Advertisement

Migration Flows: Measurement, Analysis and Modeling

Chapter
Part of the International Handbooks of Population book series (IHOP, volume 6)

Abstract

This chapter is an introduction to the study of migration flows. It starts with a review of major definition and measurement issues. Comparative studies of migration are particularly difficult because different countries define migration differently and measurement methods are not harmonized. Insight in data collection practices is a first requirement to study flows. In the paper, several migration indicators are presented that describe the level and direction of migration. Scientists attempt to model migration flows since Ravenstein presented his migration laws at the end of the nineteenth century. Initially theories and models were borrowed from physics. The gravity model, based on Newton’s law of gravitation, has been the main model of migration flows for decades and continues to be popular today. Gradually human behaviour replaced the laws of physics and spatial interaction models the gravity model. Spatial interaction models emphasize interaction between geographically dispersed populations. Initially the gravity model had a strong influence on spatial interaction models, but the interest shifted gradually to probability theory and probability models. A parallel development was the life course perspective on migration, resulting from the increased awareness that some life events trigger migration and that during some stages of life, a person has an elevated propensity to migrate. These links produce the typical and universal age patterns of migration. Today, population projection models incorporate models of these typical age patterns (model schedules) and models of spatial interaction. Projection models may be turned into policy models to infer the migration flows required to meet demographic objectives, i.e. to offset population decline resulting from low fertility.

Keywords

Spatial interaction Gravity model Multiregional Model migration schedule Migration policy Spatial focus Migration system Replacement migration Migration statistics Occurrence-exposure rate Migration preference index Migration effectiveness index 

