Computable General Equilibrium (CGE) models are a class of economy-wide models that incorporate macroeconomic behaviors widely used in policy analysis. The term
“computable” refers to the fact that the model solution can be computed, a prerequisite when a model is used for applied purposes. A general equilibrium model explicitly recognizes that an exogenous change (in policy or from some other sources, such as world markets) that affects any one part of the economy can produce repercussions throughout the system. General equilibrium model are preferable to partial equilibrium models for understanding the impact of exogenous shocks. Mathematically, a standard CGE model consists of a set of simultaneous nonlinear equations. Economically, its starting point is Walras’ neoclassical world. However, CGE models used for applied policy analysis tend to deviate considerable from this starting point, incorporating a relatively large amount of detailed real-world structure (Löfgren 2003: 1). This kind of applied, real-world policy analysis is based on the structuralist approach taking into account structural characteristics of developing countries and questioning the applicability of orthodox neoclassical economics to these countries.
There are a number of CGE models and analyses on Thailand produced by various institutions and authors. However, most of them focus on trade, change in fiscal policy, exchange rate movement, structural change, and foreign direct investment (FDI) issues. The objective of the CGE analyses in this study is, however, different from the previous ones. The CGE model for this analysis aims to analyze impacts from and on the factor input allocations as discussed in section 5.1.
The historical development of the CGE models for Thailand is presented as follows. First, we discuss the major, well-known CGE model patterns for the Thai economy developed for
various policy simulations. Next, specific studies that applied CGE models, which are relevant to this study, will be reviewed.
Grais (1981) developed a CGE model for Thailand called “SIAM1” based on a SAM to analyze the adjustments of the economy after the second oil shock (1979) over the period 1980-1990. Drud, Grais, and Vujovic (1982) adjusted SIAM1 slightly and used it to analyze the effects of a structural adjustment loan to Thailand in terms of the macroeconomic implication of alternative packages of policy measures and of other changes which might affect the Thai economy.
Taylor and Rosensweig (1984) designed the first CGE model for Thailand which includes the financial sector to examine the impacts of fiscal and monetary policies and of currency devaluation.
In the early 1990s, a CGE model for Thailand called “CAMGEM” 47was developed to analyze policy impact (comparative static analysis) and for forecasting. CAMGEM has been applied in several policy studies in Thailand including Arunsmith and Trirut (1995), Arunsmith (1997a), Arunsmith (1997b), Arunsmith (1998), Arunsmith (1999), and Siksamat (2002)48. The applications of CAMGEM have been concentrated in the area of international trade policies. It was also used to assess the pattern of changes in structural variables such as technological changes, changes in preferences and changes in other observable variables in Thailand.
Another CGE model for Thailand called “PARA”49 makes use of 1985 input-output data and was designed to address microeconomic policy issues in Thailand. It incorporated a highly disaggregated and detailed representation of the Thai economy. PARA provided a major
47 CAMGEM stands for Chulalongkorn and Monash Universities General Equilibrium Model, which is a product of joint effort between Faculty of Economics, Chulalongkorn University of Thailand and Center of Policy Studies (COPs), Monash University of Australia during 1991-1993. CAMGEM is closely related to ORANI, the internationally well-known CGE model of the Australian economy.
48 Siksamat (2002)’s model is called GEM-H, which is patterned from CAMGEM-H.
49 PARA stands for Protection Areas of Regional Agriculture, which was developed through the collaboration
contribution by incorporating the results of a large econometric research program directed toward estimating the economic behavioral parameters underlying the model based on the Thai data. It was used to analyze the effects of Thailand’s protection policy (reduced protection under the Uruguay round of the General Agreement on Tariffs and Trade (GATT)) on the economy, particularly on the agricultural sector at the regional level, and on the distribution of income, and welfare (Siksamat 1998a, 1998b).
The most recent CGE model developed for Thailand is “GEMREG”,50 which is developed from ORANI and ORANI-F models. GEMREG is a multi-regional general equilibrium model of the Thai economy, which makes it different from other existing Thai CGE models. The GEMREG model contains two main parts. The first part is a national model and the second part is a regional equation system. Policy shocks are first imposed on the national model, after that the outcomes, which are projected by the national model, are allocated to the regions.
Other empirical studies using CGE models to simulate the Thai economy and related to this study are observed as follows.
Nitsmer (1992) used CGE analysis to examine the impacts of agricultural-led development on economic growth and income distribution in Thailand. Agricultural-led development was defined in model specification as simultaneous increases in agricultural productivity and government investment in agriculture, and a reduction in export taxes. Base period data used for simulations was year 1980. This study found that when world prices for agricultural commodities were assumed to be lower than in the base period, agricultural-led development sustained agricultural growth, but income distribution shifted in favor of urban households. Alternatively if world prices for agricultural commodities were assumed to be higher than in the base period, then again this strategy increases economic growth, but income
distribution shifted in favor of rural households in the models. Results of the simulations showed that an agricultural-led development strategy was plausible for Thailand under the conditions prevailing in the early 1980s.
Wongwatanasin (1999) developed a consistent SAM and CGE model for Thailand in order to establish quantitatively the dimensions of the effects of industrial polices on particular industries, and on the Thai economy as a whole, under alternative tax transfer (replacement) policies for output, trade flows, and income distribution. The model results revealed that industrial policy enhances both economic efficiency and income equality, given appropriate government policies. Although more evidence is needed to decide which industry and tax policy was best for Thailand, the results showed clearly that the choice of an industry and a government tax policy mattered in the trade-off between economic efficiency and distributional equality. The social cost of industrial policy could be reduced by the proper industrial and fiscal policy decision. The model results revealed that industrial targeting of the 1980s strengthened the trade flows of intermediate and capital goods industries, but weakened the trade flows of other industries. Thus, industrial policy has been a contributing factor in the evolution of Thai industrial structure and trade patterns. However, the magnitudes of changes in the composition of output induced by industry-specific policies have been relatively small, implying that the industrial policy has had little input in driving growth. The model results also indicated that the industrial policy during the 1980s clearly coincided with a rising disparity in income distribution in Thailand’s outward-oriented phase. However, the trend of income distribution from the period 1981-85 appeared to have improved as the policy moved toward trade and industrial liberalization.