A Measurement of Structural Changes in the Thai Economy ( ): A Computable General Equilibrium Approach

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1 1 A Measurement of Structural Changes in the Thai Economy (199995): A Computable General Equilibrium Approach Somsajee Siksamat* Monetary Policy Group Abstract In this study, a computable general equilibrium model is developed and is used as a tool for measuring and analysing structural changes in the Thai economy. The technique of historical simulation is used to trace the patterns of structural changes during 199 to These patterns of structural changes are classified into five major factors, i.e., technology, taste, trade, investment, and general macro factors. After obtaining results of structural changes (from historical simulation), decomposition simulation is carried out to quantify the contributions of structural changes to the output growth of industries and the growth of selected macro variables. 1. Introduction The government policies towards economic development together with the favourable world economy helped Thailand to achieve high economic growth with the low inflation. As seen in Table 1, the Thai economy during 199 to 1995 expanded by 8.7 percent and inflation was 4.8 percent. The acceleration of Thailand s growth was closely related to favourable government policies and surge in foreign capital, which was believed to bring in higher level of technology usage. It is found that the economy s production structure continued to shift from primary agriculture towards manufacturing and services (non-agricultural sector). This was evidenced by the fall in the share of agricultural value-added to GDP from percent in 198 to percent in 1995 while that of non-agricultural sector rose from 79.8 percent to 89.7 percent. Since favourable government policies and world economic climate helped stimulate growth and caused structural changes in the economy, the objectives of this study are to assess the patterns of changes and examine their impacts on the economy during 199 to The structural changes include technology, taste, trade, and investment factors. To achieve our objectives, we need an accurate instrument to assist us in quantifying the patterns of structural changes and their impacts. Therefore, this study involves an application of a Computable General Equilibrium Model (CGE). * The author would like to thank Associate Professor Kwanjai Arunsmith for her valuable advice and support of GEMPACK software program and data, and also to Mr. Taweechai Charoensedtasin for his kind assistance.

2 In this study, a CGE model, hereafter GEM-H, is developed for measuring structural changes in the Thai economy and for analysing the impact of structural changes on the economy during 199 to GEM-H is patterned from CAMGEM-H which is an extension CGE model of the original CAMGEM (Chulalongkorn and Monash Universities General Equilibrium Model). In this application, the CGE approach is selected because it captures both institutional and behavioural relationships, hence providing more accurate and detailed results. For institutional relationship, CGE models exhibit distribution of output across industries and final users. Also, the models are concerned with uses of intermediate inputs and primary factors to industries production and investment. Regarding behaviour relationship, the models have various behavioural equations expressing behaviours of economic agents. With those relationships, we can explicitly take into account interactions of all commodities and factor markets together with decisionmaking agents. GEM-H is not limited to the question of structural changes. The model can be used to analyse a wide range of policy issues and forecast at the economy-wide and industry levels. In addition, the model can be extended to include other features of the economy, such as multiregion and multi-period features 1/. With an inclusion of a multi-region feature, the model aims to explain regional effects and the linkages between regions. With an inclusion of a multi-period feature, the model aims to explain the dynamic linkages between variables and to provide a forecasting element. The paper is composed of six sections. Section provides a brief overview of the theoretical framework of GEM-H. Section 3 is concerned with database for GEM-H. Section 4 presents the setting up of GEM-H for this current application. Two sets of simulations are conducted: historical and decomposition simulations. Section 5 presents simulation results of GEM-H. The results are divided into two parts: patterns of structural changes and impacts of structural changes on industries output growth and some selected macro variables. The final section, Section 6, summarises our major findings. Some uses and limitations of the study are pointed out.. Theoretical Framework of GEM-H GEM-H contains five main sectors: production, investment, household, government and external sectors. The model is derived from solutions to optimisation problems of producers, investors and households. Government demand is linked to household demand. Foreign demand for Thai commodities depends on the commodities prices in terms of foreign currency. The model also contains market clearing conditions; prices determination; and some other related equations. A brief overview of the model is as follows. 1/ see GEMREG, a multi-regional computable general equilibrium model of the Thai economy: A surge in foreign capital (1998).

3 3 Current production A schematic diagram of a nesting production is shown in Figure 1. Producers demands for inputs are derived from solutions to optimisation problems. It is assumed that producers choose their inputs to minimise costs subject to nested Leontief/CES (Constant Elasticity of Sustitution) constant-return-to-scale production functions. The inputs used in production process are the combination of intermediate goods and primary factors. Figure 1: Structure of Production Activity Level Leontief Good 1 up to Good C Primary Other Factors Costs CES CES CES Domestic Good 1 Imported Good 1 Domestic Good C Imported Good C Land Labour Capital KEY Functional Form CES Inputs or Outputs Skilled labour Unskilled labour

