Lee, James R. 2009. Climate Change and Armed Conflict: Hot and Cold Wars. Routledge studies in peace and conflict resolution. London: Routledge.
There are two areas where conflict arising from climate change is likely: the Equatorial Tension Belt and the Polar Tension Belt. The ETB represents conflicts that will arise from the following causes: the age of the society and the overall environmental impact; the size of populations and the resource pressures implied; the kind of environment-desert or tropics-lend themselves to increased impact from climate change; historical legacy; and resource distribution (9-10). In the PTB, conflict will arise over the need to extract resources that become available due to receding ice stocks.
“Climate change will tend to make the existing Equatorial Tension Belt hotter and drier, and these twin conditions are likely to lead to greater conflict. Forecasts suggest that problems will intensify as demographic and socio-economic factors add further pressures on resources” (10).
The conceptualization of the relationship between climate change and conflict involves a framing of perspectives on the future: some are optimists, some are pessimists. These can be generally grouped into idealist and realist camps.
Some argue that climate change is not going to create substantial impetus for conflict. Others argue that conflict will emerge in certain zones, and not in others. These are referred to as “tame” zones and “untamed” zones (22).
The first tame zone is those that are interdependent because of trade. The second tame zone involve those who are democratic.
Chapter 2 reviews some historic instances of climate change and conflict.
Chapter 3 looks out at forecasts of climate change and conflict.
The beginning focuses on an overview of IPCC reports and findings.
Compares ACTOR forecasts for conflict with historic prevalence of conflict (from Uppsala) and then juxtaposes this with IPCC forecasts for low, intermediate and high temperature change. Again, forecasts of climate change are compared with Fund for Peace forecasts of state failure. IPCC regions are then each specifically explored.
Six scenarios are then deployed.
Showing posts with label Forecasting. Show all posts
Showing posts with label Forecasting. Show all posts
Monday, December 28, 2009
Thursday, April 10, 2008
Hughes, et. al.: Long-Term Socio-Economic Modeling
Hughes, Barry. (2004). "Long-Term Socio-Economic Modeling". unpublished IFs working paper on the Frederick S. Pardee Center for International Futures website: Denver, CO. http://www.ifs.du.edu/reports.htm.
The purpose of this paper is two-fold: The first desire of the authors is to document the socio-economic sub model. The second purpose is to provide an analysis of how IFs can be used as a tool to understand social support systems (iii).
The paper beings by outlining the changing nature of social, economic and political interactions globally. These changes are referred to as globalization, and IFs is seen as a tool that will be helpful in understanding the possible effects of these changes. This introduction is brief.
The model is then introduced generally. Much of the data has changed since the original writing of this document (the authors refer to it as a living document).
An approach to understanding the socio-economic side of the model is put forth. I was not familiar with this approach. It is summed up in Table 2.1 (8). From this table. There are three key aspects of the socio-economic sub-model. These are demographic, goods and services and financial. These three columns can then be broken down into six rows: organizing structure, stocks, flows, key aggregate relationships and key agent-class behavior relationships.
The authors then briefly situate the IFs model within the broader modeling literature. Firstly, they say that it has characteristics of systems dynamics models (stocks and flows, etc.), but that it isn’t limited to systems dynamics. Then, they highlight agent-class interactions, but they claim that what they are not doing is micro-level modeling. There are three different systemic/structural elements to the model: the agent-class relationships, the market of goods and services (as created through the production function) and the financial flow element.
The SAM is then examined, and the IFs SAM is situated vis-à-vis the literature on SAMs. The authors contend that they differ from the SAM literature in 5 ways: 1.) the universality of the SAM representation in IFs; 2.) the connection of this universal SAM to the global financial system; 3.) there is a representation of both stocks and flows which is driven by a construction of the interaction of assets and liabilities; 4.) is a temporal addition through the connection of the SAM to the broader, long-term model of IFs; and 5.) additional sub-models that are horizontally tied to the SAM in the interest of long-range forecasting.
The IFs Preprocessor makes a brief appearance in this paper, as it is important to understand the mechanism of how data is translated and run through the model, sub-model by sub-model. The preprocessing begins cleaning and filling data holes. Then, it moves to calculate demographic data in age-cohort structures. Then it calculates both agricultural and energy numbers, both of that are used in the economic calculations. The economic sub-model is the next to be calculated.
Page 16 represents a helpful mini-legend of commonly used IFs subscripts.
Other key formulas used in the model are discussed briefly, as they will be important in the rest of this text.
Chapter 3: The Goods and Services Market Foundation
The IFs economic sub-model draws on two different modeling traditions: the dynamic growth model of classical economics and the general equilibrium model of neo-classical economics (20). The goods and services market builds on the production function and the demand market created by the Cobb-Douglas production function as well as the endogenously created MFP. This then is situated within a larger SAM. The goods and services market creates and produces supply and demand features for households, firms and governments as well as embedding the production function within six sectors of production.
