C. Simulations on Tax and Subsidy Policies (Simulations 5, 6 and 7)
D. Simulation on Government Expenditure Policy (Simulation 8) E. Simulation on Government Transfer Policy (Simulation 9)
F. Simulation on Change in Export Price (Simulation 10) G. Simulation on Change in Import Price (Simulation 11)
H. Simulations on Exchange Rate Policies (Simulations 12 and 13) I. Simulation on Improvement in Production Technology (Simulation 14) 5.4 Distributional Impacts of Simulations
5.5 Concluding Remarks
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This chapter proposes a new development strategy to reallocate primary agricultural workers to agro-industry. Since the structural transformation in terms of employment and income distribution in Thailand is happening quite slowly, the objective of this strategy is to improve the real wage of agricultural workers by channeling them into other productive sectors.
Agro-industry is selected as the destination sector because it has the best intersectoral linkages as tested in the previous chapter. By imposing this strategy, the real wage in primary agriculture should increase due to the higher productivity of labor when it becomes less abundant. The wage rate in the recipient sector is projected to decline but not excessively. Since agro-industry’s production technology is closely related to that of the agricultural sector, it should be easy to shift primary agricultural workers into agro-industry. Moreover, since agro-industry has very strong interindustrial linkages as evidenced in the previous chapter, at least the same speed of growth should be maintained when applying this labor allocation strategy. This labor allocation strategy will be tested using a Computable General Equilibrium (CGE) model. Other kinds of
policy simulations related to the new development strategy will also be experimented under the same model, such as simulations on capital allocation, tax and subsidy incentives, protective policies, government expenditures and transfers, price movements in rest-of-world, exchange rate policies, and improvements in production technology. The reason the CGE analysis is used to test this new strategy is elaborated in Section 5.1. Section 5.2 discusses previous CGE models of Thailand. Section 5.3 elaborates features of the CGE model of Thailand used in this study and explains the simulations design and simulation results. Section 5.4 summarizes the distributional impacts on labor demand, wage rate in primary agriculture, and household incomes of all simulations. The last section will conclude the analysis of this strategy.
5.1 Objective of the CGE Analysis on the Thai Economy
In the previous SAM and input-output analyses in Chapter IV, we have found that the agricultural-related sectors, to be specific, the high value-added agricultural sectors such as livestock and agro-industry are the promising sectors which give highest multiplier effects and linkage effects. These sectors have served as an engine of growth for the Thai economy in the past and present, and should be able to do so in the future.
However, although we can be sure of the potential of these promising agricultural sectors, we are in doubt about the economic environments and policies which can facilitate their growth.
As described in Chapter II and III, Thai governments’ development policies have long focused on the country’s structural transformation from a primary agricultural-based country to an advanced manufacturing industrial-based one. The objective itself is reasonable, but the process does not occur so smoothly and the results, in terms of poverty reduction and income distribution, are not so satisfactory. Our hypothesis set earlier to solve this problem is to move primary agricultural labor out of this sector into other production sectors. The previous SAM
and input-output analyses show clearly that agro-industry and high value-added agricultural sectors are the promising and more sustainable ones, in terms of the less dependency on imported inputs, for promotion over other non-agricultural manufacturing industrial sectors.
However, the SAM and the input-output analyses cannot explicitly examine the effects resulting from factor input movements (labor and capital) that we want to test in our hypothesis of the proposed new development strategy. This is because the SAM and the input-output analyses are not good tools to deal with factor input and relative price movement issues. This job is therefore left to the CGE model, though the SAM and the input-output analyses are very good to test the effects resulting from quantity change. We therefore rely on the simulation results from the SAM and the input-output analyses on effects from the quantity demand increase (from rest-of-world and the government) in the previous chapter.
The CGE model will be used to, firstly, test the impacts from the factor input movements.
