Governance

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The most recent and complete governance model documentation is available on Pardee's website. Although the text in this interactive system is, for some IFs models, often significantly out of date, you may still find the basic description useful to you.

Governance is the two-way interaction between government and the broader socio-political or, even more broadly, socio-cultural system. Although our documentation and the IFs model itself focuses primarily on three dimensions of that governance interaction, we will need also to direct some attention specifically to that broader socio-cultural system and how it might change over time.

Overview

The conceptual foundation for the representation of governance in IFs owes much to an analysis of the evolution of governance in countries around the world over several centuries. That analysis (see Chapter 1 of the Strengthening Governance Globally volume by Hughes et al. 2014) identified three dimensions of governance: security, capacity, and inclusion. It traced them over time and noted their largely sequential unfolding for currently developed countries and their currently simultaneous progression in many lower-income countries.

The three dimensions interact closely and bi-directionally with each other. They also interact bi-directionally with broader human development systems. The level of well-being, often captured quantitatively by GDP per capita or the more inclusive human development index, may be especially important, but is hardly alone in helping drive forward advance in governance; for instance, the age structures of populations and economic structures also interact with governance patterns both indirectly through well-being and directly.
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The conceptualization of governance further divides each of the three primary dimensions into two sub-dimensions partly based on the desire to quantify them historically and to facilitate forecasting. For security those are the probability of intrastate conflict and the general level of country performance and risk. The two sub-dimensions of capacity are the ability to raise revenue and the effective use of it and the other tools of government—that is, the competence or quality of governance. We use corruption (that is, control of it) as a proxy for such competence. The first sub-dimension of inclusion is the level of formal democratization, typically assessed in terms of competitive elections. More broadly democratization involves inclusion of population groupings across lines such as ethnicity, religion, sex, and age; we use gender equity as a proxy for the second dimension.

See Hughes et al. (2014), especially Chapter 4, for more background on the development of the governance representations of IFs than this documentation provides. See also Hughes (2002) for earlier and/or complementary work in IFs on socio-political representations (domestic and international); for example, here we do not discuss the formulations for power, interstate threat, and conflict, but that is available in documentation on the International Political model of the IFs system. Finally, we not provide here the important information about the forward linkages of governance to other elements of IFs, including to the production function of the economic model and to the broader financial flows of the social accounting matrix representation. See documentation on the economic model for that information.

Structure and Agent System: Governance

System/Subsystem
Governance
Organizing Structure
Three dimensions with two sub-dimensions each; highly interactive, bi-directional relationships among dimensions and with socio-economic development, demographics, and economics
Stocks
Socio-economic development levels (e.g. level of education, gender relationships, size of the economy); past patterns of governance; also cultural patterns are a stock
Flows
Government spending on human capital, infrastructure, development generally; accretion of changes in governance over time
Key Aggregate  Relationships 
(illustrative, not comprehensive)
Probability of intrastate conflict is a function of past conflict, neighborhood effects, economic growth rate (inverse), trade openness (inverse), youth bulge, infant mortality, democracy (inverted-U), state repression (inverse), and external intervention.
 
Vulnerability to intrastate conflict is a function of past intrastate conflict, energy trade dependence (as a proxy for broader natural resource dependence), economic growth rate (inverse), youth bulge, urbanization rate, poverty level, infant mortality, life expectancy (inverse) undernutrition, HIV prevalence, primary net enrollment (inverse), adult education levels (inverse), corruption, democracy (inverse), gender empowerment (inverse), governance effectiveness (inverse), freedom (inverse), inequality, and water stress
 
Government revenues are a function of past revenue as percentage of GDP, GDP per capita, and social expenditures (that is, inversely to fiscal balance).
 
Corruption is a function of past corruption level, GDP per capita (inverse), energy trade dependence, democracy (inverse), gender empowerment (inverse), and probability of intrastate conflict.
 
Democracy is a function of past democracy level, youth bulge (inverse), and gender empowerment.
 