Growth in the goods and services market responds directly to endogenous labor supply growth, endogenous growth the stock of capital as well as MFP.
In terms of equilibrium seeking, this aspect of the goods and services market is promoted through price changes by sector that attempt to reconcile supply with demand. “Prices respond to stock levels” (20). There are three mechanisms that IFs uses to maintain supply and demand: price-driven changes in domestic demand, price-driven changes in trade and stock-driven changes in investment by destination.
IFs is not an equilibrium seeking model in each year, but rather an equilibrium chasing model over time. This is similar to the GLOBUS model and the SARUM model.
The production function is established starting with a Cobb-Douglas function, and then building upon that based on the work of Solow in 57. Solow saw that much economic growth could not simply be addressed by additions of capital and labor, and introduced technology change. This becomes multifactor productivity in IFs. This concept had been exogenously modeled in previous models, but, with the work of Romer in 1994, it became endogenized in IFs.
Convergence is also a crucial aspect of MFP in IFs. There are four factors that can positively or negatively influence convergence. They are the following: the convergence base, knowledge creation and diffusion, human capital quality, social capital quality and physical capital quality.
After calculating value added for each sector, IFs goes on to determine gross production and intersectoral flows. This is done by imposing an exogenously determined imput-output matrix on the model.
Trade is the next critical component to be calculated (though there are other components calculated in the interim: labor supply, government demand, GDP at PPP, etc.). This is done by creating an international supply/demand matrix. Imports and exports respond to relative prices. On the production side, the export base and export ceiling is computed. The difference between trade levels and domestic prices is imposed on the model through standard elasticity numbers. Import demands are tied to final demands and intersectoral flows. These are responsive to changes in incomes and prices relative to elasticities.
The computation of stocks is then examined. As is noted throughout the literature, IFs is a general equilibrium seeking model. This, however, is not the full story. IFs is also referred to as a “chasing equilibrium” model because it does not look for market clearing behavior in any given year. Instead, inventories are the key for keeping the model from clearing in every year. Prices, on the other hand, drive the market towards equilibrium.
The paper then goes on to examine how consumption, expenditures, transfers and economic interactions are computed for governments, firms and households. This is accomplished and accounted for in the SAM structure. The actual details of this computation involves each agent/class to interact their needs/desires with material constraints. For example, government pension transfers are accounted for based on the size of the population cohorts over 65 coupled with the tax revenues derived from firms and households. Additionally, firms establish their levels of production and investment based on their supply of capital as well as the relative attractiveness of different sectors. Households also are responsive to their relative economic positions and are distinguished into skilled and unskilled, as well as either saving or consuming based on the relative levels of interest rates and prices. “The SAM structure in IFs is really a combination of an accounting system and an equilibrating system” (58).
The purpose of this paper is two-fold: The first desire of the authors is to document the socio-economic sub model. The second purpose is to provide an analysis of how IFs can be used as a tool to understand social support systems (iii).
The paper beings by outlining the changing nature of social, economic and political interactions globally. These changes are referred to as globalization, and IFs is seen as a tool that will be helpful in understanding the possible effects of these changes. This introduction is brief.
The model is then introduced generally. Much of the data has changed since the original writing of this document (the authors refer to it as a living document).
An approach to understanding the socio-economic side of the model is put forth. I was not familiar with this approach. It is summed up in Table 2.1 (8). From this table. There are three key aspects of the socio-economic sub-model. These are demographic, goods and services and financial. These three columns can then be broken down into six rows: organizing structure, stocks, flows, key aggregate relationships and key agent-class behavior relationships.
The authors then briefly situate the IFs model within the broader modeling literature. Firstly, they say that it has characteristics of systems dynamics models (stocks and flows, etc.), but that it isn’t limited to systems dynamics. Then, they highlight agent-class interactions, but they claim that what they are not doing is micro-level modeling. There are three different systemic/structural elements to the model: the agent-class relationships, the market of goods and services (as created through the production function) and the financial flow element.
The SAM is then examined, and the IFs SAM is situated vis-à-vis the literature on SAMs. The authors contend that they differ from the SAM literature in 5 ways: 1.) the universality of the SAM representation in IFs; 2.) the connection of this universal SAM to the global financial system; 3.) there is a representation of both stocks and flows which is driven by a construction of the interaction of assets and liabilities; 4.) is a temporal addition through the connection of the SAM to the broader, long-term model of IFs; and 5.) additional sub-models that are horizontally tied to the SAM in the interest of long-range forecasting.
The IFs Preprocessor makes a brief appearance in this paper, as it is important to understand the mechanism of how data is translated and run through the model, sub-model by sub-model. The preprocessing begins cleaning and filling data holes. Then, it moves to calculate demographic data in age-cohort structures. Then it calculates both agricultural and energy numbers, both of that are used in the economic calculations. The economic sub-model is the next to be calculated.