4 4 Demand for labour: The model allows for substitution between labour types (skilled, unskilled). Producers choose their inputs of each type of labour to minimise the total costs of meeting their labour requirements. The labour requirements are the CES aggregate of the inputs of skilled and unskilled labour. On solving cost minimisation problem we obtain equations for industry demand for skilled and unskilled labour as functions of wage rates and of industry labour requirements. Industry labour requirements are defined by industry output and by prices of capital and land relative to average price of labour. If unskilled labour becomes cheaper relative to the average wages of the composite labour, producers will substitute in favour of unskilled labour. The flexibility in shifting from one type of labour to another depends on skilled-unskilled labour substitution elasticities. Demand for primary factors: Land, labour and capital are primary factors in GEM-H. They are assumed to be substitutable within the group. Producers choose a primary factor such that the combined costs of using the primary factors are minimised subject to the CES aggregates of primary factors. The optimal solution to the demand for each primary factor is determined by the total usage of a composite primary factor and the price of that primary factor relative to the average price of the composite primary factor. Demand for intermediate inputs: Intermediate inputs are aggregates of imported and domestic commodities which are taken to be imperfect substitute. There is no substitution between input categories or between them and primary inputs and other costs, reflecting a fixed proportion of these inputs combination (Leontief production function). The solution to the optimisation of the Leontief function has no price variables in the demand equation. Investment The nesting structure of the production for new units of fixed capital is shown in Figure. Figure : Structure of Investment New Capital for Industry i Leontief Good 1 up to Good C CES CES Imported Good 1 Domestic Good 1 Imported Good C Domestic Good C

5 5 Similar to the current production, demands for inputs to produce capital goods are derived from the solutions to cost minimisation problems. At the bottom-level of the nest, the total cost of imported and domestically produced goods is minimised subject to the CES composite-source inputs production function. At the top-level, the total cost of a composite commodity is minimised subject to the Leontief production function. It should be noted that primary factors are not used directly as inputs to capital formation. The use of primary factors is recognised via the inputs of construction..3 Household Figure 3 shows the nesting structure of household demand. Figure 3: Structure of Household demand Household Utility Stone- Geary Good 1 up to Good C CES CES Imported Good 1 Domestic Good 1 Imported Good C Domestic Good C The bottom-level in the nesting structure for household demand is identical to that of investment demand. The difference is at the top-level. The commodity composites are aggregated by a Stone-Geary, rather than Leontief function. According to a Stone-Geary function, the composite commodities are split into two parts, subsistence and luxury. The subsistence demand for composite commodities is determined by the number of households and taste variable. Only the luxury components enter the per household Cobb-Douglas utility function. Each consumer maximises his utility subject to budget constraint. The solution to the optimising utility results a

6 6 linear expenditure system. This means the household expenditure on a commodity is a linear function of price and income..4 Export demand Export commodities are aggregated into a composite commodity using the Leontief function. Then, it is assumed that the foreign demand for units of the composite exports is a declining function of the export price..5 Government demand and special export demand Government consumption is not derived from the solution to the optimisation problem. Here, we assume that aggregate government consumption is linked to household consumption. Similarly, special export demand is linked to household demand..6 Demand for margins commodities It is assumed that, in an absence of technological change, margins goods (trade and transport) are required in fixed proportions to the commodity flows with which they are associated..7 Zero-pure profit condition By the assumptions of zero pure profits and constant returns to scale production technology, the basic-value prices (production costs) of commodities and supply prices of units of capital are functions only of the relevant input prices and taxes. The basic values of imports in the domestic economy are the domestic currency equivalents of the foreign currency at CIF prices plus tariffs. The basic prices are assumed to be uniform across users. Purchasers prices are the sums of basic value, sales taxes and margins. They can differ across users due to the differences in the mark-up costs. Sales taxes are treated as ad valorem on a basic-value base. The purchasers prices of exports are defined as the prices at the port of exit..8 Market clearing condition This condition ensures that the supply and demand in domestic production and in factor markets are equal. The supply of domestically produced goods is equal to total domestic demands plus exports. Imports are treated as distinct commodities, thus there is a separate market clearing condition for imports. The supply of imported goods is equal to total demand by all users..9 Miscellaneous equations Along with the system of equations explaining behaviour of the agents, the model also includes a series of identities describing, for example, gross domestic product (expenditure-and income-side), aggregate price index and balance of trade.

7 7 Structural changes In this current application, we are interested in patterns of structural changes in the economy. Structural shift variables are added to all activities in our model to allow for the impact of structural changes. (1) Technology changes: variables measuring percentage changes in technology (a1 s for intermediate inputs to current production and a s for inputs to capital creation) are added to production functions (producers and investors demand equations). a1primgen a1cap(i) a1lab_o(i) a1lnd(i) a1tot(i) a1oct(i) a1(c,s,i) a1_s(c,i) a1_si(c) atot(i) a(c,s,i) a_s(c,i) a_si(c) = primary factor saving technology = capital augmenting technical change in industry i = labour saving technology change in industry i = land augmenting technical change in industry i = all inputs augmenting technical change in industry i = other cost ticket augmenting technical change in industry i = intermediate input saving technical change in commodity c from source s by industry i = intermediate input saving in commodity c by i from both sources = intermediate input saving technical change in commodity c = all inputs augmenting technical change for investment in i = technical changes affecting demands in capital creation = technical changes for domestic/import composite of demand in investment = technical change in overall demand for investment These shift variables (technological coefficients) represent some extra percentage changes in output not explained by the economic mechanisms of the model. To understand how technological coefficients are incorporated, we give some examples of producer s demand equation in our model. From cost minimisation problem, producers in industry i choose primary factors (quantity of land, labour and capital): X1LND(i), X1LAB_O(i), and X1CAP(i) to minimise total costs of primary factors: [P1LND(i) x X1LND(i)] + [P1LAB_O(i) x X1LAB_O(i)] + [P1CAP(i) x X1CAP(i)]

