Computable general equilibrium (CGE) models are a class of economic models that use actual economic data to estimate how an economy might react to changes in policy, technology or other external factors. CGE models are also referred to as AGE (applied general equilibrium) models. A CGE model consists of equations describing model variables and a database (usually very detailed) consistent with these model equations. The equations tend to be neoclassical in spirit, often assuming cost-minimizing behaviour by producers, average-cost pricing, and household demands based on optimizing behaviour.
CGE models are useful whenever we wish to estimate the effect of changes in one part of the economy upon the rest. They have been used widely to analyse trade policy. More recently, CGE has been a popular way to estimate the economic effects of measures to reduce greenhouse gas emissions.
CGE models account for changes in prices and how they influence the relative use of various factors of production in producing a good or service. In contrast to input-output models, which estimate the quantities of inputs like wheat, energy, labour, and capital required to produce bread, a CGE model can assess how a wage increase might affect the amount of labour used in bread production.
Main features
A CGE model consists of equations describing model variables and a database (usually very detailed) consistent with these model equations. The equations tend to be neoclassical in spirit, often assuming cost-minimizing behaviour by producers, average-cost pricing, and household demands based on optimizing behaviour. However, most CGE models conform only loosely to the theoretical general equilibrium paradigm. For example, they may allow for: non-market clearing, especially for labour (unemployment) or for commodities (inventories) imperfect competition (e.g., monopoly pricing) demands not influenced by price (e.g., government demands)
CGE models always contain more variables than equations—so some variables must be set outside the model. These variables are termed exogenous; the remainder, determined by the model, are called endogenous. The choice of which variables are to be exogenous is called the model closure, and may give rise to controversy. For example, some modelers hold employment and the trade balance fixed; others allow these to vary. Variables defining technology, consumer tastes, and government instruments (such as tax rates) are usually exogenous.
A CGE model database consists of: tables of transaction values, showing, for example, the value of coal used by the iron industry. Usually the database is presented as an input-output table or as a social accounting matrix (SAM). In either case, it covers the whole economy of a country (or even the whole world), and distinguishes a number of sectors, commodities, primary factors and perhaps types of households. Sectoral coverage ranges from relatively simple representations of capital, labor and intermediates to highly detailed representations of specific sub-sectors (e.g., the electricity sector in GTAP-Power."GTAP Data Bases: GTAP 10 Satellite Data and Utilities", Global Trade Analysis Project (GTAP)) elasticities: dimensionless parameters that capture behavioural response. For example, export demand elasticities specify by how much export volumes might fall if export prices went up. Other elasticities may belong to the constant elasticity of substitution class. Amongst these are Armington elasticities, which show whether products of different countries are close substitutes, and elasticities measuring how easily inputs to production may be substituted for one another. Income elasticity of demand shows how household demands respond to income changes.
Role of separability and nested structures
In practical computable general equilibrium (CGE) models, economic behavior is typically represented using nested functional forms, most commonly nested Constant elasticity of substitution (CES) functions for production and utility. These specifications rely on the assumption of weak separability, under which groups of goods or inputs can be treated as composite aggregates. Blackorby, C., Primont, D., & Russell, R. (1978). Duality, Separability, and Functional Structure. North-Holland.Deaton, A., & Muellbauer, J. (1980). Economics and Consumer Behavior. Cambridge University Press. Mak, K. T. (1986). On Separability: Functional Structure. Journal of Economic Theory, 40, 250-282.
Separability allows a high-dimensional optimization problem to be decomposed into a sequence of lower-dimensional problems. For example, a firm may first choose between broad input categories—such as capital, labor, materials, and energy inputs—and then determine the composition within each category. Similarly, households may allocate expenditure across consumption groups (e.g., food, housing, transport) before allocating within each group. This multi-stage optimization is theoretically consistent only if preferences or technologies are separable in the relevant partitions.
The use of separable, nested structures serves several purposes in CGE modeling :
Dimensionality reduction: Large numbers of goods and factors can be represented by a smaller number of aggregates. Parameterization: Substitution patterns can be summarized by a limited set of elasticities at each level of the nesting hierarchy. Calibration: Model parameters can be chosen so that the model reproduces observed base-year data (e.g., a social accounting matrix) as an equilibrium, using observed expenditure or cost shares together with assumed elasticities. Shoven, J. B., & Whalley, J. (1992). Applying General Equilibrium. Cambridge University Press.