Gender empowerment is a function of past gender empowerment level, GDP per capita, youth bulge (inverse), and primary net enrollment.
Key Agent-Class Behavior  Relationships
(illustrative, not comprehensive)
Social sub-group relationships, especially historical conflict patterns and gender relationships; government revenue and expenditure

Dominant Relations: Governance

The drivers of change on each dimension and sub-dimension of governance range widely.  A quick summary (see also the table below) is that:

  • Probability of intrastate conflict is a function of past conflict, neighborhood effects, economic growth rate (inverse), trade openness (inverse), youth bulge, infant mortality, democracy (inverted-U), state repression (inverse), and external intervention (inverse).
  • Vulnerability to intrastate conflict is a function of energy trade dependence, economic growth rate (inverse), urbanization rate, poverty level, infant mortality, undernutrition, HIV prevalence, primary net enrollment (inverse), intrastate conflict probability, corruption, democracy (inverse), governance effectiveness (inverse), freedom (inverse), and water stress.
  • Government revenues are a function of past revenue as percentage of GDP, GDP per capita, and fiscal balance (inverse).
  • Corruption is a function of past corruption level, GDP per capita (inverse), energy trade dependence, democracy (inverse), gender empowerment (inverse), and probability of intrastate conflict.
  • Democracy is a function of past democracy level, economic growth rate (inverse), youth bulge (inverse), and gender empowerment.
  • Gender empowerment is a function of past gender empowerment level, GDP per capita, youth bulge (inverse), and primary net enrollment.
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There are some general insights with respect to elaboration of the formulations (equations and algorithms) that drive change on each dimension and sub-dimension of governance:

  • In almost each case there are path dependencies that supplement the basic relationships—social change has considerable inertia.
  • The driving and driven variables clearly constitute a complex syndrome of mutually interdependent developmental interactions, not a simple causal sequence.
  • There is a tendency for the dimensions of governance traditionally developing later to feed back to earlier ones, notably for inclusion to affect capacity via reduced corruption and also for inclusion and capacity to reduce the probability of internal conflict.
  • Behaviorally, the bi-directional structures suggest the possibility that reinforcing processes may accelerate as governance strengthens, setting up a kind of tipping from one equilibrium to another; vicious cycles of deterioration would also be possible.

For detailed discussion of the model's causal dynamics, see the discussions of flow charts (block diagrams) and equations.

Governance Flow Charts

Overview

We can show and briefly describe a block diagram for each of the three dimensions of governance and the two sub-dimensions of those: security (probability of intrastate or internal war and risk of conflict); capacity (ability to mobilize revenues and the effectiveness of their use); inclusiveness (formal democracy and broader inclusiveness, using gender empowerment as a proxy).

Please click on the links below to access these topics.

Security

Internal War

Internal or intrastate war (SFINTLWAR) is heavily determined by a moving average of a society's past experience with such conflict (SFINTLWARMA) in what is a positive feedback system. The probability of such conflict will, however, typically converge to that determined by more basic underlying drivers, and the user can control the speed of such convergence by specifying the years to convergence (sfconv ).
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The major driving variables in a statistical estimation are the level of infant mortality (INFMORT) as a proxy for quality of government performance and trade openness or exports (X) plus imports (M) as a share of GDP. In addition democracy level (DEMOCPOLITY) enters in a non-linear and algorithmic fashion, as do youth bulge (YTHBULGE) and a moving average of economic growth rate (GDPRMA).

Although less often used and turned off in the Base Case scenario, external interventions (wpextinterv ) and mass repression (sfmassrep ) can cause or at least temporarily dampen internal war, respectively.

Finally, the user can multiply resultant endogenous values of internal war (sfintlwarm ) in order to generate user-controlled scenarios.

The IFs system also includes a representation of instability short of internal war (SFINSTABALL and SFINSTABMAG), linking them to the category of abrupt regime change in the classification developed by Ted Robert Gurr and used by the Political Instability Task Force. The forecasting representation was developed before the revision and update of that for internal war, however, and we recommend less attention to it until its own revision is done.

