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Handbook of COMPUTABLE GENERAL EQUILIBRIUM MODELING
VOLUME 1A
By Peter B. Dixon, Dale W. Jorgenson Elsevier Science
Copyright © 2013 Elsevier B.V.
All rights reserved.
ISBN: 978-0-444-62631-8
Excerpt
CHAPTER 1
Introduction
1.1 OVERVIEW
Computable general equilibrium (CGE) modeling is a challenging field. It requires mastery of economic theory, meticulous preparation of data and familiarity with underlying accounting conventions, knowledge of econometric methods, and an understanding of solution algorithms and associated software for solving large equation systems. However, the most important requirement is the ability to communicate. CGE modeling is primarily about shedding light on real-world policy issues. For CGE analyses to be influential, modelers must explain their results in a way that is comprehensible and convincing to their fellow economist, and eventually to policy makers.
While CGE modeling is challenging, it is also rewarding. CGE models are used in almost every part of the world to generate insights into the effects of policies and other shocks in the areas of trade, taxation, public expenditure, social security, demography, immigration, technology, labor markets, environment, resources, infrastructure and major-project expenditures, natural and man-made disasters, and financial crises. CGE modeling is the only practical way of quantifying these effects on industries, occupations, regions and socioeconomic groups.
In 2010, CGE modeling had its 50th birthday, marked by a celebration in Oslo commemorating the publication in 1960 of Leif Johansen's A Multisectoral Study of Economic Growth. In that book, Johansen describes a 22-sector model of Norway which is generally recognized as the first CGE model. What distinguishes his model from other economy-wide models of that time is the explicit identification of behavior by separate agents. In Johansen's model, households maximize utility subject to their budget constraints, industries choose their inputs to minimize the costs of producing the level of output that will satisfy demand and capitalists allocate the economy's capital stock between industries so that rates of return reflect historical relativities. The behavior of these individual agents is coordinated through prices determined by interaction of demand and supply. In contrast to Johansen's multiple-agent approach, earlier and contemporaneous economy-wide models in the 1960s [e.g. the input-output and linear programming systems of Leontief (1936, 1941), and Sandee (1960) and Manne (1963)] visualized the economy as a single agent. In those models, the economy produced the output vector necessary to satisfy exogenous final demands or to optimize an economy-wide welfare function.
Fifty years on, CGE modeling is an established field. It has textbooks [e.g. Dervis et al., (1982), Dixon et al., (1992) and Burfisher (2011)], survey articles [e.g. Shoven and Whalley (1984), Robinson (1989), Bandara (1991), and Partridge and Rickman (1998, 2010)] and conference volumes [e.g. Kelley et al., (1983), Scarf and Shoven (1984), Bergman et al., (1990), and Mercenier and Srinivasan (1994)]. It is the subject of many monographs and journal articles, and provides the substance of thousands of consultancy reports. So what is added by the Handbook of Computable General Equilibrium Modeling?
This Handbook is not a textbook, a survey or a conference volume. It is a collection of chapters setting out the experience of leading CGE modelers. Each chapter contains essential knowledge but just as importantly, each chapter points to new horizons. As evidenced in the Handbook, CGE modeling embraces new problems as they arise in a real-world context. Important current focuses are aging, greenhouse gases and global financial imbalances.
In planning the Handbook we had a four-part structure in mind: single-country models; global models; technical aspects of CGE modeling covering data, parameter estimation, computation and validation, and current cutting-edge methodological areas. Broadly, this is how things worked out; but of course chapters that were initially planned for one section describe contributions that fit into other sections as well.
1.2 SINGLE-COUNTRY MODELS
Norway and Australia are the two countries in which CGE modeling has had its highest profile in policy formation. Both these countries have long-standing CGE modeling projects with continuous histories up to the present day. In Norway, the MSG (multi-sectoral growth) project located in the Norwegian government's statistical agency (Statistics Norway) goes back to Johansen in the 1960s. In Australia, the MONASH project, currently located in Monash University's Centre of Policy Studies, goes back to the setting up in the Australian government bureaucracy of the IMPACT project in 1975. The MSG and MONASH projects are related. The MONASH project adopted and extended Johansen's techniques and carried his style of CGE modeling to the rest of the world.
As explained in Chapter 2 by Peter Dixon, Bob Koopman and Maureen Rimmer, creation of the first MONASH model, ORANI, involved a series of enhancements to Johansen's model, including: (i) a computational procedure that eliminated Johansen's linearization errors without sacrificing simplicity, (ii) endogenization of trade flows by introducing into CGE modeling imperfect substitution between imported and domestic varieties (the Armington assumption), (iii) increased dimensionality allowing for policy-relevant detail such as transport margins, (iv) flexible closures, and (v) complex functional forms to specify production technologies. ORANI was a large-scale comparative statical model used in Australia's tariff debate of the 1970s. Modern MONASH models are dynamic. They have proved remarkably flexible and operate in numerous countries on a wide variety of policy issues. As well as broad theoretical features of MONASH models, the chapter covers data preparation and introduces the GEMPACK purpose-built CGE software, discussed in detail in Chapter 20.