8 8 subject to total primary factor requirements: X1LND(i) X1LAB_O(i) X1CAP(i) X1PRIM(i) = CES,, A1LND(i) A1LAB_O(i) A1CAP(i) In this example, technological coefficients of land, labour and capital (A1LND(i), A1LAB_O(i) and A1CAP(i)) enter the model via demands for primary factor equations. If there are technological improvements in land, labour and capital, i.e., A s become smaller, then a given level of X1PRIM(i) can be achieved with lower levels of land, labour and capital. On solving the cost minimisation problem, we find that the linearised equations / for primary factor demands are: x1lab_o(i) - a1lab_o(i) = x1prim(i) - SIGMA1PRIM(i) x [p1lab_o(i) + a1lab_o(i) - p1prim(i)] i ΙΝD (1) x1cap(i) - a1cap(i) = x1prim(i) - SIGMA1PRIM(i) x [p1cap(i) + a1cap(i) - p1prim(i)] i ΙΝD () x1lnd(i) - a1lnd(i) = x1prim(i) - SIGMA1PRIM(i) x [p1lnd(i) + a1lnd(i) - p1prim(i)] i ΙΝD (3) V1PRIM(i) x p1prim(i) = V1LAB_O(i) x {p1lab_o(i) + a1lab_o(i)} + V1CAP(i) x {p1cap(i) + a1cap(i)} + V1LND(i) x {p1lnd(i) + a1lnd(i)} i ΙΝD (4) where V1PRIM(i) = V1LND(i)+V1LAB_O(i)+V1CAP(i) i ΙΝD Equation (1) specifies that the percentage change in industry i s demand for labour in general (i.e., both skill types), x1lab_o(i), depends upon the growth of total primary factor requirements (x1prim(i)), the growth of effective unit cost of labour (p1lab_o(i)+a1lab_o(i)) relative to the growth of overall primary factor cost (p1prim(i)) and factor-saving technical change for labour (a1lab_o(i)), which is uniform for each skill type. The weighted average costs of primary factors are given by equation (4). The effective unit cost of over-all factor, p1prim(i), is a weighted sum of the factor prices and the factor saving technical variables. The positive parameter / In linearised equations, all variables are in percentage changes or changes. These are denoted by lower-case symbols. But when referring to the levels of variables, the corresponding upper-case symbols are used.

9 9 SIGMA1PRIM(i), substitution elasticities, determines the sensitivity of industry i s demand for labour to variations in the price to industry i of labour relative to the prices to industry i of all primary factors. Similarly, equations () and (3) specify respectively the percentage change in industry i s demands for capital and land. These demands are proportional to the growth of overall factor demand, x1prim(i), to a technical change term, and to a relative price term. It should be noted that the substitution elasticities (SIGMA1PRIM(i)) between each pair of primary factors, i.e., between labour and capital, labour and land, and capital and land, in an industry are identical by the CES assumption. To interpret (1) we first consider the case in which there is no change in factor-saving technology for labour, i.e., a1lab_o(i) is zero. If there are no changes in the cost of a unit of labour relative to a weighted average of the prices of the three primary factors, then a one percent increase in industry i s activity level leads to a one percent increase in the industry i s requirements for labour in general. This reflects our assumption of constant returns to scale. If x1prim(i) and a1lab_o(i) are zero (i.e. X1PRIM(i) and A1LAB_O(i) are constant), then an increase in the labour cost to industry i relative to a weighted average of the costs of the three primary factors results in a substitution away from labour in favour of capital and land. The degree of substitution depends upon the industry s substitution elasticity (SIGMA1PRIM(i)). Next, we will investigate the case in which there is a labour-saving technical improvement. This is of our interest in this current study. We assume the prices of all primary factors and the industry activity levels are unchanged but there is a labour-saving technical improvement in industry i, i.e., a fall in A1LAB_O(i). By using equation (4) to substitute p1prim(i) out of equation (1), equation (1) becomes: x1lab_o(i) - a1lab_o(i) = x1prim(i) - SIGMA1PRIM(i) x [p1lab_o(i) + a1lab_o(i) V1LAB_O(i) - { V1PRIM(i) x (p1lab_o(i) + a1lab_o(i)) V1CAP(i) + V1PRIM(i) x (p1cap(i) + a1cap(i)) V1LND(i) + V1PRIM(i) x (p1lnd(i) + a1lnd(i))}] (5) If there is a change in labour-saving technical variable, given other variables in the RHS of equation (5) zero, then equation (5) becomes: V1LAB_O(i) x1lab_o(i) = a1lab_o(i) x [1-SIGMA1PRIM(i) x {1- }] (6) V1PRIM(i)

