Guide to Scenario Analysis in International Futures (IFs)

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Contents

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.

International Trade and Finance

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

Parameter

Variable of Interest

Description

Type

labinformshrm

LABINFORMSHR

Informal labor force share multiplier

Multiplier

gdpinformshrm

GDPINFORMSHR

Informal GDP share multiplier

Multiplier

gdpshadowshrm

GDPSHADOWSHR

Shadow economy GDP share multiplier

Multiplier

gdpshadowon

GDPSHADOWSHR

Switch to turn on GDPSHADOWSHR instead of GDPINFORMSHR. Default is 0 (GDPINFORMSHR). 1 turns on GDPSHADOWSHR instead.

Switch

Edyrsagm

EDYRSAG15

Years of education ages 15 and up multiplier

Multiplier

Firmtaxrm

FIRMTAX

Tax rate on firms multiplier

Multiplier

Gdsm

GDS

Government R&D (and education) spending distribution multiplier

Multiplier

govbusregindm

GOVBUSREGIND

Government business regulation index multiplier

Multiplier

Govcorruptm

GOVCORRUPT

Government corruption multiplier

Multiplier

govhhtrnpenm

GOVHHTRN

Government pension spending multiplier

Multiplier

govhhtrnwelm

GOVHHTRN

Government welfare spending multiplier

Multiplier

randdexpm

RANDEXP

Total (public and private) expenditure on research and development multiplier

Multiplier

Taxinfadjm

HHTAXR, FIRMTAXR,  SSWELTAXR

Multiplier of impact of informal GDP share on tax rates

Multiplier

Tefinfadjm

SAVINGS

Multiplier of impact of informal GDP share on productivity

Multiplier

labinformcoeffbus

LABINFORMSHR

Informal labor share, coefficient of business index

Coefficient

labinformcoeffed

LABINFORMSHR

Informal labor share, coefficient of education years

Coefficient

labinformcoeffhhtrn

LABINFORMSHR

Informal labor share, coefficient of household transfers

Coefficient

Labinformcoeffintercept

LABINFORMSHR

Informal labor share, intercept of model

Coefficient

labinformcoefftax

LABINFORMSHR

Informal labor share, coefficient of tax rate

Coefficient

Gdpinformcoeffcorrupt

GDPINFORMSHR

Informal GDP share, coefficient of government corruption

Coefficient

Gdpinformcoeffintercept

GDPINFORMSHR

Informal GDP share, intercept of model

Coefficient

gdpinformcoefflabinf

GDPINFORMSHR

Informal GDP share, coefficient of informal labor share

Coefficient

gdpinformcoeffRandD

GDPINFORMSHR

Informal GDP share, coefficient of R&D spending

Coefficient

Gdpshadowcoeffintercept

GDPSHADOWSHR

Shadow economy GDP share, intercept of model

Coefficient

Gdpshadowcoefflabinf

GDPSHADOWSHR

Shadow economy GDP share, coefficient of informal labor share

Coefficient

Gdpshadowcoeffcorrupt

GDPSHADOWSHR

Shadow economy GDP share, coefficient of government corruption

Coefficient

gdpshadowcoeffRandD

GDPSHADOWSHR

Shadow economy GDP share, coefficient of R&D spending

Coefficient

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.

The model includes the shadow economy share of GDP (GDPSHADOWSHR) as an additional measure of informality in the economy. Under the Base Case, GDPSHADOWSHR is initialized but does not have active forward linkages (the Base Case drives all informality-related forward linkages with GDPINFORMSHR instead). The shadow economy GDP share can be affected directly by changing gdpshardowshrm or indirectly by changing its drivers, which as with the informal economy share, are: informal labor, government corruption, and spending on R&D. The shadow economy uses a similar set of parameters to the informal economy (gdpshadowcoeffintercept,gdpshadowcoeffcorrupt,gdpshadowcoefflabinf,gdpshadowcoeffRandD) to control the calculation of the shadow economy from its 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.

Access to Infrastructure

Variable Name

Sub Categories

Description

Transportation

INFRAROADRAI*

 

Access to rural roads

Electricity

INFRAELECACC*

Rural, urban, total

Access to electricity

ENSOLFUEL

 

Solid fuel use

Water and Sanitation

WATSAFE*

None, other improved, piped

Access to improved water

SANITATION*

Other unimproved, shared, improved

Access to improved sanitation

WATWASTE

 

Access to wastewater collection

WATWASTETREAT*

 

Access to wastewater treatment

ICT

INFRATELE*

 

Fixed telephone lines

ICTBROAD*

 

Fixed broadband subscriptions

ICTMOBIL*

 

Mobile telephone subscriptions

 

ICTBROADMOBIL*

 

Mobile broadband subscriptions

  • 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. Please see the note below for a more comprehensive description of the difference6

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.

Roads

Parameter

Variable of Interest

Description

Type

infraroadm

INFRAROAD

Multiplier on road density expected/demanded

Multiplicative

infraroadpavedpcntm

INFRAROADPAVED

PCNT

Multiplier on percentage of roads paved

Multiplicative

Infraroadraitrgtval, Infraroadraitrgtyr

INFRAROADRAI

Target value/years after 2010 for achievement for population percentage living w/in 2km of an all-weather road

Absolute target

infraroadraisetar, infraroadraiseyrtar

INFRAROADRAI

Standard error target and years after 2010 for achievement for rural road access index

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.

