Globalizing labor and the world economy: the role of human capital

Abstract

We develop a dynamic model of the world economy that jointly endogenizes individual decisions about fertility, education and migration. We then use it to compare the short- and long-term effects of immigration restrictions on the world distribution of income. Our calibration strategy replicates the economic and demographic characteristics of the world, and allows us to proxy bilateral migration costs and visa costs for two classes of workers and for each pair of countries. In our benchmark simulations, the world average level of income per worker increases by 12% in the short term and by approximately 52% after one century. These results are highly robust to our identifying strategy and technological assumptions. Sizable differences are obtained when our baseline (pre-liberalization) trajectory involves a rapid income convergence between countries or when we adjust visa costs for a possible upward bias. Our quantitative analysis reveals that the effects of liberalizing migration on human capital accumulation and income are gradual and cumulative. Whatever is the size of the short-term gain, the long-run impact is 4 to 5 times greater (except under a rapid convergence in income).

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Notes

  1. 1.

    In comparison, removing the remaining barriers to trade and capital flows would generate small increases in world GDP ranging from 0.5 to 4% for trade and from 0.1 to 1.7% for capital (Clemens 2011).

  2. 2.

    Kennan (2014) extends the model to two skills and still finds large gains from open borders. The effective supply of unskilled workers more than doubles in a world with open borders. Assuming constant skill premia, income increases by 130% for unskilled workers and by 90% for skilled workers.

  3. 3.

    The determinants of the size and structure of international migration have been studied in a growing number of papers (Beine et al. 2011; Grogger and Hanson 2011; Belot and Hatton 2012; Bertoli and Fernández-Huertas Moraga 2013; Razin and Wahba 2015).

  4. 4.

    In Germany, the average GDP per inhabitant in Hamburg (EUR 47,100) is 2.3 times greater than that in Brandenburg (EUR 20,500). In Italy, GDP per inhabitant is two times larger in Lombardy (EUR 33,500) than it is in Campania or Calabria (EUR 16,400; values for 2008 from Eurostat 2011). The same ratio is observed in the US between Connecticut (USD 68,167) and Mississippi (USD 32,348; values for 2008; see Bureau of Economic Analysis 2014).

  5. 5.

    The semi-elasticity is defined as the percentage of deviation in world GDP divided by the change in the world proportion of migrants.

  6. 6.

    The recent paper by Desmet et al. (2017) is an exception as it contrasts the short- and long-run impact of an abolition of migration restrictions in a dynamic model accounting for idiosyncratic tastes, location-specific amenities and agglomeration effects. However, it abstracts from endogenous population growth and education decisions.

  7. 7.

    An exception is Mountford and Rapoport (2011), who develop a stylized model with endogenous education and fertility by individuals in the sending countries and in one representative receiving economy. They show that (exogenous) high-skilled migration shocks may improve the growth rate, and reduce the fertility rate of all the economies in the world.

  8. 8.

    Identification strategies rely on cross-country regressions (Stark et al. 1997; Mountford 1997; Beine et al. 2001, 2008; Easterly and Nyarko 2009; Docquier and Rapoport 2012), survey data on the student population (Gibson and McKenzie 2011; Kangasniemi et al. 2007), regional heterogeneity in emigration and education patterns (Batista et al. 2012; McKenzie and Rapoport 2011; Yi et al. 2009), and quasi-natural emigration shocks (Clemens and Chand 2008; Shrestha 2017).

  9. 9.

    Many studies on internal migration find that it leads to a convergence of fertility rates between migrants and urban natives (see among others, Lee and Pol 1993 or Brockerhoff 1995). Convergence is also obtained in studies of international migration, including Stephen and Bean (1992), Lindstrom and Saucedo (2002) for women of Mexican origin living in the US (see also Chiswick and Miller 2012; Fernández and Fogli 2009).

  10. 10.

    Throughout the paper, the GDP per adult worker is the income measure of interest.

  11. 11.

    Although Grogger and Hanson (2011) find that a linear utility specification fits well the patterns of positive selection and sorting in the migration data, most studies rely on a concave (logarithmic) utility function (Bertoli and Fernández-Huertas Moraga 2013; Beine et al. 2013a; Beine and Parsons 2015; Ortega and Peri 2013). In our microfounded framework, using a concave function ensures interior solutions for consumption, fertility and education.