Page 16 represents a helpful mini-legend of commonly used IFs subscripts.
Other key formulas used in the model are discussed briefly, as they will be important in the rest of this text.
Chapter 3: The Goods and Services Market Foundation
The IFs economic sub-model draws on two different modeling traditions: the dynamic growth model of classical economics and the general equilibrium model of neo-classical economics (20). The goods and services market builds on the production function and the demand market created by the Cobb-Douglas production function as well as the endogenously created MFP. This then is situated within a larger SAM. The goods and services market creates and produces supply and demand features for households, firms and governments as well as embedding the production function within six sectors of production.
Growth in the goods and services market responds directly to endogenous labor supply growth, endogenous growth the stock of capital as well as MFP.
In terms of equilibrium seeking, this aspect of the goods and services market is promoted through price changes by sector that attempt to reconcile supply with demand. “Prices respond to stock levels” (20). There are three mechanisms that IFs uses to maintain supply and demand: price-driven changes in domestic demand, price-driven changes in trade and stock-driven changes in investment by destination.
IFs is not an equilibrium seeking model in each year, but rather an equilibrium chasing model over time. This is similar to the GLOBUS model and the SARUM model.
The production function is established starting with a Cobb-Douglas function, and then building upon that based on the work of Solow in 57. Solow saw that much economic growth could not simply be addressed by additions of capital and labor, and introduced technology change. This becomes multifactor productivity in IFs. This concept had been exogenously modeled in previous models, but, with the work of Romer in 1994, it became endogenized in IFs.
Convergence is also a crucial aspect of MFP in IFs. There are four factors that can positively or negatively influence convergence. They are the following: the convergence base, knowledge creation and diffusion, human capital quality, social capital quality and physical capital quality.
After calculating value added for each sector, IFs goes on to determine gross production and intersectoral flows. This is done by imposing an exogenously determined imput-output matrix on the model.
Trade is the next critical component to be calculated (though there are other components calculated in the interim: labor supply, government demand, GDP at PPP, etc.). This is done by creating an international supply/demand matrix. Imports and exports respond to relative prices. On the production side, the export base and export ceiling is computed. The difference between trade levels and domestic prices is imposed on the model through standard elasticity numbers. Import demands are tied to final demands and intersectoral flows. These are responsive to changes in incomes and prices relative to elasticities.
The computation of stocks is then examined. As is noted throughout the literature, IFs is a general equilibrium seeking model. This, however, is not the full story. IFs is also referred to as a “chasing equilibrium” model because it does not look for market clearing behavior in any given year. Instead, inventories are the key for keeping the model from clearing in every year. Prices, on the other hand, drive the market towards equilibrium.
The paper then goes on to examine how consumption, expenditures, transfers and economic interactions are computed for governments, firms and households. This is accomplished and accounted for in the SAM structure. The actual details of this computation involves each agent/class to interact their needs/desires with material constraints. For example, government pension transfers are accounted for based on the size of the population cohorts over 65 coupled with the tax revenues derived from firms and households. Additionally, firms establish their levels of production and investment based on their supply of capital as well as the relative attractiveness of different sectors. Households also are responsive to their relative economic positions and are distinguished into skilled and unskilled, as well as either saving or consuming based on the relative levels of interest rates and prices. “The SAM structure in IFs is really a combination of an accounting system and an equilibrating system” (58).
Friday, April 4, 2008
Hughes, et. al.: The Structure of IFs
Hughes, Barry, Anwar Hossain and Mohammod T. Irfan. (2004). "The Structure of IFs". unpublished IFs working paper on the Frederick S. Pardee Center for International Futures website: Denver, CO. http://www.ifs.du.edu/reports.htm.
This document begins by identifying a large number of global trends that are unfolding. It makes the claim that the International Futures forecasting platform will offer assistance in a variety of ways in light of these trends. IFs will be able to identify tensions in political, economic and environmental risk (3). It will also explore long-term trends. IFs explores the world through examining dynamic systems.
The authors then claim that three goals of forecasting are important in light of the above global trends and possibilities afforded by the IFs model. We should use these tools and trends to create and clarify global priorities. Also, we need to explore the world as it changes in a variety of directions through scenario analysis. Finally, examining our changing world through the perspective of an agent-class based approach allows us to look at points of contingency.
Four assumptions underlie the development of IFs: issues affecting human development must be explored globally and locally; goals of humans are increasingly being iterated; understandings related to human systems is growing rapidly; and the domain of human choice is broadening.
There are then four ways in that the IFs project was taken from an abstract understanding of global system interactions to a forma computer model. These involved the highlighting of different parameters necessary for formal forecasting, the selection of global systems and sub-models, the identification of both theoretical and philosophical foundations for modeling and the embrace of a technical approach to modeling. These four parts are further identified as this paper progresses.
