# Introduction

The purpose of this document is to facilitate the development of scenarios with the International Futures (IFs) system. This document supplements the IFs Training Manual. That manual provides a general introduction to IFs and assistance with the use of the interface (e.g., how do I create a graphic?). In turn, the broader Help system of IFs supplements this manual. It provides detailed information on the structure of IFs, including the underlying equations in the model (e.g., what does the economic production function look like?). This document should help users understand the leverage points that are available to change parameters (and in a few cases even equations) and create alternative scenarios relative to the Base Case scenario of IFs (e.g., how do I decrease fertility rates or increase agricultural production?). It proceeds across the modules of IFs, such as demographic, economic, energy, health, and infrastructure, to (1) identify some of the key variables that you might want to influence to build scenarios and (2) the parameters that you will want to manipulate to affect your variables of interest. The Training Manual will help you actually make the parameter changes in the computer program and the Help system will facilitate your understanding of the structures, equations and algorithms that constitute the model. We begin by introducing the types of parameters within IFs and then proceed to a discussion of variables and parameters within each of the IFs modules.

## A Note on Parameter Names

In this manual we will provide the internal computer program names of variables and parameters, as well as their descriptions. Those names are especially important for use of the Self-Managed Display form, which provides model users with complete access to all variables and parameters in the system. Most model use, however, employs the Scenario Tree form to build scenarios and the Flexible Display form to show scenario-specific forecasts, and both of those forms rely primarily on natural language descriptions of variables and parameters. To match the names provided here with the options in those forms, you can use the Search feature from the menu. The Training Manual describes how to use features such as the Flexible Display form to see computed forecast variables in natural language. And it also describes how to use the Scenario Tree form to access parameters in something close to natural language. Nonetheless, it helps very much in the use of those features and the model generally to know the actual variable and parameter names.

## Types of Parameters in IFs

Equations in IFs have the general form of a dependent or computed variable, as a function of one or more driving or independent variables. Variables, like population and GDP, are the dynamic elements of forecasts in which you are ultimately interested. For instance, total fertility rate or TFR (the number of children a woman has in her lifetime) is a function of GDP per capita at purchasing power parity (GDPPCP), education of adults 15 or more years of age (EDYRSAG15), the use of contraception within a country (CONTRUSE), and the level of infant mortality (INFMORT). In the most general terms the equation is

$TFR=F(GDPPCP,EDYRSAG15,CONTRUSE,INFMORT)$

Parameters of several kinds can alter the details of such a relationship. That is, parameters are numbers (also represented by names in IFs), that help specify the exact relationship between independent and dependent variables in equations or other formulations (including logical procedures called algorithms). For instance, the model may contains different parameters that tell us how much TFR rises or falls per unit change in GDP per 6 capita, education levels, contraception use, and infant mortality1 and it contains still others to set bounds on the lower and/or upper values of TFR over the long run (obviously TFR should never go negative and probably we will not even want it to go, at least for a long time, to a very low level such as an average of 0.5 children per woman). Some of these parameters are more technical than others in the sense that they may significantly affect the overall stability of the model if users are not very careful with the magnitude or direction of the changes they make; we will focus heavily in this manual on parameters that are easiest to interpret and modify.

In many cases, we are more interested in using a parameter to make a direct change to a variable, rather than indirectly affecting a variable like TFR through one of its drivers. We often refer to this as the "brute force" method of changing a variable, and this can be done by multiplying the entire result of a basic equation like that above by a number, adding something to that result, or simply over-riding the result with an exogenously (externally) specified series of values. In the case of TFR we use the multiplier approach, which is described below. The strengths of this approach should be obvious: it preserves model stability, and makes the model more accessible for users. However, the weakness is that in many instances it is more realistic to affect one of the drivers of TFR rather than TFR directly.

Beyond multipliers, there are many other types of parameters that IFs uses, although we are forced to abandon TFR to provide examples. For instance, a switch parameter may turn on or off a particular formulation in preference to another. A target may specify a value towards which we want a variable to move gradually (we would need to specify both the target level and the years of convergence to it).

Overall, key parameter types are:

1. Equation Result Parameters. Most users will use these parameter types far more often than any other. The three types are:

a. Multipliers. This most common of all parameter types in scenario analysis comes into play after an equation has been calculated. They multiply the result by the value of the parameter. The default value, i.e. the value for which the parameter has no effect and to which multipliers almost invariably are set in the Base Case, is 1.0. These parameters are usually denoted with the suffix -m at the end of the parameter name.
b. Additive factors. Like multipliers, these change the results after an equation computation, but add to the result rather than multiplying. The default value is normally 0.0. These are usually denoted with the suffix -add at the end of the parameter name.
c. Exogenous Specification. Sometimes these parameters override the computation of an equation. In other cases, they are actually substitutes for having an equation; that is, they are actually equivalent to specifying the values of a variable over time for which the model has no equation. This typically means establishing a new exogenous series. They typically will have the name of the variable that they over-ride within their own name.

2. Targets. Especially for the purposes of policy analysis, we often want to force the result of an equation toward a particular value over time (e.g. to achieve the elimination of indoor use of solid fuels). Target parameters are generally paired, one for the target level and one for the number of years to reach the target (from the initial year of the model forecast, 2010). Targets have different types:

a. Absolute targets. In this case the target value and year define the absolute value the variable should move toward and the number of years after the first model year over which the goal should be achieved. Together they determine a path in which the value for the variable moves quite directly2 from the value in first year to the target value in the target year. Trgtval and trgtyr are the parameter suffixes used for this parameter type. The first of these changes the target itself, and the second alters the number of years to the target. The default value of *trgtyr parameters should normally be 10 years, but in some cases it is 0, meaning that users must set the number of years to target as well as the target value in order to use these parameters.
b. Relative (standard error) targets. In this case, the target value and year define a relative value towards which the variable should move and the number of years that will pass before the target is reached. The relative value is defined as the number of standard errors above or below the “predicted” value of the variable of interest (a prediction usually based on the country's GDP per capita). Target values less than 0 set the target below the typical or predicted (as indicated by cross-sectional estimations) value of the variable. Target values above 0 set the target above the predicted value. As with the absolute targets, the value calculated using relative targeting is compared to the default value estimated in the model. The computed value then gradually moves from the normal or default-equation based value to the target value. If, however, the computed value already is at or beyond the target (that could be above or below depending on whether the target is above or below the default or predicted value), the model will not move it toward the target. Two different parameter suffixes direct relative targeting: setar and seyrtar. The first of these changes the target itself and the second alters the number of years to the target. The default value of the *seyrtar parameters varies based on the module and even variable. Governance parameters are set to a default of 10 years from the year of model initialization, while infrastructure parameters are set to a default of 20 years. These defaults mean that users do not have to change *seyrtar as well as *setar in order to build standard error target scenarios. Changing *setar should be enough.

3. Rates of change. Some parameters specify an annual percentage rate of change. Unfortunately, IFs does not consistently use percentage rates (5 percent per year) versus proportional rates (0.05 increase rate per year, which is equivalent to 5 percent), so the user should be attentive to definitions. There are multiple suffixes that may apply to these, including -r (changes in the rate) and -gr (changes the rate of change, growth or decline).

4. Limits. As indicated for the TFR example, long-term national rates are unlikely to fall and stay below a minimum value. Limits can be minimum or maximum values. These are typically denoted by the suffixes - min, -max, or -lim.

5. Switches. These turn off and on elements in the model. These most often affect linkages between modules, but can also change relationships within modules. They are typically denoted by the suffix -sw.

6. Other parameters in equations and algorithms. Equations within IFs can become quite complicated. The parameter types discussed to this point provide the easiest control over them for most model users. Relatively few users will proceed further with parameters, and to do so will typically require attention to the specific nature of the equation (e.g. whether independent variables are related to dependent ones via linear, logarithmic, exponential or other relationship forms). That is, one would normally need to understand the model via the Help system or other project documentation in order to use them meaningfully and without causing substantial risk of bad model behavior. The sections of this manual will provide very little information about these technical parameters.

a. Elasticities: These are relatively common within IFs and specify the percentage change in the dependent variable associate with a percentage change in the independent variable. They are typically prefixed el- or elas-.
b. Equilibration control parameters. IFs balances supply and demand for goods and services via prices, savings and investment with interest rates, and so on. These processes typically use an algorithmic controller system that responds to both the magnitude of imbalance or disequilibrium and the direction and extent of its change over time (see the Help system descriptions of the model). Although they are not typical elasticities, the two parameters that control each such process usually have the prefix el- and the suffixes -1 or -2. Parameters ending with 1 relate to disequilibrium magnitude; and parameters end with 2 relate to the direction of change.
c. Other coefficients in equations. Beyond elasticities, many other forms of parameter can manipulate an equation. When analysts in many fields think of parameters, this is what they mean. In IFs, most users will use them quite rarely because, in the absence of knowledge concerning equation forms and reasonable ranges, the parameters often have little transparent meaning—experts in a field may use them more often. Many analysts think of such parameters as having a constant value over time, and some are unchangeable over time in IFs. IFs allows almost all, however, to be entered as time series and vary with great flexibility across time. Some can be changed for each country and/or sub-dimensions of the associated variable, such as energy types, but others can only be changed globally.
d. Equation forms. Although most users will change parameters using the Scenario Tree (see again the Training Manual), the IFs model has made it possible over time to change an increasing number of functions directly (both bivariate and multivariate ones). The advantage this confers is the ability to alter the nature of the formulation (e.g. going from linear to logarithmic) and even, to a very limited degree, the independent or driver variables in the equation. Although some module discussions will occasionally suggest this option, most users will not avail themselves of it. Users who wish to make such changes can do so via the Change Selected Functions options, which can be accessed from the Scenario Analysis Menu on the main page.

7. Initial conditions for endogenous variables and convergence of initial discrepancies

a. Initial conditions are not, strictly speaking, true parameters, but should reflect data. Yet some users will believe that they have data superior to that in IFs, and the system allows the user to change most initial conditions. After the first year, the model will compute subsequent values internally (endogenously). Initial conditions don’t have a suffix; their names are, in fact, those of the variable itself (e.g., POP for population).
b. Convergence speed of initial-condition based discrepancies to forecasting functions. Because initial conditions taken from empirical data often vary from the values that are computed in the estimated equation used for forecasting, the model protects the empirically-based initial condition by computing shift factors that represent that initial discrepancy (they can be additive or multiplicative). For many variables, values rooted in initial conditions in the first model year should converge to the value of the estimated equation over time; convergence parameters control the speed of such convergence. Most model users will never change the convergence speed. These are denoted by the suffixes -cf or -conv.

In the use of all parameters, especially those other than equation result parameters, users will often be uncertain how much it is reasonable to move them—as are often even the model developers. The Scenario Tree form provides some support for judgments on this by indicating high and low alternatives to that of that Base Case. This manual will sometimes provide some additional information.

1Because the nature of the equation or formulation will vary (sometimes a driving variable is linearly linked to the dependent variable, sometimes the equation uses a logarithmic, exponential, or other formulation), the coefficients in the equation cannot invariably or even regularly be interpreted as units of change linked to units of change. You may need to explore the Help system and specific equations to fully understand the relationship. This is one of the key reasons we very often turn to the multipliers and additive factors explained in the next paragraph.

2The movement normally will be linear, except that it is possible to set moving targets that create non-linear progression patterns. In some cases, the model explicitly uses non-linear convergence; e.g. to accelerate movement in early years and then to slow it as the target is approached.

## Manipulating Parameters in IFs

You will typically manipulate parameters to create scenarios or internally coherent stories about the future. You may create scenarios because you wish to represent and explore the possible impact of policy interventions. Or your stories may represent views of the dynamics of global systems alternative to that in the IFs Base Case scenario. Most of the time, you will be interested in tracking the possible futures of selected variables having particular interest to you. The following sections, each covering a module of the IFs system, begin by identifying some of the variables of potentially greatest interest to you. They then provide suggestions on which parameters are likely to be of most useful in building alternative scenarios for those variables. Each section includes tables listing the most effective parameters with which to target certain outcomes. While these suggestions are intended to help you start to think about which parameters you might use to build your scenarios, it is essential that you consider seriously what the policy-based, empirical-knowledge-rooted, or theoretically informed foundations are for your changes.

## Keys to Successfully Modifying Parameters in IFs

• Test all parameter changes individually before building combinations, in order to be able to identify which parameters are having specific impacts
• After changing a parameter value and running a scenario, check the impact on the most proximate or closely related variables (identified in the tables of each module section), before checking the secondary impacts of your selected parameter on more distally related variables
• Tie parameter changes to policy options, empirical knowledge, or theoretical insight identified in literature
• Bear in mind the relevant geographical level at which a parameter operates; some parameters function directly at a global level (e.g., global migration rates), while others will be most relevant at the regional, or national level
• Some parameters are only effective when used in combination with one another (such as target values and years to reach a target)
• Some parameters cancel one another out; for example, trgtval and setar parameters cannot be used together except under very limited circumstances that we attempt to note in the subsequent text
• In many cases, variables affected by certain parameters have natural maximums (e.g. 100 percent) or minimums (e.g. fertility rate), so that changes to the parameters affecting them, where countries may already be approaching such a limit, will not have a significant impact
• The IFs systems contains many equilibrating processes, such as those around prices; interventions meant to affect one side of such an equilibration (such as efforts to reduce energy demand) may have offsetting effects (such as lower prices for energy and resultant demand increase) that make it harder than you expect to push the system in the desired direction; real-world policy makers often face such difficulties and may need to push harder than anticipated

## Prepackaged Scenarios

A number of alternative scenarios come prepackaged with the model. To access them, select Scenario Analysis from the main menu, and then the option labeled Quick Scenario Analysis with Tree. Once in the scenario display, select Add Scenario Component to view all of the .sce (scenario) files that are stored on your computer normally at the path C:/Users/Public/IFs/Scenario. Exploring several simple interventions contained in the folder structure should give users an overview of some of the leverage points in that they may wish to use in each module

# Demographic Module

## Variables of Interest

 Variable Description POP Total population POPLE15 Population, age 15 or less POP15TO65 Population, age 15 to 65 POPGT65 Population, greater than 65 POPPREWORK Population, pre-working years POPWORKING Population, working years POPRETIRED Population, retired YTHBULGE % of the population between 15 and 29 POPMEDAGE Population, median age LAB Labor force size BIRTHS Births DEATHS Deaths MIGRANTS Net migration (inward) CBR Crude birth rate CDR Crude death rate TFR Total fertility rate CONTRUSE Contraceptive usage LIFEXP Life expectancy MIGRATE Net migration rate (inward)

The IFs demographic module breaks country populations down into 21 fiveyear age groups, each one subdivided by gender. This allows the model to create an age-sex cohort structure that responds to changes in the three fundamental drivers of population: fertility, mortality, and migration. Births are calculated as a function of each country’s fertility distribution and age distribution. As children are born, they enter the lowest band of the agesex structure, the layer representing people aged 0 through 5. Each country’s population growth is reduced by deaths at each age level; like births, deaths are calculated as a function of the mortality distribution and the age distribution. Finally, migration patterns either add to, or subtract from, each country’s population, depending on the balance of immigration and emigration3 . Each of the three proximate drivers of population is influenced by deeper social processes: births are a product of fertility patterns; deaths are linked to life expectancy; and net migrants are determined by an overall global migration rate.

Total population is represented in millions of people via POP, but users may also choose to explore the age structure within society. Three variables break population down into broad age groups: POPLE15, people age 15 or younger, POP15TO65, people age 15 to age 65, and POPGT65, people older than age 65. Three additional variables provide a similar disaggregation of population: POPPREWORK, POPWORKING, POPRETIRED—as the names suggest, they measure the number of people who have yet to enter their working years, the number of people currently in their working years, and the number of people who have completed their working years. The years comprising an adult’s working life may vary from country to country, depending on education systems and retirement ages. Users can explore additional population characteristics via the variables YTHBULGE, the percent of all adults (15 and older) between the ages 15 and 29; POPMEDAGE, the median age of a country’s population; and LAB, the size of the labor force, recorded in millions of people. For any country, the complete age and sex breakdown is available under the Specialized Displays for Issues option under the Display sub-menu. From the Specialized Displays menu, select Population by Age and Sex, and click the button labeled Show Numbers. This will bring up detailed population figures for any of the countries in the IFs system. To view a population pyramid display, toggle the Distribution Type setting on the menu bar.

The three immediate drivers of population change—births, deaths and migration—are captured in the model as flows. Every year babies are born (BIRTHS), people die (DEATHS) and people leave countries to live elsewhere (MIGRANTS). These processes alter the stock of population in countries, regions and the world as a whole. The speed at which a population will grow or decline, and the attendant shift in a population’s age structure, depend on crude birth rates (CBR) and crude death rates (CDR)—the number of births and deaths per 1,000 people.

Each of the immediate drivers is linked to deeper determinants of population. For instance, fertility rates are responsive to income, education and infant mortality rates, offering points of access elsewhere in the model. Total Fertility Rate (TFR) is a variable that is essential to our understanding of populations’ reproductive behavior. TFR is, essentially, the number of children the average woman in a country can expect to have over the course of her lifetime. In order for the overall population size to remain roughly stable, TFR must meet the replacement rate for that country. For developed countries this is approximately 2.1 children per woman, but the figure may be higher in countries with high mortality rates, and is lower in many. While TFR largely determines future population growth, it is not the only behavioral variable of note: CONTRUSE captures the percent of fertile women who routinely use some method of contraception.

For a complete discussion of mortality see the Health module, where deaths are computed. They are responsive to deep or distal factors such as income, education and technological advance, as well as to more proximate ones such as levels of undernutrition and smoking. A key indicator for the population model, linked to deaths, is LIFEXP, or life expectancy, which provides a measure of the median life expectancy of a newborn in a particular year given the current mortality distribution. Although life expectancy can be calculated for any age, IFs focuses on life expectancy at birth. This variable is key to the functioning of the IFs system because many of the parameters that affect mortality do so by changing life expectancy.

The final proximate driver of population growth is migration. MIGRANTS measures net migrants in raw figures, reported in millions of people; but this variable is determined by MIGRATE, the net migration rate, reported as percent of the total population. The basic forecasts of migration in IFs are one of the very few variables that are exogenous. Nonetheless, there is parametric control of it.

The demographic module features an array of parameters that allow users to create alternative demographic scenarios by exploring uncertainty surrounding: fertility, mortality and migration, as well as the years making up people’s working lives.

3In IFs, the age distribution of migrants is controlled by an internal vector across age categories, not available for manipulation through the model’s front-end.

## Parameters to Affect Fertility

 Parameter Variable of Interest Description Type tfrm TFR, CBR Total fertility multiplier Multiplier contrusm CONTRUSE Contraceptive use multiplier Multiplier eltfrcon TFR Elasticity of total fertility rate to contraception use Elasticity tfrmin TFR Long term TFR convergence value Limit

The single most powerful way for users to modify fertility rates is to manipulate tfrm, a parameter that directly alters the total fertility rate within a country or region. This parameter serves as a multiplier on the fertility rate calculated by the model—a 20% increase or decrease in the value of the parameter will result in a similar magnitude of change in the value of the associated variable, TFR. Because it is a brute force multiplier, users should justify their modifications to the parameter. When used thoughtfully, tfrm can be a powerful tool for scenario analysis. It can be used to model the impact of fertility control initiatives that extend beyond simple contraceptive use. An example would be the implementation of a program to offer public seminars on the benefits of having fewer children, which could lower the fertility rate even when overall contraceptive usage rates are low. Health care programs for women are a major contributor to fertility decline.

Users can also directly change the percentage of the population that uses contraceptives via contrusm, a parameter that indirectly affects the total fertility rate via CONTRUSE. As this is a multiplier, it works the same way as tfrm. It can be used to model the impact of an increase in the availability of family planning education, a campaign to promote the use of condoms, or any other intervention that would likely increase (or decrease) the percentage of a population using contraceptives. Additionally, the parameter eltfrcon allows users to control the elasticity of total fertility to contraceptive use. For example, a weaker relationship between the two variables might be justified if the contraceptive methods in use in a country or region are widely known to have high failure rates.

When creating alternative scenarios that span long time horizons, users may wish to modify fertility assumptions built into the demographic module. As countries grow richer and reach higher levels of educational attainment, total fertility rates tend to decrease. However, in forecast years, a minimum value prevents countries from dipping too far below replacement rate. As a default setting, the minimum parameter, tfrmin, is set to 1.9. Thus, in the Base Case, TFR in highly developed countries will converge to just below 2 children per woman. By increasing or decreasing the parameter, users can experiment with different long-term fertility patterns.

## Parameters to Affect Mortality

 Parameter Variable of Interest Description Type mortm DEATHS Mortality multiplier (not cause specific) Multiplier hlmortm DEATHS Mortality multiplier by cause Multiplier

The health module write-up includes a full description of the drivers of mortality in the IFs system, and explains how to manipulate each one. However, one parameter affecting mortality, mortm, is worth discussing separately. 14 This parameter functions similarly to the hlmortm parameter available in the health module, but does not disaggregate by cause of death. Similar to tfrm, mortm can be used to model the impact of events that have broad impacts across the population, such as the end of an armed conflict or the implications of a plague. Usually however, if a user is building a scenario analyzing health trends, using the hlmortm multiplier will be more useful because it disaggregates mortality on the basis of cause. Because morbidity rates in IFs are linked normally to mortality rates, these parameters will affect them also.

## Parameters to Affect Migration

 Parameter Variable of Interest Description Type wmigrm MIGRATE, MIGRANTS World migration rate multiplier Multiplier migrater MIGRATE, MIGRANTS Net migration rate (inward) Rate of change

Users interested in modifying migration patterns should bear in mind that migrant flows are subject to an accounting system that keeps the global number of net migrants equal to zero. In other words, a person leaving one country will be accounted for when they enter another country. Changing the world migration rate, wmigrm, is the easiest way to affect migration patterns in IFs. Altering this parameter will allow users to increase the overall rate at which migration occurs at a global level, enabling users to simulate large scale increases (or decreases) in migration generated by, say, reductions in visa fees, or the opening of borders as is the case in the EU’s Schengen area. The parameter migrater, on the other hand, allows users to affect the rate of migration into individual countries or regions (values can range from positive, indicating net inward migration, to negative, indicating net outward migration).

## Parameters to Affect Working Age

 Parameter Variable of Interest Description Type workingageentry POPPREWORK, POPWORKING Working age determinant Exogenous specification workingageretire POPWORKING, POPRETIRED Retirement age determinant Exogenous specification

In addition to manipulating the rate at which populations grow, users can experiment with the effects of changing a country’s working age, something that will be fiscally important in many countries as populations age. The variables POPPREWORK, POPWORKING and POPRETIRE map the typical age structure of a country or region’s work force. Two parameters, workingageentry and workingageretire, control the age at which a person is considered eligible for work and the age at which a person is eligible for retirement. Changes in the workforce’s age configuration link forward to economic production via the size of the labor force (LAB). Raising or lowering the retirement age will additionally affect government finances via the size of population of retirement age and the level of pension support provided to households (GOVHHPENT).

## Prepackaged Scenarios

An installation of IFs includes high and low population-framing scenarios. Originally created for the poverty volume of the Pardee Center’s Potential Patterns of Human Progress (PPHP) series, the two files are located in the Framing Scenarios folder under Population. Both scenarios feature the direct total fertility rate multiplier. Tfrm in the high fertility scenario is set to 1.5 globally. In the low fertility scenario, tfrm is set to .6 in non-OECD nations, and the limit parameter tfrmin is set to 1.6 globally. Although the two scenarios only feature a few interventions, the effects of such a large change in human reproductive behavior would have significant forward linkages throughout each of the model’s systems.

Four of the prepackaged scenarios located in the folder Interventions and Agent Behavior contain additional examples of the demographic module’s parameters: Non OECD Contraception Use Slowed, Non OECD Contraception Use Accelerated, World Migration High, and World Migration Low. The pair of scenarios focusing on contraceptive usage rates both utilize contrusm. In the accelerated scenario, the multiplier takes the value 1.2 in non-OECD nations; and the value 0.8 in the slowed scenario for all non-OECD nations. The two alternate migration scenarios similarly feature interventions on a single parameter: the global migration multiplier wmigrm. In the high scenario the parameter takes on a value of 2, doubling global migration flows; and in the low scenarios flows are halved, with wmigrm declining to a value of 0.5.

