Effect of the Social Safety Net on Child Labor and School Retention in Bangladesh: Empirical Evidence from Hies 2016

Abstract

ABSTRACT: In Bangladesh Social Safety Net (SSN) interventions are intended to achieve specific goals, e.g., reduction in poverty, decrease in early school dropout, and nutrition supplementation. This study investigates the effectiveness of SSN participation on less explored goals of child labor reduction and school retention in Bangladesh. Empirical evidence is provided to evaluate the success of existing programs to combat child labor and early school dropout. This study utilizes the Household Income and Expenditure Survey (HIES), 2016 of Bangladesh. We employ the Propensity Score Matching (PSM) framework to show the causal relationship between SSN participation and its impact on the use of child labor and school retention. Given the nature and availability of data, the matching method provides us an estimate of the impact of SSN based on a quasi-experimental setting. We estimate counterfactuals based on observed characteristics of the household that affect both SSN participation and our outcome variables. Our results suggest that households that receive SSN tend to employ less child labor domestic or paid compared to their counterparts. Our Propensity Score Matching (PSM) results show that SSN participation reduces child labor prevalence in Bangladesh by 2.4% to 2.9%. This is an indication that families essentially substitute SSN income with child labor earnings. Thus, SSN programs in Bangladesh are effective in reducing child labor though the magnitude is not overwhelming. On the contrary, our results show that children from SSN-participating families have a higher incidence of early school dropout. This may be due to inefficiency or poorly designed programs that effectively keep the children in school. We estimate early school dropout is higher by 3.6% to 6.6% for households that receive SSN compared to the control group. Our findings are important for policymakers to redesign the programs that retain children in school. We suggest targeted interventions that will help to keep these children in school, which is conducive to human capital accrual and increasing lifetime earnings. Additionally, our insights will assist policymakers in rethinking SSN coverage, efficient selection of beneficiaries, and initiation of the correct intervention.