Lessons from stochastic small-area population projections: The case of Waikato subregions in New Zealand

被引：11
作者：
Cameron M.P. [1 ]
Poot J. [2 ]
机构：
[1] Department of Economics, University of Waikato, Hamilton
[2] National Institute of Demographic and Economic Analysis, University of Waikato, Hamilton
来源：
Journal of Population Research | 2011年 / 28卷 / 2-3期
关键词：
Cohort-component model; Net migration rates; Small area; Stochastic population projections;
D O I：
10.1007/s12546-011-9056-3
中图分类号：
学科分类号：
摘要：
Subnational population projections in New Zealand by means of the conventional deterministic cohort-component method have had a tendency to be conservative: underprojecting fast-growing populations and overprojecting slow-growing ones. In this paper we use a stochastic population projection method as an alternative. We generate population projections for five demographically distinct administrative areas within the Waikato region of New Zealand: Hamilton City, Franklin District, Thames-Coromandel District, Otorohanga District and South Waikato District. The results are compared to official subnational deterministic projections. The accuracy of subnational population projections in New Zealand is strongly affected by the instability of migration as a component of population change. Differently from the standard cohort-component method, in which net migration levels are projected, the key parameters of our method are age-gender-area specific probabilistic net migration rates. Generally, the identified and modelled uncertainty makes the traditional 'mid-range scenario of subnational deterministic projections of limited use for policy analysis or planning beyond a relatively short projection horizon. We find that the projected range of rates of population growth is wider for smaller regions and/or regions more strongly affected by net migration. Directions for further development of the methodology are suggested. © 2011 Springer Science and Business Media B.V.
引用
收藏
页码：245 / 265
页数：20
相关论文
共 38 条
  • [31] Tuljapurkar S., Stochastic population forecasts and their uses, International Journal of Forecasting, 8, 3, pp. 385-391, (1992)
  • [32] Tuljapurkar S., Lee R.D., Li Q., Random scenario forecasts versus stochastic forecasts, International Statistical Review, 72, 2, pp. 185-199, (2004)
  • [33] Vensim Version 5, (2005)
  • [34] Wilson T., Application of a probabilistic framework to New Zealand's official national population projections, New Zealand Population Review, 31, 1, pp. 51-76, (2005)
  • [35] Wilson T., The forecast accuracy of Australian Bureau of Statistics national population projections, Journal of Population Research, 24, 1, pp. 91-117, (2007)
  • [36] Wilson T., Bell M., Comparative empirical evaluations of internal migration models in subnational population projections, Journal of Population Research, 21, 2, pp. 127-160, (2004)
  • [37] Wilson T., Bell M., Probabilistic regional population forecasts: The example of Queensland, Australia, Geographical Analysis, 39, 1, pp. 1-25, (2007)
  • [38] Wilson T., Rees P., Recent developments in population projection methodology: A review, Population, Space and Place, 11, pp. 337-360, (2005)
1234