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Migration Systems in Europe: Evidence From Harmonized Flow Data

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Demography

Abstract

Empirical tests of migration systems theory require consistent and complete data on international migration flows. Publicly available data, however, represent an inconsistent and incomplete set of measurements obtained from a variety of national data collection systems. We overcome these obstacles by standardizing the available migration reports of sending and receiving countries in the European Union and Norway each year from 2003–2007 and by estimating the remaining missing flows. The resulting harmonized estimates are then used to test migration systems theory. First, locating thresholds in the size of flows over time, we identify three migration systems within the European Union and Norway. Second, examining the key determinants of flows with respect to the predictions of migration systems theory, our results highlight the importance of shared experiences of nation-state formation, geography, and accession status in the European Union. Our findings lend support to migration systems theory and demonstrate that knowledge of migration systems may improve the accuracy of migration forecasts toward managing the impacts of migration as a source of social change in Europe.

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Notes

  1. The methodology and estimates are available online (www.nidi.knaw.nl/en/projects/230211/).

  2. We use the term harmonize to mean both standardization of available migration data and estimation of the remaining missing flows. We distinguish these and the methods associated with each throughout this article.

  3. 420 = 15 sending countries × 14 receiving countries × 2 reports per flow (i.e., sender and receiver).

  4. Retrieved online (http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=migr_imm5prv&lang=en and http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=migr_emi3nxt&lang=en).

  5. For emigration flows from Country A to C, D, E, and F, the standardized figures are 20 × 1.33 = 27, 175 × 1.33 = 233, 35 × 1.33 = 47, and 40 × 1.33 = 53, respectively. The standardized figures for Country B to C, D, E, and F are 25 × 1.25 = 31, 40 × 1.25 = 50, 65 × 1.25 = 81, and 100 × 1.25 = 125, respectively.

  6. The standardized flows are 55 × 1.05 = 58, 65 × 1.05 = 68, and 100 × 1.05 = 105, respectively.

  7. The standardized flows are 90 × 1.03 = 93, 75 × 1.03 = 77, and 45 × 1.03 = 46, respectively.

  8. Nordic countries: Denmark, Finland, Norway, and Sweden. Non-Nordic countries with reliable data: Austria, Germany, Netherlands, and Spain. Non-Nordic countries with semi-reliable data: Cyprus, Czech Republic, Italy, Latvia, Lithuania, Luxembourg, Poland, Slovakia, Slovenia, and the United Kingdom. Non-Nordic countries with unreliable data: Ireland, Portugal, and Romania (emigration only). Countries with missing data: Belgium, Bulgaria, Estonia, France, Greece, Hungary, and Malta.

  9. The x and y coordinates are (95, 178), (105, 196), and (143, 268) at Times 1, 2, and 3, respectively.

  10. In our analysis, 1 ≤ k ≤ 4 because pairs of sending and receiving countries with partially complete data have between one and four years of valid data from 2003 to 2007.

  11. \( {126 } = { 115}\, \times \,\,\left[ {{{{0.0{49}}} \left/ {{\left( {0.0{49 } + { }0.0{12}} \right)}} \right.}} \right] + { 173}\,\, \times \,\,\left[ {{{{0.0{12}}} \left/ {{\left( {0.0{49 } + { }0.0{12}} \right)}} \right.}} \right] \).

  12. In our analysis, the maximum value of k is 125.

  13. To save space, we show only the results of these calculations, which can be replicated by expanding the equation in Step 2. For example, the missing flow from Country F to E at Time 1 is estimated as follows: \( {148 } = { 118} \times \left[ {{{{0.0{56}}} \left/ {{\left( {0.0{56 } + { }0.0{14 } + { }0.00{5 } + { }0.00{4}} \right)}} \right.}} \right]{ } + { 13}0 \times \left[ {{{{0.0{14}}} \left/ {{\left( {0.0{56 } + { }0.0{14 } + { }0.00{5 } + { }0.00{4}} \right)}} \right.}} \right]{ } + { 45}0 \times \left[ {{{{0.00{5}}} \left/ {{\left( {0.0{56 } + { }0.0{14 } + { }0.00{5 } + { }0.00{4}} \right)}} \right.}} \right]{ } + { 263} \times \left[ {{{{0.00{4}}} \left/ {{\left( {0.0{56 } + { }0.0{14 } + { }0.00{5 } + { }0.00{4}} \right)}} \right.}} \right] \).

  14. Abel (2010) and Poulain (1993, 1999) developed harmonized migration estimates, but for fewer sending and receiving countries.

  15. N Y = 3,779 = 28 sending countries × 27 receiving countries × 5 years of data – 1 flow in cluster X.

  16. 3,760 = 28 sending countries × 27 receiving countries × 5 years of data – 20 zero flows.