# Health Module

## Variables of Interest

 Variable Name Description LIFEXP/LIFEXPHLM Life Expectancy CDR Crude Death Rate DEATHCAT Deaths by Mortality Type HLYLL Years of Life Lost HLYLLWORK Years of Working Life Lost HLYLD Years Lived with Disability HLDALY Disability Adjusted Life Years Lost INFMOR Infant mortality rate HLSTUNT Percentage of population stunted MALNCHP Percentage of children malnourished MALNPOPP Percentage of population malnourished HLBMI Body Mass Index HLOBESITY Percentage of population obese HLSMOKING Percentage of population that smokes

The primary variables of interest in the IFs health module are those that pertain to mortality and morbidity due to a variety of causes. LIFEXP and CDR, discussed in the population module, provide basic measures of population health. DEATHCAT provides a measure of the number of deaths (in thousands) due to different categories of mortality. IFs can display health variables in the following categories of disease: Other Communicable Disease, Malignant Neoplasm, Cardiovascular, Digestive, Respiratory, Other NonCommunicable Diseases, Unintentional Injuries, Intentional Injuries, Diabetes, AIDs, Diarrhea, Malaria, Respiratory Infections, and Mental Health. Using the Flexible Display form, it is also possible to see many of these variables in the rolled-up categories of Communicable Disease, Non-Communicable Disease, and Injuries or Accidents. Because different health conditions affect age cohorts differentially, the above measure is insufficient in understanding the full impact of ill health. For this reason, it is also possible to break down the actual number of deaths accruing to each cohort, sex, and cause via the Specialized Display menu under the health heading. For example, both the Mortality by Age, Sex, and Cause and the J-Curve displays provide useful information about the health status of a country.

Three other measures help to enrich the picture: HLYLL, HLYLD and HLDALY. Like DEATHCAT, these aggregate (across age-cohort) measures are available by cause and country. HLYLL is a measure of the number of life years lost due to premature death. It differs from the DEATHCAT variable because it represents the burden of premature mortality In terms of life years lost, which allows us to account for the fact that some diseases, like HIV/AIDS, primarily affect younger people, while others, like cardiovascular disease, are primarily fatal in older adults. Although the total number of deaths may be the same between two countries for each cause, there may be significant differences between two countries’ health profiles in terms of YLLs.

HLYLD is another measure that represents the burden of ill health in terms of life years of impact. It indicates the burden of years lived with disability or disease. In calculating YLD, IFs uses the disability weights that WHO created to rank the relative severity of different conditions and their impact on productivity.

Finally, Disability Adjusted Life Years (DALYs) are a measure of morbidity (disability or infirmity due to ill health). HLDALY sums YLLs and YLDs to create a measure of the number of years of life lost to both premature mortality and morbidity due to ill health. Like the other measures discussed above, DALYs can be broken down by different disease categories within IFs. The DALY is probably the most expansive measure of ill-health within a population because it includes mortality burden by age of death and the lost quality of life for those who did not die from health events, but who are disabled by them in some way.

Other measures provide indicators of health in regard to certain specific risk factors for disease or among certain segments of the population. Infant mortality, INFMOR, can be used to assess the burden of ill health among children under one year of age. HLSTUNT, displays the percentage of the population who are stunted (have low height for age),while MALNCHP and MALNPOPP, provide information on the percentage of the child and adult population who are malnourished respectively. The variables INFMOR, HLSTUNT and MALNCHP are especially useful for assessing the burden of ill health due to communicable diseases and other conditions that primarily affect children. By contrast, the variables HLBMI, HLOBESITY, and HLSMOKING provide risk factor information on diseases that affect primarily adults. HLBMI represents the body mass index in a country while HLOBESITY and HLSMOKING provide information on the percentage of the population that is obese or smokes.

Other variables that will be useful to users interested in specific conditions or subpopulations include indicators on stunting and BMI, as well as smoking and obesity. Variables for HIV/AIDS are also available and discussed separately below in the subsection on the HIV/AIDS sub-module.

## Parameters to Affect Overall Health and Burden of Disease

 Parameter Variable of Interest Description Type hlmortm DEATHCAT/HLYLL/HLDALY Multiplier on Mortality (by cause) Multiplier hlmorbm YLD Multiplier on morbidity Multiplier hlstddthsw DEATHCAT Switches DEATHCAT from absolute numbers to deaths/1000 Switch

The above parameters provide simple ways to directly affect the burden of disease within a country. The most important parameter for modifying mortality rates is hlmortm, a parameter that allows users to increase or decrease the prevalence of deaths in any particular category of illness. IFs modifies mortality in the following categories: Other Communicable Disease, Malignant Neoplasm, Cardiovascular, Digestive, Respiratory, Other NonCommunicable Diseases, Unintentional Injuries, Intentional Injuries, diabetes, AIDs, Diarrhea, Malaria, Respiratory Infections, and Mental Health. Altering the mortality burden will affect the variables DEATHCAT, HLYLL, and HLDALYs. The parameter will indirectly affect morbidity because of its direct link to mortality. In the case of Mental Health Diseases, the parameter will not have much impact on DEATHCAT, but may have a significant impact on the number of DALY’s experienced by a population. Because hlmortm is a multiplier, increasing its value from 1 to 1.2 represents a 20% increase in the burden of mortality from a particular cause. A similar parameter, hlmorbm, allows users to affect morbidity directly through a brute force multiplicative parameter. This allows users to affect the years lost to disability in a working life and by extension multifactor productivity due to human capital (MFPHC). The hlstddthsw allows users to switch between displaying DEATHCAT in absolute numbers to deaths per thousand people.

## Parameters that Affect Communicable Diseases

 Parameter Variable of Interest Description Type watsafem WATSAFE, INFMOR Percentage of population with access to safe water Multiplier sanitationm SANITATION, INFMOR Percentage of population with access to improved sanitation Multiplier malnm MALNCHPSH Prevalence of child malnutrition Multiplier ylm YL Yield multiplier on agriculture Multiplier hivm HIVCASES Rate of HIV infection Multiplier

Above are a number of the parameters that users may wish to use to manipulate communicable diseases (which predominantly affect children). Ylm is a multiplicative parameter in the agriculture module that can be used to change the yield of agricultural lands within a country, affecting the number of calories available for consumption, and thereby altering the rates of malnutrition and obesity. Watsafem and sanitationm, in the infrastructure module, influence the percentage of the population that has access to safe water and sanitation respectively, thus decreasing childhood exposure to diarrheal disease, malnutrition and premature death. Other parameters to control safe water and sanitation access are discussed in the infrastructure section of the model. Finally, although HIV is more thoroughly discussed in the HIV/AIDs submodule, one brute force parameter is worth noting here. Hivm allows users to directly affect the rate of infection with the HIV virus.

## Parameters that Affect Non-Communicable Disease

 Parameter Variable of Interest Description Type envpm2pt5m ENVPM2PT5 Increases levels of environmental pollution Multiplier hlsmokingm HLSMOKING Increases rate of smoking Multiplier hlobesitym HLOBESITY Prevalence of obesity Multiplier hlbmim HLBMI Multiplier on body mass index Multiplier

Hlsmokingm is a multiplicative parameter that will change the rate of smoking, which will affect the prevalence of respiratory diseases. Envpm2pt5m is a multiplicative parameter that will change the level of ambient environmental pollution in terms of parts per million; higher levels of environmental pollution are a risk factor for both communicable and non-communicable respiratory diseases.

Hlobesitym works similarly to affect the prevalence of obesity within a society in the absence of overall caloric intake changes. This parameter can be used to model the impact of changing levels of physical activity within a society. Both of the above parameters work similarly to other multiplicative parameters: increasing the value of the parameter to 1.2 from 1, represents a 20% increase in the value of the parameter over the base case. By the same token, users can use hlbmim to affect the body mass index in a country, a major risk factor for cardiovascular diseases, diabetes, and overall morbidity. Please note: hlobesitym affects only obesity rates and has no affect on health; in contrast, hlbmim will affect body mass index, obesity, and deaths from heart disease and diabetes.

## Parameters that Affect Injuries and Accidents

 Parameter Variable of Interest Description Type deathtrpvm DEATHTRPV Multiplier on traffic deaths per vehicle Multiplier deathtrpvsetar, deathtrpseyrtar DEATHTRPV Standard error target for traffic deaths per vehicle Relative target Value/Year

Only a small set of parameters allow users to affect injuries and accidents, and these primarily revolve around reducing traffic deaths. Users may reduce traffic deaths as a ratio of the number of vehicles in a country using either a multiplier, deathtrpvm, or a pair of standard error targeting parameters, deathtrpvsetar and deathtrpseyrtar. Standard error targeting is discussed in detail in the infrastructure module. These parameters allow users to model the impact of road safety on mortality and, by extension, on economic productivity.

## Parameters to Affect Technology

 Parameter Variable of Interest Description Type hlmortmodsw CDR Reduces crude death rate in Africa, Europe, Southeast Asia, West Pacific Switch hltechshift CDR Rate of change in health technology Additive factor hltechlinc CDR Rate of change in health technology in low income countries Additive factor hltechssa CDR Rate of change in health technology in Sub-Saharan Africa Additive factor hltechbase CDR Rate of change in health technology at base Exogenous specification

Aside from the direct and indirect parameters affecting health, the distal drivers of health include per capita GDP, years of education, and technology. Per capita GDP is an element of the economic module and can be changed in a number of ways, but especially by changing the elements that make up multifactor productivity. Years of education is an element of the education module and can be changed by altering the duration of schooling, and the completion rate.

Moving to the third distal driver of health, there are a number of parameters built into the health module that can be used to alter the rate of technological change. Hlmortmodsw is a master switch that, when set to 1 as in the Base Case default, reduces technological progress for low-income countries of Africa, Europe, Southeast Asia, and West Pacific based on geographic and income categories. There are parameters available to alter these assumptions about differentials in mortality declines in these regions, but they only have an effect in the base case; when hlmortmodsw is set to 0 these parameters have no impact.

Once hlmortmodsw is set to 1, users can manipulate mortality patterns through several parameters. Hltechshift, alters the rate of change for health technology impacts relative to GDP. The hltechshift parameter allows users to change the mortality rate using a shift parameter that alters the technology factor affecting mortality decline relative to initial GDP. Hltechlinc and hltechssa can be used to change the rate of technological advance resulting in mortality decline in low-income countries (hltechlinc) and sub-Saharan Africa (hltechssa) specifically. Meanwhile, the hltechbase parameter allows users to change the base level of technological change across the 20 world, rather than country by country as you can do using the hltechshift parameter. All of these parameters pertain to all causes of mortality except cardiovascular mortality, which uses a different regression equation.

## Prepackaged Scenarios

Three major integrated scenarios on health were developed by the Pardee Center for the health volume of the Patterns of Potential Human Progress series (Hughes et al., 2011). The World Integrated Scenario Sets folder contains the scenarios that were built for this volume, of which three are worth an extended discussion. The first is the Proximate Drivers Excluding Environment folder, which contains parameters to individually alter four of the major risk factors for several causes of mortality. These are Body Mass Index which is a risk factor for cardiovascular disease; under nutrition, which is a risk factor for communicable diseases; smoking which is a risk factor for respiratory disease; and large increases in the number of cars per person coupled with poor pedestrian safety, which is a major risk factor for accidental death. This scenario also includes increased to improved water sources and piped sanitation taken from the infrastructure module, and parameters to reduce environmental exposure to poor air quality. This scenario reduces these risk factors to their theoretical minima, to simulate aggressive efforts to reduce, high BMI, the obesity rate, childhood malnutrition, smoking, and traffic mortality. Malnutrition is set to 0.01, smoking and obesity multipliers are set to 0, BMI multiplier to 0.8, vehicle fleets to 0.5, and traffic mortality to 0.

Another important pair of prepackaged scenarios contains the optimistic Luck and Enlightenment scenario, and a scenario that considers what happens when Things Go Wrong. The Luck and Enlightenment scenario includes improvements to HIV/AIDS, sanitation access, improved air quality, and reduced smoking rates which help lower the burden of NCDs. It also features changes to the burden of communicable disease designed to increase the levels of these. A variation to Luck and Enlightenment has add-ins that also increase foreign aid donations and agricultural yields, effectively modeling a situation in which increased global cooperation supports these efforts. Things Go Wrong models a world in which air quality worsens, smoking and obesity rates increase and there is little international cooperation on addressing these challenges.

# HIV/AIDS Submodule

## Variables of Interest

 Variable Name Description HIVCASES Number of HIV cases HIVRATE HIV infection rate HIVTECCNTL Rate of technical control of infection, cumulative reduction in infection rate AIDSDTHS Number of AIDS deaths AIDSDRATE Death rate from AIDS AIDSDTHSCM Cumulative Number of AIDS deaths since first year of model

HIV and AIDS have attracted significant interest among policy makers because of the tremendous toll that these diseases have on populations in both human and economic terms. Because of this interest, it is worth discussing the HIV/AIDS submodule separately from the rest of the health module. That submodule represents both the extent of HIV prevalence in a population (a stock variable) and the annual deaths from AIDS (a flow variable driven in substantial part by the prevalence rate, but also responsive to technological advance in the fight against AIDS). A number of key variables are available to represent the burden of HIV and AIDS within a country.

Three variables are key to understanding the progression of infection within a country. HIVCASES provides the total number of HIV cases, HIVRATE represents a flow variable showing the rate at which people are being infected with HIV, and HIVTECCNTL indicates the progress being made in reducing the rate of infection within a country.

Three other variables assess mortality due to HIV and AIDs within a country. Similar to HIV, the variables AIDSDTHS and AIDSDRATE indicate the number of AIDs deaths and the rate of mortality from AIDs respectively, while AIDSDTHSCM represents the cumulative number of deaths due to the disease.

## Parameters to Affect Prevalence

 Parameter Variable of Interest Description Type hivm HIVRATE HIV infection rate, multiplier of percent of population infected Multiplier hivtadvr HIV CASES/ HIVRATE Technical advance rate in of control of infection Rate of change hivmdcm HIVRATE HIV infection rate maximum for MDCs, multiplier Rate of change hivpeakr HIVCASES/ HIVRATE HIV infection rate at year of peak Target value hivpeakyr HIVRATE Sets year of epidemic peak Target year hivincr HIVCASES HIV increase rate, only used prior to 2000 Rate of change

Modifying the infection rate with hivm is probably the easiest way to adjust the burden of HIV infection within a country. Like hlmortm, hivm is a multiplicative parameter. In other words, increasing the value of the parameter in scenario analysis from 1 to 1.2 represents a 20% increase in the rate of infection relative to the base case. Hivtadvr allows users to change the prevalence of HIV, once the epidemic has peaked, by a certain percent annually to model different assumptions about the rate at which control technologies will improve, reducing the prevalence of the disease over time. Unlike the mortality multiplier, which takes effect once the model has calculated the base Variable Name Description HIVCASES Number of HIV cases HIVRATE HIV infection rate HIVTECCNTL Rate of technical control of infection, cumulative reduction in infection rate AIDSDTHS Number of AIDS deaths AIDSDRATE Death rate from AIDS AIDSDTHSCM Cumulative Number of AIDS deaths since first year of model 22 case, this parameter will affect the actual calculations the model makes while running. This parameter functions as additive factor to a growth rate within IFs. In other words, a 0.01 increase in the parameter represents a 0.01 increase in the growth rate for the technical advance rate in HIV infection control (hivtadvr).

The HIV submodule is designed to allow users to affect the course of the epidemic across countries and across time. The multiplier hivmdcm is a multiplicative parameter that affects the maximum infection rate in middleincome developing countries. Another way to alter the course of the epidemic is by manipulating the coefficient on hivpeakr, which is an additive parameter that will increase the peak rate of infection over the course of the epidemic. Thus a 0.01 increase in the value of the coefficient represents a 0.01 increase in the peak infection rate. An associated parameter, hivpeakyr sets the date at which the epidemic will peak before the infection rate begins to decline. Changing this parameter in the Scenario Analysis page will allow users to set any year between 2010 and 2100 as the year of peak infection rate depending on their assumptions regarding the technical rate of advance in controlling the disease. Finally, the parameter hivincr controls the increased rate in infection prior to 2000, when our knowledge of the epidemic was much less complete and control efforts were far less effective.

## Parameters to Affect Mortality

 Parameter Variable of Interest Description Type aidsdrtadvr AIDSDTHS/AIDSRATE AIDs death rate, technical annual advance rate in control Rate of change aidsdratem AIDSRATE AIDs death rate as % of HIV infection rate, multiplier Multiplier

Just as there are a variety of parameters available to control the prevalence of HIV within a population, there are also a number of parameters that allow users to control the lethality of the epidemic. The first of these parameters allow user to change the death rate as a percentage of the infection rate via the parameter aidsdratem. This parameter directly alters the lethality of the disease; it serves as a proxy for the presence or absence of control measures within a country since the availability of anti-retroviral medications will affect the rate at which people who are HIV positive die from AIDs. Of course, new research strongly suggests that ART therapies may also significantly reduce the HIV infection rate as well, but because these are not yet linked in the model, users should be aware that a more realistic use of this parameter would alter not only the AIDs mortality rate, but the infection rate as well. The other parameter available to users to control mortality from AIDs is aidsdrtadvr, a parameter which changes the technical annual advance rate in control. This parameter simulates the annual advance in technologies to control AIDs mortality, altering the lethality of the disease.

## Prepackaged Scenarios

There are several prepackaged scenarios that deal with the HIV/AIDS epidemic. The first, under the heading Hivaids in the Technological Advance section of the Framing Scenarios folder, models two scenarios around technological advance to control the epidemic. One models rapid technical advances to control HIV infection, while the other presents a scenario in which technological progress slows, slowing the resulting decline in infections.

A second set of prepackaged scenarios are available to affect HIV/AIDS are focused on altering the course of the epidemic in key countries, rather than at a global level. They are called: Intermediate HIV/AIDS, Intermediate for New School Paper, Severe HIV Aids and Total Failure to Control HIV AIDs and are located in the Surprises and Wildcards folder, under the heading AIDs. These scenarios modify the course of the AIDS epidemic in Russia, China, India, and the world at large. Each one affects parameters controlling the infection rate at the peak year of the epidemic, the peak infection rate, the initial rate of infection, the rate of advance in the infection, and the elasticity of multifactor productivity to life expectancy. They give a good example of how to modify combinations of parameters in specific countries to create different trajectories for the epidemic.

# Education Module

## Variables of Interest

 Variable Name Description EDYRSAG15 EDYRSAG25 EDYRSAG15TO24 Educational attainment, adults by age group EDPRIPER EDSECPER EDTERPER Percent of the population completing primary, secondary and tertiary EDPRIENRG EDSECENRG EDSECLOWRENRG EDSECUPPRENRG EDTERENRG Gross enrollment rate in primary, secondary, lower secondary, upper secondary and tertary EDPRIENRN EDSECENRN Net enrollment rate in primary, secondary EDPRIINT EDPRIINTN EDTERINT Primary intake rates, gross and net, and tertiary intake rates, gross EDSECLOWRTRAN EDSECUPPRTRAN Transition rates from primary to lower secondary and from lowers secondary to upper secondary EDPRISUR EDSECLOWRSUR EDSECUPPRSUR Survival rates for primary, lower secondary, upper secondary EDPRICR EDSECLOWRGRATE EDSECUPPRGRATE EDTERGRATE Graduation rates in primary, lower, secondary, upper secondary and tertiary EDSECLOWRVOC EDSECUPPRVOC Vocational enrollment rates, lower and upper secondary EDEXPERPRI EDEXPERSEC EDEXPERTER Government spending per student as a percentage of GDP for primary, secondary and tertiary GDSED Spending shares by level of education

Like all of IFs, the education module is amenable to systems thinking, or conceptualizing elements as stocks and flows. Students flow through primary, lower-secondary, upper-secondary and tertiary education levels. Each time an age cohort completes a grade level, a year is added to that group’s stock of educational attainment; as adults (aged either 15 and older or 25 and older) they will have attained some number of years of formal education based on grades completed. For any given grade, the number of students enrolled is determined by the intake rate—or, at the secondary level, the transition rate from the previous level of education—and by the percentage of students passing through the last grade. Government spending influences the system by restraining the number of students that can be sustained at a given level of education.

Most analysis of the international education system focuses on educational attainment. IFs operationalizes this stock as the average years of education successfully completed by adults in all countries, following a typology designed by Harvard economists Robert Barro and Jong-Wha Lee (2000, 2001). EDYRSAG15 captures the mean years of education attained for all adults older than 15; this important variable links forward to several other sectors of the model, including economics through multifactor productivity, health through fertility rate, and even governance through state fragility indicators. Users may wish to view mean years of education by other age breakdowns, available through EDYRSAG25 and EDYRSAG15TO24 though these variables do not have the forward linkages in the model that EDYRSAG15 does. As an alternative to average years of education, EDPRIPER, EDSECPER and EDTERPER track attainment as a percentage of the population to successfully complete each level of education.

In addition to attainment measures, most users will focus their attention on enrolment rates, the most common measure of student flows. Enrolment rates answer the simple question: at a given time, how many children are in school?

Enrollment is captured in the variables EDPRIENRG and EDPRIENRN for primary,EDSECENRG,EDSECENRN,EDSECLOWERENRG and EDSECUPPRENRG for secondary and EDTERENRG for the tertiary level. Many of the indicators used to measure education systems are either gross rates or net rates. This important distinction has to do with student age. Gross Enrollment Rates (GER), for example, are calculated by dividing the total student body by the official school-age population; Angola’s primary GER would be the number of primary students enrolled in school divided by the primary age population of the country and has often exceeded 100 percent because of primary enrollment by over-aged students who enter or return to school for various reasons. Net Enrollment Rates (NER), on the other hand, measure only the students of official school age. A country’s NER is the enrolled age-appropriate students divided by only the school-age population and should not exceed 100 percent. Net enrolment data are often more difficult to find than gross, simply because it requires the age of enrollees to calculate. Intake rates are also presented in both net and gross formulations. For a more detailed description of commonly used education indicators see UNESCO’s technical guidelines: http://www.uis.unesco.org/Library/Documents/eiguide09-en.pdf.

For each of the countries in IFs, two factors ultimately control enrolment rates: intake rates and survival rates. Drawing on drivers such as GDP per capita and educational spending, the model forecasts intake rates for primary and tertiary levels (EDPRIINT, EDPRIINTN, and EDTERINT) and transition rates from the previous level of schooling for secondary education (EDECLOWRTRAN EDSECUPPRTRAN). The model also forecasts a student persistence rate for all levels (except tertiary), often referred to as survival rate, captured in the variables EDPRISUR, EDSECLOWRSUR, and EDSECUPPRSUR. Enrolment rates at each level are calculated as the combined result of these student flows. Or thought of more simply, enrolment rates track the number of students who enter school and stay in school. Graduation rates at all levels are also forecast and the corresponding variables are: EDPRICR, EDSECLOWRGRATE, EDSECUPPRGRATE and EDTERGRATE. The model also forecasts vocational shares in lower secondary and upper secondary and the variables are EDSEECLOWRVOC and EDSECUPPRVOC.

All indicators in the model that measure the stock of educational attainment or student flows can be disaggregated by gender. Thus, the user is able to evaluate each country’s progress toward eliminating gender-based disparity in the classroom. In order to disaggregate performance by gender, a second dimension of almost all education variables allows the selection of display by male, female or total. In addition to setting normative goals, separate treatment of female education allows the model to establish powerful forward linkages. For example, female educational attainment and fertility rates are negatively related, because schooling increases the opportunity cost of having and raising a child.

In addition to student flows, the model captures financial flows tied to each country’s education system. Educational finance is the result of interplay between supply-side and demand-side forces. Demand for education can be conceptualized as the product of spending per student and enrollment rates at each level of schooling—EDEXPERPRI, EDEXPERSEC and EDEXPERTER track spending per student as a percent of GDP per capita for primary, secondary and tertiary education. However, such a formulation of demand is crude, as the number of children already in the system is constrained by the preexisting structure of government spending. Financial supply, captured as a percent of GDP by the variable GDSED, is generated by the government finance submodule. Due to a lack of reliable data, IFs does not capture private educational funding, though it is important in some countries.

