Abstract
Workers' remittances have become a major source of income for developing countries. However, little is still known about their impact on poverty and inequality. Using a large cross-country panel dataset, the authors find that remittances in Latin American and Caribbean (LAC) countries have increased growth and reduced inequality and poverty. These results are robust to the use of different instruments that attempt to correct for the potential endogeneity of remittances. Household survey-based estimates for 10 LAC countries confirm that remittances have negative albeit relatively small inequality and poverty-reducing effects, even after imputations for the potential home earnings of migrants.
Highlights
Flows of workers’ remittances have become a major source of external finance for developing countries
By combining the results on the effects of remittances on growth and inequality with initial country conditions, we find that remittances generally lower poverty
This paper has shown that migration and remittances have statistically significant poverty reducing effects that appear to operate mainly through increases in per capita income of remittances-receiving countries
Summary
Flows of workers’ remittances have become a major source of external finance for developing countries. To anticipate some of our main results, our cross-country estimates suggest that remittances have a positive and statistically significant effect on growth that is of a similar magnitude in Latin America and in the rest of the world This result is robust to the use of different instruments (internal and external) for the indicator of remittances. We need a parsimonious model as the number of available observations dramatically falls once we estimate the equations for inequality, a variable for which data availability is quite limited This choice ensures comparability with existing work in the growth literature Third, the selected controls are relevant as explanatory variables both for our growth and inequality regressions. We have to admit that there is little that we can do other than, as stressed above, relying on a control set that has been extensively used in the literature, and on the fixed effects strategy to somewhat correct the problem, when the omitted variables are time invariant or have low variability
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