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The Gravity of High-Skilled Migration Policies

Abstract

Combining unique, annual, bilateral data on labor flows of highly skilled immigrants for 10 OECD destinations between 2000 and 2012, with new databases comprising both unilateral and bilateral policy instruments, we present the first judicious cross-country assessment of policies aimed to attract and select high-skilled workers. Points-based systems are much more effective in attracting and selecting high-skilled migrants than requiring a job offer, labor market tests, and shortage lists. Offers of permanent residency, while attracting the highly skilled, overall reduce the human capital content of labor flows because they prove more attractive to non-high-skilled workers. Bilateral recognition of diploma and social security agreements foster greater flows of high-skilled workers and improve the skill selectivity of immigrant flows. Conversely, double taxation agreements deter high-skilled migrants, although they do not alter overall skill selectivity. Our results are robust to a variety of empirical specifications that account for destination-specific amenities, multilateral resistance to migration, and the endogeneity of immigration policies.

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Fig. 1

Notes

  1. http://europa.eu/rapid/press-release_SPEECH-12-312_en.htm?locale=fr

  2. We thank an anonymous referee for this suggestion.

  3. An important ongoing innovation in this regard is the IMPALA project (Beine et al. 2015), which uses lawyers—experts on the texts of migration legislation—to code dummy variables for destination countries.

  4. Following Gröschl (2012), the MRM terms are calculated as follows:

    $$ {MRDIST}_{odt}=\left[\left({\sum}_{k=1}^C{\uptheta}_{k t} \ln {Dist}_{ok}\right)+\left({\sum}_{m=1}^C{\uptheta}_{m t} \ln {Dist}_{m d}\right)-\left({\sum}_{k=1}^C{\sum}_{m=1}^C{\uptheta}_{k t}{\uptheta}_{m t} \ln {Dist}_{k m}\right)\right] $$
    $$ {MRADJ}_{odt}=\left[\left({\sum}_{k=1}^C{\uptheta}_{k t}{Adj}_{ok}\right)+\left({\sum}_{m=1}^C{\uptheta}_{m t}{Adj}_{m d}\right)-\left({\sum}_{k=1}^C{\sum}_{m=1}^C{\uptheta}_{k t}{\uptheta}_{m t}{Adj}_{k m}\right)\right]. $$

    θ refers to a country’s share of population as a fraction of the world population: N kt / N and N mt / N. Dist stands for our measure of bilateral distances; Adj is a binary variable equal to 1 if two countries in a pair border each other.

  5. The data collection underpinning the current analysis took more than two years to complete and proved particularly expensive. For the sake of brevity, interested readers are referred to Czaika and Parsons (2016) for a more complete overview of the data collection.

  6. This, of course, means that for family members who enter countries’ labor markets through family reunification channels, at which point their occupations are not recorded, our estimates will represent lower bounds.

  7. The majority of immigrants arriving in Israel during the period (74 %) comprised individuals from the countries of the former Soviet Union, which is recorded as a single entity in the data set. This no doubt reduces any discrepancies between the two series.

  8. The single exception to this is our inclusion of H1-B visa data for the United States.

  9. Therefore, our estimates for countries adopting immigration policies that admit greater numbers of high-skilled migrants through on-shore channels will represent lower bounds.

  10. Although this number is somewhat artificially inflated because of the inclusion of H1-B visa data for the United States, which are based on I-94 admissions data (Czaika and Parsons 2016), the results remain robust to their inclusion and exclusion.

  11. For a fuller discussion as to how countries conceptualize the time horizon of their migration policies, interested readers are directed to Parsons et al. (2014).

  12. See Czaika and Parsons (2016) for a more detailed description on the evolution and diffusion of these policy instruments across Western immigration destinations since 2000.

  13. The accuracy of the underlying shortage analysis in identifying and assessing labor market needs has often been criticized (e.g., Sumption 2013). Therefore, the effect of a shortage list on the overall number of high-skilled immigrants is rather ambiguous, even more so when shortage lists also include occupations that require lower skill levels.

