Abstract
The most common regression-based approach to estimating the total population of a given area is the ratio-correlation method. This multiple regression method involves relating changes between several variables known as symptomatic indicators on the one had to population changes on the other hand. Among its many advantages is the fact that regression has a firm foundation in statistical inference, which leads to the construction of meaningful measures of uncertainty around the estimates it produces. No population technique other than those based on survey samples has this characteristic. In this paper, we provide a comprehensive evaluation of this method of population estimation, which has only been partially accomplished in prior studies of it. We discuss not only how some of its weaknesses can be overcome, but also how they can be leveraged into producing more accurate population estimates.
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References
Bryan, T. (2004). Population estimates. In J. S. Siegel & D. A. Swanson (Eds.), The methods and materials of demography, (2nd ed., pp. 523–560). Amsterdam: Elsevier.
Causey, B. (1988). Evaluation of census ratio estimation and synthetic estimation. Statistical Research Division report no. Census/SRD/RR/88/15, http://www.census.gov/srd/papers/pdf/rr88-15.pdf.
Cohen, M., & Zhang, X. (1988). The difficulty of improving statistical synthetic estimation. Statistical Research Division report no. CENSUS/SRD/RR-88/12. Washington, DC: U. S. Bureau of the Census, http://www.census.gov/srd/papers/pdf/rr88-12.pdf.
Crosetti, A., & Schmitt, R. (1956). A method of estimating the inter-censal population of counties. Journal of the American Statistical Association, 5, 587–590.
D’Allesandro, F., & Tayman, J. (1980). Ridge regression for population estimation: Some insights and clarifications. Staff document No. 56. Olympia. Washington: Office of Financial Management.
Ericksen, E. (1973). A method for combining sample survey data and symptomatic indicators to obtain population estimates for local areas. Demography, 10, 137–160.
Ericksen, E. (1974). A regression method for estimating population changes of local areas. Journal of the American Statistical Association, 69, 867–875.
Ford, B. (1981). The development of county estimates in North Carolina. Staff report AGES 811119. Agriculture statistical reporting service, Research Division. Washington, DC: U.S. Department of Agriculture.
Fox, J. (1991). Regression diagnostics. Quantitative applications in the social sciences, no. 79. Newbury Park: Sage Publications.
Ghosh, M., & Rao, J. N. K. (1994). Small area estimation: An appraisal. Statistical Science, 9, 55–76.
Gonzalez, M., & Hoza, C. (1978). Small area estimation with application to unemployment and housing estimates. Journal of the American Statistical Association, 73, 7–15.
Hamilton, C. H., & Perry, J. (1962). A short method for projecting population by age from one decennial census to another. Social Forces, 41, 163–170.
Hansen, M., Hurwitz, W., & Madow, W. (1953). Sample survey methods and theory, volume I: Methods and applications. New York: Wiley.
Jaffe, A. J. (1951). Handbook of statistical methods for demographers: Selected problems in the analysis of census data, preliminary edition, second printing. Washington, DC: U.S. Government Printing Office.
Levy, P. (1979). Small area estimation: Synthetic and other procedures, 1968–1978. In J. Steinberg (Ed.), Synthetic estimates for small areas: Statistical workshop papers and discussion. NIDA research monograph 24 (pp. 4–19). Rockville: U.S. Department of Health, Education, and Welfare, Public Health Service, Alcohol, Drug Abuse, and Mental Health Administration, National Institute on Drug Abuse.
Mandel, M., & Tayman, J. (1982). Measuring temporal stability in regression models of population estimation. Demography, 19, 135–146.
McKibben, J., & Swanson, D. A. (1997). Linking substance and practice: A case study of the relationship between socio-economic structure and population estimation. Journal of Economic and Social Measurement, 24, 135–147.
Namboodiri, N. K., & Lalu, N. (1971). The average of several simple regression estimates as an alternative to the multiple regression estimate in post-censal and inter-censal population estimation: A case study. Rural Sociology, 36, 187–194.
National Center for Health Statistics. (1968). Synthetic state estimates of disability. PHS publication no. 1759. U.S. Public Health Service. Washington, DC: U.S. Government Printing Office.
O’Hare, W. (1980). A note on the use of regression methods in population estimates. Demography, 17, 341–343.
Platek, R., Rao, J. N. K., Särndal, C. E., & Singh, M. P. (Eds.). (1987). Small area statistics: An international symposium. New York: Wiley.
