The Effect of Border Trade on Income Distribution Case study: Thaphabath District, Bolikhamxai Province - SOUKAVONG , Bounthone; CHANTHANASIN , Vadsana; SIOUDOMPHAN , Soutthavone; VONGHACHACK , Souphada; CHANDAKHAM , Phonethip (Souphanouvong University Journal Multidisciplinary Research and Development, 2022)
This research aims to examine the effects of border trade on income distribution, specifically in the case study on Thaphabath District, Bolikhamxay Province. The objectives of this study are: to study the distribution of income of households that are near a village and far from a traditional border check point and to study the factors that impact the households' income distribution. This study's data set includes secondary and primary sources, as well as questionnaire responses from 335 households. The data analysis used the decomposition of the Gini Coefficient log to analyze income distribution and multiple regression analysis by OLS model (Ordinary Least Square) by STATA12 to seek the significance of the factors that affect citizen income distribution. Our results of this study included that the income distribution of households near a village and far from the traditional border check point had represented less equality by the Gini index. The Gini index was 0.2291 and 0.266 respectively. Additionally, we found that the selling of agriculture products by households near the traditional border check point covered 58.43 percent of their income, and the income of distant households deriving from retail and wholesale businesses covered 38.06 percent of their income. The results illustrate that the effects on a household’s income distribution based on a case study in Thaphabath District, Bolikhamxay Province are caused by five independent variables. These variables include: the number of family workers in a household, the number of users of the check, the distance from the household to the checkpoint, the age of the head of the household, and the level of education of the head of the household. When the number of workers increased by 1, it caused the household’s income distribution to increase by 1,277,266 kip per month with statistical significance at 1 percent. When the number of users of checkpoint increased by 1, it caused the household’s income distribution to increase by 218,138.9 kip per month with statistical significance at 5 percent. When the distance from the household to the checkpoint rises by 1, it caused the household’s income distribution to decrease by12,861.13 kip per month with statistical significance of 5 percent. When the age of the head household increased by 1, it caused the household’s income distribution to rise by 17,788.31 kip per month with statistical significance at 10 percent. Finally, when the level of education of the household head rose by 1, it caused the household’s income distribution to rise 38,940.67 kip per month with statistical significance at 5 percent.