This paper studies the welfare effects of encouraging rural–urban migration in the developing world. To do so, we build and analyze a dynamic general-equilibrium model of migration that features a rich set of migration motives. We estimate the model to replicate the results of a field experiment that subsidized seasonal migration in rural Bangladesh, leading to significant increases in migration and consumption. We show that the welfare gains from migration subsidies come from providing better insurance for vulnerable rural households rather than from correcting spatial misallocation by relaxing credit constraints for those with high productivity in urban areas that are stuck in rural areas.
This paper studies the welfare effects of encouraging rural–urban migration in the developing world. To do so, we build and analyze a dynamic general-equilibrium model of migration that features a rich set of migration motives. We estimate the model to replicate the results of a field experiment that subsidized seasonal migration in rural Bangladesh, leading to significant increases in migration and consumption. We show that the welfare gains from migration subsidies come from providing better insurance for vulnerable rural households rather than from correcting spatial misallocation by relaxing credit constraints for those with high productivity in urban areas that are stuck in rural areas.
Recent studies find that observational returns to rural-urban migration are near zero in three developing countries. We revisit this result using panel tracking surveys from six countries, finding higher returns on average. We then interpret these returns in a multi-region Roy model with heterogeneity in migration costs. In the model, the observational return to migration confounds the urban premium and the individual benefits of migrants, and is not directly informative about the welfare gain from lowering migration costs. Patterns of regional heterogeneity in returns, and a comparison of experimental to observational returns, are consistent with the model’s predictions.