Because of these properties, separability is a key element in making CGE models computationally tractable. However, it also imposes strong structural assumptions on preferences and production technologies. In particular, it restricts substitution patterns by requiring that goods within a group behave uniformly with respect to goods in other groups, and that interactions across groups are mediated only through higher-level aggregates. As a result, the choice of nesting structure and associated elasticities is not merely a technical convenience but a substantive modeling assumption that should be informed by empirical evidence and consistency with observed behavior.
History CGE models are descended from the input–output models pioneered by Wassily Leontief, but assign a more important role to prices. Thus, where Leontief assumed that, say, a fixed amount of labour was required to produce a ton of iron, a CGE model would normally allow wage levels to (negatively) affect labour demands.
CGE models derive too from the models for planning the economies of poorer countries constructed (usually by a foreign expert) from 1960 onwards.Sandee, J. (1960), A Demonstration Planning Model for India, Asia Publishing House, Calcutta. Compared to the Leontief model, development planning models focused more on constraints or shortages—of skilled labour, capital, or foreign exchange.
CGE modelling of richer economies descends from Leif Johansen's 1960Johansen, Leif (1960). A Multi-Sectoral Study of Economic Growth, North-Holland (2nd enlarged edition 1974). MSG model of Norway, and the static model developed by the Cambridge Growth ProjectCambridge Growth Project in the UK. Both models were pragmatic in flavour, and traced variables through time. The Australian MONASH model is a modern representative of this class. Perhaps the first CGE model similar to those of today was that of Taylor and Black (1974).Taylor, L. and S. L. Black (1974), "Practical General Equilibrium Estimation of Resources Pulls under Trade Liberalization", Journal of International Economics, vol. 4(1), April, pp. 37–58.
Areas of use CGE models are useful whenever we wish to estimate the effect of changes in one part of the economy upon the rest. For example, a tax on flour might affect bread prices, the CPI, and hence perhaps wages and employment. They have been used widely to analyse trade policy. More recently, CGE has been a popular way to estimate the economic effects of measures to reduce greenhouse gas emissions.
Trade policy Many CGE models cover only a single country. Others cover the whole world, divided into different countries or regions. These have been used widely to analyse trade policy. One of the most well-known global CGE models is the Global Trade Analysis Project (GTAP) model of world trade.Hertel, Tom (ed.) (1997). Global Trade Analysis: Modeling and Applications, Cambridge University Press.
Developing economies CGE models are useful to model the economies of countries for which time series data are scarce or not relevant (perhaps because of disturbances such as regime changes). Here, strong, reasonable, assumptions embedded in the model must replace historical evidence. Thus developing economies are often analysed using CGE models, such as those based on the IFPRI template model.Löfgren, Hans; Rebecca Lee Harris and Sherman Robinson (2002). A Standard Computable General Equilibrium (CGE) in GAMS, Microcomputers in Policy Research, vol. 5, International Food Policy Research Institute.
Climate policy CGE models can specify consumer and producer behaviour and simulate effects of climate policy on various economic outcomes. They can show economic gains and losses across different groups (e.g., households that differ in income, or in different regions). The equations include assumptions about the behavioural response of different groups. By optimising the prices paid for various outputs the direct burdens are shifted from one taxpayer to another.Kolstad C., K. Urama, J. Broome, A. Bruvoll, M. Cariño Olvera, D. Fullerton, C. Gollier, W. M. Hanemann, R. Hassan, F. Jotzo, M. R. Khan, L. Meyer, and L. Mundaca, 2014: "Social, Economic and Ethical Concepts and Methods", p. 238. In: AR5 Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J. C. Minx (eds.). Cambridge University Press, Cambridge, United Kingdom and New York
The Australia Institute engaged Victoria University's Centre of Policy Studies to model the economic impacts of a phased coal production reduction, using a CGE model aligned with the International Energy Agency's central scenario. The model, commonly used by industry and government, accounts for resource constraints but excludes health and environmental costs beyond direct emissions.
Another research argues that CGE models like Australian Bureau of Agricultural and Resource Economics' (ABARE) MEGABARE have been employed in ways that overstate the economic costs of reducing greenhouse gas emissions.
Comparing the Coalition and Labor’s climate modelling, the Coalition's reliance on a Computable General Equilibrium (CGE) model to project emission reductions by 2050 is based on optimistic assumptions (e.g., low carbon price, unproven technologies, and no new spending), lacking clear causative policy measures. In contrast, Labor uses a more grounded, sector-specific, "bottom-up" modelling approach that connects actual policy interventions with realistic emission reductions by 2030.
Comparative-static and dynamic models