  14. Given the heterogeneity of high-skilled migration policies across countries, methodologically we adopt a set of statements against which a 0 or 1 can be assigned to ensure consistency when coding our policy variables. Our data are always coded for the most attractive and most relevant HSM policies (in terms of volumes). Thus, while glossing over some detail, we primarily aim to capture the existence of some major policy instruments of skill-selective immigration systems. For example, for a labor market test, the guiding statement is simply, “Is there a mechanism in place to attempt to ensure the position cannot be filled by domestic workers?” The remaining statements can be found in Table 5 in the appendix. Nevertheless, because destination countries typically implement numerous policies that often relate to more than one class of migrant (Czaika and de Haas 2013), we adhere to a series of coding assumptions in order to ensure that the data are comparable both across countries and over time. These assumptions can also be found in Table 5 in the appendix. The aim is thus to imagine how a migrant with full knowledge of policies views the incentives presented within policies at the point of deciding to move. In many instances, such incentives all or predominantly fall under a single visa, which offer an array of specific provisions for high-skilled migrants. In the United States for example, we select the conjunction of H1B and EB visas because few applications for EB visas are made by new arrivals. With a processing and wait time of several years for the EB2/3 categories (and only a very narrow band of applicability within EB1), such policies are unlikely to allow migrants to respond to immediate opportunities. Therefore, the EB visas alone cannot be used to characterize immediately available incentives. They might, however, factor into long-term plans, making them attractive in terms of providing possibilities for permanent residence, spousal work rights, greater scope for family reunification, and so forth. In other words, since visa switching is clearly a strategy used by skilled migrants, looking at the provisions of these two visas in conjunction makes most sense in terms of capturing the incentives available to most skilled migrants.

  15. OECD Data Unemployment: https://data.oecd.org/unemp/unemployment-rate.htm

  16. OECD Data Earnings and Wages: https://data.oecd.org/earnwage/average-wages.htm

  17. U.S. Census Bureau, International Database: http://www.census.gov/population/international/data/idb/informationGateway.php

  18. OECD Database on Immigrants in OECD and non-OECD Countries: http://www.oecd.org/els/mig/dioc.htm

  19. OECD Tax Database: http://www.oecd.org/tax/tax-policy/tax-database.htm

  20. The results do not change when alternative annual salaries of $150,000, $200,000, and $250,000 are considered.

  21. These were calculated from data available from United Nations International Civil Service Commission, Post Adjustment Reports: http://icsc.un.org/secretariat/cold.asp?include=par

  22. OECD, PISA: http://www.oecd.org/pisa/

  23. UN Data: http://data.un.org/Default.aspx

  24. Trade union density corresponds to the ratio of wage and salary earners that are trade union members, divided by the total number of wage and salary earners. See Trade Union Density - OECD.Stat: https://stats.oecd.org/Index.aspx?DataSetCode=UN_DEN

  25. As a test of reverse causality, we ran additional logit panel regressions of respective policy changes on high-skilled migration flows. Based on these tests (available from the authors on request), we can conclude that we find little to no evidence for a reverse causality of high-skilled migration inflows on the likelihood of a policy change in the expected direction. These results, in combination with the respective GMM result, give confidence that our policy estimates are consistent and do not suffer from endogeneity bias.

  26. When calculating the shares of the highly skilled in the total (Models 1–3), regressions cannot be run if the total number highly skilled is equal to 0 because these observations are dropped from the estimation.

  27. However, the SUR regressions show no significant difference of the effect of common language on skill selectivity.

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Acknowledgments

The research presented in this article is part of the Drivers and Dynamics of High-Skilled Migration (DDHSM) project, which received generous funding from the Alfred P. Sloan Foundation (Grant 2011-10-22) and KNOMAD. The authors would like to thank Laurin Janes, Sebastien Rojon, Farhan Samanani, and Lena Wettach for nothing short of exemplary research assistance. We are grateful to attendees of the DEMIG Conference, Wolfson College, Oxford, September 2014; the Drivers and Dynamics of High-Skilled Migration Workshop, Oxford Martin School, October 2014, in particular Rey Koslowski, Çağlar Özden, and Martin Ruhs; the IRES Research Seminar, Université Catholique de Louvain, February 2015, especially Frédéric Docquier and David de la Croix; and finally to the participants of the 8th International Conference on Migration and Development, World Bank, June 2015. This article also reflects comments by three anonymous reviewers received through the KNOMAD peer review process and benefited from editing by Sherrie Brown as well as two anonymous referees during the Demography peer review process.

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Correspondence to Christopher R. Parsons.

Appendix

Appendix

Table 4 List of countries and economies
Table 5 High-skilled migration policy database
Fig. 2
figure 2figure 2

High-skilled migration policies across 10 Western destinations, 2000–2012. Light gray = policy does not exist; dark gray = policy implemented

Table 6 Descriptive statistics

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Czaika, M., Parsons, C.R. The Gravity of High-Skilled Migration Policies. Demography 54, 603–630 (2017). https://doi.org/10.1007/s13524-017-0559-1

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Keywords

  • High-skilled immigration
  • Human capital
  • Immigration policy