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Projecting spatial population and labour force growth in Australian districts

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Abstract

Projecting population in a local community is especially important to deliver public good provisions such as health services, education and social security. In addition to the size of the population and its age distribution, sex and location are also of importance for understanding needs and developing future infrastructure plans and growth policies. The projection of labour force growth is particularly important as labour is one of the primary inputs of economic production. This study aims to project the distribution of the future Australian population and its detailed characteristics. We analyse the projection of labour force growth in Australia and the proportion of people born outside the country, both at the district level, as examples of the model’s capability and applications. The microsimulation model used is designed to capture a detailed picture of the demographic evolution of the Australian population given current population trends, while also enabling us to understand the spatial distribution of migrants among the future Australian population. We validate our projections by making comparisons with recently released census data from 2016. The validation procedures we use show that our model has managed to not only project the population for sub-state/territory regions but also its age distribution as well as whether a person was born outside Australia. However, although our projection of the total size of the labour force produces reasonably accurate results, we found that estimates of the growth of the labour force were not as reliable, as the model was unable to capture the current negative growth of the labour force that is reflected in recent census data.

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Source: NOM data from Department of Home Affairs

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Source: ABS 2016 Census of Population and Housing and the microsimulation projection

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Source: ABS 2016 Census of Population and Housing and the microsimulation projection

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Source: ABS 2016 Census of Population and Housing and the microsimulation projection

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Source: ABS 2016 Census of Population and Housing and the microsimulation projection

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References

  • Australian Bureau of Statistics. (2013a). 2011 Census of population and housing, TableBuilder. Findings based on use of ABS TableBuilder data from https://auth.censusdata.abs.gov.au/webapi/jsf/dataCatalogueExplorer.xhtml. Accessed 1 June 2018.

  • Australian Bureau of Statistics. (2013b). Population projections, Australia, 2012 (base) to 2101, cat. no. 3222.0, Canberra.

  • Australian Bureau of Statistics. (2014a). Births, Australia, cat. no. 3301.0, Canberra.

  • Australian Bureau of Statistics. (2014b). Deaths, Australia, cat. no. 3302.0, Canberra.

  • Australian Bureau of Statistics. (2017). 2073.02016 Census of population and housing: TableBuilder Pro. Findings based on use of ABS TableBuilder data from https://auth.censusdata.abs.gov.au/webapi/jsf/dataCatalogueExplorer.xhtml. Accessed 1 June 2018.

  • Ballas, D., Clarke, G., Dorling, D., Eyre, H., Thomas, B., & Rossiter, D. (2005). SimBritain: A spatial microsimulation approach to population dynamics. Population, Space and Place, 11(1), 13–34.

    Article  Google Scholar 

  • Ballas, D., Clarke, G., Dorling, D., Rigby, J., & Wheeler, B. (2006). Using geographical information systems and spatial microsimulation for the analysis of health inequalities. Health Informatics Journal, 12(1), 65–79.

    Article  Google Scholar 

  • Bélanger, A., & Sabourin, P. (2017). Microsimulation and population dynamics: An introduction to Modgen 12 (Vol. 43). Basel: Springer.

    Book  Google Scholar 

  • Boarnet, M. G., Chalermpong, S., & Geho, E. (2005). Specification issues in models of population and employment growth. Papers in Regional Science, 84(1), 21–46.

    Article  Google Scholar 

  • Brueckner, J. K., & Zenou, Y. (2003). Space and unemployment: The labor-market effects of spatial mismatch. Journal of Labor Economics, 21(1), 242–262.

    Article  Google Scholar 

  • Clark, S., Birkin, M., Heppenstall, A., & Rees, P. (2017). Using 2011 Census data to estimate future elderly health care demand. In J. Stillwell & O. Duke-Williams (Eds.), The Routledge handbook of census resources, methods and applications: Unlocking the UK 2011 Census. London: Routledge.

    Google Scholar 

  • Faini, R. (1996). Increasing returns, migrations and convergence. Journal of Development Economics, 49(1), 121–136.

    Article  Google Scholar 

  • Fujita, M., Krugman, P. R., & Venables, A. J. (2001). The spatial economy: Cities, regions, and international trade. Cambridge, MA: MIT Press.

    Google Scholar 

  • Haberman, S., & Renshaw, A. (2009). On age-period-cohort parametric mortality rate projections. Insurance: Mathematics and Economics, 45(2), 255–270.

    Google Scholar 

  • Harding, A., Vidyattama, Y., & Tanton, R. (2011). Demographic change and the needs-based planning of government services: Projecting small area populations using spatial microsimulation. Journal of Population Research, 28(2–3), 203–224.

