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Financial liberalization and the selection of emigrants: a cross-national analysis

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Abstract

This paper explores the impact of financial liberalization on the migration of high skilled labor from 46 countries to the Organization for Economic Cooperation and Development, taken at 5-year intervals over the period 1985–2000. Using an exploratory factor analysis, we are able to distinguish between two dimensions of financial liberalization, namely the robustness of the markets and their freedom from direct government control. We find that a standard deviation improvement in the robustness of the source country financial sector magnifies the extent of skilled emigration by a factor of about 3.9–5.1 % points on the average. However, a corresponding increase in the freedom of the source country financial sector from government control has a statistically insignificant impact. Further, the impact of improved financial sector robustness on selection is more pronounced for countries with a better quality of institutions in terms of the perceived credibility of the regime in terms of its ability to protect property rights.

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Notes

  1. It should be mentioned that evidence on the investment impact of financial liberalization is ambigu- ous and most studies find that it stimulates growth primarily by increasing total factor productivity (Bekaert et al. 2011). At the same time, it bears repetition that the impact of financial integration on economic growth depends critically on the existing quality of institutions (Chinn and Ito 2006; Claessens and Perotti 2007). In fact, there has been a concern that financial liberalization may promote economic growth only in economies that have attained a certain level of institutional and financial development (Kose et al. 2009). However, while the literature is fairly unanimous in emphasizing the role of institutions in determining the ultimate impact of financial liberalization, the existence of threshold effects with respect to the existing level of financial development is by no means a consensus in the field.

  2. Research has also emphasized the importance of the bequest motive in migration. Since financial development makes it easier for the poor to educate their children and reduces labor market discrimination that disproportionately affects poor minority groups (Levine 2008), it reduces the need to migrate for the poor (Docquier and Rapoport 2003).

  3. Focusing on the six major OECD destinations is less restrictive than it may appear to be: The six countries considered accounted for 77 % of the OECD skilled immigration stock in the year 2000 (Beine et al. 2011a, b). This is significant considering that 90 percent of all high skilled international migrants were found to be living in the OECD in that year (Docquier et al. 2007). Further, the United States, Germany, France, Canada, and the United Kingdom were, in descending order, the five largest remittance-sending countries in 2005; together accounting for approximately half of the global remittance flow (Ratha and Shaw 2007). Australia was the ninth largest, being further superseded by Saudi Arabia, Spain, and Hong Kong in descending order. For other studies based on the Defoort (2008) dataset that gives us our dependent variable, see Beine et al. (2011a, b) and Bang and Mitra (2013).

  4. The original dataset accounts for migration from 147 source countries at 5-year intervals over the period 1975–2000 and may be accessed from http://perso.uclouvain.be/frederic.docquier/oxlight.htm. The unavailability of financial and institutional variables restricts our sample to 52, 60, 53, and 59 countries for the years 1985, 1990, 1995, and 2000, respectively. Leaving out countries that emerged as autonomous political entities over the sample period and others with intermittent availability of data on the control variables gives us our present balanced sample comprising 46 countries in each of the four periods. It should, however, be mentioned that all of our results are confirmed with an unbalanced sample of 66 countries that yields 229 observations for the OLS and 220 observations for the 2SLS model.

  5. On one hand, an increase in GDP in the source country reduces international income differentials and hence the incentive to migrate. On the other hand, it increases the ability to incur the costs of migration and hence, increases the incentive to migrate. Together, the two effects induce a non-monotonic response of skilled migration to GDP per capita that typically takes the form of an inverted U-shaped relationship. See Vogler and Rotte (2000) for more on the issue.

  6. See Marshall et al. (2009) for a description of the Polity IV variables and the underlying methodology. The document can be accessed at http://www.systemicpeace.org/inscr/p4manualv2009.pdf. Corresponding information for the ICRG variables can be found at the homepage of the PRS Group: http://www.prsgroup.com/ICRG_Methodology.aspx.

  7. See Beck et al. (2001) for a description of the variables and the underlying methodology.

  8. The risk of expropriation is perhaps the most commonly used measure of property rights used in the literature (Acemoglu et al. 2005; Knack and Keefer 1995; Rodrik et al. 2004).

  9. We could alternatively include credit ceilings rather than the combined credit controls variable, but this leads to a considerable reduction of our sample. Nevertheless, both our exploratory factor analysis and the final regression exercise yield identical results when we replace (20) with credit ceiling \(s\). These results are available on request.