## Parameters to Affect Intake Rates and Survival Rates: Annual Growth

 Parameter Variable of Interest Description Type edpriintngr EDPRIINTN Primary net intake rate Annual growth edterintgr EDTERINT Tertiary intake rate Annual growth edseclowrtrangr EDSECLOWRTRAN Lower secondary transition rate Annual growth edsecupprtrangr EDSECUPPRTRAN Upper secondary transition rate Annual growth edprisurgr EDPRISUR Primary survival rate Annual growth edseclowrsurvgr EDSECLOWRSUR Lower secondary survival rate Annual growth edsecupprsurvgr EDSECUPPRSUR Upper secondary survival rate Annual growth edtergradgr EDTERGRAD Tertiary graduation rates Annual growth

In the IFs model, improved education outcomes can be achieved most directly through changes to intake and survival rates. The number of students entering the first grade of each schooling level can be pushed up or down using annual (percentage point) growth parameters: edpriintngr for primary school, edseclowrtrangr for transfer rates between primary and secondary, edsecupprtrangr for transition between lower and upper secondary andedterintgr for intake into the tertiary level. However, any improvements to enrolment stemming from intake rate boosts are restrained when coupled with high dropout or repetition rates. Like intake rates, survival rates can be directly manipulated through annual growth parameters. Edprisurgr controls survival growth (in percentage points) at the primary level. Secondary school rates can be shifted using two parameters, edseclowrsurvgr for lower secondary and edsecupprsurvgr for upper. And finally, edtergradgr controls tertiary survival rates (also referred to as graduation rates).

Though intake rates and survival rates can be manipulated separately, users will often wish to manipulate them in conjunction due to the presence of interaction effects between the two sets of variables. For example, a rapid increase in intake/transition rates can result in a decline in country’s survival rate. The educational system may simply not have the resources to cope with an influx of students. Furthermore, the incoming cohort, which has spent years outside the system, may represent individuals from disadvantaged backgrounds—a group prone to dropping out. On the other hand, rising intake rates may have a positive threshold effect on survival rates. In many countries, once intake rates reach a certain level, dropout and repetition have declined. At this time such relationships are not captured in IFs. Thus, the user may want to build interaction and threshold effects into education policy interventions.

## Parameters to Affect Intake Rates and Survival Rates: Target Year for Universal Education

 Parameter Variable of Interest Description Type edpriintntrgtyr EDPRIINTN Primary net intake rate No. of years to reach 100% edseclowrtrantrgtyr EDSECLOWRTRAN Lower secondary transition rate No. of years to reach 100% edsecupprtrantrgtyr EDSECUPPRTRAN Upper secondary transition rate No. of years to reach 100% edterinttrgtyr EDTERINT Tertiary Intake Rate No. of years to reach 100% edprisurtrgtyr EDPRISUR Primary survival rate No. of years to reach 100% edseclowrsurtrgtyr EDSECLOWRSUR Lower secondary survival rate No. of years to reach 100% edsecupprsurtrgtyr EDSECUPPRSUR Upper secondary survival rate No. of years to reach 100% edtergradtrgtyr EDTERGRAD Tertiary graduation rates No. of years to reach 100%

Development goals like universal access to education with a specific target date can be explored through target year parameters on intake (transition) or survival (graduation) rates. These parameters ramp up the base year value of the variable of interest to one hundred percent within the number of years marked by the parameter. For countries with limited availability of government resources, these target parameters might need an education budget set aside through the edbudgon parameter described later in the budget sub-section .

## Parameters to Affect Intake Rates and Survival Rates: Multiplier

 Parameter Variable of Interest Description Type edpriintnm EDPRIINTN Primary net intake rate Multiplier on base case edterintm EDTERINT Tertiary intake rate Multiplier on base case edseclowrtranm EDSECLOWRTRAN Lower secondary transition rate Multiplier on base case edsecupprtranm EDSECUPPRTRAN Upper secondary transition rate Multiplier on base case edprisurm EDPRISUR Primary survival rate Multiplier on base case edseclowrsurvm EDSECLOWRSUR Lower secondary survival rate Multiplier on base case edsecupprsurvm EDSECUPPRSUR Upper secondary survival rate Multiplier on base case edtergradm EDTERGRAD Tertiary survival rates Multiplier on base case

In addition to the growth and target parameters described above, intake (or transition) and survival (or graduation) rates can also be modified using a set of multipliers listed in the table above. Like other multipliers in the model these work by ramping up (or down) the base case forecast over a horizon chosen in the scenario design.

## Parameters to Affect Education Spending

 Parameter Variable of Interest Description Type gdsm (education) GDS Government spending on education Multiplier edbudgon GDS Education funding impact and priority Exogenous specification gdsedm GDS Education spending distribution Multiplier edexppconv EDEXPERPRI Education spending per student, primary Convergence speed edexpslconv EDEXPERSEC Education spending per student, secondary Convergence speed edexpsuconv EDEXPERSEC Education spending per student, secondary Convergence speed edexptconv EDEXPERTER Education spending per student, tertiary Convergence speed edqtqltrm EDEXPERPRI, EDEXPERSEC , EDEXPERTER and GDS Education quantity-quality balance Multiplier

Attainment levels also respond indirectly to changes in the educational spending structure. The number of students enrolled at all grade levels is constrained by total public spending on education. In IFs, public finance allocations are distributed between transfer payments, the military, education, health and infrastructure in the government budget submodule. Bottom-up factors like demographic changes and policies targeting intake or survival will pressure the government to increase education spending. But the model features somewhat rigid top-down control of the budget—spending on education competes with other government spending and IFs maintains accounting of both total government revenues and expenditures. Most countries spend something close to the global average of 5 percent of GDP on education each year. Thus demand-side shifts may secure an increase in resources, but it is difficult to increase total education spending much in excess of historically observed values. Users can intervene in this accounting process by manipulating gdsm (education), a multiplier that directly increases the share of government expenditure accounted for by education, at the expense of spending in other arenas such as military or health. Alternatively, users can prioritize education spending over other government expenditure targets. The priority parameter edbudgon, takes on values from zero to one, with lower values representing lower priority to education funding demands and one representing maximum priority. Any non-zero value pushes education allocations away from those determined by historical spending patterns and level of economic development—as calculated in the economic module—and toward the bottom-up demand for expenditures calculated in the education model. The parameter edbudgon can also be used to completely turn off the budget impacts on student flow rates through a zero value of the parameter. The turning off of budget impacts is helpful in projecting the demand for funds for certain types of education intervention, e.g., universal primary education. However, fund demand in such a scenario is calculated in the EDTOTCOST and not in the GDS(Education) variable, which is determined, in this case, completely through the government budget allocation algorithm without using any input from the education model.

The government-budget submodule automatically allocates resources to the educational level most in need (although that interacts with historical patterns of educational preference), starting with primary education. As enrolment rates in a country’s primary education system reach a very high level, the model reallocates funding up the chain to secondary. Users may wish to change this pattern and create normative scenarios that prioritize higher levels of education, or balance spending between primary, secondary and tertiary. The gdsedm parameter allows for such experimentation via a second dimension that allots the spending increase to a specific level of schooling.

When developing education budgets, planners must account for trade-offs between equity and efficiency. What is more important, increasing total spending on the system in an effort to support more students, or increasing spending per student in an effort to improve the experience of being in school? In a crude way this can be thought of as the difference between increasing intake and increasing survival rates. IFs includes parameters that shift the spending focus in the direction of system efficiency. Edexppconv can be used to decrease the time it takes a country’s initial spending per primary student to converge with the model’s expected function. Edexppslconv and edexppuconv control the student expenditure function for lower and upper secondary, and edexpptconv does the same for the tertiary level. Increasing the target value used in a convergence parameter will increase per student spending for countries below the expected value, but the opposite is true in countries exhibiting higher than expected values. If a user wishes to boost per student expenditure in a high performing country, he or she should make use of the edqtqltrm parameter, a multiplier that shifts emphasis away from education quantity and towards education quality—with values less than one indicating a shift towards quality.

## Parameters to Affect Gender Parity

 Parameter Variable of Interest Description Type edprigndreqintn EDPRIINTN Gender parity for primary intake Convergence speed edprigndreqsur EDPRISUR Gender parity for primary survival Convergence speed edseclowrgndreqsurv EDSECLWRSUR Gender parity for lower secondary survival Convergence speed edseclowrgndreqtran EDSECLWRTRAN Gender parity for lower secondary transition Convergence speed edsecupprgndreqsurv EDSECUPPRSUR Gender parity for upper secondary survival Convergence speed edsecupprgndreqtran EDSECUPPRTRAN Gender parity for upper secondary transition Convergence speed edtergndreqint EDTERINT Gender parity for tertiary intake Convergence speed edtergndreqgrad EDTERSUR Gender parity for tertiary survival Convergence speed

Some development projects focus on gender disparity in education, rather than aggregate enrolment. In recent decades male and female enrolment rates have converged rapidly across all regions of the world. In fact, primary, secondary and tertiary enrolment taken as a global aggregate has already surpassed a female-male ratio of 0.97—commonly considered an indicator of parity. However this abstracted view obscures struggles facing women in low-performing areas, especially sub-Saharan Africa. Users may explore effects linked to improvements in female-specific enrolment rates via the intake and survival parameters described above; each parameter has a dimension that applies the annual increase to males, females, or the total student population. Alternatively, users can experiment with closing the gender attainment gap existing in some countries by setting time sensitive goals. Goals around intake can be set through edprigndreqintn for primary,edseclowrgndreqsurv and edsecupprgndreqtran for secondary and edtergndreqint for the tertiary level. Gender conversion goals may also be set for the other direct driver of enrolment, survival rates: edprigndreqsur controls female survival rates at the primary level, edseclowrgndreqsurv and edsecupprgndreqsurv control rates for lower and upper secondary, and edtergndreqgrad controls tertiary rates. Each of the gender-focused parameters are convergence speed goals, meaning the user sets the number of years that will elapse before the country’s gender parity in the corresponding measure converges to the expected function.

## Prepackaged Scenarios

An installation of IFs includes scenarios used in each entry in the Pardee Center’s Potential Patterns of Human Progress (PPHP) series. Scenarios created for the education volume include: a best and worst case framing scenario; case study interventions in several countries and regions; high spending and low spending scenarios; and over twenty normative scenarios (Dickson et al., 2010). This section will briefly describe the parameters used to construct a single normative scenario titled, Norm Mar 1 2009.

Norm Mar 1 2009 simulates a worldwide improvement in educational performance through interventions to: intake rates, student persistence, gender parity and educational spending, including boosts to foreign aid. From 2010 to 2100, the parameter edpriintngr is set to 2.2, and edprisurgr is set to 1.2, setting up annual increases to primary school intake and survival rates. The scenario includes similar annual growth increases for intake and student persistence across lower secondary and upper secondary schooling. Edseclowrtrangr is set to one percent, edseclowrsurvgr to 0.8 percent, edsecupprtrangr to 0.5 percent, and edsecupprsurvgr is set to 0.3 percent. The corresponding parameters for tertiary education are not assigned a value other than the default of zero. Additional interventions speed up the narrowing of persisting gender gaps in intake, survival and transition rates. The parameters edprigndreqintn and edprigndreqsur set a ten year time goal for gender parity at the primary level; edseclowrgndreqtran and edseclowrgndreqsurv set a thirteen year goal for parity at the lower secondary level; and,edsecupprgndreqtran and edsecupprgndreqsurv set a 20 year time goal for parity at the upper secondary level. In order to simulate improved education quality, across levels, edexppconv, edexpslconv, edexpsuconv and edexptconv all take the value of 20, signifying a convergence to the expected function after 20 forecast years. Because this scenario was created as a tool to explore the potential impacts of a world in which educational goals are achieved by all nations, the budget switch, edbudgon, is set to 0, the highest prioritization of education funding.

# Economic Module

## Variables of Interest

The treatment of economics in IFs draws on both the classical tradition’s focus on economic growth (with great attention in IFs to the newer work on endogenous growth theory) and the neoclassical perspective's general equilibrium approach.

The economic module is a core component of the IFs system for multiple reasons, in particular for its close interactions with all other modules. On the input side, variables from almost all other modules affect production levels. On the output side, the magnitude of GDP and the level of GDP per capita are critical, in turn, for essentially all other modules. Mostly closely linked to the economic module are the energy and agriculture modules, both of which use a partial equilibrium structure that echoes the one in the economic module, and both of which provide physical values that fully determine the currency value-based representations of their respective sectors in the economic model.

Basic economic variables include: GDP at market exchange rates (GDP), GDP at purchasing power parity (GDPP), GDP per capita at market exchange rates (GDPPC), and GDP per capita at purchasing power parity (GDPPCP). The model represents all of these in constant 2011 dollars (the interface allows the user to convert to other currencies).   The model also includes a representation of the portion of the economy that is informal.

#### Multifactor Productivity

 Variable Name Description GDP Gross domestic product GDPP GDP at purchasing power parity GDPPCP Per capita GDP at purchasing power parity GDPPC Per capita GDP LAB Labor KS Capital stock I Investment POPRETIRED Retired Population MFP (HC,SC,PC,KN) Multifactor productivity

The supply side of the economic module is based on the Cobb-Douglas Production Function and uses labor (LAB), capital (KS), and multifactor productivity (MFP) as the primary drivers of economic growth. Capital stock (KS) is a function of investment (I) and depreciation rates. Labor supply (LAB) is determined from population and endogenously derived labor force participation rates.

While the treatment of capital and labor in the IFs system will be familiar to users with an understanding of neoclassical economics, the treatment of productivity within IFs deserves greater explanation. Unlike most neoclassical models, which primarily focus on technology as the determining factor of productivity in their equations, the IFs system uses a broader definition of productivity called multifactor productivity (MFP). This multi-factor productivity term in IFs has four basic components: human (MFPHC), social (MFPSC), physical (MFPPC), and knowledge capital productivity (MFPKN). Each of these components can take on a positive or negative value depending on whether the calculated value of the component is providing a positive or negative impact to economic growth rates relative to what would be expected based on the country’s level of development. (See the Development Profile form in IFs to display the magnitude and direction of the four productivity elements.)

Drivers of multifactor productivity vary by component. MFPHC is driven by years of education, education expenditures, life expectancy and health expenditure. MFPSC is driven by Freedom House’s measure of political freedom (a variable describing democracy), governance effectiveness, corruption perceptions, and economic freedom. MFPPC is driven by two separate indices of infrastructure: traditional (roads, electricity, and water and sanitation), and information and communications technology (ICT). Finally, MFPKN is driven by R&D expenditures and economic integration (in the model, trade serves as a proxy for trade). This final component of MFP represents a measure of connectedness to the global economy. Altering any of these using the appropriate parameters will result in changes to the relevant component of multifactor productivity and therefore to economic growth.

#### The Social Accounting Matrix and Domestic Finance

 Variable Name Description C Private consumption SAVINGS Net national savings HHINC Household income HHSAV Household savings FIRMINC Firm income FIRMSAV Firm savings IDS Investment by destination sector GOVREV Government revenue GOVEXP Government expenditures GOVHHTRN Government household transfers GOVCON Government consumption GDS Government spending by destination GOVBAL Government balance GOVDEBT Government debt

The production function is embedded in a six sector model of the economy featuring agriculture, raw materials, energy, manufactures, services, and ICT that balances domestic demand and trade in a general equilibrium seeking structure. Production and consumption of goods and services are in turn incorporated into a larger social accounting matrix (SAM) which represents the behavior and financial interaction of households, firms and government. A social accounting matrix traditionally represents flows among different economic sectors and agent categories (i.e., households, firms, and government). For instance, it represents private consumption (C) and net national savings (SAVINGS), as well as household income (HHINC) and savings (HHSAV); firm income (FIRMINC), investment by sector (IDS), and savings (FIRMSAV); government revenues (GOVREV), total expenditures with transfers (GOVEXP), transfers to households (GOVHHTRN), directed consumption in total (GOVCON), and by sector (GDS), and balance (GOVBAL). IFs builds a full and balanced social accounting matrix of these and many other inter-agent flows. It also creates a second matrix which represents financial stocks (assets and liabilities) of different agent categories for all countries in the system, including for instance, government debt (GOVDEBT). The representation of stocks in this fashion provides the foundation upon which the system adjusts flows of finance among different agents and among countries over time (see, for instance, the net foreign debt or XDEBT of countries), maintaining consistency with the liability-asset approach used in standard accounting systems. The behavior of agents within this system is not fixed, like it is in many computable general equilibrium models (which use SAMs commonly). Instead, agent behavior is partially endogenized using algorithms that allow the behavior of agents to shift depending on the levels of stocks of relevant variables within the SAM. So, for example, different levels of government debt trigger different patterns of government spending in IFs.

Because of its centrality to the IFs system, users should understand the basic character of the social accounting matrix. Both the stock and flow matrices are available in the specialized displays menu in the display section of the main menu. The social accounting matrix loads the flow matrix by default, but the stock matrix can be accessed via the Show Stocks button on the bar at the top. Full breakouts of the SAM into its component parts is also available on this screen by clicking the button Expand SAM on the same top bar.

 Variable Name Description CURACT Current account balance CAPACT Capital account TRADEBAL Trade balance XWORKREMIT Worker remittances from abroad AID Net foreign aid receipts X or XS Exports and exports by sector M or MS Imports and Imports by sector ENX/ENM Energy exports/imports AGX/AGM Agricultural exports/imports XFDIFIN Inward flow of FDI XFDIFOUT Outward flow of FDI XFDISTOCK Inward stocks of FDI XFDISTOUT Outward stocks of FDI XPORTFIN Inward flows of portfolio investment XPORTFOUT Outward flows of portfolio investment XPORTFOLIO Inward stocks of portfolio investment XPORTSTOUT Outward stocks of portfolio investment XDEBTRPA External debt, relative price adjusted EXRATE Exchange rate

The international financial position of a country is typically represented by the balance of payments, which is equal to zero if all flows of goods and finance into and out of a country are included. The balance of payments is determined by the status of three indicators, the current account (CURACT), the capital account (CAPACT), and the foreign reserve account (not explicitly modeled in IFs). Imbalances can exist in any of these if imports of goods and services outweigh exports for example, or if governments spend down foreign reserves.

In IFs, the status of the current account balance (CURACT), which reflects the many flows into and out of a country, is a function of the trade balance (TRADEBAL), net foreign worker remittances (XWORKREMIT), net foreign aid (AID), and net interest on foreign debt. Worker remittances are calculated on the basis of the size of the population of a country that is living and working abroad.

Of these the trade balance and treatment of trade within IFs more generally is worth further discussion. Trade in the IFs system is part of the social accounting matrix structure and is modeled using a pooled rather than bilateral approach. This means that IFs tracks information on gross exports and imports of countries by sector and in total. An algorithm sums price-adjusted import demand and export capacity across all countries that trade in a given sector and defines world trade as the average of those two values. Demand and capacity are then normalized to the total of world trade to determine total and sectoral exports (X or XS by sector) and imports (M or MS by sector) by country. As already noted, the agricultural and energy modules each represent production, consumption and trade that supersede the results of the economic module’s production function and SAM. For instance, exports and imports in these modules are represented separately within the IFs system via ENX/ENM and AGX/AGM.

Interacting with the current account is the capital account (CAPACT). It captures the flows of foreign direct and portfolio investment into (XFDIFIN and XPORTFIN) and out of countries (XFDIFOUT and XPORTFOUT). The system also represents the stocks of inward and outward FDI and portfolio investment (XFDISTOCK/ XPORTFOLIO and XFDISTOUT/XPORTOUT). Together the current and capital account flows shape the stock of relative-price-adjusted net foreign indebtedness (XDEBTRPA), which in turn via an equilibration process changes the exchange rate (EXRATE). The exchange rate, in interaction with local relative prices, affects trade and financial flows over time as a key part of that equilibration process.

#### Informal Economy

 Variable Name Description LABINFORMSHR Informal share of the labor force LABINFORMPCNTINF Portion of informal labor employed inside informal sector LABINFORMPCNTNONINF Portion of informal labor employed outside the informal sector (in formal enterprises or households) GDPINFORMSHR Informal share of GDP GDPSHADOWSHR Shadow economy share of GDP EDYRSAG15 Educational attainment of adults 15 and older GOVBUSREGIND Government business regulation index (higher is better) GOVCORRUPT Government corruption (higher is less corrupt) GOVHHTRN Government transfers to households for welfare and pensions GDS, R&D Government spending on research and development FIRMTAXR Tax rate (direct) on firms HHTAXR Tax rate (direct) on households SSWELTAXR Tax rate (direct) on households and firms for social welfare RANDDEXP Total (public and private) spending on research and development TEFF Stock of total (multifactor)productivity

The economic module represents the informal economy as the share of the total labor force employed informally (LABINFORMSHR), the share of total GDP generated by informal activities (GDPINFORMSHR), and the share of total GDP generated by the shadow economy (GDPSHADOWSHR). The informal labor share is driven by four main variables: the educational attainment of adults 15 years of age and older (EDYSAG15, see the education model for parameters to affect this variable), the government business index (GOVBUSREGIND), the ratio of government transfers to households as a portion of GDP (GOVHHTRN), and the tax rate on firms (FIRMTAXR).

The informal labor share is divided into two sectors, the portion of informal labor employed inside the informal sector (LABINFORMPCNTINF, informal) and the portion of informal labor employed in the formal sector (represented by a simple residual LABINFORMPCNTNONINF). Each sector is further divided by sex.

The share of informal labor in total labor and the portion of informal labor employed inside the informal sector both help drive informal and shadow GDP shares. Other drivers include the level of corruption (GOVCORRUPT), public expenditure on research and development as a percentage of its GDP (GDS, R&D), and total (public and private) spending on research and development (RANDDEXP).

While informal and shadow GDP shares are initialized in the Base Case, only informal (GDPINFORMSHR) has active forward linkages. To drive the forward linkages with the shadow economy share instead, you must change the switch, gdpshadowon, from its default, Base Case setting of 0 to 1 (see Parameters for Affecting the Informal Economy section below).

The informal GDP shares, in turn, have forward linkages to total or multifactor productivity (TEFF), effective household (HHTAXR) and firm tax rates, as well as to the effective rate for social welfare (SSWELTAXR).

## Parameters to Affect Production and Growth

 Parameter Variable of Interest Description Type Labor lapoprm LAB Labor participation rate multiplier Multiplier labfemshrm LAB Female labor force participation rate multiplier Multiplier labretagem LAB Retirement age, labor force participation multiplier Multiplier workageentry POPRETIRED Age at which individuals enter the labor force Exogenous specification workageretire POPRETIRED Age at which individuals leave the labor force Exogenous specification Capital invm IDS Investment by destination sector Multiplier Productivity mfpadd MFP components Multifactor productivity, additive Additive factor mfpleadr MFP MFP growth rate of the technological lead country Growth Rate mfpedspn GDS-educ Increase portion of spending going to education Elasticity malnm HLSTUNT Malnutrition multiplier Multiplier ylm YL Land yield Multiplier aginvm AGINV Investment in agriculture Multiplier econfreem ECONFREE Economic freedom Multiplier goveffectm GOVEFFECT Government effectiveness Multiplier govcorruptm GOVCORRUPT Government corruption Multiplier freedomm FREEDOM Freedom/democracy Multiplier mfpinfrindtrad INFRAINDTRAD Traditional infrastructure Elasticity mfpinfrindict INFRAINDICT ICT infrastructure Elasticity mfpenpri WEP World energy price Elasticity gdsm, R&D GDS, R&D Connectivity Multiplier

Based upon the discussion of key variables, the areas of likely interest to users are clear: parameters that affect production and growth most directly, parameters that affect domestic finance (including government) and the flows of the social accounting system, and parameters that affect trade and finance. We start with production and growth and the key parameters affect labor, capital and multifactor productivity. Parameters exist in the IFs system that allows users to modify any of the component parts of the Cobb Douglas Production function. Parameters affecting labor supply include lapoprm, labfemshrm, and labretagem. The first of these, lapoprm, modifies the laborforce participation rate, while the second changes the female share of the existing labor force, and the last, labretagem, changes the number of retired persons who participate in the labor market. Other parameters, discussed in the demographic section, also impact the size of the labor force. The parameters workingageentry and workingageretire, will change when people enter and leave the labor force, thus altering the length of time they are employed and the size of the labor pool.