10 1 In equation (6), if the labour-saving technical variable improves at a rate of one percent, V1LAB_O(i) i.e., a1lab_o(i) =, then it will lead to a [1-SIGMA1PRIM(i) x {1- }] percent V1PRIM(i) change in industry i s demand for labour in general, x1lab_o(i). Provided that the value in the square bracket, [...], is greater than zero this will be a reduction in i s demand for labour. In order for the value in the square bracket to be greater than zero, the value of SIGMA1PRIM(i) x {1 - V1LAB_O(i)/ V1PRIM(i)} must be less than one. For the value of SIGMA1PRIM(i) x {1 - V1LAB_O(i)/V1PRIM(i)} to be less than one, we require either of the following cases: 1. SIGMA1PRIM(i) is sufficiently low, i.e., low substitution in each pair of primary factors in industry i; or/and. V1LAB_O(i)/V1PRIM(i) is sufficiently high, i.e., high labour intensity in industry i. Let consider the first case where SIGMA1PRIM(i) is low. The lower is SIGMA1PRIM(i), the less flexible is for industry i in switching from the use of capital or land towards labour. Thus, it is possible that x1lab_o(i) will fall when there is a labour-saving technical improvement. For labour intensity, if industry i is labour intensive, i.e., industry i has a high V1LAB_O(i)/ V1PRIM(i), it will gain directly from the labour-saving technical improvement. Thus given the industry activity levels, industry i is likely to reduce its use of labour and be able to maintain the same level of production. For example, the simulation result produces a figure of 1 percent for technical change and a figure of 5 percent for output change. This means that only 4 percent in output increase can be attributed to movement in quantity of inputs and relative prices. The extra 1 percent increase in output comes from an increase in production efficiency. The same structure of demand equations and interpretation are also applied to the demands for other inputs to investment. () Taste changes: variables measuring taste changes are added to household (a3 s), government (a5 s) and special export (a7 s) demand equations. a3(c,s) a3_s(c) a3lux(c) = taste change variables for household demands = taste change variables for domestic/import composite = taste change variables for supernumery demand a3sub(c) = taste change variables for subsistence demand a5_s(c) a7_s(c) = taste change variables for government demand = taste change variables for special export

11 11 The same logic applies to the case of consumption. For example, the model produces 1 percent increase in consumer taste in textiles products, while the corresponding increase in consumption is 3 percent. This means only an 18 percent increase in consumption is caused by the normal behavioural pattern of the consumer due to relative prices and income. The extra 1 percent increase is due to the impact of a taste shift on textiles products use. (3) Trade factor: this factor is composed of two main sources, i.e., export price shift (structural shift for export) and all import twists (structural shift for import). f4p(c) twist_src(c) = shift in export price of commodity c = all import twists for c (a weighted sum of import twist for good c in current production, investment household consumption, government spending, and tourist spending) (4) Investment factor: this factor is related to average rate of return on investment and rate of capital accumulation. To measure the pattern of changes in these two variables, we endogenised these variables in historical simulation. r1cap_i = average rate of return f_accum_c = rate of capital accumulation (5) General macro factor: this factor becomes as a residual source of changes in the economy. It includes change in trade balance to GDP, government policies and other structural shift that could not be implied by the model due to lack of observed data. However, some of their effects are captured via these variables. delb f5tot f7tot = trade balance to GDP ratio = the link between government spending to real consumption = the link between tourist spending to real consumption 1 Closing the model GEM-H has more variables than equations. To solve the model, the variables are chosen to be endogenous and exogenous in which the number of endogenous variables must equal the number of equations. This is known as a model closure. The endogenous-exogenous variables to be chosen depend upon the specification of the economic environment in which simulations are carried out. More detail of model closure is discussed in Section 4 of the paper. 3. Database for GEM-H The data files for GEM-H consist of input-output (I-O) data, statistical information of observed changes at commodity and industry level and some macro variables, and parameters.

12 1 In this application, we want to measure and analyse structural changes in the economy from 199 to Two sets of I-O data are required, i.e., the 199 and the 1995 I-O data, to compute the average growth rates of output, investment, consumption, export and import at industry level during 199 to The largest I-O account (18x18 matrix representing flows of commodities for intermediate uses, and 18x1 vectors of final demands, including household consumption, government and private investment, government consumption, exports and changes in inventories) provided by the National Economic and Social Development Board (NESDB) are aggregated to reduce its size to 53 x 53 sectors. Some adjustments are made to the NESDB s I-O data to obtain required information which is suitable for our model specification and assumptions. Statistical information on the 5-year growth patterns of some important macro variables is shown in Table 1. Plausible parameters from other studies, particularly from CAMGEM and GEMREG, are adopted or are used as references to derive the parameters in GEM-H. 4. Setting up GEM-H for the analysis of structural changes Two sets of simulations are performed in this study, namely, historical and decomposition simulations. Historical simulations are aimed at assessing quantitatively the pattern of changes in structural variables, e.g., technological changes, changes in consumer preferences and changes in other unobservable variables. Decomposition simulations are aimed at analysing the impacts of structural changes on the economy. The model closure of decomposition simulations is a reverse of that of historical simulations. In historical simulations, all observed variables such as macro variables and output growth by commodity are treated as exogenous. Many unobservable variables such as technical and taste changes are endogenised. We supply to the model the observed values of the exogenous variables (historical data). Then the model can estimate the values of endogenous variables. The simulation results (the estimates of endogenous variables) are consistent with all the statistical information on the exogenous variables. On a general level, historical simulation exercise is carried out as part of a quality control technique used to improve predictive ability of the model. That is, if the model can predict well for the past, as confirmed by recent statistics, there is likely that it may also do well for future forecasting. In addition to the quality control feature, historical simulation can also be used as an updating device for an old I-O database. Generally, the compilation and publication of inputoutput tables are behind the recent available economic statistics. For Thailand, the latest I-O data is the table of 1998 but the completed table is the table of The use of 1995 I-O data may not reflect the actual structure of the economy if there have been structural changes from 1995

