Skip to main content
SpringerLink
Log in

Modelling Age Patterns of Internal Migration at the Highest Ages

  • Published:
Spatial Demography Aims and scope Submit manuscript

Abstract

Model migration age schedules have proved valuable to demographers for a range of applications for over 40 years. The original Rogers-Castro curve has been extended over time to include a retirement curve, a post-retirement curve, and a student peak. With demographic analyses extending to higher age groups than in the past due to population ageing, it is important for the model schedule to faithfully reflect migration patterns at advanced ages. Recent data on internal migration in the nonagenarian and centenarian ages reveals several examples of rising then falling mobility with increasing age. This paper suggests an alternative specification of the post-retirement curve of the model schedule to reflect this pattern. The modified model migration schedule is successfully fitted to example internal migration age patterns from Australia, Canada and the Netherlands. The modified schedule should prove useful in preparing input data for population projections and analyses of migration age patterns extending to the highest ages.

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.

Fig. 1
Fig. 2
Fig. 3

Source: Author’s calculations based on ABS 2016 Census data

Fig. 4

Source: Author’s calculations based on Statistics Canada 2016 Census data

Fig. 5

Source: Author’s calculations based on Statistics Netherlands 2018 register data

Fig. 6

Source: Author’s calculations based on ABS 2011 and 2016 census data

Similar content being viewed by others

Data Availability

The Excel workbook used to fit the model migration schedule is available at https://doi.org/10.6084/m9.figshare.12415475. The VBA code is available within the workbook.

References

  • ABS. (2019). Australian census data extracted via TableBuilder Pro. https://www.abs.gov.au/websitedbs/D3310114.nsf/Home/2016%2520TableBuilder. Accessed 11 Aug 2019.

  • Bates, J., & Bracken, I. (1987). Migration age profiles for local authority areas in England, 1971–1981. Environment and Planning A,19, 521–535.

    Article  Google Scholar 

  • Bell, M. (1996). Understanding Internal Migration. Canberra: Australian Government Publishing Service.

    Google Scholar 

  • Bernard, A., & Bell, M. (2015). Smoothing internal migration age profiles for comparative research. Demographic Research,32(33), 915–948.

    Article  Google Scholar 

  • Bernard, A., Bell, M., & Charles-Edwards, E. (2014). Life-course transitions and the age profile of internal migration. Population and Development Review,40(2), 213–239.

    Article  Google Scholar 

  • Congdon, P. (1993). Graduation in local demographic analysis and projection. Journal of the Royal Statistical Society Series A,156(2), 237–270.

    Article  Google Scholar 

  • Dittgen, A., & Dutreuilh, C. (2005). Housing and household size in local population dynamics. The example of Paris, Population (English edition),60(3), 259–298.

    Google Scholar 

  • Dyrting, S. (2019). Smoothing migration intensities with P-TOPALS. Northern Institute, Charles Darwin University (Unpublished manuscript).

  • Ediev, D. M. (2018). Constrained mortality extrapolation to old age: an empirical assessment. European Journal of Population,34(3), 441–457.

    Article  Google Scholar 

  • Hofmeyer, B. E. (1988). Application of a mathematical model to South African migration data, 1975–1980. Southern African Journal of Demography,2(1), 24–28.

    Google Scholar 

  • Hugo, G., Harris, K. (2011). Population distribution effects of migration in Australia. In Report for Department of Immigration and Citizenship. National Centre for Social Applications of GIS, The University of Adelaide.

  • Hunsinger, E. (2019). Eddie’s R code for fitting the multi-exponential model migration schedule with student peak. https://applieddemogtoolbox.github.io/Toolbox/#SPMMSRCode. Accessed 23 Sep 2019.

  • Ishikawa, Y. (2001). Migration turnarounds and schedule changes in Japan, Sweden and Canada. Review of Urban and Regional Development Studies,13(1), 20–33.

    Article  Google Scholar 

  • Jdanov, D. A., Scholz, R. D., & Shkolnikov, V. M. (2005). Official population statistics and the Human Mortality Database estimates of populations aged 80+ in Germany and nine other European countries. Demographic Research,13(14), 335–362.

    Article  Google Scholar 

  • Liaw, K., & Nagnur, D. N. (1985). Characterization of metropolitan and nonmetropolitan outmigration schedules of the Canadian population system, 1971–1976. Canadian Studies in Population,12(1), 81–102.

    Article  Google Scholar 

  • Peristera, P., & Kostaki, A. (2007). Modeling fertility in modern populations. Demographic Research,16(6), 141–194.

    Google Scholar 

  • Pittenger, D. B. (1974). A typology of age-specific net migration rate distributions. Journal of the American Institute of Planners,40(4), 278–283.

    Article  Google Scholar 

  • Pittenger, D. B. (1978). On making flexible projections of age-specific net migration. Environment and Planning A,10(11), 1253–1272.

    Article  Google Scholar 

  • Potrykowska, A. (1988). Age patterns and model migration schedules in Poland. Geographia Polonica,54, 63–80.

