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

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

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

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

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

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Authors and Affiliations

  1. DISEA, Università degli studi di Sassari, Via Muroni 25, Sassari, Italy

    Marco Delogu

  2. IRES, Université Catholique de Louvain, 3 Place Montesquieu (bte L2.06.01), 1348, Louvain-la-Neuve, Belgium

    Frédéric Docquier

  3. Luxembourg Institute of Socio-Economic Research (LISER), Maison des Sciences Humaines, 11 Porte des Sciences, 4366, Esch-sur-Alzette, Luxembourg

    Joël Machado

  4. National Fund for Scientific Research (FNRS), 5, rue d’Egmont, 1000, Brussels, Belgium

    Frédéric Docquier

Authors
  1. Marco Delogu
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Correspondence to Frédéric Docquier.

Additional information

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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