13 13 up to the present. Hence, we want to update the I-O data of 1995 to. Via the technique of historical simulation, we can generate a new I-O table for. We assume that structural changes during 1995 to are identical to those assessed in historical simulation during 199 to We provide the model the values of structural changes (results of historical simulation during 199 to 1995) and observed changes in macro variables during 1995 to (historical data). The model will produce updated I-O data of that reflect structural changes and recent actual structure of the economy. Decomposition or structural simulation is performed by partially reversing the closure used in historical simulation. That is, some of the exogenous variables (e.g., industry outputs and employment) that were shocked in historical simulation become endogenous and some of the endogenous variables (e.g., taste and technological changes and shifters) become exogenous. By setting these technology and taste changes at their values estimated from the historical simulation, we obtain in the structural simulation results for macro variables, output by commodity and other endogenous variables identical to those in the historical simulation. With technology and taste changes exogenous in the decomposition simulation, we can assess the impacts of changes in these variables on the economy. More generally, we can decompose history into the parts attributable to changes in the structural variables (with their values estimated from the historical simulation). For example, we may want to quantify the effects of consumer preferences (or taste change variables). This is done by setting taste change variables at their values estimated from the historical simulation. The results from the decomposition simulation tell us changes in the economy due to those taste changes. Diagram 1 presents historical and decomposition closures. The observable variables are set as exogenous in historical closure. These observed variables can imply changes in corresponding structural variables shown in the box of decomposition closure. However, there are usually many more structural variables than observed variables. Therefore, some structural shifts that may not be implied by the observed patterns of changes would have to be set as exogenous with the value zero. They are not shown in Diagram 1 and we do not consider them as active variables. As mentioned earlier, decomposition simulation is performed by swapping the exogenous/endogenous in the historical and decomposition closure in Diagram 1. That is, the observed variables in the box of historical closure become endogenous and the unobserved variables in the box of decomposition closure become exogenous. Then, we simulate the model by feeding the estimated values of variables in the box of decomposition closure (obtained from the conduct of historical simulation) to obtain the impact of structural changes on the economy.

14 14 Diagram 1 : Swapping Variables between Historical Closure and Decomposition Closure HISTORICAL CLOSURE DECOMPOSITION CLOSURE Macro variables Macro variables xtot_i total investment f_accum_u rate of capital accumulation x1cap_i total capital stock r1cap_i average rate of return employ_i total employment f1lab_io real wage rate p3tot consumer price index phi exchange rate ptoft terms of trade delb trade balance to GDP ratio xcif_c import volume f_imp adjustment factor for import x4tot export volume f_v4_c adjustment factor for export x3tot real consumption a1primgen primary factor saving technology x5tot real government spending f5tot link between x3tot and x5tot x7tot special exports f7tot link between x3tot and x7tot a1all neutralisation scale for a1_si(c) f_v1_isc adjustment factor for v1_i_obs1-53 aall neutralisation scale for a_si(c) f_v_isc adjustment factor for v_i_obs1-53 a3all neutralisation scale for a3_si(c) f_v3_isc adjustment factor for v3_i_obs1-53 a5all neutralisation scale for a5_si(c) f_v5_isc adjustment factor for v5_i_obs1-53 a7all neutralisation scale for a7_si(c) f_v7_isc adjustment factor for v7_i_obs1-53 Sectoral variables Sectoral variables v1_i_obs1-53 value of domestic intmed inputs v_i_obs1-53 value of domestic inputs for investment v3_obs1-53 domestic goods for consumption v4_obs1-53 value of export v5_obs1-53 value of gov t spending v7_obs1-53 value of special export v1_i_obs546 value of imported intmed inputs v_i_obs546 value of imported inputs for investment v3_i_obs546 imported goods for consumption v5_i_obs546 govt spending on imports v7_obs546 imported components of export wbill_obs1-53 wage bill by industry a1_si(c) a_si(c) a3_s(c) f4p(c) a5_s(c) a7_s(c) twist1_src(c) twist_src(c) twist3_src(c) twist5_src(c) twist7_src(c) a1lab_o(i) tech change in intmed input tech change in input for investment taste change in HH consumption export price shift taste change in gov t consumption taste change in special export import twist in intmed input usage import twist in creating capital import twist in HH consumption import twist in gov t consumption import twist in special export technology change in labour 5. Simulation results Tables -5 present a set of numbers calculated by GEM-H in the process of historical simulation. These numbers may be interpreted as an index measuring technology shift, taste change and change in trade factor in the Thai economy during 199 to Tables 61 show the impacts of structural changes on the growth in industry output and some macro variables.