M1-M4 represent, “…four design parameter decisions” (5). I1-I4 explores issues of interface vis-à-vis such a model.
M1: the geographic representation of IFs at the time of this writing was 164 countries with the plan to move to 182. Current versions do forecast 182 countries and are currently in the process of breaking those countries down into provinces.
The model uses a pre-processor to prepare the raw data for the base year forecast. The pre-processor structure allows the re-running of the base year to happen with relative ease. Also, the structure allows for a relatively easy addition of new countries into the model.
M2: The issue areas that are represented in IFs are demographic, economic, energy, food, environmental and socio-political. There has since been the addition of an education model and a health model.
M3: Time Horizon: the model runs to 2100.
M4: The model uses extensive sets of data bases in order to dynamically link sub-models. Simple extrapolation is adequate for some short-term forecasts, but dynamic linkage is required for mid or long-term forecasts.
I1: History of IFs availability.
I2: Usability.
I3: Interventions and Scenario Development: This has developed substantially to allow users to create and save their own global scenarios.
I4: Transparency: While IFs tries to be as simple as possible, any long-term model must be sufficiently complex to represent the dynamic linkages between different variables. Therefore, transparency is important. This is accomplished where possible through help systems, etc.
There is a discussion of the philosophy of the structure of IFs. Important aspects of this discussion involve the ability to represent both stocks and flows within a system. This is accomplished, in part, thorough an input-output matrix, as in the SAM. Additionally, the structure must be able to represent non-linear, or dynamic changes within the model. This is accomplished by imposing a disequilibrium causing lever that the model enacts and an equilibrium seeking structure. Therefore, equilibrium is never quarantined to one year, but rather always exists and is sought somewhere in the future. The ability of the model to accomplish this relies on stocks and flows: if trade balance, for example, is not achieved in one year, stocks can be held and this affects future year trade interactions. Also, agents and classes are represented through households, firms and governments. Some of this behavior is clearly market based, but other behavior is not (i.e., governmental).
Relative to other modeling platforms, the authors claim that IFs represents an “eclectic” approach (13). This is because IFs is neither fully an econometric, systems dynamics or optimization model. They authors describe the model as, “…structure based…agent-class driven, dynamic modeling” (13). While it pays close attention to stocks and flows, it isn’t a system dynamics model because it represents some systems embedded in the structure of other systems. Also, the model is not a micro-agent model either because it represents classes. Finally, it isn’t fully econometric but it does rely heavily on data.
The authors also posit that there should be an additional question posed of modelers that tends to be left out: how easy is it to intervene in your model? The IFs model is designed to allow users to quickly and easily make parameter or variable changes within the model.
Demographics:
The demographic sub-model of IFs relies on stocks and flows being represented by age/sex cohorts similar to the ones used the UN or US census. These are then used to determine births within a year. However, the TFR must be determined by some metric derived from other areas of the model. Simply regressing different data sets allows patterns to emerge. In Table 3.1, the authors present a few different estimations of TFR using GDP per capita at purchasing power parity, total education, female education and contraceptive use. While they find a higher adjusted R-squared with one set of variables, they opt for a second estimation set that gives less priority to the education component, as this is not fully robustly forecasted in the model at the time of this writing.
Also, the authors point out that the relationship between TFR and GDP changes over time. The curve essentially shifts “down” showing a relationship where levels of TFR are associated with lower and lower levels of GDP. This is incorporated into the model by using a time dependent factor that decays over time due to saturation effects.
Also, longevity is determined by the model. It is found that GDP highly correlates to this, though there are exogenous factors that may speed the increase in life expectancy, like technology, etc. These are forecasted in the model by once again adding a time lag factor.
Finally, migration is forecasted. Large migrations throughout history have not at all been sustainable. Therefore, there is an upper limit placed on migration patterns so that the “maximum inflow or outflow of population is reduced over about 20 years to one percent of a country’s population” (21). Also, because it is typically largely populated countries that experience migration, the flow is capped if there is an overall decline in one year of more than 0.5%.
Economics:
The best approach to understanding the robust IFs economic sub-model is taken in three steps: first, understand how goods and services are produced. Secondly, broaden your attention to understand how the larger goods and services market interacts with consumption and exchange. Thirdly, the social accounting system must be explored to understand how the stocks and flows in the above systems are represented.
Production of Goods and Services:
The model uses a modified Cobb-Douglas production function. The Cobb-Douglas production function states that total output of an economy can be characterized by the interaction of labor multiplied by an exponent representing elasticity of labor time capital multiplied by an exponent representing elasticity of capital. The “elasticity of capital” and the “elasticity of labor” measures how quickly output will respond to a change in either labor our capital.
