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Determinants and dynamics of migration to OECD countries in a three-dimensional panel framework

Abstract

This paper investigates the determinants of bilateral immigrant flows to 19 OECD countries between 1998 and 2007 from both advanced and developing origin countries. We pay particular attention to dynamics by including both the lagged migrant flow and the migrant stock to capture partial adjustment and network effects. To correct for the dynamic panel data bias of the fixed effects estimator we use a bootstrap algorithm. Our results indicate that immigrants are primarily attracted by better income opportunities and higher growth rates abroad. Also short-run increases in the host country’s employment rate positively affect migration from both advanced and developing countries. High public services, on the other hand, discourage migration from advanced countries but exert a pull on migration from developing sources, in line with the welfare state hypothesis. Finally, we find evidence for both partial adjustment and the presence of strong network effects. This confirms that both should be considered as crucial elements of the migration model and that a correction for their joint inclusion is required.

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Notes

  1. The inclusion of public services might also be linked to the cost of migration, \(z_{it}\). In that sense, immigrants are expected to prefer countries with a generous system of public services since the presence of a safety net lowers the psychological cost of migration.

  2. As shown by Hatton (1995) this is consistent with rational expectations if \(d_{it}\) follows an AR(1) process.

  3. First, we can write \(\ln \left\{ MST_{dot}/\left[\left(1 -\delta _{do}\right)MST_{dot-1}\right]\right\} \) as \(\ln \left\{ 1+\exp \left[\ln M_{dot-1}- \left(1-\delta _{do}\right)\text{ ln}\right.\right.\) \(\left.\left.MST_{dot-1}\right]\right\} \). A first-order Taylor expansion of the latter around the mean values of \(M_{dot-1}\) and \(\left(1-\delta _{do}\right)MST_{dot-1}\) gives \(\ln \left\{ MST_{dot}/\left[\left(1-\delta _{do}\right)MST_{dot-1}\right]\right\} \approx \varOmega \left[\ln M_{dot-1} - \left(1-\delta _{do}\right)\ln \right. \left. MST_{dot-1}\right] + c\) where \(c\) is an arbitrary constant which we ignore for notational convenience. Now add \(\ln \left[\left(1-\delta _{do}\right) MST_{dot-1} \right]\) to both sides of the equation to approximate \(\ln MST_{dot} = \ln \left[\left(1-\delta _{do}\right) MST_{dot-1} + M_{dot-1} \right]\) which gives (9) in the text.

  4. A popular motivation for not including both lagged migrant flow and migrant stock is that the latter is, as presented in Eq. (8), the sum of all past immigrant flows minus deaths and return migrants. Hence, the migrant stock is itself a function of all those factors which influenced the earlier immigrant flows. Therefore, it will be correlated with all the explanatory variables. However, multicollinearity is no reason to omit the migrant stock variable as this may result in a specification bias as well as in a loss of information regarding the network effect.

  5. The long-run impact is defined from imposing a no change condition, i.e. the criterion that the squared difference between two subsequent values of the dynamic response should be less than or equal to \(0.0001^2\).

  6. Ln \(MST_{dot}\) is predetermined as it is defined as the migrant stock at the beginning of the period.

  7. The matlab code for the BCFE estimator is available upon request.

  8. The estimation results for this model are available upon request.

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Acknowledgments

We are grateful to Bruno Merlevede for a valuable discussion of a previous version of the paper, to an anonymous referee and the associated editor for constructive comments and suggestions and to participants to the ‘Seventh GEP Postgraduate Conference’ (Nottingham, April 2008) and the ‘European Trade Study Group Conference’ (Warsaw, September 2008) as well as internal seminars at Ghent University, Belgium. Responsibility for any remaining errors lies with the authors. We acknowledge financial support from the Interuniversity Attraction Poles Program - Belgian Science Policy, contract no. P5/21.

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Correspondence to Ilse Ruyssen.

Appendix

Appendix

See Tables 5, 6, 7, 8 and 9

Table 5 Total yearly immigrant flows in our sample of destination countries (thousands)
Table 6 Yearly immigrant flows from our sample of advanced origin countries (thousands)
Table 7 Yearly immigrant flows from our sample of developing origin countries (thousands)
Table 8 Descriptive statistics
Table 9 GMM estimation results

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Ruyssen, I., Everaert, G. & Rayp, G. Determinants and dynamics of migration to OECD countries in a three-dimensional panel framework. Empir Econ 46, 175–197 (2014). https://doi.org/10.1007/s00181-012-0674-1

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Keywords

  • International migration
  • Network effects
  • Dynamic panel data model
  • Bias correction

JEL Classification

  • F22
  • J61
  • C33