  10. The Basel I Accord of 1988 was a set of recommendations on banking sector regulation published by a committee of central bank governors from the Group of Ten nations, called the Basel Committee on Banking Supervision. It was replaced by the more comprehensive Basel II in 2004 and the recent financial crisis has resulted in further modifications in the form of Basel III, though this remains a work in progress. See http://www.bis.org/publ/bcbsca.htm for the original Basel document and subsequent updates.

  11. Given the inherent problem of heteroskedasticity in cross-country growth regressions (Durlauf et al. 2005), we compute robust standard errors of our estimated coefficients, making the Hansen J-test the appropriate test for over-identification.

  12. Other contributions (Acemoglu et al. 2005; Hall et al. 2010; Rodrik et al. 2004) focus on the subset of institutions that preserve the security of property rights.

  13. Highlighting this problem, Langbein and Knack (2010) undertake a confirmatory factor analysis of the World Governance Indicators (WGI) to determine if these measures are causally related to single latent variable good governance and fail to confirm this hypothesis.

  14. Beck and Levine (2004) consider the impact of stock market development; Bekaert et al. (2005) the impact of equity market liberalization; while Bekaert et al. (2011) and Chinn and Ito (2006) consider both capital and equity market liberalization. See Levine (2005) for a survey of the finance and growth literature.

  15. See Abiad and Mody (2005) for a dissenting view on the role of institutions as determinants of financial liberalization.

  16. For studies using EFA, see Bang and Mitra (2011) and Langbein and Knack (2010) in the context of institutions and Jong-A-Pin (2009) in the context of political instability.

  17. This is why the latent financial factors described subsequently have a different range than the observed financial indices which range between 0 and 3.

  18. As demonstrated subsequently, this may not be an appropriate assumption in our context.

  19. Recall that Credit Controls combines the directed credit variable with the absence of credit ceilings. Since the variation induced by the former is already accounted for by including it separately from the combined variable, the weight of the combined variable is essentially capturing the impact of credit ceilings. Including these variables in tandem does not seriously compromise the stability of our EFA specification, even though they are very highly correlated for some countries. As an example, consider the most extreme case, in which two variables are perfectly correlated. In this case, the solution to the EFA that includes both of these will simply report a duplicate set of factor loadings corresponding to the correlated variables. In our case, the credit controls variable captures the additional impact on financial freedom that derives from the absence of credit ceilings.

  20. A Heywood case occurs if the variance in an observed variable accounted for by the common factors or the communality of that variable equals or exceeds 1.

  21. We are grateful to an anonymous referee for this suggestion.

  22. It should be mentioned, however, that we get closely comparable results even when we exclude these dummies.

  23. This is not surprising since the financial principle factors obtained from the EFA are highly orthogonal.

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Acknowledgments

We are grateful to two anonymous referees and the editor of this journal for their insightful suggestions that helped to improve the paper considerably. We would also like to thank Robert Prasch for his thoughtful comments and advice; and Sarah B. King, Christian A. Johnson summer intern at Middlebury College, for her able research assistance. Earlier version of this paper was circulated as an IZA (Institute for the Study of Labor, Bonn, Germany) discussion paper # 5953, and as an Employment Policy Research Network research paper. The usual caveat applies.

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Authors and Affiliations

  1. Bard College, Annandale-on-Hudson, NY, USA

    Aniruddha Mitra

  2. St. Ambrose University, Davenport, IA, USA

    James T. Bang

  3. IZA & EPRN, Middlebury College, Middlebury, VT, USA

    Phanindra V. Wunnava

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  1. Aniruddha Mitra
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  2. James T. Bang
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  3. Phanindra V. Wunnava
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Correspondence to Phanindra V. Wunnava.

Appendixis

Appendixis

See the Tables 6, 7, 8 and 9.

Table 6 List of countries
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Table 7 Rotated factor loadings (principle factor method; orthomax rotation)
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Table 8 Rotated factor loadings (iterated principle factor method; oblique promax rotation)
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Table 9 Rotated factor loadings (maximum likelihood method; oblique promax rotation)
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Mitra, A., Bang, J.T. & Wunnava, P.V. Financial liberalization and the selection of emigrants: a cross-national analysis. Empir Econ 47, 199–226 (2014). https://doi.org/10.1007/s00181-013-0735-0

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