This production function is modified with an endogenously created multi-factor production variable. The authors note a variety of others who have determined that the Cobb-Douglas function is applicable, but limited. Some have pointed out that up to 50% of production increase comes from technological increase.
There were two ways that a MFP could be introduced into the model, one simple, the other complex. The more complex, endogenously derived model was selected, as it provided the opportunity for a more thoroughly responsive model to policy interventions. The derivation of this value involves five categories:
The convergence base: This is the base rate of MFP growth. It involves a convergence principle whereby developing countries converge on the technological superiority of a hegemon, assuming that the developing countries have passed some threshold of development. The base rate of the technological leader is a value pulled out of the air by the developers, but that can be changed to represent technological waves of advance and stagnation.
Knowledge creation and diffusion: Government spending on R&D.
Human capital quality: This addition to MFP involves spending on both health and education relative to GDP. The estimation is that a 1.5% increase in government spending on education equates to a 0.3% increase in economic growth.
Social capital quality: This tracks the effect that economic freedom can have on GDP growth. This was accomplished by looking at economic freedom and GDP cross sectionally to determine a possible relationship.
Physical capital quality: This tracks the relationship between energy supply availability and economic growth. If world energy price increases substantially, then much capital stock depreciates. IFs computes this price change by looking at the previous year’s price.
The Goods and Services Market:
This is the demand side of the model. It has embedded inside of it the supply, or production side of the model: the production function. This aspect of the model is equilibrium chasing, which means that it is never fully in equilibrium, as an economy would never fully be in equilibrium. It is able to do this through sectoral and country based inventories. These allow aspects of the economy that are not in equilibrium to rest year to year, as well as providing signals which can move both the supply and demand side towards equilibrium. This is accomplished through the setting of desired levels of stocks.
Total consumption is directly linked to income, which is based on labor earnings, returns on capital and transfer payments. These features are all addressed in the SAM discussion. Additionally, household consumption and savings is responsive to an interest rate. Consumption also takes place by sector, as households with higher levels of income tend to consume more services.
The size of the government’s share of involvement in the economy also continues to grow with time. This interaction is capped over time. IFs also does not represent trade bilaterally, but rather as a pooled feature.
IFs is equilibrium seeking in its economic sub-model through maintain a balance between supply and demand. This is accomplished through prices and stocks. Prices are mediated by elasticities and control supply and demand domestically. Stocks drive investment by destination. This equilibrium seeking behavior is controlled by a PID. “A PID-driven adjustment process responds proportionately to the integral of the error (the stock of discrepancy) and to the derivative of the error (the change in stock term)” (30).
The production function is the driver of both supply and, in many ways, demand. It accomplished this by driving consumption (through incomes). Consumption can change the dynamics of the goods and services market, but only at the margin.
The Social Accounting Matrix:
The SAM is embedded in the Goods and Services Market. It “…tracks and dynamically represents the financial stocks (assets and liabilities) and flows associated with key agent-classes” (31-2). This feature is an accounting tool for looking at how different sectoral and inter-state financial flows create abundance or dearth of certain stocks. For example, if a population is aging and a country doesn’t spend enough resources on pensions, possible adverse effects can be seen through the SAMs.
The internal SAM is a tool for looking at the distribution of stocks and flows among different sectors of the economy, households, firms and the government. The SAM is, once again, equilibrium chasing. It also does not represent a large lever that allows for policy interventions within the model, but is rather a tool for tracking the distribution of stocks, flows, and therefore production, a feature that can have effects on the model more broadly (see the MFP in the Production Function).
There is also an inter-state SAM which tracks the relationship between FDI and different countries as well as international debt. Once again, the SAM looks at assets and liabilities and uses signals from stocks to chase equilibrium over time. This is also accomplished by states acting as either agents of providing FDI or demanding FDI. These levels are then used to produce flows of capital gains to both asset country and liability country.
Energy:
Stopped here to focus on the Economic sub-model in other literature.
This document begins by identifying a large number of global trends that are unfolding. It makes the claim that the International Futures forecasting platform will offer assistance in a variety of ways in light of these trends. IFs will be able to identify tensions in political, economic and environmental risk (3). It will also explore long-term trends. IFs explores the world through examining dynamic systems.
The authors then claim that three goals of forecasting are important in light of the above global trends and possibilities afforded by the IFs model. We should use these tools and trends to create and clarify global priorities. Also, we need to explore the world as it changes in a variety of directions through scenario analysis. Finally, examining our changing world through the perspective of an agent-class based approach allows us to look at points of contingency.
Four assumptions underlie the development of IFs: issues affecting human development must be explored globally and locally; goals of humans are increasingly being iterated; understandings related to human systems is growing rapidly; and the domain of human choice is broadening.