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.

To directly alter INFRAROADRAI users can use one or the other of the parameter combinations infraroadraitrgtval/infraroadraitrgtyr or infraroadraisetar/infraroadraiseyrtar. Either set will affect desired levels of access to road infrastructure. In the event that initially computed achieved road access levels do not meet the target value, the model will determine the levels of physical infrastructure stocks required to meet the desired levels of road access and will, subject to financial resource constraints, adjust they physical stocks so as to move the achieved access level to the target.

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

Parameter

Variable of Interest

Description

Type

landirareaequipm

LANDIRAREAEQUIP

Multiplier on land area equipped for irrigation

Multiplier

sanitationm

SANITATION

Multiplier on percentage of people with access to sanitation

Multiplier

watsafem

WATSAFE

Multiplier on percentage of people with access to safe water

Multiplier

watwastem

WATWASTE

Multiplier on percentage of people connected to wastewater collection system,

Absolute target

watwastetreatm

WATWASTETREAT

Multiplier on percentage of people connected to wastewater treatment

Absolute target

sanitationtrgtval/ sanitationtrgtyr

SANITATION

Target value and years after 2010 for target achievement for population percentage with improved access to improved sanitation, by category of

Relative target

watsafetrgtval/ watsafetrgtyr

WATSAFE

Target value and years after 2010 for target achievement for population percentage without access to improved sources of water

Relative target

sanitnoconsetar/ sanitimpconsetar/ sanithhconsetar/ sanitnoconseyrtar

SANITATION

Standard error targets and years after 2010 for target achievement for population percentage with varying access to sanitation

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.

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.

ICT

Parameter

Variable of Interest

Description

Type

infratelem

INFRATELE

Multiplier on fixed-line telephone lines per 100 persons

Multiplier

ictbroadm

ICTBROAD

Multiplier on fixed-line broadband subscriptions per 100 persons

Multiplier

ictbroadmobilm

ICTBROADMOBIL

Multiplier on mobile broadband subscriptions per 100 persons

Multiplier

ictbroadtrgtval, ictbroadtrgtyr

ICTBROAD

Target value and years after 2010 for target achievement for number of subscriptions per 100 persons

Absolute target

ictbroadmobiltrgtval, ictbroadmobiltrgtyr

ICTBROADMOBIL

Target value and years after 2010 for target achievement for number of subscriptions per 100 persons; varies between 0 and 150

Absolute target

ictbroadmobilsetar, ictbroadmobilseyrtar

ICTBROADMOBIL

Standard error target and years after 2010 for target achievement for ICT mobile broadband access

Relative target

ictbroadsetar, ictbroadseyrtar

ICTBROAD

Standard error target and years after 2010 for target achievement for fixed broadband access

Relative target

ictmobilsetar, ictmobilseyrtar

ICTMOBIL

Standard error target and years after 2010 for target achievement for mobile phone access

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.

Infratelem, ictbroadm and ictbroadmobilm are the only three multiplicative parameters that apply to ICT, and they serve to modify expected or demanded levels of access to telephones, fixed broadband, and mobile broadband respectively. IFs calculates broadband ICT slightly differently than other infrastructure types. Rather than being mutually exclusive, both absolute target values and relative targets (discussed in the paragraph below) apply to the calculation of ICTBROAD. Rather than calculating the value of the parameter adding the multiplier effect and then applying the relevant absolute and relative targets, which is what occurs in the rest of the infrastructure module, the calculation process differs markedly in regards to broadband. In this case, the value of ICTBROAD is calculated twice, once with and once without the application of the target access levels, and the larger of these two values is used by the model for calculations going forward. Then the multiplier ictbroadm is applied.

Ictbroadtrgtval/ictbroadtrgtyr and ictbroadmobiltrgtval/ictbroadmobiltrgtvr set absolute target values and years for fixed and mobile broadband access. Ictbroadsetar/ictbroadseyrtar, ictbroadmobilsetar/ictbroadmobilseyrtar, and ictmobilsetar/ictmobilseyrtar establish relative target access values for desired broadband access, mobile broadband, and mobile access respectively. Absolute and relative target parameters behave differently when used in combination with one another in regards to fixed broadband than in other infrastructure areas. Instead of canceling one another out, combiningictbroadtrgtval and ictbroadsetar/ictbroadseyrtar, causes the model to calculate both and then take the larger of the two values and use that.

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

Parameters to Affect Demand

Parameters to Affect Supply

Prepackaged Scenarios

Environment Module

Variables of Interest

Parameters Affecting Carbon

Parameters Affecting Water Resources

Parameters Affecting Air Pollution

Prepackaged Scenarios

Governance Module

Variables of Interest

Parameters to Affect Security

Parameters to Affect Capacity

Parameters to Affect Inclusiveness

Prepackaged Scenarios

International Politics Module

Variables of Interest

Parameters Affecting Power

Parameters Affecting Threat Levels

Parameters Affecting War Simulation

Parameters Affecting Diplomacy

Prepackaged Scenarios

Parameter Dictionary

Population

Health

HIV/AIDS

Education

Economics

Infrastructure

Agriculture

Energy

Environment

Governance

International Politics