  17. The Calinski Index for the three-cluster solution is 6,743.92; the Duda-Hart Index and its corresponding pseudo t-squared ratio are 0.387 and 443.80, respectively. Relative to a four-cluster (or higher-cluster) solution, with values of 4,634.51 on the Calinski Index and 0.335 and 3,252.41 for the Duda-Hart Index and pseudo t-squared ratio, respectively, the stopping rules employed suggest three optimal clusters (Milligan and Cooper 1985; Rabe-Hesketh and Everett 2006).

  18. \( R_{{MARG}}^2 = 1 - \frac{{\sum\limits_{{t = 1}}^T {\sum\limits_{{i = 1}}^n {\mathop{{\left( {\mathop{Y}\nolimits_{{it}} - \mathop{{\hat{Y}}}\nolimits_{{it}} } \right)}}\nolimits^2 } } }}{{\sum\limits_{{t = 1}}^T {\sum\limits_{{i = 1}}^n {\mathop{{\left( {\mathop{Y}\nolimits_{{it}} - \overline Y } \right)}}\nolimits^2 } } }} \), where \( \bar{Y} = \frac{1}{{nT}}\sum\limits_{{t = 1}}^T {\sum\limits_{{i = 1}}^n {\mathop{Y}\nolimits_{{it}} } } \).

  19. \( 0.{7 } = { 1}00 \times \left( {{{e}^{{0.00{7}}}}-{ 1}} \right);{ 1}0.{8 } = { 1}00 \times \left( {{{e}^{{0.{1}0{3}}}}-{ 1}} \right) \).

  20. \( -{5}.{8 } = { 1}00 \times \left( {{1}.{1}{{0}^{{ - 0.{622}}}}-{ 1}} \right) \).

  21. Recall that the MIMOSA project used covariate information, including shared language family, to estimate missing flows (Raymer and Abel 2008; Raymer et al. 2011).

  22. A summary of the IMEM is available online (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, 797–825.

    Article  Google Scholar 

  • Andrienko, Y., & Guriev, S. (2004). Determinants of interregional mobility in Russia. The Economics of Transition, 12, 1–27.

    Article  Google Scholar 

  • Bauer, T. K., & Zimmermann, K. F. (1999). Assessment of possible migration pressure and its labour market impact following EU enlargement to central and eastern Europe (IZA Research Report No. 3). Bonn, Germany: Institute for the Study of Labor.

    Google Scholar 

  • Bijak, J. (2006). Forecasting international migration: Selected theories, models and methods (CEFMR Working Paper 4/2006). Warsaw, Poland: Central European Forum for Migration Research.

    Google Scholar 

  • Bilsborrow, R. E., Hugo, G., Oberai, A. S., & Zlotnik, H. (1997). International migration statistics: Guidelines for improving data collection systems. Geneva, Switzerland: International Labour Office.

    Google Scholar 

  • Bongaarts, J. (2004). Population aging and the rising cost of public pensions. Population and Development Review, 30, 1–23.

    Article  Google Scholar 

  • Bonifazi, C., Okolski, M., Schoorl, J., & Simon, P. (2008). International migration in Europe: New trends and new methods of analysis. Amsterdam, The Netherlands: Amsterdam University Press.

    Book  Google Scholar 

  • Boyd, M. (1989). Family and personal networks in international migration: Recent developments and new agendas. International Migration Review, 23, 638–670.

    Article  Google Scholar 

  • Calavita, K. (2003). Italy: Immigration, economic flexibility and policy responses. In W. Cornelius, P. L. Martin, & J. F. Hollifield (Eds.), Controlling immigration: A global perspective (2nd ed.). Stanford, CA: Stanford University Press.

    Google Scholar 

  • Castles, S., & Miller, M. (2003). The age of migration: International population movements in the modern world (3rd ed.). New York: The Guilford Press.

    Google Scholar 

  • Cohen, J. E., Roig, M., Reuman, D. C., & GoSwilt, C. (2008). International migration beyond gravity: A statistical model for use in population projections. Proceedings of the National Academy of Sciences of the United States of America, 105, 15269–15274.

    Article  Google Scholar 

  • Cover, T. M., & Hart, P. E. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13, 21–27.

    Article  Google Scholar 

  • Cui, J. (2007). QIC program and model selection in GEE. The Stata Journal, 7, 209–220.

    Google Scholar 

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

    Article  Google Scholar 

  • DeWaard, J., & Raymer, J. (2012). The temporal dynamics of international migration in Europe: Recent trends. Demographic Research. doi:10.4054/DemRes.2012.26.21

  • Fassmann, H. (2009). European migration: Historical overview and statistical problems. In H. Fassmann, U. Reeger, & W. Sievers (Eds.), Statistics and reality: Concepts and measurements of migration in Europe (pp. 21–44). Amsterdam, The Netherlands: Amsterdam University Press.