  12. 12.

    Note that in the present framework, migration is permanent and irreversible. Kennan and Walker (2011) use a richer decision framework that allows for sequential migration decisions (i.e., multiple moves). As noted by these authors, the addition of more dimensions complicates the computation exponentially. This is particularly problematic in a large multi-country framework. Nevertheless, we consider temporary migrants in a robustness check.

  13. 13.

    The dynastic model is much less tractable, and its properties are highly sensitive to the choice of the elasticities of substitution (Jones and Schoonbroodt 2010). In addition, Kollmann (1997) demonstrates that the qualitative predictions of the non-dynastic model are strikingly similar to those of a properly calibrated dynastic model.

  14. 14.

    Capital adjustments are rapid in open economies. Ortega and Peri (2009) find that flows of immigrants increase one-for-one employment and capital stocks in the receiving country in the short term (i.e., within 1 year), leaving the capital/labor ratio unchanged. In the long term, this condition also holds in a closed economy with endogenous savings since the steady state interest rate is determined by the intertemporal discount rate of individuals.

  15. 15.

    A simple OLS regression gives \(\ln \sigma _{i,2000}=0.25-0.31\ln \frac{h_{i,2000}}{1-h_{i,2000}}\) with \(R^{2}=0.57\).

  16. 16.

    Desmet et al. (2017) obtain that 78.2% of the world population migrates if migration costs are lifted in a dynamic model accounting for idiosyncratic tastes, location-specific amenities and agglomeration effects.

  17. 17.

    In Sect. 4, we test the robustness of our results by assessing different variants of the number of potential migrants.

  18. 18.

    The inequality effects are discussed in “Appendix C.2”, being aware that our analysis does not account for redistributive policies (i.e., taxes and transfers). Clearly, liberalizing migration could challenge the financing of public infrastructure and education. For example, Tanaka et al. (2014) show that public spending per student decreased by 11% after a “large episode of immigration in 2008” in Spain.

  19. 19.

    Variants of the benchmark model with exogenous wages or with endogenous TFP are discussed in Sect. 5, and the strong robustness of our results to any value of the elasticity of substitution from the range estimated in the literature is provided in “Appendix C.1”.

  20. 20.

    As shown in Sect. 5.1, simulating the model without this brain gain effect reduces the semi-elasticity to 1.3 in 2000, which is in line with previous studies.

  21. 21.

    We have aggregated the results at the regional level for the sake of clarity and comparability with other studies. The results for selected countries are reported in “Appendix C.4”.

  22. 22.

    See Biavaschi et al. (2016) for a discussion on the population composition effects in the measurement of the skill selection of migrants (relative to non-migrants).

  23. 23.

    In di Giovanni et al. (2015), repatriating immigrants reduces remittances and thereby the income and utility of individuals in origin countries which is not compensated by the positive market size effect (i.e. a higher number of varieties produced at origin by the repatriated individuals).

  24. 24.

    The existing literature estimates an elasticity of remittances to the stock of emigrants between − 0.5 and − 1.0 (see Docquier and Machado 2015 for an overview of the literature).

  25. 25.

    “Appendix C.2” provides a discussion of the impact of liberalization on the worldwide income distribution. Moreover, we evaluate the impact of current migration levels by simulating a counterfactual world without migration in “Appendix C.5”.

  26. 26.

    A change in the preference parameters \(\theta \) and \(\lambda \) implies a re-calibration of the ratio between children and low-skilled adults’ wage rate, \(\omega _{k,1975}\), and the ratio of basic education costs to the high-skilled wage rate, \(\xi _{k,1975}\), in order to match the fertility and basic education decisions in 1975 (see Eqs. 13 and 14).

  27. 27.

    Desmet et al. (2017) develop a model with population-density dependent agglomeration and congestion effects to which they apply liberalization. In their baseline, the most populated (i.e., poor developing countries) become the most developed and populated regions in the long run. Technological progress increases with the population density and dominates the congestion effects. Opening borders allows developed countries to remain at the production frontier in the future. Free mobility implies that 78.2% of the population emigrates to high-amenity countries and a 353% increase in welfare. However, their model does not consider endogenous fertility and education responses.

