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What Makes Brain Drain More Likely? Evidence from Sub-Saharan Africa


  • Romuald Méango


In Sub-Saharan Africa, high-skilled workers are 13 times more likely to migrate than low-skilled ones. This sheer number has fueled fears about “Brain Drain” as only 3% of the population obtains tertiary education. Although migration prospects might give incentives to invest in schooling, it is still unclear for which households they exist and whether these can compensate for the selection of high-skilled workers into migration. This papers measures the selection, incentive and net effects of emigration from DR Congo, Ghana and Senegal to Europe. Institutional contexts and household characteristics are strong determinants of the three effects. Rich households experience a strong selection of high-skilled workers into migration, thereby decreasing the average schooling level in the origin countries. However, stronger incentives to invest in schooling partly or fully compensate for this decrease. By contrast, poor households experience small selection and equally small incentives, except in Senegal, where they exhibit negative incentives to invest in early schooling. This is possibly due to low returns to secondary education in Europe and/or binding liquidity constraints.

Suggested Citation

  • Romuald Méango, 2016. "What Makes Brain Drain More Likely? Evidence from Sub-Saharan Africa," CESifo Working Paper Series 6209, CESifo.
  • Handle: RePEc:ces:ceswps:_6209

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    References listed on IDEAS

    1. Linguère Mbaye, 2014. "“Barcelona or die”: understanding illegal migration from Senegal," IZA Journal of Migration and Development, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 3(1), pages 1-19, December.
    2. Batista, Catia & Lacuesta, Aitor & Vicente, Pedro C., 2012. "Testing the ‘brain gain’ hypothesis: Micro evidence from Cape Verde," Journal of Development Economics, Elsevier, vol. 97(1), pages 32-45.
    3. Steinmayr, Andreas, 2014. "When a random sample is not random: Bounds on the effect of migration on household members left behind," Kiel Working Papers 1975, Kiel Institute for the World Economy (IfW).
    4. Michael Clemens & Satish Chand, 2008. "Human Capital Investment under Exit Options: Evidence from a Natural Quasi-Experiment," Working Papers 152, Center for Global Development, revised Feb 2019.
    5. Slesh A. Shrestha, 2017. "No Man Left Behind: Effects of Emigration Prospects on Educational and Labour Outcomes of Non‐migrants," Economic Journal, Royal Economic Society, vol. 127(600), pages 495-521, March.
    6. Murard, Elie, 2019. "The Impact of Migration on Family Left Behind: Estimation in Presence of Intra-Household Selection of Migrants," IZA Discussion Papers 12094, Institute of Labor Economics (IZA).
    7. Slesh A. Shrestha, 2017. "No Man Left Behind: Effects of Emigration Prospects on Educational and Labour Outcomes of Non‐migrants," Economic Journal, Royal Economic Society, vol. 127(600), pages 495-521, March.
    8. Girsberger, Esther Mirjam, 2017. "Migration, Education and Work Opportunities," IZA Discussion Papers 11028, Institute of Labor Economics (IZA).
    9. repec:dau:papers:123456789/13410 is not listed on IDEAS
    10. Philippe De Vreyer & François Roubaud, 2013. "Urban Labor Markets in Sub-Saharan Africa," World Bank Publications, The World Bank, number 15808, June.
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    More about this item


    migration; brain drain; brain gain; Sub-Saharan Africa;

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • I25 - Health, Education, and Welfare - - Education - - - Education and Economic Development
    • J61 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Geographic Labor Mobility; Immigrant Workers


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