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Interregional migration within the European Union in the aftermath of the Eastern enlargements: a spatial approach

Interregionale Migration innerhalb der Europäischen Union in den Folgejahren der Osterweiterungen – Ein räumlicher Ansatz

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Abstract

This paper investigates interregional migration on a pan-EU level for the era immediately following the accession of new member states with relatively low income levels. It is shown that it is possible to account for spatial effects of interregional migration despite the lack of data on region-to-region migration flows. In the paper, a spatial model framework of interregional migration is developed that corresponds to a spatial lag of X model or, by inclusion of a spatial autocorrelation term, a spatial Durbin error model. The framework shows that within a system, a linear model of migration inevitably results in a function of net-migration which is based on a column-standardised weight matrix. A region’s migration level is assumed to be simultaneously affected by determinants at home as well as in other regions, where the latter’s influences decrease with distance. The specifications are subsequently applied to data on net-migration rates in 250 European NUTS2 regions over the period 2006–2008. The empirical results reveal a robust association between a region’s net-migration rate and its relative location in space. Moreover, migration is driven by income opportunities, labour market conditions, economic growth, human capital endowments as well as temporarily imposed restrictions on the freedom of movement of workers.

Zusammenfassung

Im vorliegenden Artikel werden interregionale Migrationsflüsse innerhalb der gesamten Europäischen Union für die unmittelbar auf den Beitritt von Staaten mit relativ niedrigen Einkommensniveaus folgende Zeit untersucht. Trotz der Nichtverfügbarkeit direkter Migrationsflussdaten können räumliche Effekte interregionaler Migration gemessen und interpretiert werden. Zu diesem Zweck wird ein räumliches Modell interregionaler Migration entwickelt, das als räumlich-ökonometrische Spezifikation dem Spatial-Lag-Of-X-Modell, bzw. – bei Inklusion eines räumlichen Autokorrelationsterms – dem Spatial-Durbin-Error-Modell entspricht. Es wird gezeigt, dass ein lineares Migrationsmodell zwangsläufig zu einer Nettomigrationsfunktion führt, die auf einer spaltenstandardisierten Gewichtsmatrix basiert. Die Spezifikationen werden in weiterer Folge für 250 europäische NUTS2-Regionen für den Beobachtungszeitraum 2006–2008 geschätzt, wobei die empirischen Ergebnisse eine robuste Beziehung zwischen der Nettomigrationsquote und der relativen räumlichen Lage einer Region anzeigen. Darüber hinaus zeigen die Ergebnisse signifikante Effekte der Einkommenshöhen, der Arbeitsmarktbedingungen, des Wirtschaftswachstums, der Humankapitalausstattungen sowie temporärer Einschränkungen der Arbeitnehmerfreizügigkeit.

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Notes

  1. NUTS is short for Nomenclature des Unités Territoriales Statistiques.

  2. Specifically, their proximity to relatively lower wage countries and the expectation of detrimental effects of immigration on domestic unemployment and wages were used as arguments to justify these temporary repeals of the EU’s four freedoms.

  3. Net-migration as dependent variable has also recently been used by Ederveen et al. (2007) and Rodríguez-Pose and Ketterer (2012) in EU15 migration studies.

  4. Krugman (1991, pp. 484) refers to mobile “workers with industry-specific skills” and distinguishes them from immobile peasants. In this sense, industry workers represent human capital as defined by “the total contribution of workers of different skill levels to production” (Romer 2005, pp. 134).

  5. Recently, the research investigating the role of migrant networks as drivers of international migration has received considerable attention (e.g. Pedersen et al. 2008; Mayda 2007; Nowotny and Pennerstorfer 2011). Such data are not available for the sample underlying the present study.

  6. Greenwood (1978) considers two separate equations for in-migration and out-migration which consist of the same variables but one, namely the percentage of urban population, which appears in his in-migration equation only. Considering our first assumption, we may ask why the percentage of urban population is expected to have no effect on out-migration, if it is expected to affect in-migration. In our perception, if one migrant chooses to migrate from region i to region j because j is more urban, then it follows that he or she chooses to migrate from region i to region j because i is less urban.

  7. The distances are originally calculated by travel times between the central cities of NUTS3 regions. Based on these results, Schürmann and Talaat (2000) estimate the distances between the corresponding NUTS2 regions.

  8. The classification is primarily based on institutional divisions, where the threshold levels for the number of NUTS2 regions’ inhabitants are 800,000 and 3,000,000.

  9. For some regions, data on net-migration is not available for all of the observation period’s three years, and the average has been taken of the respective available periods. This is the case for all Danish, Finnish, Greek, and Irish (Republic of Ireland) regions as well as Sardinia (Sardegna) for 2006, for all Belgian, English and Welsh regions for 2008, and for all four Scottish regions as well as for Northern Ireland for 2006 and 2008, which amounts to a total of 67 estimated values out of 750.