There are then four ways in that the IFs project was taken from an abstract understanding of global system interactions to a forma computer model. These involved the highlighting of different parameters necessary for formal forecasting, the selection of global systems and sub-models, the identification of both theoretical and philosophical foundations for modeling and the embrace of a technical approach to modeling. These four parts are further identified as this paper progresses.
M1-M4 represent, “…four design parameter decisions” (5). I1-I4 explores issues of interface vis-à-vis such a model.
M1: the geographic representation of IFs at the time of this writing was 164 countries with the plan to move to 182. Current versions do forecast 182 countries and are currently in the process of breaking those countries down into provinces.
The model uses a pre-processor to prepare the raw data for the base year forecast. The pre-processor structure allows the re-running of the base year to happen with relative ease. Also, the structure allows for a relatively easy addition of new countries into the model.
M2: The issue areas that are represented in IFs are demographic, economic, energy, food, environmental and socio-political. There has since been the addition of an education model and a health model.
M3: Time Horizon: the model runs to 2100.
M4: The model uses extensive sets of data bases in order to dynamically link sub-models. Simple extrapolation is adequate for some short-term forecasts, but dynamic linkage is required for mid or long-term forecasts.
I1: History of IFs availability.
I2: Usability.
I3: Interventions and Scenario Development: This has developed substantially to allow users to create and save their own global scenarios.
I4: Transparency: While IFs tries to be as simple as possible, any long-term model must be sufficiently complex to represent the dynamic linkages between different variables. Therefore, transparency is important. This is accomplished where possible through help systems, etc.
There is a discussion of the philosophy of the structure of IFs. Important aspects of this discussion involve the ability to represent both stocks and flows within a system. This is accomplished, in part, thorough an input-output matrix, as in the SAM. Additionally, the structure must be able to represent non-linear, or dynamic changes within the model. This is accomplished by imposing a disequilibrium causing lever that the model enacts and an equilibrium seeking structure. Therefore, equilibrium is never quarantined to one year, but rather always exists and is sought somewhere in the future. The ability of the model to accomplish this relies on stocks and flows: if trade balance, for example, is not achieved in one year, stocks can be held and this affects future year trade interactions. Also, agents and classes are represented through households, firms and governments. Some of this behavior is clearly market based, but other behavior is not (i.e., governmental).
Relative to other modeling platforms, the authors claim that IFs represents an “eclectic” approach (13). This is because IFs is neither fully an econometric, systems dynamics or optimization model. They authors describe the model as, “…structure based…agent-class driven, dynamic modeling” (13). While it pays close attention to stocks and flows, it isn’t a system dynamics model because it represents some systems embedded in the structure of other systems. Also, the model is not a micro-agent model either because it represents classes. Finally, it isn’t fully econometric but it does rely heavily on data.
The authors also posit that there should be an additional question posed of modelers that tends to be left out: how easy is it to intervene in your model? The IFs model is designed to allow users to quickly and easily make parameter or variable changes within the model.
Demographics:
The demographic sub-model of IFs relies on stocks and flows being represented by age/sex cohorts similar to the ones used the UN or US census. These are then used to determine births within a year. However, the TFR must be determined by some metric derived from other areas of the model. Simply regressing different data sets allows patterns to emerge. In Table 3.1, the authors present a few different estimations of TFR using GDP per capita at purchasing power parity, total education, female education and contraceptive use. While they find a higher adjusted R-squared with one set of variables, they opt for a second estimation set that gives less priority to the education component, as this is not fully robustly forecasted in the model at the time of this writing.
Also, the authors point out that the relationship between TFR and GDP changes over time. The curve essentially shifts “down” showing a relationship where levels of TFR are associated with lower and lower levels of GDP. This is incorporated into the model by using a time dependent factor that decays over time due to saturation effects.
Also, longevity is determined by the model. It is found that GDP highly correlates to this, though there are exogenous factors that may speed the increase in life expectancy, like technology, etc. These are forecasted in the model by once again adding a time lag factor.
Finally, migration is forecasted. Large migrations throughout history have not at all been sustainable. Therefore, there is an upper limit placed on migration patterns so that the “maximum inflow or outflow of population is reduced over about 20 years to one percent of a country’s population” (21). Also, because it is typically largely populated countries that experience migration, the flow is capped if there is an overall decline in one year of more than 0.5%.
Economics:
The best approach to understanding the robust IFs economic sub-model is taken in three steps: first, understand how goods and services are produced. Secondly, broaden your attention to understand how the larger goods and services market interacts with consumption and exchange. Thirdly, the social accounting system must be explored to understand how the stocks and flows in the above systems are represented.
Production of Goods and Services:
The model uses a modified Cobb-Douglas production function. The Cobb-Douglas production function states that total output of an economy can be characterized by the interaction of labor multiplied by an exponent representing elasticity of labor time capital multiplied by an exponent representing elasticity of capital. The “elasticity of capital” and the “elasticity of labor” measures how quickly output will respond to a change in either labor our capital.
