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Immigrants’ location choice in Belgium

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Abstract

This paper analyzes international migration streams to Belgian municipalities between 1994 and 2007. The Belgian population register constitutes a rich and unique database of yearly migrant inflows and stocks broken down by nationality, allowing us to empirically explain the location choice of newly arriving immigrants at the municipality level. Specifically, we aim at separating the network effect from other location-specific characteristics such as local labor or housing market conditions and the presence of public amenities. Our main contribution to the migration literature is to model labor and housing market variables as operating at different levels, assuming that immigrants first select a region roughly corresponding to a labor market and subsequently choose a municipality within this region that maximizes their utility. Among other things, this allows us to shed new light on the still ongoing discussion in the literature concerning the impact of labor market characteristics on the location of immigrants. We find that the spatial repartition of immigrants in Belgium is determined by both network effects and local characteristics. The relative importance of the determinants of location choice varies by nationality, as expected, but for all nationalities, local factors matter more than networks.

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Notes

  1. Illegal migrants and asylum seekers do not appear in these statistics. Most origin countries included in our empirical analysis are, however, unlikely to be sending out many asylum seekers and illegal migrants.

  2. An average municipality is 52 squared kilometers large, hosts about 17,500 inhabitants or 695 inhabitants per squared kilometer. Furthermore, the available surface varies between 1 and 214 square kilometers; the population size ranges from 963 to 466 203; and population densities take values between 20 and 23,785 inhabitants per square kilometer.

  3. Recently, Spain and Germany have also made available data on immigrant flows at the local level compiled from population registers (see Bertoli and Fernández-Huertas Moraga 2013; Bertoli et al. 2013, respectively). Yet, they do not seem to issue any data on immigrant stocks.

  4. Municipalities that never received an immigrant throughout the period of observation get disregarded, as the sample size would be too small to obtain consistent and therefore reliable parameter estimates for these countries.

  5. Note that the immigrant stock is reported each year on January 1.

  6. For more geographical details on the three Belgian regions, as well as the exact location of the main cities, see Figure B-1 in the Online Appendix.

  7. Note that we consider new arrivals only, i.e., immigrants coming from abroad choosing a location within the country.

  8. In the empirical section, locations are defined as 1 of the 588 Belgian municipalities, while areas correspond to 1 of the 43 government districts.

  9. This type of hierarchical structure has been used for analyzing the location of firms (e.g., Mayer and Mucchielli 1999; Crozet et al. 2004), but we do not know of any example in the international migration literature.

  10. More details on this specification are available in the Online Appendix C.

  11. As usual when an independent variable is discrete, only differences are identified. This is not a problem, as only these differences matter.

  12. More details on this specification may be found in the Online Appendix D.

  13. In order to avoid taking the log of zero, we add unity to the immigrant stock variables before calculating population shares.

  14. The time fluctuations in the immigrant stock are to some extent related to naturalization allowed by modifications in the Belgian nationality law. Our variable is, however, robust to these fluctuations as long as immigrants’ naturalization behavior is homogenous across municipalities (See Online Appendix D).

  15. One exception is Damm (2009) who investigates the influence of regional factors on the secondary location choices of Danish refugees who were randomly assigned to their initial location by the authorities between 1986 and 1998.

  16. The same holds for variables capturing environmental conditions: There is not much climatological variation across locations which renders its inclusion uninformative.

  17. The minimum distance to the national border is measured as the geodesic distance to the nearest national border from the centroid of each municipality, measured in thousands of kilometers.

  18. Descriptive statistics and correlation matrices are available in the Online Appendix Tables A-3 to A-5. Overall, pairwise correlations are fairly limited.

  19. We tested the robustness of our results for regional policy differences affecting immigrants (mainly regarding integration courses that were compulsory in Flanders from 2003 onwards but optional in Brussels and Wallonia). Estimation results—available upon request—confirm our results. We are very thankful to a referee for pointing this out.

  20. It might be argued that a large inflow of immigrants in a municipality could create pressure on the housing market, boosting housing prices and the number of transactions. Given that we consider bilateral immigrant flows, however, the effect of migration on housing prices and transactions is likely to be minor. In order to test for potential reverse causality, we re-estimated the model using the first, second or third lag of housing prices. Though not reported here for brevity, the results (available upon request) are robust to whether these variables are lagged or not.

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Acknowledgments

The authors are much indebted to comments on an earlier draft of this paper by two anonymous referees. We are grateful to participants to the “Economics of Global Interactions: New Perspectives on Trade, Factor Mobility and Development Conference” (Bari, 2011), “North American Regional Science Council Conference” (Miami, 2011), “Annual Conference of the European Regional Science Association” (Lausanne, 2011), “18th International Panel Data Conference” (Paris, 2012), “Norface Migration: Global Development, New Frontiers” (London, 2013) as well as research seminars at SHERPPA (Ghent University) and IRES (Université Catholique de Louvain). Responsibility for any remaining errors lies with the authors.

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Authors and Affiliations

  1. Faculté des Sciences Economiques et Sociales, Université de Lille - LEM CNRS UMR 9221, Bât SH2 - Cité scientifique, 59655, Villeneuve d’Ascq, France

    Hubert Jayet

  2. Faculty of Business and Economics, Ghent University - SHERPPA, Tweekerkenstraat 2, 9000, Ghent, Belgium

    Glenn Rayp & Ilse Ruyssen

  3. Faculté des Sciences Juridiques, Politiques et Sociales, Université de Lille - LEM CNRS UMR 9221, Place Déliot 1, 59000, Lille, France

    Nadiya Ukrayinchuk

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  1. Hubert Jayet
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  4. Nadiya Ukrayinchuk
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Correspondence to Ilse Ruyssen.

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Appendix

Appendix

See Tables 6, 7 and 8.

Table 6 Time-varying determinants of immigrants’ location choice—conditional logit
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Table 7 Determinants of time-invariant local effects—OLS
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Table 8 Determinants of time-invariant local effects using conditional logit estimates—SDM
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Jayet, H., Rayp, G., Ruyssen, I. et al. Immigrants’ location choice in Belgium. Ann Reg Sci 57, 63–89 (2016). https://doi.org/10.1007/s00168-016-0761-x

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