  10. As in most studies (see Decressin and Fatás 1995), the data include external migration. The analyses would possibly benefit from data that capture migration flows between each pair of regions in the sample. As of today, however, such data are available only for particular countries, and where it exists, the data are limited to intra-country flows.

  11. GRP at PPP is standardised so that for the EU as a whole, GRP at current market prices and GRP at PPP have identical values.

  12. For details see http://www.euractiv.com/enlargement/eu-25-member-states-grapple-free-labour-market/article-117775; accessed 12-May-2015.

  13. The estimation results with the alternative, highly correlated explanatory variables suggested above are available upon request.

  14. This roughly coincides with the approach taken in Rodríguez-Pose and Ketterer (2012).

  15. With these parameters, the most centrally located region is Cologne (Köln) with a row sum of 1.982, the most peripheral region is Sardinia (Sardegna) with 0.099, the median regions are Central Hungary (Közép-Magyarország) and Central Transdanubia (Közép-Dunántúl) with 0.995 each.

  16. Note that with these changes in parameters, respective row sums and centrality/peripherality rankings change, too. With r = 0.25, Cologne (Köln) remains the most centrally located region with a row sum of 1.994, but the most peripheral region is now Northern Finland (Pohjois-Suomi) with 0.087. With r = 0, Brussels (Région de Bruxelles-Capitale/Brussels Hoofdstedelijk Gewest) becomes the most centrally located region with is with a row sum of 1.932, and Sardinia (Sardegna) is again the most peripheral region with a row sum of 0.091.

  17. A natural extension of this specification would be to include all variables in their weighted form. However, the interdependence of employment, young population, human capital and population density as well as the weak variation of the price level among the regions within a country lead to estimation problems, which is why the respective results are not listed here.

  18. With k = 125 and r = 0.5, the least central of Austrian and German regions is Burgenland with a row sum of 1.265.

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Acknowledgements

The data for car travel time distances has been provided by courtesy of the Institute of Spatial Planning, TU Dortmund. We also wish to thank Roger Bivand, James LeSage and two anonymous referees for their helpful comments.

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Authors and Affiliations

  1. Vienna University of Economics and Business, Welthandelsplatz 1/D4.3.68, 1020, Vienna, Austria

    Sascha Sardadvar Ph.D

  2. Austrian Institute of Economic Research, Arsenal, Objekt 20, 1030, Vienna, Austria

    Silvia Rocha-Akis Ph.D

Authors
  1. Sascha Sardadvar Ph.D
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  2. Silvia Rocha-Akis Ph.D
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Correspondence to Sascha Sardadvar Ph.D.

Appendices

Appendix A: Regions of the observation area

The study covers the European territory of the EU on the NUTS2 level. Due to lack of data, the classification used in this study deviates from the official classification as of December 2011 in the following cases: Cyprus, Estonia, Latvia, Lithuania, Luxemburg, and Malta are not included; the NUTS2 regions Brandenburg–Nordost and Brandenburg–Südwest as well as the NUTS2 regions of Denmark and Slovenia have been merged to one region, respectively. By focussing on Europe, the study excludes the French regions Guadeloupe, Martinique, Guyana and Réunion, the Portuguese regions Região Autónoma dos Açores and Região Autónoma da Madeira, and the Spanish regions Ciudad Autónoma de Ceuta, Ciudad Autónoma de Melilla and Canarias. The following list contains the official names of all included regions sorted alphabetically by the corresponding nation states:

  • Austria (9 regions): Burgenland; Niederösterreich; Wien; Kärnten; Steiermark; Oberösterreich; Salzburg; Tirol; Vorarlberg

  • Belgium (11 regions): Région de Bruxelles-Capitale/Brussels Hoofdstedelijk Gewest; Prov. Antwerpen; Prov. Limburg (BE); Prov. Oost-Vlaanderen; Prov. Vlaams-Brabant; Prov. West-Vlaanderen; Prov. Brabant Wallon; Prov. Hainaut; Prov. Liège; Prov. Luxembourg (BE); Prov. Namur

  • Bulgaria (6 regions): Severozapaden; Severen tsentralen; Severoiztochen; Yugoiztochen; Yugozapaden; Yuzhen tsentralen

  • Czech Republic (8 regions): Praha; Střední Čechy; Jihozápad; Severozápad; Severovýchod; Jihovýchod; Střední Morava; Moravskoslezsko

