CGE Projection of Economic and Potential Environmental Effects
of the Principal Trade Items between Thailand and Its FTA Partners
By Sompote Kunnoot [1]
Abstract
The economic gain and shadow environmental costs of Thailand’s free trade agreements (FTAs) were projected to measure true economic gain, based on Pigou’s (1960) social welfare principle, across 180 sectors using a general equilibrium model simulation. The projection covered 50 principal export and 50 principal import items in five effective FTAs and four FTA negotiations in progress. FTAs, as a whole, were found to yield a net economic gain because true economic gain outweighed true economic loss. The results call attention to policy curbing the import of selected items from Australia, China, and the EU and to diversifying domestic production to improve economic gain from the export of selected items.
Keywords: free trade agreements; environmental cost; gain from trade; CGE model
1. Introduction
Thailand’s free trade agreement (FTA) policy became effective at the beginning of 2004. By 2009, Thailand had finalized FTAs with Australia, China, India, Japan, and New Zealand. Negotiations have also been advanced with four other partners: BIMSTEC, the European Union, Peru, and South Korea.
An FTA involves liberalization in a number of areas, including trade in goods and services, investment rules and regulations, labor rights and mobility, and access to professions, banking, and finance. The main purpose of an FTA is to find a channel for both parties to increase the production of goods and services for mutual benefit (Thaveechaiyagarn, 2010). The FTA has proven to be a successful alternative in light of the slowing of the World Trade Organization (WTO) trade liberalization negotiations (NowPublic, 2007; Public Citizen, 2010; The Daily Mail, 2008; Wallach and James,2006).
In Thailand, a number of studies have been commissioned to determine the rational justification before finalizing each FTA. An FTA is perceived and publicized as providing economic gain, which is achieved by augmenting production, over economic loss, which is caused by a contraction of import-competing production. However, economic loss is perceived and publicized as counterweighted by economic gain. FTA costs are usually considered an investment in exchange for economic gain and must be minimized through a number of measures and policy changes. Supporters of FTAs accept that costs exist, but these costs can be outweighed by economic gain. While losers in competition are entitled to liability, they are encouraged to move to more productive sectors.FTA funds have been used to facilitate these changes caused by damages from the FTA.
A complete account of the effects of FTAs involves a complex, interdependent network of economic elements as the flow of economic currents multiplies through both expansion and contraction. The expansion effects are driven by production-increasing exports, and the contraction effects are driven by a reduction in competing import production. While the expansion effects can be offset by the contraction effects and vice versa, the net effects can be either expansion or contraction, depending on the strength of the current.
This study is an attempt to determine whether Thailand’s FTAs are a mission of fortune or of misfortune, a question that involves two issues. First, the underlying expectation of an FTA is that the export expansion yields real GDP gain, and the import expansion causes real GDP loss. An FTA is considered a fortune mission if the real GDP gain outweighs the real GDP loss. One approach to the issue is to obtain the real GDP gain or loss projection and identify the differences between the two measures. The initial finding is not final because the real GDP gain or loss is not considered the true gain or loss until the external costs are accounted for. This criterion is known as Pigou’s social welfare principle (Pigou, 1960). According to this principle, an FTA is truly a fortune mission if the true real GDP gain outweighs the true real GDP loss. An approach to this second issue is to obtain an environmental cost projection and identify the differences between the real GDP gain and the environmental cost. Through this approach, the true real GDP gain can be obtained, and the differences can be identified between the real GDP loss and the environmental cost avoidance to obtain the true real GDP loss. The true economic gain from an FTA is the difference between the true real GDP gain and the true real GDP loss.
A general equilibrium approach is considered the most relevant way to capture all of the effects in an economy driven by an increase in the export or import of a single item. Accordingly, a computable general equilibrium (CGE) model was devised to capture the economic gain and loss of an increase in the export or import of a single selected item. Computation results were interpreted as an opportunity to gain and a threat to lose the real GDP driven by exports and imports, respectively. The real GDP gain or loss for a selected item was summed to provide an overview of the status of a single FTA. The results for a single FTA were summed to provide an overview of the status of all FTAs.
The underlying principle for a comprehensive environmental cost accounting is that the output of all production sectors is accompanied by the environmental cost. It is common to consider the environmental cost proportional to the output. In a physical sense, waste emission is considered a fraction of the input such that material input equals material output plus material waste (e.g., Xue et al., 2006). A change in demand in a single sector drives a change in output in a network of connected production. The comprehensive environmental cost accounting for this single change is to capture the total change in the environmental cost associated with the change in output across sectors of an economy.
The environmental cost involves a number of limitations. Although the environmental cost is real, its presence is external to all consumption and production and, therefore, is normally left to the common. This nature drives the environmental cost to an area for which the market fails to account. As a result of these conditions, the environmental cost data are officially unavailable.