  28. 28.

    The elasticity found in Alesina et al. (2016) is somewhat conservative. More optimistic results are found in Ortega and Peri (2014), who suggest that an increase in the diversity of migrants from 0.05 (the value for Sri Lanka, the country with the lowest diversity index) to 0.96 (the value for the UK) implies a corresponding increase in output per person by a factor of 3.5.

  29. 29.

    Another channel of transmission advocated by Collier (2013) is trust (or mutual regard). The effect of trust on TFP has been identified in Knack and Keefer (1997), and Alesina and La Ferrara (2002) show that diversity by race reduces trust.

  30. 30.

    Log-linearizing this expression implies: \(\ln (A_{k,t+1}/{A_{k,t}})=25\ln (1.015)+b\ln (A_{{ US},t}/A_{k,t})\).

References

  1. Acosta, P., Fajnzylber, P., & Lopez, J. H. (2007). The impact of remittances on poverty and human capital: Evidence from Latin American household surveys. In World Bank Research Working Paper 4247. Washinghton, DC: World Bank.

  2. Alesina, A., Harnoss, J., & Rapoport, H. (2016). Birthplace diversity and economic prosperity. Journal of Economic Growth, 21(2), 101–138.

    Article  Google Scholar 

  3. Alesina, A., & La Ferrara, E. (2002). Who trusts others? Journal of Public Economics, 85(2), 207–234.

    Article  Google Scholar 

  4. Amuedo-Dorantes, C., & Pozo, S. (2010). Accounting for remittance and migration effects on children’s schooling. World Development, 38(12), 1747–1759.

    Article  Google Scholar 

  5. Andersen, T. B., & Dalgaard, C.-J. (2011). Flows of people, flows of ideas, and the inequality of nations. Journal of Economic Growth, 16(1), 1–32.

    Article  Google Scholar 

  6. Artuç, E., Docquier, F., Özden, Ç., & Parsons, C. (2015). A global assessment of human capital mobility: The role of non-OECD destinations. World Development, 65(C), 6–26.

    Article  Google Scholar 

  7. Bansak, C., & Chezum, B. (2009). How do remittances affect human capital formation of school-age boys and girls? The American Economic Review, 99(2), 145–148.

    Article  Google Scholar 

  8. Barro, R. J., & Lee, J. W. (2013). A new data set of educational attainment in the world, 1950–2010. Journal of Development Economics, 104(C), 184–198.

    Article  Google Scholar 

  9. Batista, C., Lacuesta, A., & Vicente, P. C. (2012). Testing the “brain gain” hypothesis: Micro evidence from Cape Verde. Journal of Development Economics, 97(1), 32–45.

    Article  Google Scholar 

  10. Becker, G. S., & Barro, R. J. (1988). A reformulation of the economic theory of fertility. The Quarterly Journal of Economics, 103(1), 1–25.

    Article  Google Scholar 

  11. Beine, M., Bourgeon, P., & Bricongne, J. -C. (2013a). Aggregate fluctuations and international migration. In CESifo Working Paper Series 4379. CESifo Group Munich.

  12. Beine, M., Docquier, F., & Özden, Ç. (2011). Diasporas. Journal of Development Economics, 95(1), 30–41.

    Article  Google Scholar 

  13. Beine, M., Docquier, F., & Özden, Ç. (2015). Dissecting network externalities in international migration. Journal of Demographic Economics (JODE), 81(4), 379–408.

    Article  Google Scholar 

  14. Beine, M., Docquier, F., & Rapoport, H. (2001). Brain drain and economic growth: Theory and evidence. Journal of Development Economics, 64(1), 275–289.

    Article  Google Scholar 

  15. Beine, M., Docquier, F., & Rapoport, H. (2008). Brain drain and human capital formation in developing countries: Winners and losers. The Economic Journal, 118(528), 631–652.

    Article  Google Scholar 

  16. Beine, M., Docquier, F., & Schiff, M. (2013b). International migration, transfer of norms and home country fertility. Canadian Journal of Economics/Revue canadienne d’économique, 46(4), 1406–1430.