15 The patterns of structural changes 5. The pattern of technology shift The technology shifts of our concern are: a1primgen a1lab_o(i) a1_si(c) a_si(c) = overall primary factor saving technology = labour saving technology change in industry i = intermediate input saving technology change in commodity c = technical change in overall demand for investment It is found that a1primgen declined by.5 percent per year, indicating less usage of primary factors required to produce a unit of output in an economy-wide context. At the industry level, the figures in Tables are interpreted as percentage changes in corresponding inputs required for producing a unit of output. Therefore, negative figures imply that saving has occurred in the usage of such input, and vice versa. A glance at Table shows that there is favourable technology change (negative technical index) in Paddy(1), Food crops(), Beverage crops(3), Textiles crops(4), Tobacco(5) and Rubber(6), indicating less usage of these inputs in production. This perhaps implies a certain degree of efficiency in agricultural processing activity in industries that used those inputs in their production, such as milling, preserved food and other food industries. There is also less usage of several manufacturing inputs. The technical index in heavy manufacturing inputs such as Iron steel(33), Industrial machinery(36), Electric machinery appliances(37) and Other transport equipments(39) was positive, indicating more use of these inputs. It is possible that an increase in the usage of industrial machinery and iron steel may imply a certain degree of technical deterioration in the production of heavy manufactured goods. It may be argued that the increase in input usage is not necessarily to reflect the lack of production efficiency if there are changes in pattern of production technique. In other words, there are changes in intermediate input mix in production. For example, producers may replace less effective intermediate inputs by more effective intermediate inputs. However, it is assumed in this study that cost structure of each industry does not change during the period of study (no change in production technique). This assumption seems to be consistent with the usual revision of the I-O table every five years. In the service sectors, Trade(46) was used less. This indicates a reduction in the services required from the trade industry to facilitate the flow of any given volume of commodities from producers to users, perhaps due to the effects of the replacement of grocers by large supermarkets and department stores. Banking&Finance(5) was used more. The result seems to be consistent with the observed trend of economic development in modernised society that resulted in more requirement for banking services. The increase in Real estate(51) technical index, suggesting more use of real estate service, seems to be consistent with increased demand for renting or leasing work places and retails spaces.

16 16 For capital creation technology, Construction(45), the most important input used in producing capital goods, was used less. This probably implies a slowdown in the activity of the property sector. Plastic &Glass(3) and Other transport equipments (39) were used more. This reflects the pattern of shift in the structure of input use in capital creation. With labour saving technology (Table 3), most sectors, except some heavy-product sectors (e.g., Cement Concrete Product(3), Iron Steel(33) Non-ferrous metal Product(34) and Fabricated metal Product(35)), have been found to use their labour efficiently The pattern of taste change From Table 4, household taste change was found to be in favour of luxury goods, such as Leather Products(3) and Other manufacturing Product(4) (including Jewelry). The taste index of Transport(48) was big positive number. This may suggest an improvement in the provision of public transport. However, there was also taste shift towards Motor vehicles(38). This reflects a surge of demand for private cars in the economy. If there was no further improvement in the provision of public transport in the near future, traffic problems in Thailand would be more severe. As expected, household taste also shifted towards Preserved food(16) and Other chemical Products(7) (including drug, medicine and cosmetics). Interestingly, household taste shifted away from unhealthy products like Tobacco Products(1). For government, several items are zero, indicating insignificant changes. For those of significant changes, the noticeable positive numbers are Vehicle repair(41) and Post Tele Communication(49), indicating an increase in government spending on these commodities. Government s taste shifted away from Industrial machinery(36) and Construction(45). With regard to special export, the large positive taste indices are for Preserved food(16), Petroleum refinery(8), Other Transport Equipment(39), and Banking &Finance(5). 5. The pattern of change in trade factor Export price shift seems to indicate a strong bias towards manufactured products with large positive figures. Several primary agricultural commodities, such as Paddy(1), Beverage crops(3) and Rubber(6), also benefited from the export price shift due to their forward linkages to agricultural processing commodities like rice (Milling(15)), Soft drink() and Rubber Products(9). For import twist, results are in favour of several agricultural and manufactured products. It has been found that import twist index of Other service(5) is large positive number. This is consistent with the observed increase in demands for overseas education and travelling. 5. Comparative analysis of sources of growth In this part, we examine the impacts of various factors on the output growth of different industries (Tables 6) and on the performance of the Thai economy in general (Table 11).

17 17 5. The impact of technology shift From Table 6, primary factors, comprising land, labour and capital, had positive impact on all industries, particularly capital-intensive industries. Similarly, labour produced positive impacts in most industries, except Fertilizer(6). For the macro effects, although labour input was used effectively with 1.5 percent contributing to GDP growth (Table 11), capital appeared to be used less effectively. However, the positive impact from labour saving outweighed the negative impact from capital. This, therefore, induced to an increase in GDP growth by percent when considering the impact of primary factors as a whole. Intermediate input technical change attributed to GDP growth, though its impact seems to be insignificant. As a result, technology arising from primary factor saving seems to be one of the factors explaining the high growth rate of the Thai economy during 199 to Technology changes contributed to growth in export and import. One of the implications of this was an increase of Thailand s international trade. The changes in technology helped improved quality of Thai export commodities and/or raised quantity produced given level of input used. The positive contribution to export growth has implication to relatively export-oriented industries. It seems that export-oriented industries gained from technical progress. The growth in import, deriving from technology, seems to reflect increased requirements for imported goods used in production Technology change was not a significant factor contributing to investment and hence capital growth. This indicates an inefficient utilisation of technology for long-term development of the economy. 5. The impact of taste change From Table 7, several industries suffered from the overall taste changes. Although, there were strong taste shifts towards several commodities, their impacts on industry s output growth were insignificant. Changes in government tastes produced positive impacts on services. They were public-welfare creating, favouring spending on public-service activities. The increase in government spending on these activities is believed to help improve quality of life and human skills, which in turn boosts the country s development The impact of trade factor It is interesting that there be significant positive export price shifts towards several commodities (Table 5). These shifts produced strong positive impacts on industry output growth. It is possible that favourable export shifts together with technical improvement had led to strong

