Skip to main content
Log in

Between a rock and a hard place: The evaluation of demographic forecasts

  • Estimates And Projections
  • Published:
Population Research and Policy Review Aims and scope Submit manuscript

Abstract

Forecasting, in general, has been described as an unavoidable yet impossible task. This irony, which comprises the ‘rock’ and the ‘hard place’ in the title, creates a high level of cognitive dissonance, which, in turn, generates stress for those both making and using forecasts that have non-trivial impacts. Why? Because the forecasted numbers that are invariably accorded a high degree of precision inexorably reveal their inevitable imprecision when the numbers forming the actuality finally take place and the numbers comprising the forecast's errors are precisely measured. The current state of the art in demography for dealing with the ‘rock’ and the ‘hard place’ is a less-than-successful strategy because it is based on an acceptance of accuracy as the primary evaluation criterion, which is the source of cognitive dissonance. One way to reduce cognitive dissonance is to change the relationship of the very cognitive elements creating it. We argue that forecast evaluations currently focused on accuracy and based on measures like RMSE and MAPE be refocused to include utility and propose for this purpose the ‘Proportionate Reduction in Error’ (PRE) measure. We illustrate our proposal with examples and discuss its advantages. We conclude that including PRE as an evaluation criterion can reduce stress by reducing cognitive dissonance without, at the same time, either trivializing the evaluation process or substantively altering how forecasts are done and presented.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Agresti, A. (1990).Categorical data analysis. New York: John Wiley.

    Google Scholar 

  • Armstrong, J. (1982–83). The 15 most common pitfalls and how to avoid them,Journal of Business Forecasting 1(6): 12–15.

    Google Scholar 

  • Ascher, W. (1978).Forecasting: An appraisal for policy-makers and planners. Baltimore, MD: The Johns Hopkins Press.

    Google Scholar 

  • Brettschneider, S. & Gorr, W. (1992). Alternatives to forecast error based evaluation: Communicability, manipulability, credibility, and policy relevance, pp. 114–123, in:Proceedings of the Federal Forecasters Conference, 1992.

  • Burch, T., Swanson, D.A. & Tedrow, L. (1996). What is applied demography? What can it be? How can we get there from here?,Population Research And Policy Review, forthcoming in a special issue (Spring, Vol. 15).

  • Costner, H. (1965). Criteria for measures of association,American Sociological Review 30: 341–353.

    Google Scholar 

  • D'Allesandro, F. (1987). Should applied demographers take out liability insurance?,Applied Demography 3(Fall): 1–3.

    Google Scholar 

  • Dorn, H. (1950). Pitfalls in population forecasts and projections,Journal of the American Statistical Association 45: 311–334.

    Google Scholar 

  • Espenshade, T. & Grummer-Strawn, L. (1991). Evaluating the accuracy of US population projection models. Paper presented at the Population Association of America Conference, Washington, DC.

  • Festinger, L. (1957).A theory of cognitive dissonance. Evanston, IL: Row, Peterson.

    Google Scholar 

  • Festinger, L., Riecken, H., & Schachter, S. (1956).When prophesy fails. Minneapolis, MN: University of Minnesota Press.

    Google Scholar 

  • Hajnal, J. (1995). The prospects for population forecasts,Journal of The American Statistical Association 50: 309–322.

    Google Scholar 

  • Keyfitz, N. (1972). On future population,Journal of the American Statistical Association 67: 347–363.

    Google Scholar 

  • Keyfitz, N. (1981). The limits of population forecasting,Population and Development Review 7: 579–594.

    Google Scholar 

  • Keyfitz, N. (1982). Can knowledge improve forecasts?,Population and Development Review 8: 729–751.

    Google Scholar 

  • Keyfitz, N. (1987). The social and political context of population forecasting, pp. 235–258, in: W. Alonso & P. Starr (eds.),The Politics of Numbers. New York: Russell Sage Foundation.

    Google Scholar 

  • Kintner, H. & Swanson, D. (1994). Forecasting health benefit populations. Paper presented at the 14th International Symposium on Forecasting, Stockholm, Sweden.

  • Makridakis, S. & Hibon, M. (1979). Accuracy of forecasting: An empirical investigation,Journal of the Royal Statistical Society, Series A: 142: 97–145.

    Google Scholar 

  • Moen, E. (1984). Voodoo forecasting: Technical, political, and ethical issues regarding the projection of local population growth,Population Research and Policy Review 3: 1–25.

    Google Scholar 

  • Murdock, S. & Leistritz, L. (1980). Selecting socio-economic assessment models: A discussion of criteria and selected models,Journal of Environmental Management 10: 1–12.

    Google Scholar 

  • Murdock, S., Hamm, R., Fannin, D., Pecotte, B. & Voss, P. (1989).Evaluating small-area population estimates and projections. Applied Community Research Monograph E3. Alexandria, VA: American Chamber of Commerce Researchers Association.

    Google Scholar 

  • Murdock, S., Leistritz, F.L. Hamm, R., Hwang, S. & Parpia, B. (1984) An assessment of the accuracy of a regional economic-demographic projection model,Demography 21: 383–404.