    Article  Google Scholar 

  17. Beine, M., & Parsons, C. (2015). Climatic factors as determinants of international migration. Scandinavian Journal of Economics, 117(2), 723–767.

    Article  Google Scholar 

  18. Belot, M. V. K., & Hatton, T. J. (2012). Immigrant selection in the OECD. Scandinavian Journal of Economics, 114(4), 1105–1128.

    Article  Google Scholar 

  19. Benhabib, J., & Jovanovic, B. (2012). Optimal migration: A world perspective. International Economic Review, 53(2), 321–348.

    Article  Google Scholar 

  20. Bertoli, S., & Fernández-Huertas Moraga, J. (2013). Multilateral resistance to migration. Journal of Development Economics, 102(C), 79–100.

    Article  Google Scholar 

  21. Bertoli, S., & Marchetta, F. (2015). Bringing it all back home-return migration and fertility choices. World Development, 65(C), 27–40.

    Article  Google Scholar 

  22. Bertoli, S., & Ruyssen, I. (2016). Networks and migrants’ intended destination. In IZA Discussion Papers 10213. Institute for the Study of Labor (IZA).

  23. Biavaschi, C., Burzynski, M., Elsner, B., & Machado, J. (2016). The gain from the drain: Skill-biased migration and global welfare. In IZA Discussion Papers 10275. Institute for the Study of Labor (IZA).

  24. Bollard, A., McKenzie, D., Morten, M., & Rapoport, H. (2011). Remittances and the brain drain revisited: The microdata show that more educated migrants remit more. The World Bank Economic Review, 25(1), 132–156.

    Article  Google Scholar 

  25. Borjas, G. J. (2015). Immigration and globalization: A review essay. Journal of Economic Literature, 53(4), 961–974.

    Article  Google Scholar 

  26. Brockerhoff, M. (1995). Fertility and family planning in African cities: The impact of female migration. Journal of Biosocial Science, 27(03), 347–358.

    Article  Google Scholar 

  27. Bureau of Economic Analysis. (2014). Per capita real GDP by state (chained 2009) dollars. In Interactive data-regional data-GDP & personal income. U.S. Bureau of Economic Analysis.

  28. Calero, C., Bedi, A. S., & Sparrow, R. (2009). Remittances, liquidity constraints and human capital investments in Ecuador. World Development, 37(6), 1143–1154.

    Article  Google Scholar 

  29. Chiswick, B. R., & Miller, P. W. (2012). Negative and positive assimilation, skill transferability, and linguistic distance. Journal of Human Capital, 6(1), 35–55.

    Article  Google Scholar 

  30. Ciccone, A., & Hall, R. E. (1996). Productivity and the density of economic activity. The American Economic Review, 86(1), 54.

    Google Scholar 

  31. Clemens, M., & Chand, S. (2008). Skilled emigration and skill creation: A quasi-experiment. In Working Papers 152. Center for Global Development.

  32. Clemens, M. A. (2011). Economics and emigration: Trillion-dollar bills on the sidewalk? Journal of Economic Perspectives, 25(3), 83–106.

    Article  Google Scholar 

  33. Clemens, M. A., & Pritchett, L. (2016). The new economic case for migration restrictions: An assessment. In IZA Discussion Papers 9730. Institute for the Study of Labor (IZA).

  34. Collier, P. (2013). Exodus: How migration is changing our world. Oxford: Oxford University Press.

    Google Scholar 

  35. de la Croix, D., & Docquier, F. (2015). An incentive mechanism to break the low-skill immigration deadlock. Review of Economic Dynamics, 18(3), 593–618.

    Article  Google Scholar 

  36. de la Croix, D., & Doepke, M. (2003). Inequality and growth: Why differential fertility matters. American Economic Review, 93(4), 1091–1113.

    Article  Google Scholar 

  37. de la Croix, D., & Doepke, M. (2004). Public versus private education when differential fertility matters. Journal of Development Economics, 73(2), 607–629.

    Article  Google Scholar 

  38. de la Croix, D., & Gosseries, A. (2009). Population policy through tradable procreation entitlements. International Economic Review, 50(2), 507–542.

