations in output and labor from the computation of labor coefficient. This adjustment is applied on the coefficients computed from the function. The equation below shows a simplified form of these computations.<br />
<br />
:<math>LabCoeffAdjFac_{r,k,s,t}=f(igdpr_{r,t=2},(LAB_{r,t=2}/LAB_{r,t=1}),(LABCOEFFS_{r,t}/LABCOEFFS_{r,t-1}))</math><br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}=LABCOEFFS_{r,sk,s,t}(1-LabCoeffAdjFac_{r,k,s,t})</math><br />
<br />
Model users can use a global parameter (labcoeffsm) to change the labor coefficients by skill level for any or all of the six sectors –<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= LABCOEFFS_{r,sk,s,t}*'''labcoeffsm_{s,sk}'''</math><br />
<br />
To forecast the total labor demand, the labor coefficients (LABCOEFFS) are multiplied to the total projected output for each of the economic sectors. The forecast is adjusted for any discrepancy between data and model. The adjustment factor (LABDemsAdjFac) is computed as the initial ratio between the actual and computed employment. Actual employment is obtained from historical data (LABEMPS) processed using the GTAP database. The computed employment is obtained by multiplying the labor coefficients (LABCOEFFS) with the final output of the sector (VADD).<br />
<br />
:<math>LabDemsAdjFac_{r,s,sk}= LABEMPS_{r,s,sk,t=1}/(VADD_{r,s,t=1}*LABCOEFFS_{r,sk,s,t})</math><br />
<br />
The projected output is obtained by applying the growth rate (IGDPRCOR) on the sectoral value added from the previous year (VADD). The total labor demand is given by the product of the labor coefficients, projected output, demand adjustments and wage impacts (labwageimpactmul) and the number 1000 which adjusts the units for the equation. Wage impact comes from the level of unemployment and is computed in an equilibration process described in the next section. Model users can use a multiplicative parameter (labdemsm) to slide the demand upward or downward.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}=1000*VADD_{r,s,t-1}*(1+IGDPRCOR_{r})*LABCOEFFS_{r,sk,s,t}*LabDemsAdjFac_{r,s,sk}*labwageimpactmul_{r,s,sk}*'''labdemsm_{r,s}'''</math><br />
<br />
== Unemployment and Wage: Labor Market Equilibration ==<br />
<br />
The IFs labor model balances the labor market through an equilibrium seeking algorithm rather than computing an exact equilibrium at each time step. We use an algorithm borrowed from the control systems engineering. This PID controller algorithm, described also in the IFs economic model documentation, works by computing corrective signals for equilibrating variables using the deviations of a buffer variable, for example unemployment rate (LABUNEMPR), from a target value. The signal is computed from two quantities, the distance of the buffer from the target and the current rate of change of the buffer. The computation is tuned with PID elasticities to avoid oscillations. The computed signal is applied on the variable/s which need to be balanced, for example, demand and supply in the event of a market equilibration, thus getting closer to a balance at each step of simulation. The target value for the buffer variable and the tuning parameters of the control algorithm are obtained through rules-of-thumb and model calibration. The IFs labor model uses unemployment rate (LABUNEMPR) as the buffer variable for the market equilibration of labor demand and labor supply. The multiplier (i.e., corrective signal) obtained from the PID is applied on the wage index (LABWAGEIND). Changes in wage indices comparative to the base year, moderated through a second PID controller, is used to compute the final signal (labwageimpactmul) that drives labor demand and labor supply. Even though the model forecasts labor demand by sector and skill, and computes labor supply for both skill types, the equilibration algorithm works over the entire pool of labor. In other words, we assume that the skills are replaceable across sectors and the lack (or abundance) of jobs affects skilled and unskilled persons equally.<br />
<br />
At each annual timestep, the model computes the unemployment rate (LABUNEMPR) as the gap in between the total supply of labor (LAB) and the total demand. The gap (EmplGap) is expressed as a share of the total labor, the standard way to express unemployment rate.<br />
<br />
:<math>sumld=∑_{s,sk}LADEMS_{r,s,sk,t}</math><br />
<br />
:<math>EmplGap= LAB_{r,t}*sumld</math><br />
<br />
:<math>LABUMENPR_{r,t}= (EmplGap/LAB_{r,t})*100</math><br />
<br />
As the target value (LabUnEmpRateTar) for the PID controller that modulates unemployment rate we use either the historical unemployment rate or a ten percent unemployment rate when the historical rate is higher than ten. Model users can override the historical target through a model parameter (labunemprtrgtval).<br />
<br />
:<math>LABUMENPRi_{r,t}= LABUMENPR_{r,t}</math><br />
<br />
:<math>LabUnempRateTarget_{r}=labunemptargetval_{r}</math><br />
:<math>If LabUnempRateTarget_{r}=0,<br />
LabUnempRateTarget_{r}= AMIN(LABUMENPRi_{r,t},10) </math><br />
<br />
Unemployment rate target, when it is different from the base year value, is reached gradually with a convergence period of forty years . The target rate is converted to count (LabUnEmplTar) to make it equivalent to the employment gap (EmplGap) computed earlier.<br />
<br />
:<math>LabUnEmplTar_{r}= LAB_{r,t}*ConvergeOverTime(LABUMENPRi_{r,t},0,100)</math><br />
<br />
The first order difference (Diffl1) between the target unemployment and the demand-supply gap is used to compute a second order difference (Diffl2) accounting for changes in the rate of movement. The two differences and the PID multipliers (elwageunemp1, elwageunemp2) are provided to the PID function (ADJSTR). Working age population (POP15TO65r,t) works as the scaling base of the PID controller. The controller algorithm gives a multiplier (mullw) that is used in the subsequent year to adjust wage.<br />
<br />
:<math>Diffl1_{t}=LabUnEmplTar_{r}-EmplGap</math><br />
<br />
:<math>Diffl2_{t}=Diffl1_{t}-Diffl1_{t-1}</math><br />
<br />
:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},elwageunemp1_{r},elwageunemp2_{r})</math><br />
<br />
Wage adjustments affect demand and supply with an increase in wage drawing demand downward and supply upward. The opposite affects occur with a downward movement of wage. The wage variable affected by the PID multiplier (LABWAGEIND) is an index initialized at one. We use an indexed rather than a dollar wage in the equilibration process to avoid affecting the process from other economic phenomena that affects wage, for example, a rise in real wage as GDP or the labor share of income grows.<br />
<br />
:<math>LABWAGEIND_{r,t=1}=1</math><br />
<br />
In the subsequent years of the model run, the wage index is first adjusted with the equilibration signal obtained from the unemployment rate PID controller in the previous period<br />
<br />
:<math>LABWAGEIND_{r,t=1}= LABWAGEIND_{r,t=1}* mullw_{r,t-1} </math><br />
<br />
A wage impact (labwageimpact) is then computed using the changes in the wage index relative to the base value. The impact is smoothed with a moving average algorithm.<br />
<br />
:<math>labwageimpact_{r}= labwageimpact_{r,t-1}*0.9+ (1-LABWAGEIND_{r,t})*0.1</math><br />
<br />
The smoothed impact is used as the equilibration signal for labor supply. As we have already described in the section on labor supply, a small fraction of the impact (labwageimpact) is applied to the labor participation rate. The impact is scaled down to account for the slow pace of changes on the supply side.<br />
<br />
:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact_{r,t}*0.05)</math><br />
<br />
For the impacts of wage on labor demand we use a second PID multiplier as opposed to using the changes in wage index that we have done on the supply side. The second PID uses the wage index itself as the process variable and uses the base year value of 1 as the target. The reason we had to use this second PID is to control the pace at which wage disequilibrium can affect demand, especially in the event of an abrupt shock. The smoothing and scaling down that works on the supply side is not enough to control oscillations on the demand side.<br />
<br />
:<math>Diffl1_{t}=LABWAGEIND_{r,t=1}-1</math><br />
<br />
:<math>Diffl2_{t}=LABWAGEIND_{r,t}-LABWAGEIND_{r,t-1} </math><br />
<br />
:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},ellabwage1_{r},ellabwage1_{r})</math><br />
<br />
A second impact factor (labwageimpactmul) is computed using the correction signal from this second multiplier:<br />
<br />
:<math>labwageimpactmul_{r,t}= labwageimpactmul_{r,t-1}*mullw_{r,t}</math><br />
<br />
This impact factor is applied on the labor demand as described in the section on labor demand.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}= LABDEMS_{r,s,sk,t}* labwageimpactmul_{r,t}</math><br />
<br />
== Informal Labor ==<br />
<br />
IFs forecast labor and GDP share of the informal sector. Informal labor forecast is not explicitly endogenized in the labor market though. They are rather driven by development, skill and regulatory factors[[#_ftn1|[1]]]. However, the productivity and revenue impacts of changes in informality affects output and thus labor demand implicitly as a very distal driver.<br />
<br />
= References =<br />
<br />
<references /><br />
</div></div></div></div></div></div></div></div></div></div></div>Wikiadmin//pardee.du.edu/wiki/LaborLabor2018-09-07T22:47:10Z<p>Wikiadmin: </p>
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<div>Workers in an economy supply the expertise and the efforts needed to produce goods and services. In return the labor receives wages that they use to meet their current and future consumption needs. On one hand, shortage of labor with required skills prevents economies from realizing their growth potential. On the other hand, individuals falling short of the right qualifications might remain unemployed or underemployed failing to secure income needed for a decent living. The ongoing adjustments to find the best match between skills, jobs and wages can only be studied through a dynamic model of the labor market.&nbsp; &nbsp;<br />
<br />
Such a model should go beyond providing a reasonable answer to the obvious question of why employment and wages go up and down. An aggregate labor market must deal with issues that have strong interconnections with various other dynamic changes in the greater society. What kind of dividend of deficit can a society expect from its labor force given the phase of demographic transition in which it is situated? How severely would aging affect the pool of working age adults? Might increasing female participation rates offset some of the losses from aging? What is the level of skills and educational attainment in a society? These supply phenomena move relatively slowly unless there are huge disruptions, like a war or famine, or an aggressive policy push. The demand side, in contrast, needs to be more responsive in adjusting wages and employment given the investment and technology in the various sectors of the broader economy. In general, though, the labor market demonstrates some sluggishness compared to the goods and services markets as it involves moving human beings with various limitations. Consumption of goods and services depend on the income earned by the labor. Uneven distribution of employment and wages among labors of various types or between labor and capital for a long period of time can give rise to persistent inequality in a society. &nbsp;&nbsp;&nbsp;&nbsp;<br />
<br />
== Conceptual Framework ==<br />
<br />
Labor markets are markets for workers and jobs. In a labor market, employers meet their demand for labor with the supply of people willing to work at the wage the employers can offer. The employers raise the wage when there is a shortage of workers. Workers agree to take a lower wage when there are more of them than the firms need. In the real-world labor markets do not always clear at perfect equilibrium. Frinctional unemployment results for various reasons, for example, the search time between jobs. Structural unemployment can result from technology induced disruptions. Some unemployment could thus persist in the labor market even when there aren’t any short-term fluctuations. There is also the phenomenon of informal employment that consists of less sophisticated workers and entrepreneurs engaged in unregulated economic activities. &nbsp;In a dynamic model that covers the entire economy, the real wage earned by the labor drives the income and social mobility.&nbsp;&nbsp;&nbsp;<br />
<br />
To understand the long-term dynamics of the labor market, we need also examine the deeper determinants of labor demand and supply, the determinants that can shift the curves. Labor demand changes over time with the changes in demand for goods and services and the labor input needed to produce those. Labor productivity itself improves with technological progress. Long term transitions in the supply of labor are mostly demographic. &nbsp;<br />
<br />
Labor supply is determined by the working age population and the share of that population who are available for participation in the workforce. The labor supply is relatively stable as the demographic changes are slow in pace. As the share of elderly in the population increases, a recent trend in many societies, the rate of participation declines. Some of the aging impacts will be offset by the greater female participation rates, a second trend that surfaces as economies develop and women attain more education. Educational attainment also drives the general skill level of workers, male and female. Specific skills are obtained through training and experience that augment the knowledge obtained through general and specialized education. &nbsp;&nbsp;&nbsp;&nbsp;<br />
<br />
It is the demand side that causes most of the short-term imbalances in the labor market. &nbsp;In the long term, as said earlier, the important driver of demand for labor and their skills is technological progress. Labor requirement drops with advances in technology, more so for less skilled labor. Labor composition changes accordingly both within and across sectors. Rapid advances in technology can also cause disruption in the system when there is not much opening in the other sectors. Labor displacement is offset to some extent by the growth in the economy and the resulting increase in total demand. &nbsp;&nbsp;&nbsp;<br />
<br />
As we have already mentioned, employees maximize income and the firms minimize labor costs. When there are more laborers than the firms can hire, there is unemployment. Shifts in the rates of unemployment impacts wage, the price of labor. For example, wages drop in the event of rising unemployment as there are more people to hire from. Wage adjustments feed back to the demand for labor seeking to bring the market back to equilibrium.<br />
<br />
The challenges around the conceptual distinction between unemployment and employment is further complicated by the phenomenon of informal employment. In many developing countries there is a large urban non-agricultural informal sector where low-skilled workers work for wages typically lower than a formal employment.<br />
<br />
[[File:LMFlowchart1.png|frame|center|Description of the labor model]]<br />
<br />
== Dominant Relations ==<br />
<br />
The labor model in the International Futures system (IFs) balances the total supply of labor with the total labor demanded by all economic sectors. Total labor (LAB) is computed from the working age population and the labor participation rate. Population forecasts are obtained from the IFs demographic model. Participation rates (LABPARR) are computed by sex with a catchup algorithm for the female participation towards that for the male. Labor is also disaggregated by skill level, as determined by educational attainment, in a separate labor supply variable (LABSUP) which is used to distribute labor earnings by skill level. [** LABSUP do not affect the demand/supply balance now]<br />
<br />
Labor demands (LABDEMS) are driven by sectoral technology functions used to compute the labor requirement by skill level for each unit of potential valued added in the sector. These labor coefficients (LABCOEFFS) are multiplied with the projected value added for the sector to compute the needed manpower. The balancing mechanisms determines the labor employed in each of the sectors (LABS).&nbsp;&nbsp;<br />
<br />
The balancing, in the current version of the model, can be done in one of the two ways. In the first method, total needs combined from all economic sectors is normalized to the available pool of labor computed by subtracting the unemployed from those who are at or looking for work. The rate of unemployment is kept at its natural rate for which we use the base year rate of unemployment. (** This might need to be changed for countries where the market is undergoing some abrupt transition.)<br />
<br />
In the second balancing method, added in a recent revision of the model, total demand is equilibrated to supply through a CGE like market equilibrium model. An indexed wage (LABWAGEIND) and the rate of unemployment (LABUNEMPR) work as the equilibrating variables. As unemployment deviates from the target, PID algorithms send a signal for the wage to adjust. Wage adjustments cause adjustments in the “base” labor demands by sector computed from the labor-coefficient functions as described earlier. Wage signals also affects the labor participation rate. The magnitude of impact on the supply side is much lower than that on the demand side.<br />
<br />
Wage and unemployment rate are aggregated for the total labor market. The wage index starts with a base year value of 1 and the unemployment rates start with the historical data for the base year. Initial year unemployment rate works as the target for long term unemployment.<br />
<br />
== Key Dynamics ==<br />
<br />
The following key dynamics are directly related to the dominant relations:<br />
<br />
*Labor supply is determined from population of appropriate age in the population model (see its dominant relations and dynamics) and endogenous labor force participation rates, influenced exogenously by the growth of female participation.<br />
*Labor demand is driven by sectoral demand functions driven by technological progress<br />
<br />
== Structure and Agent System ==<br />
<br />
{| border="1" cellspacing="0" cellpadding="0" width="0" style="width:502px;"<br />
|-<br />
| style="width:242px;height:49px;" | <br />
'''System/Subsystem'''<br />
<br />
| style="height:49px;" | <br />
Labor market<br />
<br />
|-<br />
| <br />
'''Organizing Structure'''<br />
<br />
| <br />
Labor supply by skill level and labor demand by sector for each skill category represented within an equilibrium-seeking model with wage and unemployment rate as the equilibrating variables<br />
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|-<br />
| <br />
'''Stocks'''<br />
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| <br />
Population, labor, education, &nbsp;accumulated technology<br />
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|-<br />
| <br />
'''Flows'''<br />
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| <br />
Participation rate; Coefficients of labor demand; Employment (unemployment); Wage<br />
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|-<br />
| <br />
'''Key Aggregate&nbsp;''''''Relationships&nbsp;'''<br />
<br />
'''(illustrative, not comprehensive)'''<br />
<br />
| <br />
Labor supply is driven by demographic changes; Participation of female change over time; Labor requirement changes with technological development; Unemployment rate drives wage; Wage movements affect labor demand and participation rate<br />
<br />
|-<br />
| <br />
'''Key Agent-Class Behavior&nbsp;''''''Relationships'''<br />
<br />
'''(illustrative, not comprehensive)'''<br />
<br />
| <br />
Households and work/leisure, and female participation patterns;<br />
<br />
&nbsp;<br />
<br />
Firms and hiring;<br />
<br />
&nbsp;<br />
<br />
<br />
<br />
<br />
<br />
|}<br />
<br />
= Labor Model Data =<br />
<br />
The labor supply and unemployment data that we use in our model is from International Labor Organization (ILO). For data on the demand side, we used data from the Global Trade Analysis Project. Wage variable used in the equilibration algorithm&nbsp;is an index anchored to the base year of the model.<ref>GTAP database helped us compute wage rates by sector and skill.</ref> IFs preprocessor prepared these data for model use using various estimation, conversion and reconciliation processes.&nbsp; &nbsp; &nbsp;<br />
<br />
== Definitional Issues ==<br />
<br />
There are ambiguities in the way some of the labor market variables are defined. Labor participation rates and the rate of unemployment are two that need special attention.<br />
<br />
The size of the labor supply available for economic activities is expressed with the labor force participation rate. ILO defines this as a “measure of the proportion of country’s working-age population that engages actively in the labor market, either by working or looking for work.”<ref>http://www.ilo.org/ilostat-files/Documents/description_LFPR_EN.pdf</ref>&nbsp;National labor force surveys and census data are used to estimate this rate. The definition of labor force here includes both employed and unemployed and the rate is expressed as a percentage of working-age population. Working-age population is defined here as the population above legal working-age. For international comparability, ILO adopts a convenient minimum threshold of fifteen years as working age and avoids putting any upper age limit. In practice, both the minimum and the upper-age limits can vary by country. For example, the working-age in the USA is sixteen years. In the Netherlands the upper age limit is seventy-five years, whereas South African data uses an upper age limit of 64.<ref>https://www.bls.gov/fls/flscomparelf/technical_notes.pdf</ref><br />
<br />
Ambiguities are more abundant in the definition of unemployment. ILO came up with a guideline on this as well. Per the ILO guideline, the unemployed are those among the working-age population who are not employed, are available for work and are actively looking for jobs<ref>The definitions around employed and unemployed were agreed upon by nations through the ‘Resolution concerning statistics of work, employment and labor underutilization’ adopted by the 19th International Conference of Labor Statisticians (ICLS) in 2013. (Bourmpoula et al, 2017: 6).</ref>; the unemployment rate is expressed as a percentage of those who are in the labor force. The availability and job-seeker status could be defined in different ways giving rise to incompatibility in data. &nbsp;While there seems to be little room for disagreement on whether someone is at work or not, whether that work should be considered as employment is contested at many times.<br />
<br />
The debates around the nature and type of employment can range from gainfulness to workplace setting. For example, a large number of workers in the low-income low-regulation developing countries work outside the purview of formal enterprises. According to an ILO estimate, more than half of the global labor force and more than 90% of Micro and Small Enterprises (MSEs) worldwide are in the so called informal economy.<ref>http://www.ilo.org/global/topics/employment-promotion/informal-economy/lang--en/index.htm</ref> This might explain the apparently counterintuitive pattern of low unemployment rate in some low-income countries (e.g., 2.2% for Guatemala) and relatively higher numbers for some of the developed nations. The low numbers in the poorer countries hide the prevalence of extremely low wage jobs in the informal sectors in these countries, the only options for the vulnerable people in the absence of any kind of social safety net. &nbsp;Contrastingly, in the developed countries the so called ‘gig-economy’ is attracting more and more workers who choose to work on their own rather than in a formal enterprise. ILO conceptualization makes the informal work part of total employment. The stacked Venn diagram below presents the relationship among the labor force metric including informal employment. IFs also models informal economy both in terms of GDP share and employment share of informal in the total economy and employment.<br />
<br />
[[File:LaborSubsets.png|frame|right|Relationship among various labor measurement]]<br />
<br />
Incompatibility can arise in the treatment of various population groups for the computation of the denominator for participation and unemployment rates.<ref>For example, the USA excludes people in the defense services and those in the prisons or mental asylums in their computation of the civilian non-institutional working-age population. There are also variations in the treatments of students, those recently laid-off, and family workers. Please see https://stats.bls.gov/opub/mlr/2000/06/art1full.pdf for a discussion </ref> ILO makes their best efforts to make adjustments in the data for the sake of international comparison. For example, ILO asks countries that deviate from ILO guidelines to collect data needed to convert national figures to ILO figures. It is likely that some differences might have slipped past the adjustment process. We use ILO data and continue to update our database&nbsp;&nbsp;<br />
<div><div id="ftn4"><div id="ftn1"><br />
The GTAP data that we use for the demand side of the labor model is taken as labor headcounts and is thus immune from ambiguities around rate computation. As far as we could gather<ref>Please see the webpage for documentation on GTAP labor data statistic: https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=3248</ref>, the data includes both the formal and informal employment. We also need mention here that the GTAP database reconciles the labor data to calibrate the general equilibrium modeling that they do for the trade analyses. The data could thus be somewhat different from data collected through direct surveys. As a CGE model IFs is benefited by using calibrated data.<br />
<br />
== Sources of Labor Data ==<br />
<br />
IFs model uses ILO data for labor participation rates and for the unemployment rate. The data in IFs are collected from World Bank’s World Development Indicators (WDI) database. According to their documentation, WDI obtained the data from the ILO.<br />
<br />
Unemployment rate data in IFs is also collected from WDI. Like the participation rates WDI also obtains their unemployment data from ILO.<ref>The name of the IFs table is SeriesLaborUnemploy%</ref><br />
<br />
For employment and labor demand data IFs uses Purdue University’s Global Trade Analysis Project (GTAP) database. GTAP collects and compiles factor payments, imports, and intersectoral flow data to calibrate CGE models of national economies for trade and other analyses. In their ninth release in 2016, GTAP published data for 140 countries and regions for the year 2011. The earlier GTAP releases, which the IFs model used for its previous versions, compiled data for the years 2004 and 2007. GTAP data release aggregates economic activities into 57 commodities and activities following International Standard Industrial Classification (ISIC). The IFs model maps the 57 GTAP sectors into six economic sectors of IFs – agriculture, energy, material and mining, manufacture, services and ICT. Appendix 2 presents two tables listing the sectors mapping between IFs and GTAP, and GTAP and ISIC. GTAP further disaggregates labor in each of the commodities/activities into five occupation and skill categories following the nine category International Standard Classification of Occupations (ISCO-88). The IFs model collapses five GTAP occupation categories into the simple IFs dichotomy of skilled and unskilled. The mapping of occupations and skills are presented in the third appendix of this document. &nbsp;<br />
<br />
The data in the main GTAP database, prepared for CGE modeling, are all in dollar unit and thus do not include labor headcounts. We have used a ‘satellite’ GTAP database<ref>See Weingarden and Tsigas, 2010 for the details on the preparation of this database.</ref>&nbsp;for labor headcounts by skill and sector. The labor counts were also used to plot labor requirement functions for each of the IFs economic sectors and skill categories. The wage share of skilled and unskilled labor in each sector was computed using the labor headcounts and labor payments.<br />
<br />
== Scope of IFs Labor Model ==<br />
<br />
The IFs labor model simulates labor market at the national level. Each national labor market forecasts labor demand and employment by six sectors - agriculture, energy, mining, manufacture, services and ICT- and two skill levels - skilled and unskilled. The supply side do not have sectoral representation. IFs forecasts total labor force and labor supply by the two skill levels. Labor participation rate is computed in IFs by gender. Wage and unemployment rate is forecast for the overall labor market only.&nbsp;&nbsp;<br />
<br />
== Labor Model Pre-processor ==<br />
<br />
IFs system has a data preprocessor that prepares the initial conditions for the model using historical databases and various assumptions and estimated relationships to fill in the missing data and make data adjustments as needed.<ref>For more details, please see ‘The Data Pre-Processor of International Futures (IFs)” by Barry B. Hughes (with Mohammod Irfan) at http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf</ref> Pre-processing of labor data takes place in two IFs pre-processing modules. Labor participation rate data, which is closely related to demography, is processed in the population pre-processor. Unemployment rate and labor demand data are processed in the economic pre-processor.&nbsp;<br />
<div><div id="ftn1"><br />
=== Pre-processing Labor participation rate and unemployment ===<br />
<br />
For initializing labor participation rates by sex (LABPARR) the model uses the historical values from the base year or the most recent year with data.<ref>The data tables that the IFs model pre-processor use for initializing labor participation rates are: SeriesLaborParRate15PlusFemale%, SeriesLaborParRate15PlusMale%.</ref> For countries with no data we use regression relationships of the participation rates, for men and for women, with income per capita. The relationships, shown in the next figure, are not great. However, the functions affect only five countries for which we do not have any data at all: Grenada, Kosovo, Micronesia, Seychelles and South Sudan.<ref>We should try to collect participation rate for these countries from country sources.</ref><br />
<div><div id="ftn2"><br />
IFs data series SeriesLaborUnemploy% is used for the initialization of unemployment rates. That series has annual unemployment rates for one or more years between 1980 and 2016, for 181 of the 186 IFs countries. For five countries (Grenada, Kosovo, Micronesia, Taiwan and South Sudan<ref>These are pretty much the same countries for which we do not have any participation rate data. This indicates ILO might have some administrative limitation in reporting data for these countries (notice Kosovo, Seychelles etc in the list)</ref>) there is no data at all. To fill in the missing data we use a regression function of unemployment rate against GDP per capita. Like the participation rate functions, this function does also not have much of an explanatory power.<br />
<div><div id="ftn1"><br />
=== Pre-processing labor demand and unemployment from GTAP ===<br />
<br />
The IFs economic pre-processor reads labor headcount and labor payment data from the GTAP database. In addition to performing sector and occupation/skill mapping between GTAP and IFs, pre-processor also use the labor headcount data to compute labor coefficient functions, the principal driver of labor demand in the IFs model.<br />
<br />
Labor coefficients are defined as the amount of labor needed to produce one unit of value added in a certain sector of the economy. The coefficients depend on the level of technology. The model uses GDP per capita as an indicator of the level of technological development. IFs pre-processor estimates labor coefficient functions for labor of different skill levels for the different sectors of the economy.<br />
<br />
The functions are derived from GTAP data we described earlier. The model pre-processor reads data on factor payments and aggregates data from 57 GTAP sectors to six IFs sectors. Shares of payment going to skilled and less-skilled workers in each of the sectors are then computed. Countries are grouped according to their level of technological development as represented by per capita income. For each group labor coefficients are obtained by taking an average of the country coefficients. &nbsp;We also convert labor payments data to labor headcount data using per capita income as a proxy for average wage. Labor coefficients and income are then plotted into a power function relationship. The figure below plots some of those labor functions.&nbsp;The functions fit quite well with a power law formulation.<ref>This is interesting given the prevalence of power law in all sorts of scale-up activities (West 2017).</ref><br />
<div><div id="ftn1"></div></div></div></div></div></div></div></div><br />
<br />
= Labor Model Flowcharts =<br />
<br />
The diagram below shows an outline of the IFs labor model. On the supply side, the total labor pool (LAB) is computed from the labor force participation rates, by sex, (LABPARR) and the population (POP) in their working age, i.e., population over 15 (POP15TO65 + POPGT65). Participation rates are driven by the demographic changes with an additional negative impact from aging and a catch-up in female participation rate. Skill level of the labor supply (LABSUP) is driven by the level of development (GDPPCP) and the demand for labor is driven by labor-coefficients (LABCOEFFS) computed from coefficient function representing shifts in demand with technological progress as proxied by the level of development (GDPPCP). Coefficients computed by sector and skill gives the labor requirement by skill type for each unit of value added (VADD) in the sector. Multiplying these coefficients with projected value added in each sector gives an estimate of the labor demand. &nbsp;<br />
<br />
Any surplus or shortage between total labor demand and supply is used to compute the rate of unemployment. Deviations in the unemployment rate (LABUNEMPR) signal wage changes through an equilibrium seeking algorithm. Both demand and supply respond to the wage variable (LABWAGEIND) indexed to the base year. The supply responses are much slower than the demand responses.<br />
<br />
[[File:FLOCHART2.png|frame|center|Labor Model Flowchart]]<br />
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= Labor Model Equations =<br />
<br />
== Overview ==<br />
<br />
The labor model is a part of the IFs economic model that uses labor model output as an input to a Cobb-Douglas production function in a multi-sector general equilibrium model. IFs is a very long-run dynamic model. Instead of computing fixed short-run equilibria that clear the relevant markets IFs uses an equilibrium seeking algorithm to balance the various systems over the longer run. The algorithm is known as the PID (proportion-integral-derivative) controller algorithm and is used widely in industrial control systems. It makes equilibrium seeking variables in IFs move towards a set target. The algorithm works by computing a multiplier based on the movement of the variable towards the target, as obtained by an integral (I) of the path traversed, and the rate of movement towards the target, the derivative term. The multiplier is applied on the process variable (the P term), or a response variable, in the subsequent time period. In the labor model, unemployment rate (LABUNEMPR) is used as the process variable and the PID multiplier is used on the wage rate (LABWAGEIND). Job availability (LABDEMS) and participation rate (LABPARR) get affected by changes in wage. &nbsp;&nbsp;<br />
<br />
Throughout this section we use subscripts and notations common to other modules of IFs. For example, we use t for time period. Subscripts p and r represent sex and country/region, respectively, c is the cohort number, with cohort 1 representing the newborns, cohort1 the the one-year to four-year-olds, cohort two five-year to nine-year-olds etc. Values for p are 1 for male, 2 for female and 3 for both sexes combined. For economic sectors we use s and for skill levels sk.<br />
<br />
== Labor Supply: Equations ==<br />
<br />
The total pool of labor is computed by multiplying the population of working age with the labor force participation rate (LABPARR). &nbsp;Population forecasts come from IFs demographic model which computes both five-year and single-year age-sex cohorts (''agedst'', ''fagedst''). &nbsp;<br />
<br />
The labor model forecasts participation rates by country/region&nbsp; and gender. Participation rates in the model move with the changes in the demographic composition. Female participation rates, which have historically been lower than the same for the male in all societies, but has moved up in modern and affluent societies, get a catch-up boost in the model. Participation rates can also change when there is labor shortage or surplus and the employers try to incentivize or discourage workers by changing wage. This last impact is much less slow than similar wage impacts on the demand side.<br />
<br />
== Labor Participation Rate ==<br />
<br />
Labor participation rates (''LABPARR'') for male and female are first initialized with historical data.<br />
<br />
:<math>LABPARR_{r,p}= LABPARR_{r,p,t=1} </math><br />
<br />
A ‘catch-up’ boost is added to the female participation rate. The boost added (FemParLabMul) starts at a third of a percentage point and withers away following a non-linear path as the female rates approaches the catch-up target (FemParTar), The maximum catch-up that can occur over the horizon of the model is thirty percent.<br />
<br />
:<math>FemParTar_{r}=Amin(LabParRI_{r,p=1},LabParRI_{r,p=2}+30)</math><br />
<br />
:<math>FemParLabMul_{r}=(FemParTar_{r}-LABPARR_{r,p=2,t-1})/(FemParTar_{r}-LABPARR_{r,p=2,t-1})</math><br />
<br />
:<math>LABPARR_{r,p=2,t}=LABPARR_{r,p=2,t-1}+FemParLabMul_{r}*0.3</math><br />
<br />
Next, we compute and apply the aging impact on the participation rate. As the relative share of people over the retirement age increases, the participation rate declines. The model keeps track of the changes in the demographic ratio (PopAgingRatio) of the population who are in their prime working age of 15 to 64 (POPWORKING) to those at a common retirement age of sixty-five or older (POPGT65). This ratio declines as countries age. The percentage drop in the ratio comparative to the base year is scaled appropriately to compute the aging impact (aging_impact). This impact is added to the male and female labor participation rates, with the impact on the female participation rate being slightly lower than that on male rates.<br />
<br />
:<math>POPAgingRatio_{r,t}=POPWORKING_{r,t}/POPGT65_{r,t}</math><br />
<br />
:<math>aging_impact_{r,t}=100*((POPAgingRatio_{r,t}/POPAgingRatio_{r,t=1})-1)*0.2</math><br />
<br />
:<math>LABPARR_{r,p=1,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t} </math><br />
<br />
:<math>LABPARR_{r,p=2,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t}*0.95 </math><br />
<br />
Participation rates respond slowly to changes in wage and unemployment rate. The impact is implemented through a wage impact factor computed from annual changes in the wage index (labwageimpact). The base participation rates can be changed by model user through two model parameters: a direct multiplier on the participation rate (labparm), or one that changes participation by moving the retirement age (labretagem)<br />
<br />
<br />
<br />
:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact*0.05)*labparm_{r,p,t}*labretagem_{r,p,t}</math><br />
<br />
Total participation rate (LABPARRr,p=3,t) is computed by an weighted average of male and female participation rates.<br />
<br />
:<math>LABPARR_{r,p=3,t}= (∑_{p=1 to 2}∑_{c=4 to 21}(agedst{r,c,p,t}*LABPARR_{r,p,t}))/(∑_{p=1 to 2}∑_{c=4 to 21}agedst{r,c,p,t})</math><br />
<br />
== Total Labor ==<br />
<br />
Finally, the total number of labor available for work (LAB) is computed by multiplying the total participation rate with the population of fifteen-year-olds or older.<br />
<br />
:<math>LAB_{r,t}= LABPARR_{r,p=3,t}*∑_{p=1 to 2,c=4 to 21}agedst_{r,c,p,t}</math><br />
<br />
== Labor by skill level ==<br />
<br />
The labor model forecasts labor supply (LABSUP) by two skill categories. The variable (''LABSUP'') is initialized in the pre-processor by reading the employment by skill/occupation (''LABEMPS'') data from GTAP[[#_ftn1|[1]]] &nbsp;and adding the unemployment numbers. We assume same unemployment rate (''LABUMEMPR'') for skilled and unskilled labor.<br />
<br />
:<math>LABSUP_{r,t=1,sk}=∑_{s=1 to 6}(LABEMPS_{r,s,t=1}/(1-(LABUNEMPR_{r,t=1}/100))</math><br />
<br />
The model forecasts labor by skill through a model of the skilled share of the labor. Education, training, exposure, and experience of the employees all improve with the level of development. The model captures this with an analytic function of the skilled share (perskilled) driven by GDP per capita at PPP (GDPPCP) -<br />
<br />
:<math>perskilled_{r}=f(GDPPCP_{r})</math><br />
<br />
Among the causal drivers of skill, education is considered to be the most proximate. Education is strongly correlated with the level of development, the deeper driver of skill in the model. However, the recent increase in education and/or a policy driven educational expansion might add to the impact of education on skill. Additional impacts from education on skill, when there is any, is computed through an expected function formulation. For example, in a society where an average adult has more (or less) education than the adults in other societies at that level of development, the skill share is given a slight upward push (or downward pull). The expectation function is a logarithmic function of educational attainment of working age population (EDYRSAG15) driven by GDP per capita at PPP. Attainment above (or below) the expected level (YearsEdExp) is computed by the function output (YearsEd) adjusted for country situation (yearseddiff). The percentage adjustment to the skilled share (LabSupSkiAdj) is computed using additional (limited) education, i.e., the difference between actual (EDYRSAG15) and expected values of educational attainment, expressed as a percentage of the expected value. The adjustment is scaled appropriately and peters off over time.<br />
<br />
:<math>YearsEd_{r,t}= f(GDPPCP_{r,t})</math><br />
<br />
:<math>yearsdeddiff_{r}= EDYRSAG15_{r,p=3,t=2}-YearsEd_{r,t=2}</math><br />
<br />
:<math>YearsEdExp_{r,t}=YearsEd_{r,t}+yearsdeddiff_{r}</math><br />
<br />
:<math>LabSupSkiAdj_{r,t}=0.3*(EDYRSAG15_{r,p=3,t=2}*YearsEdExp_{r,t})/YearsEd_{r,t}</math><br />
<br />
:<math>LabSupSkiAdj_{r,t}=ConvergeOverTime(0,LabSupSkiAdj_{r,t},70)</math><br />
<br />
:<math>perskilled_{r,t}= perskilled_{r,t}*(1+LabSupSkiAdj_{r,t})</math><br />
<br />
The skilled share (perskilled) is multiplied with the total labor supply (LAB) to obtain the number of labors who are skilled (LABSUPskilled)<br />
<br />
:<math>LABSUP_{r,skilled,t}=LAB_{r,p,t}*perskilledI_{r,t} </math><br />
<br />
As a last step, the model adjusts for the country specific variations in the skilled labor count not captured by the deeper and the proximate models. This is done by saving a ratio (LABSUPSkilledRI) of the actual historical data and the model computed value in the initial year. In the subsequent years this ratio is used to adjust the skilled labor forecast gradually.<br />
<br />
:<math>LABSUPCompSkilled_{r}=LAB_{r}*perskilled_{r,t=1}/100 </math><br />
<br />
:<math>LABSUPSkilledRI_{r}=LABSUP_{r,skilled,t=1}/LABSUPCompSkilled_{r}</math><br />
<br />
:<math>LABSUP_{r,skilled,t}= LABSUP_{r,skilled,t}*ConvergeOverTime(LABSUPSkilledRI_{r},1,85)</math><br />
<br />
Number of unskilled labor is obtained by subtracting the skilled labor from the total pool.<br />
<br />
<br />
<br />
:<math>LABSUP_{r,unskilled,t}= LAB_{r,p,t}- LABSUP_{r,skilled,t}</math><br />
<br />
== Labor Demand: Equations ==<br />
<br />
IFs economic model forecasts production in six economic sectors. IFs labor model computes the longer-term and shorter-term determinants of demand for skilled and unskilled labor (LABDEMS) for the production processes. The long-term drivers of labor requirement are technological progress or the lack of it. In the shorter-term wage affects the labor demand most. Wage in turn is affected by labor supply or skill shortage.<br />
<br />
The IFs model divides economic activities into six economic sectors – agriculture, energy, materials, manufacture, services and information, and communication technologies. Workers in the IFs labor model are disaggregated into two skill types. While the skill composition varies by the technology used in the sector and starts tilting towards the more skilled with the progress in technology, absolute number of labors needed to produce the same output goes down with technological development for both skilled and unskilled labor. This is illustrated in the next figure which plots the changes in labor requirement against GDP per capita at PPP, a proxy for level of development. Agriculture is a much less skill-intensive process than the manufacture, however, with technological progress skill requirement improves rapidly in both sectors. The IFs labor model computes these labor requirement functions in the model pre-processor. As we have already described in the pre-processor section, the computation of these functions use GTAP data on employment by occupation and economic activity. Appendices 3 and 4 lists sector and occupation mapping between GTAP and IFs.<br />
<br />
These functions are used to compute the labor coefficients (LABCOEFFS), i.e., number of skilled and unskilled labor needed to produce unit amount of output with the technology available, for which we use GDP per capita at PPP as a proxy.<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= f(GDPPCP_{r})</math><br />
<br />
manufacture, services and ICTech) and the subscrip sk stands for skill categories with 1 denoting unskilled and 2 skilled. The labor coefficients obtained from the analytical functions require some adjustments to incorporate country deviations from the functions for various factors not captured in the regression relationship. The first of these adjustments is a gradual removal of impacts of short-run fluctuations in output and labor from the computation of labor coefficient. This adjustment is applied on the coefficients computed from the function. The equation below shows a simplified form of these computations.<br />
<br />
:<math>LabCoeffAdjFac_{r,k,s,t}=f(igdpr_{r,t=2},(LAB_{r,t=2}/LAB_{r,t=1}),(LABCOEFFS_{r,t}/LABCOEFFS_{r,t-1}))</math><br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}=LABCOEFFS_{r,sk,s,t}(1-LabCoeffAdjFac_{r,k,s,t})</math><br />
<br />
Model users can use a global parameter (labcoeffsm) to change the labor coefficients by skill level for any or all of the six sectors –<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= LABCOEFFS_{r,sk,s,t}*'''labcoeffsm_{s,sk}'''</math><br />
<br />
To forecast the total labor demand, the labor coefficients (LABCOEFFS) are multiplied to the total projected output for each of the economic sectors. The forecast is adjusted for any discrepancy between data and model. The adjustment factor (LABDemsAdjFac) is computed as the initial ratio between the actual and computed employment. Actual employment is obtained from historical data (LABEMPS) processed using the GTAP database. The computed employment is obtained by multiplying the labor coefficients (LABCOEFFS) with the final output of the sector (VADD).<br />
<br />
:<math>LabDemsAdjFac_{r,s,sk}= LABEMPS_{r,s,sk,t=1}/(VADD_{r,s,t=1}*LABCOEFFS_{r,sk,s,t})</math><br />
<br />
The projected output is obtained by applying the growth rate (IGDPRCOR) on the sectoral value added from the previous year (VADD). The total labor demand is given by the product of the labor coefficients, projected output, demand adjustments and wage impacts (labwageimpactmul) and the number 1000 which adjusts the units for the equation. Wage impact comes from the level of unemployment and is computed in an equilibration process described in the next section. Model users can use a multiplicative parameter (labdemsm) to slide the demand upward or downward.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}=1000*VADD_{r,s,t-1}*(1+IGDPRCOR_{r})*LABCOEFFS_{r,sk,s,t}*LabDemsAdjFac_{r,s,sk}*labwageimpactmul_{r,s,sk}*'''labdemsm_{r,s}'''</math><br />
<br />
== Unemployment and Wage: Labor Market Equilibration ==<br />
<br />
The IFs labor model balances the labor market through an equilibrium seeking algorithm rather than computing an exact equilibrium at each time step. We use an algorithm borrowed from the control systems engineering. This PID controller algorithm, described also in the IFs economic model documentation, works by computing corrective signals for equilibrating variables using the deviations of a buffer variable, for example unemployment rate (LABUNEMPR), from a target value. The signal is computed from two quantities, the distance of the buffer from the target and the current rate of change of the buffer. The computation is tuned with PID elasticities to avoid oscillations. The computed signal is applied on the variable/s which need to be balanced, for example, demand and supply in the event of a market equilibration, thus getting closer to a balance at each step of simulation. The target value for the buffer variable and the tuning parameters of the control algorithm are obtained through rules-of-thumb and model calibration. The IFs labor model uses unemployment rate (LABUNEMPR) as the buffer variable for the market equilibration of labor demand and labor supply. The multiplier (i.e., corrective signal) obtained from the PID is applied on the wage index (LABWAGEIND). Changes in wage indices comparative to the base year, moderated through a second PID controller, is used to compute the final signal (labwageimpactmul) that drives labor demand and labor supply. Even though the model forecasts labor demand by sector and skill, and computes labor supply for both skill types, the equilibration algorithm works over the entire pool of labor. In other words, we assume that the skills are replaceable across sectors and the lack (or abundance) of jobs affects skilled and unskilled persons equally.<br />
<br />
At each annual timestep, the model computes the unemployment rate (LABUNEMPR) as the gap in between the total supply of labor (LAB) and the total demand. The gap (EmplGap) is expressed as a share of the total labor, the standard way to express unemployment rate.<br />
<br />
:<math>sumld=∑_{s,sk}LADEMS_{r,s,sk,t}</math><br />
<br />
:<math>EmplGap= LAB_{r,t}*sumld</math><br />
<br />
:<math>LABUMENPR_{r,t}= (EmplGap/LAB_{r,t})*100</math><br />
<br />
As the target value (LabUnEmpRateTar) for the PID controller that modulates unemployment rate we use either the historical unemployment rate or a ten percent unemployment rate when the historical rate is higher than ten. Model users can override the historical target through a model parameter (labunemprtrgtval).<br />
<br />
:<math>LABUMENPRi_{r,t}= LABUMENPR_{r,t}</math><br />
<br />
:<math>LabUnempRateTarget_{r}=labunemptargetval_{r}</math><br />
:<math>If LabUnempRateTarget_{r}=0,<br />
LabUnempRateTarget_{r}= AMIN(LABUMENPRi_{r,t},10) </math><br />
<br />
Unemployment rate target, when it is different from the base year value, is reached gradually with a convergence period of forty years . The target rate is converted to count (LabUnEmplTar) to make it equivalent to the employment gap (EmplGap) computed earlier.<br />
<br />
:<math>LabUnEmplTar_{r}= LAB_{r,t}*ConvergeOverTime(LABUMENPRi_{r,t},0,100)</math><br />
<br />
The first order difference (Diffl1) between the target unemployment and the demand-supply gap is used to compute a second order difference (Diffl2) accounting for changes in the rate of movement. The two differences and the PID multipliers (elwageunemp1, elwageunemp2) are provided to the PID function (ADJSTR). Working age population (POP15TO65r,t) works as the scaling base of the PID controller. The controller algorithm gives a multiplier (mullw) that is used in the subsequent year to adjust wage.<br />
<br />
:<math>Diffl1_{t}=LabUnEmplTar_{r}-EmplGap</math><br />
<br />
:<math>Diffl2_{t}=Diffl1_{t}-Diffl1_{t-1}</math><br />
<br />
:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},elwageunemp1_{r},elwageunemp2_{r})</math><br />
<br />
Wage adjustments affect demand and supply with an increase in wage drawing demand downward and supply upward. The opposite affects occur with a downward movement of wage. The wage variable affected by the PID multiplier (LABWAGEIND) is an index initialized at one. We use an indexed rather than a dollar wage in the equilibration process to avoid affecting the process from other economic phenomena that affects wage, for example, a rise in real wage as GDP or the labor share of income grows.<br />
<br />
:<math>LABWAGEIND_{r,t=1}=1</math><br />
<br />
In the subsequent years of the model run, the wage index is first adjusted with the equilibration signal obtained from the unemployment rate PID controller in the previous period<br />
<br />
:<math>LABWAGEIND_{r,t=1}= LABWAGEIND_{r,t=1}* mullw_{r,t-1} </math><br />
<br />
A wage impact (labwageimpact) is then computed using the changes in the wage index relative to the base value. The impact is smoothed with a moving average algorithm.<br />
<br />
:<math>labwageimpact_{r}= labwageimpact_{r,t-1}*0.9+ (1-LABWAGEIND_{r,t})*0.1</math><br />
<br />
The smoothed impact is used as the equilibration signal for labor supply. As we have already described in the section on labor supply, a small fraction of the impact (labwageimpact) is applied to the labor participation rate. The impact is scaled down to account for the slow pace of changes on the supply side.<br />
<br />
:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact_{r,t}*0.05)</math><br />
<br />
For the impacts of wage on labor demand we use a second PID multiplier as opposed to using the changes in wage index that we have done on the supply side. The second PID uses the wage index itself as the process variable and uses the base year value of 1 as the target. The reason we had to use this second PID is to control the pace at which wage disequilibrium can affect demand, especially in the event of an abrupt shock. The smoothing and scaling down that works on the supply side is not enough to control oscillations on the demand side.<br />
<br />
:<math>Diffl1_{t}=LABWAGEIND_{r,t=1}-1</math><br />
<br />
:<math>Diffl2_{t}=LABWAGEIND_{r,t}-LABWAGEIND_{r,t-1} </math><br />
<br />
:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},ellabwage1_{r},ellabwage1_{r})</math><br />
<br />
A second impact factor (labwageimpactmul) is computed using the correction signal from this second multiplier:<br />
<br />
:<math>labwageimpactmul_{r,t}= labwageimpactmul_{r,t-1}*mullw_{r,t}</math><br />
<br />
This impact factor is applied on the labor demand as described in the section on labor demand.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}= LABDEMS_{r,s,sk,t}* labwageimpactmul_{r,t}</math><br />
<br />
== Informal Labor ==<br />
<br />
IFs forecast labor and GDP share of the informal sector. Informal labor forecast is not explicitly endogenized in the labor market though. They are rather driven by development, skill and regulatory factors[[#_ftn1|[1]]]. However, the productivity and revenue impacts of changes in informality affects output and thus labor demand implicitly as a very distal driver.<br />
<br />
= References =<br />
<br />
<references /><br />
</div></div></div></div></div></div></div></div></div></div></div>Wikiadmin//pardee.du.edu/wiki/LaborLabor2018-09-07T22:44:54Z<p>Wikiadmin: </p>
<hr />
<div>Workers in an economy supply the expertise and the efforts needed to produce goods and services. In return the labor receives wages that they use to meet their current and future consumption needs. On one hand, shortage of labor with required skills prevents economies from realizing their growth potential. On the other hand, individuals falling short of the right qualifications might remain unemployed or underemployed failing to secure income needed for a decent living. The ongoing adjustments to find the best match between skills, jobs and wages can only be studied through a dynamic model of the labor market.&nbsp; &nbsp;<br />
<br />
Such a model should go beyond providing a reasonable answer to the obvious question of why employment and wages go up and down. An aggregate labor market must deal with issues that have strong interconnections with various other dynamic changes in the greater society. What kind of dividend of deficit can a society expect from its labor force given the phase of demographic transition in which it is situated? How severely would aging affect the pool of working age adults? Might increasing female participation rates offset some of the losses from aging? What is the level of skills and educational attainment in a society? These supply phenomena move relatively slowly unless there are huge disruptions, like a war or famine, or an aggressive policy push. The demand side, in contrast, needs to be more responsive in adjusting wages and employment given the investment and technology in the various sectors of the broader economy. In general, though, the labor market demonstrates some sluggishness compared to the goods and services markets as it involves moving human beings with various limitations. Consumption of goods and services depend on the income earned by the labor. Uneven distribution of employment and wages among labors of various types or between labor and capital for a long period of time can give rise to persistent inequality in a society. &nbsp;&nbsp;&nbsp;&nbsp;<br />
<br />
== Conceptual Framework ==<br />
<br />
Labor markets are markets for workers and jobs. In a labor market, employers meet their demand for labor with the supply of people willing to work at the wage the employers can offer. The employers raise the wage when there is a shortage of workers. Workers agree to take a lower wage when there are more of them than the firms need. In the real-world labor markets do not always clear at perfect equilibrium. Frinctional unemployment results for various reasons, for example, the search time between jobs. Structural unemployment can result from technology induced disruptions. Some unemployment could thus persist in the labor market even when there aren’t any short-term fluctuations. There is also the phenomenon of informal employment that consists of less sophisticated workers and entrepreneurs engaged in unregulated economic activities. &nbsp;In a dynamic model that covers the entire economy, the real wage earned by the labor drives the income and social mobility.&nbsp;&nbsp;&nbsp;<br />
<br />
To understand the long-term dynamics of the labor market, we need also examine the deeper determinants of labor demand and supply, the determinants that can shift the curves. Labor demand changes over time with the changes in demand for goods and services and the labor input needed to produce those. Labor productivity itself improves with technological progress. Long term transitions in the supply of labor are mostly demographic. &nbsp;<br />
<br />
