- 1 Overview
- 2 Goods and Services Market
- 3 Financial Flows / Social Accounting
- 4 Dominant Relations: Economics
- 5 Social Accounting Matrix Approach in IFs
- 6 Economic Flow Charts
- 7 Value Added
- 8 Multifactor Productivity
- 9 Economic Aggregates and Indicators
- 10 Household Accounts
- 11 Firm Accounts
- 12 Government Accounts
- 13 Savings and Investment
- 14 Trade
- 15 International Finance
- 16 Income Distribution
- 17 Poverty
The most recent and complete economic model documentation is available on Pardee's website. Although the text in this interactive system is, for some IFs models, often significantly out of date, you may still find the basic description useful to you.
The economics model of IFs forecasting system draws on two general modeling traditions. The first is the dynamic growth model of classical economics. Within IFs the growth rates of labor force, capital stock, and multifactor productivity largely determine the overall size of production and therefore of the economy. The second tradition is the general equilibrium model of neo-classical economics. IFs contains a six-sector (agriculture, raw materials, energy, manufactures, services, and ICT) equilibrium-seeking representation of domestic supply, domestic demand, and trade. Further, the goods and services market representation is embedded in a larger social accounting matrix structure that introduces the behavior of household, firm, and government agent classes and the financial flows they determine.
Goods and Services Market
Goods and Services
Endogenously driven production function represented within a dynamic general equilibrium-seeking model
Capital, labor, accumulated technology
Production, consumption, trade, investment
Key Aggregate Relationships
(illustrative, not comprehensive)
Production function with endogenous technological change; price movements equilibrate markets over time
Key Agent-Class Behavior Relationships
(illustrative, not comprehensive)
Households and work/leisure, consumption, and female participation patterns;
Firms and investment;
Government decisions on revenues and on both direct expenditures and transfer payments
Households, firms, and the government interact via markets in goods and services. There are obvious stock and flow components of markets that are desirable and infrequently changed in model representation. Perhaps the most important key aggregate relationship is the production function. Although the firm is an implicit agent-class in that function, the relationships of production even to capital and labor inputs, much less to the variety of technological and social and human capital elements that enter a specification of endogenous productivity change (Solow 1957; Romer 1994), involve multiple agent-classes. In the representation of the market now in IFs there are also many key agent-class relationships as suggested by the table.
Financial Flows / Social Accounting
Market plus socio-political transfers in Social Accounting Matrix (SAM)
Government, firm, household assets/debts
Savings, consumption, FDI, foreign Aid, IFI credits/grants, government expenditures (military, health, education, other) and transfers (pensions and social transfers)
Key Aggregate Relationships
(illustrative, not comprehensive)
Exchange rate, movements with net asset/current account level; interest rate movements with savings and investment
Key Agent-Class Behavior Relationships
(illustrative, not comprehensive)
Firm investment/profit returns and FDI decisions;
Government revenue, expenditure/transfer payments;
IFI credits and grants
Households, firms, and the government interact in markets, but more broadly also via financial flows, including those related to the market (like foreign direct investment), but extending also to those that have a socio-political basis (like government to household transfers). A key structural representation is the Social Accounting Matrix (SAM).
The structural system portrayed by SAMs is well represented by stocks, flows, and key relationships. Although the traditional SAM matrix itself is a flow matrix, IFs has introduced a parallel stock matrix that captures the accumulation of assets and liabilities across various agent-classes. The dynamic elements that determine the flows within the SAM involve key relationships, such as that which constrains government spending or forces increased revenue raising when government indebtedness rises. Many of these, as indicated in the table, represent agent-class behavior.
The model can represent the behavior of households with respect to use of time for employment and leisure, the use of income for consumption and savings, and the specifics of consumption decisions across possible goods and services. And it represents the behavior of governments with respect to search for income and targeting of transfers and expenditures, in interaction with other agents including households, firms, and international financial institutions (IFIs).
IFs thus represents equilibrating markets (domestically and globally) in goods and services and in financial flows. It does not yet include labor market equilibration.
Dominant Relations: Economics
In any long-term economic model the supply side has particular importance. In IFs, gross domestic product (GDP) is a function of multifactor productivity (MFP), capital stocks (KS), and labor inputs (LABS), all specified for each of six sectors. This approach is sometimes called a Solowian Cobb-Douglas specification, but IFs helps the user get inside the multifactor productivity term, rather than leaving it as a totally exogenous residual.