Users can affect capital accumulation by changing the parameter invm, which alters investment in the economy using a multiplicative approach. Unlike spending multipliers discussed later under Domestic Finance, this multiplier cannot be broken out by destination. It will also affect savings because domestic savings rates are directly tied to investment rates. In addition to altering savings, investment rate changes affect the rate of capital accumulation.

Two brute force multipliers on productivity are available to users. First, mfpadd allows users to increase productivity growth in an additive manner on a country-by-country basis. Second, the parameter mfpleadr allows users to set the growth rate of the world’s technological leader, the United States. Since all other country’s productivity is tied to the leader, changes to this parameter model assumptions about global economic growth rates, which will tend to change productivity growth rates across a number of countries. Although both these parameters represent powerful ways to affect GDP and growth, users should note that they do not carry a cost. Like other brute force multipliers, there is no cost accounting system that might limit the impact of changes introduced, so users must be extremely careful to justify any changes made with these parameters.

Users can change any one of the components of multifactor productivity by affecting the drivers of the component. Any of the parameters that impact life expectancy, education levels, or health and education spending, as discussed in the health or education modules, will impact MFPHC. Examples include but are not limited to: mfpedspn, malnm, ylm, and aginvm.

Similarly, anything that affects drivers of social capital will affect MFPSC. These include many of the governance parameters, such as: econfreem,goveffectm,govcorruptm, and freedomm.

Physical capital is primarily impacted by two indices of physical infrastructure development, one for traditional infrastructure, which includes electricity, transportation, and water and sanitation, and a separate index for ICT. Users can affect the elasticity of MFP to these indices using mfpinfrindtrad, and mfpinfrindict. By changing these parameters users can make the value of MFPPC more or less responsive to the speed of growth in the two areas of infrastructure discussed above. They can also alter the elasticity of MFP to the world energy prices using mfpenpri, which performs a similar function for changing the relationship of MFPPC to energy prices globally. See the discussion of the infrastructure module to see the large number of parameters that affect the specific elements of the two general infrastructure indices.

Knowledge capital will respond to changes in the parameter gdsm, targeted at changing the amount of government spending directed towards research and development. It will also respond to the level of trade interconnectedness, which the level of protectionism (protecm) can affect—to be discussed again below.

## Parameters to Affect Domestic Financial Flows and the Social Accounting System

 Parameter Variable of Interest Description Type govrevm GOVREV Government revenue multiplier Multiplier gdsm GDS Government spending by destination sector Multiplier gdsedm GDS Education spending distribution multiplier Multiplier infrabudsdrat GDS Alters the priority of infrastructure in the budget reconciliation Exogenous specification govhhtrnpenm GOVHHTRN Government pension spending multiplier Multiplier govhhtrnwlm GOVHHTRN Government welfare spending multiplier Multiplier firmtaxrm, SAVINGS National tax rates on firms, households, and indirect taxation Multiplier hhtaxrm (skilled, unskilled) SAVINGS National tax rates on households, broken down by skilled and unskilled Multiplier indirecttaxrm­­­ SAVINGS National tax rates on firms, households, and indirect taxation Multiplier ginidomr GINIDOM Domestic Gini Index growth rate Growth Rate ginidomm GINIDOM Domestic Gini Index multiplier Multiplier gdprext GDP GDP growth rate Growth Rate

The leverage points within the area of domestic financial flows and the social accounting matrix of greatest interest for most analyses and government revenues and expenditures. The brute force multiplier on government revenues (govrevm) will increase or decrease those and have wide secondary effects as processes play out to adjust revenue raising and expenditure patterns to the new level. On the outflow side, the government spending by destination multiplier (gdsm) will change patterns of government consumption using a multiplier that acts on military, health, education, R&D, infrastructure, other infrastructure, and other (the residual). Education spending can also be modified using gdsedm, which directly modifies spending on education by level (as discussed in the education module). Infrastructure can also be affected by infrabudsdrat, an infrastructure module parameter that increases the proportion of infrastructure spending needs that are met through the budgeting process. Unless total government consumption (GOVON) is altered, however, changes in spending patterns by target will create shifts from one category to another rather than changes in the total, because the model will normalize the sum of spending by sector to that total. Similarly, the user can use multipliers on government pension (govhhtrnpenm) and/or welfare (govhhtrnwlm) spending to change levels in either of those categories, but subject along with direct consumption expenditure to the constraint of total government expenditures (GOVEXP). Hence, parameters that affect government revenues are very important.

Changes in the tax rates can have profound implications for government capacity, and for this reason IFs contains a number of parameters to alter taxation rates for different actors, in addition to the direct multiplier on government revenues, which will cause the model to endogenously adjust such tax rates. Of these, three stand out. Hhtaxrm affects household savings rates, firmtaxrm modifies firm taxation rates, and indirecttaxrm changes the indirect taxation rate on goods and services. The household taxation rate multiplier is subdivided according to whether households are skilled or unskilled. Users who are interested in scenarios that change domestic income inequality may want to change ginidomr, which alters the ratio of the domestic Gini (GINIDOM) to the initial condition. Domestic Gini (GINIDOM) can also be altered using the parameter ginidomm, which acts as a multiplier on the calculated value of GINIDOM in the model. Finally, gdprext offers users a power way to speed up, or slow down, total GDP growth.

## Parameters to Affect Trade and International Finance

 Parameter Variable of Interest Description Type Xshift X, XS Export shift as a result of trade promotion Additive factor Protecm M, MS Protectionism in trade, multiplier on import prices Multiplier Termtrm TERMTR Terms of trade balance, higher favors global south Multiplier xfdistockm XFDISTOCK Inward stocks of FDI multiplier Multiplier Xfdistoutm XFDISTOUT Outward stocks FDI multiplier Multiplier Xfdiwgrm XFDI STOCK, XFDIOUT World FDI growth rate multiplier Multiplier Xportfoliom XPORTFOLIO Inward portfolio investment multiplier Multiplier Xportstoutm XPORTFOLIO Outward portfolio investment stock multiplier Multiplier Aiddon AID Aid foreign loan donations as % GDP Additive factor Aidrec AID Aid receipts as % GDP Additive factor aidlpm AID Aid foreign loan percentage multiplier Multiplier

Users can alter imports and exports via two major channels. First, they can force a shift towards export promotion using the additive xshift parameter. Increasing the parameter value to .05 would result in an additional 5% being added to the value of exports for that year and all subsequent years until the dynamics of the model dampen the effect. Protectionism in imports can be modeled by changing the value of protecm, which is a multiplicative parameter that increases the cost of imports and thereby proxies an increase in tariff rates or of non-tariff barriers to trade.

Finally the distribution of the global terms of trade can be impacted by termtrm. Raising this multiplicative parameter will alter the balance of global trade patterns to favor southern developing countries (colloquially known as the Global South). This means that an increase in this parameter will increase the financial value of the exports from these developing nations, while reducing the relative financial value of exports from developed nations.

Affecting global financial flows is a relatively complex process in the IFs economic system. Multiple channels exist to modify the different major sources of international financial flows. Users can alter foreign direct investment flows using the xfdistockm parameter, which increases the stocks of FDI within a country and adjusts flows accordingly. Conversely, xfdistoutm will increase the size of the stock of FDI slated to exit a country. Users may also wish to alter the growth rate for FDI at a global level via the parameter xfdiwgrm.

Portfolio investment can be modified using a similar set of parameters. Xportfoliom allows users to increase the stocks of portfolio investment within a country, while xportstout controls the amount of portfolio investment that a country’s citizens hold abroad.

Aid parameters differ somewhat from those available for FDI and portfolio investment. Users can change the amount of foreign loan donations as a percent of GDP via aiddon, which specifies the portion of donor country GDP given in the form of aid. They may change the amount of aid a country receives using the aidrec parameter, which determine the portion of the global aid pool created by donors that any individual country will receive (the total claims on that pool determined by aidrec will be normalized to the total size of the pool). Finally, users may alter the portion of aid that comes in the form of loans that accrue interest and must be repaid within a certain time frame using the multiplicative parameter aidlpm.

## Parameters to Affect the Informal Economy

Users can affect the labor informal share of the total labor force directly (labinformshrm).  In most cases they will do so indirectly by affecting the driving variables of that share.  One of the driving variables is educational attainment of adults, so any parameter affecting that attainment will have a potential impact (see the parameters for education including the prepackaged scenarios).  Another is the government business index (govbusregindm).  A third is government to household transfers for either pensions or welfare (govhhtrnpenm and govhhtrnwelm).  A fourth is the tax rate on firms (firmtaxm).

The forward linkage from informal labor share to informal GDP share is affected by a multiplier (gdpinformshrm) that can totally turn off that linkage if set to 0.  The informal GDP share can be directly affected via gdpinformshrm, or indirectly by affecting its drivers, including informal labor share. Other drivers include: the level of government corruption (govcorruptm), government spending on research and development (gdsm, R&D) and total (public and private) spending on research and development (randdexpm). Manipulating randdexpm itself will increase private sector spending while changing gdsm will also impact the public portion of RANDDEXP.

The linkage from informal GDP share to multifactor productivity is controlled by tefinfadjm and the linkage to tax rates is controlled by taxinfadjm.  Again, a zero value would turn off the linkage.

A set of parameters (labinformcoeffintercept , labinformcoeffbus, labinformcoeffed, labinformcoeffhhtrn, labinformcoefftax) control the calculation of informal labor share from its assorted drivers.

A similar set of parameters (gdpinformcoeffintercept, gdpinformcoeffcorrupt, gdpinformcoefflabinf, gdpinformcoeffRandD) control the calculation of informal GDP from its assorted drivers.

Users wanting to drive the forward linkages from informality with the shadow economy instead of the informal economy can do so by changing the switch gdpshadowon from its default, Base Case setting of 0 (which tells the model to drive the forward linkages with GDPINFORMSHR) to 1 (which tells the model to drive them with GDPSHADOWSHR instead).

The informal and shadow GDP shares (whichever is activated—see above) use the same multipliers for their linkages to multifactor productivity (tefinfadjm) and tax rate (taxinfadjm)

## Prepackaged Scenarios

A large number of prepackaged scenarios that revolve around economic development and poverty alleviation are available for user access, located under the PPHP Poverty heading in the World Integrated Scenario Sets. The best and worst case Framing Scenarios include changes to the growth in MFP for the world leader, the additive growth rate factor for MFP, TFR, and the Gini index.

More integrated scenario sets are available as well to model the effects of combinations of interventions. These are labeled Combined Dom Intl Interventions (Rev, Rev2). This scenario is built from two different series of interventions: Leverage Testing Intl, and Leverage Testing Domestic, which user can explore if they wish to look more closely at individual interventions from the combined scenario. Users should also use these scenarios and the Combined Dom Intl Interventions (Rev, Rev2) to get a sense of how to scale interventions to different regions of the world or even globally, rather than altering a single country at a time.

Revision 2 models a number of parameter changes including: growth in multifactor productivity (mfpadd), declines in TFR (tfrm), increases in the female share of the labor force(labfemshrm) and world migration(wmigrm), increases in investment (invm), foreign aid, FDI and portfolio investment (xfdistockm and xportfoliom), declines in protectionism and resulting increase in exports (protecm and xshift), increased government expenditures on education, health, and R&D (gdsm), greater welfare transfers to unskilled labor(govhhtrnwelm),improvements to governance (declines in corruption-govcorruptm, increases in effectiveness- goveffectm, and economic freedom- econfreem), improved infrastructure in roads, ICT, and telephone network density (infranetm, infraroadm,infratelem), and increased energy production from renewable resources (enpm).

Of these, the economic changes deserve special attention. This scenario includes an improvement in multifactor productivity in the World Bank developing economies to 0.002 over a ten year time horizon, growth in the female share of the labor force to 1.5 time the base case over a 50 year time frame and increases in world migration multiplier to 1.5 over a 15 year time frame. It also increases investment multiplier to between 1.3 and 2 over a 15 to 25 year time frame depending on the region triggering increased capital accumulation and savings.

Government expenditure is also affected. Expenditure to education increases to between 1.2 and 1.8 times the base case implemented over ten years. Expenditure to health and research and development also increase by similar amounts over approximately the same time frame. Government to household transfers increase in different regions by 50 to 100% (multiplier values of 1.5 to 2) over a 20-year time frame. Trade and export promotion increase as well. Levels of protectionism decline by 20% over 20 years (a multiplier value of .8) and the ratio of exports to imports grows by .04.

And finally, FDI and portfolio investment are affected. FDI doubles over 25 years in the World Bank developing economies while portfolio investment increases by 50% over the same time frame. Foreign aid by donor nations increases to .5% of GDP over ten years. There are also increases in the availability of IMF funding that go beyond the scope of this guide, but which may be of interest to specialists in international finance.

With respect to the informal economy, eight scenarios have been developed for Peru. Six of them each manipulate one of the drivers of either informal labor or informal GDP share.  In each case an "aggressive but reasonable" value of the driver works to reduce informality.  The other two involve more general impacts on information.  The first (Comb Lab GDP 1.5 drivers.sce) combines all of the six individual drivers into a single scenario to reduce informality. The second or counterfactual scenario (Informal GDP Share Total Decline Peru 15 years.sce) use a brute force multiplier on the informal GDP share to reduce it to the minimum value allowed (1 percent) between 2016 and 2030.

# Infrastructure Module

## Variables of Interest

The Infrastructure module in the IFs system forecasts infrastructure development and its consequences via a five stage process that is driven primarily by the expected/demanded levels of infrastructure and the funding available to meet these expectations or demands.

The model first estimates the expected/demanded level of infrastructure within a country in relation to key drivers like GDP per capita and population.Second, it translates these expectations into financial requirements, accounting for both new construction and maintenance. Third, the system balances these desired funding levels with the actual resources available for infrastructure construction and maintenance. Fourth, it forecasts the actual attained levels of infrastructure (both in raw physical terms and in terms of population access rates). Fifth, these levels of infrastructure have specific and direct social, economic and environmental impacts related to these attained levels (processes discussed in connection with other modules).

The distinction between expected and demanded levels of infrastructure is a function of the scenario being modeled. In general scenarios, e.g., the Base Case, we use the term expected because the underlying equations are based on historical data reflecting both underlying demands and supply constraints. In scenarios with targets, however, these equations are overridden by equations reflecting the target path, when the expected values lag behind the values defined by the target path. In such instances, therefore, we are more clearly identifying demands for infrastructure, rather than expectations.

Infrastructure variables can be divided into three major types: physical infrastructure, access, and funding.Road density, percentage of road paved, electricity generation capacity, and the amount of land equipped for irrigation all represent key physical infrastructure variables that indicate actual infrastructure stocks within a country. Key access variables, which indicate the degree to which people are able to benefit from existing infrastructure, include: rural population living within 2 kilometers of an all-season road (the road access index); population with access to electricity; reliance on solid fuels for energy; population with access to improved safe water, sanitation, and wastewater treatment; and subscriptions per 100 people to fixed line telephones, mobile phones, and fixed or mobile broadband.

4This calculation treats physical stocks differently from access indicators depending on the type of infrastructure being considered. For roads, expected access is calculated as a function of the physical stocks, while for electricity, water and sanitation, and ICT, physical stocks are calculated on the basis of the expected levels of access. This has implications for the functioning of the parameters discussed below.

5IFs also distinguishes between ‘core’ and ‘other’ infrastructure. Core infrastructure refers to those types of infrastructure that we represent explicitly in IFs—roads, electricity generation, improved water and sanitation, and ICT. Other infrastructure refers to those types that we do not represent explicitly—e.g., railroads, ports, airports, and future types of infrastructure yet to be envisioned. Please note that although we do not represent these other forms of infrastructure explicitly, we do estimate spending on them in order to avoid under-representing the total demand for infrastructure. The choice of what to include as core infrastructure reflects the availability of historical data and our determination of what can be modelled within IFs at this time.

### Physical Infrastructure

 Variable Name Description Transportation INFRAROAD* Total road density INFRAROADPAVEDPCNT* Percentage of roads paved Electricity INFRAELECGENCAP* Electricity generation capacity per capita INFRAELECTRANSLOSS Electricity transmission loss INFRAELECADJFACT Electricity adjustment factor ENELECSHRENDEM Ratio of electricity use to total primary energy use Water and Sanitation LANDIRAREAEQUIP Area equipped with irrigation * each variable marked with an asterisk has a companion variable with the suffix DEM, which indicates the expected/demanded level of the variable in the absence of funding constraints

Key physical Infrastructure variables for transportation are INFRAROAD, and INFRAROADPAVEDPCNT. The variable INFRAROAD represents a measure of total road density (paved and unpaved), while the variable INFRAROADPAVEDPCNT offers a measure of the quality of the transportation infrastructure within a country using paved percentage as a proxy.

Key variables for electricity are the amount of electricity generation capacity within a country, denoted by INFRAELECGENCAP and expressed in kilowatts per person. Other variables, which represent quality indicators, are INFRAELECTRANSLOSS—the transmission and distribution loss in percent—and INFRAELECADJFACT—the capacity factor for electricity production, expressed as a fraction, and ENELECSHRENDEM, the ratio of electricity use to total primary energy use. It is also possible to calculate electricity connections using the results on access to electricity, discussed below, and population.

The only physical infrastructure variable for water and sanitation is the area equipped for irrigation, LANDIRAREAEQUIP, which represents the amount of land in thousands of hectares that is equipped with irrigation. There are no key physical infrastructure variables associated with ICT, only access rates. Users wishing to affect ICT infrastructure should modify access, which, along with population, can be used to calculate actual physical infrastructure levels.

Physical infrastructure may be essential, but unless people have access to it, they cannot benefit. Because of this, access variables are included wherever possible in the IFs system. Transportation contains only one key access variable. This variable, INFRAROADRAI, provides a measure of degree of access people have to the existing road infrastructure. It is defined as the percent of the rural population living within 2km of an all season road.

INFRAELECACC provides a measure of access to electricity, disaggregated by rural and urban users. Another measure of access to electricity that users may be interested in from a health perspective is ENSOLFUEL which represents the percentage of the population relying on solid fuels for energy, and a significant factor in both environmental change and certain health conditions.

The two fundamental access variables of interest for water and sanitation are WATSAFE and SANITATION, which measure the percentage of the population with access to improved water sources and sanitation, respectively, by type of access. Water access is subdivided into unimproved, other improved, and piped. Sanitation access is subdivided into other unimproved, shared, and improved (note that shared access is not considered improved). WATWASTE and WATWASTETREAT represent the percentage of the population whose wastewater is collected and treated, respectively.

Four ICT variables represent access to the major components of information and communication technologies. INFRATELE represents the number of fixed line telephones per 100 people while the other three indicators, ICTBROAD, ICTMOBIL, and ICTBROADMOBIL, provide the number of subscriptions per 100 people for fixed broadband, mobile telephones, and mobile broadband, respectively. The dynamics of the model are structured in such a way that the long term trend is for fixed line telephones to decline, while use of mobile and mobile broadband increases.

6The vast majority of Infrastructure variables have both a standard variable, representing the actual achieved level of infrastructure stock or access, and a demand/expectations related variable suffixed by *DEM. The interpretation of the *DEM variables should change subtly depending on the analytical context. When discussing the Base Case or when applying any of the multiplicative parameters, it is more useful to think of the *DEM variables as expected levels; when building scenarios that manipulate the *setar/*seyrtar parameters, it is more accurate to think of them as demand variables.

### Infrastructure Funding

 Variable Name Description GDS (infrastructure, infraother) Government consumption, by category INFRAINVESTMAINT Total (public plus private) investment for core infrastructure maintenance, by type of infrastructure INFRAINVESTMAINTPUB* Public investment for core infrastructure maintenance, by type of infrastructure INFRAINVESTNEW Total (public plus private) investment for construction of new core infrastructure, by type of infrastructure INFRAINVESTNEWPUB* Public investment for construction of new core infrastructure, by type of infrastructure the companion variables, INFRABUDDEMMNT and INFRABUDDEMNEW, are the amounts of public spending originally requested prior to the government budget allocation process

The infrastructure module in IFs incorporates a cost accounting system that ensures that all infrastructure improvements are funded prior to construction, which allows for a gap between the amount of infrastructure that is expected/demanded and the amount that is actually achieved.

As in other modules, GDS provides a good overview of total public spending on infrastructure, but this cannot be broken down by type of infrastructure any more specifically than core infrastructure and other infrastructure. INFRAINVESTMAINT provides the measure of the total investment for infrastructure maintenance, while INFRAINVESTMAINTPUB provides infrastructure investment for maintenance provided by public sector funds. INFRAINVESTNEW provides an indicator of the total spending on construction of new infrastructure, while INFRAINVESTNEWPUB provides the spending that came from public sources for the purposes of new investment. For the purposes of parameter manipulation, we will discuss the types of infrastructure separately because their dynamics vary widely with regards to how the model calculates physical infrastructure versus access. Absolute and relative target parameters to affect infrastructure access are discussed together because all share similarities in terms of structure and function within the model. Finally, there is some discussion of how to combine the two different types of parameter. These variables are subdivided based on whether the spending is for maintenance purposes or for the building of new infrastructure, and whether funding comes from public or private sources. Private funding is related to public funding in some ratio in all cases but ICT, where private funding has historically been the driver of infrastructure investment.

The two public spending variables, INFRAINVESTMAINTPUB and INFRAINVESTNEWPUB, have companion variables like many of the physical infrastructure and access variables discussed above. Specifically, the variables INFRABUDDEMMNT and INFRABUDDEMNEW represent the amounts of public spending originally requested prior to the government budget allocation process. When there are shortages, INFRAINVESTMAINTPUB and INFRAINVESTNEWPUB will be less than INFRABUDDEMMNT and INFRABUDDEMNEW.7 In addition to producing a shortfall of public investment, this will also reduce private spending, except for the case of ICT infrastructure. The treatment of ICT differs because private funding has historically been the driver of investment in ICT infrastructure, whereas the public sector has been the driver of investment in other infrastructure.

7It is also possible for INFRAINVESTMAINTPUB and INFRAINVESTNEWPUB to be less than INFRABUDDEMMNT and INFRABUDDEMNEW for another reason. This occurs when countries try to increase infrastructure spending more rapidly than is considered feasible. In this case, some of the funds allocated are held back and released over several years. Please refer to the technical documentation on the infrastructure model for more details on this process.

## Parameters to Affect Infrastructure

Four basic types of parameter are used in this module. They are, multiplicative parameters, technological shift factors, absolute, and relative targets. In general, multiplicative and technological shift factors operate in the same way; likewise, absolute and relative targets have similar functionalities.

There are some general rules that govern the interaction among the different types of parameters. 8The first rule states that, if multiplicative or tech shift factor parameters are used in conjunction with absolute or relative targets, the multiplicative/technological shift parameters are applied first and then the relative or absolute targets are calculated. The second rule states that absolute and relative targets cannot be applied together. If they are, the model interprets this as an inconsistent application of parameters and will not implement either. The exception to both these rules are ICT fixed and mobile broadband, where the multipliers ictbroadm (for fixed broadband) andictbroadmobilm (for mobile broadband), are applied after the absolute or relative target parameters are applied. Fixed broadband is also the exception to the rule that absolute and relative targets cannot be applied together. Instead they are both calculated within the model and then the larger of the two values is taken.

In analyzing scenarios involving changes to the majority of the parameters described below, users should first check variables with the suffix DEM, where available, because many of the parameters modify expected/demanded levels of access. Assuming that the model has modified the expected/demanded level as desired, the user should next check the actual achieved levels of infrastructure. The reason is that if a parameter change increased the level of demand, but the impact did not raise demand above achieved levels otherwise computed in the model, there would likely be no apparent impact on the achieved level. For instance, users wishing to affect access to sanitation via sanitationm should first check SANITATIONDEM to examine the impact on expected/demanded levels, before checking SANITATION to determine the impact on the actual level fuels of access to sanitation within the population.