    Article  Google Scholar 

  • Khan, R., Orazem, P. F., & Otto, D. M. (2001). Deriving empirical definitions of spatial labor markets: The roles of competing versus complementary growth. Journal of Regional Science, 41(4), 735–756.

    Article  Google Scholar 

  • Kontis, V., Bennett, J. E., Mathers, C. D., Li, G., Foreman, K., & Ezzati, M. (2017). Future life expectancy in 35 industrialised countries: Projections with a Bayesian model ensemble. The Lancet, 389(10076), 1323–1335.

    Article  Google Scholar 

  • Lee, R. D., & Carter, L. R. (1992). Modeling and forecasting US mortality. Journal of the American Statistical Association, 87(419), 659–671.

    Google Scholar 

  • Li, J. (2013). A Poisson common factor model for projecting mortality and life expectancy jointly for females and males. Population Studies, 67(1), 111–126.

    Article  Google Scholar 

  • Li, J., & O’Donoghue, C. (2013). A survey of dynamic microsimulation models: Uses, model structure and methodology. International Journal of Microsimulation, 6(2), 3–55.

    Google Scholar 

  • Lomax, N., & Smith, A. (2017). Microsimulation for demography. Australian Population Studies, 1(1), 73–85.

    Google Scholar 

  • Longhi, S., & Nijkamp, P. (2007). Forecasting regional labor market developments under spatial autocorrelation. International Regional Science Review, 30(2), 100–119.

    Article  Google Scholar 

  • Lovelace, R., Ballas, D., & Watson, M. (2014). A spatial microsimulation approach for the analysis of commuter patterns: From individual to regional levels. Journal of Transport Geography, 34, 282–296.

    Article  Google Scholar 

  • Lymer, S., Brown, L., Yap, M., & Harding, A. (2008). 2001 regional disability estimates for New South Wales, Australia, using spatial microsimulation. Applied Spatial Analysis and Policy, 1(2), 99–116.

    Article  Google Scholar 

  • Mankiw, N. G., Romer, D., & Weil, D. N. (1992). A contribution to the empirics of economic growth. The Quarterly Journal of Economics, 107(2), 407–437.

    Article  Google Scholar 

  • Nelissen, J. H. (1991). Household and education projections by means of a microsimulation model. Economic Modelling, 8(4), 480–511.

    Article  Google Scholar 

  • Procter, K. L., Clarke, G. P., Ransley, J. K., & Cade, J. (2008). Micro-level analysis of childhood obesity, diet, physical activity, residential socioeconomic and social capital variables: Where are the obesogenic environments in Leeds? Area, 40(3), 323–340.

    Article  Google Scholar 

  • Rayer, S., & Smith, S. K. (2014). Population projections by age for Florida and its counties: Assessing accuracy and the impact of adjustments. Population Research and Policy Review, 33(5), 747–770.

    Article  Google Scholar 

  • Renkow, M. (2003). Employment growth, worker mobility, and rural economic development. American Journal of Agricultural Economics, 85(2), 503–513.

    Article  Google Scholar 

  • Stillwell, J., & Dennett, A. (2012). A comparison of internal migration by ethnic group in Great Britain using a district classification. Journal of Population Research, 29(1), 23–44.

    Article  Google Scholar 

  • Tanton, R., Vidyattama, Y., Nepal, B., & McNamara, J. (2011). Small area estimation using a reweighting algorithm. Journal of the Royal Statistical Society: Series A (Statistics in Society), 174(4), 931–951.

    Article  Google Scholar 

  • Vidyattama, Y. (2016). Inter-provincial migration and 1975–2005 regional growth in Indonesia. Papers in Regional Science, 95, S87–S105.

    Article  Google Scholar 

  • Williamson, P. (1996). Community care policies for the elderly, 1981 and 1991: A microsimulation approach. In G. Clarke (Ed.), Microsimulation for urban and regional policy analysis (pp. 64–87). London: Pion.

    Google Scholar 

  • Wilson, T., & Bell, M. (2007). Probabilistic regional population forecasts: The example of Queensland, Australia. Geographical Analysis, 39(1), 1–25.

    Article  Google Scholar 

  • Wilson, T., & Rees, P. (2005). Recent developments in population projection methodology: A review. Population, Space and Place, 11(5), 337–360.

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank the editor and the two reviewers for their advice. We also thank Hang To for her work in the data collection and the help in the early version of the paper. The work is partially funded by the Department of Home Affairs, Australia.

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Correspondence to Jinjing Li.

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Li, J., Vidyattama, Y. Projecting spatial population and labour force growth in Australian districts. J Pop Research 36, 205–232 (2019). https://doi.org/10.1007/s12546-019-09229-6

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