The following key dynamics are directly related to the dominant relations:
- Multifactor productivity is a function partly of exogenous specification of an annual growth rate in it for the systemic technology leader, base rates of relative technological advance in other countries determined via an inverted U-shaped function that assumes convergence with the leader, and of an exogenously specified additive factor for control of specific regions or countries.
- Multifactor productivity is, however, largely an endogenous function of variables determined in other models of the IFs system representing the extent of human, social, physical, and knowledge capital; their influence on production involves coefficients that the user can control.
- Capital stock is a function of investment and depreciation rates. Endogenously determined investment can be influenced exogenously by a multiplier and the lifetime of capital can be changed.
- Labor supply is determined from population of appropriate age in the population model (see its dominant relations and dynamics) and endogenous labor force participation rates, influenced exogenously by the growth of female participation.
The larger economic model provides also representation of and some control over sector-specific consumption patterns; trade including protectionism levels and terms of trade; taxation levels; economic freedom levels; and financial dynamics around foreign aid, borrowing, and external debt.
Social Accounting Matrix Approach in IFs
A SAM integrates a multi-sector input-output representation of an economy with the broader system of national accounts, also critically representing flows of funds among societal agents/institutions and the balance of payments with the outside world. Richard Stone is the acknowledged father of social accounting matrices, which emerged from his participation in setting up the first systems of national accounts or SNA (see Pesaran and Harcourt 1999 on Stone’s work and Stone 1986). Many others have pushed the concepts and use of SAMs forward, including Pyatt (Pyatt and Round 1985) and Thorbecke (2001). So, too, have many who have extended the use of SAMs into new frontiers. One such frontier is the additional representation of environmental inputs and outputs and the creation of what are coming to be known as social and environmental accounting matrices or SEAMs (see Pan 2000). Another very productive extension is into the connection between SAMs and technological systems of a society (see Khan 1998; Duchin 1999). It is fitting that the 1993 revision of the System of National Accounts by the United Nations began explicitly to move the SNA into the world of SAMs.
The SAM of IFs is integrated with a dynamic general equilibrium-seeking model. The structural representation is a variant and to some degree an extension of the computable general equilibrium formulations that often surround SAMs. In wrapping SAMs into CGEs, Stone was a pioneer, leading the Cambridge Growth Project with Alan Brown. That project placed SAMs into a broader modeling framework so that the effects of changes in assumptions and coefficients could be analyzed, the predecessor to the development and use of computable general equilibrium (CGE) models by the World Bank and others. Some of the Stone work continued with the evolution of the Cambridge Growth Model of the British economy (Barker and Peterson, 1987). Kehoe (1996) reviewed the emergence of applied general equilibrium (GE) models and their transformation from tools used to solve for equilibrium under changing assumptions at a single point in time to tools used for more dynamic analysis of societies.
The approach of IFs is both within these developing traditions and an extension of them on five fronts. The first extension is in universality of the SAM representation. Most SAMS are for a single country or a small number of countries or regions within them (e.g.. see Bussolo, Chemingui, and O’Connor 2002 for a multi-regional Indian SAM within a CGE). The IFs project has created a procedure for constructing relatively highly aggregated SAMs from available data for all of the countries it represents, relying upon estimated relationships to fill sometimes extensive holes in the available data. Jansen and Vos (1997: 400-416) refer to such aggregated systems as using a "Macroeconomic social Accounting Framework." Each SAM has an identical structure and they can therefore be easily compared or even aggregated (for regions of the world).
The second extension is the connecting of the universal set of SAMs through representation of the global financial system. Most SAMs treat the rest of the world as a residual category, unconnected to anything else. Because IFs contains SAMs for all countries, it is important that the rest-of –the-world categories are mutually consistent. Thus exports and imports, foreign direct investment inflows and outflows, government borrowing and lending, and many other inter-country flows must be balanced and consistent.
The third extension is a representation of stocks as well as flows. Both domestically and internationally, many flows are related to stocks. For instance, foreign direct investment inflows augment or reduce stocks of existing investment. Representing these stocks is very important from the point of view of understanding long-term dynamics of the system because those stocks, like stocks of government debt, portfolio investment, IMF credits, World Bank loans, reserve holdings, and domestic capital stock invested in various sectors, generate flows that affect the future. Specifically, the stocks of assets and liabilities will help drive the behavior of agent classes in shaping the flow matrix.