8There are some exceptions to the rules laid out below, primarily as pertains to the treatment of fixed broadband within ICT.

All the multiplicative parameters that users can use to affect roads affect physical infrastructure variables. This means that changes to infraroadm directly impact the variable INFRAROAD, which are in turn used to calculate the variable INFRAROADRAI, which is the infrastructure access variable for road infrastructure. Changes to infraroadpavedpcntm actually alter the percentage of roads in a country which are paved. There are no multiplicative parameters which directly affect road access.

### Electricity

 Parameter Variable of Interest Description Type infraelecgencapm INFRAELECGENCAP Multiplier on electricity generation capacity Multiplicative Infraelecaccm INFRAELECACC Multiplier on electricity access Multiplicative enelecshrendemm INFRAELECDEM Multiplier on ratio of electricity use to total primary energy use Multiplicative infraelectranlossm INFRAELECTRANLOSS Multiplier on the loss of electricity in transmission and distribution Multiplicative ensolfuelm ENSOLFUEL Multiplier on the reliance on solid fuel as a source of energy Multiplicative infraelecacctrgtval/ infraelecacctrgtyr INFRAELECACC Target value and years after 2010 for target achievement for population percentage w/ access to electricity Absolute target infraelecaccsetar/ infraelecaccseyrtar INFRAELECACC Standard error target and years after 2010 for population percentage w/ access to electricity Relative target ensolfueltrgtval/ ensolfueltrgtyr ENSOLFUEL Target value and years after 2010 for target achievement for percentage of households reliant on solid fuels Absolute target ensolfuelsetar/ ensolfuelseyrtar ENSOLFUEL Standard error target and years after 2010 for target achievement for percentage of households reliant on solid fuels Relative target Note: Using both *trgtval/trgtyr and the *setar/*seyrtar parameters to affect the same outcome variable will block the impact of both, so users should be careful to only use one or the other of these types at a time, except in the case of fixed broadband, where the larger of the two effects will have an impact.

Electricity in the infrastructure model contains parameters that alter both physical infrastructure and access levels using multiplicative parameters. The parameter infraelecgencapm directly affects INFRAELECGEN, which represents the physical capacity to generate electricity and is a physical infrastructure variable.

Access to electricity, which is represented by INFRAELECACC, can be directly affected by infraelecaccm, a parameter that directly impacts access to electricity. This parameter is subdivided to allow users to affect urban and rural access separately.

Three other multiplicative parameters are relevant to discussion of electricity access in the infrastructure module. These are ensolfuelm, enelecshrendemm, and infraelectranlossm. Ensolfuelm changes solid fuel reliance as a primary energy source for domestic cooking and heating (ENSOLFUEL), which is also driven by INFRAELECACC. Enelecshrendemm, changes the ratio of electricity use to total primary energy use (this is a variable of relevance to the energy module, but is not yet linked to it). It allows users to change how reliant a country is on electricity relative to other sources of final energy use. It indirectly affects the expected/demanded amount of electricity generation capacity as represented by the variable INFRAELECACCDEM. Infraelectranlossm affects the loss of electricity during transmission and distribution. It is a parameter that allows users to affect the efficiency and quality of the electricity infrastructure. Like enelecshrendemm, this parameter indirectly affects INFRAELECACCDEM.

There are also target access parameters to allow users to set targets for desired levels of infrastructure access. These include infraelecacctrgtval/infraelecacctrgtyr, infraelecaccsetar/infraelecaccseyrtar, ensolfueltrgtval/ensolfueltrgtyr, and ensolfuelsetar/ensolfuelseyrtar. These set absolute and relative targets and target years for desired levels of electricity access and solid fuel use respectively.

### Water and Sanitation

Water and sanitation only contains one parameter which directly affects physical infrastructure levels. This parameter is landirareaequipm, a parameter that affects the land area equipped for irrigation. All other parameters affect access to water, wastewater collection and treatment, and sanitation; the access rate then determines the expected/demanded level of the physical stock. There are no direct multipliers on physical stocks for these types of infrastructure. Sanitationm, watsafem, watwastem, and watwastetreatm are access parameters that affect access to sanitation, safe water, waste water collection, and waste water treatment, respectively.

Targeting parameters for water and sanitation are:sanitationtrgtval/sanitationtrgtyr, watsafetrgtval/watsafetrgtyr, sanitnoconsetar/sanitimpconsetar/sanihhconsetar/sanitnoconseyrtar, watsafenoconsetar/watsafimpconsetart/watsafhhconsetar/watsafenoconseyrtar, and watwastetreatsetar/watwastetreatseyrtar. The first two pairs provide absolute target values for sanitation and safe water access. The second two pairs define relative target values and years for reducing the number of people with no access to improved sanitation, safe water, or wastewater treatment.

## Parameters to Affect Funding

 Parameter Variable of Interest Description Type gdsm (infrastructure) GDS Gross domestic spending multiplier (infrastructure) Multiplier infrabudsdrat GDS Alters the priority of infrastructure in the budget reconciliation. Exogenous ratio infrainvnewpubshrm INFRAINVESTNEWPUB Portion of maintenance funding coming from public sources Multiplier Infrainvmaintpubshrm INFRAINVESTMAINTPUB Portion of new infrastructure funding coming from public sources Multiplier hhsizem GDS Household size multiplier Multiplier infraroadpavedcostm INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB Multiplier on unit cost of paved roads Multiplier infraroadunpavedcostm INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB Multiplier on unit cost of unpaved roads Multiplier infraelecgencostm INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB Multiplier on unit cost of electricity generation Multiplier infraelecaccruralcostm INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB Multiplier on unit cost of rural electricity access Multiplier infraeleaccurbancostm INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB Multiplier on unit cost of urban electricity access Multiplier watsafecostm INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB Multiplier on unit cost of safe water Multiplier watsafeimpcostm INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB Multiplier on unit cost of improved safe water sources Multiplier sanitationcostm INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB Multiplier on unit cost of basic sanitation Multiplier sanitationimpcostm INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB Multiplier on unit cost of improved sanitation Multiplier watwastetreatcostm INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB Multiplier on unit cost of waste water treatment Multiplier landircostm INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB Multiplier on unit cost of equipping land for irrigation Multiplier infratelecostm INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB Multiplier on unit cost of telephone Multiplier ictmobilcostm INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB Multiplier on unit cost of mobile Multiplier ictbroadcostm INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB Multiplier on unit cost of broadband Multiplier ictbroadmobilcostm INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB Multiplier on mobile broadband Multiplier

A number of major parameters for affecting the funding process in the infrastructure module exist. The first, more completely discussed in the economic module (in association with the social accounting matrix and government expenditures), is the government spending by destination multiplier gdsm. By manipulating the infrastructure component of gdsm users can change the proportion of government spending that goes towards infrastructure thus altering the amount of funding available to achieved the expected or demanded levels of core and other infrastructure; it can also be used to increase or decrease the spending on other infrastructure. Infrabudsdrat allows users to increase the priority given to infrastructure in the government budgeting process. As the value increases from 0 to 1, a larger fraction of the initial request for public funds for infrastructure is set aside before the budget reconciliation process, increasing the likelihood of achieving the expected or demanded values of infrastructure.

Public spending on infrastructure, both for new infrastructure and maintenance purposes, can be increased using the parameters infrainvnewpubshrm and infrainvmaintpubshrm respectively. Each of these parameters can be manipulated separately for different types of infrastructure, and changes to the public share will also affect private sector in a positive relationship (as public funding goes up so does private). Users can change the average household size using the parameter hhsizem, which has an indirect effect on budget by altering the total number of infrastructural connections required for a population. 9Since budget availability affects attainment of infrastructure, as explained above, this parameter can ultimately change access rates.

Finally, users can also directly alter the unit cost of certain kinds of infrastructure, using one of a series of cost multipliers. The parameters infraroadpavedcostm, and infraroadunpavedcostm alter the unit cost of paved and unpaved roads respectively. Infraelecgencostm alters the cost of electricity generation, while infraelecaccruralcostm and infraeleaccurbancostm alter the cost of electricity access in rural and urban areas. The parameters watsafecostm and sanitationcostm, alter the cost of basic water and sanitation access while watsafeimpcostm and sanitationimpcostm change the cost of providing improved access to each. Landircostm changes the unit cost of equipping land for irrigation. Infratelecostm, ictmobilcostm, ictbroadcostm, and ictbroadmobilcostm all change the cost of different types of ICT.

9Household size parameters are applicable only for those types of infrastructure that are delivered at a household level, e.g., water, sanitation and electricity connections.

## Prepackaged Scenarios

Because of the complexity of the infrastructure module, and the sheer number of parameters it contains, users may wish to rely more heavily on prepackaged scenarios in order to find effective combinations of multipliers than they do in other IFs modules. One package of scenarios that was developed to model extensions of ICT broadband connectivity and resulting potential gains in efficiency of energy use was developed for the EU and includes modifications to a number of parameters. Ictbroadm is used to increase broadband penetration rates while lke (lifetime of capital in energy) increases the efficiency of energy capital investments, making energy production more efficient. This prepackaged scenario set can be accessed from the Scenario Development form of IFs, using the Add Scenario Component menu option. The set can be found within the World Integrated Scenario Sets grouping where it is labeled ICT Scenarios for the EU. See Acknowledging Limits, Betting on Silver Bullets, Broadband Stupidity, and Networked Solutions (Moyer and Hughes, 2012).

Other Prepackaged Scenarios were developed for Chapter 6 of the Infrastructure volume of the Patterns of Potential Human Progress series (Rothman et al., 2013). This set of scenarios can be located in the PPHP InfrastructureChapter6 grouping of the World Integrated Scenario Sets. It contains a large number of scenarios using infrastructure targets. The scenario TarAllNNoBudgPriority, referred to as Universal Targets Pursuit in the volume, represents a future in which efforts are made to achieve universal access to water and sanitation, electricity, rural roads, and mobile broadband, as well as the elimination of solid fuel use, by 2030; no budget priority is given to infrastructure in this scenario. The scenario TarSENoBudgPriority has the same target date of 2030, but instead of universal access, countries only aim to achieve levels of access that exceed the expected value for their level of development by one standard deviation; again no budget priority is given to infrastructure. The other scenarios, TarAll2050NoBudgPriority, TarElecNoBudgPriority, TarICTNoBudgPriority, TarSE0NoBudgPriority, TarSENoP2050, TarTranNoBudgPriority, and TarWatNoBudgPriority, build on these two scenarios, adjusting the target level, the target date, or the categories of infrastructure with targets.

# Agriculture Module

## Variables of Interest

 Variable Name Description AGDEM Agricultural demand FDEM Food demand for consumption FEDDEM Feed demand for livestock INDEM Industrial demand for food AGP Agricultural production YL Yield per hectare of land LD Land area FISH Fish production from ocean and mariculture AGM Agriculture Imports AQUACUL Million metric tons of fish produced via aquaculture AGX Agriculture Exports LOSS Portion of food produced that never reaches markets CLPC Calories per capita MALNCHP Percent of children malnourished MALNPOPP Percent of population malnourished

The IFs system models agriculture in terms of supply and demand. Consumption, from the demand side, links forward into food availability and nutrition. Both supply and demand dynamics in the agricultural module are closely linked to dynamics in the population and economic modules. For simplicity of representation, IFs models three primary foodstuffs: meat, crops and fish. Stocks (inventories) of food are driven by interaction between production and demand. Stocks affect prices and prices generate movement to supply and demand equilibration, a process that also involves trade.

The agriculture model represents demand for food (AGDEM) as a function of population size, population income, the share of income spent on food, and agricultural prices. A number of other variables also help to illustrate different aspects of the demand side of the equation. The first is food demand FDEM, which represents the amount of food demanded for human consumption. The second is FEDDEM, which represents the amount of food demanded for use as feed by livestock and the third is INDEM which represents the amount of food demanded for use in industrial processes. As a country develops, the balance between these types of demand change; in general, nations with more industry will have a higher level of industrial demand for food, and major meat producing nations will have a higher demand for crops to use as feed.

At a basic level, the supply of food produced within a country (AGP) is a product of the amount of land available for cultivation (LD) and the productivity or yield of that land represented by YL in the IFs model. A number of factors affect yield (YL) including: the level of technology, the amount of labor available for agriculture, the investment of capital into agriculture, and the propensity for yields to saturate at some point even in the face of progressively increased input levels. Investment in agriculture is driven by product prices relative to the additional capital investment required and therefore profit levels. Given the common cycles in agriculture and long lead times in response to investment, however, farmers cannot always make decisions based on current conditions. Investment is also directly responsive to global inventory or stock levels and has an inertial component.

IFs also represents losses to the agricultural production system, and they can have a powerful impact on the availability of food in a country. Not all of the food produced within a country makes it to a market, because of poor infrastructure, such as a lack of refrigeration or other inefficiencies in the production system. In IFs this process is represented via the LOSS variable. For higher income countries, a larger portion of the loss is actually on the demand or consumption side. Because agricultural products are globally traded commodities, imports and exports of food, discussed in the economic module as well, play an important role in moderating the availability of food within a country. In IFs these are represented by the variablesAGX, agricultural exports, and AGM, agricultural imports.

The dynamics of the above processes ultimately affect the ability of people in a country to avoid malnutrition in the form of either obesity or starvation. In many developing nations, meeting daily caloric needs is a struggle and large portions of the population in these countries may be malnourished, leaving them vulnerable to death by preventable diseases. This is especially true of children, for whom the consequences of both short and long term under nutrition are especially severe. To help users keep track of this essential developmental indicator, IFs includes a number of variables that represent the nutritional challenges faced within a country. First, calories per capita (CLPC) provides a rough measure of the total number of calories available to individuals. It provides a rough guide to the kinds of nutritional challenges a nation may face. Populations getting less than approximately 2100 calories a day may experience higher levels of malnutrition, while those that are getting significantly above 3000 calories a day may experience increasing levels of overweight and obesity. However, more important than a crude measure of calorie availability is the impact that chronic malnourishment poses from a development standpoint. To illustrate this burden, IFs includes variables that provide the percentage of the population that is malnourished (MALNPOPP). Data tend to be better for child undernutrition and we typically have more confidence in and pay more attention to the variable that indicates the percentage of a country’s children (under five) who are malnourished (MALNCHP).

## Parameters to Affect Demand

 Parameter Variable of Interest Description Type agdemm AGDEM Demand for agricultural production Multiplier calmeatm AGDEM Demand for meat as a portion of total calories Multiplier elascd AGDEM Elasticity of crop demand to changes in food price Elasticity elasmd AGDEM Elasticity of crop demand to changes in food price Elasticity

A brute force multiplier on agricultural demand (agdemm) allows direct manipulation of demand and can be used to represent a wide range of scenarios relative to the Base Case, including changing tastes among consumers, changing responses to prices, and changes in incomes or the movement of individuals into the middle class. Changes in consumer desires for certain foods could also be modeled using the parameter calmeatm which adjusts the portion of calories obtained from meat. Like agdemm, it is a multiplicative parameter, so a 0.2 increase in the value of the parameter from its base value of 1.0 would generate a 20% increase in the value of the variable relative to the base case. However, this is a relatively difficult parameter for which to identify reasonable values for such changes. Similarly, changes to elascd (elasticity of crop demand to changes in price) and elasmd (elasticity of meat demand to changes in price) should be made with caution because they involve changing the responsiveness of demand to changes in price. Such parametric interventions might be appropriate in cases where you want to model the impact of a mass movement to change diet patterns, such as in response to a national campaign for healthy eating or decreased meat consumption due to preference changes. In all three cases the relevant outcome variable is AGDEM.

## Parameters to Affect Supply (Production)

 Parameter Variable of Interest Description Type tgrld AGP Target growth rate in cultivated land Initial ylm YL Crop yield (agricultural production) Multiplier ylmax YL Maximum crop yield Exogenous specification envylchgadd ENVYLDCHNG Agricultural yield change Additive factor slr AGP Livestock slaughter rate Exogenous specification livhdpro AGP Livestock herd productivity gain with grain feeding Exogenous specification aquaculm FISH Fish production through aquaculture, multiplier Multiplier aquaculgr AQUACUL Growth in the use of aquaculture Growth Rate ofscth, rfssh FISH Global ocean fish catch and geographic distribution Exogenous specification lossm LOSS Agricultural wastage Multiplier aginvm YL, IDS (agriculture) Investment in agriculture Multiplier elagind AGP Elasticity of industrial use of crops with price Elasticity elinag1 IDS (agriculture) Elasticity of investment in agriculture to profit levels Elasticity elinag2 IDS (agriculture) Elasticity of investment in agriculture to changes in profit level Elasticity

There are two primary channels to think about changing the supply of agricultural production. The first is via increases in the amount of resources devoted to agricultural production such as by increasing the amount of land under cultivation. This can be done via the tgrld parameter. The earth’s arable land is relatively finite, however, and land under cultivation has not been a major driver of increased production for decades in most countries.

A more appropriate channel to think about is technological change that increases the efficiency of food production systems thereby increasing yield. The yield multiplier (ylm), which affects the yield of land being used for crop production, could be used, for example, to model the effect of a second green revolution. This parameter does not carry an intrinsic cost to use, making it both a powerful way of affecting the agricultural model, but also making it essential to justify the reason for any yield change. For example, this multiplier could be effective in representing the impact of improvements to climate or technology that increase the output of a particular unit of land in the absence of increased labor or investment. Users can also alter the maximum yield that can be harvested from a plot of land using ylmax, an exogenous parameter which sets a maximum level for yield. Changes to this parameter can model extreme changes to land yield, such as a second green revolution.

Another parameter that can be used to affect yield is envylchgadd, also discussed in the environmental module. It can be used to model the effect global warming is having on agricultural yields. Modifying this parameter allows users to test different assumptions about the effect that increased CO2 and increased temperatures will have on crop yield.

Changes in the patterns of production of meat and fish can be modeled in the IFs system through a number of parameters. Meat production can be affected by changing the rate at which livestock are slaughtered via slr. This parameter could be altered to model the transition from subsistence to factory farming, and the increasing use of modern slaughterhouses in developing countries. The parameter livhdpro is also useful for modeling the impact of increased meat production from livestock as a result of the transition to grain feeding, which allows producers to extract more meat from each animal they own.

Similarly the parameters aquaculm and aquaculgr can be altered to model the impact of increasing reliance on aquaculture for fish production because of technological improvements to this method of food production. The variable of interest will be FISH. The parameter aquaculm works like other multiplicative parameters, while the parameter aquaculgr alters the rate at which the use of aquaculture increases. The first parameter targets the actual amount of aquaculture a country uses, and is easiest to manipulate for the average user. The second, aquaculgr, is set within the model and alters the rate at which countries begin to develop aquaculture resources.

Other parameters that affect the level of fish production include ofscth and rfssh. The first, ofscth, modifies the amount of fish caught globally while the second rfssh, changes the global share of the catch that each country receives. Landlocked countries receive an extremely small share of ofscth while countries with a large amount of coastline receive a much larger share. Modifications to ofscth may be made to adjust for the impact of overfishing on global fish stocks.

Changing the loss rate of agricultural output to waste (rotting in fields, lost/spoiled during transportation, or not used in the home) via the lossm parameter is a powerful way to model the impact of changing agricultural loss of food. It is a multiplicative parameter.

Technological improvement is not the only pathway through which yield (YL) may be affected within IFs. Financial pathways can change the amount of capital being invested in agriculture, resulting in higher yields. For example, parameter aginvm, directly changes the investment in agriculture. This parameter can be used to model the effects of investment in improved agricultural technologies like genetically modified crops or new methods of aquaculture. Because of the financial accounting systems within IFs, changes in agricultural investment will affect investment in other sectors. Elagind is a parameter that may be useful those who wish to adjust the percentage of crop production that is diverted for industrial uses such as ethanol, or beer.

## Parameters to Affect Nutrition

 Parameter Variable of Interest Description Type malnm MALNCHP Malnourished children Multiplier malnconv MALNCHP Convergence time with cross-sectional norm Exogenous specification

Finally, model users may wish to use parameters to explore the health and economic impacts of different levels of nutritional deficiencies. In the health model, nutritional deficiency manifests itself in terms of mortality and morbidity related to communicable diseases. It can have especially large effects on children, who if malnourished for a significant period of their childhood, suffer developmental and cognitive delays that can impact their future productivity. The most important of these parameters is malnm, which is a multiplicative parameter that affects the number of malnourished children in a country as a percent of the population. This parameter could also be used to represent the impact of school based nutrition programs or other targeted feeding programs.

## Prepackaged Scenarios

One prepackaged scenario that makes significant improvements to agricultural yield in the developing world was developed as a policy brief, and models the impact of a green revolution in Africa. The scenario combines multipliers and other parameters, including ylm, ylmax, aginvm, andtgrld. It was designed to model the impact of a green revolution type of event occurring in the developing world and involving improved yield of crops through improvements in cultivation techniques and new technologies. Users will find it saved under the African Policy Briefs section of the World Integrated Scenario Sets, under the heading Green Revolution, which is also the title of the scenario. It increases agricultural yields by 76 percent over a 32-year period (increasing the parameter ylm to 1.76). It also increases the maximum yield in all African countries, as well as modeling 20 percent growth in agricultural investment and an initial target for growth in cultivated land of 8 percent.

Another prepackaged scenario that makes significant changes to agriculture is the Politics of Belly scenario developed for the African Futures 2050 project. This scenario is located in the Afp 2050 folder in the World Integrated Scenario Sets heading. This posits a much less positive outlook for African agriculture, simulating a 20 percent decline in yields that could represent the negative impact of climate change, much more than offsetting the mitigating effect that plant responsiveness to C02 increases might have on yields.

# Energy Module

## Variables of Interest

 Variable Name Description ENDEM Energy demand WEP World energy prices WEPBYEAR Translates global energy prices into dollars per barrel of oil equivalent ENPRI Energy prices ENST Energy stock ENRGDP Ratio of energy demand to GDP ENP Energy production WENP Global energy production KS Capital stock IDS Investment in energy QE Capital output ratio RESER Accessible energy reserves RESOR Ultimate conventional energy resources RESORUNCON Ultimate unconventional energy resources ENM Energy imports ENX Energy exports ENSHO Energy shortage VADD Value added, energy

Rapid development of unconventional oil and gas production—particularly from shale sources—and high rates of growth in energy from renewable sources make the future of energy highly uncertain. Several research organizations have forecasting projects that provide long-term analysis. In the United States, the Department of Energy's Energy information Agency produces a yearly report titled, International Energy Outlook; the International Energy Agency produces similar forecasts for the world as a whole, as well as for many individual countries, in its annual World Energy Outlook. The IFs energy module contributes to this existing body of research on international energy futures. A selection of variables and parameters allows users to explore alternative patterns of energy demand, production, resource discovery and technological developments.

The structure of the IFs energy model is similar to that of the agriculture module, discussed in the preceding section. Both systems are driven by demand and supply dynamics within an equilibrium-seeking system that involves prices, trade, and investment. In both cases, the model translates forecasts of the physical system into value terms for the economic module and interaction with other sectors. The size of the economy and population shape demand, which IFs represents in the aggregate (not within sub-categories such as transportation, residential/commercial and industrial). The supply side differentiates production dynamics for oil, gas, coal, hydroelectric, nuclear, and other renewable energy and separately represents conventional and unconventional energy resources, such as shale deposits.

Beginning our elaboration with the demand side first, IFs derives each country’s energy demand from the overall size of the economy, the level of development as measured by GDP per capita, and world energy prices. The variable ENDEM, displayed as a default in billion barrels of oil equivalent (BBOE), captures demand from all sectors, and for all types of energy. The energy intensity of an economy—the usage of energy per unit of GDP (ENRGDP)—is a very important measure, influenced by the development level, the advance of technology, and energy prices. Reductions in that, or improvements of efficiency over time, have been occurring in most countries. Users may explore several different formulations of energy price, which is a key factor used to derive demand and affect the energy intensity. WEP is a single composite measure of the global price of energy, expressed as an index with a base of 100 in the first model year (WEPBYEAR translates global energy prices into dollars per barrel of oil equivalent). Like WEP, ENPRI displays country-level energy price as an index, base 100. Each country’s price is a function of global capital costs of energy production and a regional markup factor that reflects the level of country-specific energy stocks (ENST).