Labor supply is determined by the working age population and the share of that population who are available for participation in the workforce. The labor supply is relatively stable as the demographic changes are slow in pace. As the share of elderly in the population increases, a recent trend in many societies, the rate of participation declines. Some of the aging impacts will be offset by the greater female participation rates, a second trend that surfaces as economies develop and women attain more education. Educational attainment also drives the general skill level of workers, male and female. Specific skills are obtained through training and experience that augment the knowledge obtained through general and specialized education. &nbsp;&nbsp;&nbsp;&nbsp;<br />
<br />
It is the demand side that causes most of the short-term imbalances in the labor market. &nbsp;In the long term, as said earlier, the important driver of demand for labor and their skills is technological progress. Labor requirement drops with advances in technology, more so for less skilled labor. Labor composition changes accordingly both within and across sectors. Rapid advances in technology can also cause disruption in the system when there is not much opening in the other sectors. Labor displacement is offset to some extent by the growth in the economy and the resulting increase in total demand. &nbsp;&nbsp;&nbsp;<br />
<br />
As we have already mentioned, employees maximize income and the firms minimize labor costs. When there are more laborers than the firms can hire, there is unemployment. Shifts in the rates of unemployment impacts wage, the price of labor. For example, wages drop in the event of rising unemployment as there are more people to hire from. Wage adjustments feed back to the demand for labor seeking to bring the market back to equilibrium.<br />
<br />
The challenges around the conceptual distinction between unemployment and employment is further complicated by the phenomenon of informal employment. In many developing countries there is a large urban non-agricultural informal sector where low-skilled workers work for wages typically lower than a formal employment.<br />
<br />
[[File:LMFlowchart1.png|frame|center|Description of the labor model]]<br />
<br />
== Dominant Relations ==<br />
<br />
The labor model in the International Futures system (IFs) balances the total supply of labor with the total labor demanded by all economic sectors. Total labor (LAB) is computed from the working age population and the labor participation rate. Population forecasts are obtained from the IFs demographic model. Participation rates (LABPARR) are computed by sex with a catchup algorithm for the female participation towards that for the male. Labor is also disaggregated by skill level, as determined by educational attainment, in a separate labor supply variable (LABSUP) which is used to distribute labor earnings by skill level. [** LABSUP do not affect the demand/supply balance now]<br />
<br />
Labor demands (LABDEMS) are driven by sectoral technology functions used to compute the labor requirement by skill level for each unit of potential valued added in the sector. These labor coefficients (LABCOEFFS) are multiplied with the projected value added for the sector to compute the needed manpower. The balancing mechanisms determines the labor employed in each of the sectors (LABS).&nbsp;&nbsp;<br />
<br />
The balancing, in the current version of the model, can be done in one of the two ways. In the first method, total needs combined from all economic sectors is normalized to the available pool of labor computed by subtracting the unemployed from those who are at or looking for work. The rate of unemployment is kept at its natural rate for which we use the base year rate of unemployment. (** This might need to be changed for countries where the market is undergoing some abrupt transition.)<br />
<br />
In the second balancing method, added in a recent revision of the model, total demand is equilibrated to supply through a CGE like market equilibrium model. An indexed wage (LABWAGEIND) and the rate of unemployment (LABUNEMPR) work as the equilibrating variables. As unemployment deviates from the target, PID algorithms send a signal for the wage to adjust. Wage adjustments cause adjustments in the “base” labor demands by sector computed from the labor-coefficient functions as described earlier. Wage signals also affects the labor participation rate. The magnitude of impact on the supply side is much lower than that on the demand side.<br />
<br />
Wage and unemployment rate are aggregated for the total labor market. The wage index starts with a base year value of 1 and the unemployment rates start with the historical data for the base year. Initial year unemployment rate works as the target for long term unemployment.<br />
<br />
== Key Dynamics ==<br />
<br />
The following key dynamics are directly related to the dominant relations:<br />
<br />
*Labor supply is determined from population of appropriate age in the population model (see its dominant relations and dynamics) and endogenous labor force participation rates, influenced exogenously by the growth of female participation.<br />
*Labor demand is driven by sectoral demand functions driven by technological progress<br />
<br />
== Structure and Agent System ==<br />
<br />
{| border="1" cellspacing="0" cellpadding="0" width="0" style="width:502px;"<br />
|-<br />
| style="width:242px;height:49px;" | <br />
'''System/Subsystem'''<br />
<br />
| style="height:49px;" | <br />
Labor market<br />
<br />
|-<br />
| <br />
'''Organizing Structure'''<br />
<br />
| <br />
Labor supply by skill level and labor demand by sector for each skill category represented within an equilibrium-seeking model with wage and unemployment rate as the equilibrating variables<br />
<br />
|-<br />
| <br />
'''Stocks'''<br />
<br />
| <br />
Population, labor, education, &nbsp;accumulated technology<br />
<br />
|-<br />
| <br />
'''Flows'''<br />
<br />
| <br />
Participation rate; Coefficients of labor demand; Employment (unemployment); Wage<br />
<br />
|-<br />
| <br />
'''Key Aggregate&nbsp;''''''Relationships&nbsp;'''<br />
<br />
'''(illustrative, not comprehensive)'''<br />
<br />
| <br />
Labor supply is driven by demographic changes; Participation of female change over time; Labor requirement changes with technological development; Unemployment rate drives wage; Wage movements affect labor demand and participation rate<br />
<br />
|-<br />
| <br />
'''Key Agent-Class Behavior&nbsp;''''''Relationships'''<br />
<br />
'''(illustrative, not comprehensive)'''<br />
<br />
| <br />
Households and work/leisure, and female participation patterns;<br />
<br />
&nbsp;<br />
<br />
Firms and hiring;<br />
<br />
&nbsp;<br />
<br />
<br />
<br />
<br />
<br />
|}<br />
<br />
= Labor Model Data =<br />
<br />
The labor supply and unemployment data that we use in our model is from International Labor Organization (ILO). For data on the demand side, we used data from the Global Trade Analysis Project. Wage variable used in the equilibration algorithm&nbsp;is an index anchored to the base year of the model.<ref>GTAP database helped us compute wage rates by sector and skill.</ref> IFs preprocessor prepared these data for model use using various estimation, conversion and reconciliation processes.&nbsp; &nbsp; &nbsp;<br />
<br />
== Definitional Issues ==<br />
<br />
There are ambiguities in the way some of the labor market variables are defined. Labor participation rates and the rate of unemployment are two that need special attention.<br />
<br />
The size of the labor supply available for economic activities is expressed with the labor force participation rate. ILO defines this as a “measure of the proportion of country’s working-age population that engages actively in the labor market, either by working or looking for work.”<ref>http://www.ilo.org/ilostat-files/Documents/description_LFPR_EN.pdf</ref>&nbsp;National labor force surveys and census data are used to estimate this rate. The definition of labor force here includes both employed and unemployed and the rate is expressed as a percentage of working-age population. Working-age population is defined here as the population above legal working-age. For international comparability, ILO adopts a convenient minimum threshold of fifteen years as working age and avoids putting any upper age limit. In practice, both the minimum and the upper-age limits can vary by country. For example, the working-age in the USA is sixteen years. In the Netherlands the upper age limit is seventy-five years, whereas South African data uses an upper age limit of 64.<ref>https://www.bls.gov/fls/flscomparelf/technical_notes.pdf</ref><br />
<br />
Ambiguities are more abundant in the definition of unemployment. ILO came up with a guideline on this as well. Per the ILO guideline, the unemployed are those among the working-age population who are not employed, are available for work and are actively looking for jobs<ref>The definitions around employed and unemployed were agreed upon by nations through the ‘Resolution concerning statistics of work, employment and labor underutilization’ adopted by the 19th International Conference of Labor Statisticians (ICLS) in 2013. (Bourmpoula et al, 2017: 6).</ref>; the unemployment rate is expressed as a percentage of those who are in the labor force. The availability and job-seeker status could be defined in different ways giving rise to incompatibility in data. &nbsp;While there seems to be little room for disagreement on whether someone is at work or not, whether that work should be considered as employment is contested at many times.<br />
<br />
The debates around the nature and type of employment can range from gainfulness to workplace setting. For example, a large number of workers in the low-income low-regulation developing countries work outside the purview of formal enterprises. According to an ILO estimate, more than half of the global labor force and more than 90% of Micro and Small Enterprises (MSEs) worldwide are in the so called informal economy.<ref>http://www.ilo.org/global/topics/employment-promotion/informal-economy/lang--en/index.htm</ref> This might explain the apparently counterintuitive pattern of low unemployment rate in some low-income countries (e.g., 2.2% for Guatemala) and relatively higher numbers for some of the developed nations. The low numbers in the poorer countries hide the prevalence of extremely low wage jobs in the informal sectors in these countries, the only options for the vulnerable people in the absence of any kind of social safety net. &nbsp;Contrastingly, in the developed countries the so called ‘gig-economy’ is attracting more and more workers who choose to work on their own rather than in a formal enterprise. ILO conceptualization makes the informal work part of total employment. The stacked Venn diagram below presents the relationship among the labor force metric including informal employment. IFs also models informal economy both in terms of GDP share and employment share of informal in the total economy and employment.<br />
<br />
[[File:LaborSubsets.png|frame|right|Relationship among various labor measurement]]<br />
<br />
Incompatibility can arise in the treatment of various population groups for the computation of the denominator for participation and unemployment rates.<ref>For example, the USA excludes people in the defense services and those in the prisons or mental asylums in their computation of the civilian non-institutional working-age population. There are also variations in the treatments of students, those recently laid-off, and family workers. Please see https://stats.bls.gov/opub/mlr/2000/06/art1full.pdf for a discussion </ref> ILO makes their best efforts to make adjustments in the data for the sake of international comparison. For example, ILO asks countries that deviate from ILO guidelines to collect data needed to convert national figures to ILO figures. It is likely that some differences might have slipped past the adjustment process. We use ILO data and continue to update our database&nbsp;&nbsp;<br />
<div><div id="ftn4"><div id="ftn1"><br />
The GTAP data that we use for the demand side of the labor model is taken as labor headcounts and is thus immune from ambiguities around rate computation. As far as we could gather<ref>Please see the webpage for documentation on GTAP labor data statistic: https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=3248</ref>, the data includes both the formal and informal employment. We also need mention here that the GTAP database reconciles the labor data to calibrate the general equilibrium modeling that they do for the trade analyses. The data could thus be somewhat different from data collected through direct surveys. As a CGE model IFs is benefited by using calibrated data.<br />
<br />
== Sources of Labor Data ==<br />
<br />
IFs model uses ILO data for labor participation rates and for the unemployment rate. The data in IFs are collected from World Bank’s World Development Indicators (WDI) database. According to their documentation, WDI obtained the data from the ILO.<br />
<br />
Unemployment rate data in IFs is also collected from WDI. Like the participation rates WDI also obtains their unemployment data from ILO.<ref>The name of the IFs table is SeriesLaborUnemploy%</ref><br />
<br />
For employment and labor demand data IFs uses Purdue University’s Global Trade Analysis Project (GTAP) database. GTAP collects and compiles factor payments, imports, and intersectoral flow data to calibrate CGE models of national economies for trade and other analyses. In their ninth release in 2016, GTAP published data for 140 countries and regions for the year 2011. The earlier GTAP releases, which the IFs model used for its previous versions, compiled data for the years 2004 and 2007. GTAP data release aggregates economic activities into 57 commodities and activities following International Standard Industrial Classification (ISIC). The IFs model maps the 57 GTAP sectors into six economic sectors of IFs – agriculture, energy, material and mining, manufacture, services and ICT. Appendix 2 presents two tables listing the sectors mapping between IFs and GTAP, and GTAP and ISIC. GTAP further disaggregates labor in each of the commodities/activities into five occupation and skill categories following the nine category International Standard Classification of Occupations (ISCO-88). The IFs model collapses five GTAP occupation categories into the simple IFs dichotomy of skilled and unskilled. The mapping of occupations and skills are presented in the third appendix of this document. &nbsp;<br />
<br />
The data in the main GTAP database, prepared for CGE modeling, are all in dollar unit and thus do not include labor headcounts. We have used a ‘satellite’ GTAP database<ref>See Weingarden and Tsigas, 2010 for the details on the preparation of this database.</ref>&nbsp;for labor headcounts by skill and sector. The labor counts were also used to plot labor requirement functions for each of the IFs economic sectors and skill categories. The wage share of skilled and unskilled labor in each sector was computed using the labor headcounts and labor payments.<br />
<br />
== Scope of IFs Labor Model ==<br />
<br />
The IFs labor model simulates labor market at the national level. Each national labor market forecasts labor demand and employment by six sectors - agriculture, energy, mining, manufacture, services and ICT- and two skill levels - skilled and unskilled. The supply side do not have sectoral representation. IFs forecasts total labor force and labor supply by the two skill levels. Labor participation rate is computed in IFs by gender. Wage and unemployment rate is forecast for the overall labor market only.&nbsp;&nbsp;<br />
<br />
== Labor Model Pre-processor ==<br />
<br />
IFs system has a data preprocessor that prepares the initial conditions for the model using historical databases and various assumptions and estimated relationships to fill in the missing data and make data adjustments as needed[[#_ftn1|[1]]]. Pre-processing of labor data takes place in two IFs pre-processing modules. Labor participation rate data, which is closely related to demography, is processed in the population pre-processor. Unemployment rate and labor demand data are processed in the economic pre-processor. &nbsp;<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] For more details, please see ‘The Data Pre-Processor of International Futures (IFs)” by Barry B. Hughes (with Mohammod Irfan) at [http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf]<br />
<br />
=== Pre-processing Labor participation rate and unemployment ===<br />
<br />
For initializing labor participation rates by sex (LABPARR) the model uses the historical values from the base year or the most recent year with data[[#_ftn1|[1]]]. For countries with no data we use regression relationships of the participation rates, for men and for women, with income per capita. The relationships, shown in the next figure, are not great. However, the functions affect only five countries for which we do not have any data at all: Grenada, Kosovo, Micronesia, Seychelles and South Sudan[[#_ftn2|[2]]].<br />
<br />
&nbsp;<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] The data tables that the IFs model pre-processor use for initializing labor participation rates are: SeriesLaborParRate15PlusFemale%, SeriesLaborParRate15PlusMale%.<br />
</div><div id="ftn2"><br />
[[#_ftnref2|[2]]] We should try to collect participation rate for these countries from country sources.<br />
<br />
IFs data series SeriesLaborUnemploy% is used for the initialization of unemployment rates. That series has annual unemployment rates for one or more years between 1980 and 2016, for 181 of the 186 IFs countries. For five countries (Grenada, Kosovo, Micronesia, Taiwan and South Sudan[[#_ftn1|[1]]]) there is no data at all. To fill in the missing data we use a regression function of unemployment rate against GDP per capita. Like the participation rate functions, this function does also not have much of an explanatory power.<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] These are pretty much the same countries for which we do not have any participation rate data. This indicates ILO might have some administrative limitation in reporting data for these countries (notice Kosovo, Seychelles etc in the list)<br />
<br />
=== Pre-processing labor demand and unemployment from GTAP ===<br />
<br />
The IFs economic pre-processor reads labor headcount and labor payment data from the GTAP database. In addition to performing sector and occupation/skill mapping between GTAP and IFs, pre-processor also use the labor headcount data to compute labor coefficient functions, the principal driver of labor demand in the IFs model.<br />
<br />
Labor coefficients are defined as the amount of labor needed to produce one unit of value added in a certain sector of the economy. The coefficients depend on the level of technology. The model uses GDP per capita as an indicator of the level of technological development. IFs pre-processor estimates labor coefficient functions for labor of different skill levels for the different sectors of the economy.<br />
<br />
The functions are derived from GTAP data we described earlier. The model pre-processor reads data on factor payments and aggregates data from 57 GTAP sectors to six IFs sectors. Shares of payment going to skilled and less-skilled workers in each of the sectors are then computed. Countries are grouped according to their level of technological development as represented by per capita income. For each group labor coefficients are obtained by taking an average of the country coefficients. &nbsp;We also convert labor payments data to labor headcount data using per capita income as a proxy for average wage. Labor coefficients and income are then plotted into a power function relationship. The figure below plots some of those labor functions.&nbsp; The functions fit quite well with a power law formulation[[#_ftn1|[1]]].<br />
<div><br/><br />
----<br />
<div id="ftn1">[[#_ftnref1|[1]]] This is interesting given the prevalence of power law in all sorts of scale-up activities (West 2017).</div></div></div></div></div></div></div></div></div></div><br />
<br />
= Labor Model Flowcharts =<br />
<br />
The diagram below shows an outline of the IFs labor model. On the supply side, the total labor pool (LAB) is computed from the labor force participation rates, by sex, (LABPARR) and the population (POP) in their working age, i.e., population over 15 (POP15TO65 + POPGT65). Participation rates are driven by the demographic changes with an additional negative impact from aging and a catch-up in female participation rate. Skill level of the labor supply (LABSUP) is driven by the level of development (GDPPCP) and the demand for labor is driven by labor-coefficients (LABCOEFFS) computed from coefficient function representing shifts in demand with technological progress as proxied by the level of development (GDPPCP). Coefficients computed by sector and skill gives the labor requirement by skill type for each unit of value added (VADD) in the sector. Multiplying these coefficients with projected value added in each sector gives an estimate of the labor demand. &nbsp;<br />
<br />
Any surplus or shortage between total labor demand and supply is used to compute the rate of unemployment. Deviations in the unemployment rate (LABUNEMPR) signal wage changes through an equilibrium seeking algorithm. Both demand and supply respond to the wage variable (LABWAGEIND) indexed to the base year. The supply responses are much slower than the demand responses.<br />
<br />
[[File:FLOCHART2.png|frame|center|Labor Model Flowchart]]<br />
<br />
= Labor Model Equations =<br />
<br />
== Overview ==<br />
<br />
The labor model is a part of the IFs economic model that uses labor model output as an input to a Cobb-Douglas production function in a multi-sector general equilibrium model. IFs is a very long-run dynamic model. Instead of computing fixed short-run equilibria that clear the relevant markets IFs uses an equilibrium seeking algorithm to balance the various systems over the longer run. The algorithm is known as the PID (proportion-integral-derivative) controller algorithm and is used widely in industrial control systems. It makes equilibrium seeking variables in IFs move towards a set target. The algorithm works by computing a multiplier based on the movement of the variable towards the target, as obtained by an integral (I) of the path traversed, and the rate of movement towards the target, the derivative term. The multiplier is applied on the process variable (the P term), or a response variable, in the subsequent time period. In the labor model, unemployment rate (LABUNEMPR) is used as the process variable and the PID multiplier is used on the wage rate (LABWAGEIND). Job availability (LABDEMS) and participation rate (LABPARR) get affected by changes in wage. &nbsp;&nbsp;<br />
<br />
Throughout this section we use subscripts and notations common to other modules of IFs. For example, we use t for time period. Subscripts p and r represent sex and country/region, respectively, c is the cohort number, with cohort 1 representing the newborns, cohort1 the the one-year to four-year-olds, cohort two five-year to nine-year-olds etc. Values for p are 1 for male, 2 for female and 3 for both sexes combined. For economic sectors we use s and for skill levels sk.<br />
<br />
== Labor Supply: Equations ==<br />
<br />
The total pool of labor is computed by multiplying the population of working age with the labor force participation rate (LABPARR). &nbsp;Population forecasts come from IFs demographic model which computes both five-year and single-year age-sex cohorts (''agedst'', ''fagedst''). &nbsp;<br />
<br />
The labor model forecasts participation rates by country/region&nbsp; and gender. Participation rates in the model move with the changes in the demographic composition. Female participation rates, which have historically been lower than the same for the male in all societies, but has moved up in modern and affluent societies, get a catch-up boost in the model. Participation rates can also change when there is labor shortage or surplus and the employers try to incentivize or discourage workers by changing wage. This last impact is much less slow than similar wage impacts on the demand side.<br />
<br />
== Labor Participation Rate ==<br />
<br />
Labor participation rates (''LABPARR'') for male and female are first initialized with historical data.<br />
<br />
:<math>LABPARR_{r,p}= LABPARR_{r,p,t=1} </math><br />
<br />
A ‘catch-up’ boost is added to the female participation rate. The boost added (FemParLabMul) starts at a third of a percentage point and withers away following a non-linear path as the female rates approaches the catch-up target (FemParTar), The maximum catch-up that can occur over the horizon of the model is thirty percent.<br />
<br />
:<math>FemParTar_{r}=Amin(LabParRI_{r,p=1},LabParRI_{r,p=2}+30)</math><br />
<br />
:<math>FemParLabMul_{r}=(FemParTar_{r}-LABPARR_{r,p=2,t-1})/(FemParTar_{r}-LABPARR_{r,p=2,t-1})</math><br />
<br />
:<math>LABPARR_{r,p=2,t}=LABPARR_{r,p=2,t-1}+FemParLabMul_{r}*0.3</math><br />
<br />
Next, we compute and apply the aging impact on the participation rate. As the relative share of people over the retirement age increases, the participation rate declines. The model keeps track of the changes in the demographic ratio (PopAgingRatio) of the population who are in their prime working age of 15 to 64 (POPWORKING) to those at a common retirement age of sixty-five or older (POPGT65). This ratio declines as countries age. The percentage drop in the ratio comparative to the base year is scaled appropriately to compute the aging impact (aging_impact). This impact is added to the male and female labor participation rates, with the impact on the female participation rate being slightly lower than that on male rates.<br />
<br />
:<math>POPAgingRatio_{r,t}=POPWORKING_{r,t}/POPGT65_{r,t}</math><br />
<br />
:<math>aging_impact_{r,t}=100*((POPAgingRatio_{r,t}/POPAgingRatio_{r,t=1})-1)*0.2</math><br />
<br />
:<math>LABPARR_{r,p=1,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t} </math><br />
<br />
:<math>LABPARR_{r,p=2,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t}*0.95 </math><br />
<br />
Participation rates respond slowly to changes in wage and unemployment rate. The impact is implemented through a wage impact factor computed from annual changes in the wage index (labwageimpact). The base participation rates can be changed by model user through two model parameters: a direct multiplier on the participation rate (labparm), or one that changes participation by moving the retirement age (labretagem)<br />
<br />
<br />
<br />
:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact*0.05)*labparm_{r,p,t}*labretagem_{r,p,t}</math><br />
<br />
Total participation rate (LABPARRr,p=3,t) is computed by an weighted average of male and female participation rates.<br />
<br />
:<math>LABPARR_{r,p=3,t}= (∑_{p=1 to 2}∑_{c=4 to 21}(agedst{r,c,p,t}*LABPARR_{r,p,t}))/(∑_{p=1 to 2}∑_{c=4 to 21}agedst{r,c,p,t})</math><br />
<br />
== Total Labor ==<br />
<br />
Finally, the total number of labor available for work (LAB) is computed by multiplying the total participation rate with the population of fifteen-year-olds or older.<br />
<br />
:<math>LAB_{r,t}= LABPARR_{r,p=3,t}*∑_{p=1 to 2,c=4 to 21}agedst_{r,c,p,t}</math><br />
<br />
== Labor by skill level ==<br />
<br />
The labor model forecasts labor supply (LABSUP) by two skill categories. The variable (''LABSUP'') is initialized in the pre-processor by reading the employment by skill/occupation (''LABEMPS'') data from GTAP[[#_ftn1|[1]]] &nbsp;and adding the unemployment numbers. We assume same unemployment rate (''LABUMEMPR'') for skilled and unskilled labor.<br />
<br />
:<math>LABSUP_{r,t=1,sk}=∑_{s=1 to 6}(LABEMPS_{r,s,t=1}/(1-(LABUNEMPR_{r,t=1}/100))</math><br />
<br />
The model forecasts labor by skill through a model of the skilled share of the labor. Education, training, exposure, and experience of the employees all improve with the level of development. The model captures this with an analytic function of the skilled share (perskilled) driven by GDP per capita at PPP (GDPPCP) -<br />
<br />
:<math>perskilled_{r}=f(GDPPCP_{r})</math><br />
<br />
Among the causal drivers of skill, education is considered to be the most proximate. Education is strongly correlated with the level of development, the deeper driver of skill in the model. However, the recent increase in education and/or a policy driven educational expansion might add to the impact of education on skill. Additional impacts from education on skill, when there is any, is computed through an expected function formulation. For example, in a society where an average adult has more (or less) education than the adults in other societies at that level of development, the skill share is given a slight upward push (or downward pull). The expectation function is a logarithmic function of educational attainment of working age population (EDYRSAG15) driven by GDP per capita at PPP. Attainment above (or below) the expected level (YearsEdExp) is computed by the function output (YearsEd) adjusted for country situation (yearseddiff). The percentage adjustment to the skilled share (LabSupSkiAdj) is computed using additional (limited) education, i.e., the difference between actual (EDYRSAG15) and expected values of educational attainment, expressed as a percentage of the expected value. The adjustment is scaled appropriately and peters off over time.<br />
<br />
:<math>YearsEd_{r,t}= f(GDPPCP_{r,t})</math><br />
<br />
:<math>yearsdeddiff_{r}= EDYRSAG15_{r,p=3,t=2}-YearsEd_{r,t=2}</math><br />
<br />
:<math>YearsEdExp_{r,t}=YearsEd_{r,t}+yearsdeddiff_{r}</math><br />
<br />
:<math>LabSupSkiAdj_{r,t}=0.3*(EDYRSAG15_{r,p=3,t=2}*YearsEdExp_{r,t})/YearsEd_{r,t}</math><br />
<br />
:<math>LabSupSkiAdj_{r,t}=ConvergeOverTime(0,LabSupSkiAdj_{r,t},70)</math><br />
<br />
:<math>perskilled_{r,t}= perskilled_{r,t}*(1+LabSupSkiAdj_{r,t})</math><br />
<br />
The skilled share (perskilled) is multiplied with the total labor supply (LAB) to obtain the number of labors who are skilled (LABSUPskilled)<br />
<br />
:<math>LABSUP_{r,skilled,t}=LAB_{r,p,t}*perskilledI_{r,t} </math><br />
<br />
As a last step, the model adjusts for the country specific variations in the skilled labor count not captured by the deeper and the proximate models. This is done by saving a ratio (LABSUPSkilledRI) of the actual historical data and the model computed value in the initial year. In the subsequent years this ratio is used to adjust the skilled labor forecast gradually.<br />
<br />
:<math>LABSUPCompSkilled_{r}=LAB_{r}*perskilled_{r,t=1}/100 </math><br />
<br />
:<math>LABSUPSkilledRI_{r}=LABSUP_{r,skilled,t=1}/LABSUPCompSkilled_{r}</math><br />
<br />
:<math>LABSUP_{r,skilled,t}= LABSUP_{r,skilled,t}*ConvergeOverTime(LABSUPSkilledRI_{r},1,85)</math><br />
<br />
Number of unskilled labor is obtained by subtracting the skilled labor from the total pool.<br />
<br />
<br />
<br />
:<math>LABSUP_{r,unskilled,t}= LAB_{r,p,t}- LABSUP_{r,skilled,t}</math><br />
<br />
== Labor Demand: Equations ==<br />
<br />
IFs economic model forecasts production in six economic sectors. IFs labor model computes the longer-term and shorter-term determinants of demand for skilled and unskilled labor (LABDEMS) for the production processes. The long-term drivers of labor requirement are technological progress or the lack of it. In the shorter-term wage affects the labor demand most. Wage in turn is affected by labor supply or skill shortage.<br />
<br />
The IFs model divides economic activities into six economic sectors – agriculture, energy, materials, manufacture, services and information, and communication technologies. Workers in the IFs labor model are disaggregated into two skill types. While the skill composition varies by the technology used in the sector and starts tilting towards the more skilled with the progress in technology, absolute number of labors needed to produce the same output goes down with technological development for both skilled and unskilled labor. This is illustrated in the next figure which plots the changes in labor requirement against GDP per capita at PPP, a proxy for level of development. Agriculture is a much less skill-intensive process than the manufacture, however, with technological progress skill requirement improves rapidly in both sectors. The IFs labor model computes these labor requirement functions in the model pre-processor. As we have already described in the pre-processor section, the computation of these functions use GTAP data on employment by occupation and economic activity. Appendices 3 and 4 lists sector and occupation mapping between GTAP and IFs.<br />
<br />
These functions are used to compute the labor coefficients (LABCOEFFS), i.e., number of skilled and unskilled labor needed to produce unit amount of output with the technology available, for which we use GDP per capita at PPP as a proxy.<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= f(GDPPCP_{r})</math><br />
<br />
manufacture, services and ICTech) and the subscrip sk stands for skill categories with 1 denoting unskilled and 2 skilled. The labor coefficients obtained from the analytical functions require some adjustments to incorporate country deviations from the functions for various factors not captured in the regression relationship. The first of these adjustments is a gradual removal of impacts of short-run fluctuations in output and labor from the computation of labor coefficient. This adjustment is applied on the coefficients computed from the function. The equation below shows a simplified form of these computations.<br />
<br />
:<math>LabCoeffAdjFac_{r,k,s,t}=f(igdpr_{r,t=2},(LAB_{r,t=2}/LAB_{r,t=1}),(LABCOEFFS_{r,t}/LABCOEFFS_{r,t-1}))</math><br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}=LABCOEFFS_{r,sk,s,t}(1-LabCoeffAdjFac_{r,k,s,t})</math><br />
<br />
Model users can use a global parameter (labcoeffsm) to change the labor coefficients by skill level for any or all of the six sectors –<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= LABCOEFFS_{r,sk,s,t}*'''labcoeffsm_{s,sk}'''</math><br />
<br />
To forecast the total labor demand, the labor coefficients (LABCOEFFS) are multiplied to the total projected output for each of the economic sectors. The forecast is adjusted for any discrepancy between data and model. The adjustment factor (LABDemsAdjFac) is computed as the initial ratio between the actual and computed employment. Actual employment is obtained from historical data (LABEMPS) processed using the GTAP database. The computed employment is obtained by multiplying the labor coefficients (LABCOEFFS) with the final output of the sector (VADD).<br />
<br />
:<math>LabDemsAdjFac_{r,s,sk}= LABEMPS_{r,s,sk,t=1}/(VADD_{r,s,t=1}*LABCOEFFS_{r,sk,s,t})</math><br />
<br />
The projected output is obtained by applying the growth rate (IGDPRCOR) on the sectoral value added from the previous year (VADD). The total labor demand is given by the product of the labor coefficients, projected output, demand adjustments and wage impacts (labwageimpactmul) and the number 1000 which adjusts the units for the equation. Wage impact comes from the level of unemployment and is computed in an equilibration process described in the next section. Model users can use a multiplicative parameter (labdemsm) to slide the demand upward or downward.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}=1000*VADD_{r,s,t-1}*(1+IGDPRCOR_{r})*LABCOEFFS_{r,sk,s,t}*LabDemsAdjFac_{r,s,sk}*labwageimpactmul_{r,s,sk}*'''labdemsm_{r,s}'''</math><br />
<br />
== Unemployment and Wage: Labor Market Equilibration ==<br />
<br />
The IFs labor model balances the labor market through an equilibrium seeking algorithm rather than computing an exact equilibrium at each time step. We use an algorithm borrowed from the control systems engineering. This PID controller algorithm, described also in the IFs economic model documentation, works by computing corrective signals for equilibrating variables using the deviations of a buffer variable, for example unemployment rate (LABUNEMPR), from a target value. The signal is computed from two quantities, the distance of the buffer from the target and the current rate of change of the buffer. The computation is tuned with PID elasticities to avoid oscillations. The computed signal is applied on the variable/s which need to be balanced, for example, demand and supply in the event of a market equilibration, thus getting closer to a balance at each step of simulation. The target value for the buffer variable and the tuning parameters of the control algorithm are obtained through rules-of-thumb and model calibration. The IFs labor model uses unemployment rate (LABUNEMPR) as the buffer variable for the market equilibration of labor demand and labor supply. The multiplier (i.e., corrective signal) obtained from the PID is applied on the wage index (LABWAGEIND). Changes in wage indices comparative to the base year, moderated through a second PID controller, is used to compute the final signal (labwageimpactmul) that drives labor demand and labor supply. Even though the model forecasts labor demand by sector and skill, and computes labor supply for both skill types, the equilibration algorithm works over the entire pool of labor. In other words, we assume that the skills are replaceable across sectors and the lack (or abundance) of jobs affects skilled and unskilled persons equally.<br />
<br />
At each annual timestep, the model computes the unemployment rate (LABUNEMPR) as the gap in between the total supply of labor (LAB) and the total demand. The gap (EmplGap) is expressed as a share of the total labor, the standard way to express unemployment rate.<br />
<br />
:<math>sumld=∑_{s,sk}LADEMS_{r,s,sk,t}</math><br />
<br />
:<math>EmplGap= LAB_{r,t}*sumld</math><br />
<br />
:<math>LABUMENPR_{r,t}= (EmplGap/LAB_{r,t})*100</math><br />
<br />
As the target value (LabUnEmpRateTar) for the PID controller that modulates unemployment rate we use either the historical unemployment rate or a ten percent unemployment rate when the historical rate is higher than ten. Model users can override the historical target through a model parameter (labunemprtrgtval).<br />
<br />
:<math>LABUMENPRi_{r,t}= LABUMENPR_{r,t}</math><br />
<br />
:<math>LabUnempRateTarget_{r}=labunemptargetval_{r}</math><br />
:<math>If LabUnempRateTarget_{r}=0,<br />
LabUnempRateTarget_{r}= AMIN(LABUMENPRi_{r,t},10) </math><br />
<br />
Unemployment rate target, when it is different from the base year value, is reached gradually with a convergence period of forty years . The target rate is converted to count (LabUnEmplTar) to make it equivalent to the employment gap (EmplGap) computed earlier.<br />
<br />
:<math>LabUnEmplTar_{r}= LAB_{r,t}*ConvergeOverTime(LABUMENPRi_{r,t},0,100)</math><br />
<br />
The first order difference (Diffl1) between the target unemployment and the demand-supply gap is used to compute a second order difference (Diffl2) accounting for changes in the rate of movement. The two differences and the PID multipliers (elwageunemp1, elwageunemp2) are provided to the PID function (ADJSTR). Working age population (POP15TO65r,t) works as the scaling base of the PID controller. The controller algorithm gives a multiplier (mullw) that is used in the subsequent year to adjust wage.<br />
<br />
:<math>Diffl1_{t}=LabUnEmplTar_{r}-EmplGap</math><br />
<br />
:<math>Diffl2_{t}=Diffl1_{t}-Diffl1_{t-1}</math><br />
<br />
:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},elwageunemp1_{r},elwageunemp2_{r})</math><br />
<br />
Wage adjustments affect demand and supply with an increase in wage drawing demand downward and supply upward. The opposite affects occur with a downward movement of wage. The wage variable affected by the PID multiplier (LABWAGEIND) is an index initialized at one. We use an indexed rather than a dollar wage in the equilibration process to avoid affecting the process from other economic phenomena that affects wage, for example, a rise in real wage as GDP or the labor share of income grows.<br />
<br />
:<math>LABWAGEIND_{r,t=1}=1</math><br />
<br />
In the subsequent years of the model run, the wage index is first adjusted with the equilibration signal obtained from the unemployment rate PID controller in the previous period<br />
<br />
:<math>LABWAGEIND_{r,t=1}= LABWAGEIND_{r,t=1}* mullw_{r,t-1} </math><br />
<br />
A wage impact (labwageimpact) is then computed using the changes in the wage index relative to the base value. The impact is smoothed with a moving average algorithm.<br />
<br />
:<math>labwageimpact_{r}= labwageimpact_{r,t-1}*0.9+ (1-LABWAGEIND_{r,t})*0.1</math><br />
<br />
The smoothed impact is used as the equilibration signal for labor supply. As we have already described in the section on labor supply, a small fraction of the impact (labwageimpact) is applied to the labor participation rate. The impact is scaled down to account for the slow pace of changes on the supply side.<br />
<br />
:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact_{r,t}*0.05)</math><br />
<br />
For the impacts of wage on labor demand we use a second PID multiplier as opposed to using the changes in wage index that we have done on the supply side. The second PID uses the wage index itself as the process variable and uses the base year value of 1 as the target. The reason we had to use this second PID is to control the pace at which wage disequilibrium can affect demand, especially in the event of an abrupt shock. The smoothing and scaling down that works on the supply side is not enough to control oscillations on the demand side.<br />
<br />
:<math>Diffl1_{t}=LABWAGEIND_{r,t=1}-1</math><br />
<br />
:<math>Diffl2_{t}=LABWAGEIND_{r,t}-LABWAGEIND_{r,t-1} </math><br />
<br />
:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},ellabwage1_{r},ellabwage1_{r})</math><br />
<br />
A second impact factor (labwageimpactmul) is computed using the correction signal from this second multiplier:<br />
<br />
:<math>labwageimpactmul_{r,t}= labwageimpactmul_{r,t-1}*mullw_{r,t}</math><br />
<br />
This impact factor is applied on the labor demand as described in the section on labor demand.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}= LABDEMS_{r,s,sk,t}* labwageimpactmul_{r,t}</math><br />
<br />
== Informal Labor ==<br />
<br />
IFs forecast labor and GDP share of the informal sector. Informal labor forecast is not explicitly endogenized in the labor market though. They are rather driven by development, skill and regulatory factors[[#_ftn1|[1]]]. However, the productivity and revenue impacts of changes in informality affects output and thus labor demand implicitly as a very distal driver.<br />
<br />
= References =<br />
<br />
<references /><br />
</div></div></div></div></div></div></div></div></div></div></div>Wikiadmin//pardee.du.edu/wiki/LaborLabor2018-09-07T22:44:20Z<p>Wikiadmin: </p>
<hr />
<div>Workers in an economy supply the expertise and the efforts needed to produce goods and services. In return the labor receives wages that they use to meet their current and future consumption needs. On one hand, shortage of labor with required skills prevents economies from realizing their growth potential. On the other hand, individuals falling short of the right qualifications might remain unemployed or underemployed failing to secure income needed for a decent living. The ongoing adjustments to find the best match between skills, jobs and wages can only be studied through a dynamic model of the labor market.&nbsp; &nbsp;<br />
<br />
Such a model should go beyond providing a reasonable answer to the obvious question of why employment and wages go up and down. An aggregate labor market must deal with issues that have strong interconnections with various other dynamic changes in the greater society. What kind of dividend of deficit can a society expect from its labor force given the phase of demographic transition in which it is situated? How severely would aging affect the pool of working age adults? Might increasing female participation rates offset some of the losses from aging? What is the level of skills and educational attainment in a society? These supply phenomena move relatively slowly unless there are huge disruptions, like a war or famine, or an aggressive policy push. The demand side, in contrast, needs to be more responsive in adjusting wages and employment given the investment and technology in the various sectors of the broader economy. In general, though, the labor market demonstrates some sluggishness compared to the goods and services markets as it involves moving human beings with various limitations. Consumption of goods and services depend on the income earned by the labor. Uneven distribution of employment and wages among labors of various types or between labor and capital for a long period of time can give rise to persistent inequality in a society. &nbsp;&nbsp;&nbsp;&nbsp;<br />
<br />
== Conceptual Framework ==<br />
<br />
Labor markets are markets for workers and jobs. In a labor market, employers meet their demand for labor with the supply of people willing to work at the wage the employers can offer. The employers raise the wage when there is a shortage of workers. Workers agree to take a lower wage when there are more of them than the firms need. In the real-world labor markets do not always clear at perfect equilibrium. Frinctional unemployment results for various reasons, for example, the search time between jobs. Structural unemployment can result from technology induced disruptions. Some unemployment could thus persist in the labor market even when there aren’t any short-term fluctuations. There is also the phenomenon of informal employment that consists of less sophisticated workers and entrepreneurs engaged in unregulated economic activities. &nbsp;In a dynamic model that covers the entire economy, the real wage earned by the labor drives the income and social mobility.&nbsp;&nbsp;&nbsp;<br />
<br />
To understand the long-term dynamics of the labor market, we need also examine the deeper determinants of labor demand and supply, the determinants that can shift the curves. Labor demand changes over time with the changes in demand for goods and services and the labor input needed to produce those. Labor productivity itself improves with technological progress. Long term transitions in the supply of labor are mostly demographic. &nbsp;<br />
<br />
Labor supply is determined by the working age population and the share of that population who are available for participation in the workforce. The labor supply is relatively stable as the demographic changes are slow in pace. As the share of elderly in the population increases, a recent trend in many societies, the rate of participation declines. Some of the aging impacts will be offset by the greater female participation rates, a second trend that surfaces as economies develop and women attain more education. Educational attainment also drives the general skill level of workers, male and female. Specific skills are obtained through training and experience that augment the knowledge obtained through general and specialized education. &nbsp;&nbsp;&nbsp;&nbsp;<br />
<br />
It is the demand side that causes most of the short-term imbalances in the labor market. &nbsp;In the long term, as said earlier, the important driver of demand for labor and their skills is technological progress. Labor requirement drops with advances in technology, more so for less skilled labor. Labor composition changes accordingly both within and across sectors. Rapid advances in technology can also cause disruption in the system when there is not much opening in the other sectors. Labor displacement is offset to some extent by the growth in the economy and the resulting increase in total demand. &nbsp;&nbsp;&nbsp;<br />
<br />
As we have already mentioned, employees maximize income and the firms minimize labor costs. When there are more laborers than the firms can hire, there is unemployment. Shifts in the rates of unemployment impacts wage, the price of labor. For example, wages drop in the event of rising unemployment as there are more people to hire from. Wage adjustments feed back to the demand for labor seeking to bring the market back to equilibrium.<br />
<br />
The challenges around the conceptual distinction between unemployment and employment is further complicated by the phenomenon of informal employment. In many developing countries there is a large urban non-agricultural informal sector where low-skilled workers work for wages typically lower than a formal employment.<br />
<br />
[[File:LMFlowchart1.png|frame|center|Description of the labor model]]<br />
<br />
== Dominant Relations ==<br />
<br />
The labor model in the International Futures system (IFs) balances the total supply of labor with the total labor demanded by all economic sectors. Total labor (LAB) is computed from the working age population and the labor participation rate. Population forecasts are obtained from the IFs demographic model. Participation rates (LABPARR) are computed by sex with a catchup algorithm for the female participation towards that for the male. Labor is also disaggregated by skill level, as determined by educational attainment, in a separate labor supply variable (LABSUP) which is used to distribute labor earnings by skill level. [** LABSUP do not affect the demand/supply balance now]<br />
<br />
Labor demands (LABDEMS) are driven by sectoral technology functions used to compute the labor requirement by skill level for each unit of potential valued added in the sector. These labor coefficients (LABCOEFFS) are multiplied with the projected value added for the sector to compute the needed manpower. The balancing mechanisms determines the labor employed in each of the sectors (LABS).&nbsp;&nbsp;<br />
<br />
The balancing, in the current version of the model, can be done in one of the two ways. In the first method, total needs combined from all economic sectors is normalized to the available pool of labor computed by subtracting the unemployed from those who are at or looking for work. The rate of unemployment is kept at its natural rate for which we use the base year rate of unemployment. (** This might need to be changed for countries where the market is undergoing some abrupt transition.)<br />
<br />
In the second balancing method, added in a recent revision of the model, total demand is equilibrated to supply through a CGE like market equilibrium model. An indexed wage (LABWAGEIND) and the rate of unemployment (LABUNEMPR) work as the equilibrating variables. As unemployment deviates from the target, PID algorithms send a signal for the wage to adjust. Wage adjustments cause adjustments in the “base” labor demands by sector computed from the labor-coefficient functions as described earlier. Wage signals also affects the labor participation rate. The magnitude of impact on the supply side is much lower than that on the demand side.<br />
<br />
Wage and unemployment rate are aggregated for the total labor market. The wage index starts with a base year value of 1 and the unemployment rates start with the historical data for the base year. Initial year unemployment rate works as the target for long term unemployment.<br />
<br />
== Key Dynamics ==<br />
<br />
The following key dynamics are directly related to the dominant relations:<br />
<br />
*Labor supply is determined from population of appropriate age in the population model (see its dominant relations and dynamics) and endogenous labor force participation rates, influenced exogenously by the growth of female participation.<br />
*Labor demand is driven by sectoral demand functions driven by technological progress<br />
<br />
== Structure and Agent System ==<br />
<br />
{| border="1" cellspacing="0" cellpadding="0" width="0" style="width:502px;"<br />
|-<br />
| style="width:242px;height:49px;" | <br />
'''System/Subsystem'''<br />
<br />
| style="height:49px;" | <br />
Labor market<br />
<br />
|-<br />
| <br />
'''Organizing Structure'''<br />
<br />
| <br />
Labor supply by skill level and labor demand by sector for each skill category represented within an equilibrium-seeking model with wage and unemployment rate as the equilibrating variables<br />
<br />
|-<br />
| <br />
'''Stocks'''<br />
<br />
| <br />
Population, labor, education, &nbsp;accumulated technology<br />
<br />
|-<br />
| <br />
'''Flows'''<br />
<br />
| <br />
Participation rate; Coefficients of labor demand; Employment (unemployment); Wage<br />
<br />
|-<br />
| <br />
'''Key Aggregate&nbsp;''''''Relationships&nbsp;'''<br />
<br />
'''(illustrative, not comprehensive)'''<br />
<br />
| <br />
Labor supply is driven by demographic changes; Participation of female change over time; Labor requirement changes with technological development; Unemployment rate drives wage; Wage movements affect labor demand and participation rate<br />
<br />
|-<br />
| <br />
'''Key Agent-Class Behavior&nbsp;''''''Relationships'''<br />
<br />
'''(illustrative, not comprehensive)'''<br />
<br />
| <br />
Households and work/leisure, and female participation patterns;<br />
<br />
&nbsp;<br />
<br />
Firms and hiring;<br />
<br />
&nbsp;<br />
<br />
<br />
<br />
<br />
<br />
|}<br />
<br />
= Labor Model Data =<br />
<br />
The labor supply and unemployment data that we use in our model is from International Labor Organization (ILO). For data on the demand side, we used data from the Global Trade Analysis Project. Wage variable used in the equilibration algorithm&nbsp;is an index anchored to the base year of the model.<ref>GTAP database helped us compute wage rates by sector and skill.</ref> IFs preprocessor prepared these data for model use using various estimation, conversion and reconciliation processes.&nbsp; &nbsp; &nbsp;<br />
<br />
== Definitional Issues ==<br />
<br />
There are ambiguities in the way some of the labor market variables are defined. Labor participation rates and the rate of unemployment are two that need special attention.<br />
<br />
The size of the labor supply available for economic activities is expressed with the labor force participation rate. ILO defines this as a “measure of the proportion of country’s working-age population that engages actively in the labor market, either by working or looking for work.”<ref>http://www.ilo.org/ilostat-files/Documents/description_LFPR_EN.pdf</ref>&nbsp;National labor force surveys and census data are used to estimate this rate. The definition of labor force here includes both employed and unemployed and the rate is expressed as a percentage of working-age population. Working-age population is defined here as the population above legal working-age. For international comparability, ILO adopts a convenient minimum threshold of fifteen years as working age and avoids putting any upper age limit. In practice, both the minimum and the upper-age limits can vary by country. For example, the working-age in the USA is sixteen years. In the Netherlands the upper age limit is seventy-five years, whereas South African data uses an upper age limit of 64.<ref>https://www.bls.gov/fls/flscomparelf/technical_notes.pdf</ref><br />
<br />
Ambiguities are more abundant in the definition of unemployment. ILO came up with a guideline on this as well. Per the ILO guideline, the unemployed are those among the working-age population who are not employed, are available for work and are actively looking for jobs<ref>The definitions around employed and unemployed were agreed upon by nations through the ‘Resolution concerning statistics of work, employment and labor underutilization’ adopted by the 19th International Conference of Labor Statisticians (ICLS) in 2013. (Bourmpoula et al, 2017: 6).</ref>; the unemployment rate is expressed as a percentage of those who are in the labor force. The availability and job-seeker status could be defined in different ways giving rise to incompatibility in data. &nbsp;While there seems to be little room for disagreement on whether someone is at work or not, whether that work should be considered as employment is contested at many times.<br />
<br />
The debates around the nature and type of employment can range from gainfulness to workplace setting. For example, a large number of workers in the low-income low-regulation developing countries work outside the purview of formal enterprises. According to an ILO estimate, more than half of the global labor force and more than 90% of Micro and Small Enterprises (MSEs) worldwide are in the so called informal economy.<ref>http://www.ilo.org/global/topics/employment-promotion/informal-economy/lang--en/index.htm</ref> This might explain the apparently counterintuitive pattern of low unemployment rate in some low-income countries (e.g., 2.2% for Guatemala) and relatively higher numbers for some of the developed nations. The low numbers in the poorer countries hide the prevalence of extremely low wage jobs in the informal sectors in these countries, the only options for the vulnerable people in the absence of any kind of social safety net. &nbsp;Contrastingly, in the developed countries the so called ‘gig-economy’ is attracting more and more workers who choose to work on their own rather than in a formal enterprise. ILO conceptualization makes the informal work part of total employment. The stacked Venn diagram below presents the relationship among the labor force metric including informal employment. IFs also models informal economy both in terms of GDP share and employment share of informal in the total economy and employment.<br />
<br />
[[File:LaborSubsets.png|frame|right|Relationship among various labor measurement]]<br />
<br />
Incompatibility can arise in the treatment of various population groups for the computation of the denominator for participation and unemployment rates.<ref>For example, the USA excludes people in the defense services and those in the prisons or mental asylums in their computation of the civilian non-institutional working-age population. There are also variations in the treatments of students, those recently laid-off, and family workers. Please see https://stats.bls.gov/opub/mlr/2000/06/art1full.pdf for a discussion </ref> ILO makes their best efforts to make adjustments in the data for the sake of international comparison. For example, ILO asks countries that deviate from ILO guidelines to collect data needed to convert national figures to ILO figures. It is likely that some differences might have slipped past the adjustment process. We use ILO data and continue to update our database&nbsp;&nbsp;<br />
<div><div id="ftn4"><div id="ftn1"><br />
The GTAP data that we use for the demand side of the labor model is taken as labor headcounts and is thus immune from ambiguities around rate computation. As far as we could gather<ref>Please see the webpage for documentation on GTAP labor data statistic: https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=3248</ref>, the data includes both the formal and informal employment. We also need mention here that the GTAP database reconciles the labor data to calibrate the general equilibrium modeling that they do for the trade analyses. The data could thus be somewhat different from data collected through direct surveys. As a CGE model IFs is benefited by using calibrated data.<br />
<br />
== Sources of Labor Data ==<br />
<br />
IFs model uses ILO data for labor participation rates and for the unemployment rate. The data in IFs are collected from World Bank’s World Development Indicators (WDI) database. According to their documentation, WDI obtained the data from the ILO.<br />
<br />
Unemployment rate data in IFs is also collected from WDI. Like the participation rates WDI also obtains their unemployment data from ILO.<ref>The name of the IFs table is SeriesLaborUnemploy%</ref><br />
