Wage inequality in manufacturing sector

Abstract

This study adopts regression-based decomposition proposed by Fields on Thailand Labor Force Surveys to investtigate the wage inequality in the manufacturing sector in Thailand during 1985-2005. The included variables are workers’ individual characteristics and their working status such as gender, marital status, family size, urbanization, education categories, experience, types of occupation, minimum wage zone, fringe benefit, domestic expenditure, and international trade. Ordinary regression states the significance of all explanatory variables in all study periods. However, the log variance of inequality or the factor weight inequality indicates that there were only a few variables that accounted for largeshares of the inequality level. In 1985, the significant variables were education, experience, and minimum wage zone. In 1995 and in 2005, education, occupation, and minimum wage zone accounted for large inequality shares. With the regression-based decomposition, this study can examine the contribution of each variable to the change of the inequality. It has been found that an increase of the Gini during 1985-1995 was attributed to education, occupation, and minimum wage zone and was opposed by experience, marital status, and gender. A decrease of the Gini during 1995-2005 was explained by education, urbanization, and occupation. Since these explanatory variables dominated the opposing factors such as international trade, minimum wage zone, and gender, the Gini in 2005 is less than that in 1995. It is noteworthy that the regression based decomposition can capture both level and dynamism of inequality. In all study periods, the elementary and lower secondary education is an inequality-decreasing factor whereas the other education levels are inequality-increasing factors. However, the lower inequality share of all education levels in recent periods implies increased accessibility of education. In other words, although most of education levels are inequality-increasing factors, they can be a tool to improve inequality as well. With this more precise source of inequality, this study can propose more prioritization towards targeting the effectiveness of government budgeting policies.