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Labour mobility and regional disparities: the role of female labour participation

Abstract

Unemployment rates, as well as income per capita, differ vastly across the regions of Europe. Labour mobility can play a role in resolving regional disparities. This paper focuses on the questions of why labour mobility is low in the EU and how it is possible that it remains low. We explore whether changes in male and female labour participation act as an important alternative adjustment mechanism. We answer this question in the affirmative. We argue that female labour participation is very important in adjusting to regional disparities.

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Notes

  1. 1.

    This is disputed, however, by Fatás (1998) and Frankel and Rose (1997).

  2. 2.

    It is not a priori clear that the disappearance of the exchange rate instrument is an unambiguous loss. Many countries have abstained from using the exchange rate as an instrument for shock adjustment.

  3. 3.

    Gross flows of workers that are not identical with respect to skill might be resolving disequilibria on specific labour markets. Due to a lack of suitable data this issue could not be analysed.

  4. 4.

    This approach had a number of early forerunners in Smith (1776), Ravenstein (1889) and Hicks (1932).

  5. 5.

    The network approach indicates that the cost of moving may be substantially reduced for the relatives and friends of migrants, and this could increase the probability of migration. Our model, however, does not explicitly consider network hypothesis. Our model combines adjustment to the desired level of migration using other considerations such as lagged effects and discouraged worker effect in times of recession.

  6. 6.

    There is a structural break in the GDP data due to a revision in the European accounting system in 1995. We have constructed the GDP series by assuming that GDP growth is correctly measured.

  7. 7.

    That is: 2,200,000*3/1,000

  8. 8.

    In the econometric analysis we include both time dummies and fixed effects to account for this event. The main findings that we present in the next section stand upright when we exclude Germany.

  9. 9.

    We show the median of net migration/Δpopulation instead of the mean; the mean is not as informative, as the denominator tends towards zero for several observations.

  10. 10.

    This finding is robust to the method of computing the variation in expected income. The coefficient of variation also produces an upward trend, as does this statistic with the level of income (without taking logs).

  11. 11.

    GMM generates efficient estimates of the coefficients as well as consistent estimates of parameters. Standard errors are arbitrary heteroscedasticity and autocorrelation robust.

  12. 12.

    An alternative interpretation of these numbers is that a permanent adverse shock is absorbed for more than 90% by unemployment changes and less than 10% by net migration. In the US, these numbers are 30 and 65%, respectively, in the short run.

  13. 13.

    The Ramsey–Pesaran test indicates that the hypothesis of no neglected non-linearities is rejected for the equations in Table 4 when the test is conducted using the levels equation.

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Acknowledgement

Comments by Ruud de Mooij, three anonymous referees and Professor Dr. Klaus F. Zimmermann considerably improved this paper. Richard Crum, Jan Fidrmuc, George Gelauff, Joeri Gorter and Theo van de Klundert are acknowledged for comments on a previous version of this paper.

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Correspondence to Ashok Parikh.

Additional information

Sjef Ederveen and Ashok Parikh would like to dedicate this article to the memory of Richard Nahuis, who died so unexpectedly a few months ago.

At the time of writing this paper, Sjef Ederveen was affiliated with the CPB Netherlands Bureau for Economic Policy Analysis.

Responsible editor: Klaus F. Zimmermann

Appendices

Appendix 1: data

Regions in the panel: We use regional data at the NUTS 2 level from Eurostat. NUTS 2 subdivides the territory of the European Community into 211 regions. Two regions at the NUTS 2 level are identical to countries: Luxembourg and Denmark. We use data on population changes and economic variables, like GDP, and labour market data, as unemployment and participation rates.

From the available regions, we selected the regions which provided the necessary data to construct net migration rates. This provides us with an unbalanced panel of 191 regions with time series of up to 18 years (1983–2000).

