An Expert-Based Framework for Probabilistic National Population Projections: The Example of Austria
- 100 Downloads
The traditional way of dealing with uncertainty in population projections through high and low variants is unsatisfactory because it remains unclear what range of uncertainty these alternative paths are assumed to cover. But probabilistic approaches have not yet found their way into official population projections. This paper proposes an expert-based probabilistic approach that seems to meet important criteria for successful application to national and international projections: 1) it provides significant advantages to current practice, 2) it presents an evolution of current practice rather than a discontinuity, 3) it is scientifically sound, and 4) it is applicable to all countries.
In a recent Nature article (Lutz et al., 1997) this method was applied to 13 world regions. This paper discusses the applicability to national projections by directly taking the alternative assumptions defined by the Austrian Statistical Office. Sensitivity analyses that resolve some methodological questions about the approach are also presented.
KeywordsSensitivity Analysis Current Practice Public Finance Important Criterion Probabilistic Approach
Unable to display preview. Download preview PDF.
- Adler, M. and Ziglio, E., 1996. Gazing into the Oracle. The Delphi Method and its Application to Social Policy and Public Health. Jessica Kingsley Publishers, London.Google Scholar
- Alho, J. M. and Spencer, B. D., 1985. ‘Uncertain population forecasting’, Journal of the American Statistical Association 80(390): 306-314.Google Scholar
- Eurostat, 1991. Two Long-Term Population Scenarios for the European Community. Scenarios Prepared for the International Conference on Human Resources in Europe at the Dawn of the 21st Century, November 27-29, Luxembourg.Google Scholar
- Hanika, A., Lutz, W. and Scherbov, S., 1997. ‘Ein probabilistischer Ansatz zur Bevölkerungsvorausschätzung für Österreich’, Statistische Nachrichten 12: 984-988.Google Scholar
- Keilman, N. and Cruijsen, H. (eds), 1992. National Population Forecasting in Industrialized Countries. Swets and Zeitlinger, Amsterdam.Google Scholar
- Lee, R., 1998. ‘Probabilistic approaches to population forecasting’, forthcoming in: W. Lutz, J. Vaupel and D. Ahlburg (eds), Rethinking Population Projections. A Special Supplement of Population and Development Review.Google Scholar
- Lee, R. and Tuljapurkar, S., 1994. ‘Stochastic population projections for the United States: Beyond high, medium and low’, Journal of the American Statistical Association 89(428): 1175-1189.Google Scholar
- Lee, R. and Carter, L., 1992. ‘Modeling and forecasting the time series of U.S. mortality’, Journal of the American Statistical Association 87(419): 659-671.Google Scholar
- Linstone, H. A. and Turoff, M. (eds), 1975. The Delphi Method. Techniques and Applications. Addison-Wesley Publishing Company, Reading, MA, USA.Google Scholar
- Lutz, W. (ed), 1994. The Future Population of the World. What Can We Assume Today? Earthscan, London.Google Scholar
- Lutz, W. (ed), 1996. The Future Population of the World. What Can We Assume Today? Earthscan, London, revised edition.Google Scholar
- Lutz, W., Sanderson, W. and Scherbov, S., 1996. ‘Probabilistic population projections based on expert opinion’, in: W. Lutz (ed), The Future Population of the World. What Can We Assume Today? Earthscan, London, revised edition 397-428.Google Scholar