Simulations on labor allocation and capital allocation will be conducted. However, since the labor movement simulation is not applicable as a real policy implementation, other kinds of simulations related to our strategy and are policy-applicable must also be conducted. These policies can be related to the relative price movements to create incentives for producers to act in the real economy. These simulations are the capital allocation policy, the tax and subsidy incentive policies, the protective policies, the exchange rate policy, the improvement in production technology policy, and the change in rest-of-world environment such as the change in export and import prices. In addition, simulations related to the government expenditures, which are increases in government demand for specific products and government transfers to specific household groups, will also be tested since they are directly applicable as policies related to the income distribution issue.
5.2 Literature Survey of CGE Models of Thailand
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.
5.3 Simulating the New Strategy on the Thai Economy—A CGE Analysis
This section presents features of the real sector CGE model for the Thai economy used in the analysis. The model, which is broken down into blocks, is discussed in Section 5.3.2 with the full list of model equations and variables presented in Section 5.3.2.1. Section 5.3.3 discusses the equilibrium conditions and the model calibration. Section 5.3.4 discusses the simulation design and the simulation results.
5.3.1 Model Specification
The CGE model developed to use for policy analysis of Thailand is a standard CGE model for an open economy, developed by Hans Löfgren of the International Food Policy Research Institute (IFPRI).51 The model is run by using GAMS (General Algebraic Modeling System) software which is specifically designed for modeling linear, nonlinear and mixed integer optimization problems, and is useful with large, complex modeling problems.52 The mathematical statements and the GAMS codes for this model follow the standard notation used in CGE models developed in IFPRI’s Trade and Macroeconomic Division. All endogenous variables are written in uppercase Latin letters, whereas parameters (including variables with fixed or exogenous values) have lower-case Latin or Greek letters. Subscripts refer to set indexes, with one to three letters per index. Superscripts are part of the parameter name (that is, not an index). In terms of letter choices, variables and parameters for commodity and factor quantities start with the letter q; for commodity and factor prices, the first letters are p and w, respectively.
51 Löfgren, Hans. 2003. Exercises in General Equilibrium Modeling Using GAMS (and Key to Exercises in CGE Modeling Using GAMS). Washington DC: International Food Policy Research Institute.
5.3.2 The Real Sector in Standard CGE model
The standard CGE model of Thailand built for this study comprises the Price block, the Production and Commodity block, the Institution block, and the System Constraint block. Most of the model explanations in this and the next sections follow Löfgren (2003).
The model uses the data from 1998 SAM of Thailand produced by Jennifer Chung-I Li from the University of North Carolina at Chapel Hill and IFPRI (2002). The original SAM is disaggregated into 61 productive sectors with a total of 78 accounts. For this CGE model, productive sectors are aggregated into six activities and associated commodities which are:
primary agriculture (PRIMA),53 agro-industry (AINDUS), 54 other industries (MANU),55 utility and construction (UTICON),56 trade and transport (TRADE),57 and services (SER).58
The three household groups (agricultural (A-HHD), government-employed (G-HHD), and non-agricultural (N-HHD)) remained the same as in the original SAM. The government-employed household is distinguished because it is considered an important household type with a quite significant number in employment (ranging from 7.5 to 9.5 percent of total labor force in 2001-2003 depends on quarters).59 They are also the household group, other than the non-agricultural households, which benefit from the lower food costs maintained by the governments in order to reduce the need to increase pay for civil servants. Government-employed households represent a household group whose income is relatively neutral.
53 Primary agriculture account comprises paddy, other crops, vegetable and fruits, other raw agricultural products, livestock, fishing, forestry, coal and lignite, crude petroleum, natural gas, and other mining.
54 Agro-industry account comprises rice and flour, meat, canned food, other food, other agricultural products, beverage, and tobacco.
55 Other industries account comprises gasoline, diesel, aviation fuel, fuel oil, textiles, apparel, leather and footwear, wood products, furniture, paper, printing and publishing, basic chemicals, plastic and rubber, non-metal products, basic metals, fabric metals, machines, electrical manufacturing, transport equipment, and other industries.
56 Utility and construction account comprises electricity, gas distribution, water, and construction.
57 Trade and transport account comprises retail trade, land transportation, ocean transportation, inland water transportation, air transportation, and other transportation.