Key indicators for the other side of the energy system, the supply side, include production, capital/output ratio and available natural resources. Production is measured in the variable ENP; production can be displayed as a total, or disaggregated through the energy type dimension to produce individual displays of oil, natural gas, coal, hydroelectric power, nuclear, and other renewables. WENP is energy production for the world as a whole, disaggregated by production type. For each country, energy production is partially determined by relationships around capital. The portion of the initial capital stock dedicated to the energy sector, KS, limits the scale of possible production. And the capital-output ratio, QE, translates capital stock (KS) into production level. Capital stock itself is augmented over time by investment,IDS, dependent on the profitability of investment in energy.

The concept of capital-output ratio (QE) warrants clarification because it is important to the energy module's equilibrating dynamics. When production requires relatively little invested capital to produce a unit of output, the capital-output ratio is low. Conversely, a high ratio would mean a large amount of capital is required to produce a unit of output. All things equal, the lower the capital-output ratio, the greater the opportunity for profits. Thus, in the model, a low QE will generally trigger an increase in production.

Available natural resources are the final factor constraining supply. Levels of proven reserves, RESER, drive the price structure component of production—by affecting QE. Proven reserves are in turn bounded by the availability of ultimately recoverable resources, displayed in the variable RESOR for conventional resources, and the variableRESORUNCON for unconventional resources, both of which must be discovered (RD) to become reserves. As we shall see in the discussion of parameters, QE is affected not just by resource constraints, but also by the advance of technology.

A set of trade variables measures countries’ external reliance on energy. While high levels of energy exports can enrich public finances through taxation, an overreliance on either energy imports or exports increases vulnerability to external price shocks. Energy imports and exports are measured in billion barrels of oil equivalent by ENM and ENX and represent aggregate trade in energy without distinguishing energy types. A more direct indication of vulnerability to shock is located in Flexible Display, under the heading Energy Imports as a Percent of Total Demand.

Disruptive price swings are inherent to any commodity market. In energy markets they can result from either political disruption in energy producers such as Iraq or from collusion among producers—evidenced by the cartel behavior of The Organization of Petroleum Exporting Countries (OPEC). As economists point out, prices can theoretically always rise or fall to balance demand and supply—to clear the market. But as history has shown, severe energy shocks, sometimes in environments where leaderships do not allow prices to fully adjust, can result in actual physical shortages (ENSHO). In normal years the level of energy stocks in a country is visible through the variable ENST.

A final set of indicators, measured not in physical but in monetary value terms, represents the overlap between the energy and economic modules in IFs. The variable VADD displays the total value added that is contributed to a country's GDP by various sectors, including energy; this gives a quick look at how much a country relies on energy production as a source of wealth. Investment (IDS) and capital stock (KS) present additional measures of the size of the energy sector in absolute terms and relative to other sectors.

## Parameters to Affect Demand

 Parameter Variable of Interest Description Type endemm ENDEM Energy demand Multiplier elasde ENP, WEP Price elasticity of energy demand Elasticity carbtax ENP Carbon tax Exogenous specification enrgdpgr ENP, QE Growth in energy efficiency Rate of change

The demand side of the energy system features several leverage points that can be used to create alternative scenarios. The multiplier endemm directly manipulates aggregate energy demand in a country or region relative to its underlying calculation from economic size and other driving variables. A second key demand-side parameter, enrgdpgr, allows the user to change the default rate of change in energy intensity (the inverse of energy efficiency). Turning from the more brute-force parametric intervention approaches to a more policy specific one, country-level energy prices and therefore energy demand can also be manipulated directly through the additive parameter carbtax. As the name implies, this parameter simulates the effects of a tax on carbon emissions—even employed at a reasonable level, a carbon tax can have substantial impacts on energy demand.

In the energy analysis community there is always some uncertainty and debate concerning the strength of the relationship between energy price and demand. The parameter elasde allows users to change the long-term price elasticity of demand. This elasticity is negative in the model, corresponding to traditional formulations of supply and demand—as prices rise demand for energy falls. Users building scenarios may wish to experiment with different values of elasde, while bearing in mind the possibility that long-term and short-term elasticities may differ.

These demand-side leverage points offer a rich area for experimentation. Work by groups such as the International Energy Agency (IEA) and the International Panel on Climate Change (IPCC), have simulated several alternative future forecasts for energy efficiency development.

## Parameters to Affect Supply

 Parameter Variable of Interest Description Type enpm ENP Production multiplier Multiplier eprodr ENP Production growth rate Growth rate resorm RESOR Conventional resource multiplier Multiplier resorunconm RESORUNCON Unconventional resource multiplier Multiplier rdm RESER Discovery multiplier Multiplier etechadv QE Rate of technical advance in energy Exogenous specification etechadvuncon QE Rate of technical advance in unconventional energy Exogenous specification qem QE Capital output multiplier Multiplier eninvm ENIV Energy investment Multiplier enprix ENPRI Regional energy price Exogenous specification enml, enxl ENM, ENX Limits on energy imports and exports Exogenous specification

Turning to the supply side of the IFs energy module, two parameters in the model directly alter the relative production of oil, gas, coal, hydroelectric, nuclear, and other renewable energy. Enpm, a multiplier, scales up or down existing production of a specified energy type; eprodr, a growth rate parameter, forces a specified yearly percentage increase in production on the desired energy type. Both types of direct production modification can be used to trace the effects that a shift in energy production profile may have on life in a country, region, or the entire global community. For some observers, renewables represent the most important source of uncertainty surrounding the character global energy production. The IFs Base Case forecast assumes that renewable energy will grow rapidly in importance in the decades to come – see WENP across all energy types for a clear depiction. What if the model’s baseline assumptions are too optimistic, or too pessimistic? Scenarios created with enpm andeprodr can explore variations on the Base Case renewable energy story.

Moving below the direct interventions on production to parameters that affect the drivers of that production, both conventional and unconventional deposits of oil, gas and coal resources offer possible points of leverage for supply-side energy scenarios. As mentioned above, IFs divides raw materials used in energy production into resources and reserves. Scenario interventions are most effective when they include simultaneous changes to the reserves that are very closely tied to production, and to the more distil element of total resources (resources only become reserves when they are discovered and deemed to be economically exploitable with current technology and prices). An increase to the resorm orresorunconm parameters will expand the amount of materials that can potentially transition to reserves and become economically viable. As a second step, users can speed up this transition by manipulating rdm, a parameter that represents the rate of geologic discovery (or improved economic exploitability) in a country. An increase in the stock of fossil fuel reserves creates the possibility that they will be produced and that they will have forward impacts on energy price (ENPRI) and GDP of energy producing countries.

A key source of uncertainty around the future of energy supply concerns production costs. Technological progress could dramatically increase the amount of energy output possible at current levels of investment. The parameters etechadv and etechadvuncon directly control the rate of technological advance and cost reduction by energy type, whether based on fossil fuels or renewable energy. For fossil-based energy in particular, several other parameters enable users to explore the consequences of various energy production cost structures. Qem, a capital-output multiplier, directly scales up—or down—the capital cost per output unit of each energy type at the country level.

Government policies could additionally affect the future of energy production. Actions such as tax increases or direct subsidies can be simulated using eninvm, an investment multiplier. The parameter either increases or decreases overall investment in energy—the model still controls which energy-type is affected, based on price signals. Remember that the equilibrating mechanisms of the model will respond to any change in investment patterns. For example, an increase in investment flowing to oil, would increase production, eventually reducing oil prices, and leading to a subsequent decrease in investment. Remember also that the accounting mechanisms of the model will assure that any additional investment in energy will come from somewhere, such as investment in other sectors, other government expenditures, or household consumption.

Government decisions can also affect energy prices quickly, through intentional price shocks similar to those initiated by OPEC in the 1970s. The enprix parameter can simulate a concerted effort on the part of energy exporters to fix prices; enprix exogenously sets the type-specific energy price—ENPRI— for a region. As in real life, any price fixing scenario will have wide-ranging, and potentially perverse economic consequences. Two additional parameters exist that can simulate short-term supply disruptions via trade restrictions: enml exogenously imposes a limit to energy imports and enxl imposes a limit to energy exports.

## Prepackaged Scenarios

Currently, none of the prepackaged scenario files demonstrate the use of parameters linked to the energy module.

# Environment Module

## Variables of Interest

 Variable Name Description CO2PPM CO2 parts per million CO2PER Percentage increase in atmospheric CO2 CARANN Annual carbon emissions from fossil fuels CARFOROTH Annual net carbon release from forests WTEMP Average annual world temperature ENVTPCHG Annual average temperature change from 1990 ENVPRCHG Annual average precipitation change from 1990 WFORST World forest area LD Land area by type WATUSE Annual water usage WATUSEPC Annual water usage per capita ENVPM2PT5 PM2.5 levels in residential areas ENSOLFUEL Solid fuel use

As the global population has grown, and more and more countries have industrialized, sustainable development has come to occupy a permanent place on national, regional and global agendas. Growing concern for the environment was brought to the fore at the 1992 UN Conference on the Environment in Rio de Janiero. More than 2 decades have passed since the Rio conference, and the global community’s dedication to environmental concerns (if not always to action) has remained strong. In 2012 a follow up conference was held in Rio—the United Nation’s Conference on Sustainable Development, or Rio + 20—at which leaders discussed strategies to end poverty, address environmental destruction and build a bridge to the future. Other forward-looking environmental initiatives include the United Nations Environment Program’s (UNEP) Global Environmental Outlook Series. Since the first entry in the series, published in 1997, the UNEP has followed up with four additional reports (the International Futures model contributed to the most recent report). Despite the creation of dozens of internationally agreed upon goals, 2012’s Global Environmental Outlook 5 warned that the world remains on an unsustainable path. Around the world, forestland is decreasing, centuries-old glaciers are retreating, aquifers are being depleted and the amount of atmospheric carbon dioxide is on the rise. Not all environmental trends are negative, however. Developed countries have made progress on urban air pollution. Developing countries have experienced improved drinking water quality and safe sanitation access. And globally, the volume of CFCs damaging the ozone layer has dropped dramatically over the course of recent decades.

Environmental issues have potential or actual connections to all of the modules in IFs. For instance, processes associated with energy, agriculture, infrastructure, and the broader economy directly impact the earth’s forests, water supply, and atmosphere, as does governance more indirectly. The environmental module elaborates a subset of these potential connections in four specific areas: the global carbon cycle, land use (notably forest area), water use (agricultural), and air quality (urban and indoor).

The numerous ways in which human actions influence the environment are evident throughout the carbon cycle. Carbon is continuously moving within and across the boundaries of the Earth’s atmosphere, hydrosphere and biosphere. In systems dynamics terminology, the elements in the cycle are known as stocks and flows. Put simply, stocks are long-term repositories of carbon such as the world's fossil fuels, biomass and oceans, and flows are movements from one stock to another. This stock-flow dichotomy serves as a useful heuristic for users who are interested in creating scenarios in the IFs environment model more generally, but especially with respect to carbon. Interventions can target carbon stocks through processes that affect flows, such as energy production and reforestation. Key carbon stock indicators are CO2PPM, the level of atmospheric carbon dioxide measured in parts per million, and CO2PER, atmospheric carbon dioxide, measured as a percent relative to pre-industrial levels. On the flow side, key indicators available in the model include CARANN, the annual flow of carbon emissions from fossil fuel use, and CARFOROTH, global net carbon released annually from forests and other sources (discussed below). As we shall see, the model also allows parametric control of the flow of carbon from the atmospheric stock to that dissolved in the oceans, another stock.

The atmospheric carbon stocks have important forward linkages. One of the most important, temperature change, can be tracked via WTEMP, the global average annual temperature in degrees centigrade. A derivative temperature variable but at the country level, ENVTPCHG, captures the average annual temperature change from the year 1990, and ENVPRCHG similarly tracks the change in precipitation. These have further forward linkages to agriculture.

Several variables in the model measure the configuration of the world’s land area. Two variables capture trends in forest coverage: WFORST is an aggregate measure of the world’s land area covered by forest, reported in millions of hectares; and the land variable LD is disaggregated by land type, enabling country- or region-level displays of the following categories: crop, grazing, forest, urban and other. (See the agricultural module for a complete discussion of land use variables and parameters). Land use links back to the carbon representation because trees and other plants making up the world’s forests absorb carbon dioxide through photosynthesis and their carbon content is still another important global stock. As indicated above, change in forest area, such as clearing of land for agricultural use, generates one of the carbon flow terms of the model.

The environment submodule includes water indicators. The annual use of freshwater resources is reported in cubic centimeters via WATUSE, tied in IFs to the major source of water use, agricultural production. Users can additionally choose to explore water use per capita, expressed in thousands of cubic meters, via the variable WATUSEPC. Currently, the model’s treatment of water does not feature the stock and flow elements seen in the carbon cycle. Although WATUSE is a flow of water used each year, no corresponding variable captures the total stock of water—say in nonrenewable (or at least very slowly recharging) fossil water aquifers. This is a shortcoming that we hope to address in the near future. However, a parameter, watres (see the discussion below), does provide an estimate of the level of annually renewable water resources in countries and regions. The ratio of use to renewable resources serves as a measure of relative water stress. With respect to forward linkages, water stress is one of the drivers of state fragility (see the government module).

A final set of environmental indicators pertains to air pollution. The amount of solid matter suspended in the Earth’s atmosphere serves as a measure of relative air quality; high levels of atmospheric particulate matter (PM) can have adverse effects on human health. Depending on the data source, PM concentration readings may include particles that are either 10 or 2.5 micrometers in diameter. In the model, the variableENVPM2PT5 captures levels of fine particulate matter, PM2.5, found in residential areas (although our current data source is actually for 10 micrometer particulates). The prevalence of solid fuel use is a second indicator of air pollution. The variable ENSOLFUEL captures the percent of a population relying on solid fuel as the primary source of domestic energy. Burning solid fuel, such as wood, peat, coal, dung, and certain grains, produces a toxic environment indoors (see the infrastructure module).

Alternative scenarios, built in any IFs module, are likely to have bearing on environmental indicators. Users exploring the consequences of scenario interventions in economics, energy or agriculture are likely to see the greatest environmental effects. Interventions in other modules will still affect the environment, though through weaker, more distal, drivers. The environmental sub-module, however, allows users to directly explore the effects of environmental change. Environmental parameters can be categorized as those that affect carbon stocks and flows, land use, water resources, and air quality.

## Parameters Affecting Carbon

 Parameter Variable of Interest Description Type enpm ENP Energy production by type Multiplicative endemm ENDEM, WEP Energy demand multiplier Multiplicative carbtax ENP, CARANN Carbon tax Exogenous Specification qem ENP Capital-output ratio multiplier Multiplicative resorm RESOR Resource multiplier Multiplicative enrgdpgr ENP Energy efficiency Growth rate carfuel1 CARANN Carbon generated by gas Coefficient carfuel2 CARANN Carbon generated by natural gas Coefficient carfuel3 CARANN Carbon generated by coal Coefficient envclimsens CO2PPM, WTEMP Time sensitivity of global temperature to carbon dioxide Limit carabr CO2PPM, WTEMP Carbon absorbed by oceans Coefficient carabrchng CO2PPM, WTEMP annual change in this absorption rate of carbon by oceans Coefficient envco2fert AGP Crop sensitivity to environmental change Elasticity envylchgadd ENVYLDCHNG Agricultural yield change Additive factor envylchgm ENVYLDCHNG Agricultural yield change Multiplicative

There is no way to directly target atmospheric carbon stocks in the model. Instead, points of intervention involve flows, the most important of which is the burning of fossil fuels and therefore the rate of flow of carbon from its stock in those fuels to the stock in the atmosphere With respect to fossil fuel use, there are leverage points on both the production and consumption side—see the energy model discussion. Enpm, the energy production multiplier, will alter the flow of carbon into the atmosphere and can serve as a powerful leverage point in energy-driven environmental scenarios. Users can alter the mix of energy types produced within a country, thereby reducing or increasing the use of fossil fuels, and trace the environmental effects.

On the demand side, a reduction in endemm, the energy demand multiplier, can likewise simulate conservation/efficiency enhancement programs that alter the flow of carbon into the atmosphere. It is important to note that reductions in energy use triggered by endemm are free; that is to say, they are not accompanied in the model by economic costs. In reality, conservation or efficiency efforts occasionally do reduce energy use without a large cost—as evidenced by voluntary behavioral shifts on the part of citizens, or regulations enforced by the government. However, because it may be unrealistic to assume costless energy demand changes, users may wish to include financial considerations in their scenarios. The parameter carbtax simulates a reduction in demand triggered by tax; users would pay an additional fee on fossil fuels proportional to carbon content. This would encourage conservation or a transition to less expensive forms of energy. Users experimenting with carbon taxes should observe the effects on the energy production system (ENP), the level of annual carbon emissions (CARANN), world temperature (WTEMP) and the level of economic output (GDP).

Users can also simulate government interventions designed to encourage research and development focused on renewable energy or energy efficiency. Each energy type’s cost profile is linked to a capital-output ratio multiplier, qem. Users can simulate a decrease in the cost of renewables by reducing the multiplier’s initial value from 1.0 to 0.8—this would reduce all production costs by 20 percent (on top of whatever cost changes the model endogenously computes). Energy prices can also be indirectly controlled by changes to natural resources needed to produce fossil fuels. As oil and gas resources are exploited, the remainder becomes more expensive to extract. Users can use resorm to enforce alternative assumptions about the scale of ultimate resources available for discovery, and eventually, production. Mechanisms involving resources, reserves, and energy price operate over a long time horizon. Any scenario using resorm should be run out to at least the year 2050 in order to display significant forward linkages.

Changes in the efficiency of energy use from oil and gas sources would have bearing on carbon emissions as well. Changes in energy efficiency are controlled by the growth rate parameter enrgdpgr in addition or instead of the use of endemm. Changes to this parameter simulate the impact of successful research and development programs in the public or private sphere. Three parameters offer an additional way to simulate improvements to energy efficiency: carfuel1, carfuel2 and carfuel3 set, respectively, the amount of carbon emitted by burning oil, natural gas and coal. Normally these should be considered physical constants and not changed, but the model does not contain a representation of carbon sequestration and carfuel3 could be used to represent its impact, although not its cost. For a complete discussion on parameters pertaining to energy production see the energy section.

Returning to the issue of stock-to-stock flows of carbon, so far we have focused only on the movement of carbon from fossil fuels to the atmosphere. Not all carbon emitted into the atmosphere remains there, and the model contains parameters to control the absorption of atmospheric carbon by the oceans. Two parameters control this process in the model. The first is carabr, an exogenous specification of the billion tons of carbon that annually moves to or is absorbed by the oceans. The second is carabrchng, which sets the annual change in the absorption rate (a measure of flow). At this point the parameter is not functional, and the user should be aware that change in it will have no effect. The final stock-to-stock flow represented in the model is between the atmosphere and forests (to be discussed below). Because the model does not explicitly represent the flow between the atmosphere and other non-forest vegetation (or organic matter in the soil), users with interest in this area could turn to the parameter carabr to implicitly capture, not just absorption into oceans, but into other matter as well.

Turning to forward linkages, future trends in the carbon system will affect the world’s agricultural sector. The stock of atmospheric carbon dioxide (CO2PPM) and the related level of global temperature (WTEMP) are the variables that transmit the carbon system's effects. The model represents two major paths of impact, one related to each variable. The first path is the so-called carbon-fertilization effect. Because plants need carbon, an increase in CO2PPM can actually increase agricultural yields, and the parameter that controls that is envco2fert, crop sensitivity to atmospheric carbon dioxide. A value of zero would turn off this linkage.

The second path is from global temperature (WTEMP) to country-specific temperature and precipitation changes (ENVTPCHG andENVPRCHG) and then to agricultural yield. The translation in the model of changes in global temperature to changes in country-specific temperature and precipitation uses information from climate-specific models and is not subject to parametric control by users (in modeling terminology, they are “hard-coded” within the model). The user can, however, affect the further impact of country-specific values to yield. While the model's formulation for that impact (ENVYLCHNG) on yield is quite complex, two parameters exist for influencing it, the multiplicative parameter envylchgadd and the additive parameter envylchgm. Their effects are sequential, so it is best that the user rely on one or the other, not both.

One additional parameter is important in this consideration of forward linkages, namely that which controls the translation of atmospheric carbon levels (CO2PPM) into global temperature (WTEMP). That parameter is envclimsens, the time sensitivity of global temperature to carbon dioxide. The very large-scale global circulation models of climate change struggle to make the connection between these two variables and IFs uses this simple parametric linkage to summarize some of what we have learned from them. But the relationship is uncertain, so a scenario that would raise or lower the parameter and the impact on global temperature might be of interest. Moreover, the impact of carbon on temperature is likely to be one that involves lags (increasing temperature slowly even at a given fixed level of raised carbon dioxide), so one could consider a dynamic value of the parameter over time in order to capture that.

#### Land Use

 Parameter Variable of Interest Description Type forestm WFOREST World forest multiplier Multiplicative tgrld WFOREST, LD Land growth by type Initial condition carforst CO2PPM Level of carbon sequestered by forests Coefficient

The importance of land extends beyond its role in carbon cycle. We are interested in forest area, for instance, due to its involvement in species preservation, in water cycle control, and for its own intrinsic value. A direct multiplier, forestm, imposes an increase or a decrease on a country’s forest area. Any change in land area is constrained by a country’s geographic size because an increase in forest area requires a corresponding decrease in the land available for other types of use, such as crop production. As another way to affect land use, the parameter tgrld sets an initial target for annual growth in cropland. This second parameter achieves a change in land devoted to crops primarily as a trade off with forest area. For a complete discussion of parameters concerning patterns of land use, see the agricultural section. Changes in land use will, of course, have secondary consequences visible in GDP, AGP, AGX, and AGM, among other variables

Returning to the role of forests in the carbon cycle, the key parameter is the amount of carbon sequestered by each million hectares of the earth’s forests, carforst. By default, this coefficient is set to 0.12—meaning that every million hectares of forest sequesters 0.12 billion tons of carbon. By increasing or decreasing the value, users can explore futures in which forests are more or less effective at removing carbon from the atmosphere.

## Parameters Affecting Water Resources

 Parameter Variable of Interest Description Type watres Unknown Renewable water resources Exogenous specification tgrld AGP Land growth by type Initial condition ylm YL Agricultural yield multiplier Multiplier

Currently, only one parameter in the model is directly linked to water variables. Watres allows users to set the renewable resources located in each of the countries in IFs. Changes to this parameter or exogenous variable (we would like it at some point to be a variable) can either be instantaneous or gradual, as specified by the user. Though each country’s renewable water resources, and associated water stress, has bearing on state fragility, it is important to note that the watres parameter does not constrain water use. As mentioned in the discussion above, modeling work is currently envisioned to create a dynamic water system.

Users can, however, affect water use via deeper drivers. Any increase (or decrease) in crop land, and subsequent increases (or decreases) in production, will require a corresponding change in water usage (WATUSE). Water usage per capita is linked to crop production per capita by a function in the model (Crop Production/Capita Versus Water Use/Capita). Thus, changing that function or manipulating the land growth by type parameter, tgrld, can trigger patterns of water uses differing from those featured in the Base Case. The yield multiplier ylm provides a very direct method of indirect water use modification; increases in production will require more water, often (implicitly in the model) through irrigation.