  • Denmark (1 region): Danmark

  • Finland (5 regions): Itä-Suomi; Etelä-Suomi; Länsi-Suomi; Pohjois-Suomi; Åland

  • France (22 regions): 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

  • Germany (38 regions): Stuttgart; Karlsruhe; Freiburg; Tübingen; Oberbayern; Niederbayern; Oberpfalz; Oberfranken; Mittelfranken; Unterfranken; Schwaben; Berlin; Brandenburg—Nordost & Brandenburg—Südwest; Bremen; Hamburg; Darmstadt; Gießen; Kassel; Mecklenburg-Vorpommern; Braunschweig; Hannover; Lüneburg; Weser-Ems; Düsseldorf; Köln; Münster; Detmold; Arnsberg; Koblenz; Trier; Rheinhessen-Pfalz; Saarland; Chemnitz; Dresden; Leipzig; Sachsen-Anhalt; Schleswig-Holstein; Thüringen

  • Greece (13 regions): Anatoliki Makedonia, Thraki; Kentriki Makedonia; Dytiki Makedonia; Thessalia; Ipeiros; Ionia Nisia; Dytiki Ellada; Sterea Ellada; Peloponnisos; Attiki; Voreio Aigaio; Notio Aigaio; Kriti

  • Hungary (7 regions): Közép-Magyarország; Közép-Dunántúl; Nyugat-Dunántúl; Dél-Dunántúl; Észak-Magyarország; Észak-Alföld; Dél-Alföld

  • Ireland (2 regions): Border, Midland and Western; Southern and Eastern

  • Italy (21 regions): Piemonte; Valle d’Aosta/Vallée d’Aoste; Liguria; Lombardia; Provincia Autonoma Bolzano/Bozen; Provincia Autonoma Trento; Veneto; Friuli-Venezia Giulia; Emilia-Romagna; Toscana; Umbria; Marche; Lazio; Abruzzo; Molise; Campania; Puglia; Basilicata; Calabria; Sicilia; Sardegna

  • Netherlands (12 regions): Groningen; Friesland; Drenthe; Overijssel; Gelderland; Flevoland; Utrecht; Noord-Holland; Zuid-Holland; Zeeland; Noord-Brabant; Limburg (NL)

  • Poland (16 regions): Łódzkie; Mazowieckie; Małopolskie; Śląskie; Lubelskie; Podkarpackie; Świętokrzyskie; Podlaskie; Wielkopolskie; Zachodniopomorskie; Lubuskie; Dolnośląskie; Opolskie; Kujawsko-Pomorskie; Warmińsko-Mazurskie; Pomorskie

  • Portugal (5 regions): Norte; Algarve; Centro (PT); Lisboa; Alentejo

  • Romania (8 regions): Nord-Vest; Centru; Nord-Est; Sud-Est; Sud—Muntenia; Bucuresti—Ilfov; Sud-Vest Oltenia; Vest

  • Slovakia (4 regions): Bratislavský kraj; Západné Slovensko; Stredné Slovensko; Východné Slovensko

  • Slovenia (1 region): Slovenija

  • Spain (16 regions): Galicia; Principado de Asturias; Cantabria; País Vasco; Comunidad Foral de Navarra; La Rioja; Aragón; Comunidad de Madrid; Castilla y León; Castilla-La Mancha; Extremadura; Cataluña; Comunidad Valenciana; Illes Balears; Andalucía; Región de Murcia

  • Sweden (8 regions): Stockholm; Östra Mellansverige; Sydsverige; Norra Mellansverige; Mellersta Norrland; Övre Norrland; Småland med öarna; Västsverige

  • United Kingdom (37 regions): Tees Valley and Durham; Northumberland and Tyne and Wear; Cumbria; Cheshire; Greater Manchester; Lancashire; Merseyside; East Riding and North Lincolnshire; North Yorkshire; South Yorkshire; West Yorkshire; Derbyshire and Nottinghamshire; Leicestershire, Rutland and Northamptonshire; Lincolnshire; Herefordshire, Worcestershire and Warwickshire; Shropshire and Staffordshire; West Midlands; East Anglia; Bedfordshire and Hertfordshire; Essex; Inner London; Outer London; Berkshire, Buckinghamshire and Oxfordshire; Surrey, East and West Sussex; Hampshire and Isle of Wight; Kent; Gloucestershire, Wiltshire and North Somerset; Dorset and Somerset; Cornwall and Isles of Scilly; Devon; West Wales and the Valleys; East Wales; North Eastern Scotland; Eastern Scotland; South Western Scotland; Highlands and Islands; Northern Ireland

Appendix B: Additional results and summary statistics

Table 4 Estimations with method 1 and r = 0.5 for different values of k
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Table 5 Descriptive statistics
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Table 6 Correlation coefficients
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Sardadvar, S., Rocha-Akis, S. Interregional migration within the European Union in the aftermath of the Eastern enlargements: a spatial approach. Rev Reg Res 36, 51–79 (2016). https://doi.org/10.1007/s10037-015-0100-1

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