Remittances, income inequality and investment in Bangladesh

Abstract

The effect of remittances on income inequality and investment on human capital is an arguable issue in the development literature. As Bangladesh is one of the top remittance receiver countries in the world, it is essential to identify the distributional impact of remittances on households in Bangladesh. In this study, we use a unique national dataset from Bangladesh Household Income and Expenditure Survey (HIES) to analyze the impact of internal remittances (within Bangladesh) and international remittances (from outside of Bangladesh) on income inequality and investment on human capital at the household level. To investigate the impact of both internal and international remittances on household expenditure and investment in Bangladesh, this research applies quantile regression model to examine the distributional impact of remittances and the Working-Leser model to examine the impact of remittances on human capital. Quantile regression shows how covariates influence the location, scale, and shape of a response distribution more vigorously and naturally. Moreover, this research estimates the income distribution by kernel density (KD) estimation. This research also employs the Working-Leser model for linear budget share specification specifically for education and health. Our results suggest that remittances from both domestic and international migrants improve expenditures, but they have different impacts on income inequality. While internal remittances are more likely to reduce household income inequality, international remittances increase it significantly. The results are also found consistent in the KD estimation. The Working-Leser model estimation suggests that if remittances increase then expenditure on health and education also increases. While overall impact of remittances found positive, the government should take the initiative and extra incentives to reduce the costs of sending remittances and they should also provide procedural and structural support for the migrants.