## Parameters Affecting Air Pollution

 Parameter Variable of Interest Description Type envpm2pt5m ENVPM2PT5 PM2.5 levels multiplier Multiplier ensolfuelm ENSOLFUEL Solid fuel use multiplier Multiplier ensolfuelsetar ENSOLFUEL Solid fuel use SE target Standard error target ensolfuelseyrtar ENSOLFUEL Solid fuel use SE target Standard error target ensolfueltrgtval ENSOL FUEL Solid fuel elimination target Target ensolfueltrgtyr ENSOLFUEL Solid fuel elimination target Target hlsolfuelsw DEATHCAT Relationship between solid fuel and respiratory diseases Switch

Several environmental parameters allow users to directly manipulate levels of air pollution. Fine grain aerial particulate matter levels in residential areas (ENVPM2PT5) are controlled by a multiplicative parameter, envpm2pt5m. Like any multiplier, assigning higher or lower values will result in a proportional change in the associated variable—in this case ENVPM2PT5. As an alternative, users can explore variations of solid fuel use in a specific country, region, or the world as a whole. The percentage of a population using solid fuel as their primary source of electricity (ENSOLFUEL) is connected to a multiplier, ensolfuelm. The model also features a pair of standard error target parameters: one that establishes the target, ensolfuelsetar, and one that establishes the year by which the target will be reached, ensolfuelseyrtar. Users can alternatively (not simultaneously) utilize a second pair of parameters to set a year by which solid fuel use will be either reduced, or eliminated completely. Ensolfueltrgtval sets the desired reduction, and ensolfueltrgtyr sets the target year. Finally, and related to forward linkages, users can control whether there is a relationship between the burning of solid fuel and health. When the switch parameter hlsolfuelsw is set to 1 (the default), a positive relationship between solid fuel use and respiratory disease is active; when the switch is set to 0, the relationship is deactivated.

## Prepackaged Scenarios

Exploring an alternative scenario conceptualized by the Netherlands Environmental Assessment Agency (PBL) can give users a quick overview of some of the powerful leverage points in the environment module. Using their integrated assessment model, called IMAGE, PBL constructed a set of forecasts representing a future in which carbon emissions drop dramatically. A replication of this scenario comes pre-packaged with the IFs installation, in the World Integrated Scenario Sets folder and the NCAR IPCC subfolder, under the title IMAGE_hirenew_lowcarbon. The folder contains, in total, five scenarios, all of which were developed during the course of a joint project between the Pardee Center and the National Center for Atmospheric Research (NCAR), which is located in Boulder, Colorado. Each alternative scenario uses IFs parameters to approximate work done by another integrated assessment model: BPL’s Integrated Model to Assess the Global Environment (IMAGE), Asia-Pacific Environment Innovation Strategy’s AIM, the International Atomic Energy Agency’s MESSAGE, and Pacific Northwest National Laboratory’s Mini-Climate Assessment Model, or MiniCAM,

In order to simulate a significant reduction in global carbon emissions, the IMAGE_hirenew_lowcarbon scenario includes interventions in capital-output ratio, fossil fuel burning behavior, and carbon tax policy. For oil, gas, coal and nuclear production in every country, the parameter qem is set to rise steadily, reaching a value of four by mid-century and remaining there until 2100. Conversely, the capital cost of each unit of renewable energy produced in the world is set to decline, with qem approaching 0.3, and remaining at that value until 2100. As fossil fuel production becomes expensive and green energy becomes relatively cheaper, price signals will push global energy producers to reduce carbon emissions. The use of the carbtax parameter augments the economic incentives attached to renewables in the scenario. By 2100 a carbon tax of 400 billion U.S. dollars is in place in every country in the world. Finally, the scenario adds an element of environmentally friendly technological change, through a reduction in the parameters carfuel1, carfuel2, and carfuel3. This would simulate worldwide discoveries allowing for the cleaner burning of oil, natural gas, and coal.

# Governance Module

## Variables of Interest

 Variable Name Description GOVINDSECUR Governance security index GOVINDCAPAC Governance capacity index GOVINDINCLUS Government inclusiveness index GOVINDTOTAL Total governance index SFINTLWARALL Internal war, event occurrence SFINTLWARMAG Internal war, magnitude GOVRISK Government risk index GOVREV Government revenues AID Foreign Aid GOVCORRUPT Government Corruption REGQUALITY Regulatory quality ECONFREE Economic freedom measure GOVEFFECT Government effectiveness DEMOCPOLITY Level of democracy FREEDOM Freedom measure GEM Gender empowerment measure

The IFs governance module contributes to a growing body of work dedicated to quantifying the performance of governments around the world. The IFs model adds to that literature especially by facilitating forecasting and scenario analysis. The module represents governance as a composite system, made up of three interconnected dimensions: security, capacity and inclusiveness. The modelling process builds on a large number of data series and governance indices constructed by various research groups, mostly in academia, non-profit organizations, and international organizations. Variables capturing regulatory quality and government effectiveness, for example, come from the World Bank’s World Governance Indicator project; Freedom House and Transparency International provide measures of economic freedom and corruption; and the Center for Systemic Peace provides state fragility and democracy indicators. Based on such data and series, the IFs system builds up to aggregate indices for its three dimensions of governance: GOVINDSECUR for security, GOVINDCAPAC for capacity, and GOVINDINCLUS for inclusivity. These three indices are then combined into an overall governance index,GOVINDTOTAL.

The building blocks of the three indices, however, are sub dimensions and component variables. Security’s sub-dimensions are the probability of intrastate conflict (SFINTLWARALL) and a general level of government performance with associated risk of instability (GOVRISK). Although not used in the security index, SFINTLWARMAG supplements SFINTLWARALL by forecasting the average magnitude of internal conflict.

Forecasts of intrastate war probability (SFINTLWARALL) are heavily path dependent, driven by a moving average of countries’ past experiences with violence. Over time, however, other factors—infant mortality, level of democracy, economic growth rate, and trade openness, among others—affect the probability of conflict as well. IFs uses an index, GOVRISK, to measure the second sub-dimension of security, government performance and associated risk. For the 186 countries included in the model, the overall index value ranges roughly from 15 to 60, with a higher number signifying increased levels of risk. Low risk countries such as Sweden will generally fall at the bottom of the range, while high risk ones such as Somalia will be at the top. Like the probability of war, each country’s performance risk profile is driven by an array of factors across demographic, environmental, economic, health and education subsystems. A comprehensive look at each country’s performance and risk profile is available through Specialized Display under the option Performance Risk Evaluation. The display includes raw values and a global ranking, for each of the index’s nine components. Weighting and relative risk levels associated with each component are also listed.

The sub-dimensions of the second dimension of governance, capacity, are government revenue and government quality. Each country’s ability to raise revenue is captured by a simple ratio of GOVREV toGDP—net of foreign aid (AID) received by governments, which the index does not include. The more revenue a government is able to raise relative to GDP, the higher the capacity score will be—for a discussion of the equilibrating processes used to calculate government revenue, expenditure and transfer payments see the economic module. Governance quality, capacity’s second sub-dimension, is represented by corruption. In the model, GOVCORRUPT measures relative levels of political corruption, with 0 representing extremely corrupt practices, and 10 representing extreme transparency, or lack of corruption. Indices tracking regulatory quality (REGQUALITY), economic freedom (ECONFREE), and government effectiveness (GOVEFFECT) offer alternative formulations of governance quality. Although the GOVCORRUPT score is the only element used to generate the composite index GOVINDCAP, the other indicators link forward to economic and infrastructure development processes.

The third dimension of governance, inclusiveness, is made up of the level of formal democratization and an element that measures gender empowerment in society. The DEMOCPOLITY index represents the strength of democratic institutions; in IFs, values range from 0 to 20, higher numbers signifying greater levels of democracy. In forecast years, several factors—the age structure of society, dependence on raw materials, energy exports and energy price—determine the strength of democracy. The model uses the gender empowerment measure (GEM) as another index of inclusiveness. The measure takes its historical values—and its name—from the United Nations Development Program’s Gender Empowerment Measure. Forecasts of gender empowerment are driven by a country’s youth population size, GDP per capita at purchasing power parity, and years of formal education attained by female adults. The index FREEDOM, created by Freedom House, offers a final formulation of civil participation in society. This measure is not used to generate the aggregate inclusiveness index, but it links forward to the social capital component of multifactor productivity.

While the three dimensions represent distinct facets of governance, they are tightly interrelated. Indeed, the drivers of security, capacity and inclusion often overlap. For example, gender empowerment, a sub-dimension of inclusion, drives security’s vulnerability to conflict index and capacity’s corruption index. And the three composite indices—GOVINDSECUR, GOVINDCAPAC andGOVINDINCLUS—interact bi-directionally with each other. Beyond the sub-module, strong relationships link the key governance indices and other measures of human well-being, represented by GDP per capita and the Human Development Index (HDI). For instance, economic freedom (ECONFREE) links forward to multifactor productivity (MFP) and thus on to broader economic growth. And conversely, other systems affect governance forecasts, as evidenced by the relationship between levels of per capita income and HDI and DEMOCPOLITY—higher levels of well-being lead to increased pressure for democracy.

The tri-part conceptualization of governance originated in the Pardee Center’s analysis of historical trends, as discussed in Potential Patterns of Human Progress: Strengthening Governance Globally (Hughes et al. (2014)). Over time, the story of governance has unfolded differently in countries across the world—some countries have developed security, capacity and inclusiveness sequentially, and some countries have developed all three dimensions simultaneously. A suite of parameters attached to the sub-dimensions of each dimension allows users to explore a range of potential patterns of global governance.

## Parameters to Affect Security

 Parameter Variable of Interest Description Type sfintlwaradd SFINTLWAR State failure/internal war addition Additive sfconv SFINTLWAR State failure convergence Convergence speed (years) sfmassrep SFINTLWAR Mass repression factor Additive wpextinterv SFINTLWAR External intervention factor Additive govriskm GOVRISK Government risk multiplier Multiplicative govriskweight GOVRISK Government risk weight Weight

The most direct way in which to change the probability of internal war, and thus its broader consequences, is to manipulate the additive parameter affecting the variable, sfintlwaradd. Other parameters connected to internal war and the country performance risk evaluation allow users to explore alternate scenarios around security. Over time, a country’s probability of internal war will converge to the model’s expected function, a function driven by demographic and economic factors. However, users can control the speed of convergence through the parameter sfconv—accelerating this convergence would be an effective way to improve the fortunes of countries with especially bad histories of intrastate violence, because path dependency is a driver of internal war probability. Though set to 0.0 in the Base Case, two additional parameters exist that can directly affect the likelihood of internal war. Wpextinterv is a parameter that, when activated, simulates a conflict-creating external intervention; when the parameter is set to 1.0 the probability of intrastate conflict rises to 1.0 also. Another parameter, sfmassrep, works in a similar way but with a reverse valence. Assigning it a value other than 0.0 simulates a campaign of mass repression by the central government that lowers the probability of overt intra-state violence.

Turning to government performance and risk evaluation, the multiplier govriskm directly controls the overall risk index, GOVRISK. Changes to the risk profile link forward to the higher order GOVINDVSECUR index, which in turn links to the overall governance score captured in GOVINDTOTAL. Users can also experiment with different risk configurations of a country or region via govriskweight; as the name implies, this parameter sets the weighting of GOVRISK’s component parts. As a final option, users can tailor their interventions to the components’ values, rather than simply the weighting. Any of the government risk index’s determining factors can be manipulated. The list of potential intervention targets includes: youth bulge, the ratio of water use to renewable resources, climate change, resource export dependence, rate of per capita GDP growth, infant mortality, life expectancy, malnutrition, HIV prevalence rate, primary net enrolment and adult education years. Scenario interventions could target one of these elements or several, cutting across IFs modules.

## Parameters to Affect Capacity

 Parameter Variable of Interest Description Type govrevm GOVREV Government Revenue Multiplier Multiplier firmtaxrm GOVREV Firm tax multiplier Multiplier hhtaxrm GOVREV Household tax rate multiplier Multiplier ssweltaxrm GOVREV Social Security multiplier Multiplier indirecttaxrm GOVREV Indirect tax multiplier Multiplier govcorruptm GOVCORRUPT Corruption Multiplier Multiplier govcorruptsetar GOVCORRUPT Corruption target Standard error target govcorruptseyrtar GOVCORRUPT Corruption target Standard error target goveffectm GOVEFFECT Effectiveness multiplier Multiplier goveffectsetar GOVEFFECT Effectiveness target Standard error target goveffectseyrtar GOVEFFECT Effectiveness target Standard error target govregqualm REGQUALITY Regulatory quality multiplier Multiplier govregqualsetar REGQUALITY Regulatory quality target Standard error target govregqualseyrtar REGQUALITY Regulatory quality target Standard error target econfreem ECONFREE Economic freedom multiplier Multiplier

The governance module includes parameters connected to both sub-dimensions of capacity: government revenue and government effectiveness. Govrevm is a direct multiplier of government revenue, GOVREV; assigning values to this parameter would signify either growing or diminishing government capacity to gather revenue. However, various, more nuanced, government revenue collecting programs can be simulated via several taxation parameters. The module includes multipliers to the tax rate paid by firms (firmtaxrm) and households (hhtaxrm), taxes paid on social security (ssweltaxrm) and indirect taxes such as sales tax or value added tax (indirecttaxrm). For a full discussion of government revenue interventions see the economic module discussion.

Each of the components of government effectiveness represents a potential target for scenario intervention. The variable GOVCORRUPT, measuring corruption levels, is connected to a multiplicative parameter, govcorruptm. Although corruption is the only representation of effectiveness tied to the GOVINDCAPAC, users may wish to explore the consequences of changes to other measures. Multipliers govregqualm andeconfreem directly control the level of a country’s regulatory quality (REGQUALITY) and economic freedom (ECONFREE). Increases to regulatory quality link forward to broadband connectivity and other information and communication technology (ICT) indicators; and changes in economic freedom affect multifactor productivity through social capital. Finally, users can manipulate the GOVEFFECT variable, a formulation of effectiveness taken from the World Bank’s World Governance Indicators project. Goveffectm is a straightforward multiplier, with assigned values acting to scale effectiveness up or down.

Users can alternatively choose to manipulate governance quality indicators via standard error targeting. Two parameters are linked to each corruption, regulatory quality and effectiveness. The first parameter sets the target increase or decrease in the index, expressed in standard errors away from the expected function, and the second parameter sets the number of years that will pass before the target is reached. These parameters are govcorruptsetar and govcorruptseyrtar for corruption, govregqualsetar and govreqqualseyrtar for regulatory quality, andgoveffectsetar andgoveffectseyrtar for government effectiveness.

## Parameters to Affect Inclusiveness

 Parameter Variable of Interest Description Type democm DEMOCPOLITY Democracy multiplier Coefficient swseffects DEMOCPOLITY Swing state effect Additive factor democimpus DEMOCPOLITY Democracy impetus by leading power Coefficient democimpoth DEMOCPOLITY Democracy impetus by other powers Coefficient democwvlen DEMOCPOLITY Democracy wave length Limit democwvmax DEMOCPOLITY Democracy wave upper limit Limit democpolitysetar DEMOCPOLITY Democracy SE target Standard error target democpolityseyrtar DEMOCPOLITY Democracy SE year Standard error target freedomm FREEDOM Freedom multiplier Multiplier gemm GEM Gender empowerment multiplier Multiplier gemsetar GEM Gender empowerment target Standard error target gemseyrtar GEM Gender empowerment year target Standard error target

The two sub-dimensions of government inclusiveness are democracy and gender empowerment. Users wishing to explore alternative scenarios around levels of democracy can choose from an array of leverage points. First, the multiplier democm allows direct manipulation of the variable DEMOCPOLITY for a country or region. Users choosing this brute force method should direct their attention to linkages in multifactor productivity, state fragility, and the overall GOVINDINCLUS index. Scenario interventions could also target any of the deeper drivers of democracy. In the model, forecasts of democracy levels are a function of gender empowerment, the youth bulge, dependency on raw material exports and energy prices. Two other parameters—disabled in the base case—can affect DEMOCPOLITY. The first simulates a neighborhood affect: if swseffects is set to a value of 1, swing states in a geographic region will be pressured to conform to their neighbors’ democratic practices—or lack thereof. The second additional method is rather complicated and will not be of interest to most users. The variable DEMOCWAVE either adds to or subtracts from the democracy levels determined by the model, simulating a wider neighborhood, in fact global, effect known as a wave of democracy. The magnitude of upswings or downswings is specified exogenously through several parameters: democimpus, the impetus provided to democracy by the leading power; democimpoth, the impetus provided to democracy by other powers; democwvlen, the wave of democracy’s duration in years; and, democwvmax, the maximum rise in democracy levels. When a democracy wave is active, two variables track the process: DEMOCWVCOUNT measures the duration of the wave’s run and recession, and DEMOCWVDIR contains the direction of the wave, whether it supports or hinders the consolidation of democracy. As an alternative to other types of intervention, users may override the polity function through the use of external targeting. Democpolitysetar specifies a standard error target, and democpolityseyrtar specifies the year in which this target will be achieved.

The degree of civilian participation – on the part of all citizens – is another driver of inclusiveness. Whereas the polity measure of democracy (DEMOCPOLITY) measures formal, electoral inclusion, the variable FREEDOM is a more comprehensive measure of civil participation. FREEDOM is computed from levels of GDP and general educational attainment—among both men and women; and like the other elements inclusion, it is linked to a brute force multiplier, freedomm. Unlike the other parameters, however, standard error targeting is not available for the civil society freedom variable.

The second dimension of government inclusiveness, gender empowerment, offers a final target for scenario analysis. The three primary drivers of the gender empowerment measure (GEM) are youth bulge size, GDP per capita at purchasing power parity, and years of formal education obtained by females over age fifteen. Users can build alternate scenarios around a combination of driving factors, or, more directly, through a multiplicative parameter. The multiplier gemm allows users to directly control the progression/regression of gender empowerment in a country or region. Additional parameters control standard error targeting around GEM: used together, gemsetar andgemseyrtar establish a target value, measured in distance from the expected, cross-sectional function, and the time required to reach the target.

## Prepackaged Scenarios

An installation of IFs includes scenarios used in each entry in the Pardee Center’s Potential Patterns of Human Progress (PPHP) series. Scenarios created for the governance volume include: a high conflict scenario; a scenario representing challenges to the world’s governments; a set of scenarios that speed up economic growth via multifactor productivity; and, finally, a set of government strengthening scenarios. This section will briefly describe the parameter interventions used to construct several of the enhanced government scenarios, located in the folder Strengthened Governance.

The collection of governance scenarios includes one scenario for each of the three dimensions: Strengthened Security, Strengthened Capacity SE1 and Strengthened Inclusiveness SE1. Each scenario includes a single parameter shift that targets one sub-dimension of security, capacity, or inclusion. Users wishing to load the three interventions simultaneously can select the combined scenario, Strengthened Governance SE1. The boost to capacity operates through govrevm, the multiplier tied to the revenue sub-dimension of capacity; the parameter is set to 1.1 for the majority of the forecast horizon, raising revenue collection each year. The intervention in inclusion targets the democracy sub-dimension. For the entire forecast horizon, from 2010 to 2100, democpolitysetar is set to 1, establishing an improvement target in democracy levels of one standard error. As the corresponding year parameter, democpolityseyrtar, is not changed, it takes on its default value of 10.0. This means that the target of one standard error will be reached in 10 years. The final intervention simulates a decline in intra-state violence the world over. The additive factor sfintlwaradd interpolates from 0 to -1 over thirty years; after reaching negative 1, it remains at that value through 2100.

The final scenario in the governance set, Strengthened Policies, features interventions to deeper drivers of each governance dimension, including: educational improvements at primary, lower secondary, upper secondary and tertiary schooling levels; reductions in childhood malnutrition; increased government transfers and international development assistance; steady decline in total fertility rates across western, central and eastern Africa as well as Oceania; rising agricultural yields; less solid fuel use, and a corresponding reduction in atmospheric particulate matter.

The two comprehensive scenarios contained in the PPHP Governance folder represent distinct methods of alternative forecast analysis. Strengthened Governance SE1 explores the effects of directly manipulating governance indicators. Strengthened Policies, on the other hand, explores the forward linkages of governance drivers.

# International Politics Module

## Variables of Interest

 Variable Name Description POWER Total power index CPOW Conventional power NPOW Nuclear power WCPOW World nuclear power PCONGREAT Power concentration of great powers PCONGREATEU Power concentration of great powers, single Europe PCONGREATEUF Power concentration of great powers, flexibly defined, with single Europe THREAT Probability of militarized conflict THREATREC Threat received THREATSENT Threat received THREATSENT Threat sent WGDP World GDP WTRADE World trade GOVEMBED Government embeddedness CWAR Conventional war CWARPB Conventional war probability NWAR Nuclear war NWARPB Nuclear war probability CIVDM Civilian damage (multiplier) DEMOCPOLITY Level of democracy DIPLOEMBASSY10 Number of embassies held in other countries DIPLOIGO International organization membership DIPLOTREATY Treaty participation

10For DIPLOEMBASSY, DIPLOIGO and DIPLOTREATY, there exist three corresponding variables with the suffix MAX that cap forecast values.

Unlike other systems, such as demographics or economics, there is no standard organizational structure used to represent international politics. In order to establish a theoretical structure that describes interactions between states, IFs draws on in-house expertise, as well as work conducted by other research organizations, academics and governments and, of course, international relations theory and empirical work. The two foundational elements in the model are power and threat levels, which trigger alliances, conflicts and military spending. IFs takes conceptual and theoretical inputs from projects that have been dedicated to quantifying power and threat dynamics, including J. David Singer’s Correlates of War project, initiated at the University of Michigan, which produced the Composite Index of National Capability (CINC) –a measure of hard power, the CINC formulation includes demographic, economic and military components. IFs reaches out for data to a variety of sources including the Stockholm International Peace Research Institute’s (SIPRI) datasets on military expenditures, arms trade, and international conflicts. The IFs international politics module contributes to existing work in the field by forecasting measures of hard power and of threat levels between states. It is increasingly also developing data and forecasting capability around diplomacy.

IFs includes several formulations of power. The variable POWER is an index, computed in the model, that measures each country’s share of world power. The index is comprised of weighted elements for: GDP, population, GDP per capita (as a proxy for technological sophistication), government size, public military spending, conventional power, and nuclear power. Levels of conventional (CPOW) and nuclear (NPOW) power have their own dynamics, but both are primarily dependent on military spending patterns, as captured in the variable GDS and an associated parameter that determines the amount of funding available for nuclear weapons research. (For a full discussion of government spending flowing to the military and to other destinations, see the economic section.) NPOW is not an index – like POWER or CPOW – but rather the number of nuclear warheads owned by each country. Two additional variables measure global power. The indicator of the world’s stock of nuclear power, WNPOW, is measured in megatons. And WCPOW is an aggregate value of all countries’ conventional power, expressed as an index.

Depending on the configuration of power at a given point in time, the international system may be multipolar, bipolar, or unipolar. A set of variables in the model capture the degree to which power is concentrated in the hands of a few nations. The index PCONGREAT measures the percentage of global power held by dominant, or great powers— as values approach zero, smaller shares of power would be held by so-called great powers. PCONGREATEU measures the percentage of global power held by great powers, considering the European Union to be a single actor. PCONGREATEUF measures the percentage of global power held by great powers, with the European Union considered a single actor and the category “great power” flexibly defined; the flexible definition refers to the phasing in of great power status above a threshold share of global power up to full great power status upon attaining a higher share. And finally, the variable PCONSYS is an index indicating the power concentration of the global system, regardless of the number of great powers.

In IFs, the balance of power between actors in the international system links forward to threat (THREAT) of militarized interstate disputes (MIDs). The model calculates the threat that any state poses to any other state as a product of historical conflict data and a predictive function. Three especially important factors drive the dyadic function: contiguity, whether or not either of the countries is a great power, and the existence of a territorial dispute between the countries. Additional drivers of international threat include world trade (WTRADE), energy production (WENP), and strength of democracy (DEMOCPOLITY). We also compute a level of government embeddedness (GOVEMBED) that may interact with power structures in future model development. (Government embeddedness is a concept taken from international political economy; a government is considered embedded if its actions are constrained by domestic institutions, firms and households). The results of computations are stored in an index, THREAT, which can be interpreted very roughly as the probability that a pair of countries will experience a militarized dispute in a given year (but expressed in percentage terms of 0-100 instead of probabilistic ones of 0-1). Countries can be theorized as both recipients and deliverers of threat: THREATREC is of the total of all threat received from all senders; THREATSENT is, as the name indicates, the total threat sent by a country. And the variable WTHREAT is an average of threat levels between all country pairs, thus offering an indication of global military tension.