<br />
For employment and labor demand data IFs uses Purdue University’s Global Trade Analysis Project (GTAP) database. GTAP collects and compiles factor payments, imports, and intersectoral flow data to calibrate CGE models of national economies for trade and other analyses. In their ninth release in 2016, GTAP published data for 140 countries and regions for the year 2011. The earlier GTAP releases, which the IFs model used for its previous versions, compiled data for the years 2004 and 2007. GTAP data release aggregates economic activities into 57 commodities and activities following International Standard Industrial Classification (ISIC). The IFs model maps the 57 GTAP sectors into six economic sectors of IFs – agriculture, energy, material and mining, manufacture, services and ICT. Appendix 2 presents two tables listing the sectors mapping between IFs and GTAP, and GTAP and ISIC. GTAP further disaggregates labor in each of the commodities/activities into five occupation and skill categories following the nine category International Standard Classification of Occupations (ISCO-88). The IFs model collapses five GTAP occupation categories into the simple IFs dichotomy of skilled and unskilled. The mapping of occupations and skills are presented in the third appendix of this document. &nbsp;<br />
<br />
The data in the main GTAP database, prepared for CGE modeling, are all in dollar unit and thus do not include labor headcounts. We have used a ‘satellite’ GTAP database<ref>See Weingarden and Tsigas, 2010 for the details on the preparation of this database.</ref>&nbsp;for labor headcounts by skill and sector. The labor counts were also used to plot labor requirement functions for each of the IFs economic sectors and skill categories. The wage share of skilled and unskilled labor in each sector was computed using the labor headcounts and labor payments.<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] The name of the IFs table is SeriesLaborUnemploy%<br />
</div><div id="ftn2">[[#_ftnref2|[2]]] See Weingarden and Tsigas, 2010 for the details on the preparation of this database.</div></div><br />
<br />
== Scope of IFs Labor Model ==<br />
<br />
The IFs labor model simulates labor market at the national level. Each national labor market forecasts labor demand and employment by six sectors - agriculture, energy, mining, manufacture, services and ICT- and two skill levels - skilled and unskilled. The supply side do not have sectoral representation. IFs forecasts total labor force and labor supply by the two skill levels. Labor participation rate is computed in IFs by gender. Wage and unemployment rate is forecast for the overall labor market only.&nbsp;&nbsp;<br />
<br />
== Labor Model Pre-processor ==<br />
<br />
IFs system has a data preprocessor that prepares the initial conditions for the model using historical databases and various assumptions and estimated relationships to fill in the missing data and make data adjustments as needed[[#_ftn1|[1]]]. Pre-processing of labor data takes place in two IFs pre-processing modules. Labor participation rate data, which is closely related to demography, is processed in the population pre-processor. Unemployment rate and labor demand data are processed in the economic pre-processor. &nbsp;<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] For more details, please see ‘The Data Pre-Processor of International Futures (IFs)” by Barry B. Hughes (with Mohammod Irfan) at [http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf]<br />
<br />
=== Pre-processing Labor participation rate and unemployment ===<br />
<br />
For initializing labor participation rates by sex (LABPARR) the model uses the historical values from the base year or the most recent year with data[[#_ftn1|[1]]]. For countries with no data we use regression relationships of the participation rates, for men and for women, with income per capita. The relationships, shown in the next figure, are not great. However, the functions affect only five countries for which we do not have any data at all: Grenada, Kosovo, Micronesia, Seychelles and South Sudan[[#_ftn2|[2]]].<br />
<br />
&nbsp;<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] The data tables that the IFs model pre-processor use for initializing labor participation rates are: SeriesLaborParRate15PlusFemale%, SeriesLaborParRate15PlusMale%.<br />
</div><div id="ftn2"><br />
[[#_ftnref2|[2]]] We should try to collect participation rate for these countries from country sources.<br />
<br />
IFs data series SeriesLaborUnemploy% is used for the initialization of unemployment rates. That series has annual unemployment rates for one or more years between 1980 and 2016, for 181 of the 186 IFs countries. For five countries (Grenada, Kosovo, Micronesia, Taiwan and South Sudan[[#_ftn1|[1]]]) there is no data at all. To fill in the missing data we use a regression function of unemployment rate against GDP per capita. Like the participation rate functions, this function does also not have much of an explanatory power.<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] These are pretty much the same countries for which we do not have any participation rate data. This indicates ILO might have some administrative limitation in reporting data for these countries (notice Kosovo, Seychelles etc in the list)<br />
<br />
=== Pre-processing labor demand and unemployment from GTAP ===<br />
<br />
The IFs economic pre-processor reads labor headcount and labor payment data from the GTAP database. In addition to performing sector and occupation/skill mapping between GTAP and IFs, pre-processor also use the labor headcount data to compute labor coefficient functions, the principal driver of labor demand in the IFs model.<br />
<br />
Labor coefficients are defined as the amount of labor needed to produce one unit of value added in a certain sector of the economy. The coefficients depend on the level of technology. The model uses GDP per capita as an indicator of the level of technological development. IFs pre-processor estimates labor coefficient functions for labor of different skill levels for the different sectors of the economy.<br />
<br />
The functions are derived from GTAP data we described earlier. The model pre-processor reads data on factor payments and aggregates data from 57 GTAP sectors to six IFs sectors. Shares of payment going to skilled and less-skilled workers in each of the sectors are then computed. Countries are grouped according to their level of technological development as represented by per capita income. For each group labor coefficients are obtained by taking an average of the country coefficients. &nbsp;We also convert labor payments data to labor headcount data using per capita income as a proxy for average wage. Labor coefficients and income are then plotted into a power function relationship. The figure below plots some of those labor functions.&nbsp; The functions fit quite well with a power law formulation[[#_ftn1|[1]]].<br />
<div><br/><br />
----<br />
<div id="ftn1">[[#_ftnref1|[1]]] This is interesting given the prevalence of power law in all sorts of scale-up activities (West 2017).</div></div></div></div></div></div></div></div></div></div><br />
<br />
= Labor Model Flowcharts =<br />
<br />
The diagram below shows an outline of the IFs labor model. On the supply side, the total labor pool (LAB) is computed from the labor force participation rates, by sex, (LABPARR) and the population (POP) in their working age, i.e., population over 15 (POP15TO65 + POPGT65). Participation rates are driven by the demographic changes with an additional negative impact from aging and a catch-up in female participation rate. Skill level of the labor supply (LABSUP) is driven by the level of development (GDPPCP) and the demand for labor is driven by labor-coefficients (LABCOEFFS) computed from coefficient function representing shifts in demand with technological progress as proxied by the level of development (GDPPCP). Coefficients computed by sector and skill gives the labor requirement by skill type for each unit of value added (VADD) in the sector. Multiplying these coefficients with projected value added in each sector gives an estimate of the labor demand. &nbsp;<br />
<br />
Any surplus or shortage between total labor demand and supply is used to compute the rate of unemployment. Deviations in the unemployment rate (LABUNEMPR) signal wage changes through an equilibrium seeking algorithm. Both demand and supply respond to the wage variable (LABWAGEIND) indexed to the base year. The supply responses are much slower than the demand responses.<br />
<br />
[[File:FLOCHART2.png|frame|center|Labor Model Flowchart]]<br />
<br />
= Labor Model Equations =<br />
<br />
== Overview ==<br />
<br />
The labor model is a part of the IFs economic model that uses labor model output as an input to a Cobb-Douglas production function in a multi-sector general equilibrium model. IFs is a very long-run dynamic model. Instead of computing fixed short-run equilibria that clear the relevant markets IFs uses an equilibrium seeking algorithm to balance the various systems over the longer run. The algorithm is known as the PID (proportion-integral-derivative) controller algorithm and is used widely in industrial control systems. It makes equilibrium seeking variables in IFs move towards a set target. The algorithm works by computing a multiplier based on the movement of the variable towards the target, as obtained by an integral (I) of the path traversed, and the rate of movement towards the target, the derivative term. The multiplier is applied on the process variable (the P term), or a response variable, in the subsequent time period. In the labor model, unemployment rate (LABUNEMPR) is used as the process variable and the PID multiplier is used on the wage rate (LABWAGEIND). Job availability (LABDEMS) and participation rate (LABPARR) get affected by changes in wage. &nbsp;&nbsp;<br />
<br />
Throughout this section we use subscripts and notations common to other modules of IFs. For example, we use t for time period. Subscripts p and r represent sex and country/region, respectively, c is the cohort number, with cohort 1 representing the newborns, cohort1 the the one-year to four-year-olds, cohort two five-year to nine-year-olds etc. Values for p are 1 for male, 2 for female and 3 for both sexes combined. For economic sectors we use s and for skill levels sk.<br />
<br />
== Labor Supply: Equations ==<br />
<br />
The total pool of labor is computed by multiplying the population of working age with the labor force participation rate (LABPARR). &nbsp;Population forecasts come from IFs demographic model which computes both five-year and single-year age-sex cohorts (''agedst'', ''fagedst''). &nbsp;<br />
<br />
The labor model forecasts participation rates by country/region&nbsp; and gender. Participation rates in the model move with the changes in the demographic composition. Female participation rates, which have historically been lower than the same for the male in all societies, but has moved up in modern and affluent societies, get a catch-up boost in the model. Participation rates can also change when there is labor shortage or surplus and the employers try to incentivize or discourage workers by changing wage. This last impact is much less slow than similar wage impacts on the demand side.<br />
<br />
== Labor Participation Rate ==<br />
<br />
Labor participation rates (''LABPARR'') for male and female are first initialized with historical data.<br />
<br />
:<math>LABPARR_{r,p}= LABPARR_{r,p,t=1} </math><br />
<br />
A ‘catch-up’ boost is added to the female participation rate. The boost added (FemParLabMul) starts at a third of a percentage point and withers away following a non-linear path as the female rates approaches the catch-up target (FemParTar), The maximum catch-up that can occur over the horizon of the model is thirty percent.<br />
<br />
:<math>FemParTar_{r}=Amin(LabParRI_{r,p=1},LabParRI_{r,p=2}+30)</math><br />
<br />
:<math>FemParLabMul_{r}=(FemParTar_{r}-LABPARR_{r,p=2,t-1})/(FemParTar_{r}-LABPARR_{r,p=2,t-1})</math><br />
<br />
:<math>LABPARR_{r,p=2,t}=LABPARR_{r,p=2,t-1}+FemParLabMul_{r}*0.3</math><br />
<br />
Next, we compute and apply the aging impact on the participation rate. As the relative share of people over the retirement age increases, the participation rate declines. The model keeps track of the changes in the demographic ratio (PopAgingRatio) of the population who are in their prime working age of 15 to 64 (POPWORKING) to those at a common retirement age of sixty-five or older (POPGT65). This ratio declines as countries age. The percentage drop in the ratio comparative to the base year is scaled appropriately to compute the aging impact (aging_impact). This impact is added to the male and female labor participation rates, with the impact on the female participation rate being slightly lower than that on male rates.<br />
<br />
:<math>POPAgingRatio_{r,t}=POPWORKING_{r,t}/POPGT65_{r,t}</math><br />
<br />
:<math>aging_impact_{r,t}=100*((POPAgingRatio_{r,t}/POPAgingRatio_{r,t=1})-1)*0.2</math><br />
<br />
:<math>LABPARR_{r,p=1,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t} </math><br />
<br />
:<math>LABPARR_{r,p=2,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t}*0.95 </math><br />
<br />
Participation rates respond slowly to changes in wage and unemployment rate. The impact is implemented through a wage impact factor computed from annual changes in the wage index (labwageimpact). The base participation rates can be changed by model user through two model parameters: a direct multiplier on the participation rate (labparm), or one that changes participation by moving the retirement age (labretagem)<br />
<br />
<br />
<br />
:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact*0.05)*labparm_{r,p,t}*labretagem_{r,p,t}</math><br />
<br />
Total participation rate (LABPARRr,p=3,t) is computed by an weighted average of male and female participation rates.<br />
<br />
:<math>LABPARR_{r,p=3,t}= (∑_{p=1 to 2}∑_{c=4 to 21}(agedst{r,c,p,t}*LABPARR_{r,p,t}))/(∑_{p=1 to 2}∑_{c=4 to 21}agedst{r,c,p,t})</math><br />
<br />
== Total Labor ==<br />
<br />
Finally, the total number of labor available for work (LAB) is computed by multiplying the total participation rate with the population of fifteen-year-olds or older.<br />
<br />
:<math>LAB_{r,t}= LABPARR_{r,p=3,t}*∑_{p=1 to 2,c=4 to 21}agedst_{r,c,p,t}</math><br />
<br />
== Labor by skill level ==<br />
<br />
The labor model forecasts labor supply (LABSUP) by two skill categories. The variable (''LABSUP'') is initialized in the pre-processor by reading the employment by skill/occupation (''LABEMPS'') data from GTAP[[#_ftn1|[1]]] &nbsp;and adding the unemployment numbers. We assume same unemployment rate (''LABUMEMPR'') for skilled and unskilled labor.<br />
<br />
:<math>LABSUP_{r,t=1,sk}=∑_{s=1 to 6}(LABEMPS_{r,s,t=1}/(1-(LABUNEMPR_{r,t=1}/100))</math><br />
<br />
The model forecasts labor by skill through a model of the skilled share of the labor. Education, training, exposure, and experience of the employees all improve with the level of development. The model captures this with an analytic function of the skilled share (perskilled) driven by GDP per capita at PPP (GDPPCP) -<br />
<br />
:<math>perskilled_{r}=f(GDPPCP_{r})</math><br />
<br />
Among the causal drivers of skill, education is considered to be the most proximate. Education is strongly correlated with the level of development, the deeper driver of skill in the model. However, the recent increase in education and/or a policy driven educational expansion might add to the impact of education on skill. Additional impacts from education on skill, when there is any, is computed through an expected function formulation. For example, in a society where an average adult has more (or less) education than the adults in other societies at that level of development, the skill share is given a slight upward push (or downward pull). The expectation function is a logarithmic function of educational attainment of working age population (EDYRSAG15) driven by GDP per capita at PPP. Attainment above (or below) the expected level (YearsEdExp) is computed by the function output (YearsEd) adjusted for country situation (yearseddiff). The percentage adjustment to the skilled share (LabSupSkiAdj) is computed using additional (limited) education, i.e., the difference between actual (EDYRSAG15) and expected values of educational attainment, expressed as a percentage of the expected value. The adjustment is scaled appropriately and peters off over time.<br />
<br />
:<math>YearsEd_{r,t}= f(GDPPCP_{r,t})</math><br />
<br />
:<math>yearsdeddiff_{r}= EDYRSAG15_{r,p=3,t=2}-YearsEd_{r,t=2}</math><br />
<br />
:<math>YearsEdExp_{r,t}=YearsEd_{r,t}+yearsdeddiff_{r}</math><br />
<br />
:<math>LabSupSkiAdj_{r,t}=0.3*(EDYRSAG15_{r,p=3,t=2}*YearsEdExp_{r,t})/YearsEd_{r,t}</math><br />
<br />
:<math>LabSupSkiAdj_{r,t}=ConvergeOverTime(0,LabSupSkiAdj_{r,t},70)</math><br />
<br />
:<math>perskilled_{r,t}= perskilled_{r,t}*(1+LabSupSkiAdj_{r,t})</math><br />
<br />
The skilled share (perskilled) is multiplied with the total labor supply (LAB) to obtain the number of labors who are skilled (LABSUPskilled)<br />
<br />
:<math>LABSUP_{r,skilled,t}=LAB_{r,p,t}*perskilledI_{r,t} </math><br />
<br />
As a last step, the model adjusts for the country specific variations in the skilled labor count not captured by the deeper and the proximate models. This is done by saving a ratio (LABSUPSkilledRI) of the actual historical data and the model computed value in the initial year. In the subsequent years this ratio is used to adjust the skilled labor forecast gradually.<br />
<br />
:<math>LABSUPCompSkilled_{r}=LAB_{r}*perskilled_{r,t=1}/100 </math><br />
<br />
:<math>LABSUPSkilledRI_{r}=LABSUP_{r,skilled,t=1}/LABSUPCompSkilled_{r}</math><br />
<br />
:<math>LABSUP_{r,skilled,t}= LABSUP_{r,skilled,t}*ConvergeOverTime(LABSUPSkilledRI_{r},1,85)</math><br />
<br />
Number of unskilled labor is obtained by subtracting the skilled labor from the total pool.<br />
<br />
<br />
<br />
:<math>LABSUP_{r,unskilled,t}= LAB_{r,p,t}- LABSUP_{r,skilled,t}</math><br />
<br />
== Labor Demand: Equations ==<br />
<br />
IFs economic model forecasts production in six economic sectors. IFs labor model computes the longer-term and shorter-term determinants of demand for skilled and unskilled labor (LABDEMS) for the production processes. The long-term drivers of labor requirement are technological progress or the lack of it. In the shorter-term wage affects the labor demand most. Wage in turn is affected by labor supply or skill shortage.<br />
<br />
The IFs model divides economic activities into six economic sectors – agriculture, energy, materials, manufacture, services and information, and communication technologies. Workers in the IFs labor model are disaggregated into two skill types. While the skill composition varies by the technology used in the sector and starts tilting towards the more skilled with the progress in technology, absolute number of labors needed to produce the same output goes down with technological development for both skilled and unskilled labor. This is illustrated in the next figure which plots the changes in labor requirement against GDP per capita at PPP, a proxy for level of development. Agriculture is a much less skill-intensive process than the manufacture, however, with technological progress skill requirement improves rapidly in both sectors. The IFs labor model computes these labor requirement functions in the model pre-processor. As we have already described in the pre-processor section, the computation of these functions use GTAP data on employment by occupation and economic activity. Appendices 3 and 4 lists sector and occupation mapping between GTAP and IFs.<br />
<br />
These functions are used to compute the labor coefficients (LABCOEFFS), i.e., number of skilled and unskilled labor needed to produce unit amount of output with the technology available, for which we use GDP per capita at PPP as a proxy.<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= f(GDPPCP_{r})</math><br />
<br />
manufacture, services and ICTech) and the subscrip sk stands for skill categories with 1 denoting unskilled and 2 skilled. The labor coefficients obtained from the analytical functions require some adjustments to incorporate country deviations from the functions for various factors not captured in the regression relationship. The first of these adjustments is a gradual removal of impacts of short-run fluctuations in output and labor from the computation of labor coefficient. This adjustment is applied on the coefficients computed from the function. The equation below shows a simplified form of these computations.<br />
<br />
:<math>LabCoeffAdjFac_{r,k,s,t}=f(igdpr_{r,t=2},(LAB_{r,t=2}/LAB_{r,t=1}),(LABCOEFFS_{r,t}/LABCOEFFS_{r,t-1}))</math><br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}=LABCOEFFS_{r,sk,s,t}(1-LabCoeffAdjFac_{r,k,s,t})</math><br />
<br />
Model users can use a global parameter (labcoeffsm) to change the labor coefficients by skill level for any or all of the six sectors –<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= LABCOEFFS_{r,sk,s,t}*'''labcoeffsm_{s,sk}'''</math><br />
<br />
To forecast the total labor demand, the labor coefficients (LABCOEFFS) are multiplied to the total projected output for each of the economic sectors. The forecast is adjusted for any discrepancy between data and model. The adjustment factor (LABDemsAdjFac) is computed as the initial ratio between the actual and computed employment. Actual employment is obtained from historical data (LABEMPS) processed using the GTAP database. The computed employment is obtained by multiplying the labor coefficients (LABCOEFFS) with the final output of the sector (VADD).<br />
<br />
:<math>LabDemsAdjFac_{r,s,sk}= LABEMPS_{r,s,sk,t=1}/(VADD_{r,s,t=1}*LABCOEFFS_{r,sk,s,t})</math><br />
<br />
The projected output is obtained by applying the growth rate (IGDPRCOR) on the sectoral value added from the previous year (VADD). The total labor demand is given by the product of the labor coefficients, projected output, demand adjustments and wage impacts (labwageimpactmul) and the number 1000 which adjusts the units for the equation. Wage impact comes from the level of unemployment and is computed in an equilibration process described in the next section. Model users can use a multiplicative parameter (labdemsm) to slide the demand upward or downward.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}=1000*VADD_{r,s,t-1}*(1+IGDPRCOR_{r})*LABCOEFFS_{r,sk,s,t}*LabDemsAdjFac_{r,s,sk}*labwageimpactmul_{r,s,sk}*'''labdemsm_{r,s}'''</math><br />
<br />
== Unemployment and Wage: Labor Market Equilibration ==<br />
<br />
The IFs labor model balances the labor market through an equilibrium seeking algorithm rather than computing an exact equilibrium at each time step. We use an algorithm borrowed from the control systems engineering. This PID controller algorithm, described also in the IFs economic model documentation, works by computing corrective signals for equilibrating variables using the deviations of a buffer variable, for example unemployment rate (LABUNEMPR), from a target value. The signal is computed from two quantities, the distance of the buffer from the target and the current rate of change of the buffer. The computation is tuned with PID elasticities to avoid oscillations. The computed signal is applied on the variable/s which need to be balanced, for example, demand and supply in the event of a market equilibration, thus getting closer to a balance at each step of simulation. The target value for the buffer variable and the tuning parameters of the control algorithm are obtained through rules-of-thumb and model calibration. The IFs labor model uses unemployment rate (LABUNEMPR) as the buffer variable for the market equilibration of labor demand and labor supply. The multiplier (i.e., corrective signal) obtained from the PID is applied on the wage index (LABWAGEIND). Changes in wage indices comparative to the base year, moderated through a second PID controller, is used to compute the final signal (labwageimpactmul) that drives labor demand and labor supply. Even though the model forecasts labor demand by sector and skill, and computes labor supply for both skill types, the equilibration algorithm works over the entire pool of labor. In other words, we assume that the skills are replaceable across sectors and the lack (or abundance) of jobs affects skilled and unskilled persons equally.<br />
<br />
At each annual timestep, the model computes the unemployment rate (LABUNEMPR) as the gap in between the total supply of labor (LAB) and the total demand. The gap (EmplGap) is expressed as a share of the total labor, the standard way to express unemployment rate.<br />
<br />
:<math>sumld=∑_{s,sk}LADEMS_{r,s,sk,t}</math><br />
<br />
:<math>EmplGap= LAB_{r,t}*sumld</math><br />
<br />
:<math>LABUMENPR_{r,t}= (EmplGap/LAB_{r,t})*100</math><br />
<br />
As the target value (LabUnEmpRateTar) for the PID controller that modulates unemployment rate we use either the historical unemployment rate or a ten percent unemployment rate when the historical rate is higher than ten. Model users can override the historical target through a model parameter (labunemprtrgtval).<br />
<br />
:<math>LABUMENPRi_{r,t}= LABUMENPR_{r,t}</math><br />
<br />
:<math>LabUnempRateTarget_{r}=labunemptargetval_{r}</math><br />
:<math>If LabUnempRateTarget_{r}=0,<br />
LabUnempRateTarget_{r}= AMIN(LABUMENPRi_{r,t},10) </math><br />
<br />
Unemployment rate target, when it is different from the base year value, is reached gradually with a convergence period of forty years . The target rate is converted to count (LabUnEmplTar) to make it equivalent to the employment gap (EmplGap) computed earlier.<br />
<br />
:<math>LabUnEmplTar_{r}= LAB_{r,t}*ConvergeOverTime(LABUMENPRi_{r,t},0,100)</math><br />
<br />
The first order difference (Diffl1) between the target unemployment and the demand-supply gap is used to compute a second order difference (Diffl2) accounting for changes in the rate of movement. The two differences and the PID multipliers (elwageunemp1, elwageunemp2) are provided to the PID function (ADJSTR). Working age population (POP15TO65r,t) works as the scaling base of the PID controller. The controller algorithm gives a multiplier (mullw) that is used in the subsequent year to adjust wage.<br />
<br />
:<math>Diffl1_{t}=LabUnEmplTar_{r}-EmplGap</math><br />
<br />
:<math>Diffl2_{t}=Diffl1_{t}-Diffl1_{t-1}</math><br />
<br />
:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},elwageunemp1_{r},elwageunemp2_{r})</math><br />
<br />
Wage adjustments affect demand and supply with an increase in wage drawing demand downward and supply upward. The opposite affects occur with a downward movement of wage. The wage variable affected by the PID multiplier (LABWAGEIND) is an index initialized at one. We use an indexed rather than a dollar wage in the equilibration process to avoid affecting the process from other economic phenomena that affects wage, for example, a rise in real wage as GDP or the labor share of income grows.<br />
<br />
:<math>LABWAGEIND_{r,t=1}=1</math><br />
<br />
In the subsequent years of the model run, the wage index is first adjusted with the equilibration signal obtained from the unemployment rate PID controller in the previous period<br />
<br />
:<math>LABWAGEIND_{r,t=1}= LABWAGEIND_{r,t=1}* mullw_{r,t-1} </math><br />
<br />
A wage impact (labwageimpact) is then computed using the changes in the wage index relative to the base value. The impact is smoothed with a moving average algorithm.<br />
<br />
:<math>labwageimpact_{r}= labwageimpact_{r,t-1}*0.9+ (1-LABWAGEIND_{r,t})*0.1</math><br />
<br />
The smoothed impact is used as the equilibration signal for labor supply. As we have already described in the section on labor supply, a small fraction of the impact (labwageimpact) is applied to the labor participation rate. The impact is scaled down to account for the slow pace of changes on the supply side.<br />
<br />
:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact_{r,t}*0.05)</math><br />
<br />
For the impacts of wage on labor demand we use a second PID multiplier as opposed to using the changes in wage index that we have done on the supply side. The second PID uses the wage index itself as the process variable and uses the base year value of 1 as the target. The reason we had to use this second PID is to control the pace at which wage disequilibrium can affect demand, especially in the event of an abrupt shock. The smoothing and scaling down that works on the supply side is not enough to control oscillations on the demand side.<br />
<br />
:<math>Diffl1_{t}=LABWAGEIND_{r,t=1}-1</math><br />
<br />
:<math>Diffl2_{t}=LABWAGEIND_{r,t}-LABWAGEIND_{r,t-1} </math><br />
<br />
:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},ellabwage1_{r},ellabwage1_{r})</math><br />
<br />
A second impact factor (labwageimpactmul) is computed using the correction signal from this second multiplier:<br />
<br />
:<math>labwageimpactmul_{r,t}= labwageimpactmul_{r,t-1}*mullw_{r,t}</math><br />
<br />
This impact factor is applied on the labor demand as described in the section on labor demand.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}= LABDEMS_{r,s,sk,t}* labwageimpactmul_{r,t}</math><br />
<br />
== Informal Labor ==<br />
<br />
IFs forecast labor and GDP share of the informal sector. Informal labor forecast is not explicitly endogenized in the labor market though. They are rather driven by development, skill and regulatory factors[[#_ftn1|[1]]]. However, the productivity and revenue impacts of changes in informality affects output and thus labor demand implicitly as a very distal driver.<br />
<br />
= References =<br />
<br />
<references /><br />
</div></div></div></div></div></div></div></div></div></div></div>Wikiadmin//pardee.du.edu/wiki/LaborLabor2018-09-07T22:43:13Z<p>Wikiadmin: </p>
<hr />
<div>Workers in an economy supply the expertise and the efforts needed to produce goods and services. In return the labor receives wages that they use to meet their current and future consumption needs. On one hand, shortage of labor with required skills prevents economies from realizing their growth potential. On the other hand, individuals falling short of the right qualifications might remain unemployed or underemployed failing to secure income needed for a decent living. The ongoing adjustments to find the best match between skills, jobs and wages can only be studied through a dynamic model of the labor market.&nbsp; &nbsp;<br />
<br />
Such a model should go beyond providing a reasonable answer to the obvious question of why employment and wages go up and down. An aggregate labor market must deal with issues that have strong interconnections with various other dynamic changes in the greater society. What kind of dividend of deficit can a society expect from its labor force given the phase of demographic transition in which it is situated? How severely would aging affect the pool of working age adults? Might increasing female participation rates offset some of the losses from aging? What is the level of skills and educational attainment in a society? These supply phenomena move relatively slowly unless there are huge disruptions, like a war or famine, or an aggressive policy push. The demand side, in contrast, needs to be more responsive in adjusting wages and employment given the investment and technology in the various sectors of the broader economy. In general, though, the labor market demonstrates some sluggishness compared to the goods and services markets as it involves moving human beings with various limitations. Consumption of goods and services depend on the income earned by the labor. Uneven distribution of employment and wages among labors of various types or between labor and capital for a long period of time can give rise to persistent inequality in a society. &nbsp;&nbsp;&nbsp;&nbsp;<br />
<br />
== Conceptual Framework ==<br />
<br />
Labor markets are markets for workers and jobs. In a labor market, employers meet their demand for labor with the supply of people willing to work at the wage the employers can offer. The employers raise the wage when there is a shortage of workers. Workers agree to take a lower wage when there are more of them than the firms need. In the real-world labor markets do not always clear at perfect equilibrium. Frinctional unemployment results for various reasons, for example, the search time between jobs. Structural unemployment can result from technology induced disruptions. Some unemployment could thus persist in the labor market even when there aren’t any short-term fluctuations. There is also the phenomenon of informal employment that consists of less sophisticated workers and entrepreneurs engaged in unregulated economic activities. &nbsp;In a dynamic model that covers the entire economy, the real wage earned by the labor drives the income and social mobility.&nbsp;&nbsp;&nbsp;<br />
<br />
To understand the long-term dynamics of the labor market, we need also examine the deeper determinants of labor demand and supply, the determinants that can shift the curves. Labor demand changes over time with the changes in demand for goods and services and the labor input needed to produce those. Labor productivity itself improves with technological progress. Long term transitions in the supply of labor are mostly demographic. &nbsp;<br />
<br />
Labor supply is determined by the working age population and the share of that population who are available for participation in the workforce. The labor supply is relatively stable as the demographic changes are slow in pace. As the share of elderly in the population increases, a recent trend in many societies, the rate of participation declines. Some of the aging impacts will be offset by the greater female participation rates, a second trend that surfaces as economies develop and women attain more education. Educational attainment also drives the general skill level of workers, male and female. Specific skills are obtained through training and experience that augment the knowledge obtained through general and specialized education. &nbsp;&nbsp;&nbsp;&nbsp;<br />
<br />
It is the demand side that causes most of the short-term imbalances in the labor market. &nbsp;In the long term, as said earlier, the important driver of demand for labor and their skills is technological progress. Labor requirement drops with advances in technology, more so for less skilled labor. Labor composition changes accordingly both within and across sectors. Rapid advances in technology can also cause disruption in the system when there is not much opening in the other sectors. Labor displacement is offset to some extent by the growth in the economy and the resulting increase in total demand. &nbsp;&nbsp;&nbsp;<br />
<br />
As we have already mentioned, employees maximize income and the firms minimize labor costs. When there are more laborers than the firms can hire, there is unemployment. Shifts in the rates of unemployment impacts wage, the price of labor. For example, wages drop in the event of rising unemployment as there are more people to hire from. Wage adjustments feed back to the demand for labor seeking to bring the market back to equilibrium.<br />
<br />
The challenges around the conceptual distinction between unemployment and employment is further complicated by the phenomenon of informal employment. In many developing countries there is a large urban non-agricultural informal sector where low-skilled workers work for wages typically lower than a formal employment.<br />
<br />
[[File:LMFlowchart1.png|frame|center|Description of the labor model]]<br />
<br />
== Dominant Relations ==<br />
<br />
The labor model in the International Futures system (IFs) balances the total supply of labor with the total labor demanded by all economic sectors. Total labor (LAB) is computed from the working age population and the labor participation rate. Population forecasts are obtained from the IFs demographic model. Participation rates (LABPARR) are computed by sex with a catchup algorithm for the female participation towards that for the male. Labor is also disaggregated by skill level, as determined by educational attainment, in a separate labor supply variable (LABSUP) which is used to distribute labor earnings by skill level. [** LABSUP do not affect the demand/supply balance now]<br />
<br />
Labor demands (LABDEMS) are driven by sectoral technology functions used to compute the labor requirement by skill level for each unit of potential valued added in the sector. These labor coefficients (LABCOEFFS) are multiplied with the projected value added for the sector to compute the needed manpower. The balancing mechanisms determines the labor employed in each of the sectors (LABS).&nbsp;&nbsp;<br />
<br />
The balancing, in the current version of the model, can be done in one of the two ways. In the first method, total needs combined from all economic sectors is normalized to the available pool of labor computed by subtracting the unemployed from those who are at or looking for work. The rate of unemployment is kept at its natural rate for which we use the base year rate of unemployment. (** This might need to be changed for countries where the market is undergoing some abrupt transition.)<br />
<br />
In the second balancing method, added in a recent revision of the model, total demand is equilibrated to supply through a CGE like market equilibrium model. An indexed wage (LABWAGEIND) and the rate of unemployment (LABUNEMPR) work as the equilibrating variables. As unemployment deviates from the target, PID algorithms send a signal for the wage to adjust. Wage adjustments cause adjustments in the “base” labor demands by sector computed from the labor-coefficient functions as described earlier. Wage signals also affects the labor participation rate. The magnitude of impact on the supply side is much lower than that on the demand side.<br />
<br />
Wage and unemployment rate are aggregated for the total labor market. The wage index starts with a base year value of 1 and the unemployment rates start with the historical data for the base year. Initial year unemployment rate works as the target for long term unemployment.<br />
<br />
== Key Dynamics ==<br />
<br />
The following key dynamics are directly related to the dominant relations:<br />
<br />
*Labor supply is determined from population of appropriate age in the population model (see its dominant relations and dynamics) and endogenous labor force participation rates, influenced exogenously by the growth of female participation.<br />
*Labor demand is driven by sectoral demand functions driven by technological progress<br />
<br />
== Structure and Agent System ==<br />
<br />
{| border="1" cellspacing="0" cellpadding="0" width="0" style="width:502px;"<br />
|-<br />
| style="width:242px;height:49px;" | <br />
'''System/Subsystem'''<br />
<br />
| style="height:49px;" | <br />
Labor market<br />
<br />
|-<br />
| <br />
'''Organizing Structure'''<br />
<br />
| <br />
Labor supply by skill level and labor demand by sector for each skill category represented within an equilibrium-seeking model with wage and unemployment rate as the equilibrating variables<br />
<br />
|-<br />
| <br />
'''Stocks'''<br />
<br />
| <br />
Population, labor, education, &nbsp;accumulated technology<br />
<br />
|-<br />
| <br />
'''Flows'''<br />
<br />
| <br />
Participation rate; Coefficients of labor demand; Employment (unemployment); Wage<br />
<br />
|-<br />
| <br />
'''Key Aggregate&nbsp;''''''Relationships&nbsp;'''<br />
<br />
'''(illustrative, not comprehensive)'''<br />
<br />
| <br />
Labor supply is driven by demographic changes; Participation of female change over time; Labor requirement changes with technological development; Unemployment rate drives wage; Wage movements affect labor demand and participation rate<br />
<br />
|-<br />
| <br />
'''Key Agent-Class Behavior&nbsp;''''''Relationships'''<br />
<br />
'''(illustrative, not comprehensive)'''<br />
<br />
| <br />
Households and work/leisure, and female participation patterns;<br />
<br />
&nbsp;<br />
<br />
Firms and hiring;<br />
<br />
&nbsp;<br />
<br />
<br />
<br />
<br />
<br />
|}<br />
<br />
= Labor Model Data =<br />
<br />
The labor supply and unemployment data that we use in our model is from International Labor Organization (ILO). For data on the demand side, we used data from the Global Trade Analysis Project. Wage variable used in the equilibration algorithm&nbsp;is an index anchored to the base year of the model.<ref>GTAP database helped us compute wage rates by sector and skill.</ref> IFs preprocessor prepared these data for model use using various estimation, conversion and reconciliation processes.&nbsp; &nbsp; &nbsp;<br />
<br />
== Definitional Issues ==<br />
<br />
There are ambiguities in the way some of the labor market variables are defined. Labor participation rates and the rate of unemployment are two that need special attention.<br />
<br />
The size of the labor supply available for economic activities is expressed with the labor force participation rate. ILO defines this as a “measure of the proportion of country’s working-age population that engages actively in the labor market, either by working or looking for work.”<ref>http://www.ilo.org/ilostat-files/Documents/description_LFPR_EN.pdf</ref>&nbsp;National labor force surveys and census data are used to estimate this rate. The definition of labor force here includes both employed and unemployed and the rate is expressed as a percentage of working-age population. Working-age population is defined here as the population above legal working-age. For international comparability, ILO adopts a convenient minimum threshold of fifteen years as working age and avoids putting any upper age limit. In practice, both the minimum and the upper-age limits can vary by country. For example, the working-age in the USA is sixteen years. In the Netherlands the upper age limit is seventy-five years, whereas South African data uses an upper age limit of 64.<ref>https://www.bls.gov/fls/flscomparelf/technical_notes.pdf</ref><br />
<br />
Ambiguities are more abundant in the definition of unemployment. ILO came up with a guideline on this as well. Per the ILO guideline, the unemployed are those among the working-age population who are not employed, are available for work and are actively looking for jobs<ref>The definitions around employed and unemployed were agreed upon by nations through the ‘Resolution concerning statistics of work, employment and labor underutilization’ adopted by the 19th International Conference of Labor Statisticians (ICLS) in 2013. (Bourmpoula et al, 2017: 6).</ref>; the unemployment rate is expressed as a percentage of those who are in the labor force. The availability and job-seeker status could be defined in different ways giving rise to incompatibility in data. &nbsp;While there seems to be little room for disagreement on whether someone is at work or not, whether that work should be considered as employment is contested at many times.<br />
<br />
The debates around the nature and type of employment can range from gainfulness to workplace setting. For example, a large number of workers in the low-income low-regulation developing countries work outside the purview of formal enterprises. According to an ILO estimate, more than half of the global labor force and more than 90% of Micro and Small Enterprises (MSEs) worldwide are in the so called informal economy.<ref>http://www.ilo.org/global/topics/employment-promotion/informal-economy/lang--en/index.htm</ref> This might explain the apparently counterintuitive pattern of low unemployment rate in some low-income countries (e.g., 2.2% for Guatemala) and relatively higher numbers for some of the developed nations. The low numbers in the poorer countries hide the prevalence of extremely low wage jobs in the informal sectors in these countries, the only options for the vulnerable people in the absence of any kind of social safety net. &nbsp;Contrastingly, in the developed countries the so called ‘gig-economy’ is attracting more and more workers who choose to work on their own rather than in a formal enterprise. ILO conceptualization makes the informal work part of total employment. The stacked Venn diagram below presents the relationship among the labor force metric including informal employment. IFs also models informal economy both in terms of GDP share and employment share of informal in the total economy and employment.<br />
<br />
[[File:LaborSubsets.png|frame|right|Relationship among various labor measurement]]<br />
<br />
Incompatibility can arise in the treatment of various population groups for the computation of the denominator for participation and unemployment rates.<ref>For example, the USA excludes people in the defense services and those in the prisons or mental asylums in their computation of the civilian non-institutional working-age population. There are also variations in the treatments of students, those recently laid-off, and family workers. Please see https://stats.bls.gov/opub/mlr/2000/06/art1full.pdf for a discussion </ref> ILO makes their best efforts to make adjustments in the data for the sake of international comparison. For example, ILO asks countries that deviate from ILO guidelines to collect data needed to convert national figures to ILO figures. It is likely that some differences might have slipped past the adjustment process. We use ILO data and continue to update our database&nbsp;&nbsp;<br />
<div><div id="ftn4"><div id="ftn1"><br />
The GTAP data that we use for the demand side of the labor model is taken as labor headcounts and is thus immune from ambiguities around rate computation. As far as we could gather<ref>Please see the webpage for documentation on GTAP labor data statistic: https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=3248</ref>, the data includes both the formal and informal employment. We also need mention here that the GTAP database reconciles the labor data to calibrate the general equilibrium modeling that they do for the trade analyses. The data could thus be somewhat different from data collected through direct surveys. As a CGE model IFs is benefited by using calibrated data.<br />
<br />
== Sources of Labor Data ==<br />
<br />
IFs model uses ILO data for labor participation rates and for the unemployment rate. The data in IFs are collected from World Bank’s World Development Indicators (WDI) database. According to their documentation, WDI obtained the data from the ILO.<br />
<br />
&nbsp;<br />
<br />
Unemployment rate data in IFs is also collected from WDI. Like the participation rates WDI also obtains their unemployment data from ILO.[[#_ftn1|[1]]]<br />
<br />
For employment and labor demand data IFs uses Purdue University’s Global Trade Analysis Project (GTAP) database. GTAP collects and compiles factor payments, imports, and intersectoral flow data to calibrate CGE models of national economies for trade and other analyses. In their ninth release in 2016, GTAP published data for 140 countries and regions for the year 2011. The earlier GTAP releases, which the IFs model used for its previous versions, compiled data for the years 2004 and 2007. GTAP data release aggregates economic activities into 57 commodities and activities following International Standard Industrial Classification (ISIC). The IFs model maps the 57 GTAP sectors into six economic sectors of IFs – agriculture, energy, material and mining, manufacture, services and ICT. Appendix 2 presents two tables listing the sectors mapping between IFs and GTAP, and GTAP and ISIC. GTAP further disaggregates labor in each of the commodities/activities into five occupation and skill categories following the nine category International Standard Classification of Occupations (ISCO-88). The IFs model collapses five GTAP occupation categories into the simple IFs dichotomy of skilled and unskilled. The mapping of occupations and skills are presented in the third appendix of this document. &nbsp;<br />
<br />
The data in the main GTAP database, prepared for CGE modeling, are all in dollar unit and thus do not include labor headcounts. We have used a ‘satellite’ GTAP database[[#_ftn2|[2]]] for labor headcounts by skill and sector. The labor counts were also used to plot labor requirement functions for each of the IFs economic sectors and skill categories. The wage share of skilled and unskilled labor in each sector was computed using the labor headcounts and labor payments.<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] The name of the IFs table is SeriesLaborUnemploy%<br />
</div><div id="ftn2"><br />
[[#_ftnref2|[2]]] See Weingarden and Tsigas, 2010 for the details on the preparation of this database.<br />
<br />
== Scope of IFs Labor Model ==<br />
<br />
The IFs labor model simulates labor market at the national level. Each national labor market forecasts labor demand and employment by six sectors - agriculture, energy, mining, manufacture, services and ICT- and two skill levels - skilled and unskilled. The supply side do not have sectoral representation. IFs forecasts total labor force and labor supply by the two skill levels. Labor participation rate is computed in IFs by gender. Wage and unemployment rate is forecast for the overall labor market only.&nbsp;&nbsp;<br />
<br />
== Labor Model Pre-processor ==<br />
<br />
IFs system has a data preprocessor that prepares the initial conditions for the model using historical databases and various assumptions and estimated relationships to fill in the missing data and make data adjustments as needed[[#_ftn1|[1]]]. Pre-processing of labor data takes place in two IFs pre-processing modules. Labor participation rate data, which is closely related to demography, is processed in the population pre-processor. Unemployment rate and labor demand data are processed in the economic pre-processor. &nbsp;<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] For more details, please see ‘The Data Pre-Processor of International Futures (IFs)” by Barry B. Hughes (with Mohammod Irfan) at [http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf]<br />
<br />
=== Pre-processing Labor participation rate and unemployment ===<br />
<br />
For initializing labor participation rates by sex (LABPARR) the model uses the historical values from the base year or the most recent year with data[[#_ftn1|[1]]]. For countries with no data we use regression relationships of the participation rates, for men and for women, with income per capita. The relationships, shown in the next figure, are not great. However, the functions affect only five countries for which we do not have any data at all: Grenada, Kosovo, Micronesia, Seychelles and South Sudan[[#_ftn2|[2]]].<br />
<br />
&nbsp;<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] The data tables that the IFs model pre-processor use for initializing labor participation rates are: SeriesLaborParRate15PlusFemale%, SeriesLaborParRate15PlusMale%.<br />
</div><div id="ftn2"><br />
[[#_ftnref2|[2]]] We should try to collect participation rate for these countries from country sources.<br />
<br />
IFs data series SeriesLaborUnemploy% is used for the initialization of unemployment rates. That series has annual unemployment rates for one or more years between 1980 and 2016, for 181 of the 186 IFs countries. For five countries (Grenada, Kosovo, Micronesia, Taiwan and South Sudan[[#_ftn1|[1]]]) there is no data at all. To fill in the missing data we use a regression function of unemployment rate against GDP per capita. Like the participation rate functions, this function does also not have much of an explanatory power.<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] These are pretty much the same countries for which we do not have any participation rate data. This indicates ILO might have some administrative limitation in reporting data for these countries (notice Kosovo, Seychelles etc in the list)<br />
<br />
=== Pre-processing labor demand and unemployment from GTAP ===<br />
<br />
The IFs economic pre-processor reads labor headcount and labor payment data from the GTAP database. In addition to performing sector and occupation/skill mapping between GTAP and IFs, pre-processor also use the labor headcount data to compute labor coefficient functions, the principal driver of labor demand in the IFs model.<br />
<br />
Labor coefficients are defined as the amount of labor needed to produce one unit of value added in a certain sector of the economy. The coefficients depend on the level of technology. The model uses GDP per capita as an indicator of the level of technological development. IFs pre-processor estimates labor coefficient functions for labor of different skill levels for the different sectors of the economy.<br />
<br />
The functions are derived from GTAP data we described earlier. The model pre-processor reads data on factor payments and aggregates data from 57 GTAP sectors to six IFs sectors. Shares of payment going to skilled and less-skilled workers in each of the sectors are then computed. Countries are grouped according to their level of technological development as represented by per capita income. For each group labor coefficients are obtained by taking an average of the country coefficients. &nbsp;We also convert labor payments data to labor headcount data using per capita income as a proxy for average wage. Labor coefficients and income are then plotted into a power function relationship. The figure below plots some of those labor functions.&nbsp; The functions fit quite well with a power law formulation[[#_ftn1|[1]]].<br />
<div><br/><br />
----<br />
<div id="ftn1">[[#_ftnref1|[1]]] This is interesting given the prevalence of power law in all sorts of scale-up activities (West 2017).</div></div></div></div></div></div></div></div></div></div><br />
<br />
= Labor Model Flowcharts =<br />
<br />
The diagram below shows an outline of the IFs labor model. On the supply side, the total labor pool (LAB) is computed from the labor force participation rates, by sex, (LABPARR) and the population (POP) in their working age, i.e., population over 15 (POP15TO65 + POPGT65). Participation rates are driven by the demographic changes with an additional negative impact from aging and a catch-up in female participation rate. Skill level of the labor supply (LABSUP) is driven by the level of development (GDPPCP) and the demand for labor is driven by labor-coefficients (LABCOEFFS) computed from coefficient function representing shifts in demand with technological progress as proxied by the level of development (GDPPCP). Coefficients computed by sector and skill gives the labor requirement by skill type for each unit of value added (VADD) in the sector. Multiplying these coefficients with projected value added in each sector gives an estimate of the labor demand. &nbsp;<br />
<br />
Any surplus or shortage between total labor demand and supply is used to compute the rate of unemployment. Deviations in the unemployment rate (LABUNEMPR) signal wage changes through an equilibrium seeking algorithm. Both demand and supply respond to the wage variable (LABWAGEIND) indexed to the base year. The supply responses are much slower than the demand responses.<br />
<br />
[[File:FLOCHART2.png|frame|center|Labor Model Flowchart]]<br />
<br />
= Labor Model Equations =<br />
<br />
== Overview ==<br />
<br />
The labor model is a part of the IFs economic model that uses labor model output as an input to a Cobb-Douglas production function in a multi-sector general equilibrium model. IFs is a very long-run dynamic model. Instead of computing fixed short-run equilibria that clear the relevant markets IFs uses an equilibrium seeking algorithm to balance the various systems over the longer run. The algorithm is known as the PID (proportion-integral-derivative) controller algorithm and is used widely in industrial control systems. It makes equilibrium seeking variables in IFs move towards a set target. The algorithm works by computing a multiplier based on the movement of the variable towards the target, as obtained by an integral (I) of the path traversed, and the rate of movement towards the target, the derivative term. The multiplier is applied on the process variable (the P term), or a response variable, in the subsequent time period. In the labor model, unemployment rate (LABUNEMPR) is used as the process variable and the PID multiplier is used on the wage rate (LABWAGEIND). Job availability (LABDEMS) and participation rate (LABPARR) get affected by changes in wage. &nbsp;&nbsp;<br />
<br />
Throughout this section we use subscripts and notations common to other modules of IFs. For example, we use t for time period. Subscripts p and r represent sex and country/region, respectively, c is the cohort number, with cohort 1 representing the newborns, cohort1 the the one-year to four-year-olds, cohort two five-year to nine-year-olds etc. Values for p are 1 for male, 2 for female and 3 for both sexes combined. For economic sectors we use s and for skill levels sk.<br />
<br />
== Labor Supply: Equations ==<br />
<br />
The total pool of labor is computed by multiplying the population of working age with the labor force participation rate (LABPARR). &nbsp;Population forecasts come from IFs demographic model which computes both five-year and single-year age-sex cohorts (''agedst'', ''fagedst''). &nbsp;<br />
<br />
The labor model forecasts participation rates by country/region&nbsp; and gender. Participation rates in the model move with the changes in the demographic composition. Female participation rates, which have historically been lower than the same for the male in all societies, but has moved up in modern and affluent societies, get a catch-up boost in the model. Participation rates can also change when there is labor shortage or surplus and the employers try to incentivize or discourage workers by changing wage. This last impact is much less slow than similar wage impacts on the demand side.<br />
<br />
== Labor Participation Rate ==<br />
<br />
Labor participation rates (''LABPARR'') for male and female are first initialized with historical data.<br />
<br />
:<math>LABPARR_{r,p}= LABPARR_{r,p,t=1} </math><br />
<br />
A ‘catch-up’ boost is added to the female participation rate. The boost added (FemParLabMul) starts at a third of a percentage point and withers away following a non-linear path as the female rates approaches the catch-up target (FemParTar), The maximum catch-up that can occur over the horizon of the model is thirty percent.<br />
<br />
:<math>FemParTar_{r}=Amin(LabParRI_{r,p=1},LabParRI_{r,p=2}+30)</math><br />
<br />
:<math>FemParLabMul_{r}=(FemParTar_{r}-LABPARR_{r,p=2,t-1})/(FemParTar_{r}-LABPARR_{r,p=2,t-1})</math><br />
<br />
:<math>LABPARR_{r,p=2,t}=LABPARR_{r,p=2,t-1}+FemParLabMul_{r}*0.3</math><br />
<br />
Next, we compute and apply the aging impact on the participation rate. As the relative share of people over the retirement age increases, the participation rate declines. The model keeps track of the changes in the demographic ratio (PopAgingRatio) of the population who are in their prime working age of 15 to 64 (POPWORKING) to those at a common retirement age of sixty-five or older (POPGT65). This ratio declines as countries age. The percentage drop in the ratio comparative to the base year is scaled appropriately to compute the aging impact (aging_impact). This impact is added to the male and female labor participation rates, with the impact on the female participation rate being slightly lower than that on male rates.<br />
<br />
:<math>POPAgingRatio_{r,t}=POPWORKING_{r,t}/POPGT65_{r,t}</math><br />
