Skip to main content
SpringerLink
Log in

Putting the Pieces of the Puzzle Together: Age and Sex-Specific Estimates of Migration amongst Countries in the EU/EFTA, 2002–2007

Assembler les pièces du puzzle: estimations de la migration entre les pays de l’UE et de l’AELE par âge et par sexe, 2002–2007

  • Published:
European Journal of Population / Revue européenne de Démographie Aims and scope Submit manuscript

Abstract

Because of inconsistencies in the reported migration flows and large amounts of missing data, our knowledge of international migration patterns in the Europe is limited. Methods for overcoming data obstacles and harmonising international migration data, however, are improving. In this article, we provide a methodology for integrating various pieces of incomplete information together, including a partial set of harmonised migration flows, to estimate a complete set of migration flows by origin, destination, age and sex for the 31 countries in the European Union and European Free Trade Association from 2002 to 2007. The results represent a synthetic data base that can be used to inform population projections, policy decisions and migration theory.

Résumé

Du fait d’incohérences dans l’enregistrement des flux migratoires et du grand nombre de données manquantes, notre connaissance des schémas de migrations internationales en Europe reste limitée. Cependant, les méthodes disponibles pour surmonter les obstacles liés aux données et pour harmoniser les données sur la migration internationale s’améliorent. Dans cet article, nous proposons une méthode pour combiner les différents éléments de ces informations incomplètes, incluant un ensemble partiel de données harmonisées sur les flux migratoires, afin d’estimer une série complète de flux migratoires par pays d’origine, pays de destination, âge et sexe pour les 31 pays de l’Union Européenne et de l’Association Européenne de Libre Echange de 2002 à 2007. Les résultats constituent une base de données synthétique pouvant servir de base pour les projections de population, les décisions politiques et les théories relatives à la migration.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. http://europa.eu/legislation_summaries/justice_freedom_security/free_movement_of_persons_asylum_immigration/l14508_en.htm.

  2. The 27 countries in the EU are Austria (AT), Belgium (BE), Bulgaria (BG), Cyprus (CY), Czech Republic (CZ), Denmark (DK), Estonia (EE), Finland (FI), France (FR), Germany (DE), Greece (GR), Hungary (HU), Irish Republic (IE), Italy (IT), Latvia (LV), Lithuania (LT), Luxembourg (LU), Malta (MT), Netherlands (NL), Poland (PL), Portugal (PT), Romania (RO), Slovakia (SK), Slovenia (SI), Spain (ES), Sweden (SE) and United Kingdom (UK).

  3. The four countries in the EFTA are Iceland (IS), Liechtenstein (LI), Norway (NO) and Switzerland (CH).

  4. The United Kingdom and Cyprus use a passenger survey to obtain information on migration flows. Ireland uses a Labour Force Survey.

  5. The 12 countries with missing data are Belgium, Bulgaria, Estonia, France, Greece, Hungary, Ireland, Lichtenstein, Malta, Portugal, Romania and Switzerland.

  6. In both cases, we used SPSS’s linear regression procedure.

  7. Here, we used SPSS’s log-linear procedure.

  8. Obtained from the Population Reference Bureau’s World Population Data Sheets (http://www.prb.org/).

  9. Obtained from the Global Migrant Origin Database (http://www.migrationdrc.org/research/typesofmigration/global_migrant_origin_database.html).

  10. Obtained from the United Nations Commodity Trade Statistics Database (http://comtrade.un.org/).

  11. We used Excel for this.

  12. We used SPSS’s log-linear procedure for this.

  13. The complete set of results is available at http://www-oud.nidi.knaw.nl/en/projects/230211/.

  14. See http://www.norface.org/migration12.html.

References

  • Abel, G. J. (2010). Estimation of international migration flow tables in Europe. Journal of the Royal Statistical Society Series A, 173(4), 797–825.

    Google Scholar 

  • Bilsborrow, R. E., Hugo, G., Oberai, A. S., & Zlotnik, H. (1998). International migration statistics: Guidelines for improving data collection systems. Geneva: International Labour Office.

    Google Scholar 

  • Champion, A. G. (1994). International migration and demographic change in the developed world. Urban Studies, 31(4/5), 653–677.

