Difference between revisions of "Test"

From Wiki
Jump to: navigation, search
(add headings)
(add another heading)
Line 1: Line 1:
= Another test. =
This page is created by Yutang for testing purposes : )
This page is created by Yutang for testing purposes : )

Revision as of 21:24, 2 December 2020

Another test.

This page is created by Yutang for testing purposes : )

This is Yutang's second edit, a link to the main page is added: Main Page

This is Yutang's third edit, Yutang's profile picture is added.

There are 380 series pulled from the World Development Indicators (WDI) in 2017. 72 of these are used in the preprocessor.

The most extensive and reliable source for socio-economic and development data is probably World Bank’s annually published World Development Indicators (WDI) database. The recent WDI, published in 2017, has approximately 1,500 indicators for 217 economies. Economies in WDI have been organized in 18 groups based on income, geography, and indebtedness criteria. Data in the WDI database are categorized in six major areas: overview, people, environment, economy, states and markets, and global links.

The major advantage of the WDI database is that it has longer time series coverage, for example, most of the indicators cover data since 1960. The World Bank collects data for particular series from the organizations who are pioneers in those areas. For example, WDI compiles external account data, like export, import, current account, debt etc. from the International Monetary Fund (IMF), and population data from United Nations Population Division. Major data sources for WDI are World Bank national accounts data, OECD National Accounts, Food and Agriculture Organization, United Nations Educational, Scientific, and Cultural Organization, World Health Organization, International Labor Organization, International Telecommunication Union etc. WDI database are available in three formats- online (https://publications.worldbank.org/subscriptions/WDI/), published report in hard copy and CDROM version.

Some financial data series from the WDI database, used in IFs analysis, have been converted into billion US$ from current prices at US$ and some series related to population have been transformed into million figures. The IFs system also needed to calibrate some data series as percent of GDP in constructing social accounting matrices.

Series pulled from 2017 WDI Batch Pull

DataDict 720





Last IFs Update




Economic, Trade

Imports of goods (current currency)





Environment, Infrastructure, Water

Land use, irrigated land (% of cropland)






Labor force size




Discontinued WDI Series

Since beginning to pull WDI data, there have been a number of series which have been discontinued in terms of collection by the WDI. We continue to keep this outdated data in IFs until proper replacements are found. Those data are:

Non-Preprocessor Series:




Preprocessor Series:





Instructions on pulling WDI data

​Steps for pulling WDI Batch Data

  1. Access the World Development Indicators through the world bank at: http://data.worldbank.org/data-catalog/world-development-indicators
  2. Use batch import tool to import the WDI data. Import as user sees best fit (it's a good idea to start with a preprocessor import, a lot fewer series that generally allow user to see where errors might lie without combing through 400 series). Choose the World Bank Countries concordance table.
  3. During 2017 import issues arose around North Korea. In order to fix this the country name in World Bank Countries Concordance table was changed to "Korea, Dem. Rep." and was also changed to this in the WDI excel file. Will need to repeat this step in order to ensure data is imported. 
  4. Begin prevetting process. Blend data points for missing years and missing country data. Can generally expect that data will differ slightly from current consiering that WDI regularly updates old data points.
  5. Once all noticeable errors are fixed send to vetter for more intensive search of data for errors. 

Eelectricity Access data


This section explains the methodology that is to be followed when importing Electricity Access data into Ifs. It also describes certain problems that a user might face in interpreting, understanding the data.

Representation of Electricity access data in IFs

Electricity access data in Ifs is broken down into electricity access for Urban areas and Electricity access for rural areas. It is represented as a percentage i.e. percentage of urban population with electricity access and percentage of rural population with electricity access. This data is used in the following computations in Ifs,

  1.  Desired level of electricity access
  2.  Electricity use as a % of total energy use
  3.  % of population that relies on solid fuels as the primary source of domestic energy 

This data is affected by the following drivers in IFs,

  1. The household size
  2. Population size
  3. Breakdown of population (Urban, Rural)
  4. Government effectiveness

Below is a simple diagrammatic representation of Electricity Access in Ifs,

                                                                                                Source: International Futures 7.22

Source for data- Electricity Access data is available from a variety of sources. The two main sources of data for the same are,

  1. WDI- the World Bank publishes data for electricity access data for 186 countries. A bifurcation is available between Rural Electrification and Urban Electrification. The data is available at  http://data.worldbank.org/indicator/EG.ELC.ACCS.ZS
  2. IEA- the International Energy Agency publishes this data as well, but only for developing countries. This data is published as a part of the world energy outlook. The data is available at http://www.worldenergyoutlook.org/resources/energydevelopment/energyaccessdatabase/  


Series to be updated

  1. National Electricity Access - SeriesEnElecAccess%National
  2. Rural Electricity Access- SeriesEnElecAccess%Rural
  3. Urban Electricity Access- SeriesEnElecAccess%Urban

Comparison of WDI and IEA data

  When the WDI and IEA data were compared, in order to verify whether the datasets could be blended, we found that the year on year data was too dissimilar to blend. For Example, for some countries the 2013 value from IEA was lower than the 2012 value from WDI. (This is not logical, since electrification rates cannot go down). Also, for some other countries the 2013 IEA value was abnormally high considering the historical growth rates of electrification for the country or region in question. Blending these datasets would create transients in the IFs forecasts for future years. Hence, it is recommended that the user only use the WDI data when updating this series.


'Country List to be used'World Bank countries