New Directions for Migration Research

If you are interested in economic research on migration, I highly recommend this presentation at the Center of African Economies 2015 conference. A great deal of fresh, puzzling recently discovered data patterns and exciting directions for future research from a group of young promising economists. The following are some quick notes. I encourage you to watch the whole presentation for your own inspiration.

Michael Clemens on Skilled Migration

– Future research questions: 1) look at surveys with details about migrants: what do they learn? How well do they assimilate into the local labor markets? 2) How does participation in the global labor market affect education systems in the origin country? 3) How should we model human capital externality (this is hard!)?

– Incorrect language can mislead our discussion on migration. “Brain drain”? Think about calling women’s labor force participation rate “family abandonment rate”. How would that feel?

Melanie Morten on Migration in Sub-Saharan Africa

– Facts: much migration in Sub-Saharan Africa in rural-urban, rural-rural, and urban-urban; there are large regional differences in migration flows.

– Apart from migration across sectors, we should think about labor flows within sector across areas as well.

– Potential reasons for the lack of migration in the presence of large wage gaps: 1) selection; 2) people care more than wages (amenities/quality of life, cost of living, congestion, etc), which is the compensating differentials argument; 3) it’s costly to migrate.

– There’s evidence for all three channels mentioned above. This points to the need for further understanding of the costs of migration and potential roles of policy to foster a better match between skill and location.

– Policies that are space based (e.g. subsidies) might prevent people from migration by making it easier to stay in low-productivity places. (I found this particularly interesting).

Clement Imbert on Seasonal Migration

– Seasonal migration is especially important for risk coping in certain areas with agricultural lean seasons (India, Bangladesh, etc); but the contexts are similar.

– Data: NSS data, REDS data, RICE survey.

– Two sectors: construction (spot market) and manufacturing (network/referral matters).

– Need to consider the general equilibrium effects of seasonal migration (on urban wages, amenities, etc).

– Little data and work done on seasonal migration in Africa.

Q&A:

1. How to study forced migration due to climate change or conflicts?

Morten: This is challenging to model because migration is not a choice. Current research has focused on the impact of large migrant inflows on the local economy. See Ran Abramitzky‘s work. In the case of climate change, which is a permanent shock, political economy might matter and the distributional impacts are important. See Esteban Rossi-Hansberg‘s recent work (he presented at Duke two weeks ago and it was fascinating!).

2. Are migrant non-migrant wage differences only seen in developing countries?

Morten: No. It seems people value amenities differently. In developing countries, the benefits can be similar with those in developed countries, but the costs can be very high. See my paper on nonparametric estimation of migration costs (my comment: gravity model in trade?). Thinking about differences between developing and developed countries can be useful for policy intervention.

Clemens: There is a broader literature on “Why don’t people make profitable investments?” There’s a lot to explore in developed countries — why do people stay in the Appalachia when they can move to Miami?

3. How do property rights affect models on migration?

Morten: This is a first-order consideration. See paper in AER: as people get formal property rights, they migrate more. Part of the lack of migration might be due to property rights.

4. What’s the role of information on migration?

Morten: Experiments. Need to find high quality data on wages that people can trust. There are many open questions and possibilities to extend existing models.

Imbert: Information seems to be more easily available for lower-skilled jobs.

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