<br />
:<math>aging_impact_{r,t}=100*((POPAgingRatio_{r,t}/POPAgingRatio_{r,t=1})-1)*0.2</math><br />
<br />
:<math>LABPARR_{r,p=1,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t} </math><br />
<br />
:<math>LABPARR_{r,p=2,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t}*0.95 </math><br />
<br />
Participation rates respond slowly to changes in wage and unemployment rate. The impact is implemented through a wage impact factor computed from annual changes in the wage index (labwageimpact). The base participation rates can be changed by model user through two model parameters: a direct multiplier on the participation rate (labparm), or one that changes participation by moving the retirement age (labretagem)<br />
<br />
<br />
<br />
:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact*0.05)*labparm_{r,p,t}*labretagem_{r,p,t}</math><br />
<br />
Total participation rate (LABPARRr,p=3,t) is computed by an weighted average of male and female participation rates.<br />
<br />
:<math>LABPARR_{r,p=3,t}= (∑_{p=1 to 2}∑_{c=4 to 21}(agedst{r,c,p,t}*LABPARR_{r,p,t}))/(∑_{p=1 to 2}∑_{c=4 to 21}agedst{r,c,p,t})</math><br />
<br />
== Total Labor ==<br />
<br />
Finally, the total number of labor available for work (LAB) is computed by multiplying the total participation rate with the population of fifteen-year-olds or older.<br />
<br />
:<math>LAB_{r,t}= LABPARR_{r,p=3,t}*∑_{p=1 to 2,c=4 to 21}agedst_{r,c,p,t}</math><br />
<br />
== Labor by skill level ==<br />
<br />
The labor model forecasts labor supply (LABSUP) by two skill categories. The variable (''LABSUP'') is initialized in the pre-processor by reading the employment by skill/occupation (''LABEMPS'') data from GTAP[[#_ftn1|[1]]] &nbsp;and adding the unemployment numbers. We assume same unemployment rate (''LABUMEMPR'') for skilled and unskilled labor.<br />
<br />
:<math>LABSUP_{r,t=1,sk}=∑_{s=1 to 6}(LABEMPS_{r,s,t=1}/(1-(LABUNEMPR_{r,t=1}/100))</math><br />
<br />
The model forecasts labor by skill through a model of the skilled share of the labor. Education, training, exposure, and experience of the employees all improve with the level of development. The model captures this with an analytic function of the skilled share (perskilled) driven by GDP per capita at PPP (GDPPCP) -<br />
<br />
:<math>perskilled_{r}=f(GDPPCP_{r})</math><br />
<br />
Among the causal drivers of skill, education is considered to be the most proximate. Education is strongly correlated with the level of development, the deeper driver of skill in the model. However, the recent increase in education and/or a policy driven educational expansion might add to the impact of education on skill. Additional impacts from education on skill, when there is any, is computed through an expected function formulation. For example, in a society where an average adult has more (or less) education than the adults in other societies at that level of development, the skill share is given a slight upward push (or downward pull). The expectation function is a logarithmic function of educational attainment of working age population (EDYRSAG15) driven by GDP per capita at PPP. Attainment above (or below) the expected level (YearsEdExp) is computed by the function output (YearsEd) adjusted for country situation (yearseddiff). The percentage adjustment to the skilled share (LabSupSkiAdj) is computed using additional (limited) education, i.e., the difference between actual (EDYRSAG15) and expected values of educational attainment, expressed as a percentage of the expected value. The adjustment is scaled appropriately and peters off over time.<br />
<br />
:<math>YearsEd_{r,t}= f(GDPPCP_{r,t})</math><br />
<br />
:<math>yearsdeddiff_{r}= EDYRSAG15_{r,p=3,t=2}-YearsEd_{r,t=2}</math><br />
<br />
:<math>YearsEdExp_{r,t}=YearsEd_{r,t}+yearsdeddiff_{r}</math><br />
<br />
:<math>LabSupSkiAdj_{r,t}=0.3*(EDYRSAG15_{r,p=3,t=2}*YearsEdExp_{r,t})/YearsEd_{r,t}</math><br />
<br />
:<math>LabSupSkiAdj_{r,t}=ConvergeOverTime(0,LabSupSkiAdj_{r,t},70)</math><br />
<br />
:<math>perskilled_{r,t}= perskilled_{r,t}*(1+LabSupSkiAdj_{r,t})</math><br />
<br />
The skilled share (perskilled) is multiplied with the total labor supply (LAB) to obtain the number of labors who are skilled (LABSUPskilled)<br />
<br />
:<math>LABSUP_{r,skilled,t}=LAB_{r,p,t}*perskilledI_{r,t} </math><br />
<br />
As a last step, the model adjusts for the country specific variations in the skilled labor count not captured by the deeper and the proximate models. This is done by saving a ratio (LABSUPSkilledRI) of the actual historical data and the model computed value in the initial year. In the subsequent years this ratio is used to adjust the skilled labor forecast gradually.<br />
<br />
:<math>LABSUPCompSkilled_{r}=LAB_{r}*perskilled_{r,t=1}/100 </math><br />
<br />
:<math>LABSUPSkilledRI_{r}=LABSUP_{r,skilled,t=1}/LABSUPCompSkilled_{r}</math><br />
<br />
:<math>LABSUP_{r,skilled,t}= LABSUP_{r,skilled,t}*ConvergeOverTime(LABSUPSkilledRI_{r},1,85)</math><br />
<br />
Number of unskilled labor is obtained by subtracting the skilled labor from the total pool.<br />
<br />
<br />
<br />
:<math>LABSUP_{r,unskilled,t}= LAB_{r,p,t}- LABSUP_{r,skilled,t}</math><br />
<br />
== Labor Demand: Equations ==<br />
<br />
IFs economic model forecasts production in six economic sectors. IFs labor model computes the longer-term and shorter-term determinants of demand for skilled and unskilled labor (LABDEMS) for the production processes. The long-term drivers of labor requirement are technological progress or the lack of it. In the shorter-term wage affects the labor demand most. Wage in turn is affected by labor supply or skill shortage.<br />
<br />
The IFs model divides economic activities into six economic sectors – agriculture, energy, materials, manufacture, services and information, and communication technologies. Workers in the IFs labor model are disaggregated into two skill types. While the skill composition varies by the technology used in the sector and starts tilting towards the more skilled with the progress in technology, absolute number of labors needed to produce the same output goes down with technological development for both skilled and unskilled labor. This is illustrated in the next figure which plots the changes in labor requirement against GDP per capita at PPP, a proxy for level of development. Agriculture is a much less skill-intensive process than the manufacture, however, with technological progress skill requirement improves rapidly in both sectors. The IFs labor model computes these labor requirement functions in the model pre-processor. As we have already described in the pre-processor section, the computation of these functions use GTAP data on employment by occupation and economic activity. Appendices 3 and 4 lists sector and occupation mapping between GTAP and IFs.<br />
<br />
These functions are used to compute the labor coefficients (LABCOEFFS), i.e., number of skilled and unskilled labor needed to produce unit amount of output with the technology available, for which we use GDP per capita at PPP as a proxy.<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= f(GDPPCP_{r})</math><br />
<br />
manufacture, services and ICTech) and the subscrip sk stands for skill categories with 1 denoting unskilled and 2 skilled. The labor coefficients obtained from the analytical functions require some adjustments to incorporate country deviations from the functions for various factors not captured in the regression relationship. The first of these adjustments is a gradual removal of impacts of short-run fluctuations in output and labor from the computation of labor coefficient. This adjustment is applied on the coefficients computed from the function. The equation below shows a simplified form of these computations.<br />
<br />
:<math>LabCoeffAdjFac_{r,k,s,t}=f(igdpr_{r,t=2},(LAB_{r,t=2}/LAB_{r,t=1}),(LABCOEFFS_{r,t}/LABCOEFFS_{r,t-1}))</math><br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}=LABCOEFFS_{r,sk,s,t}(1-LabCoeffAdjFac_{r,k,s,t})</math><br />
<br />
Model users can use a global parameter (labcoeffsm) to change the labor coefficients by skill level for any or all of the six sectors –<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= LABCOEFFS_{r,sk,s,t}*'''labcoeffsm_{s,sk}'''</math><br />
<br />
To forecast the total labor demand, the labor coefficients (LABCOEFFS) are multiplied to the total projected output for each of the economic sectors. The forecast is adjusted for any discrepancy between data and model. The adjustment factor (LABDemsAdjFac) is computed as the initial ratio between the actual and computed employment. Actual employment is obtained from historical data (LABEMPS) processed using the GTAP database. The computed employment is obtained by multiplying the labor coefficients (LABCOEFFS) with the final output of the sector (VADD).<br />
<br />
:<math>LabDemsAdjFac_{r,s,sk}= LABEMPS_{r,s,sk,t=1}/(VADD_{r,s,t=1}*LABCOEFFS_{r,sk,s,t})</math><br />
<br />
The projected output is obtained by applying the growth rate (IGDPRCOR) on the sectoral value added from the previous year (VADD). The total labor demand is given by the product of the labor coefficients, projected output, demand adjustments and wage impacts (labwageimpactmul) and the number 1000 which adjusts the units for the equation. Wage impact comes from the level of unemployment and is computed in an equilibration process described in the next section. Model users can use a multiplicative parameter (labdemsm) to slide the demand upward or downward.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}=1000*VADD_{r,s,t-1}*(1+IGDPRCOR_{r})*LABCOEFFS_{r,sk,s,t}*LabDemsAdjFac_{r,s,sk}*labwageimpactmul_{r,s,sk}*'''labdemsm_{r,s}'''</math><br />
<br />
== Unemployment and Wage: Labor Market Equilibration ==<br />
<br />
The IFs labor model balances the labor market through an equilibrium seeking algorithm rather than computing an exact equilibrium at each time step. We use an algorithm borrowed from the control systems engineering. This PID controller algorithm, described also in the IFs economic model documentation, works by computing corrective signals for equilibrating variables using the deviations of a buffer variable, for example unemployment rate (LABUNEMPR), from a target value. The signal is computed from two quantities, the distance of the buffer from the target and the current rate of change of the buffer. The computation is tuned with PID elasticities to avoid oscillations. The computed signal is applied on the variable/s which need to be balanced, for example, demand and supply in the event of a market equilibration, thus getting closer to a balance at each step of simulation. The target value for the buffer variable and the tuning parameters of the control algorithm are obtained through rules-of-thumb and model calibration. The IFs labor model uses unemployment rate (LABUNEMPR) as the buffer variable for the market equilibration of labor demand and labor supply. The multiplier (i.e., corrective signal) obtained from the PID is applied on the wage index (LABWAGEIND). Changes in wage indices comparative to the base year, moderated through a second PID controller, is used to compute the final signal (labwageimpactmul) that drives labor demand and labor supply. Even though the model forecasts labor demand by sector and skill, and computes labor supply for both skill types, the equilibration algorithm works over the entire pool of labor. In other words, we assume that the skills are replaceable across sectors and the lack (or abundance) of jobs affects skilled and unskilled persons equally.<br />
<br />
At each annual timestep, the model computes the unemployment rate (LABUNEMPR) as the gap in between the total supply of labor (LAB) and the total demand. The gap (EmplGap) is expressed as a share of the total labor, the standard way to express unemployment rate.<br />
<br />
:<math>sumld=∑_{s,sk}LADEMS_{r,s,sk,t}</math><br />
<br />
:<math>EmplGap= LAB_{r,t}*sumld</math><br />
<br />
:<math>LABUMENPR_{r,t}= (EmplGap/LAB_{r,t})*100</math><br />
<br />
As the target value (LabUnEmpRateTar) for the PID controller that modulates unemployment rate we use either the historical unemployment rate or a ten percent unemployment rate when the historical rate is higher than ten. Model users can override the historical target through a model parameter (labunemprtrgtval).<br />
<br />
:<math>LABUMENPRi_{r,t}= LABUMENPR_{r,t}</math><br />
<br />
:<math>LabUnempRateTarget_{r}=labunemptargetval_{r}</math><br />
:<math>If LabUnempRateTarget_{r}=0,<br />
LabUnempRateTarget_{r}= AMIN(LABUMENPRi_{r,t},10) </math><br />
<br />
Unemployment rate target, when it is different from the base year value, is reached gradually with a convergence period of forty years . The target rate is converted to count (LabUnEmplTar) to make it equivalent to the employment gap (EmplGap) computed earlier.<br />
<br />
:<math>LabUnEmplTar_{r}= LAB_{r,t}*ConvergeOverTime(LABUMENPRi_{r,t},0,100)</math><br />
<br />
The first order difference (Diffl1) between the target unemployment and the demand-supply gap is used to compute a second order difference (Diffl2) accounting for changes in the rate of movement. The two differences and the PID multipliers (elwageunemp1, elwageunemp2) are provided to the PID function (ADJSTR). Working age population (POP15TO65r,t) works as the scaling base of the PID controller. The controller algorithm gives a multiplier (mullw) that is used in the subsequent year to adjust wage.<br />
<br />
:<math>Diffl1_{t}=LabUnEmplTar_{r}-EmplGap</math><br />
<br />
:<math>Diffl2_{t}=Diffl1_{t}-Diffl1_{t-1}</math><br />
<br />
:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},elwageunemp1_{r},elwageunemp2_{r})</math><br />
<br />
Wage adjustments affect demand and supply with an increase in wage drawing demand downward and supply upward. The opposite affects occur with a downward movement of wage. The wage variable affected by the PID multiplier (LABWAGEIND) is an index initialized at one. We use an indexed rather than a dollar wage in the equilibration process to avoid affecting the process from other economic phenomena that affects wage, for example, a rise in real wage as GDP or the labor share of income grows.<br />
<br />
:<math>LABWAGEIND_{r,t=1}=1</math><br />
<br />
In the subsequent years of the model run, the wage index is first adjusted with the equilibration signal obtained from the unemployment rate PID controller in the previous period<br />
<br />
:<math>LABWAGEIND_{r,t=1}= LABWAGEIND_{r,t=1}* mullw_{r,t-1} </math><br />
<br />
A wage impact (labwageimpact) is then computed using the changes in the wage index relative to the base value. The impact is smoothed with a moving average algorithm.<br />
<br />
:<math>labwageimpact_{r}= labwageimpact_{r,t-1}*0.9+ (1-LABWAGEIND_{r,t})*0.1</math><br />
<br />
The smoothed impact is used as the equilibration signal for labor supply. As we have already described in the section on labor supply, a small fraction of the impact (labwageimpact) is applied to the labor participation rate. The impact is scaled down to account for the slow pace of changes on the supply side.<br />
<br />
:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact_{r,t}*0.05)</math><br />
<br />
For the impacts of wage on labor demand we use a second PID multiplier as opposed to using the changes in wage index that we have done on the supply side. The second PID uses the wage index itself as the process variable and uses the base year value of 1 as the target. The reason we had to use this second PID is to control the pace at which wage disequilibrium can affect demand, especially in the event of an abrupt shock. The smoothing and scaling down that works on the supply side is not enough to control oscillations on the demand side.<br />
<br />
:<math>Diffl1_{t}=LABWAGEIND_{r,t=1}-1</math><br />
<br />
:<math>Diffl2_{t}=LABWAGEIND_{r,t}-LABWAGEIND_{r,t-1} </math><br />
<br />
:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},ellabwage1_{r},ellabwage1_{r})</math><br />
<br />
A second impact factor (labwageimpactmul) is computed using the correction signal from this second multiplier:<br />
<br />
:<math>labwageimpactmul_{r,t}= labwageimpactmul_{r,t-1}*mullw_{r,t}</math><br />
<br />
This impact factor is applied on the labor demand as described in the section on labor demand.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}= LABDEMS_{r,s,sk,t}* labwageimpactmul_{r,t}</math><br />
<br />
== Informal Labor ==<br />
<br />
IFs forecast labor and GDP share of the informal sector. Informal labor forecast is not explicitly endogenized in the labor market though. They are rather driven by development, skill and regulatory factors[[#_ftn1|[1]]]. However, the productivity and revenue impacts of changes in informality affects output and thus labor demand implicitly as a very distal driver.<br />
<br />
= References =<br />
<br />
<references /><br />
</div></div></div></div></div></div></div></div></div></div></div>Wikiadmin//pardee.du.edu/wiki/LaborLabor2018-09-07T22:41:27Z<p>Wikiadmin: </p>
<hr />
<div>Workers in an economy supply the expertise and the efforts needed to produce goods and services. In return the labor receives wages that they use to meet their current and future consumption needs. On one hand, shortage of labor with required skills prevents economies from realizing their growth potential. On the other hand, individuals falling short of the right qualifications might remain unemployed or underemployed failing to secure income needed for a decent living. The ongoing adjustments to find the best match between skills, jobs and wages can only be studied through a dynamic model of the labor market.&nbsp; &nbsp;<br />
<br />
Such a model should go beyond providing a reasonable answer to the obvious question of why employment and wages go up and down. An aggregate labor market must deal with issues that have strong interconnections with various other dynamic changes in the greater society. What kind of dividend of deficit can a society expect from its labor force given the phase of demographic transition in which it is situated? How severely would aging affect the pool of working age adults? Might increasing female participation rates offset some of the losses from aging? What is the level of skills and educational attainment in a society? These supply phenomena move relatively slowly unless there are huge disruptions, like a war or famine, or an aggressive policy push. The demand side, in contrast, needs to be more responsive in adjusting wages and employment given the investment and technology in the various sectors of the broader economy. In general, though, the labor market demonstrates some sluggishness compared to the goods and services markets as it involves moving human beings with various limitations. Consumption of goods and services depend on the income earned by the labor. Uneven distribution of employment and wages among labors of various types or between labor and capital for a long period of time can give rise to persistent inequality in a society. &nbsp;&nbsp;&nbsp;&nbsp;<br />
<br />
== Conceptual Framework ==<br />
<br />
Labor markets are markets for workers and jobs. In a labor market, employers meet their demand for labor with the supply of people willing to work at the wage the employers can offer. The employers raise the wage when there is a shortage of workers. Workers agree to take a lower wage when there are more of them than the firms need. In the real-world labor markets do not always clear at perfect equilibrium. Frinctional unemployment results for various reasons, for example, the search time between jobs. Structural unemployment can result from technology induced disruptions. Some unemployment could thus persist in the labor market even when there aren’t any short-term fluctuations. There is also the phenomenon of informal employment that consists of less sophisticated workers and entrepreneurs engaged in unregulated economic activities. &nbsp;In a dynamic model that covers the entire economy, the real wage earned by the labor drives the income and social mobility.&nbsp;&nbsp;&nbsp;<br />
<br />
To understand the long-term dynamics of the labor market, we need also examine the deeper determinants of labor demand and supply, the determinants that can shift the curves. Labor demand changes over time with the changes in demand for goods and services and the labor input needed to produce those. Labor productivity itself improves with technological progress. Long term transitions in the supply of labor are mostly demographic. &nbsp;<br />
<br />
Labor supply is determined by the working age population and the share of that population who are available for participation in the workforce. The labor supply is relatively stable as the demographic changes are slow in pace. As the share of elderly in the population increases, a recent trend in many societies, the rate of participation declines. Some of the aging impacts will be offset by the greater female participation rates, a second trend that surfaces as economies develop and women attain more education. Educational attainment also drives the general skill level of workers, male and female. Specific skills are obtained through training and experience that augment the knowledge obtained through general and specialized education. &nbsp;&nbsp;&nbsp;&nbsp;<br />
<br />
It is the demand side that causes most of the short-term imbalances in the labor market. &nbsp;In the long term, as said earlier, the important driver of demand for labor and their skills is technological progress. Labor requirement drops with advances in technology, more so for less skilled labor. Labor composition changes accordingly both within and across sectors. Rapid advances in technology can also cause disruption in the system when there is not much opening in the other sectors. Labor displacement is offset to some extent by the growth in the economy and the resulting increase in total demand. &nbsp;&nbsp;&nbsp;<br />
<br />
As we have already mentioned, employees maximize income and the firms minimize labor costs. When there are more laborers than the firms can hire, there is unemployment. Shifts in the rates of unemployment impacts wage, the price of labor. For example, wages drop in the event of rising unemployment as there are more people to hire from. Wage adjustments feed back to the demand for labor seeking to bring the market back to equilibrium.<br />
<br />
The challenges around the conceptual distinction between unemployment and employment is further complicated by the phenomenon of informal employment. In many developing countries there is a large urban non-agricultural informal sector where low-skilled workers work for wages typically lower than a formal employment.<br />
<br />
[[File:LMFlowchart1.png|frame|center|Description of the labor model]]<br />
<br />
== Dominant Relations ==<br />
<br />
The labor model in the International Futures system (IFs) balances the total supply of labor with the total labor demanded by all economic sectors. Total labor (LAB) is computed from the working age population and the labor participation rate. Population forecasts are obtained from the IFs demographic model. Participation rates (LABPARR) are computed by sex with a catchup algorithm for the female participation towards that for the male. Labor is also disaggregated by skill level, as determined by educational attainment, in a separate labor supply variable (LABSUP) which is used to distribute labor earnings by skill level. [** LABSUP do not affect the demand/supply balance now]<br />
<br />
Labor demands (LABDEMS) are driven by sectoral technology functions used to compute the labor requirement by skill level for each unit of potential valued added in the sector. These labor coefficients (LABCOEFFS) are multiplied with the projected value added for the sector to compute the needed manpower. The balancing mechanisms determines the labor employed in each of the sectors (LABS).&nbsp;&nbsp;<br />
<br />
The balancing, in the current version of the model, can be done in one of the two ways. In the first method, total needs combined from all economic sectors is normalized to the available pool of labor computed by subtracting the unemployed from those who are at or looking for work. The rate of unemployment is kept at its natural rate for which we use the base year rate of unemployment. (** This might need to be changed for countries where the market is undergoing some abrupt transition.)<br />
<br />
In the second balancing method, added in a recent revision of the model, total demand is equilibrated to supply through a CGE like market equilibrium model. An indexed wage (LABWAGEIND) and the rate of unemployment (LABUNEMPR) work as the equilibrating variables. As unemployment deviates from the target, PID algorithms send a signal for the wage to adjust. Wage adjustments cause adjustments in the “base” labor demands by sector computed from the labor-coefficient functions as described earlier. Wage signals also affects the labor participation rate. The magnitude of impact on the supply side is much lower than that on the demand side.<br />
<br />
Wage and unemployment rate are aggregated for the total labor market. The wage index starts with a base year value of 1 and the unemployment rates start with the historical data for the base year. Initial year unemployment rate works as the target for long term unemployment.<br />
<br />
== Key Dynamics ==<br />
<br />
The following key dynamics are directly related to the dominant relations:<br />
<br />
*Labor supply is determined from population of appropriate age in the population model (see its dominant relations and dynamics) and endogenous labor force participation rates, influenced exogenously by the growth of female participation.<br />
*Labor demand is driven by sectoral demand functions driven by technological progress<br />
<br />
== Structure and Agent System ==<br />
<br />
{| border="1" cellspacing="0" cellpadding="0" width="0" style="width:502px;"<br />
|-<br />
| style="width:242px;height:49px;" | <br />
'''System/Subsystem'''<br />
<br />
| style="height:49px;" | <br />
Labor market<br />
<br />
|-<br />
| <br />
'''Organizing Structure'''<br />
<br />
| <br />
Labor supply by skill level and labor demand by sector for each skill category represented within an equilibrium-seeking model with wage and unemployment rate as the equilibrating variables<br />
<br />
|-<br />
| <br />
'''Stocks'''<br />
<br />
| <br />
Population, labor, education, &nbsp;accumulated technology<br />
<br />
|-<br />
| <br />
'''Flows'''<br />
<br />
| <br />
Participation rate; Coefficients of labor demand; Employment (unemployment); Wage<br />
<br />
|-<br />
| <br />
'''Key Aggregate&nbsp;''''''Relationships&nbsp;'''<br />
<br />
'''(illustrative, not comprehensive)'''<br />
<br />
| <br />
Labor supply is driven by demographic changes; Participation of female change over time; Labor requirement changes with technological development; Unemployment rate drives wage; Wage movements affect labor demand and participation rate<br />
<br />
|-<br />
| <br />
'''Key Agent-Class Behavior&nbsp;''''''Relationships'''<br />
<br />
'''(illustrative, not comprehensive)'''<br />
<br />
| <br />
Households and work/leisure, and female participation patterns;<br />
<br />
&nbsp;<br />
<br />
Firms and hiring;<br />
<br />
&nbsp;<br />
<br />
<br />
<br />
<br />
<br />
|}<br />
<br />
= Labor Model Data =<br />
<br />
The labor supply and unemployment data that we use in our model is from International Labor Organization (ILO). For data on the demand side, we used data from the Global Trade Analysis Project. Wage variable used in the equilibration algorithm&nbsp;is an index anchored to the base year of the model.<ref>GTAP database helped us compute wage rates by sector and skill.</ref> IFs preprocessor prepared these data for model use using various estimation, conversion and reconciliation processes.&nbsp; &nbsp; &nbsp;<br />
<br />
== Definitional Issues ==<br />
<br />
There are ambiguities in the way some of the labor market variables are defined. Labor participation rates and the rate of unemployment are two that need special attention.<br />
<br />
The size of the labor supply available for economic activities is expressed with the labor force participation rate. ILO defines this as a “measure of the proportion of country’s working-age population that engages actively in the labor market, either by working or looking for work.”<ref>http://www.ilo.org/ilostat-files/Documents/description_LFPR_EN.pdf</ref>&nbsp;National labor force surveys and census data are used to estimate this rate. The definition of labor force here includes both employed and unemployed and the rate is expressed as a percentage of working-age population. Working-age population is defined here as the population above legal working-age. For international comparability, ILO adopts a convenient minimum threshold of fifteen years as working age and avoids putting any upper age limit. In practice, both the minimum and the upper-age limits can vary by country. For example, the working-age in the USA is sixteen years. In the Netherlands the upper age limit is seventy-five years, whereas South African data uses an upper age limit of 64.<ref>https://www.bls.gov/fls/flscomparelf/technical_notes.pdf</ref><br />
<br />
Ambiguities are more abundant in the definition of unemployment. ILO came up with a guideline on this as well. Per the ILO guideline, the unemployed are those among the working-age population who are not employed, are available for work and are actively looking for jobs<ref>The definitions around employed and unemployed were agreed upon by nations through the ‘Resolution concerning statistics of work, employment and labor underutilization’ adopted by the 19th International Conference of Labor Statisticians (ICLS) in 2013. (Bourmpoula et al, 2017: 6).</ref>; the unemployment rate is expressed as a percentage of those who are in the labor force. The availability and job-seeker status could be defined in different ways giving rise to incompatibility in data. &nbsp;While there seems to be little room for disagreement on whether someone is at work or not, whether that work should be considered as employment is contested at many times.<br />
<br />
The debates around the nature and type of employment can range from gainfulness to workplace setting. For example, a large number of workers in the low-income low-regulation developing countries work outside the purview of formal enterprises. According to an ILO estimate, more than half of the global labor force and more than 90% of Micro and Small Enterprises (MSEs) worldwide are in the so called informal economy.<ref>http://www.ilo.org/global/topics/employment-promotion/informal-economy/lang--en/index.htm</ref> This might explain the apparently counterintuitive pattern of low unemployment rate in some low-income countries (e.g., 2.2% for Guatemala) and relatively higher numbers for some of the developed nations. The low numbers in the poorer countries hide the prevalence of extremely low wage jobs in the informal sectors in these countries, the only options for the vulnerable people in the absence of any kind of social safety net. &nbsp;Contrastingly, in the developed countries the so called ‘gig-economy’ is attracting more and more workers who choose to work on their own rather than in a formal enterprise. ILO conceptualization makes the informal work part of total employment. The stacked Venn diagram below presents the relationship among the labor force metric including informal employment. IFs also models informal economy both in terms of GDP share and employment share of informal in the total economy and employment.<br />
<br />
[[File:LaborSubsets.png|frame|right|Relationship among various labor measurement]]<br />
<br />
Incompatibility can arise in the treatment of various population groups for the computation of the denominator for participation and unemployment rates.<ref>For example, the USA excludes people in the defense services and those in the prisons or mental asylums in their computation of the civilian non-institutional working-age population. There are also variations in the treatments of students, those recently laid-off, and family workers. Please see https://stats.bls.gov/opub/mlr/2000/06/art1full.pdf for a discussion </ref> ILO makes their best efforts to make adjustments in the data for the sake of international comparison. For example, ILO asks countries that deviate from ILO guidelines to collect data needed to convert national figures to ILO figures. It is likely that some differences might have slipped past the adjustment process. We use ILO data and continue to update our database &nbsp;<br />
<div><div id="ftn1"><br />
&nbsp;<br />
</div></div><div><br/><div id="ftn4"><div><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] For example, the USA excludes people in the defense services and those in the prisons or mental asylums in their computation of the civilian non-institutional working-age population. There are also variations in the treatments of students, those recently laid-off, and family workers. Please see [https://stats.bls.gov/opub/mlr/2000/06/art1full.pdf https://stats.bls.gov/opub/mlr/2000/06/art1full.pdf] for a discussion&nbsp;<br />
<br />
The GTAP data that we use for the demand side of the labor model is taken as labor headcounts and is thus immune from ambiguities around rate computation. As far as we could gather[[#_ftn1|[1]]], the data includes both the formal and informal employment. We also need mention here that the GTAP database reconciles the labor data to calibrate the general equilibrium modeling that they do for the trade analyses. The data could thus be somewhat different from data collected through direct surveys. As a CGE model IFs is benefited by using calibrated data.<br />
<div><br/><br />
----<br />
<div id="ftn1">[[#_ftnref1|[1]]] Please see the webpage for documentation on GTAP labor data statistic: [https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=3248 https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=3248]</div></div></div></div></div></div><br />
<br />
== Sources of Labor Data ==<br />
<br />
IFs model uses ILO data for labor participation rates and for the unemployment rate. The data in IFs are collected from World Bank’s World Development Indicators (WDI) database. According to their documentation, WDI obtained the data from the ILO.<br />
<br />
&nbsp;<br />
<br />
Unemployment rate data in IFs is also collected from WDI. Like the participation rates WDI also obtains their unemployment data from ILO.[[#_ftn1|[1]]]<br />
<br />
For employment and labor demand data IFs uses Purdue University’s Global Trade Analysis Project (GTAP) database. GTAP collects and compiles factor payments, imports, and intersectoral flow data to calibrate CGE models of national economies for trade and other analyses. In their ninth release in 2016, GTAP published data for 140 countries and regions for the year 2011. The earlier GTAP releases, which the IFs model used for its previous versions, compiled data for the years 2004 and 2007. GTAP data release aggregates economic activities into 57 commodities and activities following International Standard Industrial Classification (ISIC). The IFs model maps the 57 GTAP sectors into six economic sectors of IFs – agriculture, energy, material and mining, manufacture, services and ICT. Appendix 2 presents two tables listing the sectors mapping between IFs and GTAP, and GTAP and ISIC. GTAP further disaggregates labor in each of the commodities/activities into five occupation and skill categories following the nine category International Standard Classification of Occupations (ISCO-88). The IFs model collapses five GTAP occupation categories into the simple IFs dichotomy of skilled and unskilled. The mapping of occupations and skills are presented in the third appendix of this document. &nbsp;<br />
<br />
The data in the main GTAP database, prepared for CGE modeling, are all in dollar unit and thus do not include labor headcounts. We have used a ‘satellite’ GTAP database[[#_ftn2|[2]]] for labor headcounts by skill and sector. The labor counts were also used to plot labor requirement functions for each of the IFs economic sectors and skill categories. The wage share of skilled and unskilled labor in each sector was computed using the labor headcounts and labor payments.<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] The name of the IFs table is SeriesLaborUnemploy%<br />
</div><div id="ftn2"><br />
[[#_ftnref2|[2]]] See Weingarden and Tsigas, 2010 for the details on the preparation of this database.<br />
<br />
== Scope of IFs Labor Model ==<br />
<br />
The IFs labor model simulates labor market at the national level. Each national labor market forecasts labor demand and employment by six sectors - agriculture, energy, mining, manufacture, services and ICT- and two skill levels - skilled and unskilled. The supply side do not have sectoral representation. IFs forecasts total labor force and labor supply by the two skill levels. Labor participation rate is computed in IFs by gender. Wage and unemployment rate is forecast for the overall labor market only.&nbsp;&nbsp;<br />
<br />
== Labor Model Pre-processor ==<br />
<br />
IFs system has a data preprocessor that prepares the initial conditions for the model using historical databases and various assumptions and estimated relationships to fill in the missing data and make data adjustments as needed[[#_ftn1|[1]]]. Pre-processing of labor data takes place in two IFs pre-processing modules. Labor participation rate data, which is closely related to demography, is processed in the population pre-processor. Unemployment rate and labor demand data are processed in the economic pre-processor. &nbsp;<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] For more details, please see ‘The Data Pre-Processor of International Futures (IFs)” by Barry B. Hughes (with Mohammod Irfan) at [http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf]<br />
<br />
=== Pre-processing Labor participation rate and unemployment ===<br />
<br />
For initializing labor participation rates by sex (LABPARR) the model uses the historical values from the base year or the most recent year with data[[#_ftn1|[1]]]. For countries with no data we use regression relationships of the participation rates, for men and for women, with income per capita. The relationships, shown in the next figure, are not great. However, the functions affect only five countries for which we do not have any data at all: Grenada, Kosovo, Micronesia, Seychelles and South Sudan[[#_ftn2|[2]]].<br />
<br />
&nbsp;<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] The data tables that the IFs model pre-processor use for initializing labor participation rates are: SeriesLaborParRate15PlusFemale%, SeriesLaborParRate15PlusMale%.<br />
</div><div id="ftn2"><br />
[[#_ftnref2|[2]]] We should try to collect participation rate for these countries from country sources.<br />
<br />
IFs data series SeriesLaborUnemploy% is used for the initialization of unemployment rates. That series has annual unemployment rates for one or more years between 1980 and 2016, for 181 of the 186 IFs countries. For five countries (Grenada, Kosovo, Micronesia, Taiwan and South Sudan[[#_ftn1|[1]]]) there is no data at all. To fill in the missing data we use a regression function of unemployment rate against GDP per capita. Like the participation rate functions, this function does also not have much of an explanatory power.<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] These are pretty much the same countries for which we do not have any participation rate data. This indicates ILO might have some administrative limitation in reporting data for these countries (notice Kosovo, Seychelles etc in the list)<br />
<br />
=== Pre-processing labor demand and unemployment from GTAP ===<br />
<br />
The IFs economic pre-processor reads labor headcount and labor payment data from the GTAP database. In addition to performing sector and occupation/skill mapping between GTAP and IFs, pre-processor also use the labor headcount data to compute labor coefficient functions, the principal driver of labor demand in the IFs model.<br />
<br />
Labor coefficients are defined as the amount of labor needed to produce one unit of value added in a certain sector of the economy. The coefficients depend on the level of technology. The model uses GDP per capita as an indicator of the level of technological development. IFs pre-processor estimates labor coefficient functions for labor of different skill levels for the different sectors of the economy.<br />
<br />
The functions are derived from GTAP data we described earlier. The model pre-processor reads data on factor payments and aggregates data from 57 GTAP sectors to six IFs sectors. Shares of payment going to skilled and less-skilled workers in each of the sectors are then computed. Countries are grouped according to their level of technological development as represented by per capita income. For each group labor coefficients are obtained by taking an average of the country coefficients. &nbsp;We also convert labor payments data to labor headcount data using per capita income as a proxy for average wage. Labor coefficients and income are then plotted into a power function relationship. The figure below plots some of those labor functions.&nbsp; The functions fit quite well with a power law formulation[[#_ftn1|[1]]].<br />
<div><br/><br />
----<br />
<div id="ftn1">[[#_ftnref1|[1]]] This is interesting given the prevalence of power law in all sorts of scale-up activities (West 2017).</div></div></div></div></div></div></div></div></div></div><br />
<br />
= Labor Model Flowcharts =<br />
<br />
The diagram below shows an outline of the IFs labor model. On the supply side, the total labor pool (LAB) is computed from the labor force participation rates, by sex, (LABPARR) and the population (POP) in their working age, i.e., population over 15 (POP15TO65 + POPGT65). Participation rates are driven by the demographic changes with an additional negative impact from aging and a catch-up in female participation rate. Skill level of the labor supply (LABSUP) is driven by the level of development (GDPPCP) and the demand for labor is driven by labor-coefficients (LABCOEFFS) computed from coefficient function representing shifts in demand with technological progress as proxied by the level of development (GDPPCP). Coefficients computed by sector and skill gives the labor requirement by skill type for each unit of value added (VADD) in the sector. Multiplying these coefficients with projected value added in each sector gives an estimate of the labor demand. &nbsp;<br />
<br />
Any surplus or shortage between total labor demand and supply is used to compute the rate of unemployment. Deviations in the unemployment rate (LABUNEMPR) signal wage changes through an equilibrium seeking algorithm. Both demand and supply respond to the wage variable (LABWAGEIND) indexed to the base year. The supply responses are much slower than the demand responses.<br />
<br />
[[File:FLOCHART2.png|frame|center|Labor Model Flowchart]]<br />
<br />
= Labor Model Equations =<br />
<br />
== Overview ==<br />
<br />
The labor model is a part of the IFs economic model that uses labor model output as an input to a Cobb-Douglas production function in a multi-sector general equilibrium model. IFs is a very long-run dynamic model. Instead of computing fixed short-run equilibria that clear the relevant markets IFs uses an equilibrium seeking algorithm to balance the various systems over the longer run. The algorithm is known as the PID (proportion-integral-derivative) controller algorithm and is used widely in industrial control systems. It makes equilibrium seeking variables in IFs move towards a set target. The algorithm works by computing a multiplier based on the movement of the variable towards the target, as obtained by an integral (I) of the path traversed, and the rate of movement towards the target, the derivative term. The multiplier is applied on the process variable (the P term), or a response variable, in the subsequent time period. In the labor model, unemployment rate (LABUNEMPR) is used as the process variable and the PID multiplier is used on the wage rate (LABWAGEIND). Job availability (LABDEMS) and participation rate (LABPARR) get affected by changes in wage. &nbsp;&nbsp;<br />
<br />
Throughout this section we use subscripts and notations common to other modules of IFs. For example, we use t for time period. Subscripts p and r represent sex and country/region, respectively, c is the cohort number, with cohort 1 representing the newborns, cohort1 the the one-year to four-year-olds, cohort two five-year to nine-year-olds etc. Values for p are 1 for male, 2 for female and 3 for both sexes combined. For economic sectors we use s and for skill levels sk.<br />
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== Labor Supply: Equations ==<br />
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The total pool of labor is computed by multiplying the population of working age with the labor force participation rate (LABPARR). &nbsp;Population forecasts come from IFs demographic model which computes both five-year and single-year age-sex cohorts (''agedst'', ''fagedst''). &nbsp;<br />
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The labor model forecasts participation rates by country/region&nbsp; and gender. Participation rates in the model move with the changes in the demographic composition. Female participation rates, which have historically been lower than the same for the male in all societies, but has moved up in modern and affluent societies, get a catch-up boost in the model. Participation rates can also change when there is labor shortage or surplus and the employers try to incentivize or discourage workers by changing wage. This last impact is much less slow than similar wage impacts on the demand side.<br />
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== Labor Participation Rate ==<br />
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Labor participation rates (''LABPARR'') for male and female are first initialized with historical data.<br />
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:<math>LABPARR_{r,p}= LABPARR_{r,p,t=1} </math><br />
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A ‘catch-up’ boost is added to the female participation rate. The boost added (FemParLabMul) starts at a third of a percentage point and withers away following a non-linear path as the female rates approaches the catch-up target (FemParTar), The maximum catch-up that can occur over the horizon of the model is thirty percent.<br />
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:<math>FemParTar_{r}=Amin(LabParRI_{r,p=1},LabParRI_{r,p=2}+30)</math><br />
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:<math>FemParLabMul_{r}=(FemParTar_{r}-LABPARR_{r,p=2,t-1})/(FemParTar_{r}-LABPARR_{r,p=2,t-1})</math><br />
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:<math>LABPARR_{r,p=2,t}=LABPARR_{r,p=2,t-1}+FemParLabMul_{r}*0.3</math><br />
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Next, we compute and apply the aging impact on the participation rate. As the relative share of people over the retirement age increases, the participation rate declines. The model keeps track of the changes in the demographic ratio (PopAgingRatio) of the population who are in their prime working age of 15 to 64 (POPWORKING) to those at a common retirement age of sixty-five or older (POPGT65). This ratio declines as countries age. The percentage drop in the ratio comparative to the base year is scaled appropriately to compute the aging impact (aging_impact). This impact is added to the male and female labor participation rates, with the impact on the female participation rate being slightly lower than that on male rates.<br />
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:<math>POPAgingRatio_{r,t}=POPWORKING_{r,t}/POPGT65_{r,t}</math><br />
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:<math>aging_impact_{r,t}=100*((POPAgingRatio_{r,t}/POPAgingRatio_{r,t=1})-1)*0.2</math><br />
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:<math>LABPARR_{r,p=1,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t} </math><br />
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:<math>LABPARR_{r,p=2,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t}*0.95 </math><br />
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Participation rates respond slowly to changes in wage and unemployment rate. The impact is implemented through a wage impact factor computed from annual changes in the wage index (labwageimpact). The base participation rates can be changed by model user through two model parameters: a direct multiplier on the participation rate (labparm), or one that changes participation by moving the retirement age (labretagem)<br />
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:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact*0.05)*labparm_{r,p,t}*labretagem_{r,p,t}</math><br />
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Total participation rate (LABPARRr,p=3,t) is computed by an weighted average of male and female participation rates.<br />
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:<math>LABPARR_{r,p=3,t}= (∑_{p=1 to 2}∑_{c=4 to 21}(agedst{r,c,p,t}*LABPARR_{r,p,t}))/(∑_{p=1 to 2}∑_{c=4 to 21}agedst{r,c,p,t})</math><br />
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== Total Labor ==<br />
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Finally, the total number of labor available for work (LAB) is computed by multiplying the total participation rate with the population of fifteen-year-olds or older.<br />
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:<math>LAB_{r,t}= LABPARR_{r,p=3,t}*∑_{p=1 to 2,c=4 to 21}agedst_{r,c,p,t}</math><br />
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== Labor by skill level ==<br />
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The labor model forecasts labor supply (LABSUP) by two skill categories. The variable (''LABSUP'') is initialized in the pre-processor by reading the employment by skill/occupation (''LABEMPS'') data from GTAP[[#_ftn1|[1]]] &nbsp;and adding the unemployment numbers. We assume same unemployment rate (''LABUMEMPR'') for skilled and unskilled labor.<br />
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:<math>LABSUP_{r,t=1,sk}=∑_{s=1 to 6}(LABEMPS_{r,s,t=1}/(1-(LABUNEMPR_{r,t=1}/100))</math><br />
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The model forecasts labor by skill through a model of the skilled share of the labor. Education, training, exposure, and experience of the employees all improve with the level of development. The model captures this with an analytic function of the skilled share (perskilled) driven by GDP per capita at PPP (GDPPCP) -<br />
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:<math>perskilled_{r}=f(GDPPCP_{r})</math><br />
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Among the causal drivers of skill, education is considered to be the most proximate. Education is strongly correlated with the level of development, the deeper driver of skill in the model. However, the recent increase in education and/or a policy driven educational expansion might add to the impact of education on skill. Additional impacts from education on skill, when there is any, is computed through an expected function formulation. For example, in a society where an average adult has more (or less) education than the adults in other societies at that level of development, the skill share is given a slight upward push (or downward pull). The expectation function is a logarithmic function of educational attainment of working age population (EDYRSAG15) driven by GDP per capita at PPP. Attainment above (or below) the expected level (YearsEdExp) is computed by the function output (YearsEd) adjusted for country situation (yearseddiff). The percentage adjustment to the skilled share (LabSupSkiAdj) is computed using additional (limited) education, i.e., the difference between actual (EDYRSAG15) and expected values of educational attainment, expressed as a percentage of the expected value. The adjustment is scaled appropriately and peters off over time.<br />
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:<math>YearsEd_{r,t}= f(GDPPCP_{r,t})</math><br />
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:<math>yearsdeddiff_{r}= EDYRSAG15_{r,p=3,t=2}-YearsEd_{r,t=2}</math><br />
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:<math>YearsEdExp_{r,t}=YearsEd_{r,t}+yearsdeddiff_{r}</math><br />
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:<math>LabSupSkiAdj_{r,t}=0.3*(EDYRSAG15_{r,p=3,t=2}*YearsEdExp_{r,t})/YearsEd_{r,t}</math><br />
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:<math>LabSupSkiAdj_{r,t}=ConvergeOverTime(0,LabSupSkiAdj_{r,t},70)</math><br />
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:<math>perskilled_{r,t}= perskilled_{r,t}*(1+LabSupSkiAdj_{r,t})</math><br />
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The skilled share (perskilled) is multiplied with the total labor supply (LAB) to obtain the number of labors who are skilled (LABSUPskilled)<br />
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:<math>LABSUP_{r,skilled,t}=LAB_{r,p,t}*perskilledI_{r,t} </math><br />
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As a last step, the model adjusts for the country specific variations in the skilled labor count not captured by the deeper and the proximate models. This is done by saving a ratio (LABSUPSkilledRI) of the actual historical data and the model computed value in the initial year. In the subsequent years this ratio is used to adjust the skilled labor forecast gradually.<br />
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:<math>LABSUPCompSkilled_{r}=LAB_{r}*perskilled_{r,t=1}/100 </math><br />
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:<math>LABSUPSkilledRI_{r}=LABSUP_{r,skilled,t=1}/LABSUPCompSkilled_{r}</math><br />
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:<math>LABSUP_{r,skilled,t}= LABSUP_{r,skilled,t}*ConvergeOverTime(LABSUPSkilledRI_{r},1,85)</math><br />
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Number of unskilled labor is obtained by subtracting the skilled labor from the total pool.<br />
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:<math>LABSUP_{r,unskilled,t}= LAB_{r,p,t}- LABSUP_{r,skilled,t}</math><br />
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== Labor Demand: Equations ==<br />
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IFs economic model forecasts production in six economic sectors. IFs labor model computes the longer-term and shorter-term determinants of demand for skilled and unskilled labor (LABDEMS) for the production processes. The long-term drivers of labor requirement are technological progress or the lack of it. In the shorter-term wage affects the labor demand most. Wage in turn is affected by labor supply or skill shortage.<br />
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The IFs model divides economic activities into six economic sectors – agriculture, energy, materials, manufacture, services and information, and communication technologies. Workers in the IFs labor model are disaggregated into two skill types. While the skill composition varies by the technology used in the sector and starts tilting towards the more skilled with the progress in technology, absolute number of labors needed to produce the same output goes down with technological development for both skilled and unskilled labor. This is illustrated in the next figure which plots the changes in labor requirement against GDP per capita at PPP, a proxy for level of development. Agriculture is a much less skill-intensive process than the manufacture, however, with technological progress skill requirement improves rapidly in both sectors. The IFs labor model computes these labor requirement functions in the model pre-processor. As we have already described in the pre-processor section, the computation of these functions use GTAP data on employment by occupation and economic activity. Appendices 3 and 4 lists sector and occupation mapping between GTAP and IFs.<br />
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These functions are used to compute the labor coefficients (LABCOEFFS), i.e., number of skilled and unskilled labor needed to produce unit amount of output with the technology available, for which we use GDP per capita at PPP as a proxy.<br />
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:<math>LABCOEFFS_{r,sk,s,t}= f(GDPPCP_{r})</math><br />
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manufacture, services and ICTech) and the subscrip sk stands for skill categories with 1 denoting unskilled and 2 skilled. The labor coefficients obtained from the analytical functions require some adjustments to incorporate country deviations from the functions for various factors not captured in the regression relationship. The first of these adjustments is a gradual removal of impacts of short-run fluctuations in output and labor from the computation of labor coefficient. This adjustment is applied on the coefficients computed from the function. The equation below shows a simplified form of these computations.<br />
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:<math>LabCoeffAdjFac_{r,k,s,t}=f(igdpr_{r,t=2},(LAB_{r,t=2}/LAB_{r,t=1}),(LABCOEFFS_{r,t}/LABCOEFFS_{r,t-1}))</math><br />
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:<math>LABCOEFFS_{r,sk,s,t}=LABCOEFFS_{r,sk,s,t}(1-LabCoeffAdjFac_{r,k,s,t})</math><br />
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Model users can use a global parameter (labcoeffsm) to change the labor coefficients by skill level for any or all of the six sectors –<br />
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:<math>LABCOEFFS_{r,sk,s,t}= LABCOEFFS_{r,sk,s,t}*'''labcoeffsm_{s,sk}'''</math><br />
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To forecast the total labor demand, the labor coefficients (LABCOEFFS) are multiplied to the total projected output for each of the economic sectors. The forecast is adjusted for any discrepancy between data and model. The adjustment factor (LABDemsAdjFac) is computed as the initial ratio between the actual and computed employment. Actual employment is obtained from historical data (LABEMPS) processed using the GTAP database. The computed employment is obtained by multiplying the labor coefficients (LABCOEFFS) with the final output of the sector (VADD).<br />
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:<math>LabDemsAdjFac_{r,s,sk}= LABEMPS_{r,s,sk,t=1}/(VADD_{r,s,t=1}*LABCOEFFS_{r,sk,s,t})</math><br />
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The projected output is obtained by applying the growth rate (IGDPRCOR) on the sectoral value added from the previous year (VADD). The total labor demand is given by the product of the labor coefficients, projected output, demand adjustments and wage impacts (labwageimpactmul) and the number 1000 which adjusts the units for the equation. Wage impact comes from the level of unemployment and is computed in an equilibration process described in the next section. Model users can use a multiplicative parameter (labdemsm) to slide the demand upward or downward.<br />
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:<math>LABDEMS_{r,s,sk,t}=1000*VADD_{r,s,t-1}*(1+IGDPRCOR_{r})*LABCOEFFS_{r,sk,s,t}*LabDemsAdjFac_{r,s,sk}*labwageimpactmul_{r,s,sk}*'''labdemsm_{r,s}'''</math><br />
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== Unemployment and Wage: Labor Market Equilibration ==<br />
