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Internal Labour Mobility in Central Europe and the Baltic Region: Evidence from Labour Force Surveys

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The Labour Market Impact of the EU Enlargement

Part of the book series: AIEL Series in Labour Economics ((AIEL))

Abstract

There are large regional disparities in labour market indicators in countries in Central Europe and the Baltic region. Such disparities appear to be persistent over time indicating, in part, a lack of flexibility in the prevailing adjustment mechanisms. Internal labour mobility is often seen as a potentially important instrument to reducing adjustment costs when other mechanisms fail. Drawing from 2004 LFS data and utilizing a common empirical framework and estimation strategy, this paper identifies patterns and statistical profiles of geographical mobility. It finds internal migration to be generally low and highly concentrated amongst better-educated, young and single workers. This suggests that migration is more likely to reinforce existing inequalities rather than act as an equalizing phenomenon. By way of contrast, commuting flows have grown over time and are more responsive to regional economic differentials. The findings suggest the need for appropriate and country-tailored policy measures designed to increase the responsiveness of labour flows to market conditions.

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Notes

  1. 1.

    See for example Huber (2007) and Ferragina and Pastore (2008) for comprehensive surveys of the theoretical and empirical literature on regional unemployment in transition countries.

  2. 2.

    This area includes the transition countries of the Baltic region and Central Europe, that is, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia and Slovenia.

  3. 3.

    Existing estimates suggest that the stock of migrants from the region in the older member states of the EU rose sharply following their EU accession, doubling between 2003 and 2005, according to the World Bank’s Quarterly Economic Report on the new EU Member States (September 2006). Estimates are, however, complicated by various technical difficulties, including the impact of the legalization of migrants already working in destination countries prior to EU accession.

  4. 4.

    Some of the figures reported by these studies, based on census or registration data, may differ from LFS-based averages as explained below. For more details see session IV of the paper.

  5. 5.

    For more details (see Paci et al. 2007).

  6. 6.

    The only country for which there is considerable difference between the rates of internal migration that emerge from the LFS data and those given by aggregate statistics and other studies (for example, Bornhorst and Commander 2006) is Hungary where the rates derived from the individual LFS data are considerably lower than those reported by other sources. Hungary is also noteworthy for its very high commuting rate (over 10 percent), which is nearly double that experienced in other EU8 countries.

  7. 7.

    See also OECD (2005), Fidrmuc (2004), Fidrmuc and Huber (2004), Burda and Profit (1996) and Sjoberg and Tammaru (1999).

  8. 8.

    See, for example,e Institute of Informatics and Statistics, 2002, Population Development in the Slovak Republic (Table 6.10).

  9. 9.

    It has been suggested, for example, that growth in commuting may reflect ‘suburbanization’ rather than labor-induced mobility. We thank the Lithuania Ministry of Economy for raising this point.

  10. 10.

    This is an interesting pattern that is difficult to compare with the existing literature for two reasons. First, none of the studies we surveyed analyse both commuting and migration flows in the same country using a common framework. Second, studies of internal migration typically use regional, rather than individual, data. In contrast, studies of commuting (of which there are only a couple), use individual-level data.

  11. 11.

    While this finding may seem to run counter to the other finding on the positive association between higher educational attainment and mobility, it should be kept in mind that these results are from multivariate regressions controlling for both educational attainment and sector of employment, among other things. Thus, between two workers who otherwise have similar demographic and socio-economic profiles except in sector of employment, construction workers appear to be relatively more mobile.

  12. 12.

    Slovenia and Latvia have been excluded from the analysis because the former does not have the necessary information on NUTS3 region of residence and the LFS for the latter does not have information on previous region of residence.

  13. 13.

    For more details (see Paci et al. 2007).

  14. 14.

    This finding for Hungary is apparently at odds with the available aggregate official data according to which Hungary has the highest rate of internal migration in the region. The sources of this discrepancy are unclear.

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Acknowledgement

This paper is a revised and abbreviated version of a regional report, Paci, P. and others, 2007, Internal Labor Mobility in Central Europe and the Baltic Region, Working Paper No. 105 (Washington: The World Bank). Supplementary analysis of data from the Czech Republic Labour Force Survey (LFS) was prepared by Daniel Munich, Martin Guzi and Franta Michal. This paper benefited, at various stages of the study, from valuable advice and comments provided by Daniela Gressani, Asad Alam, William Maloney, Zafiris Tzannatos, Arvo Kuddo, Stefano Scarpetta, Suman Mehra, Thomas Laursen, Anton Marcincin, Leszek Kasek, Paulina Bucon, Egbert Holthuis, Christoph Maier, Peter Huber, Antonio Spilimbergo and Iulia Traistaru-Siedschlag, as well as two anonymous referees.

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Correspondence to Pierella Paci .

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Paci, P., Tiongson, E.R., Walewski, M., LiwiƄski, J. (2010). Internal Labour Mobility in Central Europe and the Baltic Region: Evidence from Labour Force Surveys. In: Caroleo, F., Pastore, F. (eds) The Labour Market Impact of the EU Enlargement. AIEL Series in Labour Economics. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2164-2_8

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