    Article  Google Scholar 

  39. de Palma, A., & Kilani, K. (2007). Invariance of conditional maximum utility. Journal of Economic Theory, 132(1), 137–146.

    Article  Google Scholar 

  40. Defoort, C. (2008). Tendances de long terme des migrations internationales: Analyse à partir des six principaux pays receveurs. Population (french edition), 63(2), 317–351.

    Article  Google Scholar 

  41. Delogu, M., Docquier, F., & Machado, J. (2014). The dynamic implications of liberalizing global migration. In CESifo Working Paper Series 4596. CESifo Group Munich.

  42. Desmet, K., Nagy, D. K., & Rossi-Hansberg, E. (2017). The geography of development: Evaluating migration restrictions and coastal flooding. Journal of Political Economy (forthcoming).

  43. di Giovanni, J., Levchenko, A., & Ortega, F. (2015). A global view of cross-border migration. Journal of the European Economic Association, 13(1), 168–202.

    Article  Google Scholar 

  44. Docquier, F., & Machado, J. (2015). Remittance and migration prospects for the twenty-first century. Working Papers P133. FERDI.

  45. Docquier, F., Machado, J., & Sekkat, K. (2015). Efficiency gains from liberalizing labor mobility. The Scandinavian Journal of Economics, 117(2), 303–346.

    Article  Google Scholar 

  46. Docquier, F., Peri, G., & Ruyssen, I. (2014). The cross-country determinants of potential and actual migration. International Migration Review, 48, 37–99.

    Article  Google Scholar 

  47. Docquier, F., & Rapoport, H. (2012). Globalization, brain drain, and development. Journal of Economic Literature, 50(3), 681–730.

    Article  Google Scholar 

  48. Dustmann, C., & Okatenko, A. (2014). Out-migration, wealth constraints, and the quality of local amenities. Journal of Development Economics, 110(C), 52–63.

    Article  Google Scholar 

  49. Easterly, W., & Nyarko, Y. (2009). Is the brain drain good for Africa? In Skilled immigration: Problems, prospects and policies (Chapter 11, pp. 316–60). Oxford: Oxford University Press.

  50. Esipova, N., Ray, J., & Srinivasan, R. (2011). The world’s potential migrants: Who they are, where they want to go, and why it matters. In Gallup White Paper.

  51. Eurostat. (2011). GDP per inhabitant ranged from 28% of the EU27 average in Severoza–Paden in Bulgaria to 343% in Inner London. In Eurostat News Release, STAT/11/28. Eurostat.

  52. Fernández, R., & Fogli, A. (2009). Culture: An empirical investigation of beliefs, work, and fertility. American Economic Journal: Macroeconomics, 1(1), 146–177.

    Google Scholar 

  53. Galor, O. (2011). Unified growth theory. Number 9477 in economics books. Princeton University Press.

  54. Gibson, J., & McKenzie, D. (2011). The microeconomic determinants of emigration and return migration of the best and brightest: Evidence from the Pacific. Journal of Development Economics, 95(1), 18–29.

    Article  Google Scholar 

  55. Grogger, J., & Hanson, G. H. (2011). Income maximization and the selection and sorting of international migrants. Journal of Development Economics, 95(1), 42–57.

    Article  Google Scholar 

  56. Haveman, R., & Wolfe, B. (1995). The determinants of children’s attainments: A review of methods and findings. Journal of Economic Literature, 33(4), 1829–1878.

    Google Scholar 

  57. Hendricks, L. (2004). A database of mincerian earnings regressions. Accessed 1 January 2015. www.lhendircks.org/Mincer.htm.

  58. Iranzo, S., & Peri, G. (2009). Migration and trade: Theory with an application to the Eastern–Western European integration. Journal of International Economics, 79(1), 1–19.

    Article  Google Scholar 

  59. Iregui, A. M. (2005). Efficiency gains from the elimination of global restrictions on labour mobility: An analysis using a multiregional CGE model. In Poverty, international migration and asylum (pp. 211–238). UK: Palgrave Macmillan.

  60. Jones, L. E., & Schoonbroodt, A. (2010). Complements versus substitutes and trends in fertility choice in dynastic models. International Economic Review, 51(3), 671–699.