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The IFs labor model balances the labor market through an equilibrium seeking algorithm rather than computing an exact equilibrium at each time step. We use an algorithm borrowed from the control systems engineering. This PID controller algorithm, described also in the IFs economic model documentation, works by computing corrective signals for equilibrating variables using the deviations of a buffer variable, for example unemployment rate (LABUNEMPR), from a target value. The signal is computed from two quantities, the distance of the buffer from the target and the current rate of change of the buffer. The computation is tuned with PID elasticities to avoid oscillations. The computed signal is applied on the variable/s which need to be balanced, for example, demand and supply in the event of a market equilibration, thus getting closer to a balance at each step of simulation. The target value for the buffer variable and the tuning parameters of the control algorithm are obtained through rules-of-thumb and model calibration. The IFs labor model uses unemployment rate (LABUNEMPR) as the buffer variable for the market equilibration of labor demand and labor supply. The multiplier (i.e., corrective signal) obtained from the PID is applied on the wage index (LABWAGEIND). Changes in wage indices comparative to the base year, moderated through a second PID controller, is used to compute the final signal (labwageimpactmul) that drives labor demand and labor supply. Even though the model forecasts labor demand by sector and skill, and computes labor supply for both skill types, the equilibration algorithm works over the entire pool of labor. In other words, we assume that the skills are replaceable across sectors and the lack (or abundance) of jobs affects skilled and unskilled persons equally.<br />
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At each annual timestep, the model computes the unemployment rate (LABUNEMPR) as the gap in between the total supply of labor (LAB) and the total demand. The gap (EmplGap) is expressed as a share of the total labor, the standard way to express unemployment rate.<br />
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:<math>sumld=∑_{s,sk}LADEMS_{r,s,sk,t}</math><br />
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:<math>EmplGap= LAB_{r,t}*sumld</math><br />
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:<math>LABUMENPR_{r,t}= (EmplGap/LAB_{r,t})*100</math><br />
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As the target value (LabUnEmpRateTar) for the PID controller that modulates unemployment rate we use either the historical unemployment rate or a ten percent unemployment rate when the historical rate is higher than ten. Model users can override the historical target through a model parameter (labunemprtrgtval).<br />
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:<math>LABUMENPRi_{r,t}= LABUMENPR_{r,t}</math><br />
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:<math>LabUnempRateTarget_{r}=labunemptargetval_{r}</math><br />
:<math>If LabUnempRateTarget_{r}=0,<br />
LabUnempRateTarget_{r}= AMIN(LABUMENPRi_{r,t},10) </math><br />
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Unemployment rate target, when it is different from the base year value, is reached gradually with a convergence period of forty years . The target rate is converted to count (LabUnEmplTar) to make it equivalent to the employment gap (EmplGap) computed earlier.<br />
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:<math>LabUnEmplTar_{r}= LAB_{r,t}*ConvergeOverTime(LABUMENPRi_{r,t},0,100)</math><br />
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The first order difference (Diffl1) between the target unemployment and the demand-supply gap is used to compute a second order difference (Diffl2) accounting for changes in the rate of movement. The two differences and the PID multipliers (elwageunemp1, elwageunemp2) are provided to the PID function (ADJSTR). Working age population (POP15TO65r,t) works as the scaling base of the PID controller. The controller algorithm gives a multiplier (mullw) that is used in the subsequent year to adjust wage.<br />
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:<math>Diffl1_{t}=LabUnEmplTar_{r}-EmplGap</math><br />
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:<math>Diffl2_{t}=Diffl1_{t}-Diffl1_{t-1}</math><br />
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:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},elwageunemp1_{r},elwageunemp2_{r})</math><br />
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Wage adjustments affect demand and supply with an increase in wage drawing demand downward and supply upward. The opposite affects occur with a downward movement of wage. The wage variable affected by the PID multiplier (LABWAGEIND) is an index initialized at one. We use an indexed rather than a dollar wage in the equilibration process to avoid affecting the process from other economic phenomena that affects wage, for example, a rise in real wage as GDP or the labor share of income grows.<br />
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:<math>LABWAGEIND_{r,t=1}=1</math><br />
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In the subsequent years of the model run, the wage index is first adjusted with the equilibration signal obtained from the unemployment rate PID controller in the previous period<br />
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:<math>LABWAGEIND_{r,t=1}= LABWAGEIND_{r,t=1}* mullw_{r,t-1} </math><br />
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A wage impact (labwageimpact) is then computed using the changes in the wage index relative to the base value. The impact is smoothed with a moving average algorithm.<br />
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:<math>labwageimpact_{r}= labwageimpact_{r,t-1}*0.9+ (1-LABWAGEIND_{r,t})*0.1</math><br />
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The smoothed impact is used as the equilibration signal for labor supply. As we have already described in the section on labor supply, a small fraction of the impact (labwageimpact) is applied to the labor participation rate. The impact is scaled down to account for the slow pace of changes on the supply side.<br />
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:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact_{r,t}*0.05)</math><br />
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For the impacts of wage on labor demand we use a second PID multiplier as opposed to using the changes in wage index that we have done on the supply side. The second PID uses the wage index itself as the process variable and uses the base year value of 1 as the target. The reason we had to use this second PID is to control the pace at which wage disequilibrium can affect demand, especially in the event of an abrupt shock. The smoothing and scaling down that works on the supply side is not enough to control oscillations on the demand side.<br />
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:<math>Diffl1_{t}=LABWAGEIND_{r,t=1}-1</math><br />
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:<math>Diffl2_{t}=LABWAGEIND_{r,t}-LABWAGEIND_{r,t-1} </math><br />
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:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},ellabwage1_{r},ellabwage1_{r})</math><br />
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A second impact factor (labwageimpactmul) is computed using the correction signal from this second multiplier:<br />
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:<math>labwageimpactmul_{r,t}= labwageimpactmul_{r,t-1}*mullw_{r,t}</math><br />
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This impact factor is applied on the labor demand as described in the section on labor demand.<br />
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:<math>LABDEMS_{r,s,sk,t}= LABDEMS_{r,s,sk,t}* labwageimpactmul_{r,t}</math><br />
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== Informal Labor ==<br />
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IFs forecast labor and GDP share of the informal sector. Informal labor forecast is not explicitly endogenized in the labor market though. They are rather driven by development, skill and regulatory factors[[#_ftn1|[1]]]. However, the productivity and revenue impacts of changes in informality affects output and thus labor demand implicitly as a very distal driver.<br />
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= References =<br />
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<references /><br />
</div></div></div></div></div></div></div></div></div></div></div>Wikiadmin//pardee.du.edu/wiki/File:LaborSubsets.pngFile:LaborSubsets.png2018-09-07T22:37:02Z<p>Wikiadmin: </p>
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<div></div>Wikiadmin//pardee.du.edu/wiki/LaborLabor2018-09-07T22:35:27Z<p>Wikiadmin: </p>
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<div>Workers in an economy supply the expertise and the efforts needed to produce goods and services. In return the labor receives wages that they use to meet their current and future consumption needs. On one hand, shortage of labor with required skills prevents economies from realizing their growth potential. On the other hand, individuals falling short of the right qualifications might remain unemployed or underemployed failing to secure income needed for a decent living. The ongoing adjustments to find the best match between skills, jobs and wages can only be studied through a dynamic model of the labor market.&nbsp; &nbsp;<br />
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Such a model should go beyond providing a reasonable answer to the obvious question of why employment and wages go up and down. An aggregate labor market must deal with issues that have strong interconnections with various other dynamic changes in the greater society. What kind of dividend of deficit can a society expect from its labor force given the phase of demographic transition in which it is situated? How severely would aging affect the pool of working age adults? Might increasing female participation rates offset some of the losses from aging? What is the level of skills and educational attainment in a society? These supply phenomena move relatively slowly unless there are huge disruptions, like a war or famine, or an aggressive policy push. The demand side, in contrast, needs to be more responsive in adjusting wages and employment given the investment and technology in the various sectors of the broader economy. In general, though, the labor market demonstrates some sluggishness compared to the goods and services markets as it involves moving human beings with various limitations. Consumption of goods and services depend on the income earned by the labor. Uneven distribution of employment and wages among labors of various types or between labor and capital for a long period of time can give rise to persistent inequality in a society. &nbsp;&nbsp;&nbsp;&nbsp;<br />
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== Conceptual Framework ==<br />
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Labor markets are markets for workers and jobs. In a labor market, employers meet their demand for labor with the supply of people willing to work at the wage the employers can offer. The employers raise the wage when there is a shortage of workers. Workers agree to take a lower wage when there are more of them than the firms need. In the real-world labor markets do not always clear at perfect equilibrium. Frinctional unemployment results for various reasons, for example, the search time between jobs. Structural unemployment can result from technology induced disruptions. Some unemployment could thus persist in the labor market even when there aren’t any short-term fluctuations. There is also the phenomenon of informal employment that consists of less sophisticated workers and entrepreneurs engaged in unregulated economic activities. &nbsp;In a dynamic model that covers the entire economy, the real wage earned by the labor drives the income and social mobility.&nbsp;&nbsp;&nbsp;<br />
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To understand the long-term dynamics of the labor market, we need also examine the deeper determinants of labor demand and supply, the determinants that can shift the curves. Labor demand changes over time with the changes in demand for goods and services and the labor input needed to produce those. Labor productivity itself improves with technological progress. Long term transitions in the supply of labor are mostly demographic. &nbsp;<br />
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Labor supply is determined by the working age population and the share of that population who are available for participation in the workforce. The labor supply is relatively stable as the demographic changes are slow in pace. As the share of elderly in the population increases, a recent trend in many societies, the rate of participation declines. Some of the aging impacts will be offset by the greater female participation rates, a second trend that surfaces as economies develop and women attain more education. Educational attainment also drives the general skill level of workers, male and female. Specific skills are obtained through training and experience that augment the knowledge obtained through general and specialized education. &nbsp;&nbsp;&nbsp;&nbsp;<br />
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It is the demand side that causes most of the short-term imbalances in the labor market. &nbsp;In the long term, as said earlier, the important driver of demand for labor and their skills is technological progress. Labor requirement drops with advances in technology, more so for less skilled labor. Labor composition changes accordingly both within and across sectors. Rapid advances in technology can also cause disruption in the system when there is not much opening in the other sectors. Labor displacement is offset to some extent by the growth in the economy and the resulting increase in total demand. &nbsp;&nbsp;&nbsp;<br />
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As we have already mentioned, employees maximize income and the firms minimize labor costs. When there are more laborers than the firms can hire, there is unemployment. Shifts in the rates of unemployment impacts wage, the price of labor. For example, wages drop in the event of rising unemployment as there are more people to hire from. Wage adjustments feed back to the demand for labor seeking to bring the market back to equilibrium.<br />
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The challenges around the conceptual distinction between unemployment and employment is further complicated by the phenomenon of informal employment. In many developing countries there is a large urban non-agricultural informal sector where low-skilled workers work for wages typically lower than a formal employment.<br />
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[[File:LMFlowchart1.png|frame|center|Description of the labor model]]<br />
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== Dominant Relations ==<br />
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The labor model in the International Futures system (IFs) balances the total supply of labor with the total labor demanded by all economic sectors. Total labor (LAB) is computed from the working age population and the labor participation rate. Population forecasts are obtained from the IFs demographic model. Participation rates (LABPARR) are computed by sex with a catchup algorithm for the female participation towards that for the male. Labor is also disaggregated by skill level, as determined by educational attainment, in a separate labor supply variable (LABSUP) which is used to distribute labor earnings by skill level. [** LABSUP do not affect the demand/supply balance now]<br />
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Labor demands (LABDEMS) are driven by sectoral technology functions used to compute the labor requirement by skill level for each unit of potential valued added in the sector. These labor coefficients (LABCOEFFS) are multiplied with the projected value added for the sector to compute the needed manpower. The balancing mechanisms determines the labor employed in each of the sectors (LABS).&nbsp;&nbsp;<br />
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The balancing, in the current version of the model, can be done in one of the two ways. In the first method, total needs combined from all economic sectors is normalized to the available pool of labor computed by subtracting the unemployed from those who are at or looking for work. The rate of unemployment is kept at its natural rate for which we use the base year rate of unemployment. (** This might need to be changed for countries where the market is undergoing some abrupt transition.)<br />
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In the second balancing method, added in a recent revision of the model, total demand is equilibrated to supply through a CGE like market equilibrium model. An indexed wage (LABWAGEIND) and the rate of unemployment (LABUNEMPR) work as the equilibrating variables. As unemployment deviates from the target, PID algorithms send a signal for the wage to adjust. Wage adjustments cause adjustments in the “base” labor demands by sector computed from the labor-coefficient functions as described earlier. Wage signals also affects the labor participation rate. The magnitude of impact on the supply side is much lower than that on the demand side.<br />
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Wage and unemployment rate are aggregated for the total labor market. The wage index starts with a base year value of 1 and the unemployment rates start with the historical data for the base year. Initial year unemployment rate works as the target for long term unemployment.<br />
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== Key Dynamics ==<br />
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The following key dynamics are directly related to the dominant relations:<br />
<br />
*Labor supply is determined from population of appropriate age in the population model (see its dominant relations and dynamics) and endogenous labor force participation rates, influenced exogenously by the growth of female participation.<br />
*Labor demand is driven by sectoral demand functions driven by technological progress<br />
<br />
== Structure and Agent System ==<br />
<br />
{| border="1" cellspacing="0" cellpadding="0" width="0" style="width:502px;"<br />
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'''System/Subsystem'''<br />
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Labor market<br />
<br />
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'''Organizing Structure'''<br />
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Labor supply by skill level and labor demand by sector for each skill category represented within an equilibrium-seeking model with wage and unemployment rate as the equilibrating variables<br />
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'''Stocks'''<br />
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Population, labor, education, &nbsp;accumulated technology<br />
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'''Flows'''<br />
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Participation rate; Coefficients of labor demand; Employment (unemployment); Wage<br />
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|-<br />
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'''Key Aggregate&nbsp;''''''Relationships&nbsp;'''<br />
<br />
'''(illustrative, not comprehensive)'''<br />
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Labor supply is driven by demographic changes; Participation of female change over time; Labor requirement changes with technological development; Unemployment rate drives wage; Wage movements affect labor demand and participation rate<br />
<br />
|-<br />
| <br />
'''Key Agent-Class Behavior&nbsp;''''''Relationships'''<br />
<br />
'''(illustrative, not comprehensive)'''<br />
<br />
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Households and work/leisure, and female participation patterns;<br />
<br />
&nbsp;<br />
<br />
Firms and hiring;<br />
<br />
&nbsp;<br />
<br />
<br />
<br />
<br />
<br />
|}<br />
<br />
= Labor Model Data =<br />
<br />
The labor supply and unemployment data that we use in our model is from International Labor Organization (ILO). For data on the demand side, we used data from the Global Trade Analysis Project. Wage variable used in the equilibration algorithm&nbsp;is an index anchored to the base year of the model.<ref>GTAP database helped us compute wage rates by sector and skill.</ref> IFs preprocessor prepared these data for model use using various estimation, conversion and reconciliation processes.&nbsp; &nbsp; &nbsp;<br />
<br />
== Definitional Issues ==<br />
<br />
There are ambiguities in the way some of the labor market variables are defined. Labor participation rates and the rate of unemployment are two that need special attention.<br />
<br />
The size of the labor supply available for economic activities is expressed with the labor force participation rate. ILO defines this as a “measure of the proportion of country’s working-age population that engages actively in the labor market, either by working or looking for work.”<ref>http://www.ilo.org/ilostat-files/Documents/description_LFPR_EN.pdf</ref>&nbsp;National labor force surveys and census data are used to estimate this rate. The definition of labor force here includes both employed and unemployed and the rate is expressed as a percentage of working-age population. Working-age population is defined here as the population above legal working-age. For international comparability, ILO adopts a convenient minimum threshold of fifteen years as working age and avoids putting any upper age limit. In practice, both the minimum and the upper-age limits can vary by country. For example, the working-age in the USA is sixteen years. In the Netherlands the upper age limit is seventy-five years, whereas South African data uses an upper age limit of 64.<ref>https://www.bls.gov/fls/flscomparelf/technical_notes.pdf</ref><br />
<br />
Ambiguities are more abundant in the definition of unemployment. ILO came up with a guideline on this as well. Per the ILO guideline, the unemployed are those among the working-age population who are not employed, are available for work and are actively looking for jobs<ref>The definitions around employed and unemployed were agreed upon by nations through the ‘Resolution concerning statistics of work, employment and labor underutilization’ adopted by the 19th International Conference of Labor Statisticians (ICLS) in 2013. (Bourmpoula et al, 2017: 6).</ref>; the unemployment rate is expressed as a percentage of those who are in the labor force. The availability and job-seeker status could be defined in different ways giving rise to incompatibility in data. &nbsp;While there seems to be little room for disagreement on whether someone is at work or not, whether that work should be considered as employment is contested at many times.<br />
<br />
The debates around the nature and type of employment can range from gainfulness to workplace setting. For example, a large number of workers in the low-income low-regulation developing countries work outside the purview of formal enterprises. According to an ILO estimate, more than half of the global labor force and more than 90% of Micro and Small Enterprises (MSEs) worldwide are in the so called informal economy.<ref>http://www.ilo.org/global/topics/employment-promotion/informal-economy/lang--en/index.htm</ref> This might explain the apparently counterintuitive pattern of low unemployment rate in some low-income countries (e.g., 2.2% for Guatemala) and relatively higher numbers for some of the developed nations. The low numbers in the poorer countries hide the prevalence of extremely low wage jobs in the informal sectors in these countries, the only options for the vulnerable people in the absence of any kind of social safety net. &nbsp;Contrastingly, in the developed countries the so called ‘gig-economy’ is attracting more and more workers who choose to work on their own rather than in a formal enterprise. ILO conceptualization makes the informal work part of total employment. The stacked Venn diagram below presents the relationship among the labor force metric including informal employment. IFs also models informal economy both in terms of GDP share and employment share of informal in the total economy and employment.<br />
<div><br/><div id="ftn4"><div><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] For example, the USA excludes people in the defense services and those in the prisons or mental asylums in their computation of the civilian non-institutional working-age population. There are also variations in the treatments of students, those recently laid-off, and family workers. Please see [https://stats.bls.gov/opub/mlr/2000/06/art1full.pdf https://stats.bls.gov/opub/mlr/2000/06/art1full.pdf] for a discussion&nbsp;<br />
<br />
The GTAP data that we use for the demand side of the labor model is taken as labor headcounts and is thus immune from ambiguities around rate computation. As far as we could gather[[#_ftn1|[1]]], the data includes both the formal and informal employment. We also need mention here that the GTAP database reconciles the labor data to calibrate the general equilibrium modeling that they do for the trade analyses. The data could thus be somewhat different from data collected through direct surveys. As a CGE model IFs is benefited by using calibrated data.<br />
<div><br/><br />
----<br />
<div id="ftn1">[[#_ftnref1|[1]]] Please see the webpage for documentation on GTAP labor data statistic: [https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=3248 https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=3248]</div></div></div></div></div></div><br />
<br />
== Sources of Labor Data ==<br />
<br />
IFs model uses ILO data for labor participation rates and for the unemployment rate. The data in IFs are collected from World Bank’s World Development Indicators (WDI) database. According to their documentation, WDI obtained the data from the ILO.<br />
<br />
&nbsp;<br />
<br />
Unemployment rate data in IFs is also collected from WDI. Like the participation rates WDI also obtains their unemployment data from ILO.[[#_ftn1|[1]]]<br />
<br />
For employment and labor demand data IFs uses Purdue University’s Global Trade Analysis Project (GTAP) database. GTAP collects and compiles factor payments, imports, and intersectoral flow data to calibrate CGE models of national economies for trade and other analyses. In their ninth release in 2016, GTAP published data for 140 countries and regions for the year 2011. The earlier GTAP releases, which the IFs model used for its previous versions, compiled data for the years 2004 and 2007. GTAP data release aggregates economic activities into 57 commodities and activities following International Standard Industrial Classification (ISIC). The IFs model maps the 57 GTAP sectors into six economic sectors of IFs – agriculture, energy, material and mining, manufacture, services and ICT. Appendix 2 presents two tables listing the sectors mapping between IFs and GTAP, and GTAP and ISIC. GTAP further disaggregates labor in each of the commodities/activities into five occupation and skill categories following the nine category International Standard Classification of Occupations (ISCO-88). The IFs model collapses five GTAP occupation categories into the simple IFs dichotomy of skilled and unskilled. The mapping of occupations and skills are presented in the third appendix of this document. &nbsp;<br />
<br />
The data in the main GTAP database, prepared for CGE modeling, are all in dollar unit and thus do not include labor headcounts. We have used a ‘satellite’ GTAP database[[#_ftn2|[2]]] for labor headcounts by skill and sector. The labor counts were also used to plot labor requirement functions for each of the IFs economic sectors and skill categories. The wage share of skilled and unskilled labor in each sector was computed using the labor headcounts and labor payments.<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] The name of the IFs table is SeriesLaborUnemploy%<br />
</div><div id="ftn2"><br />
[[#_ftnref2|[2]]] See Weingarden and Tsigas, 2010 for the details on the preparation of this database.<br />
<br />
== Scope of IFs Labor Model ==<br />
<br />
The IFs labor model simulates labor market at the national level. Each national labor market forecasts labor demand and employment by six sectors - agriculture, energy, mining, manufacture, services and ICT- and two skill levels - skilled and unskilled. The supply side do not have sectoral representation. IFs forecasts total labor force and labor supply by the two skill levels. Labor participation rate is computed in IFs by gender. Wage and unemployment rate is forecast for the overall labor market only.&nbsp;&nbsp;<br />
<br />
== Labor Model Pre-processor ==<br />
<br />
IFs system has a data preprocessor that prepares the initial conditions for the model using historical databases and various assumptions and estimated relationships to fill in the missing data and make data adjustments as needed[[#_ftn1|[1]]]. Pre-processing of labor data takes place in two IFs pre-processing modules. Labor participation rate data, which is closely related to demography, is processed in the population pre-processor. Unemployment rate and labor demand data are processed in the economic pre-processor. &nbsp;<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] For more details, please see ‘The Data Pre-Processor of International Futures (IFs)” by Barry B. Hughes (with Mohammod Irfan) at [http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf]<br />
<br />
=== Pre-processing Labor participation rate and unemployment ===<br />
<br />
For initializing labor participation rates by sex (LABPARR) the model uses the historical values from the base year or the most recent year with data[[#_ftn1|[1]]]. For countries with no data we use regression relationships of the participation rates, for men and for women, with income per capita. The relationships, shown in the next figure, are not great. However, the functions affect only five countries for which we do not have any data at all: Grenada, Kosovo, Micronesia, Seychelles and South Sudan[[#_ftn2|[2]]].<br />
<br />
&nbsp;<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] The data tables that the IFs model pre-processor use for initializing labor participation rates are: SeriesLaborParRate15PlusFemale%, SeriesLaborParRate15PlusMale%.<br />
</div><div id="ftn2"><br />
[[#_ftnref2|[2]]] We should try to collect participation rate for these countries from country sources.<br />
<br />
IFs data series SeriesLaborUnemploy% is used for the initialization of unemployment rates. That series has annual unemployment rates for one or more years between 1980 and 2016, for 181 of the 186 IFs countries. For five countries (Grenada, Kosovo, Micronesia, Taiwan and South Sudan[[#_ftn1|[1]]]) there is no data at all. To fill in the missing data we use a regression function of unemployment rate against GDP per capita. Like the participation rate functions, this function does also not have much of an explanatory power.<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] These are pretty much the same countries for which we do not have any participation rate data. This indicates ILO might have some administrative limitation in reporting data for these countries (notice Kosovo, Seychelles etc in the list)<br />
<br />
=== Pre-processing labor demand and unemployment from GTAP ===<br />
<br />
The IFs economic pre-processor reads labor headcount and labor payment data from the GTAP database. In addition to performing sector and occupation/skill mapping between GTAP and IFs, pre-processor also use the labor headcount data to compute labor coefficient functions, the principal driver of labor demand in the IFs model.<br />
<br />
Labor coefficients are defined as the amount of labor needed to produce one unit of value added in a certain sector of the economy. The coefficients depend on the level of technology. The model uses GDP per capita as an indicator of the level of technological development. IFs pre-processor estimates labor coefficient functions for labor of different skill levels for the different sectors of the economy.<br />
<br />
The functions are derived from GTAP data we described earlier. The model pre-processor reads data on factor payments and aggregates data from 57 GTAP sectors to six IFs sectors. Shares of payment going to skilled and less-skilled workers in each of the sectors are then computed. Countries are grouped according to their level of technological development as represented by per capita income. For each group labor coefficients are obtained by taking an average of the country coefficients. &nbsp;We also convert labor payments data to labor headcount data using per capita income as a proxy for average wage. Labor coefficients and income are then plotted into a power function relationship. The figure below plots some of those labor functions.&nbsp; The functions fit quite well with a power law formulation[[#_ftn1|[1]]].<br />
<div><br/><br />
----<br />
<div id="ftn1">[[#_ftnref1|[1]]] This is interesting given the prevalence of power law in all sorts of scale-up activities (West 2017).</div></div></div></div></div></div></div></div></div></div><br />
<br />
= Labor Model Flowcharts =<br />
<br />
The diagram below shows an outline of the IFs labor model. On the supply side, the total labor pool (LAB) is computed from the labor force participation rates, by sex, (LABPARR) and the population (POP) in their working age, i.e., population over 15 (POP15TO65 + POPGT65). Participation rates are driven by the demographic changes with an additional negative impact from aging and a catch-up in female participation rate. Skill level of the labor supply (LABSUP) is driven by the level of development (GDPPCP) and the demand for labor is driven by labor-coefficients (LABCOEFFS) computed from coefficient function representing shifts in demand with technological progress as proxied by the level of development (GDPPCP). Coefficients computed by sector and skill gives the labor requirement by skill type for each unit of value added (VADD) in the sector. Multiplying these coefficients with projected value added in each sector gives an estimate of the labor demand. &nbsp;<br />
<br />
Any surplus or shortage between total labor demand and supply is used to compute the rate of unemployment. Deviations in the unemployment rate (LABUNEMPR) signal wage changes through an equilibrium seeking algorithm. Both demand and supply respond to the wage variable (LABWAGEIND) indexed to the base year. The supply responses are much slower than the demand responses.<br />
<br />
[[File:FLOCHART2.png|frame|center|Labor Model Flowchart]]<br />
<br />
= Labor Model Equations =<br />
<br />
== Overview ==<br />
<br />
The labor model is a part of the IFs economic model that uses labor model output as an input to a Cobb-Douglas production function in a multi-sector general equilibrium model. IFs is a very long-run dynamic model. Instead of computing fixed short-run equilibria that clear the relevant markets IFs uses an equilibrium seeking algorithm to balance the various systems over the longer run. The algorithm is known as the PID (proportion-integral-derivative) controller algorithm and is used widely in industrial control systems. It makes equilibrium seeking variables in IFs move towards a set target. The algorithm works by computing a multiplier based on the movement of the variable towards the target, as obtained by an integral (I) of the path traversed, and the rate of movement towards the target, the derivative term. The multiplier is applied on the process variable (the P term), or a response variable, in the subsequent time period. In the labor model, unemployment rate (LABUNEMPR) is used as the process variable and the PID multiplier is used on the wage rate (LABWAGEIND). Job availability (LABDEMS) and participation rate (LABPARR) get affected by changes in wage. &nbsp;&nbsp;<br />
<br />
Throughout this section we use subscripts and notations common to other modules of IFs. For example, we use t for time period. Subscripts p and r represent sex and country/region, respectively, c is the cohort number, with cohort 1 representing the newborns, cohort1 the the one-year to four-year-olds, cohort two five-year to nine-year-olds etc. Values for p are 1 for male, 2 for female and 3 for both sexes combined. For economic sectors we use s and for skill levels sk.<br />
<br />
== Labor Supply: Equations ==<br />
<br />
The total pool of labor is computed by multiplying the population of working age with the labor force participation rate (LABPARR). &nbsp;Population forecasts come from IFs demographic model which computes both five-year and single-year age-sex cohorts (''agedst'', ''fagedst''). &nbsp;<br />
<br />
The labor model forecasts participation rates by country/region&nbsp; and gender. Participation rates in the model move with the changes in the demographic composition. Female participation rates, which have historically been lower than the same for the male in all societies, but has moved up in modern and affluent societies, get a catch-up boost in the model. Participation rates can also change when there is labor shortage or surplus and the employers try to incentivize or discourage workers by changing wage. This last impact is much less slow than similar wage impacts on the demand side.<br />
<br />
== Labor Participation Rate ==<br />
<br />
Labor participation rates (''LABPARR'') for male and female are first initialized with historical data.<br />
<br />
:<math>LABPARR_{r,p}= LABPARR_{r,p,t=1} </math><br />
<br />
A ‘catch-up’ boost is added to the female participation rate. The boost added (FemParLabMul) starts at a third of a percentage point and withers away following a non-linear path as the female rates approaches the catch-up target (FemParTar), The maximum catch-up that can occur over the horizon of the model is thirty percent.<br />
<br />
:<math>FemParTar_{r}=Amin(LabParRI_{r,p=1},LabParRI_{r,p=2}+30)</math><br />
<br />
:<math>FemParLabMul_{r}=(FemParTar_{r}-LABPARR_{r,p=2,t-1})/(FemParTar_{r}-LABPARR_{r,p=2,t-1})</math><br />
<br />
:<math>LABPARR_{r,p=2,t}=LABPARR_{r,p=2,t-1}+FemParLabMul_{r}*0.3</math><br />
<br />
Next, we compute and apply the aging impact on the participation rate. As the relative share of people over the retirement age increases, the participation rate declines. The model keeps track of the changes in the demographic ratio (PopAgingRatio) of the population who are in their prime working age of 15 to 64 (POPWORKING) to those at a common retirement age of sixty-five or older (POPGT65). This ratio declines as countries age. The percentage drop in the ratio comparative to the base year is scaled appropriately to compute the aging impact (aging_impact). This impact is added to the male and female labor participation rates, with the impact on the female participation rate being slightly lower than that on male rates.<br />
<br />
:<math>POPAgingRatio_{r,t}=POPWORKING_{r,t}/POPGT65_{r,t}</math><br />
<br />
:<math>aging_impact_{r,t}=100*((POPAgingRatio_{r,t}/POPAgingRatio_{r,t=1})-1)*0.2</math><br />
<br />
:<math>LABPARR_{r,p=1,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t} </math><br />
<br />
:<math>LABPARR_{r,p=2,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t}*0.95 </math><br />
<br />
Participation rates respond slowly to changes in wage and unemployment rate. The impact is implemented through a wage impact factor computed from annual changes in the wage index (labwageimpact). The base participation rates can be changed by model user through two model parameters: a direct multiplier on the participation rate (labparm), or one that changes participation by moving the retirement age (labretagem)<br />
<br />
<br />
<br />
:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact*0.05)*labparm_{r,p,t}*labretagem_{r,p,t}</math><br />
<br />
Total participation rate (LABPARRr,p=3,t) is computed by an weighted average of male and female participation rates.<br />
<br />
:<math>LABPARR_{r,p=3,t}= (∑_{p=1 to 2}∑_{c=4 to 21}(agedst{r,c,p,t}*LABPARR_{r,p,t}))/(∑_{p=1 to 2}∑_{c=4 to 21}agedst{r,c,p,t})</math><br />
<br />
== Total Labor ==<br />
<br />
Finally, the total number of labor available for work (LAB) is computed by multiplying the total participation rate with the population of fifteen-year-olds or older.<br />
<br />
:<math>LAB_{r,t}= LABPARR_{r,p=3,t}*∑_{p=1 to 2,c=4 to 21}agedst_{r,c,p,t}</math><br />
<br />
== Labor by skill level ==<br />
<br />
The labor model forecasts labor supply (LABSUP) by two skill categories. The variable (''LABSUP'') is initialized in the pre-processor by reading the employment by skill/occupation (''LABEMPS'') data from GTAP[[#_ftn1|[1]]] &nbsp;and adding the unemployment numbers. We assume same unemployment rate (''LABUMEMPR'') for skilled and unskilled labor.<br />
<br />
:<math>LABSUP_{r,t=1,sk}=∑_{s=1 to 6}(LABEMPS_{r,s,t=1}/(1-(LABUNEMPR_{r,t=1}/100))</math><br />
<br />
The model forecasts labor by skill through a model of the skilled share of the labor. Education, training, exposure, and experience of the employees all improve with the level of development. The model captures this with an analytic function of the skilled share (perskilled) driven by GDP per capita at PPP (GDPPCP) -<br />
<br />
:<math>perskilled_{r}=f(GDPPCP_{r})</math><br />
<br />
Among the causal drivers of skill, education is considered to be the most proximate. Education is strongly correlated with the level of development, the deeper driver of skill in the model. However, the recent increase in education and/or a policy driven educational expansion might add to the impact of education on skill. Additional impacts from education on skill, when there is any, is computed through an expected function formulation. For example, in a society where an average adult has more (or less) education than the adults in other societies at that level of development, the skill share is given a slight upward push (or downward pull). The expectation function is a logarithmic function of educational attainment of working age population (EDYRSAG15) driven by GDP per capita at PPP. Attainment above (or below) the expected level (YearsEdExp) is computed by the function output (YearsEd) adjusted for country situation (yearseddiff). The percentage adjustment to the skilled share (LabSupSkiAdj) is computed using additional (limited) education, i.e., the difference between actual (EDYRSAG15) and expected values of educational attainment, expressed as a percentage of the expected value. The adjustment is scaled appropriately and peters off over time.<br />
<br />
:<math>YearsEd_{r,t}= f(GDPPCP_{r,t})</math><br />
<br />
:<math>yearsdeddiff_{r}= EDYRSAG15_{r,p=3,t=2}-YearsEd_{r,t=2}</math><br />
<br />
:<math>YearsEdExp_{r,t}=YearsEd_{r,t}+yearsdeddiff_{r}</math><br />
<br />
:<math>LabSupSkiAdj_{r,t}=0.3*(EDYRSAG15_{r,p=3,t=2}*YearsEdExp_{r,t})/YearsEd_{r,t}</math><br />
<br />
:<math>LabSupSkiAdj_{r,t}=ConvergeOverTime(0,LabSupSkiAdj_{r,t},70)</math><br />
<br />
:<math>perskilled_{r,t}= perskilled_{r,t}*(1+LabSupSkiAdj_{r,t})</math><br />
<br />
The skilled share (perskilled) is multiplied with the total labor supply (LAB) to obtain the number of labors who are skilled (LABSUPskilled)<br />
<br />
:<math>LABSUP_{r,skilled,t}=LAB_{r,p,t}*perskilledI_{r,t} </math><br />
<br />
As a last step, the model adjusts for the country specific variations in the skilled labor count not captured by the deeper and the proximate models. This is done by saving a ratio (LABSUPSkilledRI) of the actual historical data and the model computed value in the initial year. In the subsequent years this ratio is used to adjust the skilled labor forecast gradually.<br />
<br />
:<math>LABSUPCompSkilled_{r}=LAB_{r}*perskilled_{r,t=1}/100 </math><br />
<br />
:<math>LABSUPSkilledRI_{r}=LABSUP_{r,skilled,t=1}/LABSUPCompSkilled_{r}</math><br />
<br />
:<math>LABSUP_{r,skilled,t}= LABSUP_{r,skilled,t}*ConvergeOverTime(LABSUPSkilledRI_{r},1,85)</math><br />
<br />
Number of unskilled labor is obtained by subtracting the skilled labor from the total pool.<br />
<br />
<br />
<br />
:<math>LABSUP_{r,unskilled,t}= LAB_{r,p,t}- LABSUP_{r,skilled,t}</math><br />
<br />
== Labor Demand: Equations ==<br />
<br />
IFs economic model forecasts production in six economic sectors. IFs labor model computes the longer-term and shorter-term determinants of demand for skilled and unskilled labor (LABDEMS) for the production processes. The long-term drivers of labor requirement are technological progress or the lack of it. In the shorter-term wage affects the labor demand most. Wage in turn is affected by labor supply or skill shortage.<br />
<br />
The IFs model divides economic activities into six economic sectors – agriculture, energy, materials, manufacture, services and information, and communication technologies. Workers in the IFs labor model are disaggregated into two skill types. While the skill composition varies by the technology used in the sector and starts tilting towards the more skilled with the progress in technology, absolute number of labors needed to produce the same output goes down with technological development for both skilled and unskilled labor. This is illustrated in the next figure which plots the changes in labor requirement against GDP per capita at PPP, a proxy for level of development. Agriculture is a much less skill-intensive process than the manufacture, however, with technological progress skill requirement improves rapidly in both sectors. The IFs labor model computes these labor requirement functions in the model pre-processor. As we have already described in the pre-processor section, the computation of these functions use GTAP data on employment by occupation and economic activity. Appendices 3 and 4 lists sector and occupation mapping between GTAP and IFs.<br />
<br />
These functions are used to compute the labor coefficients (LABCOEFFS), i.e., number of skilled and unskilled labor needed to produce unit amount of output with the technology available, for which we use GDP per capita at PPP as a proxy.<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= f(GDPPCP_{r})</math><br />
<br />
manufacture, services and ICTech) and the subscrip sk stands for skill categories with 1 denoting unskilled and 2 skilled. The labor coefficients obtained from the analytical functions require some adjustments to incorporate country deviations from the functions for various factors not captured in the regression relationship. The first of these adjustments is a gradual removal of impacts of short-run fluctuations in output and labor from the computation of labor coefficient. This adjustment is applied on the coefficients computed from the function. The equation below shows a simplified form of these computations.<br />
<br />
:<math>LabCoeffAdjFac_{r,k,s,t}=f(igdpr_{r,t=2},(LAB_{r,t=2}/LAB_{r,t=1}),(LABCOEFFS_{r,t}/LABCOEFFS_{r,t-1}))</math><br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}=LABCOEFFS_{r,sk,s,t}(1-LabCoeffAdjFac_{r,k,s,t})</math><br />
<br />
Model users can use a global parameter (labcoeffsm) to change the labor coefficients by skill level for any or all of the six sectors –<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= LABCOEFFS_{r,sk,s,t}*'''labcoeffsm_{s,sk}'''</math><br />
<br />
To forecast the total labor demand, the labor coefficients (LABCOEFFS) are multiplied to the total projected output for each of the economic sectors. The forecast is adjusted for any discrepancy between data and model. The adjustment factor (LABDemsAdjFac) is computed as the initial ratio between the actual and computed employment. Actual employment is obtained from historical data (LABEMPS) processed using the GTAP database. The computed employment is obtained by multiplying the labor coefficients (LABCOEFFS) with the final output of the sector (VADD).<br />
<br />
:<math>LabDemsAdjFac_{r,s,sk}= LABEMPS_{r,s,sk,t=1}/(VADD_{r,s,t=1}*LABCOEFFS_{r,sk,s,t})</math><br />
<br />
The projected output is obtained by applying the growth rate (IGDPRCOR) on the sectoral value added from the previous year (VADD). The total labor demand is given by the product of the labor coefficients, projected output, demand adjustments and wage impacts (labwageimpactmul) and the number 1000 which adjusts the units for the equation. Wage impact comes from the level of unemployment and is computed in an equilibration process described in the next section. Model users can use a multiplicative parameter (labdemsm) to slide the demand upward or downward.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}=1000*VADD_{r,s,t-1}*(1+IGDPRCOR_{r})*LABCOEFFS_{r,sk,s,t}*LabDemsAdjFac_{r,s,sk}*labwageimpactmul_{r,s,sk}*'''labdemsm_{r,s}'''</math><br />
<br />
== Unemployment and Wage: Labor Market Equilibration ==<br />
<br />
The IFs labor model balances the labor market through an equilibrium seeking algorithm rather than computing an exact equilibrium at each time step. We use an algorithm borrowed from the control systems engineering. This PID controller algorithm, described also in the IFs economic model documentation, works by computing corrective signals for equilibrating variables using the deviations of a buffer variable, for example unemployment rate (LABUNEMPR), from a target value. The signal is computed from two quantities, the distance of the buffer from the target and the current rate of change of the buffer. The computation is tuned with PID elasticities to avoid oscillations. The computed signal is applied on the variable/s which need to be balanced, for example, demand and supply in the event of a market equilibration, thus getting closer to a balance at each step of simulation. The target value for the buffer variable and the tuning parameters of the control algorithm are obtained through rules-of-thumb and model calibration. The IFs labor model uses unemployment rate (LABUNEMPR) as the buffer variable for the market equilibration of labor demand and labor supply. The multiplier (i.e., corrective signal) obtained from the PID is applied on the wage index (LABWAGEIND). Changes in wage indices comparative to the base year, moderated through a second PID controller, is used to compute the final signal (labwageimpactmul) that drives labor demand and labor supply. Even though the model forecasts labor demand by sector and skill, and computes labor supply for both skill types, the equilibration algorithm works over the entire pool of labor. In other words, we assume that the skills are replaceable across sectors and the lack (or abundance) of jobs affects skilled and unskilled persons equally.<br />
<br />
At each annual timestep, the model computes the unemployment rate (LABUNEMPR) as the gap in between the total supply of labor (LAB) and the total demand. The gap (EmplGap) is expressed as a share of the total labor, the standard way to express unemployment rate.<br />
<br />
:<math>sumld=∑_{s,sk}LADEMS_{r,s,sk,t}</math><br />
<br />
:<math>EmplGap= LAB_{r,t}*sumld</math><br />
<br />
:<math>LABUMENPR_{r,t}= (EmplGap/LAB_{r,t})*100</math><br />
<br />
As the target value (LabUnEmpRateTar) for the PID controller that modulates unemployment rate we use either the historical unemployment rate or a ten percent unemployment rate when the historical rate is higher than ten. Model users can override the historical target through a model parameter (labunemprtrgtval).<br />
<br />
:<math>LABUMENPRi_{r,t}= LABUMENPR_{r,t}</math><br />
<br />
:<math>LabUnempRateTarget_{r}=labunemptargetval_{r}</math><br />
:<math>If LabUnempRateTarget_{r}=0,<br />
LabUnempRateTarget_{r}= AMIN(LABUMENPRi_{r,t},10) </math><br />
<br />
Unemployment rate target, when it is different from the base year value, is reached gradually with a convergence period of forty years . The target rate is converted to count (LabUnEmplTar) to make it equivalent to the employment gap (EmplGap) computed earlier.<br />
<br />
:<math>LabUnEmplTar_{r}= LAB_{r,t}*ConvergeOverTime(LABUMENPRi_{r,t},0,100)</math><br />
<br />
The first order difference (Diffl1) between the target unemployment and the demand-supply gap is used to compute a second order difference (Diffl2) accounting for changes in the rate of movement. The two differences and the PID multipliers (elwageunemp1, elwageunemp2) are provided to the PID function (ADJSTR). Working age population (POP15TO65r,t) works as the scaling base of the PID controller. The controller algorithm gives a multiplier (mullw) that is used in the subsequent year to adjust wage.<br />
<br />
:<math>Diffl1_{t}=LabUnEmplTar_{r}-EmplGap</math><br />
<br />
:<math>Diffl2_{t}=Diffl1_{t}-Diffl1_{t-1}</math><br />
<br />
:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},elwageunemp1_{r},elwageunemp2_{r})</math><br />
<br />
Wage adjustments affect demand and supply with an increase in wage drawing demand downward and supply upward. The opposite affects occur with a downward movement of wage. The wage variable affected by the PID multiplier (LABWAGEIND) is an index initialized at one. We use an indexed rather than a dollar wage in the equilibration process to avoid affecting the process from other economic phenomena that affects wage, for example, a rise in real wage as GDP or the labor share of income grows.<br />
<br />
:<math>LABWAGEIND_{r,t=1}=1</math><br />
<br />
In the subsequent years of the model run, the wage index is first adjusted with the equilibration signal obtained from the unemployment rate PID controller in the previous period<br />
<br />
:<math>LABWAGEIND_{r,t=1}= LABWAGEIND_{r,t=1}* mullw_{r,t-1} </math><br />
<br />
A wage impact (labwageimpact) is then computed using the changes in the wage index relative to the base value. The impact is smoothed with a moving average algorithm.<br />
<br />
:<math>labwageimpact_{r}= labwageimpact_{r,t-1}*0.9+ (1-LABWAGEIND_{r,t})*0.1</math><br />
<br />
The smoothed impact is used as the equilibration signal for labor supply. As we have already described in the section on labor supply, a small fraction of the impact (labwageimpact) is applied to the labor participation rate. The impact is scaled down to account for the slow pace of changes on the supply side.<br />
<br />
:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact_{r,t}*0.05)</math><br />
<br />
For the impacts of wage on labor demand we use a second PID multiplier as opposed to using the changes in wage index that we have done on the supply side. The second PID uses the wage index itself as the process variable and uses the base year value of 1 as the target. The reason we had to use this second PID is to control the pace at which wage disequilibrium can affect demand, especially in the event of an abrupt shock. The smoothing and scaling down that works on the supply side is not enough to control oscillations on the demand side.<br />
<br />
:<math>Diffl1_{t}=LABWAGEIND_{r,t=1}-1</math><br />
<br />
:<math>Diffl2_{t}=LABWAGEIND_{r,t}-LABWAGEIND_{r,t-1} </math><br />
<br />
:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},ellabwage1_{r},ellabwage1_{r})</math><br />
<br />
A second impact factor (labwageimpactmul) is computed using the correction signal from this second multiplier:<br />
<br />
:<math>labwageimpactmul_{r,t}= labwageimpactmul_{r,t-1}*mullw_{r,t}</math><br />
<br />
This impact factor is applied on the labor demand as described in the section on labor demand.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}= LABDEMS_{r,s,sk,t}* labwageimpactmul_{r,t}</math><br />
<br />
== Informal Labor ==<br />
<br />
IFs forecast labor and GDP share of the informal sector. Informal labor forecast is not explicitly endogenized in the labor market though. They are rather driven by development, skill and regulatory factors[[#_ftn1|[1]]]. However, the productivity and revenue impacts of changes in informality affects output and thus labor demand implicitly as a very distal driver.<br />
<br />
= References =<br />
<br />
<references /><br />
</div></div></div></div></div></div></div></div></div></div></div>Wikiadmin//pardee.du.edu/wiki/LaborLabor2018-09-07T22:30:35Z<p>Wikiadmin: </p>
<hr />
<div>Workers in an economy supply the expertise and the efforts needed to produce goods and services. In return the labor receives wages that they use to meet their current and future consumption needs. On one hand, shortage of labor with required skills prevents economies from realizing their growth potential. On the other hand, individuals falling short of the right qualifications might remain unemployed or underemployed failing to secure income needed for a decent living. The ongoing adjustments to find the best match between skills, jobs and wages can only be studied through a dynamic model of the labor market.&nbsp; &nbsp;<br />
<br />
Such a model should go beyond providing a reasonable answer to the obvious question of why employment and wages go up and down. An aggregate labor market must deal with issues that have strong interconnections with various other dynamic changes in the greater society. What kind of dividend of deficit can a society expect from its labor force given the phase of demographic transition in which it is situated? How severely would aging affect the pool of working age adults? Might increasing female participation rates offset some of the losses from aging? What is the level of skills and educational attainment in a society? These supply phenomena move relatively slowly unless there are huge disruptions, like a war or famine, or an aggressive policy push. The demand side, in contrast, needs to be more responsive in adjusting wages and employment given the investment and technology in the various sectors of the broader economy. In general, though, the labor market demonstrates some sluggishness compared to the goods and services markets as it involves moving human beings with various limitations. Consumption of goods and services depend on the income earned by the labor. Uneven distribution of employment and wages among labors of various types or between labor and capital for a long period of time can give rise to persistent inequality in a society. &nbsp;&nbsp;&nbsp;&nbsp;<br />
<br />
== Conceptual Framework ==<br />
<br />
Labor markets are markets for workers and jobs. In a labor market, employers meet their demand for labor with the supply of people willing to work at the wage the employers can offer. The employers raise the wage when there is a shortage of workers. Workers agree to take a lower wage when there are more of them than the firms need. In the real-world labor markets do not always clear at perfect equilibrium. Frinctional unemployment results for various reasons, for example, the search time between jobs. Structural unemployment can result from technology induced disruptions. Some unemployment could thus persist in the labor market even when there aren’t any short-term fluctuations. There is also the phenomenon of informal employment that consists of less sophisticated workers and entrepreneurs engaged in unregulated economic activities. &nbsp;In a dynamic model that covers the entire economy, the real wage earned by the labor drives the income and social mobility.&nbsp;&nbsp;&nbsp;<br />
<br />
To understand the long-term dynamics of the labor market, we need also examine the deeper determinants of labor demand and supply, the determinants that can shift the curves. Labor demand changes over time with the changes in demand for goods and services and the labor input needed to produce those. Labor productivity itself improves with technological progress. Long term transitions in the supply of labor are mostly demographic. &nbsp;<br />