58 Services account comprises restaurants, hotels, communication, banking, insurance, real estate, business services, public administration, education, healthcare and medical, nonprofit organizations, recreation, repairs, and personal services.
Two factors of production (labor (LAB) and capital (CAP)) are arranged for the model by combining the original two kinds of capital factor (agricultural capital and non-agricultural capital) in the SAM. The aggregation of capital accounts is done for the simplicity of the model and because the aggregation should not affect the assigned simulations.
Other institution accounts used in the model are the same as in the original SAM. There are two kinds of enterprises (public (ENT-G) and private (ENT-P) enterprises), four accounts for the government which includes the government itself (GOV) and three kinds of tax accounts (income tax (YTAX), indirect tax (ITAX), and tariff (TAR)), one saving-investment account (capital account (S-I)), and one rest-of-world account (ROW).
Note that there are some adjustments on the entries in this 1998 SAM as some entries are not at all meaningful. One of them is the entry in agricultural household’s expenditure on labor.
This entry does not have a meaning in standard SAM as households usually do not pay for labor use but are the income receivers of their labor supply. This discrepancy value is small and can be adjusted easily by dropping this entry and balancing the SAM in the entry of agricultural household’s labor income. Another adjustment is to combine the rows (and columns) of S-I and stock accounts together as the new S-I account. The last adjustment is to combine the entries in government incomes from households and enterprises (in GOV row) with the entries of household income tax and corporate tax paid to the government (in YTAX row). It is not reasonable to have entries in both GOV and YTAX rows from households and enterprises’
columns since only one of them should represent the income tax. The discrepancy is adjusted in the entry of government income from YTAX.
Table 5.1—Aggregated 1998 SAM for the CGE Model of Thailand
Table 5.1—Aggregated 1998 SAM for the CGE Model of Thailand (Cont.)
SECTORS A-HHD G-HHD N-HHD ENT-G ENT-P GOV YTAX ITAX TAR S-I ROW TOTAL
PRIMA-A 1148980
AINDUS-A 1362878
MANU-A 4326345
UTICON-A 719513
TRADE-A 1677846
SER-A 2132057
PRIMA-C 70608 27668 113860 831 15633 223689 1321775 AINDUS-C 189171 92511 356467 55 -24282 290902 1459193 MANU-C 177269 86691 334041 22112 493031 1666256 5877670 UTICON-C 9787 4787 18445 7364 295545 11366 723396 TRADE-C 34720 42795 165657 13136 100926 289891 1748924 SER-C 114914 141634 548255 457207 3675 241615 2389491 LAB 1457990
CAP 2702301
A-HHD 11443 19820 531662 G-HHD 2945 3826 497128 N-HHD 36068 35199 2138980 ENT-G 124496
ENT-P 1011 13903 28848 18976 78981 1039640 GOV 276736 413599 62037 20677 852454 YTAX 2576 41166 94444 34199 104351 276736
ITAX 413599
TAR 62037 S-I -68394 45973 478963 90297 632621 281451 390300 1851211
ROW 302668 866 966683 2882222
TOTAL 531662 497128 2138980 124496 1039640 852454 276736 413599 62037 1851211 2882222
5.3.2.1 List of Equations and Variables
The Standard CGE Model of the Thai Economy (1998) Sets
a ∊ A activities
c ∊ C commodities
c ∊ CM (⊂ C) imported commodities (all) c ∊ CE (⊂ C) exported commodities (all) f ∊ F factors
h ∊ H (⊂ ID) households ent ∊ ENT (⊂ ID) enterprises
i ∊ ID (⊂ I) institutions (ID = domestic institutions except government = households and enterprises; I = households, enterprises, government and rest of world)
Parameters
ada production function efficiency parameter
aqc shift parameter for composite supply (Armington) function atc shift parameter for output transformation (CET) function capitala net capital stock at 1998 cost (million baht)
costgapfa gap calibrated factor cost-SAM value (should be zero)
cpi consumer price index
cwtsc commodity weight in CPI
finv Thailand’s investment abroad
icaca quantity of c as intermediate input per unit of activity a
icaca quantity of c as intermediate input per unit of activity a