Describing Age Structures of Migration

  • Andrei Rogers
  • James Raymer
  • Jani Little
Part of the The Springer Series on Demographic Methods and Population Analysis book series (PSDE, volume 26)


Empirical schedules of age-specific rates exhibit remarkably persistent regularities in age pattern. Mortality schedules, for example, normally show a moderately high death rate immediately after birth, after which the rates drop to a minimum between ages 10 and 15, then increase slowly until about age 50, and thereafter rise at an increasing pace until the last years of life. Fertility rates generally start to take on nonzero values at about age 15 and attain a maximum somewhere between ages 20 and 30; the curve is unimodal and declines to zero once again at some age close to 50. Similar unimodal profiles may be found in schedules of first marriage, divorce, and remarriage (Rogers, 1986). The most prominent regularity in age-specific schedules of migration is the high concentration of migration among young adults; rates of migration also are high among children, starting with a peak during the first year of life, dropping to a low point during the teenage years, turning sharply upward to a peak near ages 20–22, and then declining regularly thereafter, except for a possible slight hump at the onset of the principal ages of retirement, and/or an upward slope at the oldest ages.


Migration Rate Model Schedule American Community Survey Interregional Migration Migration Level 
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  1. Rogers, A., & Raymer, J. (1999). Fitting observed demographic rates with the multiexponential model schedule: An assessment of two estimation programs. Review of Urban and Regional studies, 11(1), 1–10.CrossRefGoogle Scholar
  2. Rogers, A. (1986). Parameterized multistate population-dynamics and projections. Journal of the American Statistical Association, 81(393), 48–61.CrossRefGoogle Scholar
  3. Rogers, A. (1978). Special IIASA issue on migration and settlement. Environment and Planning A, 10(5), 469–474.Google Scholar
  4. Rogers, A., Raymer, J., & Newbold, K. B. (2003). Reconciling and translating migration data collected over time intervals of differing widths. Annals of Regional Science, 37(4), 581–601.CrossRefGoogle Scholar
  5. Rogers, A., & Castro, L. J. (1981). Model migration schedules. Research report 81–30. Laxenburg, Austria: International Institute for Applied Systems Analysis.Google Scholar
  6. 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
  7. Rogers, A., & Little, J. S. (1994). Parameterizing age patterns of demographic rates with the multiexponential model schedule. Mathematical Population Studies, 4(3), 175–195.CrossRefGoogle Scholar
  8. Rogerson, P. A. (1990). Migration analysis using data with time intervals of differing widths. Papers of the Regional Science Association, 68, 97–106.CrossRefGoogle Scholar
  9. Coale, A. J., & McNeil, D. R. (1972). Distribution by age of frequency of first marriage in a female cohort. Journal of the American Statistical Association, 67(340), 743–749.CrossRefGoogle Scholar
  10. Gumbel, E. J. (1941). The return period of flood flows. Annals of Mathematical Statistics, 12(2), 163–190.CrossRefGoogle Scholar
  11. Castro, L. J., & Rogers, A. (1983). What the age composition of migrants can tell us. Population Bulletin of the United Nations, 15, 63–79.Google Scholar
  12. Rogers, A. (1988). Age patterns of elderly migration: An international comparison. Demography, 25(3), 355–370.CrossRefGoogle Scholar
  13. McNeil, D. R., Trussell, T. J., & Turner, J. C. (1977). Spline interpolation of demographic data. Demography, 14(2), 245–252.CrossRefGoogle Scholar
  14. Kimball, B. F. (1956). The bias in certain estimates of the parameters of the extreme-value distribution. Annals of Mathematical Statistics, 27(3), 758–767.CrossRefGoogle Scholar
  15. Kitsul, P., & Philipov, D. (1982). High- and low-intensity model of mobility. In K. C. Land & A. Rogers (Eds.), Multidimensional mathematical demography (pp. 505–534). New York: Academic Press.Google Scholar
  16. Rogers, A., Jones, B., Partida, V., & Muhidin, S. (2007). Inferring migration flows from the migration propensities of infants: Mexico and Indonesia. Annals of Regional Science, 41(2), 443–465.CrossRefGoogle Scholar
  17. Rogers, A., & Willekens, F. (1986). Migration and settlement: A multiregional comparative study. Dordrecht: Reidel.Google Scholar
  18. Levenberg, K. (1944). A method for the solution of certain nonlinear problems in least squares. Quarterly of Applied Mathematics, 2, 164–168.Google Scholar
  19. Rogers, A., & Watkins, J. (1987). General versus elderly interstate migration and population redistribution in the United States. Research on Aging, 9(4), 483–529.CrossRefGoogle Scholar
  20. Rees, P. H. (1977). The measurement of migration, from census data and other sources. Environment and Planning A, 9(3), 247–272.CrossRefGoogle Scholar
  21. Marquardt, D. W. (1963). An algorithm for least-squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics, 11(2), 431–441.CrossRefGoogle Scholar
  22. Andersson, A. E., & Holmberg, I. (1980). Migration and settlement: 3. Sweden. Research report 80-5. Laxenburg, Austria: International Institute for Applied Systems Analysis.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.University of Colorado, Boulder Inst. Behavioral Science Population ProgramBoulderUSA
  2. 2.School of Social SciencesUniversity of SouthamptonSouthamptonUK
  3. 3.University of Colorado Institute of Behavioral ScienceBoulderUK

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