    Article  Google Scholar 

  61. Kangasniemi, M., Winters, L. A., & Commander, S. (2007). Is the medical brain drain beneficial? Evidence from overseas doctors in the UK. Social Science & Medicine, 65(5), 915–923.

    Article  Google Scholar 

  62. Kennan, J. (2013). Open borders. Review of Economic Dynamics, 16(2), 1–13.

    Article  Google Scholar 

  63. Kennan, J. (2014). Immigration restrictions and skill premia. Mimeo, Department of Economics, University of Wisconsin.

  64. Kennan, J., & Walker, J. R. (2011). The effect of expected income on individual migration decisions. Econometrica, 79(1), 211–251.

    Article  Google Scholar 

  65. Klein, P., & Ventura, G. (2009). Productivity differences and the dynamic effects of labor movements. Journal of Monetary Economics, 56(8), 1059–1073.

    Article  Google Scholar 

  66. Knack, S., & Keefer, P. (1997). Does social capital have an economic payoff? A cross-country investigation. The Quarterly Journal of Economics, 112(4), 1251–1288.

    Article  Google Scholar 

  67. Kollmann, R. (1997). Endogenous fertility in a model with non-dynastic parental altruism. Journal of Population Economics, 10(1), 87–95.

    Article  Google Scholar 

  68. Kremer, M., & Chen, D. (1999). Income-distribution dynamics with endogenous fertility. American Economic Review, 89(2), 155–160.

    Article  Google Scholar 

  69. Lee, B. S., & Pol, L. G. (1993). The influence of rural-urban migration on migrants’ fertility in Korea, Mexico and Cameroon. Population Research and Policy Review, 12(1), 3–26.

    Article  Google Scholar 

  70. Lindstrom, D. P., & Saucedo, S. G. (2002). The short-and long-term effects of US migration experience on Mexican women’s fertility. Social Forces, 80(4), 1341–1368.

    Article  Google Scholar 

  71. Manchin, M., & Orazbayev, S. (2016). Social networks and the intention to migrate. In Development Working Papers 409. Centro Studi Luca d’Agliano, University of Milano.

  72. McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers in econometrics (pp. 105–42). New York: Academic Press.

    Google Scholar 

  73. McKenzie, D., & Rapoport, H. (2011). Can migration reduce educational attainment? Evidence from Mexico. Journal of Population Economics, 24(4), 1331–1358.

    Article  Google Scholar 

  74. Mountford, A. (1997). Can a brain drain be good for growth in the source economy? Journal of Development Economics, 53(2), 287–303.

    Article  Google Scholar 

  75. Mountford, A., & Rapoport, H. (2011). The brain drain and the world distribution of income. Journal of Development Economics, 95(1), 4–17.

    Article  Google Scholar 

  76. Neumayer, E. (2006). Unequal access to foreign spaces: How states use visa restrictions to regulate mobility in a globalised world. Transactions of the Institute of British Geographers, 1(31), 72–84.

    Article  Google Scholar 

  77. Ortega, F., Peri, G. (2009). The causes and effects of international migrations: Evidence from OECD countries 1980–2005. In NBER Working Papers 14833. National Bureau of Economic Research Inc.

  78. Ortega, F., & Peri, G. (2013). The role of income and immigration policies in attracting international migrants. Migration Sudies, 1(1), 1–28.

    Google Scholar 

  79. Ortega, F., & Peri, G. (2014). Openness and income: The roles of trade and migration. Journal of International Economics, 92(2), 231–251.

    Article  Google Scholar 

  80. Ottaviano, G. I., & Peri, G. (2006). The economic value of cultural diversity: Evidence from US cities. Journal of Economic Geography, 6(1), 9–44.

    Article  Google Scholar 

  81. Ottaviano, G. I., & Peri, G. (2012). Rethinking the effect of immigration on wages. Journal of the European Economic Association, 10(1), 152–197.

    Article  Google Scholar 

  82. Parrotta, P., Pozzoli, D., & Pytlikova, M. (2014). The nexus between labor diversity and firms innovation. Journal of Population Economics, 27(2), 303–364.