This production function is modified with an endogenously created multi-factor production variable. The authors note a variety of others who have determined that the Cobb-Douglas function is applicable, but limited. Some have pointed out that up to 50% of production increase comes from technological increase.
There were two ways that a MFP could be introduced into the model, one simple, the other complex. The more complex, endogenously derived model was selected, as it provided the opportunity for a more thoroughly responsive model to policy interventions. The derivation of this value involves five categories:
The convergence base: This is the base rate of MFP growth. It involves a convergence principle whereby developing countries converge on the technological superiority of a hegemon, assuming that the developing countries have passed some threshold of development. The base rate of the technological leader is a value pulled out of the air by the developers, but that can be changed to represent technological waves of advance and stagnation.
Knowledge creation and diffusion: Government spending on R&D.
Human capital quality: This addition to MFP involves spending on both health and education relative to GDP. The estimation is that a 1.5% increase in government spending on education equates to a 0.3% increase in economic growth.
Social capital quality: This tracks the effect that economic freedom can have on GDP growth. This was accomplished by looking at economic freedom and GDP cross sectionally to determine a possible relationship.
Physical capital quality: This tracks the relationship between energy supply availability and economic growth. If world energy price increases substantially, then much capital stock depreciates. IFs computes this price change by looking at the previous year’s price.
The Goods and Services Market:
This is the demand side of the model. It has embedded inside of it the supply, or production side of the model: the production function. This aspect of the model is equilibrium chasing, which means that it is never fully in equilibrium, as an economy would never fully be in equilibrium. It is able to do this through sectoral and country based inventories. These allow aspects of the economy that are not in equilibrium to rest year to year, as well as providing signals which can move both the supply and demand side towards equilibrium. This is accomplished through the setting of desired levels of stocks.
Total consumption is directly linked to income, which is based on labor earnings, returns on capital and transfer payments. These features are all addressed in the SAM discussion. Additionally, household consumption and savings is responsive to an interest rate. Consumption also takes place by sector, as households with higher levels of income tend to consume more services.
The size of the government’s share of involvement in the economy also continues to grow with time. This interaction is capped over time. IFs also does not represent trade bilaterally, but rather as a pooled feature.
IFs is equilibrium seeking in its economic sub-model through maintain a balance between supply and demand. This is accomplished through prices and stocks. Prices are mediated by elasticities and control supply and demand domestically. Stocks drive investment by destination. This equilibrium seeking behavior is controlled by a PID. “A PID-driven adjustment process responds proportionately to the integral of the error (the stock of discrepancy) and to the derivative of the error (the change in stock term)” (30).
The production function is the driver of both supply and, in many ways, demand. It accomplished this by driving consumption (through incomes). Consumption can change the dynamics of the goods and services market, but only at the margin.
The Social Accounting Matrix:
The SAM is embedded in the Goods and Services Market. It “…tracks and dynamically represents the financial stocks (assets and liabilities) and flows associated with key agent-classes” (31-2). This feature is an accounting tool for looking at how different sectoral and inter-state financial flows create abundance or dearth of certain stocks. For example, if a population is aging and a country doesn’t spend enough resources on pensions, possible adverse effects can be seen through the SAMs.
The internal SAM is a tool for looking at the distribution of stocks and flows among different sectors of the economy, households, firms and the government. The SAM is, once again, equilibrium chasing. It also does not represent a large lever that allows for policy interventions within the model, but is rather a tool for tracking the distribution of stocks, flows, and therefore production, a feature that can have effects on the model more broadly (see the MFP in the Production Function).
There is also an inter-state SAM which tracks the relationship between FDI and different countries as well as international debt. Once again, the SAM looks at assets and liabilities and uses signals from stocks to chase equilibrium over time. This is also accomplished by states acting as either agents of providing FDI or demanding FDI. These levels are then used to produce flows of capital gains to both asset country and liability country.
Energy:
Stopped here to focus on the Economic sub-model in other literature.
Thursday, April 3, 2008
Hughes: Forecasting Globalization
Hughes, Barry. (2004). "Forecasting Globalization: The Use of International Futures". unpublished IFs working paper on the Frederick S. Pardee Center for International Futures website: Denver, CO. http://www.ifs.du.edu/reports.htm.
A standard measure of globazliation includes four metrics: political engagement, technological connectivity, personal contact and economic integration. The Globalization Index (GI) produced by A.T. Kearney and Foreign Policy is the gold standard at the moment. The IFs GLOBALIZ variable will be explored in relation to the GI. Can IFs forecast globalization? The answer provided here is both yes and no.