To a large extent MONASH models have evolved to meet the needs of clients in government and business. The models can be used for four modes of analysis: historical, decomposition, forecast and policy. Historical simulations produce up-to-date data, and estimate trends in technologies, preferences and other naturally exogenous but unobservable variables. Decomposition simulations explain historical episodes and place policy effects in historical context. Forecast simulations provide baselines using extrapolated trends from historical simulations together with specialist forecasts. Policy simulations generate the effects of policies as deviations from baselines. To emphasize the practical orientation of MONASH models, the chapter starts with a MONASH-style policy story on the effects on the US economy of restricting the number of illegal immigrants working in the US. The story is told in a way that is typical of policy briefs provided by MONASH modelers to government-sector economic advisors. It is a quantitative story that exposes the main mechanisms and data items that drive the results but does not require advisors to have a CGE background. Among the factors identified in the story is the Occupation-mix effect. This is the idea that a reduction in low-skilled immigrant employment creates job vacancies for legal residents at the low-skilled end of the occupational ladder, while closing off vacancies at the top end. In the long run, this causes a deterioration in the occupational mix of employment of legal US residents.
In Chapter 3, Erling Holmøy and Birger Strøm consider applications of MSG6, the most recent version of the MSG model of Norway originally developed by Leif Johansen (1960, 1974). Holmøy (2012) has traced the development of successive generations of the MSG model over the past half century. Although the different versions of MSG have common features, such as integration with the Norwegian national accounts and an emphasis on long-term trends, the current version bears relatively little resemblance to Johansen's model. In place of constant returns to scale and perfect competition for individual industries, as in Johansen's approach, individual industries in MSG6 are modeled as imperfectly competitive with increasing returns to scale. Second, the model incorporates a detailed microsimulation model for Norway in order to capture the role of changing demographics and the distributional impact of alternative policies.
Holmøy and Strøm consider applications of MSG6 to assessments of the fiscal sustainability of government tax and spending programs in Norway. Despite Norway's massive revenues from the sale of petroleum and natural gas in buoyant international markets, the Norwegian economy faces a severe problem of fiscal sustainability in the future. This can be traced to the relatively high level of government services, the aging of the Norwegian population and the eventual depletion of oil and gas resources. This finding is robust to variations in assumptions about productivity growth, the level of petroleum prices, longevity of the Norwegian population, immigration policies and health of the elderly. The authors emphasize the crucial role of general equilibrium effects in the outcomes of the policy simulations. General equilibrium modeling is particularly important for distinguishing between the transitory impact of large petroleum revenues and the long-term effects of an aging population on the Norwegian economy.
Apart from public sector institutions in Norway and Australia, another institution that has had a long-standing involvement in CGE modeling is the World Bank. Chapters 4, 5 and 6 discuss some of the Bank's current and past single-country CGE modeling. Later chapters (Chapters 13, 14 and 21) cover the Bank's contributions to global modeling.
In Chapter 4, Hans Lofgren, Martin Cicowiez and Carolina Diaz-Bonilla describe the World Bank's program to develop a standardized modeling approach for assessing progress toward the Millennium Development Goals (MDGs). These goals were established at the UN Millennium Summit in 2000, and called for halving poverty rates, achieving universal primary education, reducing under-5 and maternal mortality rates, and reducing the share of the population without access to improved sources of water and sanitation, all by 2015. In 2004 the World Bank established the MAMS (Maquette for MDG Simulations) framework for CGE modeling of the MDGs and launched a pilot project for Ethiopia. This framework has now been applied to more than 40 countries by the World Bank staff, the staff of the UN Department of Social and Economic Affairs and national researchers in emerging economies. Applications have been extended beyond the MDGs to include alternative targets, additional issues and longer time horizons.
Lofgren, Cicowiez and Diaz-Bonilla describe the MAMS framework, its implementation in the GAMS software package and the development of an Excel-based front end to make the model results accessible to a wider range of users. A major finding from cross-country comparisons is that achievement of the MDGs is heavily dependent on each country's initial situation. This leads to the conclusion that countries may be better off pursuing country-specific goals for poverty reduction and human development. A relatively balanced development program consisting of public infrastructure and human development services generates the most desirable outcomes. Second, a focus on human development programs such as education and health generates large demands for highly educated labor and undesirable distributional effects. The MAMS framework has also been used to analyze the potential for currency appreciation from foreign aid that could undermine competitiveness. The authors present a relatively detailed model for Yemen to illustrate the CGE approach and its application to development policy.
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Excerpted from Handbook of COMPUTABLE GENERAL EQUILIBRIUM MODELING by Peter B. Dixon. Copyright © 2013 by Elsevier B.V.. Excerpted by permission of Elsevier Science.
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