The model includes a specialized display for threat dynamics and other dyadic (country-to-country) interaction. To access the display select the Data Analysis option from the IFs main menu, then select Dyadic Interaction Display. On the resulting form, users can explore the relationship between any two countries via the list boxes labeled CountryA and CountryB. In addition to THREAT, a slew of international political variables are available for dyadic display in the Variable box: Midpresent, FatalityBinary, StandAlliance, StandDiplo, StandGO, Stand UN, StandTrade, StandCOI, StandLiberalisHigherMoreLib, StandRealismLowerMoreTension.

As an additional feature, the international politics module includes an optional war simulation. When the war option is activated, the model will stochastically (randomly) introduce a war, based on dyadic threat levels described above. The variables CWARPB and NWARPB measure the probability that a conventional or nuclear war will break out in a given year, whether or not the switch for the war option is activated. When the switch is on, a second pair of variables identifies the occurrence or absence of conventional war and nuclear war: CWAR and NWAR take on binary values, 1 indicating the presence of war, and 0 indicating the absence of war. Once a war event takes place, the countries involved will experience fatalities and material damage. CIVDM tracks the human costs to overall population and the capital lost in the fighting.

In addition to forecasting power, threat and conflict, the IFs model forecasts several diplomatic measures. The model currently includes variables containing the number of embassies each country has abroad (DIPLOEMBASSY), international organization membership (DIPLOIGO), and treaty membership (DIPLOTREATY). Like the other variables in IFs, these measures of soft power are forecast out to 2100 for each country.

Parameters in the international politics model allow users to create alternative scenarios that explore uncertainty around the international power structure, dyadic threat levels, the outbreak of war, and diplomacy.

## Parameters Affecting Power

 Parameter Variable of Interest Description Type cpowf CPOW Factor controlling military spending conversion Convergence factor cpowldcf CPOW Higher impact of military spending in LDCs Convergence factor drcpow CPOW Depreciation rate of conventional power Rate of change cpowdf CIVDM Conventional power, damage done by war Coefficient govnucwpadd NPOW Nuclear weapons stockpile change Additive factor wpwghtpow POWER Contribution of various components to power index Index weight wpgreatthresh PCONGREAT Systemic power share required for threshold of great power status Coefficient wpgreatlev PCONGREAT Systemic power share required for full great power status Coefficient wpconeusw PCONGREAT EU great power switch Switch wpconmax PCONSYS Maximum power concentration Limit wpconmin PCONSYS Minimum power concentration Limit

A suite of parameters is available to users who wish to explore power dynamics in the IFs model. Conventional power (CPOW) can be manipulated using the parameters cpowf, cpowldcf, and drcpow. The first controls the elasticity between military spending and conventional power accrual; higher values of cpowf increase the impact that military spending has on conventional power measures. Cpowldcf controls the impact of military spending within LDCs, where military spending has historically had a greater impact on measures of power, largely because the costs of military personnel are lower. And drcpow controls the depreciation rate of conventional power. This parameter controls the rate at which past investments in conventional power decline as a contribution to power in successive years. The destructiveness of interstate conflict has a negative impact on conventional power measures. The impact that conventional war has on combatant nations, as measured by CIVDM, can be controlled by cpowdf—the higher the parameter value, the greater the civilian deaths and capital destruction. Users may also increase or decrease a country’s stock of nuclear weapons via the parameter govnucwpadd. Nuclear weapons capacity influences total power through the nuclear power component, NPOW.

The aggregate measurement of power is similarly connected to parameter levers. The parameter wpwghtpow allows users to customize the set of weights used in the calculation of the power index (POWER). Individual weights are attached to each of the index’s components: population, GDPatPPP, GDP, GDPxGDPPCatPPP, GDPxGDPPC, Govt, MilSpend, ConvPow, NucPow, NucWarHeads, EnergyNetExport, TradeTotal, FDIInflows, AidNet, HumCapWkPopHDI, GovRev, InnovationR&D, and ConnectivityICTProd.

Users can also alter the role of great powers in the international political system.Wpgreatthresh controls the threshold level of the power index that, when achieved, signals the basic level of a nation’s transition into (or out of) being a great power. Wpgreatlev controls the power share held by full great powers, allowing users to model greater or lower levels of power diffusion. And, wpconeusw is a switch parameter that controls whether the EU is considered a single great power in the global power structure.

Two final parameters place limits on the levels of power that any country can experience. Wpconmax and wpconmin set the maximum and minimum level of power that a country can reach in forecast years.

## Parameters Affecting Threat Levels

 Parameter Variable of Interest Description Type wpgreat1 THREAT Dyadic dispute probability, one great power Coefficient wpgreat2 THREAT Dyadic dispute probability, two great powers Coefficient wppowtran1 THREAT Power transition threshold Coefficient wppowtran2 THREAT Increase in dispute probability with 10% greater power parity Elasticity wpcon THREAT Change in threat with power concentration Coefficient wpconsq THREAT Change in threat with power concentration Coefficient wpcontiguity THREAT Threat associated with geographic proximity Elasticity wpdemdist THREAT Response of threat to political distance Elasticity wpdemmin THREAT Response of threat to the minimum democracy level in the dyad Limit glocom THREAT Global community multiplier Exogenous specification wpcommun THREAT Response of threat to global community level Elasticity wptrade THREAT Change in threat with systemic trade Elasticity ally THREAT Alliance membership switch Switch wpally THREAT Reduction in threat with allied dyad Elasticity terdispute THREAT Territorial dispute or absence Exogenous specification wpterdisp THREAT Conflict arising from territorial disputes Elasticity wpthrconv THREAT Threat level convergence Convergence Speed

All parameters modifying threat affect the same variable, probability of dyadic conflict (THREAT). Wpgreat1 and wpgreat2 specify the likelihood of disputes involving one and two great powers, respectively. The reason these parameters exist is that great powers have historically been much more likely to experience conflict than have other states. A subset of great powers, namely the global leader in power and any power challenging that leadership and transition itself into system leadership, is especially likely to be engaged in conflict. Wppowertran1 sets the dyadic power ratio for those two countries that identifies the beginning of a power transition and thereby triggers an increase in conflict threat levels. For example, a wppowertran1 value of 0.7 means that a threat increase would begin once the weaker dyad member achieve a power score (POWER) equal to 70 percent of the more powerful member’s score. Wppowertran2 then establishes increase in the likelihood of conflict associated with increased power parity. More specifically, a wppowertran2 value of 0.05 means that, once a power transition has begun, each further reduction in a dyad’s power differential of 10 percent leads to an increase in threat levels of 5 percent.

Regardless of the number of great powers or the occurrence of a power transition, the concentration of power in the global system of great powers (PCONGREAT), as described earlier, can affect the level of threat of conflict (THREAT). Users may wish to take note of, wpcon and wpconsq, the two coefficients that control the relationship between power concentration and conflict likelihood. Wpcon directly links concentration to threat and wpconsq links the square of power concentration to threat.

Several additional parameters relate to the probability or threat of conflict in a dyad . Wpcontiguity controls the threat of dispute generated by geographic proximity, one of the strongest determinants of conflict. Wpdemdist alters threat generated in response to political incompatibility—i.e., divergent levels of democracy—between two members in a dyad (the greater the distance, the greater the threat). Wpdemmin relates conflict to the democracy level of the less democratic member of a dyad (if democracy of that member rises, the default value of that parameter would lower threat). A pair of parameters together establish the relationship between the global community and threat. Glocom represents an exogenous specification of the cohesiveness of the global community, andwpcommun is an elasticity between international cohesion and the probability of conflict. Wptrade modifies the response of threat to the level of systemic trade. The parameter ally is a switch parameter that turns on or off alliance memberships, while the parameter wpally reduces the probability of dispute if two dyadic members are allied. And finally, terdispute defines the presence or absence of a territorial dispute within a dyad, while wpterdisp changes the likelihood of conflict arising from such disputes.

One other parameter is worth noting. Wpthrconv defines the convergence speed of historical threat levels within a dyad (which in initialization for the first model year reflect the reality of past conflicts) to the level of dyadic threat as calculated using all of the drivers and relationships indicated above. If that convergence speed, expressed in terms of the number of years for full convergence, is very slow (a high value for years needed), then the path dependency of past conflict will continue much more strongly to affect future likelihood than if the convergence speed is very fast (a low value for years).

## Parameters Affecting War Simulation

 Parameter Variable of Interest Description Type wpthrwar CWARPB Portion of disputes that escalate to war Coefficient waron CWAR, NWAR War switch Switch cwarbase CWARPB Conventional war probability addition to base computation Additive factor cwarf CWARPB Conventional war forcing Switch cwarsv CIVDM Conventional war severity Multiplier nwarf NWARPB Probability that conventional war will escalate to nuclear Coefficient cdmf CIVDM Civilian war damage factor Multiplier nwarsv CIVDM Nuclear war severity Multiplier elndpow CIVDM Elasticity of nuclear damage with nuclear power Elasticity

Threat (THREAT) within a dyad is, as indicated above, related to the probability of conflict but not identical to it, because scaling of the various contributions to the threat variable is complicated and uncertain. The parameter wpthrwar translates threat within a dyad into a probability of conventional conflict (CWARPB) in that same dyad. There will never be an actual conventional war, however, unless the war switch is turned on (waron=1). When that switch is on and CWARPB for a dyad is non-zero, conventional war in any dyad-year occurs randomly in relationship to that war probability. For any given dyad-year a user can change the probability of conventional war with the additive parameter cwarbase. Or wars can be forced with the parameter switch, cwarf, when it is set to 100 or above.

The probability of a nuclear war (NWARPB) is zero unless there is a conventional war (CWAR=1). If there is a conventional war, the probability of a nuclear war is determined by the severity of conventional war (cwarsv) and the probability of escalation (nwarf). If nwarf is set to 100 or above, it will force a nuclear war when a conventional one occurs between nuclear powers.

Once a conventional or nuclear war takes place, several parameters determine how damaging the effects will be. Cdmf is a multiplier that serves as a civilian war damage factor–the higher the parameter’s value, the greater the casualties and physical destruction. Two related parameters, cwarsv andnwarsv, control the severity of conventional and nuclear war. Elndpow is an elasticity of nuclear war severity and nuclear weapon numbers.

## Parameters Affecting Diplomacy

 Parameter Variable of Interest Description Type diploembassymaxm DIPLOEMBASSYMAX Maximum embassies abroad Multiplier diploigomaxm DIPLOIGOMAX Maximum international organization membership Multiplier diplotreatymaxm DIPLOTREATYMAX Maximum bilateral treaty participation Multiplier

The diplomacy portion of the IFs international politics module is under development. Currently, three multiplicative parameters exist that allow users to manipulate forecast values of embassies abroad (DIPLOEMBASSY), international organization membership (DIPLOIGO) and bilateral treaty agreements (DIPLOTREATY). Forecast values of each measure of soft power are limited by cap variables— DIPLOEMBASSYMAX, DIPLOIGOMAX, and DIPLOTREATYMAX. Users can raise or lower these caps via the parameters diploembassymaxm, diploigomaxm and diplotreatymaxm.

## Prepackaged Scenarios

There are few prepackaged scenarios which make use of the international politics parameters. Two sets featuring limited interventions are located under the Understandings of Causality heading in the Prepackaged Scenarios folder. Under the heading Power Capabilities are a number of different formulations of global power which modify the parameter wpwghtpow. These modifications are designed to reproduce the distributions of global power that have been discussed by academic researchers. For instance, the scenario COW-Like 3-Element at PPP increases the weighting for population, GDP at PPP, and military spending in the calculation of power. Three other scenarios in this folder (Brecke Formulation, Hillebrand-Herman Base and COW-Like 3-Element at Market) create alternative structuring of the POWER variable. Two other scenarios in this folder, Slow China Formulation, and Slow China Formulation with growth, model a slow growth in power for China by underweighting the value of military spending, GDP, GDPPPC, and population, while overweighting GDPPC.

In the folder labeled Threat International are two scenarios that model the threat of international war. These two scenarios modify the parameter wpthrconv in order to frame various assumptions about the dynamics of threats at the international level.

Finally, one scenario in the World Integrated Scenario Sets demonstrates the use of power parameters. This scenario is located in the MENA Scenarios and is called Iran Nukes + Sanctions. The scenario models the possible effect of Iran acquiring nuclear weapons and the potential US response. This scenario increases govnucwpadd to simulate the effect of Iran acquiring nukes, and then alters the calculation of the power index to underweight population, GDPPP, GDPxGDPPCatPPP, and military spending, while overweighting the effect of nuclear warheads, net exports of energy, trade flows, FDI inflows, net aid, government revenue, human capital, innovation and R&D, and connectivity of ICT. It also decreases exports (xshift), protectionism (protecm), oil and gas production (enpm), to demonstrate the possible impact of an international response that favored increasing sanctions against the country.

# Parameter Dictionary

## Population

 Parameter Variable of Interest Description Type tfrm TFR, CBR Total fertility multiplier Multiplier contrusm CONTRUSE Contraceptive use multiplier Multiplier eltfrcon TFR Elasticity of total fertility rate to contraception use Elasticity tfrmin TFR Long term TFR convergence value Limit mortm DEATHS Mortality multiplier (not cause specific) Multiplier hlmortm DEATHS Mortality multiplier by cause Multiplier wmigrm MIGRATE, MIGRANTS World migration rate multiplier Multiplier migrater MIGRATE, MIGRANTS Net migration rate (inward) Rate of change workingageentry POPPREWORK, POPWORKING Working age determinant Exogenous specification workingageretire POPWORKING, POPRETIRED Retirement age determinant Exogenous specification

## Health

 Parameter Variable of Interest Description Type hlmortm DEATHCAT, HLYLL, HLDALY Multiplier on Mortality (by cause) Multiplier hlmorbm YLD Multiplier on morbidity Multiplier hlstddthsw DEATHCAT Switches DEATHCAT from absolute numbers to deaths/1000 Switch watsafem WATSAFE, INFMOR Percentage of population with access to safe water Multiplier sanitationm SANITATION, INFMOR Percentage of population with access to improved sanitation Multiplier malnm MALNCHPSH Prevalence of child malnutrition Multiplier ylm YL Yield multiplier on agriculture Multiplier hivm HIVCASES Rate of HIV infection Multiplier envpm2pt5m ENVPM2PT5 Increases levels of environmental pollution Multiplier hlsmokingm HLSMOKING Increases rate of smoking Multiplier hlobesitym HLOBESITY Prevalence of obesity Multiplier hlbmim HLBMI Multiplier on body mass index Multiplier deathtrpvm DEATHTRPV Multiplier on traffic deaths per vehicle Multiplier deathtrpvsetar, deathtrpseyrtar DEATHTRPV Standard error target for traffic deaths per vehicle Relative target Value/Year hlmortmodsw CDR Reduces crude death rate in Africa, Europe, Southeast Asia, West Pacific Switch hltechshift CDR Rate of change in health technology Additive factor hltechlinc CDR Rate of change in health technology in low income countries Additive factor hltechssa CDR Rate of change in health technology in Sub-Saharan Africa Additive factor hltechbase CDR Rate of change in health technology at base Exogenous specification

## HIV/AIDS

 Parameter Variable of Interest Description Type hivm HIVRATE HIV infection rate, multiplier of percent of population infected Multiplier hivtadvr HIV CASES, HIVRATE Technical advance rate in of control of infection Rate of change hivmdcm HIVRATE HIV infection rate maximum for MDCs, multiplier Rate of change hivpeakr HIVCASES, HIVRATE HIV infection rate at year of peak Target value hivpeakyr HIVRATE Sets year of epidemic peak Target year hivincr HIVCASES HIV increase rate, only used prior to 2000 Rate of change aidsdrtadvr AIDSDTHS, AIDSRATE AIDs death rate, technical annual advance rate in control Rate of change aidsdratem AIDSRATE AIDs death rate as % of HIV infection rate, multiplier Multiplier

## Education

 Parameter Variable of Interest Description Type edpriintngr EDPRIINTN Primary net intake rate Annual growth rate edterintgr EDTERINT Tertiary intake rate Annual growth rate edseclowrtrangr EDSECLOWRTRAN Lower secondary transition rate Annual growth rate edsecupprtrangr EDSECUPPRTRAN Upper secondary transition rate Annual growth rate edprisurgr EDPRISUR Primary survival rate Annual growth rate edseclowrsurvgr EDSECLOWRSUR Lower secondary survival rate Annual growth rate edsecupprsurvgr EDSECUPPRSUR Upper secondary survival rate Annual growth rate edtergradgr EDTERSUR Tertiary survival rates Annual growth rate gdsm (education) GDS Government spending on education Multiplier edbudgon GDS Education funding priority Exogenous specification gdsedm GDS Education spending distribution Multiplier edexppconv EDEXPERPRI Education spending per student, primary Convergence speed edexpslconv EDEXPERSEC Education spending per student, secondary Convergence speed edexpsuconv EDEXPERSEC Education spending per student, secondary Convergence speed edexptconv EDEXPERTER Education spending per student, tertiary Convergence speed edqtqltrm EDEXPERPRI, EDEXPERSEC , EDEXPERTER and GDS Education quantity-quality balance Multiplier edprigndreqintn EDPRIINTN Gender parity for primary intake Convergence speed edprigndreqsur EDPRISUR Gender parity for primary survival Convergence speed edseclowrgndreqsurv EDSECLWRSUR Gender parity for lower secondary survival Convergence speed edseclowrgndreqtran EDSECLWRTRAN Gender parity for lower secondary transition Convergence speed edsecupprgndreqsurv EDSECUPPRSUR Gender parity for upper secondary survival Convergence speed edsecupprgndreqtran EDSECUPPRTRAN Gender parity for upper secondary transition Convergence speed edtergndreqint EDTERINT Gender parity for tertiary intake Convergence speed edtergndreqgrad EDTERSUR Gender parity for tertiary survival Convergence speed

## Agriculture

 Parameter Variable of Interest Description Type agdemm AGDEM Demand for agricultural production Multiplier calmeatm AGDEM Demand for meat as a portion of total calories Multiplier elascd AGDEM Elasticity of crop demand to changes in food price Elasticity elasmd AGDEM Elasticity of crop demand to changes in food price Elasticity tgrld AGP Target growth rate in cultivated land Initial ylm YL Crop yield (agricultural production) Multiplier ylmax YL Maximum crop yield Exogenous specification envylchgadd ENVYLDCHNG Agricultural yield change Additive factor slr AGP Livestock slaughter rate Exogenous specification livhdpro AGP Livestock herd productivity gain with grain feeding Exogenous specification aquaculm FISH Fish production through aquaculture, multiplier Multiplier aquaculgr AQUACUL Growth in the use of aquaculture Growth Rate ofscth, rfssh FISH Global ocean fish catch and geographic distribution Exogenous specification lossm LOSS Agricultural wastage Multiplier aginvm YL, IDS (agriculture) Investment in agriculture Multiplier elagind AGP Elasticity of industrial use of crops with price Elasticity elinag1 IDS (agriculture) Elasticity of investment in agriculture to profit levels Elasticity elinag2 IDS (agriculture) Elasticity of investment in agriculture to changes in profit level Elasticity malnm MALNCHP Malnourished children Multiplier malnconv MALNCHP Convergence time with cross-sectional norm Exogenous specification

## Energy

 Parameter Variable of Interest Description Type endemm ENDEM Energy demand Multiplier elasde ENP, WEP Price elasticity of energy demand Elasticity carbtax ENP Carbon tax Exogenous specification enrgdpgr ENP, QE Growth in energy efficiency Rate of change enpm ENP Production multiplier Multiplier eprodr ENP Production growth rate Growth rate resorm RESOR Conventional resource multiplier Multiplier resorunconm RESORUNCON Unconventional resource multiplier Multiplier rdm RESER Discovery multiplier Multiplier etechadv QE Rate of technical advance in energy Exogenous specification etechadvuncon QE Rate of technical advance in unconventional energy Exogenous specification qem QE Capital output multiplier Multiplier eninvm ENIV Energy investment Multiplier enprix ENPRI Regional energy price Exogenous specification enml, enxl ENM, ENX Limits on energy imports and exports Exogenous specification

## Environment

 Parameter Variable of Interest Description Type enpm ENP Energy production by type Multiplicative endemm ENDEM, WEP Energy demand multiplier Multiplicative carbtax ENP, CARANN Carbon tax Exogenous Specification qem ENP Capital-output ratio multiplier Multiplicative resorm RESOR Resource multiplier Multiplicative enrgdpgr ENP Energy efficiency Growth rate carfuel1 CARANN Carbon generated by gas Coefficient carfuel2 CARANN Carbon generated by natural gas Coefficient carfuel3 CARANN Carbon generated by coal Coefficient envclimsens CO2PPM, WTEMP Time sensitivity of global temperature to carbon dioxide Limit carabr CO2PPM, WTEMP Carbon absorbed by oceans Coefficient carabrchng CO2PPM, WTEMP annual change in this absorption rate of carbon by oceans Coefficient envco2fert AGP Crop sensitivity to environmental change Elasticity envylchgadd ENVYLDCHNG Agricultural yield change Additive factor envylchgm ENVYLDCHNG Agricultural yield change Multiplicative forestm WFOREST World forest multiplier Multiplicative tgrld WFOREST, LD Land growth by type Initial condition carforst CO2PPM Level of carbon sequestered by forests Coefficient watres Unknown Renewable water resources Exogenous specification tgrld AGP Land growth by type Initial condition ylm YL Agricultural yield multiplier Multiplier envpm2pt5m ENVPM2PT5 PM2.5 levels multiplier Multiplier ensolfuelm ENSOLFUEL Solid fuel use multiplier Multiplier ensolfuelsetar ENSOLFUEL Solid fuel use SE target Standard error target ensolfuelseyrtar ENSOLFUEL Solid fuel use SE target Standard error target ensolfueltrgtval ENSOL FUEL Solid fuel elimination target Target ensolfueltrgtyr ENSOLFUEL Solid fuel elimination target Target hlsolfuelsw DEATHCAT Relationship between solid fuel and respiratory diseases Switch

## Governance

 Parameter Variable of Interest Description Type sfintlwaradd SFINTLWAR State failure/internal war addition Additive sfconv SFINTLWAR State failure convergence Convergence speed (years) sfmassrep SFINTLWAR Mass repression factor Additive wpextinterv SFINTLWAR External intervention factor Additive govriskm GOVRISK Government risk multiplier Multiplicative govriskweight GOVRISK Government risk weight Weight govrevm GOVREV Government Revenue Multiplier Multiplier firmtaxrm GOVREV Firm tax multiplier Multiplier hhtaxrm GOVREV Household tax rate multiplier Multiplier ssweltaxrm GOVREV Social Security multiplier Multiplier indirecttaxrm GOVREV Indirect tax multiplier Multiplier govcorruptm GOVCORRUPT Corruption Multiplier Multiplier govcorruptsetar GOVCORRUPT Corruption target Standard error target govcorruptseyrtar GOVCORRUPT Corruption target Standard error target goveffectm GOVEFFECT Effectiveness multiplier Multiplier goveffectsetar GOVEFFECT Effectiveness target Standard error target goveffectseyrtar GOVEFFECT Effectiveness target Standard error target govregqualm REGQUALITY Regulatory quality multiplier Multiplier govregqualsetar REGQUALITY Regulatory quality target Standard error target govregqualseyrtar REGQUALITY Regulatory quality target Standard error target econfreem ECONFREE Economic freedom multiplier Multiplier democm DEMOCPOLITY Democracy multiplier Coefficient swseffects DEMOCPOLITY Swing state effect Additive factor democimpus DEMOCPOLITY Democracy impetus by leading power Coefficient democimpoth DEMOCPOLITY Democracy impetus by other powers Coefficient democwvlen DEMOCPOLITY Democracy wave length Limit democwvmax DEMOCPOLITY Democracy wave upper limit Limit democpolitysetar DEMOCPOLITY Democracy SE target Standard error target democpolityseyrtar DEMOCPOLITY Democracy SE year Standard error target freedomm FREEDOM Freedom multiplier Multiplier gemm GEM Gender empowerment multiplier Multiplier gemsetar GEM Gender empowerment target Standard error target gemseyrtar GEM Gender empowerment year target Standard error target