The IFs stock framework has been developed with the asset-liability concept of standard accounting method. The stock framework is also an extension of the social accounting flow matrix, and the cumulative flows over time among the agents will determine the stocks of assets or liabilities for all agents. If the inflow demands repayment or return at some point in future, it is considered as liability for that agent and an asset for the agent from which the flow came. For example, in IFs, if a government receives loans (inflow) from other countries, the stock of those loans is a liability for the recipient government and an asset for the country or countries providing the loans.
The fourth extension is temporal and builds on the third. The SAM structure described here has been embedded within a long-term global model. The economic module of IFs has many of the characteristics of a typical CGE, but the representation of stocks and related agent-class driven behavior in a consciously long-term structure introduces a quite different approach to dynamics. Instead of elasticities or multipliers on various terms in the SAM, IFs seeks to build agent-class behavior that often is algorithmic rather than automatic. To clarify this distinction with an example, instead of representing a fixed set of coefficients that determine how an infusion of additional resources to a government would be spent, IFs increasingly attempts partially to endogenize such coefficients, linking them to such longer-term dynamics as those around levels of government debt. Similarly, the World Bank as an actor or agent could base decisions about lending on a wide range of factors including subscriptions by donor states to the Bank, development level of recipients, governance capacity of recipients, existing outstanding loans, debt-to-export ratios, etc. Much of this kind of representation is in very basic form at this level of development, but the foundation is in place.
The fifth and final extension has already been discussed. In addition to the SAM, The IFs forecasting system also includes a number of other models relevant to the analysis of longer-term forecasts. For example demographic, education, health, agriculture, and energy models all provide inputs to the economic model and SAM, as well as responding to behavior within it. The effort is to provide a dynamic base for forecasts can be made well into the 21st century. It is important to quickly emphasize that such forecasts are not predictions. Instead they are scenarios to be used for thinking about possible alternative longer-term futures.
As a graduate student in what is now the Josef Korbel School of International Studies, Anwar Hossain worked with Barry Hughes in the development of the SAM structure and database for IFs (see Hughes and Hossain 2003); his help was much appreciated.
Economic Flow Charts
This section presents several block diagrams that are central to the two major components of the economics model, the goods and services market—with special emphasis on the production function— and the broader SAM.
The economic model represents supply, demand, and trade in each of six economic sectors: agriculture, primary energy, raw materials, manufactures, services, and information/communications technology. The model draws upon data from the Global Trade and Analysis Project (GTAP) with 57 sectors as of GTAP 8; the pre-processor collapses those into the six IFs sectors and theoretically could collapse them into a different aggregated subset.Inventories (or stocks) are the key equilibrating variable in three negative feedback loops. As they rise, prices fall, increasing final demand (one loop), decreasing production (a second loop), and thereby in total decreasing inventories in the pursuit over time of a target value and equilibrium. Similarly, as inventories rise, capacity utilization falls, decreasing production, and restraining inventories.
Physical investment and capital stocks are the key driving variables in an important positive feedback loop. As capital rises, it increases value added and GDP, increasing final demand and further increasing investment. Similarly, government social investment can increase productivity, production and inventories in another positive feedback loop.
The figure below also shows some production detail. A-matrices, which are specified dependent on the level of development (GDP per capita), allow the computation from value added of gross production and of the production that is available, after satisfaction of intersectoral flows, for meeting final demand. It is the balance of this production for final demand with actual final demand that determines whether inventories grow or decline.
The calculation of gross production (ZS) in value terms within the economic model is overridden by calculations of physical production converted to value in the agricultural and energy models when respective switches (AGON and ENON) are thrown as in the default of the IFs Base Case scenario.
A Cobb-Douglas production function determines value added. Thus two principal factors are capital and labor. Labor is responsive not just to population size and structure, but to the labor participation rate, including the changing role of women in the work force. Accumulated growth in the level of technology or multifactor productivity (MFP), in a "disembodied" representation (TEFF), modifies these factors. Immediate energy shortages/shocks can also affect value added.
The technological factor in the production function is often called multifactor productivity (MFP). The basic value of MFP is a sum of a global productivity growth rate driven by the economically advanced or leading country/region (mfpleadr ), a technological premium dependent on GDP per capita, and an exogenous or scenario factor (mfpadd ).