<br />
Labor supply is determined by the working age population and the share of that population who are available for participation in the workforce. The labor supply is relatively stable as the demographic changes are slow in pace. As the share of elderly in the population increases, a recent trend in many societies, the rate of participation declines. Some of the aging impacts will be offset by the greater female participation rates, a second trend that surfaces as economies develop and women attain more education. Educational attainment also drives the general skill level of workers, male and female. Specific skills are obtained through training and experience that augment the knowledge obtained through general and specialized education. &nbsp;&nbsp;&nbsp;&nbsp;<br />
<br />
It is the demand side that causes most of the short-term imbalances in the labor market. &nbsp;In the long term, as said earlier, the important driver of demand for labor and their skills is technological progress. Labor requirement drops with advances in technology, more so for less skilled labor. Labor composition changes accordingly both within and across sectors. Rapid advances in technology can also cause disruption in the system when there is not much opening in the other sectors. Labor displacement is offset to some extent by the growth in the economy and the resulting increase in total demand. &nbsp;&nbsp;&nbsp;<br />
<br />
As we have already mentioned, employees maximize income and the firms minimize labor costs. When there are more laborers than the firms can hire, there is unemployment. Shifts in the rates of unemployment impacts wage, the price of labor. For example, wages drop in the event of rising unemployment as there are more people to hire from. Wage adjustments feed back to the demand for labor seeking to bring the market back to equilibrium.<br />
<br />
The challenges around the conceptual distinction between unemployment and employment is further complicated by the phenomenon of informal employment. In many developing countries there is a large urban non-agricultural informal sector where low-skilled workers work for wages typically lower than a formal employment.<br />
<br />
[[File:LMFlowchart1.png|frame|center|Description of the labor model]]<br />
<br />
== Dominant Relations ==<br />
<br />
The labor model in the International Futures system (IFs) balances the total supply of labor with the total labor demanded by all economic sectors. Total labor (LAB) is computed from the working age population and the labor participation rate. Population forecasts are obtained from the IFs demographic model. Participation rates (LABPARR) are computed by sex with a catchup algorithm for the female participation towards that for the male. Labor is also disaggregated by skill level, as determined by educational attainment, in a separate labor supply variable (LABSUP) which is used to distribute labor earnings by skill level. [** LABSUP do not affect the demand/supply balance now]<br />
<br />
Labor demands (LABDEMS) are driven by sectoral technology functions used to compute the labor requirement by skill level for each unit of potential valued added in the sector. These labor coefficients (LABCOEFFS) are multiplied with the projected value added for the sector to compute the needed manpower. The balancing mechanisms determines the labor employed in each of the sectors (LABS).&nbsp;&nbsp;<br />
<br />
The balancing, in the current version of the model, can be done in one of the two ways. In the first method, total needs combined from all economic sectors is normalized to the available pool of labor computed by subtracting the unemployed from those who are at or looking for work. The rate of unemployment is kept at its natural rate for which we use the base year rate of unemployment. (** This might need to be changed for countries where the market is undergoing some abrupt transition.)<br />
<br />
In the second balancing method, added in a recent revision of the model, total demand is equilibrated to supply through a CGE like market equilibrium model. An indexed wage (LABWAGEIND) and the rate of unemployment (LABUNEMPR) work as the equilibrating variables. As unemployment deviates from the target, PID algorithms send a signal for the wage to adjust. Wage adjustments cause adjustments in the “base” labor demands by sector computed from the labor-coefficient functions as described earlier. Wage signals also affects the labor participation rate. The magnitude of impact on the supply side is much lower than that on the demand side.<br />
<br />
Wage and unemployment rate are aggregated for the total labor market. The wage index starts with a base year value of 1 and the unemployment rates start with the historical data for the base year. Initial year unemployment rate works as the target for long term unemployment.<br />
<br />
== Key Dynamics ==<br />
<br />
The following key dynamics are directly related to the dominant relations:<br />
<br />
*Labor supply is determined from population of appropriate age in the population model (see its dominant relations and dynamics) and endogenous labor force participation rates, influenced exogenously by the growth of female participation.<br />
*Labor demand is driven by sectoral demand functions driven by technological progress<br />
<br />
== Structure and Agent System ==<br />
<br />
{| border="1" cellspacing="0" cellpadding="0" width="0" style="width:502px;"<br />
|-<br />
| style="width:242px;height:49px;" | <br />
'''System/Subsystem'''<br />
<br />
| style="height:49px;" | <br />
Labor market<br />
<br />
|-<br />
| <br />
'''Organizing Structure'''<br />
<br />
| <br />
Labor supply by skill level and labor demand by sector for each skill category represented within an equilibrium-seeking model with wage and unemployment rate as the equilibrating variables<br />
<br />
|-<br />
| <br />
'''Stocks'''<br />
<br />
| <br />
Population, labor, education, &nbsp;accumulated technology<br />
<br />
|-<br />
| <br />
'''Flows'''<br />
<br />
| <br />
Participation rate; Coefficients of labor demand; Employment (unemployment); Wage<br />
<br />
|-<br />
| <br />
'''Key Aggregate&nbsp;''''''Relationships&nbsp;'''<br />
<br />
'''(illustrative, not comprehensive)'''<br />
<br />
| <br />
Labor supply is driven by demographic changes; Participation of female change over time; Labor requirement changes with technological development; Unemployment rate drives wage; Wage movements affect labor demand and participation rate<br />
<br />
|-<br />
| <br />
'''Key Agent-Class Behavior&nbsp;''''''Relationships'''<br />
<br />
'''(illustrative, not comprehensive)'''<br />
<br />
| <br />
Households and work/leisure, and female participation patterns;<br />
<br />
&nbsp;<br />
<br />
Firms and hiring;<br />
<br />
&nbsp;<br />
<br />
<br />
<br />
<br />
<br />
|}<br />
<br />
= Labor Model Data =<br />
<br />
The labor supply and unemployment data that we use in our model is from International Labor Organization (ILO). For data on the demand side, we used data from the Global Trade Analysis Project. Wage variable used in the equilibration algorithm&nbsp;is an index anchored to the base year of the model.<ref>GTAP database helped us compute wage rates by sector and skill.</ref> IFs preprocessor prepared these data for model use using various estimation, conversion and reconciliation processes.&nbsp; &nbsp; &nbsp;<br />
<br />
== Definitional Issues ==<br />
<br />
There are ambiguities in the way some of the labor market variables are defined. Labor participation rates and the rate of unemployment are two that need special attention.<br />
<br />
The size of the labor supply available for economic activities is expressed with the labor force participation rate. ILO defines this as a “measure of the proportion of country’s working-age population that engages actively in the labor market, either by working or looking for work.”<ref>http://www.ilo.org/ilostat-files/Documents/description_LFPR_EN.pdf</ref>&nbsp;National labor force surveys and census data are used to estimate this rate. The definition of labor force here includes both employed and unemployed and the rate is expressed as a percentage of working-age population. Working-age population is defined here as the population above legal working-age. For international comparability, ILO adopts a convenient minimum threshold of fifteen years as working age and avoids putting any upper age limit. In practice, both the minimum and the upper-age limits can vary by country. For example, the working-age in the USA is sixteen years. In the Netherlands the upper age limit is seventy-five years, whereas South African data uses an upper age limit of 64.<ref>https://www.bls.gov/fls/flscomparelf/technical_notes.pdf</ref><br />
<br />
Ambiguities are more abundant in the definition of unemployment. ILO came up with a guideline on this as well. Per the ILO guideline, the unemployed are those among the working-age population who are not employed, are available for work and are actively looking for jobs<ref>The definitions around employed and unemployed were agreed upon by nations through the ‘Resolution concerning statistics of work, employment and labor underutilization’ adopted by the 19th International Conference of Labor Statisticians (ICLS) in 2013. (Bourmpoula et al, 2017: 6).</ref>; the unemployment rate is expressed as a percentage of those who are in the labor force. The availability and job-seeker status could be defined in different ways giving rise to incompatibility in data. &nbsp;While there seems to be little room for disagreement on whether someone is at work or not, whether that work should be considered as employment is contested at many times.<br />
<br />
The debates around the nature and type of employment can range from gainfulness to workplace setting. For example, a large number of workers in the low-income low-regulation developing countries work outside the purview of formal enterprises. According to an ILO estimate, more than half of the global labor force and more than 90% of Micro and Small Enterprises (MSEs) worldwide are in the so called informal economy.<ref>http://www.ilo.org/global/topics/employment-promotion/informal-economy/lang--en/index.htm Incompatibility can arise in the treatment of various population groups for the computation of the denominator for participation and unemployment rates[1]. ILO makes their best efforts to make adjustments in the data for the sake of international comparison. For example, ILO asks countries that deviate from ILO guidelines to collect data needed to convert national figures to ILO figures. It is likely that some differences might have slipped past the adjustment process. We use ILO data and continue to update our database from ILO on a regular basis.</ref> This might explain the apparently counterintuitive pattern of low unemployment rate in some low-income countries (e.g., 2.2% for Guatemala) and relatively higher numbers for some of the developed nations. The low numbers in the poorer countries hide the prevalence of extremely low wage jobs in the informal sectors in these countries, the only options for the vulnerable people in the absence of any kind of social safety net. &nbsp;Contrastingly, in the developed countries the so called ‘gig-economy’ is attracting more and more workers who choose to work on their own rather than in a formal enterprise. ILO conceptualization makes the informal work part of total employment. The stacked Venn diagram below presents the relationship among the labor force metric including informal employment. IFs also models informal economy both in terms of GDP share and employment share of informal in the total economy and employment.<br />
<div><br/><div id="ftn4"><div><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] For example, the USA excludes people in the defense services and those in the prisons or mental asylums in their computation of the civilian non-institutional working-age population. There are also variations in the treatments of students, those recently laid-off, and family workers. Please see [https://stats.bls.gov/opub/mlr/2000/06/art1full.pdf https://stats.bls.gov/opub/mlr/2000/06/art1full.pdf] for a discussion&nbsp;<br />
<br />
The GTAP data that we use for the demand side of the labor model is taken as labor headcounts and is thus immune from ambiguities around rate computation. As far as we could gather[[#_ftn1|[1]]], the data includes both the formal and informal employment. We also need mention here that the GTAP database reconciles the labor data to calibrate the general equilibrium modeling that they do for the trade analyses. The data could thus be somewhat different from data collected through direct surveys. As a CGE model IFs is benefited by using calibrated data.<br />
<div><br/><br />
----<br />
<div id="ftn1">[[#_ftnref1|[1]]] Please see the webpage for documentation on GTAP labor data statistic: [https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=3248 https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=3248]</div></div></div></div></div></div><br />
== Sources of Labor Data ==<br />
<br />
IFs model uses ILO data for labor participation rates and for the unemployment rate. The data in IFs are collected from World Bank’s World Development Indicators (WDI) database. According to their documentation, WDI obtained the data from the ILO.<br />
<br />
&nbsp;<br />
<br />
Unemployment rate data in IFs is also collected from WDI. Like the participation rates WDI also obtains their unemployment data from ILO.[[#_ftn1|[1]]]<br />
<br />
For employment and labor demand data IFs uses Purdue University’s Global Trade Analysis Project (GTAP) database. GTAP collects and compiles factor payments, imports, and intersectoral flow data to calibrate CGE models of national economies for trade and other analyses. In their ninth release in 2016, GTAP published data for 140 countries and regions for the year 2011. The earlier GTAP releases, which the IFs model used for its previous versions, compiled data for the years 2004 and 2007. GTAP data release aggregates economic activities into 57 commodities and activities following International Standard Industrial Classification (ISIC). The IFs model maps the 57 GTAP sectors into six economic sectors of IFs – agriculture, energy, material and mining, manufacture, services and ICT. Appendix 2 presents two tables listing the sectors mapping between IFs and GTAP, and GTAP and ISIC. GTAP further disaggregates labor in each of the commodities/activities into five occupation and skill categories following the nine category International Standard Classification of Occupations (ISCO-88). The IFs model collapses five GTAP occupation categories into the simple IFs dichotomy of skilled and unskilled. The mapping of occupations and skills are presented in the third appendix of this document. &nbsp;<br />
<br />
The data in the main GTAP database, prepared for CGE modeling, are all in dollar unit and thus do not include labor headcounts. We have used a ‘satellite’ GTAP database[[#_ftn2|[2]]] for labor headcounts by skill and sector. The labor counts were also used to plot labor requirement functions for each of the IFs economic sectors and skill categories. The wage share of skilled and unskilled labor in each sector was computed using the labor headcounts and labor payments.<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] The name of the IFs table is SeriesLaborUnemploy%<br />
</div><div id="ftn2"><br />
[[#_ftnref2|[2]]] See Weingarden and Tsigas, 2010 for the details on the preparation of this database.<br />
<br />
== Scope of IFs Labor Model ==<br />
<br />
The IFs labor model simulates labor market at the national level. Each national labor market forecasts labor demand and employment by six sectors - agriculture, energy, mining, manufacture, services and ICT- and two skill levels - skilled and unskilled. The supply side do not have sectoral representation. IFs forecasts total labor force and labor supply by the two skill levels. Labor participation rate is computed in IFs by gender. Wage and unemployment rate is forecast for the overall labor market only.&nbsp;&nbsp;<br />
<br />
== Labor Model Pre-processor ==<br />
<br />
IFs system has a data preprocessor that prepares the initial conditions for the model using historical databases and various assumptions and estimated relationships to fill in the missing data and make data adjustments as needed[[#_ftn1|[1]]]. Pre-processing of labor data takes place in two IFs pre-processing modules. Labor participation rate data, which is closely related to demography, is processed in the population pre-processor. Unemployment rate and labor demand data are processed in the economic pre-processor. &nbsp;<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] For more details, please see ‘The Data Pre-Processor of International Futures (IFs)” by Barry B. Hughes (with Mohammod Irfan) at [http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf]<br />
<br />
=== Pre-processing Labor participation rate and unemployment ===<br />
<br />
For initializing labor participation rates by sex (LABPARR) the model uses the historical values from the base year or the most recent year with data[[#_ftn1|[1]]]. For countries with no data we use regression relationships of the participation rates, for men and for women, with income per capita. The relationships, shown in the next figure, are not great. However, the functions affect only five countries for which we do not have any data at all: Grenada, Kosovo, Micronesia, Seychelles and South Sudan[[#_ftn2|[2]]].<br />
<br />
&nbsp;<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] The data tables that the IFs model pre-processor use for initializing labor participation rates are: SeriesLaborParRate15PlusFemale%, SeriesLaborParRate15PlusMale%.<br />
</div><div id="ftn2"><br />
[[#_ftnref2|[2]]] We should try to collect participation rate for these countries from country sources.<br />
<br />
IFs data series SeriesLaborUnemploy% is used for the initialization of unemployment rates. That series has annual unemployment rates for one or more years between 1980 and 2016, for 181 of the 186 IFs countries. For five countries (Grenada, Kosovo, Micronesia, Taiwan and South Sudan[[#_ftn1|[1]]]) there is no data at all. To fill in the missing data we use a regression function of unemployment rate against GDP per capita. Like the participation rate functions, this function does also not have much of an explanatory power.<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] These are pretty much the same countries for which we do not have any participation rate data. This indicates ILO might have some administrative limitation in reporting data for these countries (notice Kosovo, Seychelles etc in the list)<br />
<br />
=== Pre-processing labor demand and unemployment from GTAP ===<br />
<br />
The IFs economic pre-processor reads labor headcount and labor payment data from the GTAP database. In addition to performing sector and occupation/skill mapping between GTAP and IFs, pre-processor also use the labor headcount data to compute labor coefficient functions, the principal driver of labor demand in the IFs model.<br />
<br />
Labor coefficients are defined as the amount of labor needed to produce one unit of value added in a certain sector of the economy. The coefficients depend on the level of technology. The model uses GDP per capita as an indicator of the level of technological development. IFs pre-processor estimates labor coefficient functions for labor of different skill levels for the different sectors of the economy.<br />
<br />
The functions are derived from GTAP data we described earlier. The model pre-processor reads data on factor payments and aggregates data from 57 GTAP sectors to six IFs sectors. Shares of payment going to skilled and less-skilled workers in each of the sectors are then computed. Countries are grouped according to their level of technological development as represented by per capita income. For each group labor coefficients are obtained by taking an average of the country coefficients. &nbsp;We also convert labor payments data to labor headcount data using per capita income as a proxy for average wage. Labor coefficients and income are then plotted into a power function relationship. The figure below plots some of those labor functions.&nbsp; The functions fit quite well with a power law formulation[[#_ftn1|[1]]].<br />
<div><br/><br />
----<br />
<div id="ftn1">[[#_ftnref1|[1]]] This is interesting given the prevalence of power law in all sorts of scale-up activities (West 2017).</div></div></div></div></div></div></div></div></div></div><br />
<br />
= Labor Model Flowcharts =<br />
<br />
The diagram below shows an outline of the IFs labor model. On the supply side, the total labor pool (LAB) is computed from the labor force participation rates, by sex, (LABPARR) and the population (POP) in their working age, i.e., population over 15 (POP15TO65 + POPGT65). Participation rates are driven by the demographic changes with an additional negative impact from aging and a catch-up in female participation rate. Skill level of the labor supply (LABSUP) is driven by the level of development (GDPPCP) and the demand for labor is driven by labor-coefficients (LABCOEFFS) computed from coefficient function representing shifts in demand with technological progress as proxied by the level of development (GDPPCP). Coefficients computed by sector and skill gives the labor requirement by skill type for each unit of value added (VADD) in the sector. Multiplying these coefficients with projected value added in each sector gives an estimate of the labor demand. &nbsp;<br />
<br />
Any surplus or shortage between total labor demand and supply is used to compute the rate of unemployment. Deviations in the unemployment rate (LABUNEMPR) signal wage changes through an equilibrium seeking algorithm. Both demand and supply respond to the wage variable (LABWAGEIND) indexed to the base year. The supply responses are much slower than the demand responses.<br />
<br />
[[File:FLOCHART2.png|frame|center|Labor Model Flowchart]]<br />
<br />
= Labor Model Equations =<br />
<br />
== Overview ==<br />
<br />
The labor model is a part of the IFs economic model that uses labor model output as an input to a Cobb-Douglas production function in a multi-sector general equilibrium model. IFs is a very long-run dynamic model. Instead of computing fixed short-run equilibria that clear the relevant markets IFs uses an equilibrium seeking algorithm to balance the various systems over the longer run. The algorithm is known as the PID (proportion-integral-derivative) controller algorithm and is used widely in industrial control systems. It makes equilibrium seeking variables in IFs move towards a set target. The algorithm works by computing a multiplier based on the movement of the variable towards the target, as obtained by an integral (I) of the path traversed, and the rate of movement towards the target, the derivative term. The multiplier is applied on the process variable (the P term), or a response variable, in the subsequent time period. In the labor model, unemployment rate (LABUNEMPR) is used as the process variable and the PID multiplier is used on the wage rate (LABWAGEIND). Job availability (LABDEMS) and participation rate (LABPARR) get affected by changes in wage. &nbsp;&nbsp;<br />
<br />
Throughout this section we use subscripts and notations common to other modules of IFs. For example, we use t for time period. Subscripts p and r represent sex and country/region, respectively, c is the cohort number, with cohort 1 representing the newborns, cohort1 the the one-year to four-year-olds, cohort two five-year to nine-year-olds etc. Values for p are 1 for male, 2 for female and 3 for both sexes combined. For economic sectors we use s and for skill levels sk.<br />
<br />
== Labor Supply: Equations ==<br />
<br />
The total pool of labor is computed by multiplying the population of working age with the labor force participation rate (LABPARR). &nbsp;Population forecasts come from IFs demographic model which computes both five-year and single-year age-sex cohorts (''agedst'', ''fagedst''). &nbsp;<br />
<br />
The labor model forecasts participation rates by country/region&nbsp; and gender. Participation rates in the model move with the changes in the demographic composition. Female participation rates, which have historically been lower than the same for the male in all societies, but has moved up in modern and affluent societies, get a catch-up boost in the model. Participation rates can also change when there is labor shortage or surplus and the employers try to incentivize or discourage workers by changing wage. This last impact is much less slow than similar wage impacts on the demand side.<br />
<br />
== Labor Participation Rate ==<br />
<br />
Labor participation rates (''LABPARR'') for male and female are first initialized with historical data.<br />
<br />
:<math>LABPARR_{r,p}= LABPARR_{r,p,t=1} </math><br />
<br />
A ‘catch-up’ boost is added to the female participation rate. The boost added (FemParLabMul) starts at a third of a percentage point and withers away following a non-linear path as the female rates approaches the catch-up target (FemParTar), The maximum catch-up that can occur over the horizon of the model is thirty percent.<br />
<br />
:<math>FemParTar_{r}=Amin(LabParRI_{r,p=1},LabParRI_{r,p=2}+30)</math><br />
<br />
:<math>FemParLabMul_{r}=(FemParTar_{r}-LABPARR_{r,p=2,t-1})/(FemParTar_{r}-LABPARR_{r,p=2,t-1})</math><br />
<br />
:<math>LABPARR_{r,p=2,t}=LABPARR_{r,p=2,t-1}+FemParLabMul_{r}*0.3</math><br />
<br />
Next, we compute and apply the aging impact on the participation rate. As the relative share of people over the retirement age increases, the participation rate declines. The model keeps track of the changes in the demographic ratio (PopAgingRatio) of the population who are in their prime working age of 15 to 64 (POPWORKING) to those at a common retirement age of sixty-five or older (POPGT65). This ratio declines as countries age. The percentage drop in the ratio comparative to the base year is scaled appropriately to compute the aging impact (aging_impact). This impact is added to the male and female labor participation rates, with the impact on the female participation rate being slightly lower than that on male rates.<br />
<br />
:<math>POPAgingRatio_{r,t}=POPWORKING_{r,t}/POPGT65_{r,t}</math><br />
<br />
:<math>aging_impact_{r,t}=100*((POPAgingRatio_{r,t}/POPAgingRatio_{r,t=1})-1)*0.2</math><br />
<br />
:<math>LABPARR_{r,p=1,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t} </math><br />
<br />
:<math>LABPARR_{r,p=2,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t}*0.95 </math><br />
<br />
Participation rates respond slowly to changes in wage and unemployment rate. The impact is implemented through a wage impact factor computed from annual changes in the wage index (labwageimpact). The base participation rates can be changed by model user through two model parameters: a direct multiplier on the participation rate (labparm), or one that changes participation by moving the retirement age (labretagem)<br />
<br />
<br />
<br />
:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact*0.05)*labparm_{r,p,t}*labretagem_{r,p,t}</math><br />
<br />
Total participation rate (LABPARRr,p=3,t) is computed by an weighted average of male and female participation rates.<br />
<br />
:<math>LABPARR_{r,p=3,t}= (∑_{p=1 to 2}∑_{c=4 to 21}(agedst{r,c,p,t}*LABPARR_{r,p,t}))/(∑_{p=1 to 2}∑_{c=4 to 21}agedst{r,c,p,t})</math><br />
<br />
== Total Labor ==<br />
<br />
Finally, the total number of labor available for work (LAB) is computed by multiplying the total participation rate with the population of fifteen-year-olds or older.<br />
<br />
:<math>LAB_{r,t}= LABPARR_{r,p=3,t}*∑_{p=1 to 2,c=4 to 21}agedst_{r,c,p,t}</math><br />
<br />
== Labor by skill level ==<br />
<br />
The labor model forecasts labor supply (LABSUP) by two skill categories. The variable (''LABSUP'') is initialized in the pre-processor by reading the employment by skill/occupation (''LABEMPS'') data from GTAP[[#_ftn1|[1]]] &nbsp;and adding the unemployment numbers. We assume same unemployment rate (''LABUMEMPR'') for skilled and unskilled labor.<br />
<br />
:<math>LABSUP_{r,t=1,sk}=∑_{s=1 to 6}(LABEMPS_{r,s,t=1}/(1-(LABUNEMPR_{r,t=1}/100))</math><br />
<br />
The model forecasts labor by skill through a model of the skilled share of the labor. Education, training, exposure, and experience of the employees all improve with the level of development. The model captures this with an analytic function of the skilled share (perskilled) driven by GDP per capita at PPP (GDPPCP) -<br />
<br />
:<math>perskilled_{r}=f(GDPPCP_{r})</math><br />
<br />
Among the causal drivers of skill, education is considered to be the most proximate. Education is strongly correlated with the level of development, the deeper driver of skill in the model. However, the recent increase in education and/or a policy driven educational expansion might add to the impact of education on skill. Additional impacts from education on skill, when there is any, is computed through an expected function formulation. For example, in a society where an average adult has more (or less) education than the adults in other societies at that level of development, the skill share is given a slight upward push (or downward pull). The expectation function is a logarithmic function of educational attainment of working age population (EDYRSAG15) driven by GDP per capita at PPP. Attainment above (or below) the expected level (YearsEdExp) is computed by the function output (YearsEd) adjusted for country situation (yearseddiff). The percentage adjustment to the skilled share (LabSupSkiAdj) is computed using additional (limited) education, i.e., the difference between actual (EDYRSAG15) and expected values of educational attainment, expressed as a percentage of the expected value. The adjustment is scaled appropriately and peters off over time.<br />
<br />
:<math>YearsEd_{r,t}= f(GDPPCP_{r,t})</math><br />
<br />
:<math>yearsdeddiff_{r}= EDYRSAG15_{r,p=3,t=2}-YearsEd_{r,t=2}</math><br />
<br />
:<math>YearsEdExp_{r,t}=YearsEd_{r,t}+yearsdeddiff_{r}</math><br />
<br />
:<math>LabSupSkiAdj_{r,t}=0.3*(EDYRSAG15_{r,p=3,t=2}*YearsEdExp_{r,t})/YearsEd_{r,t}</math><br />
<br />
:<math>LabSupSkiAdj_{r,t}=ConvergeOverTime(0,LabSupSkiAdj_{r,t},70)</math><br />
<br />
:<math>perskilled_{r,t}= perskilled_{r,t}*(1+LabSupSkiAdj_{r,t})</math><br />
<br />
The skilled share (perskilled) is multiplied with the total labor supply (LAB) to obtain the number of labors who are skilled (LABSUPskilled)<br />
<br />
:<math>LABSUP_{r,skilled,t}=LAB_{r,p,t}*perskilledI_{r,t} </math><br />
<br />
As a last step, the model adjusts for the country specific variations in the skilled labor count not captured by the deeper and the proximate models. This is done by saving a ratio (LABSUPSkilledRI) of the actual historical data and the model computed value in the initial year. In the subsequent years this ratio is used to adjust the skilled labor forecast gradually.<br />
<br />
:<math>LABSUPCompSkilled_{r}=LAB_{r}*perskilled_{r,t=1}/100 </math><br />
<br />
:<math>LABSUPSkilledRI_{r}=LABSUP_{r,skilled,t=1}/LABSUPCompSkilled_{r}</math><br />
<br />
:<math>LABSUP_{r,skilled,t}= LABSUP_{r,skilled,t}*ConvergeOverTime(LABSUPSkilledRI_{r},1,85)</math><br />
<br />
Number of unskilled labor is obtained by subtracting the skilled labor from the total pool.<br />
<br />
<br />
<br />
:<math>LABSUP_{r,unskilled,t}= LAB_{r,p,t}- LABSUP_{r,skilled,t}</math><br />
<br />
== Labor Demand: Equations ==<br />
<br />
IFs economic model forecasts production in six economic sectors. IFs labor model computes the longer-term and shorter-term determinants of demand for skilled and unskilled labor (LABDEMS) for the production processes. The long-term drivers of labor requirement are technological progress or the lack of it. In the shorter-term wage affects the labor demand most. Wage in turn is affected by labor supply or skill shortage.<br />
<br />
The IFs model divides economic activities into six economic sectors – agriculture, energy, materials, manufacture, services and information, and communication technologies. Workers in the IFs labor model are disaggregated into two skill types. While the skill composition varies by the technology used in the sector and starts tilting towards the more skilled with the progress in technology, absolute number of labors needed to produce the same output goes down with technological development for both skilled and unskilled labor. This is illustrated in the next figure which plots the changes in labor requirement against GDP per capita at PPP, a proxy for level of development. Agriculture is a much less skill-intensive process than the manufacture, however, with technological progress skill requirement improves rapidly in both sectors. The IFs labor model computes these labor requirement functions in the model pre-processor. As we have already described in the pre-processor section, the computation of these functions use GTAP data on employment by occupation and economic activity. Appendices 3 and 4 lists sector and occupation mapping between GTAP and IFs.<br />
<br />
These functions are used to compute the labor coefficients (LABCOEFFS), i.e., number of skilled and unskilled labor needed to produce unit amount of output with the technology available, for which we use GDP per capita at PPP as a proxy.<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= f(GDPPCP_{r})</math><br />
<br />
manufacture, services and ICTech) and the subscrip sk stands for skill categories with 1 denoting unskilled and 2 skilled. The labor coefficients obtained from the analytical functions require some adjustments to incorporate country deviations from the functions for various factors not captured in the regression relationship. The first of these adjustments is a gradual removal of impacts of short-run fluctuations in output and labor from the computation of labor coefficient. This adjustment is applied on the coefficients computed from the function. The equation below shows a simplified form of these computations.<br />
<br />
:<math>LabCoeffAdjFac_{r,k,s,t}=f(igdpr_{r,t=2},(LAB_{r,t=2}/LAB_{r,t=1}),(LABCOEFFS_{r,t}/LABCOEFFS_{r,t-1}))</math><br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}=LABCOEFFS_{r,sk,s,t}(1-LabCoeffAdjFac_{r,k,s,t})</math><br />
<br />
Model users can use a global parameter (labcoeffsm) to change the labor coefficients by skill level for any or all of the six sectors –<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= LABCOEFFS_{r,sk,s,t}*'''labcoeffsm_{s,sk}'''</math><br />
<br />
To forecast the total labor demand, the labor coefficients (LABCOEFFS) are multiplied to the total projected output for each of the economic sectors. The forecast is adjusted for any discrepancy between data and model. The adjustment factor (LABDemsAdjFac) is computed as the initial ratio between the actual and computed employment. Actual employment is obtained from historical data (LABEMPS) processed using the GTAP database. The computed employment is obtained by multiplying the labor coefficients (LABCOEFFS) with the final output of the sector (VADD).<br />
<br />
:<math>LabDemsAdjFac_{r,s,sk}= LABEMPS_{r,s,sk,t=1}/(VADD_{r,s,t=1}*LABCOEFFS_{r,sk,s,t})</math><br />
<br />
The projected output is obtained by applying the growth rate (IGDPRCOR) on the sectoral value added from the previous year (VADD). The total labor demand is given by the product of the labor coefficients, projected output, demand adjustments and wage impacts (labwageimpactmul) and the number 1000 which adjusts the units for the equation. Wage impact comes from the level of unemployment and is computed in an equilibration process described in the next section. Model users can use a multiplicative parameter (labdemsm) to slide the demand upward or downward.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}=1000*VADD_{r,s,t-1}*(1+IGDPRCOR_{r})*LABCOEFFS_{r,sk,s,t}*LabDemsAdjFac_{r,s,sk}*labwageimpactmul_{r,s,sk}*'''labdemsm_{r,s}'''</math><br />
<br />
== Unemployment and Wage: Labor Market Equilibration ==<br />
<br />
The IFs labor model balances the labor market through an equilibrium seeking algorithm rather than computing an exact equilibrium at each time step. We use an algorithm borrowed from the control systems engineering. This PID controller algorithm, described also in the IFs economic model documentation, works by computing corrective signals for equilibrating variables using the deviations of a buffer variable, for example unemployment rate (LABUNEMPR), from a target value. The signal is computed from two quantities, the distance of the buffer from the target and the current rate of change of the buffer. The computation is tuned with PID elasticities to avoid oscillations. The computed signal is applied on the variable/s which need to be balanced, for example, demand and supply in the event of a market equilibration, thus getting closer to a balance at each step of simulation. The target value for the buffer variable and the tuning parameters of the control algorithm are obtained through rules-of-thumb and model calibration. The IFs labor model uses unemployment rate (LABUNEMPR) as the buffer variable for the market equilibration of labor demand and labor supply. The multiplier (i.e., corrective signal) obtained from the PID is applied on the wage index (LABWAGEIND). Changes in wage indices comparative to the base year, moderated through a second PID controller, is used to compute the final signal (labwageimpactmul) that drives labor demand and labor supply. Even though the model forecasts labor demand by sector and skill, and computes labor supply for both skill types, the equilibration algorithm works over the entire pool of labor. In other words, we assume that the skills are replaceable across sectors and the lack (or abundance) of jobs affects skilled and unskilled persons equally.<br />
<br />
At each annual timestep, the model computes the unemployment rate (LABUNEMPR) as the gap in between the total supply of labor (LAB) and the total demand. The gap (EmplGap) is expressed as a share of the total labor, the standard way to express unemployment rate.<br />
<br />
:<math>sumld=∑_{s,sk}LADEMS_{r,s,sk,t}</math><br />
<br />
:<math>EmplGap= LAB_{r,t}*sumld</math><br />
<br />
:<math>LABUMENPR_{r,t}= (EmplGap/LAB_{r,t})*100</math><br />
<br />
As the target value (LabUnEmpRateTar) for the PID controller that modulates unemployment rate we use either the historical unemployment rate or a ten percent unemployment rate when the historical rate is higher than ten. Model users can override the historical target through a model parameter (labunemprtrgtval).<br />
<br />
:<math>LABUMENPRi_{r,t}= LABUMENPR_{r,t}</math><br />
<br />
:<math>LabUnempRateTarget_{r}=labunemptargetval_{r}</math><br />
:<math>If LabUnempRateTarget_{r}=0,<br />
LabUnempRateTarget_{r}= AMIN(LABUMENPRi_{r,t},10) </math><br />
<br />
Unemployment rate target, when it is different from the base year value, is reached gradually with a convergence period of forty years . The target rate is converted to count (LabUnEmplTar) to make it equivalent to the employment gap (EmplGap) computed earlier.<br />
<br />
:<math>LabUnEmplTar_{r}= LAB_{r,t}*ConvergeOverTime(LABUMENPRi_{r,t},0,100)</math><br />
<br />
The first order difference (Diffl1) between the target unemployment and the demand-supply gap is used to compute a second order difference (Diffl2) accounting for changes in the rate of movement. The two differences and the PID multipliers (elwageunemp1, elwageunemp2) are provided to the PID function (ADJSTR). Working age population (POP15TO65r,t) works as the scaling base of the PID controller. The controller algorithm gives a multiplier (mullw) that is used in the subsequent year to adjust wage.<br />
<br />
:<math>Diffl1_{t}=LabUnEmplTar_{r}-EmplGap</math><br />
<br />
:<math>Diffl2_{t}=Diffl1_{t}-Diffl1_{t-1}</math><br />
<br />
:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},elwageunemp1_{r},elwageunemp2_{r})</math><br />
<br />
Wage adjustments affect demand and supply with an increase in wage drawing demand downward and supply upward. The opposite affects occur with a downward movement of wage. The wage variable affected by the PID multiplier (LABWAGEIND) is an index initialized at one. We use an indexed rather than a dollar wage in the equilibration process to avoid affecting the process from other economic phenomena that affects wage, for example, a rise in real wage as GDP or the labor share of income grows.<br />
<br />
:<math>LABWAGEIND_{r,t=1}=1</math><br />
<br />
In the subsequent years of the model run, the wage index is first adjusted with the equilibration signal obtained from the unemployment rate PID controller in the previous period<br />
<br />
:<math>LABWAGEIND_{r,t=1}= LABWAGEIND_{r,t=1}* mullw_{r,t-1} </math><br />
<br />
A wage impact (labwageimpact) is then computed using the changes in the wage index relative to the base value. The impact is smoothed with a moving average algorithm.<br />
<br />
:<math>labwageimpact_{r}= labwageimpact_{r,t-1}*0.9+ (1-LABWAGEIND_{r,t})*0.1</math><br />
<br />
The smoothed impact is used as the equilibration signal for labor supply. As we have already described in the section on labor supply, a small fraction of the impact (labwageimpact) is applied to the labor participation rate. The impact is scaled down to account for the slow pace of changes on the supply side.<br />
<br />
:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact_{r,t}*0.05)</math><br />
<br />
For the impacts of wage on labor demand we use a second PID multiplier as opposed to using the changes in wage index that we have done on the supply side. The second PID uses the wage index itself as the process variable and uses the base year value of 1 as the target. The reason we had to use this second PID is to control the pace at which wage disequilibrium can affect demand, especially in the event of an abrupt shock. The smoothing and scaling down that works on the supply side is not enough to control oscillations on the demand side.<br />
<br />
:<math>Diffl1_{t}=LABWAGEIND_{r,t=1}-1</math><br />
<br />
:<math>Diffl2_{t}=LABWAGEIND_{r,t}-LABWAGEIND_{r,t-1} </math><br />
<br />
:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},ellabwage1_{r},ellabwage1_{r})</math><br />
<br />
A second impact factor (labwageimpactmul) is computed using the correction signal from this second multiplier:<br />
<br />
:<math>labwageimpactmul_{r,t}= labwageimpactmul_{r,t-1}*mullw_{r,t}</math><br />
<br />
This impact factor is applied on the labor demand as described in the section on labor demand.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}= LABDEMS_{r,s,sk,t}* labwageimpactmul_{r,t}</math><br />
<br />
== Informal Labor ==<br />
<br />
IFs forecast labor and GDP share of the informal sector. Informal labor forecast is not explicitly endogenized in the labor market though. They are rather driven by development, skill and regulatory factors[[#_ftn1|[1]]]. However, the productivity and revenue impacts of changes in informality affects output and thus labor demand implicitly as a very distal driver.<br />
<br />
= References =<br />
<br />
<references /><br />
</div></div></div></div></div></div></div></div></div></div></div>Wikiadmin//pardee.du.edu/wiki/LaborLabor2018-09-07T22:26:54Z<p>Wikiadmin: </p>
<hr />
<div>Workers in an economy supply the expertise and the efforts needed to produce goods and services. In return the labor receives wages that they use to meet their current and future consumption needs. On one hand, shortage of labor with required skills prevents economies from realizing their growth potential. On the other hand, individuals falling short of the right qualifications might remain unemployed or underemployed failing to secure income needed for a decent living. The ongoing adjustments to find the best match between skills, jobs and wages can only be studied through a dynamic model of the labor market.&nbsp; &nbsp;<br />
<br />
Such a model should go beyond providing a reasonable answer to the obvious question of why employment and wages go up and down. An aggregate labor market must deal with issues that have strong interconnections with various other dynamic changes in the greater society. What kind of dividend of deficit can a society expect from its labor force given the phase of demographic transition in which it is situated? How severely would aging affect the pool of working age adults? Might increasing female participation rates offset some of the losses from aging? What is the level of skills and educational attainment in a society? These supply phenomena move relatively slowly unless there are huge disruptions, like a war or famine, or an aggressive policy push. The demand side, in contrast, needs to be more responsive in adjusting wages and employment given the investment and technology in the various sectors of the broader economy. In general, though, the labor market demonstrates some sluggishness compared to the goods and services markets as it involves moving human beings with various limitations. Consumption of goods and services depend on the income earned by the labor. Uneven distribution of employment and wages among labors of various types or between labor and capital for a long period of time can give rise to persistent inequality in a society. &nbsp;&nbsp;&nbsp;&nbsp;<br />
<br />
== Conceptual Framework ==<br />
<br />
Labor markets are markets for workers and jobs. In a labor market, employers meet their demand for labor with the supply of people willing to work at the wage the employers can offer. The employers raise the wage when there is a shortage of workers. Workers agree to take a lower wage when there are more of them than the firms need. In the real-world labor markets do not always clear at perfect equilibrium. Frinctional unemployment results for various reasons, for example, the search time between jobs. Structural unemployment can result from technology induced disruptions. Some unemployment could thus persist in the labor market even when there aren’t any short-term fluctuations. There is also the phenomenon of informal employment that consists of less sophisticated workers and entrepreneurs engaged in unregulated economic activities. &nbsp;In a dynamic model that covers the entire economy, the real wage earned by the labor drives the income and social mobility.&nbsp;&nbsp;&nbsp;<br />
<br />
To understand the long-term dynamics of the labor market, we need also examine the deeper determinants of labor demand and supply, the determinants that can shift the curves. Labor demand changes over time with the changes in demand for goods and services and the labor input needed to produce those. Labor productivity itself improves with technological progress. Long term transitions in the supply of labor are mostly demographic. &nbsp;<br />
<br />
Labor supply is determined by the working age population and the share of that population who are available for participation in the workforce. The labor supply is relatively stable as the demographic changes are slow in pace. As the share of elderly in the population increases, a recent trend in many societies, the rate of participation declines. Some of the aging impacts will be offset by the greater female participation rates, a second trend that surfaces as economies develop and women attain more education. Educational attainment also drives the general skill level of workers, male and female. Specific skills are obtained through training and experience that augment the knowledge obtained through general and specialized education. &nbsp;&nbsp;&nbsp;&nbsp;<br />
<br />
It is the demand side that causes most of the short-term imbalances in the labor market. &nbsp;In the long term, as said earlier, the important driver of demand for labor and their skills is technological progress. Labor requirement drops with advances in technology, more so for less skilled labor. Labor composition changes accordingly both within and across sectors. Rapid advances in technology can also cause disruption in the system when there is not much opening in the other sectors. Labor displacement is offset to some extent by the growth in the economy and the resulting increase in total demand. &nbsp;&nbsp;&nbsp;<br />
<br />
As we have already mentioned, employees maximize income and the firms minimize labor costs. When there are more laborers than the firms can hire, there is unemployment. Shifts in the rates of unemployment impacts wage, the price of labor. For example, wages drop in the event of rising unemployment as there are more people to hire from. Wage adjustments feed back to the demand for labor seeking to bring the market back to equilibrium.<br />
<br />
The challenges around the conceptual distinction between unemployment and employment is further complicated by the phenomenon of informal employment. In many developing countries there is a large urban non-agricultural informal sector where low-skilled workers work for wages typically lower than a formal employment.<br />
<br />
[[File:LMFlowchart1.png|frame|center|Description of the labor model]]<br />
<br />
== Dominant Relations ==<br />
<br />
The labor model in the International Futures system (IFs) balances the total supply of labor with the total labor demanded by all economic sectors. Total labor (LAB) is computed from the working age population and the labor participation rate. Population forecasts are obtained from the IFs demographic model. Participation rates (LABPARR) are computed by sex with a catchup algorithm for the female participation towards that for the male. Labor is also disaggregated by skill level, as determined by educational attainment, in a separate labor supply variable (LABSUP) which is used to distribute labor earnings by skill level. [** LABSUP do not affect the demand/supply balance now]<br />
<br />
Labor demands (LABDEMS) are driven by sectoral technology functions used to compute the labor requirement by skill level for each unit of potential valued added in the sector. These labor coefficients (LABCOEFFS) are multiplied with the projected value added for the sector to compute the needed manpower. The balancing mechanisms determines the labor employed in each of the sectors (LABS).&nbsp;&nbsp;<br />
<br />
The balancing, in the current version of the model, can be done in one of the two ways. In the first method, total needs combined from all economic sectors is normalized to the available pool of labor computed by subtracting the unemployed from those who are at or looking for work. The rate of unemployment is kept at its natural rate for which we use the base year rate of unemployment. (** This might need to be changed for countries where the market is undergoing some abrupt transition.)<br />
<br />
In the second balancing method, added in a recent revision of the model, total demand is equilibrated to supply through a CGE like market equilibrium model. An indexed wage (LABWAGEIND) and the rate of unemployment (LABUNEMPR) work as the equilibrating variables. As unemployment deviates from the target, PID algorithms send a signal for the wage to adjust. Wage adjustments cause adjustments in the “base” labor demands by sector computed from the labor-coefficient functions as described earlier. Wage signals also affects the labor participation rate. The magnitude of impact on the supply side is much lower than that on the demand side.<br />
<br />
Wage and unemployment rate are aggregated for the total labor market. The wage index starts with a base year value of 1 and the unemployment rates start with the historical data for the base year. Initial year unemployment rate works as the target for long term unemployment.<br />
<br />
== Key Dynamics ==<br />
<br />
The following key dynamics are directly related to the dominant relations:<br />
<br />
*Labor supply is determined from population of appropriate age in the population model (see its dominant relations and dynamics) and endogenous labor force participation rates, influenced exogenously by the growth of female participation.<br />
*Labor demand is driven by sectoral demand functions driven by technological progress<br />
<br />
== Structure and Agent System ==<br />
<br />
{| border="1" cellspacing="0" cellpadding="0" width="0" style="width:502px;"<br />
|-<br />
| style="width:242px;height:49px;" | <br />
'''System/Subsystem'''<br />
<br />
| style="height:49px;" | <br />
Labor market<br />
<br />
|-<br />
| <br />
'''Organizing Structure'''<br />
<br />
| <br />
Labor supply by skill level and labor demand by sector for each skill category represented within an equilibrium-seeking model with wage and unemployment rate as the equilibrating variables<br />
<br />
|-<br />
| <br />
'''Stocks'''<br />
<br />
| <br />
Population, labor, education, &nbsp;accumulated technology<br />
<br />
|-<br />
| <br />
'''Flows'''<br />
<br />
| <br />
Participation rate; Coefficients of labor demand; Employment (unemployment); Wage<br />
<br />
|-<br />
| <br />
'''Key Aggregate&nbsp;''''''Relationships&nbsp;'''<br />
<br />
'''(illustrative, not comprehensive)'''<br />
<br />
| <br />
Labor supply is driven by demographic changes; Participation of female change over time; Labor requirement changes with technological development; Unemployment rate drives wage; Wage movements affect labor demand and participation rate<br />
<br />
|-<br />
| <br />
'''Key Agent-Class Behavior&nbsp;''''''Relationships'''<br />
<br />
'''(illustrative, not comprehensive)'''<br />
<br />
| <br />
Households and work/leisure, and female participation patterns;<br />
<br />
&nbsp;<br />
<br />
Firms and hiring;<br />
<br />
&nbsp;<br />
<br />
<br />
<br />
<br />
<br />
|}<br />
<br />
= Labor Model Data =<br />
<br />
The labor supply and unemployment data that we use in our model is from International Labor Organization (ILO). For data on the demand side, we used data from the Global Trade Analysis Project. Wage variable used in the equilibration algorithm &nbsp;is an index anchored to the base year of the model. IFs preprocessor prepared these data for model use using various estimation, conversion and reconciliation processes.&nbsp; &nbsp; &nbsp;<br />
<br />
== Definitional Issues ==<br />
<br />
There are ambiguities in the way some of the labor market variables are defined. Labor participation rates and the rate of unemployment are two that need special attention.<br />
<br />
The size of the labor supply available for economic activities is expressed with the labor force participation rate. ILO defines this as a “measure of the proportion of country’s working-age population that engages actively in the labor market, either by working or looking for work.”<ref>http://www.ilo.org/ilostat-files/Documents/description_LFPR_EN.pdf</ref>&nbsp;National labor force surveys and census data are used to estimate this rate. The definition of labor force here includes both employed and unemployed and the rate is expressed as a percentage of working-age population. Working-age population is defined here as the population above legal working-age. For international comparability, ILO adopts a convenient minimum threshold of fifteen years as working age and avoids putting any upper age limit. In practice, both the minimum and the upper-age limits can vary by country. For example, the working-age in the USA is sixteen years. In the Netherlands the upper age limit is seventy-five years, whereas South African data uses an upper age limit of 64.<ref>https://www.bls.gov/fls/flscomparelf/technical_notes.pdf</ref><br />
<br />
Ambiguities are more abundant in the definition of unemployment. ILO came up with a guideline on this as well. Per the ILO guideline, the unemployed are those among the working-age population who are not employed, are available for work and are actively looking for jobs<ref>The definitions around employed and unemployed were agreed upon by nations through the ‘Resolution concerning statistics of work, employment and labor underutilization’ adopted by the 19th International Conference of Labor Statisticians (ICLS) in 2013. (Bourmpoula et al, 2017: 6).</ref>; the unemployment rate is expressed as a percentage of those who are in the labor force. The availability and job-seeker status could be defined in different ways giving rise to incompatibility in data. &nbsp;While there seems to be little room for disagreement on whether someone is at work or not, whether that work should be considered as employment is contested at many times.<br />
<br />
The debates around the nature and type of employment can range from gainfulness to workplace setting. For example, a large number of workers in the low-income low-regulation developing countries work outside the purview of formal enterprises. According to an ILO estimate, more than half of the global labor force and more than 90% of Micro and Small Enterprises (MSEs) worldwide are in the so called informal economy.<ref>http://www.ilo.org/global/topics/employment-promotion/informal-economy/lang--en/index.htm Incompatibility can arise in the treatment of various population groups for the computation of the denominator for participation and unemployment rates[1]. ILO makes their best efforts to make adjustments in the data for the sake of international comparison. For example, ILO asks countries that deviate from ILO guidelines to collect data needed to convert national figures to ILO figures. It is likely that some differences might have slipped past the adjustment process. We use ILO data and continue to update our database from ILO on a regular basis.</ref> This might explain the apparently counterintuitive pattern of low unemployment rate in some low-income countries (e.g., 2.2% for Guatemala) and relatively higher numbers for some of the developed nations. The low numbers in the poorer countries hide the prevalence of extremely low wage jobs in the informal sectors in these countries, the only options for the vulnerable people in the absence of any kind of social safety net. &nbsp;Contrastingly, in the developed countries the so called ‘gig-economy’ is attracting more and more workers who choose to work on their own rather than in a formal enterprise. ILO conceptualization makes the informal work part of total employment. The stacked Venn diagram below presents the relationship among the labor force metric including informal employment. IFs also models informal economy both in terms of GDP share and employment share of informal in the total economy and employment.<br />
<div><br/><div id="ftn4"><div><br />
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<div id="ftn1"><br />
[[#_ftnref1|[1]]] For example, the USA excludes people in the defense services and those in the prisons or mental asylums in their computation of the civilian non-institutional working-age population. There are also variations in the treatments of students, those recently laid-off, and family workers. Please see [https://stats.bls.gov/opub/mlr/2000/06/art1full.pdf https://stats.bls.gov/opub/mlr/2000/06/art1full.pdf] for a discussion&nbsp;<br />
<br />
The GTAP data that we use for the demand side of the labor model is taken as labor headcounts and is thus immune from ambiguities around rate computation. As far as we could gather[[#_ftn1|[1]]], the data includes both the formal and informal employment. We also need mention here that the GTAP database reconciles the labor data to calibrate the general equilibrium modeling that they do for the trade analyses. The data could thus be somewhat different from data collected through direct surveys. As a CGE model IFs is benefited by using calibrated data.<br />