    Article  Google Scholar 

  • de Beer, J., Raymer, J., van der Erf, R., & van Wissen, L. (2010). Overcoming the problems of inconsistent international migration data: A new method applied to flows in Europe. European Journal of Population, 26, 459–481.

    Article  Google Scholar 

  • Deming, W. E., & Stephan, F. F. (1940). On a least squares adjustment of a sampled frequency table when the expected marginal totals are known. The Annals of Mathematical Statistics, 11(4), 424–444.

    Google Scholar 

  • Fienberg, S. E. (1970). An iterative procedure for estimation in contingency tables. The Annals of Mathematical Statistics, 41(3), 907–917.

    Article  Google Scholar 

  • Haining, R., Griffith, D. A., & Bennett, R. (1984). A statistical approach to the problem of missing spatial data using a first-order Markov model. The Professional Geographer, 36(3), 338–345.

    Article  Google Scholar 

  • Jennissen, R. (2004). Macro-economic determinants of international migration in Europe. Amsterdam: Dutch University Press.

    Google Scholar 

  • Johnston, R. J., & Pattie, C. J. (1993). Entropy-maximizing and the iterative proportional fitting procedure. The Professional Geographer, 45(3), 317–322.

    Article  Google Scholar 

  • Kelly, J. J. (1987). Improving the comparability of international migration statistics: Contributions by the conference of European statisticians from 1971 to date. International Migration Review (Special Issue: Measuring International Migration: Theory and Practice), 21(4), 1017–1037.

    Google Scholar 

  • Kraly, E. P., & Gnanasekaran, K. S. (1987). Efforts to improve international migration statistics: A historical perspective. International Migration Review (Special Issue: Measuring International Migration: Theory and Practice), 21(4), 967–995.

    Google Scholar 

  • Kupiszewska, D., & Nowok, B. (2008). Comparability of statistics on international migration flows in the European Union. In J. Raymer & F. Willekens (Eds.), International migration in Europe: Data, models and estimates (pp. 41–71). Chichester, England: Wiley.

    Google Scholar 

  • Nair, P. S. (1985). Estimation of period-specific gross migration flows from limited data: Bi-proportional adjustment approach. Demography, 22(1), 133–142.

    Article  Google Scholar 

  • Nowok, B., Kupiszewska, D., & Poulain, M. (2006). Statistics on international migration flows. In M. Poulain, N. Perrin, & A. Singleton (Eds.), THESIM: Towards harmonised European statistics on international migration (pp. 203–231). Louvain-la-Neuve: Presses universitaires de Louvain.

    Google Scholar 

  • Poulain, M., Perrin, N., & Singleton, A. (Eds.). (2006). THESIM: Towards harmonised European statistics on international migration. Louvain-la-Neuve: Presses universitaires de Louvain.

    Google Scholar 

  • Raymer, J. (2007). The estimation of international migration flows: A general technique focused on the origin-destination association structure. Environment and Planning A, 12, 371–388.

    Google Scholar 

  • Raymer, J. (2008). Obtaining an overall picture of population movement in the European Union. In J. Raymer & F. Willekens (Eds.), International migration in Europe: Data, models and estimates (pp. 209–234). Chichester, England: Wiley.

    Google Scholar 

  • Raymer, J., Bonaguidi, A., & Valentini, A. (2006). Describing and projecting the age and spatial structures of interregional migration in Italy. Population, Space and Place, 12, 371–388.

    Article  Google Scholar 

  • Raymer, J., & Rogers, A. (2007). Using age and spatial flow structures in the indirect estimation of migration streams. Demography, 44(2), 199–223.

    Article  Google Scholar 

  • Rees, P. H., & Duke-Williams, O. (1997). Methods for estimating missing data on migrants in the 1991 British census. International Journal of Population Geography, 3, 323–368.

    Article  Google Scholar 

  • Rogers, A., Little, J., & Raymer, J. (2010). The indirect estimation of migration: Methods for dealing with irregular, inadequate, and missing data. Dordrecht: Springer.

    Google Scholar 

  • Rogers, A., Willekens, F. J., Little, J. S., & Raymer, J. (2002). Describing migration spatial structure. Papers in Regional Science, 81, 29–48.