In addition, however, other factors affect productivity growth over time. These include a wide range of variables, such as the years of education that adults have (EDYRSAG25) and the level of economic freedom (ECONFREE), which respectively are among the variables that affect change in MFP associated with human and social capital.
Economic Aggregates and IndicatorsBased on value added and population, it is possible to compute GDP, GDP at purchasing power parity, and a substantial number of country/region-specific and global indicators including several that show the extent of the global North/South gap.
The most important drivers of household income is the size of value added and the share of that accruing to households. That share, divided further into unskilled and skilled households, is initialized with data from the Global Trade and Analysis Project and changes a function driven by GDP per capita. Household income is augmented by flows from government and firms (dividends and interest). Most of household income will be used for consumption, but shares will go back to the government via taxes and to savings.
Once the total of household consumption is known, if is divided across the sectors of IFs using Engel elasticities that recognize changing use of consumption as levels per capital rise
Firm AccountsFirms retain as income the portion of total value added that is not sent to households in return for labor provided. Income of firms functioning within a country (ownership in IFs is not designated as domestic or international) benefit from inflows of portfolio and foreign direct investment. Firms direct their income to governments in the form of tax payments, to households as dividends and interest, to the outside world as portfolio or foreign direct investment, and to savings (available for investment).
Government revenues come from taxes levied on households and firms. The total expenditures are a sum of two sub-categories, direct consumption and transfer payments (the latter in turn being a sum of payments for pensions/retirement and welfare.
The annual government balance is the difference between revenues and expenditures and increments or decrements government debt in absolute terms and as a portion of GDP. That stock variable in turn sends back signals to both revenue and expenditure sides of the model so as to keep the debt at reasonable levels over time.
The level of government consumption and its distribution across targets are important policy-relevant variables in the model. Government consumption is spread across several target spending categories: military health, education, traditional infrastructure, other infrastructure, research and development, other/residual, and foreign aid). The distribution to most of those categories is endogenously determined by functions, but other models in the IFs system provide special signals for military, education, and traditional infrastructure spending. Demand for military spending involves action-reaction dynamics (when a switch is turned on) and threat levels. Demand for educational and infrastructure involves full models. Demands will not equal supply and all demands are normalized to the supply, but special protection can be given to the demands for education and infrastructure spending.
Educational spending by level of education (primary, secondary, and tertiary) is further broken out of total educational spending in the education model but targets can be changed via a multiplier.
Savings and Investment
Savings is a sum of the savings by households, firms, the government (its fiscal balance) and net foreign savings. Investment is most immediately a sum of gross capital formation and changes in inventories.
As in other parts of the IFs economic model, there will not be an exact equilibrium between savings and investment in any given time step. The system will chase equilibrium over time with the help of two mechanisms. The smoothed pattern of savings over time will affect investment. So, too, will interest rates that respond to changes in inventories or stocks of goods and services.
The trade algorithm of IFs relies on a pooled rather than bilateral trade approach. That is, it does not track exactly who trades with whom, maintaining instead information on gross exports and imports by sector and in total. The algorithm sums import demand and export capacity across all traders (in a given sector), defines world trade as the average of those two values, and then normalizes demand and capacity to the total of world trade to determine sectoral exports and imports by geographic unit.
The current account depends on international loan repayments and foreign aid flows as well as on the trade balance. The exchange rate floats with the debt level (in turn responsive to the current account balance) and is the key equilibrating variable in two negative feedback loops that work via import demand and export capacity (see description of trade).
Income DistributionDomestic income distribution is represented by the Gini coefficient. That is calculated with a Lorenz curve that looks at the share of population and income held by the only two subgroups for which we have that information, namely unskilled an
Given domestic Gini indices, it is also possible to compute global Gini indices, both treating countries as wholes (GINI) and computing across the world at the household level (GINIFULL).
The calculation of poverty levels is fairly straightforward if one has the average level of consumption per capita (or income) and its distribution as indicated by the Gini index (and if one assumes that the distribution underlying the Gini index is log-normal). The internal calculation using those variables will, however, almost inevitably produce a rate of poverty at odds with the provided by national surveys. We therefore compute a ratio of those in the first year to allow adjustment in forecast years of the values from the lognormal calculation.
As a rough check on the values produced by lognormal calculation we also compute a value of poverty estimated from a cross-sectional function linking GDP per capita (and PPP) and Gini to rates of poverty.