Studies on environmental cost have increased recently in the area of life-cycle assessment (LCA). These studies require that the environmental cost data throughout the production supply chain be available. One popular channel for an LCA approach is the application of Leontief’s (1936) input-output approach, known as an economic input-output LCA (EIOLCA) (e.g., Karna and Engstrom, 1994; Lave et al., 1995; Pento, 1997; Joshi, 2000). This approach requires that environmental cost data for all productions be in an input-output table.
In Thailand, because the official environmental cost data are not available, EIOLCA studies must resort to the environmental cost data using the economic data available in the input-output table. Thus, the environmental cost data are not the true environmental cost data; rather, they are interpreted as the shadow environmental cost data.
The environmental cost data requirement in this study is similar to that of EIOLCA studies. The environmental cost data are required for all production to obtain Pigou’s true real GDP gain. This study adopted a similar approach in representing the environmental cost data by using the economic data available in the input-output table. Thus, environmental cost is interpreted as the shadow environmental cost (SEC). The SEC is represented by the intermediate cost in input-output sectors, which include 025 logging, 026 charcoal and firewood, 027 other forest products, 037 chemical fertilizers, 078 saw mills, 085 fertilizer and pesticides, 092 other chemical products, 093 petroleum refineries, 094 other petroleum products, 135 electricity, 136 pipeline and gas distribution, 137 water work and supply, 149 railways, 150 road passenger transport, 151 road freight transport, 152 land transport support service, 153 ocean transport, 154 coastal and inland water transport, 155 water transport services, 156 air transport, and 166 sanitary and similar services.
In Thailand, a similar approach to the formulation of the SEC coefficient matrix was used in a study commissioned by the Pollution Control Department (PCD), Ministry of Natural Resources and Environment (PCD, 2008) and a study conducted by Kunnoot (2009). The PCD (2008) study employed Leontief’s (1936) input-output model using Thailand’s 2000 input-output table, which was published by the Office of the National Economic and Social Development Board (NESDB).
The research by Kunnoot (2009) was also an accounting of the economic and environmental effects of an FTA, but, unlike the PCD (2008), the computation employed a CGE model. The rationale for using a CGE model was primarily its theoretical property of fewer limitations compared to an input-output model. The primary limitations in the Kunnoot (2009) study, however, were aggregation problems inherent in a small CGE model that classified only 50 sectors. The aggregation problem was considered especially significant with respect to the projection accuracy for FTA effects.
In this study, a CGE model was structured to fully incorporate the 180 sectors of the Thai input-output table. At this level, the aggregation issue can be improved, though not entirely removed. Accordingly, the projection accuracy for FTA effects can be expected to improve. The specific objectives of this study were two-fold: (1) to project Pigou’s true economic gain or loss from Thailand’s FTAs and (2) to offer relevant policy recommendations about Thailand’s FTAs.
2. Research methodology
2.1 Conceptual framework
The approach for this study was Pigou’s (1960) social welfare. The projection of the true economic gain or loss from Thailand’s FTAs is shown in Figure 1. True economic gain is gross economic gain less the environmental cost burden. Likewise, true economic loss is gross economic loss less the environmental cost avoidance.
Figure 1 Approach for the projection of Pigou’s true economic gain or loss from Thailand’s FTAs
2.2 The CGE model and closure
A CGE model was devised to represent an open economy in Thailand. It was used to project the economic and environmental effects of an increase in exports and imports from Thailand’s perspective. The design adopted Johanson’s class linear platform based on the Orani model design approach (Dixon et al., 1982). The model was structured with 180 production sectors to be fully compatible with Thailand’s original input-output table, which was used as the base data.
The CGE model consists of 68,788 variables and 67,523 equations, thus leaving 1,265 exogenous variables. The lists of variables, equations, exogenous variables, coefficients, shares, and parameters are shown in Appendix A-D. The production function was configured with a two-level nest, as shown in Figure 2. The cost minimization was applied to the intermediate and primary input demand functions.
Figure 2 Composite production functions
The utility maximization was applied to the household, investment, and government consumption demand functions. The Hicks-Slutsky partition principle-total effect equaled the income effect plus the substitution effect (Dixon et al., 1982) applied to the computation of the substitution elasticity of household consumption and government consumption. The foreign demand for exports was subject to price elasticity. The GDP computed from the supply side was the sum of all value added, and the GDP computed from the demand side was the sum of all household, investment, government, and differences between exports and imports. Inflation was computed in terms of consumer price index, government consumption price index, and GDP deflator.
The elasticity of substitution between domestic and imported intermediate inputs was computed as a ratio of the total absorption to the domestic production. Accordingly, substitution is less flexible in sectors dominated by domestic production and more elastic in sectors dominated by imported goods.