18 18 response from these industries, hence stimulating output growth of industries. On the other hand, twist to import has negative impact on industry output. However, its impact was outweighed by favourable export price shift. Hence, the overall impacts of the trade factor are positive for most industries (Table 8). For the macro impacts, trade factor produced positive results for most selected macro variables in Table 11. Its effects are relatively large. This reflects that the growth in the economy during 199 to 1995 relied heavily on international trade The impact of investment factor As shown in Table 9, investment impacts arising from a fall in required rate of return (fell by.6 percent) outweighed the negative impacts of the rate of capital accumulation (fell by percent), hence producing positive impacts to all industries, particularly capital-oriented industries such as Public works(44), Construction(45) and Industrial machinery(36). Investment was regarded as the second major source of growth in the Thai economy during 199 to It is worth noting that investment, rather than trade, should have been the country s main source of growth in order to drive the economy to achieve sustainable growth The impact of employment and general macro factors Employment did not seem to produce any interesting result in our findings. Meanwhile, general macro factors, which included balance of trade, government policy, and other structural shifts 3/, was taken as endogenous residual resulting in large negative figures. The large negative number for the impact of general macro on export (-7. percent) was in line with the pattern of large deficit in current account during 199 to Concluding remarks The paper has fulfilled two prime tasks. The first task is to develop a computable general equilibrium model, namely GEM-H. The second task is to assess the patterns of changes in the Thai economy during the period of 199 to 1995, then examine the effects of structural changes on the economy. For this purpose, GEM-H was used as our instrument to simulate the patterns of structural changes and their impacts. There are some interesting findings that may be concluded from this study. They are as follows: 3/ Although several government policy variables and other structural shifts are included in GEM-H, their patterns of changes were not quantified in historical simulation because their changes may not be implied by the observed pattern of changes. Hence, their impacts could not be assesed in decomposed simulation.

19 19 1. The growth of the Thai economy during 199 to 1995 has been found to be resulted mainly from the substantial shift in the trade factor (particularly from favourable export shift), followed by favourable investment climate, i.e., decline in required rate of return on capital. Meanwhile, a more fundamental factor like technology became a mild factor attributing to GDP growth. This is quite astonishing. When we look back to the period of 199 to 1995, it was found that there was a high surge in capital inflow to Thailand during that period. This foreign capital caused to economic transformation away from labour-intensive activities to capital-intensive activities, which concentrated in manufacturing and service sectors. The surge in foreign capital has been associated with very strong growth in these sectors. Moreover, it was believed that foreign capital, particularly in the form foreign direct investment, assisted in strengthening the industrial base with new technologies and modern plants. It was found in this study that there were less usage of primary factors required to produce a unit of output in an economy-wide context and a certain degree of efficiency in some industries, however the technology factor as a whole showed small positive influence on the GDP growth. If this pattern of growth continues, it should be of some concern to the government. Looking towards Thailand s development future, it is worthwhile pondering that investment and technology factors should play an important role in stimulating growth.. With regard to sectoral performance (Table 1), trade and investment factors were key contributors of industry s output growth. However, growth was intensified in manufacturing and service sectors. Meanwhile, primary agricultural sectors gained marginally from those factors. This, perhaps, suggests the weakness of industry linkages. To enhance the distribution of benefits across industries, backward and forward linkages among industries should be intensively promoted. 3. The usefulness of this study arises from the development of a model and from the application of the model to examine structural changes and to assess their impacts on the Thai economy. The simulation results from GEM-H are the outcomes of the adjustments in the model which capture the interaction of macro and industry variables. GEM-H is not constrained to the structural change application. The model can be used to simulate other shocks. Aside from comparative static applications, GEM-H is sufficiently flexible to be used for forecasting. With an appropriate closure and specified set of information (i.e., projected values of some key macroeconomic exogenous variables which may be obtained from other sources), the model can be turned to forecasting mode and project the changes in endogenous variables for a certain period. 4. The use of GEM-H in an empirical analysis is subject to limitations and these should be pointed out. Limitations arise from the specification of the model and the database. From model specification, the model was developed under simplifying assumptions that are sometimes considered as limitations. In our model, production is assumed to exhibit constant returns to scale and all markets are assumed to be perfectly competitive. Although these assumptions