    Google Scholar 

  • Raymer, J., & Baffour, B. (2018). Subsequent migration of immigrants within Australia, 1981–2016. Population Research and Policy Review,37, 1053–1077.

    Article  Google Scholar 

  • 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 

  • Rees, P. (1997). Problems and solutions in forecasting geographical populations. Journal of the Australian Population Association,14(2), 145–166.

    Article  Google Scholar 

  • Rees, P., Bell, M., Duke-Williams, O., & Blake, M. (2000). Problems and solutions in the measurement of migration intensities: Britain and Australia compared. Population Studies,54(2), 207–222.

    Article  Google Scholar 

  • Robine, J., & Cubaynes, S. (2017). Worldwide demography of centenarians. Mechanisms of Ageing and Development,165, 59–67.

    Article  Google Scholar 

  • Rogers, A. (1978). Model migration schedules: An application using data for the Soviet Union. Canadian Studies in Population,5, 85–98.

    Article  Google Scholar 

  • Rogers, A. (1988). Age patterns of elderly migration: An international companion. Demography,25(3), 355–370.

    Article  Google Scholar 

  • Rogers, A., Castro, L. J. (1981). Model migration schedules. In Research Report RR-81–30. Laxenburg: International Institute for Applied Systems Analysis.

  • 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 Development Studies,11(1), 1–10.

    Article  Google Scholar 

  • Rogers, A., & Watkins, J. (1987). General versus elderly interstate migration and population redistribution in the United States. Research on Aging,9(4), 483–529.

    Article  Google Scholar 

  • Rogers, A., Racquillet, R., & Castro, L. J. (1978). Model migration schedules and their applications. Environment and Planning A,10(5), 475–502.

    Article  Google Scholar 

  • Rogers, A., Castro, L. J., & Lea, M. (2005). Model migration schedules: three alternative linear estimation methods. Mathematical Population Studies,12(1), 17–38.

    Article  Google Scholar 

  • Rogers, A., Little, J., & Raymer, J. (2010). The Indirect Estimation of Migration. Dordrecht: Springer.

    Book  Google Scholar 

  • Rowland, D. T. (2012). Population Aging. Dordrecht: Springer.

    Book  Google Scholar 

  • Ruiz-Santacruz, J. S., Garcés, J. (2018). migraR: prototype package for adjusting Rogers and Castro models. https://github.com/elflacosebas/migraR/ Accessed 23 Sept 2019.

  • Sander, N. (2011). Retirement migration of the Baby Boomers in Australia: Beach, Bush or Busted? PhD thesis, School of Geography, Planning and Environmental Management, The University of Queensland.

  • Statistics Netherlands. (2019). Internal migration data extracted via the Statline online tool. https://opendata.cbs.nl/statline/#/CBS/nl/. Accessed 24 Sep 2019.

  • Thatcher, R., Kannisto, V., & Andreev, K. (2002). The Survivor Ratio method for estimating numbers at high ages. Demographic Research,6(1), 1–18.

    Article  Google Scholar 

  • Thomas, M., Gillespie, B., & Lomax, N. (2019). Variations in migration motives over distance. Demographic Research,40(38), 1097–1110.

    Article  Google Scholar 

  • Wilson, T. (2010). Model migration schedules incorporating student migration peaks. Demographic Research, 23(8), 191–222.

    Article  Google Scholar 

  • Wilson, T. (2014). The impact of education-related mobility on inter-regional migration age profiles in Australia. Applied Spatial Analysis and Policy, 8(4), 371–391.

    Article  Google Scholar 

  • Wilson, T. (2015). The demographic constraints on future population growth in regional Australia. Australian Geographer, 46(1), 91–111.

    Article  Google Scholar 

  • Wilson, T. (2020). Model migration schedule fitting example. figshare. Excel workbook. https://doi.org/10.6084/m9.figshare.12415475.

    Article  Google Scholar 

  • Wilson, T., & Temple, J. (2020). More accurate estimates of Australia’s population at the highest ages. Manuscript under review. Melbourne School of Population and Global Health, The University of Melbourne.

  • Wilson, T., & Terblanche, W. (2018). New estimates of Australia’s centenarian population. International Journal of Population Data Science, 3(1), 1–10.

    Article  Google Scholar 

Download references

Acknowledgements

The author is grateful to Patrice Dion of Statistics Canada for facilitating access to Canadian census data. Eddie Hunsinger and Sigurd Dyrting kindly provided helpful feedback on an earlier draft. The suggestions of the anonymous reviewers were most helpful.

Funding

This work was funded by the Australian Research Council’s (ARC) Centre of Excellence in Population Ageing Research (CE1101029).

Author information

Authors and Affiliations

  1. Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie St., Melbourne, Vic, 3010, Australia

    Tom Wilson

Authors
  1. Tom Wilson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tom Wilson.

Ethics declarations

Conflicts of interest

The author(s) declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wilson, T. Modelling Age Patterns of Internal Migration at the Highest Ages. Spat Demogr 8, 175–192 (2020). https://doi.org/10.1007/s40980-020-00062-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40980-020-00062-7

Keywords

Search

Navigation