The aim of this study is to explore whether efforts to encourage producers to use agricultural machinery and equipment will significantly improve agricultural productivity, income distribution amongst social groups, as well as macroeconomic performance in Thailand. A 2000 Social Accounting Matrix (SAM) of Thailand was constructed as a data set, and then a 20 production-sector Computable General Equilibrium (CGE) model was developed for the Thai economy. The CGE model is employed to simulate the impact of capital-intensive farming on the Thai economy under two different scenarios: technological change and free trade. Four simulations were conducted. Simulation 1 increased the share parameter of capital in the agricultural sector by 5%. Simulation 2 shows a 5% increase in agricultural capital stock. A removal in import tariffs for agricultural machinery sector forms the basis for Simulation 3. The last simulation (Simulation 4) is the combination of the above three simulations. The results for each simulation are divided into four effects: input, output, income and macroeconomic effects. The results of the first two simulations produced opposite outcomes in terms of the four effects. Simulation 2 accelerated the capital intensification of all agricultural sectors, whereas Simulation 1 led to more capital intensity in some agricultural sectors. The effects of the input reallocation had a simultaneous impact on output in every sector. Simulation 1 led to a fall of almost all outputs in the agricultural sectors, whereas there was an increase in agricultural output in Simulation 2. In terms of domestic income effects, as a result of the decline of the average price of factors in Simulation 1, there was a decrease in factor incomes belonging to households and enterprises. Consequently, government revenue decreased by 0.7%. In contrast, Simulation 2 resulted in an increase in all incomes above. Finally, regarding macroeconomic variables, Simulation 1 had a negative impact on private consumption, government consumption, investment, imports and exports, resulting in Gross Domestic Product (GDP) decreasing by 0.8%. On the other hand, Simulation 2 had a positive impact on those same variables, affecting a 0.4% rise of GDP. The effects of Simulation 3 were very small in everything compared with the first two simulations. The effect of Simulation 4 was mostly dominated by Simulations 1 and 2; the negative results of Simulation 1 were compensated by the positive effects of Simulation 2.


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