The elasticity of substitution between the labor and capital input was computed based on the share of labor in a combined labor-capital. Accordingly, substitution is less flexible in sectors dominated by capital and more elastic in sectors dominated by labor.
The price elasticity of export demand is computed based on the ratio of combined domestic-export demand to domestic demand. Accordingly, export demand is more responsive in the sectors dominated by exports and less responsive in the sectors dominated by domestic demand.
2.3 Compatibility between the CGE model and trade data
The CGE model was run based on Thailand’s input-output table data source. However, the FTA evaluation projection was carried out for specific trade items based on trade data. The classification of trade data, the harmonized system classification (HSC), differs significantly from the input-output sector classification (IOC) in that the HSC is more detailed, whereas the IOC is more aggregated. Because official matching between the HSC and the IOC does not exist, they are arbitrarily matched. This HSC-IOC matching issue can cause the projection results to suffer from incorrect parameters due to aggregation. Many traded items may be in the same IOC although their true elasticity parameters are different.
To carry out an FTA evaluation projection, which is based on the HSC, through a CGE model, which is based on the IOC, the CGE model was run to produce multiplier value. That is, to exhibit the power of economic effects, it is necessary to obtain the economic multiplier computed in terms of the real GDP change per export or import unit. The second multiplier is the environmental cost multiplier computed in terms of the change in environmental costs associated with all affected output per export or import unit, thus exhibiting the power of environmental cost effects. The multiplier value was multiplied by the value of export and import items in the HSC platform to obtain the value of change in economic and environmental effects originating from an increase in exports and imports.
To keep the presentation of the computation and results manageable, export and import items under evaluation were arbitrarily limited to 50 principal export and import items for each FTA. The projection results must be carefully interpreted as representing part of an FTA. Similarly, policy recommendations must be interpreted as targeting only within the scope of principally traded items.
2.4 Simulation
The CGE simulation was carried out as follows. For Thailand’s exports, the CGE model was fed with a change in the foreign liberalization variable to induce an increase in the export variable, representing each of 50 export items. Likewise, for Thailand’s imports, the CGE model was fed with a change in the import tax variable to induce an increase in the import variable representing each of 50 import items. Changes in the real GDP variable and the environmental effects variable were captured for each of 50 principal traded items and converted into real GDP change and environmental costs change per export and import unit, that is, economic and environmental cost multipliers, respectively, for each item.
The multiplier value was multiplied by the value of the HSC export and import data to obtain values for the change in real GDP and the shadow environmental cost associated with the existing value of the HSC export and import data. Another benefit of computing the multiplier values was their use for comparing export and import items in terms of the power and opportunity of a particular export and import item to yield change in the real GDP and the associated SEC.
2.5 Data and computation
Thailand’s 2005 input-output table was used as base data for the CGE model. The table was compiled and published online at by the Office of the National Economic and Social Development Board. A conceptual layout of the input-output table is illustrated in a miniature 2-sector system in Figure 1.
Trade data consist of year 2009 export and import values for 50 principal items classified by country, compiled in an HSC by the Customs Department, Ministry of Finance, and published online at by the Department of Export Promotion, Ministry of Commerce.
The CGE model was computed by GEMPACK (Harrison and Pearson, 2002).
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[1] Rice / [2] Machine / [C] Household consumption / [I] Investment / [G] Government consumption / [E] Exports / [M] Imports / [GDP] GDP consumption / [X] Total commodity demandDomestically / [1] Rice / / / / / / /
produced / [2] Machine / / / / / / /
Imported / [1] Rice / / / / / /
goods / [2] Machine / / / / / /
Value added / [L] Labor wage income / / /
[K] Enterprise profit income / / /
[T] Indirect taxes / / /
[X] Total commodity supply / / /
Figure 3 Illustration of miniature 2-sector representation of the conceptual layout of input-output base data
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3. Results
3.1 Overview
The results are presented in four overviews. The first overview is a projection of real GDP gain or loss (loss indicated by a negative sign) and the associated SEC burden or associated SEC avoidance (avoidance indicated by a negative sign) for 9 FTAs, as shown in Table 1.
The second overview is the multipliers for economic gain or loss (loss indicated by a negative sign) and associated SEC burden or associated SEC avoidance (avoidance indicated by a negative sign) for 9 FTAs, shown in Table 2. The multiplier for economic gain or loss is the ratio between the value of changed real GDP and the value of changed export or import. The multiplier for the associated SEC burden and associated SEC avoidance is the ratio between the value of changed associated SEC burden or associated SEC avoidance and the value of changed export and import, respectively.
The third overview is the projection of real GDP gain or loss (loss indicated by negative sign) and the associated SEC burden and associated SEC avoidance (avoidance indicated by a negative sign) for 5 effective FTAs, as shown in Table 3.