Rao, J. N. K. (2003). Small area estimation. New York: Wiley-Interscience.
Schaible, W. (1993). Indirect estimators: Definition, characteristics, and recommendations. Proceedings of the survey research methods section, American Statistical Association Vol. I (pp. 1–10). Alexandria, VA: American Statistical Association, http://www.amstat.org/sections/srms/proceedings/y1993f.html.
Schmitt, R., & Crosetti, A. (1954). Accuracy of the ratio-correlation method for estimating post-censal population. Land Economics, 30, 279–281.
Schmitt, R., & Grier, J. (1966). A method of estimating the population of minor civil divisions. Rural Sociology, 31, 355–361.
Smith, S. K., Tayman, J., & Swanson, D. A. (2001). State and local population projections: Methodology and analysis. New York: Kluwer.
Snow, E. C. (1911). The application of the method of multiple correlation to the estimation of post-censal populations. Journal of the Royal Statistical Society, 74, 575–629. (pp. 621–629 contain the discussion).
Spar, M., & Martin, J. (1979). Refinements to regression-based estimates of post-censal population characteristics. Review of Public Data Use, 7, 16–22.
Steinberg, J. (1979). Introduction. In J. Steinberg (Ed.), Synthetic estimates for small areas: Statistical workshop papers and discussion NIDA research monograph 24 (pp. 1–2). Rockville: U.S. Department of Health, Education, and Welfare, Public Health Service, Alcohol, Drug Abuse, and Mental Health Administration, National Institute on Drug Abuse.
Stigler, S. (1986). The history of statistics: The measurement of uncertainty before 1900. Cambridge: The Belknap Press of Harvard University.
Swanson, D. A. (1978a). An evaluation of ratio and difference regression methods for estimating small, highly concentrated populations: The case of ethnic groups. Review of Public Data Use, 6, 18–27.
Swanson, D. A. (1978b). Preliminary results of an evaluation of the utility of ridge regression for making county population estimates. Presented at the annual meeting of the Pacific Sociological Association, Spokane, WA.
Swanson, D. A. (1980). Improving accuracy in multiple regression estimates of county populations using principles from causal modeling. Demography, 17, 413–427.
Swanson, D. A. (1989). Confidence intervals for postcensal population estimates: A case study for local areas. Survey Methodology, 15, 271–280.
Swanson, D. A. (2004). Advancing methodological knowledge within state and local demography: A case study. Population Research and Policy Review, 23, 379–398.
Swanson, D. A., & Beck, D. (1994). A new short-term county population projection method. Journal of Economic and Social Measurement, 21, 25–50.
Swanson, D. A., & Pol, L. (2008). Applied demography: Its business and public sector components. In Y. Zeng (Ed.), The encyclopedia of life support systems, demography volume (pp. 321–348). Oxford: UNESCO-EOLSS Publishers.
Swanson, D. A., & Prevost, R. (1985). A new technique for assessing error in ratio-correlation estimates of population: A preliminary note. Applied Demography, 1, 1–4.
Swanson, D. A., & Tayman, J. (2013). Measuring uncertainty in population forecasts: A new approach. Presented at the EUROSTAT/UNECE work session on Demographic Projections, Rome, Italy, October 29–31.
Swanson, D. A., & Tedrow, L. (1984). Improving the measurement of change in regression models used for county population estimates. Demography, 21, 373–381.
Swanson, D. A., & Tedrow, L. (2013). Exploring stable population concepts from the perspective of cohort change ratios. Presented at the annual conference of the Canadian Population Society, Victoria, British Columbia, Canada, June 5–7.
Tayman, J., & Schafer, E. (1985). The impact of coefficient drift and measurement error on the accuracy of ratio-correlation population estimates. The Review of Regional Studies, 15, 3–10.
Voss, P. (2007). Demography as a spatial social science. Population Research and Policy Review, 26, 457–476.
Weisstein, E. W. (2011). Estimator bias. From MathWorld, a Wolfram web resource, http://mathworld.wolfram.com/EstimatorBias.html.
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Appendix
Appendix
1990–2000 Ratio-Correlation Model: Data, Computations, and 2005 EstimatesWashington State Counties
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Swanson, D., Tayman, J. (2015). On the Ratio-Correlation Regression Method of Population Estimation and Its Variants. In: Hoque, M., B. Potter, L. (eds) Emerging Techniques in Applied Demography. Applied Demography Series, vol 4. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8990-5_8
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