    Chapter  Google Scholar 

  • Fawcett, J. T. (1989). Networks, linkages, and migration systems. International Migration Review, 23, 671–680.

    Article  Google Scholar 

  • Greenwood, M. J. (1997). Internal migration in developed countries. In M. R. Rozenzweig & O. Stark (Eds.), Handbook of population and family economics (Vol. 2, pp. 647–720). New York: Elsevier.

    Chapter  Google Scholar 

  • Greenwood, M. J., & McDowell, J. M. (1991). Differential economic opportunity, transferability of skills, and immigration to the United States and Canada. The Review of Economics and Statistics, 73, 612–623.

    Article  Google Scholar 

  • Hardin, J. W., & Hilbe, J. M. (2003). Generalized estimating equations. Boca Raton, FL: Chapman and Hall/CRC.

    Google Scholar 

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

    Google Scholar 

  • Kaczmarczyk, P., & Okólski, M. (2005). International migration in central and eastern Europe: Current and future trends (UN/POP/MIG/2005/12). United Nations Expert Group Meeting on International Migration and Development. New York: Population Division, Department of Economic and Social Affairs, United Nations.

    Google Scholar 

  • Karemera, D., Oguledo, V., & Davis, B. (2000). A gravity model analysis of international migration to North America. Applied Economics, 32, 1745–1755.

    Article  Google Scholar 

  • Kim, K., & Cohen, J. E. (2010). Determinants of international migration flows to and from industrialized countries: A panel data approach beyond gravity. International Migration Review, 44, 899–932.

    Article  Google Scholar 

  • Kritz, M. M., Lim, L. L., & Zlotnik, H. (1992). International migration systems: A global approach. Oxford, UK: Clarendon Press.

    Google Scholar 

  • Kritz, M. M., & Zlotnik, H. (1992). Global interactions: Migration systems, processes, and policies. In M. M. Kritz, L. L. Lim, & H. Zlotnik (Eds.), International migration systems: A global approach (pp. 1–18). Oxford, UK: Clarendon Press.

    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. Chichester, UK: Wiley and Sons.

    Google Scholar 

  • Leblang, D. A., Fitzgerald, J., & Teets, J. (2009). Defying the law of gravity: The political economy of international migration (Working paper). Cambridge, MA: Faculty Discussion Group on Political Economy, Weatherhead Center for International Affairs, Harvard University.

    Google Scholar 

  • Lemaitre, G. (2005). The comparability of international migration statistics: Problems and prospects (Statistics Brief No. 9). Paris, France: Organisation for Economic Co-operation and Development.

    Google Scholar 

  • Liang, K. Y., & Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73, 13–22.

    Article  Google Scholar 

  • Mabogunje, A. L. (1970). A systems approach to a theory of rural–urban migration. Geographic Analysis, 2, 1–18.

    Article  Google Scholar 

  • Massey, D. S., Arango, J., Hugo, G., Kouaouci, A., Pellegrino, A., & Taylor, J. E. (1998). Worlds in motion: Understanding international migration at the end of the millennium. Oxford, UK: Clarendon Press.

    Google Scholar 

  • Mayda, A. M. (2005). International migration: A panel data analysis of economic and non-economic determinants (Discussion Paper No. 1590). Bonn, Germany: Institute for the Study of Labor.

    Google Scholar 

  • Milligan, G. W., & Cooper, M. C. (1985). An examination of procedures for determining the number of clusters in a data set. Psychometrika, 50, 159–179.

    Article  Google Scholar 

  • Neumayer, E. (2005). Bogus refugees? The determinants of asylum migration to western Europe. International Studies Quarterly, 49, 389–409.

    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. Louvain-la-Neuve, Belgium: UCL Presses Universitaires de Louvain.

    Google Scholar 

  • Pan, W. (2001). Akaike’s information criterion in generalized estimating equations. Biometrics, 57, 120–125.

    Article  Google Scholar 

  • Pedersen, P. J., Pytlikova, M., & Smith, N. (2008). Selection and network effects: Migration flows into OECD countries 1990–2000. European Economic Review, 52, 1160–1186.

    Article  Google Scholar 

  • Poulain, M. (1993). Confrontation des Statistiques de Migrations Intra-Européennes: Vers plus d’harmonisation? [Statistical comparison of intra-European migration: Toward greater harmonization]. European Journal of Population, 9, 353–381.

    Article  Google Scholar 

  • Poulain, M. (1999). International migration within Europe: Towards more complete and reliable data (Working Paper 12). Geneva, Switzerland: Joint ECE-Eurostat Work Session on Migration Statistics.

    Google Scholar 

  • Poulain, M., Perrin, N., & Singleton, A. (2006). THESIM: Towards harmonized European statistics on international migration. Louvain-la-Neuve, Belgium: UCL Presses Universitaires de Louvain.