References

  1. Aalen, O. O., Borgan, Ø., & Gjessing, H. K. (2008). Survival and event history analysis: A process point of view. New York: Springer.CrossRefGoogle Scholar
  2. Abel, G. (2010). Estimation of international migration flow tables in Europe. Journal of the Royal Statistical Society, Series A, 173(4), 797–825.CrossRefGoogle Scholar
  3. Abel, G. (2013). Estimating global migration flow tables using place of birth data. Demographic Research, 28(18), 505–546.CrossRefGoogle Scholar
  4. Abel, G., & Sander, N. (2014). Quantifying global international migration flows. Science, 343(6178), 1520–1522.CrossRefGoogle Scholar
  5. Baydar, N. (1983). Analysis of the temporal stability of migration in the context of multiregional forecasting (Working Paper No 38). Voorburg: Netherlands Interuniversity Demographic Institute.Google Scholar
  6. Baydar, N., & Willekens, F. (1986). Forecasting place-to-place migration with generalized linear models. In R. Woods & P. H. Rees (Eds.), Population structures and models. Developments in spatial demography (pp. 203–244). London: Allen and Unwin.Google Scholar
  7. Bell, M., & Muhidin, S. (2009). Cross-national comparisons of internal migration (Human Development Research Paper 2009/30). New York: United Nations Development Programme, United Nations.Google Scholar
  8. Bell, M., & Muhidin, S. (2011). Chapter 7: Comparing internal migration between countries using Courgeau’s K. In J. Stillwell & M. Clarke (Eds.), Population dynamics and projection methods. New York: Springer.Google Scholar
  9. Bell, M., Blake, M., Boyle, P., Duke–Williams, O., Rees, P., Stillwell, J., & Hugo, G. (2002). Cross–national comparison of internal migration: Issues and measures. Journal of the Royal Statistical Society: Series A (Statistics in Society), 165(3), 435–464.CrossRefGoogle Scholar
  10. Blossfeld, H. P., & Rohwer, G. (2002). Techniques of event history modeling. New approaches to causal analysis (2nd ed.). Mahwah: Lawrence Erlbaum.Google Scholar
  11. Boyle, P., Halfacree, K., & Robinson, V. (1998). Exploring contemporary migration. London: Longman.Google Scholar
  12. Brierley, M. J., Forster, J. J., McDonald, J. W., & Smith, P. W. F. (2008). Bayesian estimation of migration flows. In J. Raymer & F. Willekens (Eds.), International migration in Europe. Data, models and estimates (pp. 149–174). Chichester: Wiley.Google Scholar
  13. Coale, A. J., & McNeil, D. R. (1972). The distribution by age of the frequency of first marriage in a female cohort. Journal of the American Statistical Association, 67(743), 749.Google Scholar
  14. Courgeau, D. (1973a). Migrants et migrations. Population, 28, 95–128.CrossRefGoogle Scholar
  15. Courgeau, D. (1973b). Migrations et d’ecoupages du territoire. Population, 28, 511–537.CrossRefGoogle Scholar
  16. Courgeau, D. (1982). Comparaison des migrations internes en France et aux Etats- Unis. Population, 6, 1184–1188.CrossRefGoogle Scholar
  17. Courgeau, D. (1988). Méthodes de mesure de la mobilité spatiale, migrations internes, mobilité temporaire, navettes. Paris: Editions INED.Google Scholar
  18. Courgeau, D., Muhidin, S., & Bell, M. (2012). Estimating changes of residence for cross-national comparison. Population (English edition), 67(4), 631–651.Google Scholar
  19. 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.CrossRefGoogle Scholar
  20. European Parliament. (2007). 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. http://www.europarl.europa.eu/sides/getDoc.do?objRefId=140109&language=EN. Accessed 5 July 2014.
  21. Flowerdew, R. (2010). Chapter 14: Modelling migration with Poisson regression. In J. Stillwell, O. Duke-Williams, & A. Dennett (Eds.), Technologies for migration and commuting analysis: Spatial interaction data applications (pp. 261–279). Hershey: IGI Global Publishing.CrossRefGoogle Scholar
  22. Flowerdew, R., & Aitkin, M. (1982). A method of fitting the gravity model based on the Poisson distribution. Journal of Regional Science, 22, 191–202.CrossRefGoogle Scholar
  23. Kitsul, P., & Philipov, D. (1981). The one year/five year migration problem. In A. Rogers (Ed.), Advances in multiregional demography (Research Report RR-81-6, pp. 1–33). Laxenburg: IIASA.Google Scholar
  24. Ledent, J. (1980). Multistate life tables: Movement versus transition perspectives. Environment and Planning A, 12, 533–562.CrossRefGoogle Scholar
  25. Lee, E. S. (1966). A theory of migration. Demography, 3(1), 47–57.CrossRefGoogle Scholar
  26. Long, J. F., & Boertlein, C. G. (1981). Using migration measures having different intervals, manuscript. Washington, DC: U.S. Bureau of the Census.Google Scholar
  27. Massey, D. S. (2008). Patterns and processes of international migration in the 21st century. Princeton: Princeton University Press.Google Scholar
  28. Massey, D. S., Arango, J., Koucouci, A., Pelligrino, A., & Taylor, J. E. (1998). Worlds in motion: Understanding international migration at the end of the Millennium. Oxford: Oxford University Press.Google Scholar
  29. McCullagh, P., & Nelder, J. A. (1989). Generalized linear models (2nd ed.). Boca Raton: Chapman & Hall/CRC.CrossRefGoogle Scholar
  30. Mueser, P. (1989). The spatial structure of migration: An analysis of flows between states in the USA over three decades. Regional Studies, 23(3), 185–200.CrossRefGoogle Scholar
  31. Nowok, B. (2010). Harmonization by simulation. A contribution to comparable international migration statistics in Europe. PhD dissertation. University of Groningen, The Netherlands.Google Scholar
  32. Plane, D. A., & Mulligan, G. F. (1997). Measuring spatial focusing in a migration system. Demography, 34, 251–262.CrossRefGoogle Scholar
  33. Pooler, J. (1992). Spatial uncertainty and spatial dominance in interaction modelling: A theoretical perspective on spatial competition. Environment and Planning A, 24, 995–1008.Google Scholar
  34. Preston, S. H., Heuveline, P., & Guillot, M. (2001). Demography. Measuring and modeling population processes. Oxford: Blackwell.Google Scholar
  35. Raymer, J., & Giulietti, C. (2010). Chapter 15: Analysing structures of interregional migration in England. In J. Stillwell, O. Duke-Williams, & A. Dennett (Eds.), Technologies for migration and commuting analysis: Spatial interaction data applications (pp. 280–293). Hershey: IGI Global Publishing.CrossRefGoogle Scholar
