Joining the Asean Economic Community (AEC)
15.3 Empirical Study
15.3.1 Appling the Linear Structure Relationship Model (LISREL) for Key Trade Factor Analysis
The study will apply the linear structure relationship model to formulate and ana- lyze the effects of the critical factors on the performance of Thailand’s trade policy.
Because of The LISREL covers a wide range of models useful in the social and be- havioral sciences, including confirmatory factor analysis, path analysis, econometric model for time series data, recursive and non-recursive models for cross-sectional and longitudinal data, and covariance structure models [9]. The structural equation model based on procedures have an advantage over those first-generation techniques such as principal components analysis, factor analysis, discriminate analysis, or multiple regressions because of the greater flexibility that a researcher has for the interplay between theory and data [10]. The LISREL model has several dominant features such as firstly, it allows for measurement of error in the variables. There- fore, it has preliminary agreement is less and it is possible that the data will be based on assumptions rather than general statistics analysis. Secondly, the LISREL model will provide information about the direct and indirect effects, the influence between dependent variable and independent variables, and other variables in the model.
Thirdly, the LISREL can predict or evaluate the relationships among multiple vari- ables within one single model, so lead to less error in forecasting. The last reason is the LISREL model consonant with empirical, which can provide information that will lead to generalization phenomenon and to create new important knowledge.
15.3.2 The Expected Sign
Before the Asian finance crisis 1997, expected sign effect between international trade policy and economic growth rate had a negative effect (H5a), wherewith Thailand has the highest wall tariff rate; it is influencing the foreign direct investment in Thailand. During this period, as begin the development of advanced technology in
15 Analysis of Key Factors to Develop an International Trade Policy. . . 131 agriculture sector, Thailand has a lot to inject capital to support this development.
Household sector had a positive effect on international trade policy and economic growth (H1a&H1b) because the consumption expenditure had a positive effect on international trade policy according to Jörg Mayer [11] said that domestic demand could also expand other relatively large and rapidly growing developing countries and household consumption as a driver of global growth. Business sector had a neg- ative effect on international trade policy and economic growth (H2a&H2b) because of in the business sector consists exchange rate, tariff rate, import, inflation rate and labor force which that the observed almost had a negative effect on observed vari- able of international trade policy. For example, Schuh [12] was the first to argue that the overvalued dollar caused the decline in agricultural exports due to their relative expense in other countries and argue that the over valuation of the exchange rate had a large negative effect on agricultural exports. The business sector likely the cost of developing country, so expected a negative effect on economic growth. Govern- ment sector had a positive effect on international trade policy and economic growth (H3a&H3b) because of the level of capital formation variations in the government sector is likely to influence FDI and economic growth as well. Neoclassical growth model postulates that developing economies that have a lower initial level of capital stock tend to have a higher marginal rate of returns (productivity) and growth rates if adequate capital stock is injected [13]. Supported this expected positive effect on economic growth by Soi [14], found that final government consumption had a posi- tive effect on GDP growth rate. Foreign sector had a negative effect on international trade policy and economic growth (H4a&H4b) wherewith before Asian financial crisis 1997 had high tariff rate and the international trade policy would to protect domestic trade especially the product of the agriculture sector (Fig.15.1).
After the Asian finance crisis 1997, expected sign effect between international trade policy and economic growth rate had a negative effect (H5a) wherewith Thai- land has still developed conduct mega—project in infrastructure to support the openness in Asean market. For this mega—project, it is the high cost of the drive to the economic growth in Thailand, the return in this period still doesn’t cover the cost of investment. Household sector had a positive effect on international trade policy and economic growth (H1a&H1b) that similar effect in before Asian finance crisis 1997. The business sector and government sector had a positive effect on interna- tional trade policy (H2a&H3a). In after Asian financial crisis 1997, the Thailand has strategies to join the Trade FTAs with several groups to reduce tariff rates of major import and exports of Thailand. As the expected sign of foreign sector were posi- tive effects of international trade policy (H4a). The linkage between FDI variable in foreign sector, trade openness, and economic growth ought to be positive. Not only this, this nexus should be co-integrated in the long-run [13]. But the foreign sector had impact from the high competitiveness of Asian countries to reduce of tariff rate which market share of the export market of Thailand has less than the last time. Therefore, the expected significant effect on economic growth had a negative (H4b). The government sector had a negative effect on international trade policy and economic growth (H3a&H3b) because of Thai government have to use many the
132 P. Phasuk and J.-W. Wann
Fig. 15.1 Framework of hypothesis Thailand’s trade policy effects
external long—term debt for development about infrastructure to support the trans- portation agricultures product each region of Thailand such as double track railway.
A higher level of Thailand’s public debt had a negative effect on economic growth (Fig.15.1).
15.3.3 Data Sources
The dataset of this study comprises 448 observations, which include 32 annual ob- servations (1980–2011) for fourteen economic variables represent the key factors affecting economic growth of Thailand. Because of data for analysis in this study has small size, it might cause problems about sample size effect and not be identi- fied variable of the model. This study used the generated data method to decreased sample size effect by means of added data to fifteen times of amount of parameter.
The this method used the function random data 0 to 1, defined scale to be equal to the desired volume, and determined probability of random data equal 1/number of annual. Therefore, the dataset of this study comprises 2,940 observations for fourteen economic parameters.
The observed variable data between 1980–2011 were sourced from World Bank’s World Development Indicators database (WDI) and the United National Conference on Trade and Development (UNCTAD) statistics. The details are as follows:
The agricultural raw material export, agricultural raw material import data, agri- culture labor force and the tariff data were source from The World Integrated Trade Solution (WITS) database.
The variable is measured as the weighted average tariff rate. Exchange rate data were source from WDI (2005). The exchange rate is measured as the period average
15 Analysis of Key Factors to Develop an International Trade Policy. . . 133 exchange rate—the number of local currency units that can be traded for one US dollar.
The household consumption expenditure, gross capital formation, general gov- ernment final consumption expenditure, the external long-term debt of developing economies by lending source, migrants’ remittances, and inflation rate were sourced from the UNCTAD’S statistics.
The FDI stock levels as obtained from OECD Statistics and Printed issues of UNCTAD’s World Investment Report (WIR).
The GDP data measure gross domestic product is endogenous talent variable data between 1980–2011 as sourced from the World Bank’s World Development Indicators (WDI 2005).