Vulnerability and Risk of Conflict

The IFs treatment of societal/governance performance risk and related vulnerability to conflict does not involve an estimated formulation. Instead, like other such efforts, it involves the creation of an index. The figure below, a screen capture of the form (reached via Specialized Displays) uses variables related both directly to governance and to performance. A specialized Help topic on this form is available.

Although many users will be interested in the rankings of countries (see the Global Rank column for ranks on individual variables and the summary measure for overall, variable-weighted rank), others will be interested in the summary value across all variables, shown at the bottom of the first column. Those values are also available in the model as the variable named government risk (GOVRISK).

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Capacity

Government Revenues

The ability to raise government revenues (GOVREV as a share of GDP) is one of the dimensions of capacity in governance. Its basic calculation is a very simple ratio. The key drivers of GOVREV, however, documented elsewhere, are very complex. For instance, GOVREV is responsive in an equilibration process to government expenditures, both transfer payments and direct government expenditures in categories such as military, health, education, and infrastructure, as well as to external revenues, notably foreign aid receipts.
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Effectiveness of Government

The central measure of governance effectiveness in Hughes et al. (2014) was defined to be corruption or GOVCORRUPT (actually the absence thereof, or level of transparency). The model computes several additional measures of effectiveness or ca
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pacity, however, including regulatory quality (REGQUALITY) and effectiveness (GOVEFFECT), both related to the World Bank's World Governance Indicator project (Kaufmann, Kraay, and Mastruzzi 2010). In addition, many analysts point to the level of economic freedom (ECONFREE) or liberalization as a measure of effectiveness, in spite of considerable debate around their doing so.

Among the drivers of governance corruption is resource dependence, for which we use as a proxy the value of energy exports (ENX) at energy prices (ENPRI) as a share of GDP. Energy exports tend to me the largest such category globally. Further drivers are the extent of gender empowerment (GEM) and the level of democracy (DEMOCPOLITY), both of which indicate the extent of inclusiveness but which make independent statistical contributions to corruption level.

The drivers do not, of course, fully determine the level of corruption and there is much historical path dependence in societies related to other variables. The user can control the speed of elimination of such dependence and therefore of convergence to the basic formulation with a conversion years parameter (goveffconv ).

There are times when the user will wish to introduce normatively controlled target values for corruption. One approach is use of the "brute force" multiplier on corruption (govcorruptm ). A second approach involves the specification of target values relative to a function of the key drivers estimated cross-sectionally across countries. This second approach allows, for instance, the specification of a target level 1 or 2 standard errors (SE) above the level expected of a country given those drivers. The SE target parameter is govcorruptsetar and the govcorruptseyrtar carries the years to achieve the target. There are similar control parameters (not shown the diagram) for regulatory quality (govregqualsetar and govreqqualseyrtar ) and for effectiveness (goveffectsetar and goveffectseyrtar ), but not for economic freedom.

Theoretically, internal war (SFINTLWAR) could affect all of the capacity variables, but the only linkage identified in IFs is that to economic freedom. Setting the control switch (confforsw ) to 1 turns on that impact.

Inclusiveness

Democracy

Three variables dominate the forecasting formulation for democracy (DEMOCPOLITY): the gender empowerment measure (GEM) as a measure of broad social inclusion (positive linkage), the youth bulge (YTHBULGE) as an indicator of the age structure of society (negative linkage), and the dependence of the country on raw materials exports, a negative linkage using energy export share (ENX) times energy prices (ENPRI) as a share of the GDP as a proxy. An exogenous multiplier (democm ) allows the user to directly manipulate the democracy level.

Two other variables can affect the democracy level but are turned off in the Base Case and will seldom be used. The first is the neighborhood effects of swing states in a regional neighborhood (e.g. Russia among former states of the Soviet Union). The swing states effect switch (sweffects ) turns it on when set to 1.