    Article  Google Scholar 

  • Rogers, A., Willekens, F. J., & Raymer, J. (2003). Imposing age and spatial structures on inadequate migration-flow datasets. The Professional Geographer, 55(1), 56–69.

    Google Scholar 

  • Smith, P. W. F., Raymer, J., & Giulietti, (2010). Combining available migration data in England to study economic activity flows over time. Journal of the Royal Statistical Society Series A, 173(4), 733–753.

    Google Scholar 

  • Sweeney, S. H., & Konty, K. J. (2002). Population forecasting with nonstationary multiregional growth matrices. Geographical Analysis, 34(4), 289–309.

    Google Scholar 

  • Thierry, X. (2008). Towards a harmonization of European statistics on international migration. Population and Sociétiés, 442(February), 1–4.

    Google Scholar 

  • United Nations. (1998). Recommendations on statistics of international migration. Statistical papers series M, No. 58, Rev. 1. Statistics Division, Department of Economic and Social Affairs, United Nations, New York. http://unstats.un.org/unsd/publication/SeriesM/SeriesM_58rev1E.pdf.

  • United Nations. (2002). Measuring international migration: Many questions, few answers. Population Division, Department of Economic and Social Affairs, United Nations, New York. www.un.org/esa/population/meetings/firstcoord2002/popdiv.pdf.

  • van Wissen, L., van der Gaag, N., Rees, P., & Stillwell, J. (2008). In search of a modelling strategy for projecting internal migration in European countries. In J. Poot, B. Waldorf, & L. van Wissen (Eds.), Migration and human capital (pp. 49–74). Cheltenham, England: Edward Elgar.

    Google Scholar 

  • Willekens, F. (1982). Multidimensional population analysis with incomplete data. In K. Land & A. Rogers (Eds.), Multidimensional mathematical demography (pp. 43–111). New York: Academic Press.

    Google Scholar 

  • Willekens, F. (1983). Log-linear modelling of spatial interaction. Papers of the Regional Science Association, 52, 187–205.

    Article  Google Scholar 

  • Willekens, F. (1994). Monitoring international migration flows in Europe: Towards a statistical data base combining data from different sources. European Journal of Population, 10, 1–42.

    Article  Google Scholar 

  • Willekens, F. (2008). Models of migration: Observations and judgement. In J. Raymer & F. Willekens (Eds.), International migration in Europe: Data, models and estimates (pp. 117–147). Chichester, England: Wiley.

    Google Scholar 

  • Wong, D. W. S. (1992). The reliability of using the iterative proportional fitting procedure. The Professional Geographer, 44(3), 340–348.

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported by a European Commission/Eurostat tender no 2006/S100 106607 (OJ 27/05/2006) for the supply of statistical services. The authors would like to thank the other team members of the MIMOSA (MIgration MOdelling for Statistical Analyses) project, and two reviewers of European Journal of Population for their comments and suggestions on our earlier efforts. The estimates described in this article are freely available on the Netherlands Interdisciplinary Demographic Institute’s website at http://www-oud.nidi.knaw.nl/en/projects/230211/.

Author information

Authors and Affiliations

  1. Southampton Statistical Sciences Research Institute, University of Southampton, Hampshire, UK

    James Raymer

  2. Netherlands Interdisciplinary Demographic Institute, The Hague, The Netherlands

    Joop de Beer & Rob van der Erf

Authors
  1. James Raymer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joop de Beer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rob van der Erf
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to James Raymer.

Appendices

Appendices

See Appendix in Table 8 and 9.

Table 8 Harmonised estimates of immigration by country (in thousands) and area of origin, 2002–2007
Full size table
Table 9 Harmonised estimates of emigration by country (in thousands) and area of destination, 2002–2007
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Raymer, J., de Beer, J. & van der Erf, R. Putting the Pieces of the Puzzle Together: Age and Sex-Specific Estimates of Migration amongst Countries in the EU/EFTA, 2002–2007. Eur J Population 27, 185–215 (2011). https://doi.org/10.1007/s10680-011-9230-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10680-011-9230-5

Keywords

Mots-clés

Search

Navigation