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Computable general equilibrium (CGE) models are a class of economic model
that use actual economic data to estimate how an economy might react to changes
in policy, technology or other external factors.
Overview
A CGE model consists of (a) equations describing model variables and (b) a
database (usually very detailed) consistent with the model equations. The
equations tend to be neo-classical 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 (eg, monopoly pricing)
demands not influenced by price (eg, government demands)
a range of taxes
externalities, such as pollution
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. 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 household.
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.
CGE models are also referred to as AGE (applied general equilibrium) models.
They 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. Compared
to the Leontief model, development planning models focussed more on constraints
or shortages -- of skilled labour, capital, or foreign exchange.
CGE modelling of richer economies descends from Leif Johansen's 1960 MSG model
of Norway, and the model developed by the Cambridge Growth Project in the UK.
Both models were pragmatic in flavour, and were dynamic (traced variables
through time). The Australian MONASH model is a modern representative of this
class.
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.
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 modellers 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.
Today there are many CGE models of different countries. One of the most
well-known CGE models is global: the GTAP model of world trade.
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 .
Comparative-static and dynamic CGE models
Many CGE models are comparative-static: they model the reactions of the economy
at only one point in time. For policy analysis, results from such a model are
often interpreted as showing the reaction of the economy in some future period
to one or a few external shocks or policy changes. That is, the results show the
difference (usually reported in percent change form) between two alternative
future states (with and without the policy shock). The process of adjustment to
the new equilibrium is not explicitly represented in such a model, although
details of the closure (for example, whether capital stocks are allowed to
adjust) lead modellers to distinguish between short-run and long-run equilibria.
By contrast, dynamic CGE models explicitly trace each variable through time --
often at annual intervals. These models are more realistic, but more challenging
to construct and solve -- they require for instance that future changes are
predicted for all exogenous variables, not just those affected by a possible
policy change. The dynamic elements may arise from partial adjustment processes
or from stock/flow accumulation relations: between capital stocks and
investment, and between foreign debt and trade deficits.
Recursive-dynamic CGE models are those which can be solved sequentially (one
period at a time): they assume that behaviour depends only on current and past
states of the economy. Alternatively, if agents' expectations depend on the
future state of the economy, it becomes necessary to solve for all periods
simultaneously, leading to full multi-period dynamic CGE models. Within the
latter group dynamic stochastic general equilibrium models explicitly
incorporate uncertainty about the future.
References
^ Johansen, Leif (1960). A Multi-Sectoral Study of Economic Growth,
North-Holland (2nd enlarged edition 1974).
^ Dixon, Peter and Maureen Rimmer (2002). Dynamic General Equilibrium Modelling
for Forecasting and Policy: a Practical Guide and Documentation of MONASH, North
Holland.
^ Hertel, Tom (ed.) (1997). Global Trade Analysis: Modeling and Applications,
Cambridge University Press.
^ L?fgren, 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.
Further reading
Adelman, Irma and Sherman Robinson (1978). Income Distribution Policy in
Developing Countries: A Case Study of Korea, Stanford University Press
Dervis, Kemal, Jaime de Melo and Sherman Robinson (1982). General Equilibrium
Models for Development Policy. Cambridge University Press.
Dixon, Peter, Brian Parmenter, John Sutton and Dave Vincent (1982). ORANI: A
multisectoral model of the Australian Economy, North-Holland.
Dixon, Peter, Brian Parmenter, Alan Powell and Peter Wilcoxen (1992). Notes and
Problems in Applied General Equilibrium Economics, North Holland.
Dixon, Peter (2006). Evidence-based Trade Policy Decision Making in Australia
and the Development of Computable General Equilibrium Modelling, CoPS/IMPACT
Working Paper Number G-163
Ginsburgh, Victor and Michiel Keyzer (1997). The Structure of Applied General
Equilibrium Models, MIT Press.
Kehoe, Patrick J. and Timothy J. Kehoe (1994) "A Primer on Static Applied
General Equilibrium Models," Federal Reserve Bank of Minneapolis Quarterly
Review, 18(2) .
Kehoe, Timothy J. and Edward C. Prescott (1995) Edited volume on "Applied
General Equilibrium," Economic Theory, 6.
Piermartini, Roberta and Robert Teh (2005). Demystifying Modelling Methods for
Trade Policy, Discussion Paper No. 10, World Trade Organization, Geneva.
Shoven, John and John Whalley (1984). Applied General-Equilibrium Models of
Taxation and International Trade: An Introduction and Survey. Journal of
Economic Literature, vol.22(3) 1007-51
Shoven, John and John Whalley (1992). Applying General Equilibrium, Cambridge
University Press.
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