Working Paper: CEPR ID: DP8739
Authors: Gharad Bryan; Shyamal Chowdhury; Ahmed Mushfiq Mobarak
Abstract: Pre-harvest lean seasons are widespread in the agrarian areas of Asia and Sub-Saharan Africa. Every year, these seasonal famines force millions of people to succumb to poverty and hunger. We randomly assign an $8.50 incentive to households in Bangladesh to out-migrate during the lean season, and document a set of striking facts. The incentive induces 22% of households to send a seasonal migrant, consumption at the origin increases by 30% (550-700 calories per person per day) for the family members of induced migrants, and follow-up data show that treated households continue to re-migrate at a higher rate after the incentive is removed. The migration rate is 10 percentage points higher in treatment areas a year later, and three years later it is still 8 percentage points higher. These facts can be explained by a model with three key elements: (a) experimenting with the new activity is risky, given uncertain prospects at the destination, (b) overcoming the risk requires individual-specific learning (e.g. resolving the uncertainty about matching to an employer), and (c) some migrants are close to subsistence and the risk of failure is very costly. We test a model with these features by examining heterogeneity in take-up and re-migration, and by conducting a new experiment with a migration insurance treatment. We document several pieces of evidence consistent with the model.
Keywords: Bangladesh; Migration; Risk Aversion
JEL Codes: J61; O1; O15; R23
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| risk aversion, individual-specific learning, and high costs of failure (D81) | migration dynamics (J61) |
| cash incentive (M52) | migration behavior (F22) |
| migration behavior (F22) | household consumption (D10) |
| cash incentive (M52) | household consumption (D10) |
| initial migration experience (F22) | future migration prospects (J61) |