Following Rosenbaum and Rubin (1983), we employ PSM to reconstruct counterfactuals using observational data. In our paper, the key variable of interest, đč đ (financial inclusion) is binary, thus we can set this as the treatment variable. Using one-to-one matching, Table S1 shows the balancing of the variables before and after matching.After matching, all variables are well balanced, providing a bias of less than 5 per cent (%bias<5%).Moreover, because all t-test are not significant, we cannot reject that null hypothesis that there is no systematic difference between treatment and control groups. Figure S1 shows the histogram of matched sub-samples along common support. It shows that most of the observations are on support.
Do remittances reduce labor supply in recipient economies? This paper addresses this question with aggregate level data for a panel of sixty-six developing countries from the Middle East and Africa, Asia and the Pacific, and Latin America and the Caribbean over the period 1985 to 2005. The results exhibit a positive and significant relationship between remittances and aggregate labor supply. The effect is clearly driven by men in each of the three regions. Three potential explanations are put forward to explain these empirical findings: (1) non-migrating household members are likely to increase their labor supply in order to defray migration-related expenses; (2) neighboring households increase their labor supply to help family members migrate after they become more aware of the benefits of remittances; and (3) remittances overcome credit constraints, thus generating employment.
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