Difference between revisions of "Guide to Scenario Analysis in International Futures (IFs)"
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== <span style="font-size:x-large;">Parameters to Affect Mortality</span> ==
== <span style="font-size:x-large;">Parameters to Affect Mortality</span> ==
== <span style="font-size:x-large;">Parameters to Affect Migration</span> ==
== <span style="font-size:x-large;">Parameters to Affect Migration</span> ==
Revision as of 19:28, 25 August 2017
- 1 Introduction
- 2 Demographic Module
- 3 Health Module
- 3.1 Variables of Interest
- 3.2 Parameters to Affect Overall Health and Burden of Disease
- 3.3 Parameters that Affect Communicable Diseases
- 3.4 Parameters that Affect Non-Communicable Disease
- 3.5 Parameters that Affect Injuries and Accidents
- 3.6 Parameters to Affect Technology
- 3.7 Prepackaged Scenarios
- 4 HIV/AIDS Submodule
- 5 Education Module
- 5.1 Variables of Interest
- 5.2 Parameters to Affect Intake Rates and Survival Rates: Annual Growth
- 5.3 Parameters to Affect Intake Rates and Survival Rates: Target Year for Universal Education
- 5.4 Parameters to Affect Intake Rates and Survival Rates: Multiplier
- 5.5 Parameters to Affect Education Spending
- 5.6 Parameters to Affect Gender Parity
- 5.7 Prepackaged Scenarios
- 6 Economic Module
- 7 Infrastructure Module
- 8 Agriculture Module
- 9 Energy Module
- 10 Environment Module
- 11 Governance Module
- 12 International Politics Module
- 13 Parameter Dictionary
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
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
Variables of Interest
|POPLE15|| Population, age 15 or less|
|POP15TO65|| Population, age 15 to 65|
|| Population, greater than 65|
|| Population, pre-working years|
|| Population, working years|
|| Population, retired|
|| % of the population between 15 and 29|
|| Population, median age|
|| Labor force size|
|| Net migration (inward)|
|| 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
|contrusm||CONTRUSE|| Contraceptive use multiplier
|eltfrcon||TFR|| Elasticity of total fertility rate to contraception use
|tfrmin||TFR|| Long term TFR convergence value
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)
|hlmortm||DEATHS|| Mortality multiplier by cause
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.