    Google Scholar 

  • Rabe-Hesketh, S., & Everett, B. S. (2006). A handbook of statistical analyses using Stata (4th ed.). Boca Raton, FL: Chapman & Hall/CRC.

    Google Scholar 

  • Raftery, A. E. (1995). Bayesian model selection in social research. Sociological Methodology, 25, 111–163.

    Article  Google Scholar 

  • Raymer, J., & Abel, G. (2008). The MIMOSA model for estimating international migration flows in the European Union (Working Paper 8). Geneva, Switzerland: Joint UNECE/Eurostat Work Session on Migration Statistics.

    Google Scholar 

  • Raymer, J., de Beer, J., & van der Erf, R. (2011). Putting the pieces of the puzzle together: Age and sex-specific estimates of migration between EU/EFTA countries, 2002–2007. European Journal of Population, 27, 185–215.

    Article  Google Scholar 

  • Raymer, J., Forster, J. J., Smith, P. W. F., Bijak, J., Wiśniowski, A., & Abel, G. J. (2010). The IMEM model for estimating international migration flows in the European Union (Working Paper 14). Geneva, Switzerland: Joint UNECE/Eurostat Work Session on Migration Statistics.

    Google Scholar 

  • Regulation (EC) No 862/2007 of the European parliament and of the council of 11 July 2007 on community statistics on migration and international protection and repealing council regulation (EEC) No 311/76 on the Compilation of Statistics on Foreign Workers. (2007). Official Journal of the European Union. Retrieved from http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2007:199:0023:0029:EN:PDF

  • Rogers, A. (2008). Demographic modeling of the geography of migration and population: A multiregional perspective. Geographical Analysis, 40, 276–296.

    Article  Google Scholar 

  • Salt, J. (1989). A comparative overview of international trends and types: 1950–80. International Migration Review, 23, 431–456.

    Article  Google Scholar 

  • Salt, J. (2001). Current trends in international migration in Europe (Report CDMG (2001) 33). Stasbourg, France: European Committee on Migration, Council of Europe.

    Google Scholar 

  • Svaton, P., & Warin, T. (2008). European migration: Welfare migration or economic migration? Global Economy Journal, 8, 1–30.

    Google Scholar 

  • Todaro, M. P. (1976). Internal migration in developing countries. Geneva, Switzerland: International Labor Office.

    Google Scholar 

  • United Nations. (1998). Recommendations on statistics of international migration (Statistical Papers Series M, No. 58, Rev. 1). New York: Statistics Division, Department of Economic and Social Affairs, United Nations.

    Google Scholar 

  • van der Erf, R., & van der Gaag, N. (2007). An iterative procedure to revise available data in the double entry migration matrix for 2002, 2003 and 2004 (Discussion Paper). The Hague: Netherlands Interdisciplinary Demographic Institute. Retrieved from http://mimosa.gedap.be/Documents/Erf_2007.pdf

  • van Tubergen, F., Maas, I., & Flap, H. (2004). The economic incorporation of immigrants in 18 Western societies: Origin, destination, and community effects. American Sociological Review, 69, 704–727.

    Article  Google Scholar 

  • Zheng, B. (2000). Summarizing the goodness of fit of generalized linear models for longitudinal data. Statistics in Medicine, 19, 1265–1275.

    Article  Google Scholar 

  • Zlotnik, H. (1992). Empirical identification of international migration systems. In M. M. Kritz, L. L. Lim, & H. Zlotnik (Eds.), International migration systems: A global approach (pp. 19–40). Oxford, UK: Clarendon Press.

    Google Scholar 

  • Zolberg, A. R. (2006). A nation by design: Immigration policy in the fashioning of America. New York: Russell Sage Foundation.

    Google Scholar 

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Acknowledgments

Jack DeWaard is supported by NICHD Training Grant T32-HD07014 and Center Grant R24-HD047873 to the Center for Demography and Ecology at the University of Wisconsin–Madison. James Raymer received support from the ESRC Research Centre for Population Change (Grant Reference RES-625-28-0001). The authors acknowledge the MIgration MOdeling for Statistical Analysis (MIMOSA) project in providing harmonized flow data for comparison, and comments from Theodore P. Gerber, Katherine J. Curtis, Jenna Nobles, Mary M. Kritz, Douglas T. Gurak, Joel E. Cohen, Stewart Tolnay, and three anonymous reviewers. Previous versions of this article were presented at the annual meeting of the Population Association of America on April 15, 2010 and the Integrated Modeling of European Migration (IMEM) workshop on May 27, 2011.

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Table 4 Data descriptions, sources, and variable names

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DeWaard, J., Kim, K. & Raymer, J. Migration Systems in Europe: Evidence From Harmonized Flow Data. Demography 49, 1307–1333 (2012). https://doi.org/10.1007/s13524-012-0117-9

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