20 are sometimes criticised, Abayasiri and Horridge (1996) found no significant roles for scale economies and imperfect competition in the ORANI-F 4/ model in simulating the effects of trade liberalization on the Australian economy. Due to data deficiencies, none of the elasticities used in GEM-H is estimated econometrically. Most elasticities for GEM-H are borrowed from other studies. However, the adoption of elasticities from other studies is done with caution, and checked via plausibility of simulation results. The awareness of deficiencies in the data and some adjustments in the data underlies the need for caution in interpreting and using the results. 5. For further work, GEM-H will be modified to incorporate a multi-regional aspect of the Thai economy. With the inclusion of regional disaggregation in the model, we can see how policies at the national level affect regions, and at the same time, how spatial structure influences regional growth and development. For those of available data, the substitution elasticities will be estimated econometrically. 4/ ORANI-F is patterned from ORANI model, a CGE model of the Australian economy. ORANI-F included dynamic features arising from investment-capital and trade deficit-foreign debt accumulations. The model is used for forecasting purpose.

21 1 Appendix Table 1 : Data Inputs to Historical Simulation at Macro Level, (unit : percent) Variables (real term) Average (5 years) growth during GDP 8.7 Private consumption 7.6 Investment 11. Government consumption 6.3 Export 14.3 Import (c.i.f weight) 13.9 Capital stock Employment Population 1. Inflation 4.8

22 Table : Pattern of Changes in Intermediate Input Saving and in Capital Creation (percentage of input needed to produce one unit of output) (unit : percent) Commodities 1 Paddy Food crops 3 Beverage crops 4 Textiles crops 5 Tobacco 6 Rubber 7 Other agr 8 Livestock 9 Forestry 1 Fishery 11 CoalOil 1 Metal ore 13 Non-metal ore 14 Slaughtering 15 Milling 16 Preserved food 17 Other food 18 Animal feed 19 Alcoholic beverage Soft drink 1 Tobacco Products Textiles 3 Leather Products 4 Wood Paper 5 Basic Chem Products 6 Fertilizer 7 Other Chem Products 8 Petroleum refinery 9 Rubber products 3 Plastic Glass 31 Non-metal Products 3 Cement Concrete Prd 33 Iron Steel 34 Non-ferrous metal Prd 35 Fabricated metal Prd 36 Industrial machinery 37 Electric Machinery App 38 Motor vehicles 39 Oth Transport Equip 4 Motor-Bicycles 41 Vehicle repair 4 Other manu Prod (jewelry) 43 Electricity & Gas 44 Public work 45 Construction 46 Trade 47 Restaurant & Hotel 48 Transport 49 Post TeleCommunication 5 Banking & Finance 51 Real estate 5 Other Service 53 Others Production Capital Creation

23 3 Table 3 : Pattern of Changes in Labour Saving Technology (percentage of labour needed to produce one unit of output) (unit : percent) Sectors 1 Paddy Food crops 3 Beverage crops 4 Textiles crops 5 Tobacco 6 Rubber 7 Other agr 8 Livestock 9 Forestry 1 Fishery 11 CoalOil 1 Metal ore 13 Non-metal ore 14 Slaughtering 15 Milling 16 Preserved food 17 Other food 18 Animal feed 19 Alcoholic beverage Soft drink 1 Tobacco Products Textiles 3 Leather Products 4 Wood Paper 5 Basic Chem Products 6 Fertilizer 7 Other Chem Products 8 Petroleum refinery 9 Rubber products 3 Plastic Glass 31 Non-metal Products 3 Cement Concrete Prd 33 Iron Steel 34 Non-ferrous metal Prd 35 Fabricated metal Prd 36 Industrial machinery 37 Electric Machinery App 38 Motor vehicles 39 Oth Transport Equip 4 Motor-Bicycles 41 Vehicle repair 4 Other manu Prod (jewelry) 43 Electricity & Gas 44 Public work 45 Construction 46 Trade 47 Restaurant & Hotel 48 Transport 49 Post TeleCommunication 5 Banking & Finance 51 Real estate 5 Other Service 53 Others Production

24 4 Table 4 : Pattern of Taste Changes (percentage of commodity shifts) (unit : percent) Commodities Household Government Special Export 1 Paddy Food crops 3 Beverage crops 4 Textiles crops 5 Tobacco 6 Rubber 7 Other agr 8 Livestock 9 Forestry 1 Fishery 11 CoalOil 1 Metal ore 13 Non-metal ore 14 Slaughtering 15 Milling 16 Preserved food 17 Other food 18 Animal feed 19 Alcoholic beverage Soft drink 1 Tobacco Products Textiles 3 Leather Products 4 Wood Paper 5 Basic Chem Products 6 Fertilizer 7 Other Chem Products 8 Petroleum refinery 9 Rubber products 3 Plastic Glass 31 Non-metal Products 3 Cement Concrete Prd 33 Iron Steel 34 Non-ferrous metal Prd 35 Fabricated metal Prd 36 Industrial machinery 37 Electric Machinery App 38 Motor vehicles 39 Oth Transport Equip 4 Motor-Bicycles 41 Vehicle repair 4 Other manu Prod (jewelry) 43 Electricity & Gas 44 Public work 45 Construction 46 Trade 47 Restaurant & Hotel 48 Transport 49 Post TeleCommunication 5 Banking & Finance 51 Real estate 5 Other Service 53 Others