From the unbalanced panel, we constructed a balanced panel with the time-series dimension unaltered and the number of regions reduced to 83. The following regions, Belgium (11), Denmark (1), Luxembourg (1), France (20), Germany (30), Greece (2) and Italy (18), are in the balanced panel:

Région Bruxelles-capitale/Brussels hoofdstad gewest, Antwerpen, Limburg, Oost-Vlaanderen, Vlaams Brabant, West-Vlaanderen, Hainaut, Liège, Luxembourg, Namur, Denmark, Stuttgart, Karlsruhe, Freiburg, Tübingen, Oberbayern, Niederbayern, Oberpfalz, Oberfranken, Mittelfranken, Unterfranken, Schwaben, Berlin, Brandenburg, Bremen, Hamburg, Darmstadt, Gießen, Kassel, Braunschweig, Hannover, Lüneburg, Weser-Ems, Düsseldorf, Köln, Münster, Detmold, Arnsberg, Koblenz, Trier, Rheinhessen-Pfalz, Saarland, Schleswig-Holstein, Thessalia, Kriti, Île de France, Champagne-Ardenne, Picardie, Haute-Normandie, Centre, Basse-Normandie, Bourgogne, Nord-Pas-de-Calais, Lorraine, Alsace, Franche-Comté, Pays de la Loire, Bretagne, Poitou-Charentes, Aquitaine, Midi-Pyrénées, Limousin, Rhône-Alpes, Auvergne, Languedoc-Roussillon, Provence-Alpes-Côte d’Azur, Corse, Piemonte, Valle d’Aosta, Lombardia, Trentino-Alto Adige, Veneto, Friuli-Venezia Giulia, Emilia-Romagna, Toscana, Umbria, Marche, Lazio, Abruzzo, Molise, Campania, Puglia, Basilicata, Calabria, Sicilia, Sardegna and Luxembourg.

Appendix 2: definitions of variables

Female activity rates (NACTFEM) represent the female labour force as a percentage of the female working-age population (15–64 years old). Male activity rates (NACTMALE) represent the male labour force as a percentage of the male working-age population (15–64 years old for post-1991 and 14 years old or older for the years 1983–1991). The labour force comprises persons in employment and unemployed persons. Both female and male activity rates include workers born in non-EU countries and both natives, and foreigners are included in the labour force and the working-age population.

DNACTFEM and DNACTMALE are changes in female and male participation rates.

Net migration is the difference between immigration into and emigration from the area. Net migration is negative when the number of emigrants exceeds the number of immigrants. Because most countries/regions do not have accurate figures on immigration and emigration or have no figures at all, net migration is generally estimated on the difference between population change and natural increase between two dates. It is called corrected net migration in Eurostat.

Population change is the difference between the size of the population at the end and the beginning of a period. It is equal to the algebraic sum of natural increase and net migration. Natural increase is the difference between the number of live births and the number of deaths during the year. The natural increase is negative when the number of deaths exceeds the number of births.

Unemployment rates (UNEMP) represent unemployed persons as a percentage of the labour force. Unemployed persons comprise persons aged from 15 to 74 years old who were without work during the reference week, are currently available for work and are actively seeking work.

LUNEMP is log of unemployment rates.

LGDP is the log of gross domestic product per inhabitant at purchasing power parity at NUTS 2 level. Due to a revision in the ESA, new GDP data are only available starting from 1995. The GDP data before that year (following ESA79) are not completely comparable. Changes in the system of GDP data are handled by using a dummy from 1995. This is defined as DUMGDP, a dummy from 1995 for GDP change.

NET MIGRATION (MN) includes both natives and foreigners. Immigrants include the ones living in EU region and non-EU countries and migrating to one of the 83 regions at NUTS 2 level. The net change in population due to migration to offset region-specific shocks is relevant. The source of migrants is (economically) not relevant. The information on gross flows between regions would facilitate the analysis of different push and pull factors within an economy. Such information is, however, not available between regions of the EU economies.

L. NMIGRATE is one period lagged migration rate.

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Ederveen, S., Nahuis, R. & Parikh, A. Labour mobility and regional disparities: the role of female labour participation. J Popul Econ 20, 895–913 (2007). https://doi.org/10.1007/s00148-006-0095-6

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Keywords

  • Labour mobility
  • European Union
  • Panel data methods

JEL

  • F22
  • J61
  • C33