The more complicated additional factor is that of democracy waves (DEMOCWAVE). Relative to the initial condition a democracy wave can add or subtract democracy to the basic formulation's calculation of it (an algorithm based on historical experience allows upward swings to be larger than downward ones depending on EffectMul). The basic magnitude of increments depends of an exogenous specification of the impetus provided to democracy by the leading power (democwvus ) and by other powers (democimpoth ), the former's impact controlled by an elasticity (eldemocimp ). Because waves rise and ebb, another parameter controls the length (democlen ) and still another sets the maximum rise (democwvmax ). A counter keeps track of the running and receding of a wave (DEMOCWVCOUNT) and a pointer keeps track of the direction its operation (DEMOCWVDIR); these two parameters are linked with the magnitude of the wave in a positive loop.

The calculation from the basic formulation, before the addition of wave and swing state or neighborhood effects, can also be overridden by the use of external targeting directed by specifications of standard error targets relative to the formulation (democpolitysetar ) to be achieved by a target year (democpolityseyrtar ).

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Gender Empowerment and Freedom

Gender empowerment (GEM), a broader measure of inclusion, joins democracy as the secon
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d key measure of governance inclusiveness. Its three basic drivers are youth bulge size (YTHBULGE), GDP per capita as purchasing power parity (GDPPCP), and the years of formal education obtained by female adults (EDYRSAG15).

A user can control the progression of gender empowerment with a simple multiplier (gemm ) or via setting a target value for it movement to some number of standard errors above or below a cross-sectionally estimated function (gemsetar ) across a set number of years (gemseyrtar ).

Although IFs uses the Polity measure of democracy (DEMOCPOLITY) as its main measure of more formal, electoral inclusion, Freedom House's freedom measure (FREEDOM) is a logical alternative and the second of that measure's sub-dimensions, civil liberties, is a more inclusive measure. We therefore compute it also, using again GDP per capita and educational years (of all adults, not just females) as drivers. And there is a brute force multiplier for it also (freedomm ). There is no SE targeting mechanism in place for the freedom variable.

Aggregate Governance Indicators

The major way of exploring the possible future of the three dimensions of governance is separately to use the two variables that represent each. But it is also useful to have more aggregate indices, first for each dimension and also across the three.

The governance security index (GOVINDSECUR) is computed as an unweighted average of internal war probability (SFINTLWAR) and governance/society performance risk (GOVRISK). Similarly, the governance capacity index (GOINDCAP) is an unweighted average of government revenue (GOVREV) as a portion of GDP and government corruption, while the governance inclusion index (GOVINCLIND) averages democracy (DEMOCPOLITY) and gender empowerment (GEM). The overall governance index (GOVINDTOTAL) is a simple average of those across dimensions.

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In reality, creating the indices for each dimension requires some attention to scaling issues and valence. See the description of the equations for details.

Life Conditions and the Human Development Index

The condition of individuals and society are both the ultimate focus of governance and the font of it. The IFs system computes many of the relevant variables across its various models. It also aggregates a number of those into the widely used Human Development Index (HDI), based on heath (life expectancy), education or knowledge (both expectations for youth and attainment for adults), and GDP per capita.

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Social Values and Cultural Evolution

</hgroup></header> Understanding societies fully requires going even more deeply than their governance and social conditions in order to look at the values and cultural foundations. IFs computes change in three cultural dimensions identified by the World Values Survey (Inglehart 1997). Those are dimensions of materialism/post-materialism, survival/self-expression, and traditional/secular-rational values.

Inglehart has identified large cultural regions that have substantially different patterns on these value dimensions and IFs represents those regions, using them to compute shifts in value patterns specific to them.

Levels on the three cultural dimensions are predicted not only for the country/regional populations as a whole, but in each of 6 age cohorts. Not shown in the flow chart is the option, controlled by the parameter "wvsagesw ," of computing country/region change over time in the three dimensions by functions for each cohort (value of wvsagesw = 1) or by computing change only in the first cohort and then advancing that through time (value of wvsagesw = 2).

The model uses country-specific data from the World Values Survey project to compute a variety of parameters in the first year by cultural region (English-speaking, Orthodox, Islamic, etc.). The key parameters for the model user are the three country/region-specific additive factors on each value/cultural dimension (matpostradd , etc.).

Finally, the model contains data on the size (percentage of population) of the two largest ethnic/cultural groupings. At this point these parameters have no forward linkages to other variables in the model.