The structure of IFs allows for a forecast of globalization that takes into consideration the four variables outlined above. However, this representation does not fully take into consideration all of the effects of the positive and negative feedback loops. The positive feedback loops of globalization are reinforcing relationships between, for example, increased trade flows and increased country GDP. The negative feedback loops, for example, are how increased growth leads to increased carbon emissions. The IFs model does a good job with the positive feedback loops but does not fully complete the negative feedback loops. For example, while there is a negative feedback loop between carbon emissions and GDP growth, that loop is not completed with carbon emissions affecting something else.
The paper concludes that, while globalization is very difficult to forecast, the different GEO scenarios provide a robust set of scenarios to use as a basis for understanding the possible directions in that the global trend may go. Three of the four scenarios see an increase in levels of globalization with the Security First scenario being the outlier. Additionally, the forecasting of the negative feedback loops within IFs, or any model, is fraught with difficulties. Thirdly, the author wonders whether or not there could be a saturation point within the process of globalization.
A standard measure of globazliation includes four metrics: political engagement, technological connectivity, personal contact and economic integration. The Globalization Index (GI) produced by A.T. Kearney and Foreign Policy is the gold standard at the moment. The IFs GLOBALIZ variable will be explored in relation to the GI. Can IFs forecast globalization? The answer provided here is both yes and no.
The structure of IFs allows for a forecast of globalization that takes into consideration the four variables outlined above. However, this representation does not fully take into consideration all of the effects of the positive and negative feedback loops. The positive feedback loops of globalization are reinforcing relationships between, for example, increased trade flows and increased country GDP. The negative feedback loops, for example, are how increased growth leads to increased carbon emissions. The IFs model does a good job with the positive feedback loops but does not fully complete the negative feedback loops. For example, while there is a negative feedback loop between carbon emissions and GDP growth, that loop is not completed with carbon emissions affecting something else.
The paper concludes that, while globalization is very difficult to forecast, the different GEO scenarios provide a robust set of scenarios to use as a basis for understanding the possible directions in that the global trend may go. Three of the four scenarios see an increase in levels of globalization with the Security First scenario being the outlier. Additionally, the forecasting of the negative feedback loops within IFs, or any model, is fraught with difficulties. Thirdly, the author wonders whether or not there could be a saturation point within the process of globalization.
Hughes: Forecasting the HDI
Hughes, Barry. (2004). "Forecasting the Human Development Index". unpublished IFs working paper on the Frederick S. Pardee Center for International Futures website: Denver, CO. http://www.ifs.du.edu/reports.htm.
This report presents a variety of forecasts using the IFs model regarding the HDI.
Initially, there is a discussion of the history of the HDI, which was developed by Haq and Sen in 1990. The measure looks at three aspects of human development: a logged $40,000 GDP per capita, life expectancy and literacy. The measure is from 0-1 with higher numbers representing greater levels of human development.
The forecast of HDI for OECD countries actually moves beyond the upper level of 1. There is general steady growth in this cohort. All other cohorts show steady growth in HDI except for sub-Saharan Africa. One of the important causal factors for this lack of growth is the prevalence of HIV/AIDS.
The author then deploys alternative scenarios for exploring possible alternative global scenarios. One is the failure to control for HIV/AIDS. Another is a sustainability scenario. These two scenarios are then compared with the base case vis-à-vis the HDI in sub-Saharan Africa. The results are summed up in Figure 11 with the failure to control for HIV/AIDS clearly halting HDI development early and the sustainability scenario clearly promoting this development.
This paper then goes on to explore the HDI with respect to its longevity as a metric. In later forecasts of IFs, some countries exceed the limits imposed by the HDI of $40,000 GDP per capita at purchasing power parity and 85 year life expectancy. The model gets around this by raising maximum GDP to $100,000.
This report presents a variety of forecasts using the IFs model regarding the HDI.
Initially, there is a discussion of the history of the HDI, which was developed by Haq and Sen in 1990. The measure looks at three aspects of human development: a logged $40,000 GDP per capita, life expectancy and literacy. The measure is from 0-1 with higher numbers representing greater levels of human development.
The forecast of HDI for OECD countries actually moves beyond the upper level of 1. There is general steady growth in this cohort. All other cohorts show steady growth in HDI except for sub-Saharan Africa. One of the important causal factors for this lack of growth is the prevalence of HIV/AIDS.
The author then deploys alternative scenarios for exploring possible alternative global scenarios. One is the failure to control for HIV/AIDS. Another is a sustainability scenario. These two scenarios are then compared with the base case vis-à-vis the HDI in sub-Saharan Africa. The results are summed up in Figure 11 with the failure to control for HIV/AIDS clearly halting HDI development early and the sustainability scenario clearly promoting this development.
This paper then goes on to explore the HDI with respect to its longevity as a metric. In later forecasts of IFs, some countries exceed the limits imposed by the HDI of $40,000 GDP per capita at purchasing power parity and 85 year life expectancy. The model gets around this by raising maximum GDP to $100,000.
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