    Article  Google Scholar 

  83. Peri, G., Shih, K., & Sparber, C. (2015). STEM workers, H-1B visas and productivity in US cities. Journal of Labor Economics, 33(S1), 225–255.

    Article  Google Scholar 

  84. Razin, A., & Wahba, J. (2015). Welfare magnet hypothesis, fiscal burden, and immigration skill selectivity. Scandinavian Journal of Economics, 117(2), 369–402.

    Article  Google Scholar 

  85. Salas, V. B. (2014). International remittances and human capital formation. World Development, 59(C), 224–237.

    Article  Google Scholar 

  86. Shrestha, S. A. (2017). No man left behind: Effects of emigration prospects on educational and labour outcomes of non-migrants. The Economic Journal, 127(600), 495–521.

    Article  Google Scholar 

  87. Spilimbergo, A. (2009). Foreign students and democracy. American Economic Review, 99(1), 528–43.

    Article  Google Scholar 

  88. Stark, O., Helmenstein, C., & Prskawetz, A. (1997). A brain gain with a brain drain. Economics Letters, 55(2), 227–234.

    Article  Google Scholar 

  89. Stephen, E. H., & Bean, F. D. (1992). Assimilation, disruption and the fertility of Mexican-origin women in the United States. International Migration Review, 26(1), 67–88.

    Article  Google Scholar 

  90. Tanaka, R., Farré, L., Ortega, F. (2014). Immigration, naturalization, and the future of public education. In IZA Discussion Papers 8342. Institute for the Study of Labor (IZA).

  91. United Nations. (2011). World population prospects: The 2010 revision: Highlights and advance tables. In Working Paper No. ESA/P/WP.220. United Nations, Department of Economic and Social Affairs, Population Division.

  92. Walmsley, T. L., & Winters, L. A. (2005). Relaxing the restrictions on the temporary movement of natural persons: A simulation analysis. Journal of Economic Integration, 20(4), 688–726.

    Article  Google Scholar 

  93. Weil, D. N., & Galor, O. (2000). Population, technology, and growth: From Malthusian stagnation to the demographic transition and beyond. American Economic Review, 90(4), 806–828.

    Article  Google Scholar 

  94. Winters, L. A. (2001). Assessing the efficiency gain from further liberalization: A comment. In P. Suave & A. Subramanian (Eds.), Efficiency, equity and legitimacy: The Multilateral Trading System at the Millennium (pp. 106–113). Washington: Brookings Institution Press.

    Google Scholar 

  95. World Bank. (2010). World development indicators. Washington, DC: The World Bank.

  96. Yi, J., Zhang, J., & Ha, W. (2009). Brain drain, brain gain, and economic growth in China. Technical Report 2009/37, Human Development Research Paper.

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Correspondence to Frédéric Docquier.

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This paper benefited from the helpful suggestions from four anonymous referees. We are grateful to Michel Beine, Simone Bertoli, Bastien Chabé-Ferret, Guiseppe de Arcangelis, David de la Croix, Gordon Hanson, Freddy Heylen, Giovanni Facchini, Pierre Picard, Giovanni Peri, Lionel Ragot, Hillel Rapoport, Eric Toulemonde and seminar participants for their comments. An earlier version (see Delogu et al. 2014) was presented at the IZA workshop on “Migration and Human Capital” (May 2013), the NORFACE conference in Nottingham (September 2013), the 6th EGIT conference in Düsseldorf (May 2015), and seminars in Bari, Bonn, Geneva, Gothenburg, Louvain-la-Neuve, Luxembourg and Nantes. The authors acknowledge CESifo sponsorship for the participation at the CESifo Economic Studies Conference on Migration held in December 2013 and financial support from the ARC convention on “Geographical Mobility of Factors” (convention 09/14-019). Joël Machado acknowledges funding by the Luxembourg National Research Fund (9037210).

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Delogu, M., Docquier, F. & Machado, J. Globalizing labor and the world economy: the role of human capital. J Econ Growth 23, 223–258 (2018). https://doi.org/10.1007/s10887-017-9153-z

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Keywords

  • Migration
  • Migration policy
  • Liberalization
  • Growth
  • Human capital
  • Fertility
  • Inequality

JEL Classification

  • O15
  • F22
  • F63
  • I24