    Google Scholar 

  • Musham, H.V. (1965). The use of cost functions in making assumptions for population forecasts,Proceedings of the World Population Conference. New York: United Nations.

    Google Scholar 

  • Ohio Data Users Center (1985).Population projections, Ohio and counties by age and sex: 1980 to 2000. Columbus, OH: Ohio Department of Development.

    Google Scholar 

  • Pittenger, D. (1978). The role of judgement, assumptions, techniques, and confidence limits in forecasting population,Socio-Economic Planning Sciences 12: 271–276.

    Google Scholar 

  • Rainford, P. & Masser, I (1987). Population forecasting and urban planning practice,Environment and Planning A 19: 1463–1475.

    Google Scholar 

  • Reynolds, H.T. (1977).Analysis of nominal data. Beverly Hills, CA: Sage.

    Google Scholar 

  • Robinson, J.G., Ahmed, B., Das Gupta, P. & Woodrow, K. (1991). Estimating coverage of the 1990 United States census: Demographic analysis. Paper presented at the Annual Meeting of The American Statistical Association, Atlanta, Georgia.

  • Romaniuc, A. (1994). Reflection on population forecasting: From prediction to prospective analysis,Canadian Studies in Population 21(2): 165–180.

    Google Scholar 

  • Smith, S. (1987). Tests of forecast accuracy and bias for county population projections,Journal of the American Statistical Association 82: 991–1003.

    Google Scholar 

  • Smith, S. & Bayya, R. (1992). An evaluation of population forecasts for Florida and its counties,Applied Demography 7 (Spring): 1–5.

    Google Scholar 

  • Smith, S. & Shahidullah, M. (1995). An evaluation of population projection errors for census tracts,Journal of The American Statistical Association 90(1): 69–71.

    Google Scholar 

  • Smith, S. & Sincich, T. (1990). The relationship between the length of the base period and population forecast errors,Journal of the American Statistical Association 85: 367–375.

    Google Scholar 

  • Smith, S. & Sincich, T. (1992). Evaluating the forecast accuracy and bias of alternative projections for States,International Journal of Forecasting 8: 495–508.

    Google Scholar 

  • Stoto, M. (1983). The accuracy of population projections,Journal of the American Statistical Association 78: 13–20.

    Google Scholar 

  • Starr, P. (1987). The sociology of official statistics, pp. 7–58, in: W. Alonso & P. Starr (eds.),The politics of numbers. New York: Russell Sage Foundation.

    Google Scholar 

  • Swanson, D.A. (1986). Evaluating population estimates and short-term projections,Applied Demography 2 (November): 5–6.

    Google Scholar 

  • Swanson, D.A. & Beck, D. (1994). New short-term county population projection method.Journal of Economic and Social Measurement 20: 1–26.

    Google Scholar 

  • Swanson, D.A. & Tayman, J. (1994). Measuring the utility of population projections. Paper presented at the Annual Meeting of the Ohio Academy of Science, Toledo, Ohio.

  • Swanson, D.A., Tayman, J. & Beck, D. (1995). On the utility of lagged ratio-correlation as a short-term county population projection method: A case study of Washington State,Journal of Economic and Social Measurement 21: 1–16.

    Google Scholar 

  • Sykes, Z. (1969). Some stochastic versions of the matrix model for population dynamics,Journal of the American Statistical Association 44: 111–130.

    Google Scholar 

  • Tayman, J. (1993). How accurately can we forecast small area population? Presented at the Annual Meeting of the American Statistical Association, San Francisco, California.

  • Tayman, J. (1996). The accuracy of small area population forecasts based on a spatial interaction land use modelling system,Journal of The American Planning Association (forthcoming).

  • Tayman, J., Schafer, E. & Carter, L. (1994). Confidence intervals for small area population forecast error: A repeated sampling approach. Paper presented at the Annual Meeting of the Population Association of America, Miami, Florida.

  • Tayman, J. & Swanson, D. (1995). Alternative measures for evaluating population forecasts: A comparison of state, county, and subcounty areas. Paper presented at The Annual Meeting of the Population Association of America, San Francisco, California.

  • US Bureau of the Census (1975).Coverage of population in the 1970 census and some implications for public programs. Current Population Reports, P-23, No. 56. Washington, DC: US Government Printing Office.

    Google Scholar 

  • US Bureau of the Census (1982).Coverage of the national population in the 1980 census by age, sex, and race. Current Population Reports, P-23, No. 115. Washington, DC: US Government Printing Office.

    Google Scholar 

  • US Bureau of the Census (1992).Population projections of the United States. by age, sex, race, and Hispanic origin: 1992–2050. Washington, DC: US Government Printing Office.

    Google Scholar 

  • Washington State, Office of Financial Management (1986).Forecasts of the State and County populations by year for selected age groups: 1980–2000. F86–11. Olympia, WA: Population Estimation and Forecasting Unit.

    Google Scholar 

  • Wheelwright, S. & Makridakis, S. (1980).Forecasting methods for management, 3rd ed. New York: John Wiley.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Swanson, D.A., Tayman, J. Between a rock and a hard place: The evaluation of demographic forecasts. Popul Res Policy Rev 14, 233–249 (1995). https://doi.org/10.1007/BF01074460

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01074460

Key words

Navigation