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

      Cameron, Michael Patrick; Poot, Jacques
      DOI
       10.1007/s12546-011-9056-3
      Link
       www.springerlink.com
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      Citation
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      Cameron, M.P. & Poot, J. (2011). Lessons from stochastic small-area population projections: the case of Waikato subregions in New Zealand. Journal of Population Research, published online 25 March 2011.
      Permanent Research Commons link: https://hdl.handle.net/10289/5287
      Abstract
      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.
      Date
      2011
      Type
      Journal Article
      Publisher
      Springer Science & Business Media B.V.
      Collections
      • Management Papers [1139]
      • Arts and Social Sciences Papers [1443]
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