<div><br/><br />
----<br />
<div id="ftn1">[[#_ftnref1|[1]]] Please see the webpage for documentation on GTAP labor data statistic: [https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=3248 https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=3248]</div></div></div></div></div></div><br />
<br />
== Sources of Labor Data ==<br />
<br />
IFs model uses ILO data for labor participation rates and for the unemployment rate. The data in IFs are collected from World Bank’s World Development Indicators (WDI) database. According to their documentation, WDI obtained the data from the ILO.<br />
<br />
&nbsp;<br />
<br />
Unemployment rate data in IFs is also collected from WDI. Like the participation rates WDI also obtains their unemployment data from ILO.[[#_ftn1|[1]]]<br />
<br />
For employment and labor demand data IFs uses Purdue University’s Global Trade Analysis Project (GTAP) database. GTAP collects and compiles factor payments, imports, and intersectoral flow data to calibrate CGE models of national economies for trade and other analyses. In their ninth release in 2016, GTAP published data for 140 countries and regions for the year 2011. The earlier GTAP releases, which the IFs model used for its previous versions, compiled data for the years 2004 and 2007. GTAP data release aggregates economic activities into 57 commodities and activities following International Standard Industrial Classification (ISIC). The IFs model maps the 57 GTAP sectors into six economic sectors of IFs – agriculture, energy, material and mining, manufacture, services and ICT. Appendix 2 presents two tables listing the sectors mapping between IFs and GTAP, and GTAP and ISIC. GTAP further disaggregates labor in each of the commodities/activities into five occupation and skill categories following the nine category International Standard Classification of Occupations (ISCO-88). The IFs model collapses five GTAP occupation categories into the simple IFs dichotomy of skilled and unskilled. The mapping of occupations and skills are presented in the third appendix of this document. &nbsp;<br />
<br />
The data in the main GTAP database, prepared for CGE modeling, are all in dollar unit and thus do not include labor headcounts. We have used a ‘satellite’ GTAP database[[#_ftn2|[2]]] for labor headcounts by skill and sector. The labor counts were also used to plot labor requirement functions for each of the IFs economic sectors and skill categories. The wage share of skilled and unskilled labor in each sector was computed using the labor headcounts and labor payments.<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] The name of the IFs table is SeriesLaborUnemploy%<br />
</div><div id="ftn2"><br />
[[#_ftnref2|[2]]] See Weingarden and Tsigas, 2010 for the details on the preparation of this database.<br />
<br />
== Scope of IFs Labor Model ==<br />
<br />
The IFs labor model simulates labor market at the national level. Each national labor market forecasts labor demand and employment by six sectors - agriculture, energy, mining, manufacture, services and ICT- and two skill levels - skilled and unskilled. The supply side do not have sectoral representation. IFs forecasts total labor force and labor supply by the two skill levels. Labor participation rate is computed in IFs by gender. Wage and unemployment rate is forecast for the overall labor market only.&nbsp;&nbsp;<br />
<br />
== Labor Model Pre-processor ==<br />
<br />
IFs system has a data preprocessor that prepares the initial conditions for the model using historical databases and various assumptions and estimated relationships to fill in the missing data and make data adjustments as needed[[#_ftn1|[1]]]. Pre-processing of labor data takes place in two IFs pre-processing modules. Labor participation rate data, which is closely related to demography, is processed in the population pre-processor. Unemployment rate and labor demand data are processed in the economic pre-processor. &nbsp;<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] For more details, please see ‘The Data Pre-Processor of International Futures (IFs)” by Barry B. Hughes (with Mohammod Irfan) at [http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf]<br />
<br />
=== Pre-processing Labor participation rate and unemployment ===<br />
<br />
For initializing labor participation rates by sex (LABPARR) the model uses the historical values from the base year or the most recent year with data[[#_ftn1|[1]]]. For countries with no data we use regression relationships of the participation rates, for men and for women, with income per capita. The relationships, shown in the next figure, are not great. However, the functions affect only five countries for which we do not have any data at all: Grenada, Kosovo, Micronesia, Seychelles and South Sudan[[#_ftn2|[2]]].<br />
<br />
&nbsp;<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] The data tables that the IFs model pre-processor use for initializing labor participation rates are: SeriesLaborParRate15PlusFemale%, SeriesLaborParRate15PlusMale%.<br />
</div><div id="ftn2"><br />
[[#_ftnref2|[2]]] We should try to collect participation rate for these countries from country sources.<br />
<br />
IFs data series SeriesLaborUnemploy% is used for the initialization of unemployment rates. That series has annual unemployment rates for one or more years between 1980 and 2016, for 181 of the 186 IFs countries. For five countries (Grenada, Kosovo, Micronesia, Taiwan and South Sudan[[#_ftn1|[1]]]) there is no data at all. To fill in the missing data we use a regression function of unemployment rate against GDP per capita. Like the participation rate functions, this function does also not have much of an explanatory power.<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] These are pretty much the same countries for which we do not have any participation rate data. This indicates ILO might have some administrative limitation in reporting data for these countries (notice Kosovo, Seychelles etc in the list)<br />
<br />
=== Pre-processing labor demand and unemployment from GTAP ===<br />
<br />
The IFs economic pre-processor reads labor headcount and labor payment data from the GTAP database. In addition to performing sector and occupation/skill mapping between GTAP and IFs, pre-processor also use the labor headcount data to compute labor coefficient functions, the principal driver of labor demand in the IFs model.<br />
<br />
Labor coefficients are defined as the amount of labor needed to produce one unit of value added in a certain sector of the economy. The coefficients depend on the level of technology. The model uses GDP per capita as an indicator of the level of technological development. IFs pre-processor estimates labor coefficient functions for labor of different skill levels for the different sectors of the economy.<br />
<br />
The functions are derived from GTAP data we described earlier. The model pre-processor reads data on factor payments and aggregates data from 57 GTAP sectors to six IFs sectors. Shares of payment going to skilled and less-skilled workers in each of the sectors are then computed. Countries are grouped according to their level of technological development as represented by per capita income. For each group labor coefficients are obtained by taking an average of the country coefficients. &nbsp;We also convert labor payments data to labor headcount data using per capita income as a proxy for average wage. Labor coefficients and income are then plotted into a power function relationship. The figure below plots some of those labor functions.&nbsp; The functions fit quite well with a power law formulation[[#_ftn1|[1]]].<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] This is interesting given the prevalence of power law in all sorts of scale-up activities (West 2017).<br />
<br />
= Labor Model Flowcharts =<br />
<br />
The diagram below shows an outline of the IFs labor model. On the supply side, the total labor pool (LAB) is computed from the labor force participation rates, by sex, (LABPARR) and the population (POP) in their working age, i.e., population over 15 (POP15TO65 + POPGT65). Participation rates are driven by the demographic changes with an additional negative impact from aging and a catch-up in female participation rate. Skill level of the labor supply (LABSUP) is driven by the level of development (GDPPCP) and the demand for labor is driven by labor-coefficients (LABCOEFFS) computed from coefficient function representing shifts in demand with technological progress as proxied by the level of development (GDPPCP). Coefficients computed by sector and skill gives the labor requirement by skill type for each unit of value added (VADD) in the sector. Multiplying these coefficients with projected value added in each sector gives an estimate of the labor demand. &nbsp;<br />
<br />
Any surplus or shortage between total labor demand and supply is used to compute the rate of unemployment. Deviations in the unemployment rate (LABUNEMPR) signal wage changes through an equilibrium seeking algorithm. Both demand and supply respond to the wage variable (LABWAGEIND) indexed to the base year. The supply responses are much slower than the demand responses.<br />
<br />
[[File:FLOCHART2.png|frame|center|Labor Model Flowchart]]<br />
<br />
= Labor Model Equations =<br />
<br />
== Overview ==<br />
<br />
The labor model is a part of the IFs economic model that uses labor model output as an input to a Cobb-Douglas production function in a multi-sector general equilibrium model. IFs is a very long-run dynamic model. Instead of computing fixed short-run equilibria that clear the relevant markets IFs uses an equilibrium seeking algorithm to balance the various systems over the longer run. The algorithm is known as the PID (proportion-integral-derivative) controller algorithm and is used widely in industrial control systems. It makes equilibrium seeking variables in IFs move towards a set target. The algorithm works by computing a multiplier based on the movement of the variable towards the target, as obtained by an integral (I) of the path traversed, and the rate of movement towards the target, the derivative term. The multiplier is applied on the process variable (the P term), or a response variable, in the subsequent time period. In the labor model, unemployment rate (LABUNEMPR) is used as the process variable and the PID multiplier is used on the wage rate (LABWAGEIND). Job availability (LABDEMS) and participation rate (LABPARR) get affected by changes in wage. &nbsp;&nbsp;<br />
<br />
Throughout this section we use subscripts and notations common to other modules of IFs. For example, we use t for time period. Subscripts p and r represent sex and country/region, respectively, c is the cohort number, with cohort 1 representing the newborns, cohort1 the the one-year to four-year-olds, cohort two five-year to nine-year-olds etc. Values for p are 1 for male, 2 for female and 3 for both sexes combined. For economic sectors we use s and for skill levels sk.<br />
<br />
== Labor Supply: Equations ==<br />
<br />
The total pool of labor is computed by multiplying the population of working age with the labor force participation rate (LABPARR). &nbsp;Population forecasts come from IFs demographic model which computes both five-year and single-year age-sex cohorts (''agedst'', ''fagedst''). &nbsp;<br />
<br />
The labor model forecasts participation rates by country/region&nbsp; and gender. Participation rates in the model move with the changes in the demographic composition. Female participation rates, which have historically been lower than the same for the male in all societies, but has moved up in modern and affluent societies, get a catch-up boost in the model. Participation rates can also change when there is labor shortage or surplus and the employers try to incentivize or discourage workers by changing wage. This last impact is much less slow than similar wage impacts on the demand side.<br />
<br />
== Labor Participation Rate ==<br />
<br />
Labor participation rates (''LABPARR'') for male and female are first initialized with historical data.<br />
<br />
:<math>LABPARR_{r,p}= LABPARR_{r,p,t=1} </math><br />
<br />
A ‘catch-up’ boost is added to the female participation rate. The boost added (FemParLabMul) starts at a third of a percentage point and withers away following a non-linear path as the female rates approaches the catch-up target (FemParTar), The maximum catch-up that can occur over the horizon of the model is thirty percent.<br />
<br />
:<math>FemParTar_{r}=Amin(LabParRI_{r,p=1},LabParRI_{r,p=2}+30)</math><br />
<br />
:<math>FemParLabMul_{r}=(FemParTar_{r}-LABPARR_{r,p=2,t-1})/(FemParTar_{r}-LABPARR_{r,p=2,t-1})</math><br />
<br />
:<math>LABPARR_{r,p=2,t}=LABPARR_{r,p=2,t-1}+FemParLabMul_{r}*0.3</math><br />
<br />
Next, we compute and apply the aging impact on the participation rate. As the relative share of people over the retirement age increases, the participation rate declines. The model keeps track of the changes in the demographic ratio (PopAgingRatio) of the population who are in their prime working age of 15 to 64 (POPWORKING) to those at a common retirement age of sixty-five or older (POPGT65). This ratio declines as countries age. The percentage drop in the ratio comparative to the base year is scaled appropriately to compute the aging impact (aging_impact). This impact is added to the male and female labor participation rates, with the impact on the female participation rate being slightly lower than that on male rates.<br />
<br />
:<math>POPAgingRatio_{r,t}=POPWORKING_{r,t}/POPGT65_{r,t}</math><br />
<br />
:<math>aging_impact_{r,t}=100*((POPAgingRatio_{r,t}/POPAgingRatio_{r,t=1})-1)*0.2</math><br />
<br />
:<math>LABPARR_{r,p=1,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t} </math><br />
<br />
:<math>LABPARR_{r,p=2,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t}*0.95 </math><br />
<br />
Participation rates respond slowly to changes in wage and unemployment rate. The impact is implemented through a wage impact factor computed from annual changes in the wage index (labwageimpact). The base participation rates can be changed by model user through two model parameters: a direct multiplier on the participation rate (labparm), or one that changes participation by moving the retirement age (labretagem)<br />
<br />
<br />
<br />
:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact*0.05)*labparm_{r,p,t}*labretagem_{r,p,t}</math><br />
<br />
Total participation rate (LABPARRr,p=3,t) is computed by an weighted average of male and female participation rates.<br />
<br />
:<math>LABPARR_{r,p=3,t}= (∑_{p=1 to 2}∑_{c=4 to 21}(agedst{r,c,p,t}*LABPARR_{r,p,t}))/(∑_{p=1 to 2}∑_{c=4 to 21}agedst{r,c,p,t})</math><br />
<br />
== Total Labor ==<br />
<br />
Finally, the total number of labor available for work (LAB) is computed by multiplying the total participation rate with the population of fifteen-year-olds or older.<br />
<br />
:<math>LAB_{r,t}= LABPARR_{r,p=3,t}*∑_{p=1 to 2,c=4 to 21}agedst_{r,c,p,t}</math><br />
<br />
== Labor by skill level ==<br />
<br />
The labor model forecasts labor supply (LABSUP) by two skill categories. The variable (''LABSUP'') is initialized in the pre-processor by reading the employment by skill/occupation (''LABEMPS'') data from GTAP[[#_ftn1|[1]]] &nbsp;and adding the unemployment numbers. We assume same unemployment rate (''LABUMEMPR'') for skilled and unskilled labor.<br />
<br />
:<math>LABSUP_{r,t=1,sk}=∑_{s=1 to 6}(LABEMPS_{r,s,t=1}/(1-(LABUNEMPR_{r,t=1}/100))</math><br />
<br />
The model forecasts labor by skill through a model of the skilled share of the labor. Education, training, exposure, and experience of the employees all improve with the level of development. The model captures this with an analytic function of the skilled share (perskilled) driven by GDP per capita at PPP (GDPPCP) -<br />
<br />
:<math>perskilled_{r}=f(GDPPCP_{r})</math><br />
<br />
Among the causal drivers of skill, education is considered to be the most proximate. Education is strongly correlated with the level of development, the deeper driver of skill in the model. However, the recent increase in education and/or a policy driven educational expansion might add to the impact of education on skill. Additional impacts from education on skill, when there is any, is computed through an expected function formulation. For example, in a society where an average adult has more (or less) education than the adults in other societies at that level of development, the skill share is given a slight upward push (or downward pull). The expectation function is a logarithmic function of educational attainment of working age population (EDYRSAG15) driven by GDP per capita at PPP. Attainment above (or below) the expected level (YearsEdExp) is computed by the function output (YearsEd) adjusted for country situation (yearseddiff). The percentage adjustment to the skilled share (LabSupSkiAdj) is computed using additional (limited) education, i.e., the difference between actual (EDYRSAG15) and expected values of educational attainment, expressed as a percentage of the expected value. The adjustment is scaled appropriately and peters off over time.<br />
<br />
:<math>YearsEd_{r,t}= f(GDPPCP_{r,t})</math><br />
<br />
:<math>yearsdeddiff_{r}= EDYRSAG15_{r,p=3,t=2}-YearsEd_{r,t=2}</math><br />
<br />
:<math>YearsEdExp_{r,t}=YearsEd_{r,t}+yearsdeddiff_{r}</math><br />
<br />
:<math>LabSupSkiAdj_{r,t}=0.3*(EDYRSAG15_{r,p=3,t=2}*YearsEdExp_{r,t})/YearsEd_{r,t}</math><br />
<br />
:<math>LabSupSkiAdj_{r,t}=ConvergeOverTime(0,LabSupSkiAdj_{r,t},70)</math><br />
<br />
:<math>perskilled_{r,t}= perskilled_{r,t}*(1+LabSupSkiAdj_{r,t})</math><br />
<br />
The skilled share (perskilled) is multiplied with the total labor supply (LAB) to obtain the number of labors who are skilled (LABSUPskilled)<br />
<br />
:<math>LABSUP_{r,skilled,t}=LAB_{r,p,t}*perskilledI_{r,t} </math><br />
<br />
As a last step, the model adjusts for the country specific variations in the skilled labor count not captured by the deeper and the proximate models. This is done by saving a ratio (LABSUPSkilledRI) of the actual historical data and the model computed value in the initial year. In the subsequent years this ratio is used to adjust the skilled labor forecast gradually.<br />
<br />
:<math>LABSUPCompSkilled_{r}=LAB_{r}*perskilled_{r,t=1}/100 </math><br />
<br />
:<math>LABSUPSkilledRI_{r}=LABSUP_{r,skilled,t=1}/LABSUPCompSkilled_{r}</math><br />
<br />
:<math>LABSUP_{r,skilled,t}= LABSUP_{r,skilled,t}*ConvergeOverTime(LABSUPSkilledRI_{r},1,85)</math><br />
<br />
Number of unskilled labor is obtained by subtracting the skilled labor from the total pool.<br />
<br />
<br />
<br />
:<math>LABSUP_{r,unskilled,t}= LAB_{r,p,t}- LABSUP_{r,skilled,t}</math><br />
<br />
== Labor Demand: Equations ==<br />
<br />
IFs economic model forecasts production in six economic sectors. IFs labor model computes the longer-term and shorter-term determinants of demand for skilled and unskilled labor (LABDEMS) for the production processes. The long-term drivers of labor requirement are technological progress or the lack of it. In the shorter-term wage affects the labor demand most. Wage in turn is affected by labor supply or skill shortage.<br />
<br />
The IFs model divides economic activities into six economic sectors – agriculture, energy, materials, manufacture, services and information, and communication technologies. Workers in the IFs labor model are disaggregated into two skill types. While the skill composition varies by the technology used in the sector and starts tilting towards the more skilled with the progress in technology, absolute number of labors needed to produce the same output goes down with technological development for both skilled and unskilled labor. This is illustrated in the next figure which plots the changes in labor requirement against GDP per capita at PPP, a proxy for level of development. Agriculture is a much less skill-intensive process than the manufacture, however, with technological progress skill requirement improves rapidly in both sectors. The IFs labor model computes these labor requirement functions in the model pre-processor. As we have already described in the pre-processor section, the computation of these functions use GTAP data on employment by occupation and economic activity. Appendices 3 and 4 lists sector and occupation mapping between GTAP and IFs.<br />
<br />
These functions are used to compute the labor coefficients (LABCOEFFS), i.e., number of skilled and unskilled labor needed to produce unit amount of output with the technology available, for which we use GDP per capita at PPP as a proxy.<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= f(GDPPCP_{r})</math><br />
<br />
manufacture, services and ICTech) and the subscrip sk stands for skill categories with 1 denoting unskilled and 2 skilled. The labor coefficients obtained from the analytical functions require some adjustments to incorporate country deviations from the functions for various factors not captured in the regression relationship. The first of these adjustments is a gradual removal of impacts of short-run fluctuations in output and labor from the computation of labor coefficient. This adjustment is applied on the coefficients computed from the function. The equation below shows a simplified form of these computations.<br />
<br />
:<math>LabCoeffAdjFac_{r,k,s,t}=f(igdpr_{r,t=2},(LAB_{r,t=2}/LAB_{r,t=1}),(LABCOEFFS_{r,t}/LABCOEFFS_{r,t-1}))</math><br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}=LABCOEFFS_{r,sk,s,t}(1-LabCoeffAdjFac_{r,k,s,t})</math><br />
<br />
Model users can use a global parameter (labcoeffsm) to change the labor coefficients by skill level for any or all of the six sectors –<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= LABCOEFFS_{r,sk,s,t}*'''labcoeffsm_{s,sk}'''</math><br />
<br />
To forecast the total labor demand, the labor coefficients (LABCOEFFS) are multiplied to the total projected output for each of the economic sectors. The forecast is adjusted for any discrepancy between data and model. The adjustment factor (LABDemsAdjFac) is computed as the initial ratio between the actual and computed employment. Actual employment is obtained from historical data (LABEMPS) processed using the GTAP database. The computed employment is obtained by multiplying the labor coefficients (LABCOEFFS) with the final output of the sector (VADD).<br />
<br />
:<math>LabDemsAdjFac_{r,s,sk}= LABEMPS_{r,s,sk,t=1}/(VADD_{r,s,t=1}*LABCOEFFS_{r,sk,s,t})</math><br />
<br />
The projected output is obtained by applying the growth rate (IGDPRCOR) on the sectoral value added from the previous year (VADD). The total labor demand is given by the product of the labor coefficients, projected output, demand adjustments and wage impacts (labwageimpactmul) and the number 1000 which adjusts the units for the equation. Wage impact comes from the level of unemployment and is computed in an equilibration process described in the next section. Model users can use a multiplicative parameter (labdemsm) to slide the demand upward or downward.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}=1000*VADD_{r,s,t-1}*(1+IGDPRCOR_{r})*LABCOEFFS_{r,sk,s,t}*LabDemsAdjFac_{r,s,sk}*labwageimpactmul_{r,s,sk}*'''labdemsm_{r,s}'''</math><br />
<br />
== Unemployment and Wage: Labor Market Equilibration ==<br />
<br />
The IFs labor model balances the labor market through an equilibrium seeking algorithm rather than computing an exact equilibrium at each time step. We use an algorithm borrowed from the control systems engineering. This PID controller algorithm, described also in the IFs economic model documentation, works by computing corrective signals for equilibrating variables using the deviations of a buffer variable, for example unemployment rate (LABUNEMPR), from a target value. The signal is computed from two quantities, the distance of the buffer from the target and the current rate of change of the buffer. The computation is tuned with PID elasticities to avoid oscillations. The computed signal is applied on the variable/s which need to be balanced, for example, demand and supply in the event of a market equilibration, thus getting closer to a balance at each step of simulation. The target value for the buffer variable and the tuning parameters of the control algorithm are obtained through rules-of-thumb and model calibration. The IFs labor model uses unemployment rate (LABUNEMPR) as the buffer variable for the market equilibration of labor demand and labor supply. The multiplier (i.e., corrective signal) obtained from the PID is applied on the wage index (LABWAGEIND). Changes in wage indices comparative to the base year, moderated through a second PID controller, is used to compute the final signal (labwageimpactmul) that drives labor demand and labor supply. Even though the model forecasts labor demand by sector and skill, and computes labor supply for both skill types, the equilibration algorithm works over the entire pool of labor. In other words, we assume that the skills are replaceable across sectors and the lack (or abundance) of jobs affects skilled and unskilled persons equally.<br />
<br />
At each annual timestep, the model computes the unemployment rate (LABUNEMPR) as the gap in between the total supply of labor (LAB) and the total demand. The gap (EmplGap) is expressed as a share of the total labor, the standard way to express unemployment rate.<br />
<br />
:<math>sumld=∑_{s,sk}LADEMS_{r,s,sk,t}</math><br />
<br />
:<math>EmplGap= LAB_{r,t}*sumld</math><br />
<br />
:<math>LABUMENPR_{r,t}= (EmplGap/LAB_{r,t})*100</math><br />
<br />
As the target value (LabUnEmpRateTar) for the PID controller that modulates unemployment rate we use either the historical unemployment rate or a ten percent unemployment rate when the historical rate is higher than ten. Model users can override the historical target through a model parameter (labunemprtrgtval).<br />
<br />
:<math>LABUMENPRi_{r,t}= LABUMENPR_{r,t}</math><br />
<br />
:<math>LabUnempRateTarget_{r}=labunemptargetval_{r}</math><br />
:<math>If LabUnempRateTarget_{r}=0,<br />
LabUnempRateTarget_{r}= AMIN(LABUMENPRi_{r,t},10) </math><br />
<br />
Unemployment rate target, when it is different from the base year value, is reached gradually with a convergence period of forty years . The target rate is converted to count (LabUnEmplTar) to make it equivalent to the employment gap (EmplGap) computed earlier.<br />
<br />
:<math>LabUnEmplTar_{r}= LAB_{r,t}*ConvergeOverTime(LABUMENPRi_{r,t},0,100)</math><br />
<br />
The first order difference (Diffl1) between the target unemployment and the demand-supply gap is used to compute a second order difference (Diffl2) accounting for changes in the rate of movement. The two differences and the PID multipliers (elwageunemp1, elwageunemp2) are provided to the PID function (ADJSTR). Working age population (POP15TO65r,t) works as the scaling base of the PID controller. The controller algorithm gives a multiplier (mullw) that is used in the subsequent year to adjust wage.<br />
<br />
:<math>Diffl1_{t}=LabUnEmplTar_{r}-EmplGap</math><br />
<br />
:<math>Diffl2_{t}=Diffl1_{t}-Diffl1_{t-1}</math><br />
<br />
:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},elwageunemp1_{r},elwageunemp2_{r})</math><br />
<br />
Wage adjustments affect demand and supply with an increase in wage drawing demand downward and supply upward. The opposite affects occur with a downward movement of wage. The wage variable affected by the PID multiplier (LABWAGEIND) is an index initialized at one. We use an indexed rather than a dollar wage in the equilibration process to avoid affecting the process from other economic phenomena that affects wage, for example, a rise in real wage as GDP or the labor share of income grows.<br />
<br />
:<math>LABWAGEIND_{r,t=1}=1</math><br />
<br />
In the subsequent years of the model run, the wage index is first adjusted with the equilibration signal obtained from the unemployment rate PID controller in the previous period<br />
<br />
:<math>LABWAGEIND_{r,t=1}= LABWAGEIND_{r,t=1}* mullw_{r,t-1} </math><br />
<br />
A wage impact (labwageimpact) is then computed using the changes in the wage index relative to the base value. The impact is smoothed with a moving average algorithm.<br />
<br />
:<math>labwageimpact_{r}= labwageimpact_{r,t-1}*0.9+ (1-LABWAGEIND_{r,t})*0.1</math><br />
<br />
The smoothed impact is used as the equilibration signal for labor supply. As we have already described in the section on labor supply, a small fraction of the impact (labwageimpact) is applied to the labor participation rate. The impact is scaled down to account for the slow pace of changes on the supply side.<br />
<br />
:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact_{r,t}*0.05)</math><br />
<br />
For the impacts of wage on labor demand we use a second PID multiplier as opposed to using the changes in wage index that we have done on the supply side. The second PID uses the wage index itself as the process variable and uses the base year value of 1 as the target. The reason we had to use this second PID is to control the pace at which wage disequilibrium can affect demand, especially in the event of an abrupt shock. The smoothing and scaling down that works on the supply side is not enough to control oscillations on the demand side.<br />
<br />
:<math>Diffl1_{t}=LABWAGEIND_{r,t=1}-1</math><br />
<br />
:<math>Diffl2_{t}=LABWAGEIND_{r,t}-LABWAGEIND_{r,t-1} </math><br />
<br />
:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},ellabwage1_{r},ellabwage1_{r})</math><br />
<br />
A second impact factor (labwageimpactmul) is computed using the correction signal from this second multiplier:<br />
<br />
:<math>labwageimpactmul_{r,t}= labwageimpactmul_{r,t-1}*mullw_{r,t}</math><br />
<br />
This impact factor is applied on the labor demand as described in the section on labor demand.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}= LABDEMS_{r,s,sk,t}* labwageimpactmul_{r,t}</math><br />
<br />
== Informal Labor ==<br />
<br />
IFs forecast labor and GDP share of the informal sector. Informal labor forecast is not explicitly endogenized in the labor market though. They are rather driven by development, skill and regulatory factors[[#_ftn1|[1]]]. However, the productivity and revenue impacts of changes in informality affects output and thus labor demand implicitly as a very distal driver.<br />
<br />
= References =<br />
<br />
<references /><br />
</div></div></div></div></div></div></div></div></div></div></div>Wikiadmin//pardee.du.edu/wiki/LaborLabor2018-09-07T22:25:46Z<p>Wikiadmin: </p>
<hr />
<div>Workers in an economy supply the expertise and the efforts needed to produce goods and services. In return the labor receives wages that they use to meet their current and future consumption needs. On one hand, shortage of labor with required skills prevents economies from realizing their growth potential. On the other hand, individuals falling short of the right qualifications might remain unemployed or underemployed failing to secure income needed for a decent living. The ongoing adjustments to find the best match between skills, jobs and wages can only be studied through a dynamic model of the labor market.&nbsp; &nbsp;<br />
<br />
Such a model should go beyond providing a reasonable answer to the obvious question of why employment and wages go up and down. An aggregate labor market must deal with issues that have strong interconnections with various other dynamic changes in the greater society. What kind of dividend of deficit can a society expect from its labor force given the phase of demographic transition in which it is situated? How severely would aging affect the pool of working age adults? Might increasing female participation rates offset some of the losses from aging? What is the level of skills and educational attainment in a society? These supply phenomena move relatively slowly unless there are huge disruptions, like a war or famine, or an aggressive policy push. The demand side, in contrast, needs to be more responsive in adjusting wages and employment given the investment and technology in the various sectors of the broader economy. In general, though, the labor market demonstrates some sluggishness compared to the goods and services markets as it involves moving human beings with various limitations. Consumption of goods and services depend on the income earned by the labor. Uneven distribution of employment and wages among labors of various types or between labor and capital for a long period of time can give rise to persistent inequality in a society. &nbsp;&nbsp;&nbsp;&nbsp;<br />
<br />
== Conceptual Framework ==<br />
<br />
Labor markets are markets for workers and jobs. In a labor market, employers meet their demand for labor with the supply of people willing to work at the wage the employers can offer. The employers raise the wage when there is a shortage of workers. Workers agree to take a lower wage when there are more of them than the firms need. In the real-world labor markets do not always clear at perfect equilibrium. Frinctional unemployment results for various reasons, for example, the search time between jobs. Structural unemployment can result from technology induced disruptions. Some unemployment could thus persist in the labor market even when there aren’t any short-term fluctuations. There is also the phenomenon of informal employment that consists of less sophisticated workers and entrepreneurs engaged in unregulated economic activities. &nbsp;In a dynamic model that covers the entire economy, the real wage earned by the labor drives the income and social mobility.&nbsp;&nbsp;&nbsp;<br />
<br />
To understand the long-term dynamics of the labor market, we need also examine the deeper determinants of labor demand and supply, the determinants that can shift the curves. Labor demand changes over time with the changes in demand for goods and services and the labor input needed to produce those. Labor productivity itself improves with technological progress. Long term transitions in the supply of labor are mostly demographic. &nbsp;<br />
<br />
Labor supply is determined by the working age population and the share of that population who are available for participation in the workforce. The labor supply is relatively stable as the demographic changes are slow in pace. As the share of elderly in the population increases, a recent trend in many societies, the rate of participation declines. Some of the aging impacts will be offset by the greater female participation rates, a second trend that surfaces as economies develop and women attain more education. Educational attainment also drives the general skill level of workers, male and female. Specific skills are obtained through training and experience that augment the knowledge obtained through general and specialized education. &nbsp;&nbsp;&nbsp;&nbsp;<br />
<br />
It is the demand side that causes most of the short-term imbalances in the labor market. &nbsp;In the long term, as said earlier, the important driver of demand for labor and their skills is technological progress. Labor requirement drops with advances in technology, more so for less skilled labor. Labor composition changes accordingly both within and across sectors. Rapid advances in technology can also cause disruption in the system when there is not much opening in the other sectors. Labor displacement is offset to some extent by the growth in the economy and the resulting increase in total demand. &nbsp;&nbsp;&nbsp;<br />
<br />
As we have already mentioned, employees maximize income and the firms minimize labor costs. When there are more laborers than the firms can hire, there is unemployment. Shifts in the rates of unemployment impacts wage, the price of labor. For example, wages drop in the event of rising unemployment as there are more people to hire from. Wage adjustments feed back to the demand for labor seeking to bring the market back to equilibrium.<br />
<br />
The challenges around the conceptual distinction between unemployment and employment is further complicated by the phenomenon of informal employment. In many developing countries there is a large urban non-agricultural informal sector where low-skilled workers work for wages typically lower than a formal employment.<br />
<br />
[[File:LMFlowchart1.png|frame|center|Description of the labor model]]<br />
<br />
== Dominant Relations ==<br />
<br />
The labor model in the International Futures system (IFs) balances the total supply of labor with the total labor demanded by all economic sectors. Total labor (LAB) is computed from the working age population and the labor participation rate. Population forecasts are obtained from the IFs demographic model. Participation rates (LABPARR) are computed by sex with a catchup algorithm for the female participation towards that for the male. Labor is also disaggregated by skill level, as determined by educational attainment, in a separate labor supply variable (LABSUP) which is used to distribute labor earnings by skill level. [** LABSUP do not affect the demand/supply balance now]<br />
<br />
Labor demands (LABDEMS) are driven by sectoral technology functions used to compute the labor requirement by skill level for each unit of potential valued added in the sector. These labor coefficients (LABCOEFFS) are multiplied with the projected value added for the sector to compute the needed manpower. The balancing mechanisms determines the labor employed in each of the sectors (LABS).&nbsp;&nbsp;<br />
<br />
The balancing, in the current version of the model, can be done in one of the two ways. In the first method, total needs combined from all economic sectors is normalized to the available pool of labor computed by subtracting the unemployed from those who are at or looking for work. The rate of unemployment is kept at its natural rate for which we use the base year rate of unemployment. (** This might need to be changed for countries where the market is undergoing some abrupt transition.)<br />
<br />
In the second balancing method, added in a recent revision of the model, total demand is equilibrated to supply through a CGE like market equilibrium model. An indexed wage (LABWAGEIND) and the rate of unemployment (LABUNEMPR) work as the equilibrating variables. As unemployment deviates from the target, PID algorithms send a signal for the wage to adjust. Wage adjustments cause adjustments in the “base” labor demands by sector computed from the labor-coefficient functions as described earlier. Wage signals also affects the labor participation rate. The magnitude of impact on the supply side is much lower than that on the demand side.<br />
<br />
Wage and unemployment rate are aggregated for the total labor market. The wage index starts with a base year value of 1 and the unemployment rates start with the historical data for the base year. Initial year unemployment rate works as the target for long term unemployment.<br />
<br />
== Key Dynamics ==<br />
<br />
The following key dynamics are directly related to the dominant relations:<br />
<br />
*Labor supply is determined from population of appropriate age in the population model (see its dominant relations and dynamics) and endogenous labor force participation rates, influenced exogenously by the growth of female participation.<br />
*Labor demand is driven by sectoral demand functions driven by technological progress<br />
<br />
== Structure and Agent System ==<br />
<br />
{| border="1" cellspacing="0" cellpadding="0" width="0" style="width:502px;"<br />
|-<br />
| style="width:242px;height:49px;" | <br />
'''System/Subsystem'''<br />
<br />
| style="height:49px;" | <br />
Labor market<br />
<br />
|-<br />
| <br />
'''Organizing Structure'''<br />
<br />
| <br />
Labor supply by skill level and labor demand by sector for each skill category represented within an equilibrium-seeking model with wage and unemployment rate as the equilibrating variables<br />
<br />
|-<br />
| <br />
'''Stocks'''<br />
<br />
| <br />
Population, labor, education, &nbsp;accumulated technology<br />
<br />
|-<br />
| <br />
'''Flows'''<br />
<br />
| <br />
Participation rate; Coefficients of labor demand; Employment (unemployment); Wage<br />
<br />
|-<br />
| <br />
'''Key Aggregate&nbsp;''''''Relationships&nbsp;'''<br />
<br />
'''(illustrative, not comprehensive)'''<br />
<br />
| <br />
Labor supply is driven by demographic changes; Participation of female change over time; Labor requirement changes with technological development; Unemployment rate drives wage; Wage movements affect labor demand and participation rate<br />
<br />
|-<br />
| <br />
'''Key Agent-Class Behavior&nbsp;''''''Relationships'''<br />
<br />
'''(illustrative, not comprehensive)'''<br />
<br />
| <br />
Households and work/leisure, and female participation patterns;<br />
<br />
&nbsp;<br />
<br />
Firms and hiring;<br />
<br />
&nbsp;<br />
<br />
<br />
<br />
<br />
<br />
|}<br />
<br />
= Labor Model Data =<br />
<br />
The labor supply and unemployment data that we use in our model is from International Labor Organization (ILO). For data on the demand side, we used data from the Global Trade Analysis Project. Wage variable used in the equilibration algorithm &nbsp;is an index anchored to the base year of the model. IFs preprocessor prepared these data for model use using various estimation, conversion and reconciliation processes.&nbsp; &nbsp; &nbsp;<br />
<br />
== Definitional Issues ==<br />
<br />
There are ambiguities in the way some of the labor market variables are defined. Labor participation rates and the rate of unemployment are two that need special attention.<br />
<br />
The size of the labor supply available for economic activities is expressed with the labor force participation rate. ILO defines this as a “measure of the proportion of country’s working-age population that engages actively in the labor market, either by working or looking for work.”<ref>http://www.ilo.org/ilostat-files/Documents/description_LFPR_EN.pdf</ref>&nbsp;National labor force surveys and census data are used to estimate this rate. The definition of labor force here includes both employed and unemployed and the rate is expressed as a percentage of working-age population. Working-age population is defined here as the population above legal working-age. For international comparability, ILO adopts a convenient minimum threshold of fifteen years as working age and avoids putting any upper age limit. In practice, both the minimum and the upper-age limits can vary by country. For example, the working-age in the USA is sixteen years. In the Netherlands the upper age limit is seventy-five years, whereas South African data uses an upper age limit of 64.<ref>https://www.bls.gov/fls/flscomparelf/technical_notes.pdf</ref><br />
<br />
Ambiguities are more abundant in the definition of unemployment. ILO came up with a guideline on this as well. Per the ILO guideline, the unemployed are those among the working-age population who are not employed, are available for work and are actively looking for jobs<ref>The definitions around employed and unemployed were agreed upon by nations through the ‘Resolution concerning statistics of work, employment and labor underutilization’ adopted by the 19th International Conference of Labor Statisticians (ICLS) in 2013. (Bourmpoula et al, 2017: 6).</ref>; the unemployment rate is expressed as a percentage of those who are in the labor force. The availability and job-seeker status could be defined in different ways giving rise to incompatibility in data. &nbsp;While there seems to be little room for disagreement on whether someone is at work or not, whether that work should be considered as employment is contested at many times.<br />
<br />
The debates around the nature and type of employment can range from gainfulness to workplace setting. For example, a large number of workers in the low-income low-regulation developing countries work outside the purview of formal enterprises. According to an ILO estimate, more than half of the global labor force and more than 90% of Micro and Small Enterprises (MSEs) worldwide are in the so called informal economy[[#_ftn4|[4]]]. This might explain the apparently counterintuitive pattern of low unemployment rate in some low-income countries (e.g., 2.2% for Guatemala) and relatively higher numbers for some of the developed nations. The low numbers in the poorer countries hide the prevalence of extremely low wage jobs in the informal sectors in these countries, the only options for the vulnerable people in the absence of any kind of social safety net. &nbsp;Contrastingly, in the developed countries the so called ‘gig-economy’ is attracting more and more workers who choose to work on their own rather than in a formal enterprise. ILO conceptualization makes the informal work part of total employment. The stacked Venn diagram below presents the relationship among the labor force metric including informal employment. IFs also models informal economy both in terms of GDP share and employment share of informal in the total economy and employment.<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] [http://www.ilo.org/ilostat-files/Documents/description_LFPR_EN.pdf http://www.ilo.org/ilostat-files/Documents/description_LFPR_EN.pdf]<br />
</div><div id="ftn2"><br />
[[#_ftnref2|[2]]] [https://www.bls.gov/fls/flscomparelf/technical_notes.pdf https://www.bls.gov/fls/flscomparelf/technical_notes.pdf]<br />
</div><div id="ftn3"><br />
[[#_ftnref3|[3]]] The definitions around employed and unemployed were agreed upon by nations through the ‘Resolution concerning statistics of work, employment and labor underutilization’ adopted by the 19<sup>th</sup> International Conference of Labor Statisticians (ICLS) in 2013. (Bourmpoula et al, 2017: 6).<br />
</div><div id="ftn4"><br />
[[#_ftnref4|[4]]] [http://www.ilo.org/global/topics/employment-promotion/informal-economy/lang--en/index.htm http://www.ilo.org/global/topics/employment-promotion/informal-economy/lang--en/index.htm]<br />
<br />
Incompatibility can arise in the treatment of various population groups for the computation of the denominator for participation and unemployment rates[[#_ftn1|[1]]]. ILO makes their best efforts to make adjustments in the data for the sake of international comparison. For example, ILO asks countries that deviate from ILO guidelines to collect data needed to convert national figures to ILO figures. It is likely that some differences might have slipped past the adjustment process. We use ILO data and continue to update our database from ILO on a regular basis.<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] For example, the USA excludes people in the defense services and those in the prisons or mental asylums in their computation of the civilian non-institutional working-age population. There are also variations in the treatments of students, those recently laid-off, and family workers. Please see [https://stats.bls.gov/opub/mlr/2000/06/art1full.pdf https://stats.bls.gov/opub/mlr/2000/06/art1full.pdf] for a discussion&nbsp;<br />
<br />
The GTAP data that we use for the demand side of the labor model is taken as labor headcounts and is thus immune from ambiguities around rate computation. As far as we could gather[[#_ftn1|[1]]], the data includes both the formal and informal employment. We also need mention here that the GTAP database reconciles the labor data to calibrate the general equilibrium modeling that they do for the trade analyses. The data could thus be somewhat different from data collected through direct surveys. As a CGE model IFs is benefited by using calibrated data.<br />
<div><br/><br />
----<br />
<div id="ftn1">[[#_ftnref1|[1]]] Please see the webpage for documentation on GTAP labor data statistic: [https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=3248 https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=3248]</div></div></div></div></div></div><br />
== Sources of Labor Data ==<br />
<br />
IFs model uses ILO data for labor participation rates and for the unemployment rate. The data in IFs are collected from World Bank’s World Development Indicators (WDI) database. According to their documentation, WDI obtained the data from the ILO.<br />
<br />
&nbsp;<br />
<br />
Unemployment rate data in IFs is also collected from WDI. Like the participation rates WDI also obtains their unemployment data from ILO.[[#_ftn1|[1]]]<br />
<br />
For employment and labor demand data IFs uses Purdue University’s Global Trade Analysis Project (GTAP) database. GTAP collects and compiles factor payments, imports, and intersectoral flow data to calibrate CGE models of national economies for trade and other analyses. In their ninth release in 2016, GTAP published data for 140 countries and regions for the year 2011. The earlier GTAP releases, which the IFs model used for its previous versions, compiled data for the years 2004 and 2007. GTAP data release aggregates economic activities into 57 commodities and activities following International Standard Industrial Classification (ISIC). The IFs model maps the 57 GTAP sectors into six economic sectors of IFs – agriculture, energy, material and mining, manufacture, services and ICT. Appendix 2 presents two tables listing the sectors mapping between IFs and GTAP, and GTAP and ISIC. GTAP further disaggregates labor in each of the commodities/activities into five occupation and skill categories following the nine category International Standard Classification of Occupations (ISCO-88). The IFs model collapses five GTAP occupation categories into the simple IFs dichotomy of skilled and unskilled. The mapping of occupations and skills are presented in the third appendix of this document. &nbsp;<br />
<br />
The data in the main GTAP database, prepared for CGE modeling, are all in dollar unit and thus do not include labor headcounts. We have used a ‘satellite’ GTAP database[[#_ftn2|[2]]] for labor headcounts by skill and sector. The labor counts were also used to plot labor requirement functions for each of the IFs economic sectors and skill categories. The wage share of skilled and unskilled labor in each sector was computed using the labor headcounts and labor payments.<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] The name of the IFs table is SeriesLaborUnemploy%<br />
</div><div id="ftn2"><br />
[[#_ftnref2|[2]]] See Weingarden and Tsigas, 2010 for the details on the preparation of this database.<br />
<br />
== Scope of IFs Labor Model ==<br />
<br />
The IFs labor model simulates labor market at the national level. Each national labor market forecasts labor demand and employment by six sectors - agriculture, energy, mining, manufacture, services and ICT- and two skill levels - skilled and unskilled. The supply side do not have sectoral representation. IFs forecasts total labor force and labor supply by the two skill levels. Labor participation rate is computed in IFs by gender. Wage and unemployment rate is forecast for the overall labor market only.&nbsp;&nbsp;<br />
<br />
== Labor Model Pre-processor ==<br />
<br />
IFs system has a data preprocessor that prepares the initial conditions for the model using historical databases and various assumptions and estimated relationships to fill in the missing data and make data adjustments as needed[[#_ftn1|[1]]]. Pre-processing of labor data takes place in two IFs pre-processing modules. Labor participation rate data, which is closely related to demography, is processed in the population pre-processor. Unemployment rate and labor demand data are processed in the economic pre-processor. &nbsp;<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] For more details, please see ‘The Data Pre-Processor of International Futures (IFs)” by Barry B. Hughes (with Mohammod Irfan) at [http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf]<br />
<br />
=== Pre-processing Labor participation rate and unemployment ===<br />
<br />
For initializing labor participation rates by sex (LABPARR) the model uses the historical values from the base year or the most recent year with data[[#_ftn1|[1]]]. For countries with no data we use regression relationships of the participation rates, for men and for women, with income per capita. The relationships, shown in the next figure, are not great. However, the functions affect only five countries for which we do not have any data at all: Grenada, Kosovo, Micronesia, Seychelles and South Sudan[[#_ftn2|[2]]].<br />
<br />
&nbsp;<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] The data tables that the IFs model pre-processor use for initializing labor participation rates are: SeriesLaborParRate15PlusFemale%, SeriesLaborParRate15PlusMale%.<br />
</div><div id="ftn2"><br />
[[#_ftnref2|[2]]] We should try to collect participation rate for these countries from country sources.<br />
<br />
IFs data series SeriesLaborUnemploy% is used for the initialization of unemployment rates. That series has annual unemployment rates for one or more years between 1980 and 2016, for 181 of the 186 IFs countries. For five countries (Grenada, Kosovo, Micronesia, Taiwan and South Sudan[[#_ftn1|[1]]]) there is no data at all. To fill in the missing data we use a regression function of unemployment rate against GDP per capita. Like the participation rate functions, this function does also not have much of an explanatory power.<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] These are pretty much the same countries for which we do not have any participation rate data. This indicates ILO might have some administrative limitation in reporting data for these countries (notice Kosovo, Seychelles etc in the list)<br />
<br />
=== Pre-processing labor demand and unemployment from GTAP ===<br />
<br />
The IFs economic pre-processor reads labor headcount and labor payment data from the GTAP database. In addition to performing sector and occupation/skill mapping between GTAP and IFs, pre-processor also use the labor headcount data to compute labor coefficient functions, the principal driver of labor demand in the IFs model.<br />
<br />
Labor coefficients are defined as the amount of labor needed to produce one unit of value added in a certain sector of the economy. The coefficients depend on the level of technology. The model uses GDP per capita as an indicator of the level of technological development. IFs pre-processor estimates labor coefficient functions for labor of different skill levels for the different sectors of the economy.<br />
<br />
The functions are derived from GTAP data we described earlier. The model pre-processor reads data on factor payments and aggregates data from 57 GTAP sectors to six IFs sectors. Shares of payment going to skilled and less-skilled workers in each of the sectors are then computed. Countries are grouped according to their level of technological development as represented by per capita income. For each group labor coefficients are obtained by taking an average of the country coefficients. &nbsp;We also convert labor payments data to labor headcount data using per capita income as a proxy for average wage. Labor coefficients and income are then plotted into a power function relationship. The figure below plots some of those labor functions.&nbsp; The functions fit quite well with a power law formulation[[#_ftn1|[1]]].<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] This is interesting given the prevalence of power law in all sorts of scale-up activities (West 2017).<br />
<br />
= Labor Model Flowcharts =<br />
<br />
The diagram below shows an outline of the IFs labor model. On the supply side, the total labor pool (LAB) is computed from the labor force participation rates, by sex, (LABPARR) and the population (POP) in their working age, i.e., population over 15 (POP15TO65 + POPGT65). Participation rates are driven by the demographic changes with an additional negative impact from aging and a catch-up in female participation rate. Skill level of the labor supply (LABSUP) is driven by the level of development (GDPPCP) and the demand for labor is driven by labor-coefficients (LABCOEFFS) computed from coefficient function representing shifts in demand with technological progress as proxied by the level of development (GDPPCP). Coefficients computed by sector and skill gives the labor requirement by skill type for each unit of value added (VADD) in the sector. Multiplying these coefficients with projected value added in each sector gives an estimate of the labor demand. &nbsp;<br />
<br />
Any surplus or shortage between total labor demand and supply is used to compute the rate of unemployment. Deviations in the unemployment rate (LABUNEMPR) signal wage changes through an equilibrium seeking algorithm. Both demand and supply respond to the wage variable (LABWAGEIND) indexed to the base year. The supply responses are much slower than the demand responses.<br />
<br />
[[File:FLOCHART2.png|frame|center|Labor Model Flowchart]]<br />
<br />
= Labor Model Equations =<br />
<br />
== Overview ==<br />
<br />
The labor model is a part of the IFs economic model that uses labor model output as an input to a Cobb-Douglas production function in a multi-sector general equilibrium model. IFs is a very long-run dynamic model. Instead of computing fixed short-run equilibria that clear the relevant markets IFs uses an equilibrium seeking algorithm to balance the various systems over the longer run. The algorithm is known as the PID (proportion-integral-derivative) controller algorithm and is used widely in industrial control systems. It makes equilibrium seeking variables in IFs move towards a set target. The algorithm works by computing a multiplier based on the movement of the variable towards the target, as obtained by an integral (I) of the path traversed, and the rate of movement towards the target, the derivative term. The multiplier is applied on the process variable (the P term), or a response variable, in the subsequent time period. In the labor model, unemployment rate (LABUNEMPR) is used as the process variable and the PID multiplier is used on the wage rate (LABWAGEIND). Job availability (LABDEMS) and participation rate (LABPARR) get affected by changes in wage. &nbsp;&nbsp;<br />
<br />
Throughout this section we use subscripts and notations common to other modules of IFs. For example, we use t for time period. Subscripts p and r represent sex and country/region, respectively, c is the cohort number, with cohort 1 representing the newborns, cohort1 the the one-year to four-year-olds, cohort two five-year to nine-year-olds etc. Values for p are 1 for male, 2 for female and 3 for both sexes combined. For economic sectors we use s and for skill levels sk.<br />
<br />
== Labor Supply: Equations ==<br />
<br />
The total pool of labor is computed by multiplying the population of working age with the labor force participation rate (LABPARR). &nbsp;Population forecasts come from IFs demographic model which computes both five-year and single-year age-sex cohorts (''agedst'', ''fagedst''). &nbsp;<br />
<br />
The labor model forecasts participation rates by country/region&nbsp; and gender. Participation rates in the model move with the changes in the demographic composition. Female participation rates, which have historically been lower than the same for the male in all societies, but has moved up in modern and affluent societies, get a catch-up boost in the model. Participation rates can also change when there is labor shortage or surplus and the employers try to incentivize or discourage workers by changing wage. This last impact is much less slow than similar wage impacts on the demand side.<br />
<br />
== Labor Participation Rate ==<br />
<br />
Labor participation rates (''LABPARR'') for male and female are first initialized with historical data.<br />
<br />