  36. Raymer, J., Abel, G., & Smith, P. W. F. (2007). Combining census and registration data to estimate detailed elderly migration flows in England and Wales. Journal of the Royal Statistical Society: Series A (Statistics in Society), 170(4), 891–908. doi: 10.1111/j.1467-985X.2007.00490.x.CrossRefGoogle Scholar
  37. Raymer, J., Smith, P. W. F., & Giulietti, C. (2011a). Combining census and registration data to analyse ethnic migration patterns in England from 1991 to 2007. Population, Space and Place, 17, 73–88. doi: 10.1002/psp.565.CrossRefGoogle Scholar
  38. Raymer, J., de Beer, J., & van der Erf, R. (2011b). Putting the pieces of the puzzle together: Age and sex-specific estimates of migration amongst countries in the EU/EFTA, 2002–2007. European Journal of Population, 27(2), 185–215.CrossRefGoogle Scholar
  39. Raymer, J., Wiśniowski, A., Forster, J. J., Smith, P. W. F., & Bijak, J. (2013). Integrated modeling of European migration. Journal of the American Statistical Association, 108(503), 801–819.CrossRefGoogle Scholar
  40. Raymer, J., & Rogers, A. (2008). Applying model migration schedules to represent age-specific migration flows. In J. Raymer & F. Willekens (Eds.), International migration in Europe. Data, models and estimates (pp. 175–192). Chichester: Wiley.Google Scholar
  41. Rees, P. H., & Willekens, F. J. (1986). Data and accounts. In A. Rogers & F. Willekens (Eds.), Migration and settlement: A multiregional comparative study (pp. 19–58). Dordrecht: Reidel Press.Google Scholar
  42. Rogers, A. (1985). Regional population projection models. Beverly Hills: Sage.Google Scholar
  43. Rogers, A. (1990). Requiem for the net migrant. Geographical Analysis, 22(4), 283–300.CrossRefGoogle Scholar
  44. Rogers, A., & Castro, L. (1981). Model migration schedules (Research Report RR-81-30). Laxenburg: International Institute for Applied Systems Analysis (IIASA).Google Scholar
  45. Rogers, A., & Raymer, J. (1998). The spatial focus of U.S. interstate migration flows. International Journal of Population Geography, 4, 63–80.CrossRefGoogle Scholar
  46. Rogers, A., & Sweeney, S. (1998). Measuring the spatial focus of migration patterns. Professional Geographer, 50(2), 232–242.CrossRefGoogle Scholar
  47. Rogers, A., Willekens, F., & Raymer, J. (2003a). Imposing age and spatial structures on inadequate migration flow data sets. The Professional Geographer, 55(1), 56–69.Google Scholar
  48. Rogers, A., Raymer, J., & Newbold, K. B. (2003b). Reconciling and translating migration data collected over time intervals of different widths. The Annals of Regional Science, 37(4), 581–601.CrossRefGoogle Scholar
  49. Sen, A., & Smith, T. (1995). Gravity models of spatial interaction behavior. Berlin: Springer.CrossRefGoogle Scholar
  50. Shryock, H. S., & Siegel, J. S. (1980). The methods and materials of demography (Vol. 1). Washington, DC: U.S. Bureau of the Census (Fourth printing).Google Scholar
  51. Singer, B., & Spilerman, S. (1979). Mathematical representations of development theories. In J. R. Nesselroade & P. B. Baltes (Eds.), Longitudinal research in the study of behavior and development (pp. 155–177). New York: Academic.Google Scholar
  52. Snickars, F., & Weibull, J. W. (1977). A minimum information principle. Theory and practice. Regional Science and Urban Economics, 7, 137–168.CrossRefGoogle Scholar
  53. Stouffer, S. A. (1940). Intervening opportunities: A theory relating to mobility and distance. American Sociological Review, 5(6), 845–867.CrossRefGoogle Scholar
  54. United Nations. (1998). Recommendations on statistics of international migration: Revision 1 (Statistical Papers, No. 58, Salesn No. E98.XVII.14). New York: United Nations.Google Scholar
  55. United Nations. (2001). Replacement migration. Is it a solution to declining and ageing populations? (ST/ESA/SER.A/206). New York: United Nations.Google Scholar
  56. United Nations. (2012). World urbanization prospects. The 2011 revision. Highlight (ESA/P/WP/224). New York: United Nations.Google Scholar
  57. United Nations World Tourism Organization. (2014). Yearbook of tourism statistics, Data 2008–2012, (2014 ed.). Madrid.Google Scholar
  58. Wetrogan, S. I., & Long, J. F. (1990). Creating annual state-to-state migration flows with demographic detail (Current Population Reports Special Studies Series P-23, No. 166). Washington, DC: U.S. Bureau of the Census.Google Scholar
  59. Willekens, F. (1976). Optimal migration policies (Research memorandum RM-76-50). Laxenburg: International Institute for Applied Systems Analysis (IIASA)).Google Scholar
  60. Willekens, F. (1979). Optimal migration policies. An analytical approach. Regional Science and Urban Economics, 9, 345–367.CrossRefGoogle Scholar
  61. Willekens, F. (1983). Log-linear analysis of spatial interaction. Papers of the Regional Science Association, 52, 187–205.CrossRefGoogle Scholar
  62. Willekens, F. J. (1994). Monitoring international migration in Europe. Towards a statistical data base combining data from different sources. European Journal of Population, 10(1), 1–42.CrossRefGoogle Scholar
  63. Willekens, F. (2008). Models of migration: Observations and judgements. In J. Raymer & F. Willekens (Eds.), International migration in Europe. Data, models and estimates (pp. 117–147). Chichester: Wiley.Google Scholar
  64. World Economic Forum. (2011). Global talent risk. Seven responses. Geneva: World Economic Forum.Google Scholar
  65. Zagheni, E., & Weber I. (2012, June 22–24). You are where you E-mail: Using E-mail data to estimate international migration rates. WebSci. http://www.demogr.mpg.de/publications/files/4598_1340471188_1_Zagheni&Weber_Websci12.pdf. Accessed 5 July 2014.
  66. Zipf, G. K. (1946). The P1P2/D hypothesis: On the intercity movement of persons. American Sociological Review, 11, 677–686.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Max Planck Institute for Demographic Research (MPIDR)RostockGermany
  2. 2.Netherlands Interdisciplinary Demographic InstituteThe HagueThe Netherlands

Personalised recommendations