:<math>LABPARR_{r,p}= LABPARR_{r,p,t=1} </math><br />
<br />
A ‘catch-up’ boost is added to the female participation rate. The boost added (FemParLabMul) starts at a third of a percentage point and withers away following a non-linear path as the female rates approaches the catch-up target (FemParTar), The maximum catch-up that can occur over the horizon of the model is thirty percent.<br />
<br />
:<math>FemParTar_{r}=Amin(LabParRI_{r,p=1},LabParRI_{r,p=2}+30)</math><br />
<br />
:<math>FemParLabMul_{r}=(FemParTar_{r}-LABPARR_{r,p=2,t-1})/(FemParTar_{r}-LABPARR_{r,p=2,t-1})</math><br />
<br />
:<math>LABPARR_{r,p=2,t}=LABPARR_{r,p=2,t-1}+FemParLabMul_{r}*0.3</math><br />
<br />
Next, we compute and apply the aging impact on the participation rate. As the relative share of people over the retirement age increases, the participation rate declines. The model keeps track of the changes in the demographic ratio (PopAgingRatio) of the population who are in their prime working age of 15 to 64 (POPWORKING) to those at a common retirement age of sixty-five or older (POPGT65). This ratio declines as countries age. The percentage drop in the ratio comparative to the base year is scaled appropriately to compute the aging impact (aging_impact). This impact is added to the male and female labor participation rates, with the impact on the female participation rate being slightly lower than that on male rates.<br />
<br />
:<math>POPAgingRatio_{r,t}=POPWORKING_{r,t}/POPGT65_{r,t}</math><br />
<br />
:<math>aging_impact_{r,t}=100*((POPAgingRatio_{r,t}/POPAgingRatio_{r,t=1})-1)*0.2</math><br />
<br />
:<math>LABPARR_{r,p=1,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t} </math><br />
<br />
:<math>LABPARR_{r,p=2,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t}*0.95 </math><br />
<br />
Participation rates respond slowly to changes in wage and unemployment rate. The impact is implemented through a wage impact factor computed from annual changes in the wage index (labwageimpact). The base participation rates can be changed by model user through two model parameters: a direct multiplier on the participation rate (labparm), or one that changes participation by moving the retirement age (labretagem)<br />
<br />
<br />
<br />
:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact*0.05)*labparm_{r,p,t}*labretagem_{r,p,t}</math><br />
<br />
Total participation rate (LABPARRr,p=3,t) is computed by an weighted average of male and female participation rates.<br />
<br />
:<math>LABPARR_{r,p=3,t}= (∑_{p=1 to 2}∑_{c=4 to 21}(agedst{r,c,p,t}*LABPARR_{r,p,t}))/(∑_{p=1 to 2}∑_{c=4 to 21}agedst{r,c,p,t})</math><br />
<br />
== Total Labor ==<br />
<br />
Finally, the total number of labor available for work (LAB) is computed by multiplying the total participation rate with the population of fifteen-year-olds or older.<br />
<br />
:<math>LAB_{r,t}= LABPARR_{r,p=3,t}*∑_{p=1 to 2,c=4 to 21}agedst_{r,c,p,t}</math><br />
<br />
== Labor by skill level ==<br />
<br />
The labor model forecasts labor supply (LABSUP) by two skill categories. The variable (''LABSUP'') is initialized in the pre-processor by reading the employment by skill/occupation (''LABEMPS'') data from GTAP[[#_ftn1|[1]]] &nbsp;and adding the unemployment numbers. We assume same unemployment rate (''LABUMEMPR'') for skilled and unskilled labor.<br />
<br />
:<math>LABSUP_{r,t=1,sk}=∑_{s=1 to 6}(LABEMPS_{r,s,t=1}/(1-(LABUNEMPR_{r,t=1}/100))</math><br />
<br />
The model forecasts labor by skill through a model of the skilled share of the labor. Education, training, exposure, and experience of the employees all improve with the level of development. The model captures this with an analytic function of the skilled share (perskilled) driven by GDP per capita at PPP (GDPPCP) -<br />
<br />
:<math>perskilled_{r}=f(GDPPCP_{r})</math><br />
<br />
Among the causal drivers of skill, education is considered to be the most proximate. Education is strongly correlated with the level of development, the deeper driver of skill in the model. However, the recent increase in education and/or a policy driven educational expansion might add to the impact of education on skill. Additional impacts from education on skill, when there is any, is computed through an expected function formulation. For example, in a society where an average adult has more (or less) education than the adults in other societies at that level of development, the skill share is given a slight upward push (or downward pull). The expectation function is a logarithmic function of educational attainment of working age population (EDYRSAG15) driven by GDP per capita at PPP. Attainment above (or below) the expected level (YearsEdExp) is computed by the function output (YearsEd) adjusted for country situation (yearseddiff). The percentage adjustment to the skilled share (LabSupSkiAdj) is computed using additional (limited) education, i.e., the difference between actual (EDYRSAG15) and expected values of educational attainment, expressed as a percentage of the expected value. The adjustment is scaled appropriately and peters off over time.<br />
<br />
:<math>YearsEd_{r,t}= f(GDPPCP_{r,t})</math><br />
<br />
:<math>yearsdeddiff_{r}= EDYRSAG15_{r,p=3,t=2}-YearsEd_{r,t=2}</math><br />
<br />
:<math>YearsEdExp_{r,t}=YearsEd_{r,t}+yearsdeddiff_{r}</math><br />
<br />
:<math>LabSupSkiAdj_{r,t}=0.3*(EDYRSAG15_{r,p=3,t=2}*YearsEdExp_{r,t})/YearsEd_{r,t}</math><br />
<br />
:<math>LabSupSkiAdj_{r,t}=ConvergeOverTime(0,LabSupSkiAdj_{r,t},70)</math><br />
<br />
:<math>perskilled_{r,t}= perskilled_{r,t}*(1+LabSupSkiAdj_{r,t})</math><br />
<br />
The skilled share (perskilled) is multiplied with the total labor supply (LAB) to obtain the number of labors who are skilled (LABSUPskilled)<br />
<br />
:<math>LABSUP_{r,skilled,t}=LAB_{r,p,t}*perskilledI_{r,t} </math><br />
<br />
As a last step, the model adjusts for the country specific variations in the skilled labor count not captured by the deeper and the proximate models. This is done by saving a ratio (LABSUPSkilledRI) of the actual historical data and the model computed value in the initial year. In the subsequent years this ratio is used to adjust the skilled labor forecast gradually.<br />
<br />
:<math>LABSUPCompSkilled_{r}=LAB_{r}*perskilled_{r,t=1}/100 </math><br />
<br />
:<math>LABSUPSkilledRI_{r}=LABSUP_{r,skilled,t=1}/LABSUPCompSkilled_{r}</math><br />
<br />
:<math>LABSUP_{r,skilled,t}= LABSUP_{r,skilled,t}*ConvergeOverTime(LABSUPSkilledRI_{r},1,85)</math><br />
<br />
Number of unskilled labor is obtained by subtracting the skilled labor from the total pool.<br />
<br />
<br />
<br />
:<math>LABSUP_{r,unskilled,t}= LAB_{r,p,t}- LABSUP_{r,skilled,t}</math><br />
<br />
== Labor Demand: Equations ==<br />
<br />
IFs economic model forecasts production in six economic sectors. IFs labor model computes the longer-term and shorter-term determinants of demand for skilled and unskilled labor (LABDEMS) for the production processes. The long-term drivers of labor requirement are technological progress or the lack of it. In the shorter-term wage affects the labor demand most. Wage in turn is affected by labor supply or skill shortage.<br />
<br />
The IFs model divides economic activities into six economic sectors – agriculture, energy, materials, manufacture, services and information, and communication technologies. Workers in the IFs labor model are disaggregated into two skill types. While the skill composition varies by the technology used in the sector and starts tilting towards the more skilled with the progress in technology, absolute number of labors needed to produce the same output goes down with technological development for both skilled and unskilled labor. This is illustrated in the next figure which plots the changes in labor requirement against GDP per capita at PPP, a proxy for level of development. Agriculture is a much less skill-intensive process than the manufacture, however, with technological progress skill requirement improves rapidly in both sectors. The IFs labor model computes these labor requirement functions in the model pre-processor. As we have already described in the pre-processor section, the computation of these functions use GTAP data on employment by occupation and economic activity. Appendices 3 and 4 lists sector and occupation mapping between GTAP and IFs.<br />
<br />
These functions are used to compute the labor coefficients (LABCOEFFS), i.e., number of skilled and unskilled labor needed to produce unit amount of output with the technology available, for which we use GDP per capita at PPP as a proxy.<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= f(GDPPCP_{r})</math><br />
<br />
manufacture, services and ICTech) and the subscrip sk stands for skill categories with 1 denoting unskilled and 2 skilled. The labor coefficients obtained from the analytical functions require some adjustments to incorporate country deviations from the functions for various factors not captured in the regression relationship. The first of these adjustments is a gradual removal of impacts of short-run fluctuations in output and labor from the computation of labor coefficient. This adjustment is applied on the coefficients computed from the function. The equation below shows a simplified form of these computations.<br />
<br />
:<math>LabCoeffAdjFac_{r,k,s,t}=f(igdpr_{r,t=2},(LAB_{r,t=2}/LAB_{r,t=1}),(LABCOEFFS_{r,t}/LABCOEFFS_{r,t-1}))</math><br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}=LABCOEFFS_{r,sk,s,t}(1-LabCoeffAdjFac_{r,k,s,t})</math><br />
<br />
Model users can use a global parameter (labcoeffsm) to change the labor coefficients by skill level for any or all of the six sectors –<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= LABCOEFFS_{r,sk,s,t}*'''labcoeffsm_{s,sk}'''</math><br />
<br />
To forecast the total labor demand, the labor coefficients (LABCOEFFS) are multiplied to the total projected output for each of the economic sectors. The forecast is adjusted for any discrepancy between data and model. The adjustment factor (LABDemsAdjFac) is computed as the initial ratio between the actual and computed employment. Actual employment is obtained from historical data (LABEMPS) processed using the GTAP database. The computed employment is obtained by multiplying the labor coefficients (LABCOEFFS) with the final output of the sector (VADD).<br />
<br />
:<math>LabDemsAdjFac_{r,s,sk}= LABEMPS_{r,s,sk,t=1}/(VADD_{r,s,t=1}*LABCOEFFS_{r,sk,s,t})</math><br />
<br />
The projected output is obtained by applying the growth rate (IGDPRCOR) on the sectoral value added from the previous year (VADD). The total labor demand is given by the product of the labor coefficients, projected output, demand adjustments and wage impacts (labwageimpactmul) and the number 1000 which adjusts the units for the equation. Wage impact comes from the level of unemployment and is computed in an equilibration process described in the next section. Model users can use a multiplicative parameter (labdemsm) to slide the demand upward or downward.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}=1000*VADD_{r,s,t-1}*(1+IGDPRCOR_{r})*LABCOEFFS_{r,sk,s,t}*LabDemsAdjFac_{r,s,sk}*labwageimpactmul_{r,s,sk}*'''labdemsm_{r,s}'''</math><br />
<br />
== Unemployment and Wage: Labor Market Equilibration ==<br />
<br />
The IFs labor model balances the labor market through an equilibrium seeking algorithm rather than computing an exact equilibrium at each time step. We use an algorithm borrowed from the control systems engineering. This PID controller algorithm, described also in the IFs economic model documentation, works by computing corrective signals for equilibrating variables using the deviations of a buffer variable, for example unemployment rate (LABUNEMPR), from a target value. The signal is computed from two quantities, the distance of the buffer from the target and the current rate of change of the buffer. The computation is tuned with PID elasticities to avoid oscillations. The computed signal is applied on the variable/s which need to be balanced, for example, demand and supply in the event of a market equilibration, thus getting closer to a balance at each step of simulation. The target value for the buffer variable and the tuning parameters of the control algorithm are obtained through rules-of-thumb and model calibration. The IFs labor model uses unemployment rate (LABUNEMPR) as the buffer variable for the market equilibration of labor demand and labor supply. The multiplier (i.e., corrective signal) obtained from the PID is applied on the wage index (LABWAGEIND). Changes in wage indices comparative to the base year, moderated through a second PID controller, is used to compute the final signal (labwageimpactmul) that drives labor demand and labor supply. Even though the model forecasts labor demand by sector and skill, and computes labor supply for both skill types, the equilibration algorithm works over the entire pool of labor. In other words, we assume that the skills are replaceable across sectors and the lack (or abundance) of jobs affects skilled and unskilled persons equally.<br />
<br />
At each annual timestep, the model computes the unemployment rate (LABUNEMPR) as the gap in between the total supply of labor (LAB) and the total demand. The gap (EmplGap) is expressed as a share of the total labor, the standard way to express unemployment rate.<br />
<br />
:<math>sumld=∑_{s,sk}LADEMS_{r,s,sk,t}</math><br />
<br />
:<math>EmplGap= LAB_{r,t}*sumld</math><br />
<br />
:<math>LABUMENPR_{r,t}= (EmplGap/LAB_{r,t})*100</math><br />
<br />
As the target value (LabUnEmpRateTar) for the PID controller that modulates unemployment rate we use either the historical unemployment rate or a ten percent unemployment rate when the historical rate is higher than ten. Model users can override the historical target through a model parameter (labunemprtrgtval).<br />
<br />
:<math>LABUMENPRi_{r,t}= LABUMENPR_{r,t}</math><br />
<br />
:<math>LabUnempRateTarget_{r}=labunemptargetval_{r}</math><br />
:<math>If LabUnempRateTarget_{r}=0,<br />
LabUnempRateTarget_{r}= AMIN(LABUMENPRi_{r,t},10) </math><br />
<br />
Unemployment rate target, when it is different from the base year value, is reached gradually with a convergence period of forty years . The target rate is converted to count (LabUnEmplTar) to make it equivalent to the employment gap (EmplGap) computed earlier.<br />
<br />
:<math>LabUnEmplTar_{r}= LAB_{r,t}*ConvergeOverTime(LABUMENPRi_{r,t},0,100)</math><br />
<br />
The first order difference (Diffl1) between the target unemployment and the demand-supply gap is used to compute a second order difference (Diffl2) accounting for changes in the rate of movement. The two differences and the PID multipliers (elwageunemp1, elwageunemp2) are provided to the PID function (ADJSTR). Working age population (POP15TO65r,t) works as the scaling base of the PID controller. The controller algorithm gives a multiplier (mullw) that is used in the subsequent year to adjust wage.<br />
<br />
:<math>Diffl1_{t}=LabUnEmplTar_{r}-EmplGap</math><br />
<br />
:<math>Diffl2_{t}=Diffl1_{t}-Diffl1_{t-1}</math><br />
<br />
:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},elwageunemp1_{r},elwageunemp2_{r})</math><br />
<br />
Wage adjustments affect demand and supply with an increase in wage drawing demand downward and supply upward. The opposite affects occur with a downward movement of wage. The wage variable affected by the PID multiplier (LABWAGEIND) is an index initialized at one. We use an indexed rather than a dollar wage in the equilibration process to avoid affecting the process from other economic phenomena that affects wage, for example, a rise in real wage as GDP or the labor share of income grows.<br />
<br />
:<math>LABWAGEIND_{r,t=1}=1</math><br />
<br />
In the subsequent years of the model run, the wage index is first adjusted with the equilibration signal obtained from the unemployment rate PID controller in the previous period<br />
<br />
:<math>LABWAGEIND_{r,t=1}= LABWAGEIND_{r,t=1}* mullw_{r,t-1} </math><br />
<br />
A wage impact (labwageimpact) is then computed using the changes in the wage index relative to the base value. The impact is smoothed with a moving average algorithm.<br />
<br />
:<math>labwageimpact_{r}= labwageimpact_{r,t-1}*0.9+ (1-LABWAGEIND_{r,t})*0.1</math><br />
<br />
The smoothed impact is used as the equilibration signal for labor supply. As we have already described in the section on labor supply, a small fraction of the impact (labwageimpact) is applied to the labor participation rate. The impact is scaled down to account for the slow pace of changes on the supply side.<br />
<br />
:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact_{r,t}*0.05)</math><br />
<br />
For the impacts of wage on labor demand we use a second PID multiplier as opposed to using the changes in wage index that we have done on the supply side. The second PID uses the wage index itself as the process variable and uses the base year value of 1 as the target. The reason we had to use this second PID is to control the pace at which wage disequilibrium can affect demand, especially in the event of an abrupt shock. The smoothing and scaling down that works on the supply side is not enough to control oscillations on the demand side.<br />
<br />
:<math>Diffl1_{t}=LABWAGEIND_{r,t=1}-1</math><br />
<br />
:<math>Diffl2_{t}=LABWAGEIND_{r,t}-LABWAGEIND_{r,t-1} </math><br />
<br />
:<math>mullw_{r,t}= ADJSTR(POP15TO65_{r,t},Diffl1_{t},Diffl2_{t},ellabwage1_{r},ellabwage1_{r})</math><br />
<br />
A second impact factor (labwageimpactmul) is computed using the correction signal from this second multiplier:<br />
<br />
:<math>labwageimpactmul_{r,t}= labwageimpactmul_{r,t-1}*mullw_{r,t}</math><br />
<br />
This impact factor is applied on the labor demand as described in the section on labor demand.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}= LABDEMS_{r,s,sk,t}* labwageimpactmul_{r,t}</math><br />
<br />
== Informal Labor ==<br />
<br />
IFs forecast labor and GDP share of the informal sector. Informal labor forecast is not explicitly endogenized in the labor market though. They are rather driven by development, skill and regulatory factors[[#_ftn1|[1]]]. However, the productivity and revenue impacts of changes in informality affects output and thus labor demand implicitly as a very distal driver.<br />
<br />
= References =<br />
<br />
<references /><br />
</div></div></div></div></div></div></div></div></div></div></div>Wikiadmin//pardee.du.edu/wiki/LaborLabor2018-09-07T22:24:56Z<p>Wikiadmin: </p>
<hr />
<div>Workers in an economy supply the expertise and the efforts needed to produce goods and services. In return the labor receives wages that they use to meet their current and future consumption needs. On one hand, shortage of labor with required skills prevents economies from realizing their growth potential. On the other hand, individuals falling short of the right qualifications might remain unemployed or underemployed failing to secure income needed for a decent living. The ongoing adjustments to find the best match between skills, jobs and wages can only be studied through a dynamic model of the labor market.&nbsp; &nbsp;<br />
<br />
Such a model should go beyond providing a reasonable answer to the obvious question of why employment and wages go up and down. An aggregate labor market must deal with issues that have strong interconnections with various other dynamic changes in the greater society. What kind of dividend of deficit can a society expect from its labor force given the phase of demographic transition in which it is situated? How severely would aging affect the pool of working age adults? Might increasing female participation rates offset some of the losses from aging? What is the level of skills and educational attainment in a society? These supply phenomena move relatively slowly unless there are huge disruptions, like a war or famine, or an aggressive policy push. The demand side, in contrast, needs to be more responsive in adjusting wages and employment given the investment and technology in the various sectors of the broader economy. In general, though, the labor market demonstrates some sluggishness compared to the goods and services markets as it involves moving human beings with various limitations. Consumption of goods and services depend on the income earned by the labor. Uneven distribution of employment and wages among labors of various types or between labor and capital for a long period of time can give rise to persistent inequality in a society. &nbsp;&nbsp;&nbsp;&nbsp;<br />
<br />
== Conceptual Framework ==<br />
<br />
Labor markets are markets for workers and jobs. In a labor market, employers meet their demand for labor with the supply of people willing to work at the wage the employers can offer. The employers raise the wage when there is a shortage of workers. Workers agree to take a lower wage when there are more of them than the firms need. In the real-world labor markets do not always clear at perfect equilibrium. Frinctional unemployment results for various reasons, for example, the search time between jobs. Structural unemployment can result from technology induced disruptions. Some unemployment could thus persist in the labor market even when there aren’t any short-term fluctuations. There is also the phenomenon of informal employment that consists of less sophisticated workers and entrepreneurs engaged in unregulated economic activities. &nbsp;In a dynamic model that covers the entire economy, the real wage earned by the labor drives the income and social mobility.&nbsp;&nbsp;&nbsp;<br />
<br />
To understand the long-term dynamics of the labor market, we need also examine the deeper determinants of labor demand and supply, the determinants that can shift the curves. Labor demand changes over time with the changes in demand for goods and services and the labor input needed to produce those. Labor productivity itself improves with technological progress. Long term transitions in the supply of labor are mostly demographic. &nbsp;<br />
<br />
Labor supply is determined by the working age population and the share of that population who are available for participation in the workforce. The labor supply is relatively stable as the demographic changes are slow in pace. As the share of elderly in the population increases, a recent trend in many societies, the rate of participation declines. Some of the aging impacts will be offset by the greater female participation rates, a second trend that surfaces as economies develop and women attain more education. Educational attainment also drives the general skill level of workers, male and female. Specific skills are obtained through training and experience that augment the knowledge obtained through general and specialized education. &nbsp;&nbsp;&nbsp;&nbsp;<br />
<br />
It is the demand side that causes most of the short-term imbalances in the labor market. &nbsp;In the long term, as said earlier, the important driver of demand for labor and their skills is technological progress. Labor requirement drops with advances in technology, more so for less skilled labor. Labor composition changes accordingly both within and across sectors. Rapid advances in technology can also cause disruption in the system when there is not much opening in the other sectors. Labor displacement is offset to some extent by the growth in the economy and the resulting increase in total demand. &nbsp;&nbsp;&nbsp;<br />
<br />
As we have already mentioned, employees maximize income and the firms minimize labor costs. When there are more laborers than the firms can hire, there is unemployment. Shifts in the rates of unemployment impacts wage, the price of labor. For example, wages drop in the event of rising unemployment as there are more people to hire from. Wage adjustments feed back to the demand for labor seeking to bring the market back to equilibrium.<br />
<br />
The challenges around the conceptual distinction between unemployment and employment is further complicated by the phenomenon of informal employment. In many developing countries there is a large urban non-agricultural informal sector where low-skilled workers work for wages typically lower than a formal employment.<br />
<br />
[[File:LMFlowchart1.png|frame|center|Description of the labor model]]<br />
<br />
== Dominant Relations ==<br />
<br />
The labor model in the International Futures system (IFs) balances the total supply of labor with the total labor demanded by all economic sectors. Total labor (LAB) is computed from the working age population and the labor participation rate. Population forecasts are obtained from the IFs demographic model. Participation rates (LABPARR) are computed by sex with a catchup algorithm for the female participation towards that for the male. Labor is also disaggregated by skill level, as determined by educational attainment, in a separate labor supply variable (LABSUP) which is used to distribute labor earnings by skill level. [** LABSUP do not affect the demand/supply balance now]<br />
<br />
Labor demands (LABDEMS) are driven by sectoral technology functions used to compute the labor requirement by skill level for each unit of potential valued added in the sector. These labor coefficients (LABCOEFFS) are multiplied with the projected value added for the sector to compute the needed manpower. The balancing mechanisms determines the labor employed in each of the sectors (LABS).&nbsp;&nbsp;<br />
<br />
The balancing, in the current version of the model, can be done in one of the two ways. In the first method, total needs combined from all economic sectors is normalized to the available pool of labor computed by subtracting the unemployed from those who are at or looking for work. The rate of unemployment is kept at its natural rate for which we use the base year rate of unemployment. (** This might need to be changed for countries where the market is undergoing some abrupt transition.)<br />
<br />
In the second balancing method, added in a recent revision of the model, total demand is equilibrated to supply through a CGE like market equilibrium model. An indexed wage (LABWAGEIND) and the rate of unemployment (LABUNEMPR) work as the equilibrating variables. As unemployment deviates from the target, PID algorithms send a signal for the wage to adjust. Wage adjustments cause adjustments in the “base” labor demands by sector computed from the labor-coefficient functions as described earlier. Wage signals also affects the labor participation rate. The magnitude of impact on the supply side is much lower than that on the demand side.<br />
<br />
Wage and unemployment rate are aggregated for the total labor market. The wage index starts with a base year value of 1 and the unemployment rates start with the historical data for the base year. Initial year unemployment rate works as the target for long term unemployment.<br />
<br />
== Key Dynamics ==<br />
<br />
The following key dynamics are directly related to the dominant relations:<br />
<br />
*Labor supply is determined from population of appropriate age in the population model (see its dominant relations and dynamics) and endogenous labor force participation rates, influenced exogenously by the growth of female participation.<br />
*Labor demand is driven by sectoral demand functions driven by technological progress<br />
<br />
== Structure and Agent System ==<br />
<br />
{| border="1" cellspacing="0" cellpadding="0" width="0" style="width:502px;"<br />
|-<br />
| style="width:242px;height:49px;" | <br />
'''System/Subsystem'''<br />
<br />
| style="height:49px;" | <br />
Labor market<br />
<br />
|-<br />
| <br />
'''Organizing Structure'''<br />
<br />
| <br />
Labor supply by skill level and labor demand by sector for each skill category represented within an equilibrium-seeking model with wage and unemployment rate as the equilibrating variables<br />
<br />
|-<br />
| <br />
'''Stocks'''<br />
<br />
| <br />
Population, labor, education, &nbsp;accumulated technology<br />
<br />
|-<br />
| <br />
'''Flows'''<br />
<br />
| <br />
Participation rate; Coefficients of labor demand; Employment (unemployment); Wage<br />
<br />
|-<br />
| <br />
'''Key Aggregate&nbsp;''''''Relationships&nbsp;'''<br />
<br />
'''(illustrative, not comprehensive)'''<br />
<br />
| <br />
Labor supply is driven by demographic changes; Participation of female change over time; Labor requirement changes with technological development; Unemployment rate drives wage; Wage movements affect labor demand and participation rate<br />
<br />
|-<br />
| <br />
'''Key Agent-Class Behavior&nbsp;''''''Relationships'''<br />
<br />
'''(illustrative, not comprehensive)'''<br />
<br />
| <br />
Households and work/leisure, and female participation patterns;<br />
<br />
&nbsp;<br />
<br />
Firms and hiring;<br />
<br />
&nbsp;<br />
<br />
<br />
<br />
<br />
<br />
|}<br />
<br />
= Labor Model Data =<br />
<br />
The labor supply and unemployment data that we use in our model is from International Labor Organization (ILO). For data on the demand side, we used data from the Global Trade Analysis Project. Wage variable used in the equilibration algorithm &nbsp;is an index anchored to the base year of the model. IFs preprocessor prepared these data for model use using various estimation, conversion and reconciliation processes.&nbsp; &nbsp; &nbsp;<br />
<br />
== Definitional Issues ==<br />
<br />
There are ambiguities in the way some of the labor market variables are defined. Labor participation rates and the rate of unemployment are two that need special attention.<br />
<br />
The size of the labor supply available for economic activities is expressed with the labor force participation rate. ILO defines this as a “measure of the proportion of country’s working-age population that engages actively in the labor market, either by working or looking for work.”<ref>http://www.ilo.org/ilostat-files/Documents/description_LFPR_EN.pdf</ref>&nbsp;National labor force surveys and census data are used to estimate this rate. The definition of labor force here includes both employed and unemployed and the rate is expressed as a percentage of working-age population. Working-age population is defined here as the population above legal working-age. For international comparability, ILO adopts a convenient minimum threshold of fifteen years as working age and avoids putting any upper age limit. In practice, both the minimum and the upper-age limits can vary by country. For example, the working-age in the USA is sixteen years. In the Netherlands the upper age limit is seventy-five years, whereas South African data uses an upper age limit of 64.<ref>https://www.bls.gov/fls/flscomparelf/technical_notes.pdf</ref><br />
<br />
Ambiguities are more abundant in the definition of unemployment. ILO came up with a guideline on this as well. Per the ILO guideline, the unemployed are those among the working-age population who are not employed, are available for work and are actively looking for jobs<ref>The definitions around employed and unemployed were agreed upon by nations through the ‘Resolution concerning statistics of work, employment and labor underutilization’ adopted by the 19th International Conference of Labor Statisticians (ICLS) in 2013. (Bourmpoula et al, 2017: 6).</ref>; the unemployment rate is expressed as a percentage of those who are in the labor force. The availability and job-seeker status could be defined in different ways giving rise to incompatibility in data. &nbsp;While there seems to be little room for disagreement on whether someone is at work or not, whether that work should be considered as employment is contested at many times.<br />
<br />
The debates around the nature and type of employment can range from gainfulness to workplace setting. For example, a large number of workers in the low-income low-regulation developing countries work outside the purview of formal enterprises. According to an ILO estimate, more than half of the global labor force and more than 90% of Micro and Small Enterprises (MSEs) worldwide are in the so called informal economy[[#_ftn4|[4]]]. This might explain the apparently counterintuitive pattern of low unemployment rate in some low-income countries (e.g., 2.2% for Guatemala) and relatively higher numbers for some of the developed nations. The low numbers in the poorer countries hide the prevalence of extremely low wage jobs in the informal sectors in these countries, the only options for the vulnerable people in the absence of any kind of social safety net. &nbsp;Contrastingly, in the developed countries the so called ‘gig-economy’ is attracting more and more workers who choose to work on their own rather than in a formal enterprise. ILO conceptualization makes the informal work part of total employment. The stacked Venn diagram below presents the relationship among the labor force metric including informal employment. IFs also models informal economy both in terms of GDP share and employment share of informal in the total economy and employment.<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] [http://www.ilo.org/ilostat-files/Documents/description_LFPR_EN.pdf http://www.ilo.org/ilostat-files/Documents/description_LFPR_EN.pdf]<br />
</div><div id="ftn2"><br />
[[#_ftnref2|[2]]] [https://www.bls.gov/fls/flscomparelf/technical_notes.pdf https://www.bls.gov/fls/flscomparelf/technical_notes.pdf]<br />
</div><div id="ftn3"><br />
[[#_ftnref3|[3]]] The definitions around employed and unemployed were agreed upon by nations through the ‘Resolution concerning statistics of work, employment and labor underutilization’ adopted by the 19<sup>th</sup> International Conference of Labor Statisticians (ICLS) in 2013. (Bourmpoula et al, 2017: 6).<br />
</div><div id="ftn4"><br />
[[#_ftnref4|[4]]] [http://www.ilo.org/global/topics/employment-promotion/informal-economy/lang--en/index.htm http://www.ilo.org/global/topics/employment-promotion/informal-economy/lang--en/index.htm]<br />
<br />
Incompatibility can arise in the treatment of various population groups for the computation of the denominator for participation and unemployment rates[[#_ftn1|[1]]]. ILO makes their best efforts to make adjustments in the data for the sake of international comparison. For example, ILO asks countries that deviate from ILO guidelines to collect data needed to convert national figures to ILO figures. It is likely that some differences might have slipped past the adjustment process. We use ILO data and continue to update our database from ILO on a regular basis.<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] For example, the USA excludes people in the defense services and those in the prisons or mental asylums in their computation of the civilian non-institutional working-age population. There are also variations in the treatments of students, those recently laid-off, and family workers. Please see [https://stats.bls.gov/opub/mlr/2000/06/art1full.pdf https://stats.bls.gov/opub/mlr/2000/06/art1full.pdf] for a discussion&nbsp;<br />
<br />
The GTAP data that we use for the demand side of the labor model is taken as labor headcounts and is thus immune from ambiguities around rate computation. As far as we could gather[[#_ftn1|[1]]], the data includes both the formal and informal employment. We also need mention here that the GTAP database reconciles the labor data to calibrate the general equilibrium modeling that they do for the trade analyses. The data could thus be somewhat different from data collected through direct surveys. As a CGE model IFs is benefited by using calibrated data.<br />
<div><br/><br />
----<br />
<div id="ftn1">[[#_ftnref1|[1]]] Please see the webpage for documentation on GTAP labor data statistic: [https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=3248 https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=3248]</div></div></div></div></div></div><br />
<br />
== Sources of Labor Data ==<br />
<br />
IFs model uses ILO data for labor participation rates and for the unemployment rate. The data in IFs are collected from World Bank’s World Development Indicators (WDI) database. According to their documentation, WDI obtained the data from the ILO.<br />
<br />
&nbsp;<br />
<br />
Unemployment rate data in IFs is also collected from WDI. Like the participation rates WDI also obtains their unemployment data from ILO.[[#_ftn1|[1]]]<br />
<br />
For employment and labor demand data IFs uses Purdue University’s Global Trade Analysis Project (GTAP) database. GTAP collects and compiles factor payments, imports, and intersectoral flow data to calibrate CGE models of national economies for trade and other analyses. In their ninth release in 2016, GTAP published data for 140 countries and regions for the year 2011. The earlier GTAP releases, which the IFs model used for its previous versions, compiled data for the years 2004 and 2007. GTAP data release aggregates economic activities into 57 commodities and activities following International Standard Industrial Classification (ISIC). The IFs model maps the 57 GTAP sectors into six economic sectors of IFs – agriculture, energy, material and mining, manufacture, services and ICT. Appendix 2 presents two tables listing the sectors mapping between IFs and GTAP, and GTAP and ISIC. GTAP further disaggregates labor in each of the commodities/activities into five occupation and skill categories following the nine category International Standard Classification of Occupations (ISCO-88). The IFs model collapses five GTAP occupation categories into the simple IFs dichotomy of skilled and unskilled. The mapping of occupations and skills are presented in the third appendix of this document. &nbsp;<br />
<br />
The data in the main GTAP database, prepared for CGE modeling, are all in dollar unit and thus do not include labor headcounts. We have used a ‘satellite’ GTAP database[[#_ftn2|[2]]] for labor headcounts by skill and sector. The labor counts were also used to plot labor requirement functions for each of the IFs economic sectors and skill categories. The wage share of skilled and unskilled labor in each sector was computed using the labor headcounts and labor payments.<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] The name of the IFs table is SeriesLaborUnemploy%<br />
</div><div id="ftn2"><br />
[[#_ftnref2|[2]]] See Weingarden and Tsigas, 2010 for the details on the preparation of this database.<br />
<br />
== Scope of IFs Labor Model ==<br />
<br />
The IFs labor model simulates labor market at the national level. Each national labor market forecasts labor demand and employment by six sectors - agriculture, energy, mining, manufacture, services and ICT- and two skill levels - skilled and unskilled. The supply side do not have sectoral representation. IFs forecasts total labor force and labor supply by the two skill levels. Labor participation rate is computed in IFs by gender. Wage and unemployment rate is forecast for the overall labor market only.&nbsp;&nbsp;<br />
<br />
== Labor Model Pre-processor ==<br />
<br />
IFs system has a data preprocessor that prepares the initial conditions for the model using historical databases and various assumptions and estimated relationships to fill in the missing data and make data adjustments as needed[[#_ftn1|[1]]]. Pre-processing of labor data takes place in two IFs pre-processing modules. Labor participation rate data, which is closely related to demography, is processed in the population pre-processor. Unemployment rate and labor demand data are processed in the economic pre-processor. &nbsp;<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] For more details, please see ‘The Data Pre-Processor of International Futures (IFs)” by Barry B. Hughes (with Mohammod Irfan) at [http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf]<br />
<br />
=== Pre-processing Labor participation rate and unemployment ===<br />
<br />
For initializing labor participation rates by sex (LABPARR) the model uses the historical values from the base year or the most recent year with data[[#_ftn1|[1]]]. For countries with no data we use regression relationships of the participation rates, for men and for women, with income per capita. The relationships, shown in the next figure, are not great. However, the functions affect only five countries for which we do not have any data at all: Grenada, Kosovo, Micronesia, Seychelles and South Sudan[[#_ftn2|[2]]].<br />
<br />
&nbsp;<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] The data tables that the IFs model pre-processor use for initializing labor participation rates are: SeriesLaborParRate15PlusFemale%, SeriesLaborParRate15PlusMale%.<br />
</div><div id="ftn2"><br />
[[#_ftnref2|[2]]] We should try to collect participation rate for these countries from country sources.<br />
<br />
IFs data series SeriesLaborUnemploy% is used for the initialization of unemployment rates. That series has annual unemployment rates for one or more years between 1980 and 2016, for 181 of the 186 IFs countries. For five countries (Grenada, Kosovo, Micronesia, Taiwan and South Sudan[[#_ftn1|[1]]]) there is no data at all. To fill in the missing data we use a regression function of unemployment rate against GDP per capita. Like the participation rate functions, this function does also not have much of an explanatory power.<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] These are pretty much the same countries for which we do not have any participation rate data. This indicates ILO might have some administrative limitation in reporting data for these countries (notice Kosovo, Seychelles etc in the list)<br />
<br />
=== Pre-processing labor demand and unemployment from GTAP ===<br />
<br />
The IFs economic pre-processor reads labor headcount and labor payment data from the GTAP database. In addition to performing sector and occupation/skill mapping between GTAP and IFs, pre-processor also use the labor headcount data to compute labor coefficient functions, the principal driver of labor demand in the IFs model.<br />
<br />
Labor coefficients are defined as the amount of labor needed to produce one unit of value added in a certain sector of the economy. The coefficients depend on the level of technology. The model uses GDP per capita as an indicator of the level of technological development. IFs pre-processor estimates labor coefficient functions for labor of different skill levels for the different sectors of the economy.<br />
<br />
The functions are derived from GTAP data we described earlier. The model pre-processor reads data on factor payments and aggregates data from 57 GTAP sectors to six IFs sectors. Shares of payment going to skilled and less-skilled workers in each of the sectors are then computed. Countries are grouped according to their level of technological development as represented by per capita income. For each group labor coefficients are obtained by taking an average of the country coefficients. &nbsp;We also convert labor payments data to labor headcount data using per capita income as a proxy for average wage. Labor coefficients and income are then plotted into a power function relationship. The figure below plots some of those labor functions.&nbsp; The functions fit quite well with a power law formulation[[#_ftn1|[1]]].<br />
<div><br/><br />
----<br />
<div id="ftn1"><br />
[[#_ftnref1|[1]]] This is interesting given the prevalence of power law in all sorts of scale-up activities (West 2017).<br />
<br />
= Labor Model Flowcharts =<br />
<br />
The diagram below shows an outline of the IFs labor model. On the supply side, the total labor pool (LAB) is computed from the labor force participation rates, by sex, (LABPARR) and the population (POP) in their working age, i.e., population over 15 (POP15TO65 + POPGT65). Participation rates are driven by the demographic changes with an additional negative impact from aging and a catch-up in female participation rate. Skill level of the labor supply (LABSUP) is driven by the level of development (GDPPCP) and the demand for labor is driven by labor-coefficients (LABCOEFFS) computed from coefficient function representing shifts in demand with technological progress as proxied by the level of development (GDPPCP). Coefficients computed by sector and skill gives the labor requirement by skill type for each unit of value added (VADD) in the sector. Multiplying these coefficients with projected value added in each sector gives an estimate of the labor demand. &nbsp;<br />
<br />
Any surplus or shortage between total labor demand and supply is used to compute the rate of unemployment. Deviations in the unemployment rate (LABUNEMPR) signal wage changes through an equilibrium seeking algorithm. Both demand and supply respond to the wage variable (LABWAGEIND) indexed to the base year. The supply responses are much slower than the demand responses.<br />
<br />
[[File:FLOCHART2.png|frame|center|Labor Model Flowchart]]<br />
<br />
= Labor Model Equations =<br />
<br />
== Overview ==<br />
<br />
The labor model is a part of the IFs economic model that uses labor model output as an input to a Cobb-Douglas production function in a multi-sector general equilibrium model. IFs is a very long-run dynamic model. Instead of computing fixed short-run equilibria that clear the relevant markets IFs uses an equilibrium seeking algorithm to balance the various systems over the longer run. The algorithm is known as the PID (proportion-integral-derivative) controller algorithm and is used widely in industrial control systems. It makes equilibrium seeking variables in IFs move towards a set target. The algorithm works by computing a multiplier based on the movement of the variable towards the target, as obtained by an integral (I) of the path traversed, and the rate of movement towards the target, the derivative term. The multiplier is applied on the process variable (the P term), or a response variable, in the subsequent time period. In the labor model, unemployment rate (LABUNEMPR) is used as the process variable and the PID multiplier is used on the wage rate (LABWAGEIND). Job availability (LABDEMS) and participation rate (LABPARR) get affected by changes in wage. &nbsp;&nbsp;<br />
<br />
Throughout this section we use subscripts and notations common to other modules of IFs. For example, we use t for time period. Subscripts p and r represent sex and country/region, respectively, c is the cohort number, with cohort 1 representing the newborns, cohort1 the the one-year to four-year-olds, cohort two five-year to nine-year-olds etc. Values for p are 1 for male, 2 for female and 3 for both sexes combined. For economic sectors we use s and for skill levels sk.<br />
<br />
== Labor Supply: Equations ==<br />
<br />
The total pool of labor is computed by multiplying the population of working age with the labor force participation rate (LABPARR). &nbsp;Population forecasts come from IFs demographic model which computes both five-year and single-year age-sex cohorts (''agedst'', ''fagedst''). &nbsp;<br />
<br />
The labor model forecasts participation rates by country/region&nbsp; and gender. Participation rates in the model move with the changes in the demographic composition. Female participation rates, which have historically been lower than the same for the male in all societies, but has moved up in modern and affluent societies, get a catch-up boost in the model. Participation rates can also change when there is labor shortage or surplus and the employers try to incentivize or discourage workers by changing wage. This last impact is much less slow than similar wage impacts on the demand side.<br />
<br />
== Labor Participation Rate ==<br />
<br />
Labor participation rates (''LABPARR'') for male and female are first initialized with historical data.<br />
<br />
:<math>LABPARR_{r,p}= LABPARR_{r,p,t=1} </math><br />
<br />
A ‘catch-up’ boost is added to the female participation rate. The boost added (FemParLabMul) starts at a third of a percentage point and withers away following a non-linear path as the female rates approaches the catch-up target (FemParTar), The maximum catch-up that can occur over the horizon of the model is thirty percent.<br />
<br />
:<math>FemParTar_{r}=Amin(LabParRI_{r,p=1},LabParRI_{r,p=2}+30)</math><br />
<br />
:<math>FemParLabMul_{r}=(FemParTar_{r}-LABPARR_{r,p=2,t-1})/(FemParTar_{r}-LABPARR_{r,p=2,t-1})</math><br />
<br />
:<math>LABPARR_{r,p=2,t}=LABPARR_{r,p=2,t-1}+FemParLabMul_{r}*0.3</math><br />
<br />
Next, we compute and apply the aging impact on the participation rate. As the relative share of people over the retirement age increases, the participation rate declines. The model keeps track of the changes in the demographic ratio (PopAgingRatio) of the population who are in their prime working age of 15 to 64 (POPWORKING) to those at a common retirement age of sixty-five or older (POPGT65). This ratio declines as countries age. The percentage drop in the ratio comparative to the base year is scaled appropriately to compute the aging impact (aging_impact). This impact is added to the male and female labor participation rates, with the impact on the female participation rate being slightly lower than that on male rates.<br />
<br />
:<math>POPAgingRatio_{r,t}=POPWORKING_{r,t}/POPGT65_{r,t}</math><br />
<br />
:<math>aging_impact_{r,t}=100*((POPAgingRatio_{r,t}/POPAgingRatio_{r,t=1})-1)*0.2</math><br />
<br />
:<math>LABPARR_{r,p=1,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t} </math><br />
<br />
:<math>LABPARR_{r,p=2,t}= LABPARR_{r,p=1,t}+aging_impact_{r,t}*0.95 </math><br />
<br />
Participation rates respond slowly to changes in wage and unemployment rate. The impact is implemented through a wage impact factor computed from annual changes in the wage index (labwageimpact). The base participation rates can be changed by model user through two model parameters: a direct multiplier on the participation rate (labparm), or one that changes participation by moving the retirement age (labretagem)<br />
<br />
<br />
<br />
:<math>LABPARR_{r,p,t}= LABPARR_{r,p,t}*(1-labwageimpact*0.05)*labparm_{r,p,t}*labretagem_{r,p,t}</math><br />
<br />
Total participation rate (LABPARRr,p=3,t) is computed by an weighted average of male and female participation rates.<br />
<br />
:<math>LABPARR_{r,p=3,t}= (∑_{p=1 to 2}∑_{c=4 to 21}(agedst{r,c,p,t}*LABPARR_{r,p,t}))/(∑_{p=1 to 2}∑_{c=4 to 21}agedst{r,c,p,t})</math><br />
<br />
== Total Labor ==<br />
<br />
Finally, the total number of labor available for work (LAB) is computed by multiplying the total participation rate with the population of fifteen-year-olds or older.<br />
<br />
:<math>LAB_{r,t}= LABPARR_{r,p=3,t}*∑_{p=1 to 2,c=4 to 21}agedst_{r,c,p,t}</math><br />
<br />
== Labor by skill level ==<br />
<br />
The labor model forecasts labor supply (LABSUP) by two skill categories. The variable (''LABSUP'') is initialized in the pre-processor by reading the employment by skill/occupation (''LABEMPS'') data from GTAP[[#_ftn1|[1]]] &nbsp;and adding the unemployment numbers. We assume same unemployment rate (''LABUMEMPR'') for skilled and unskilled labor.<br />
<br />
:<math>LABSUP_{r,t=1,sk}=∑_{s=1 to 6}(LABEMPS_{r,s,t=1}/(1-(LABUNEMPR_{r,t=1}/100))</math><br />
<br />
The model forecasts labor by skill through a model of the skilled share of the labor. Education, training, exposure, and experience of the employees all improve with the level of development. The model captures this with an analytic function of the skilled share (perskilled) driven by GDP per capita at PPP (GDPPCP) -<br />
<br />
:<math>perskilled_{r}=f(GDPPCP_{r})</math><br />
<br />
Among the causal drivers of skill, education is considered to be the most proximate. Education is strongly correlated with the level of development, the deeper driver of skill in the model. However, the recent increase in education and/or a policy driven educational expansion might add to the impact of education on skill. Additional impacts from education on skill, when there is any, is computed through an expected function formulation. For example, in a society where an average adult has more (or less) education than the adults in other societies at that level of development, the skill share is given a slight upward push (or downward pull). The expectation function is a logarithmic function of educational attainment of working age population (EDYRSAG15) driven by GDP per capita at PPP. Attainment above (or below) the expected level (YearsEdExp) is computed by the function output (YearsEd) adjusted for country situation (yearseddiff). The percentage adjustment to the skilled share (LabSupSkiAdj) is computed using additional (limited) education, i.e., the difference between actual (EDYRSAG15) and expected values of educational attainment, expressed as a percentage of the expected value. The adjustment is scaled appropriately and peters off over time.<br />
<br />
:<math>YearsEd_{r,t}= f(GDPPCP_{r,t})</math><br />
<br />
:<math>yearsdeddiff_{r}= EDYRSAG15_{r,p=3,t=2}-YearsEd_{r,t=2}</math><br />
<br />
:<math>YearsEdExp_{r,t}=YearsEd_{r,t}+yearsdeddiff_{r}</math><br />
<br />
:<math>LabSupSkiAdj_{r,t}=0.3*(EDYRSAG15_{r,p=3,t=2}*YearsEdExp_{r,t})/YearsEd_{r,t}</math><br />
<br />
:<math>LabSupSkiAdj_{r,t}=ConvergeOverTime(0,LabSupSkiAdj_{r,t},70)</math><br />
<br />
:<math>perskilled_{r,t}= perskilled_{r,t}*(1+LabSupSkiAdj_{r,t})</math><br />
<br />
The skilled share (perskilled) is multiplied with the total labor supply (LAB) to obtain the number of labors who are skilled (LABSUPskilled)<br />
<br />
:<math>LABSUP_{r,skilled,t}=LAB_{r,p,t}*perskilledI_{r,t} </math><br />
<br />
As a last step, the model adjusts for the country specific variations in the skilled labor count not captured by the deeper and the proximate models. This is done by saving a ratio (LABSUPSkilledRI) of the actual historical data and the model computed value in the initial year. In the subsequent years this ratio is used to adjust the skilled labor forecast gradually.<br />
<br />
:<math>LABSUPCompSkilled_{r}=LAB_{r}*perskilled_{r,t=1}/100 </math><br />
<br />
:<math>LABSUPSkilledRI_{r}=LABSUP_{r,skilled,t=1}/LABSUPCompSkilled_{r}</math><br />
<br />
:<math>LABSUP_{r,skilled,t}= LABSUP_{r,skilled,t}*ConvergeOverTime(LABSUPSkilledRI_{r},1,85)</math><br />
<br />
Number of unskilled labor is obtained by subtracting the skilled labor from the total pool.<br />
<br />
<br />
<br />
:<math>LABSUP_{r,unskilled,t}= LAB_{r,p,t}- LABSUP_{r,skilled,t}</math><br />
<br />
== Labor Demand: Equations ==<br />
<br />
IFs economic model forecasts production in six economic sectors. IFs labor model computes the longer-term and shorter-term determinants of demand for skilled and unskilled labor (LABDEMS) for the production processes. The long-term drivers of labor requirement are technological progress or the lack of it. In the shorter-term wage affects the labor demand most. Wage in turn is affected by labor supply or skill shortage.<br />
<br />
The IFs model divides economic activities into six economic sectors – agriculture, energy, materials, manufacture, services and information, and communication technologies. Workers in the IFs labor model are disaggregated into two skill types. While the skill composition varies by the technology used in the sector and starts tilting towards the more skilled with the progress in technology, absolute number of labors needed to produce the same output goes down with technological development for both skilled and unskilled labor. This is illustrated in the next figure which plots the changes in labor requirement against GDP per capita at PPP, a proxy for level of development. Agriculture is a much less skill-intensive process than the manufacture, however, with technological progress skill requirement improves rapidly in both sectors. The IFs labor model computes these labor requirement functions in the model pre-processor. As we have already described in the pre-processor section, the computation of these functions use GTAP data on employment by occupation and economic activity. Appendices 3 and 4 lists sector and occupation mapping between GTAP and IFs.<br />
<br />
These functions are used to compute the labor coefficients (LABCOEFFS), i.e., number of skilled and unskilled labor needed to produce unit amount of output with the technology available, for which we use GDP per capita at PPP as a proxy.<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= f(GDPPCP_{r})</math><br />
<br />
manufacture, services and ICTech) and the subscrip sk stands for skill categories with 1 denoting unskilled and 2 skilled. The labor coefficients obtained from the analytical functions require some adjustments to incorporate country deviations from the functions for various factors not captured in the regression relationship. The first of these adjustments is a gradual removal of impacts of short-run fluctuations in output and labor from the computation of labor coefficient. This adjustment is applied on the coefficients computed from the function. The equation below shows a simplified form of these computations.<br />
<br />
:<math>LabCoeffAdjFac_{r,k,s,t}=f(igdpr_{r,t=2},(LAB_{r,t=2}/LAB_{r,t=1}),(LABCOEFFS_{r,t}/LABCOEFFS_{r,t-1}))</math><br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}=LABCOEFFS_{r,sk,s,t}(1-LabCoeffAdjFac_{r,k,s,t})</math><br />
<br />
Model users can use a global parameter (labcoeffsm) to change the labor coefficients by skill level for any or all of the six sectors –<br />
<br />
:<math>LABCOEFFS_{r,sk,s,t}= LABCOEFFS_{r,sk,s,t}*'''labcoeffsm_{s,sk}'''</math><br />
<br />
To forecast the total labor demand, the labor coefficients (LABCOEFFS) are multiplied to the total projected output for each of the economic sectors. The forecast is adjusted for any discrepancy between data and model. The adjustment factor (LABDemsAdjFac) is computed as the initial ratio between the actual and computed employment. Actual employment is obtained from historical data (LABEMPS) processed using the GTAP database. The computed employment is obtained by multiplying the labor coefficients (LABCOEFFS) with the final output of the sector (VADD).<br />
<br />
:<math>LabDemsAdjFac_{r,s,sk}= LABEMPS_{r,s,sk,t=1}/(VADD_{r,s,t=1}*LABCOEFFS_{r,sk,s,t})</math><br />
<br />
The projected output is obtained by applying the growth rate (IGDPRCOR) on the sectoral value added from the previous year (VADD). The total labor demand is given by the product of the labor coefficients, projected output, demand adjustments and wage impacts (labwageimpactmul) and the number 1000 which adjusts the units for the equation. Wage impact comes from the level of unemployment and is computed in an equilibration process described in the next section. Model users can use a multiplicative parameter (labdemsm) to slide the demand upward or downward.<br />
<br />
:<math>LABDEMS_{r,s,sk,t}=1000*VADD_{r,s,t-1}*(1+IGDPRCOR_{r})*LABCOEFFS_{r,sk,s,t}*LabDemsAdjFac_{r,s,sk}*labwageimpactmul_{r,s,sk}*'''labdemsm_{r,s}'''</math><br />
<br />
== Unemployment and Wage: Labor Market Equilibration ==<br />
<br />
The IFs labor model balances the labor market through an equilibrium seeking algorithm rather than computing an exact equilibrium at each time step. We use an algorithm borrowed from the control systems engineering. This PID controller algorithm, described also in the IFs economic model documentation, works by computing corrective signals for equilibrating variables using the deviations of a buffer variable, for example unemployment rate (LABUNEMPR), from a target value. The signal is computed from two quantities, the distance of the buffer from the target and the current rate of change of the buffer. The computation is tuned with PID elasticities to avoid oscillations. The computed signal is applied on the variable/s which need to be balanced, for example, demand and supply in the event of a market equilibration, thus getting closer to a balance at each step of simulation. The target value for the buffer variable and the tuning par