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The Accuracy of Hamilton–Perry Population Projections for Census Tracts in the United States

Author

Listed:
  • Jack Baker

    (Transamerica Life)

  • David Swanson

    (University of California Riverside
    University of Washington)

  • Jeff Tayman

    (University of California San Diego)

Abstract

In a first-ever nation-wide census tract evaluation, we assess the accuracy of the Hamilton–Perry population projection method for 65,221 census tracts. We started with 73,607 census tracts but eliminated those for which zeros appeared in age/sex groups. The test uses 1990 and 2000 census tract data by age and gender to construct cohort change ratios, which are then applied to 2000 census tract data to generate 2010 Hamilton–Perry projections that are evaluated in an ex post facto test against the reported 2010 census tract data by age and gender. The projections include: (1) uncontrolled age and gender projections; and (2) age and gender projections controlled to a projection of the population total by census tract. Mean Absolute Percent Error (MAPE) is used to evaluate precision and Mean Algebraic Percent Error (MALPE) is used to evaluate bias. We find that controlling the Hamilton–Perry projections by age for each tract to the linearly projected total population of each tract reduces both MAPE and MALPE within age groups by gender and for total females and total males. As this result suggests, simple linear extrapolation provides more accurate projections of the total population than does the Hamilton–Perry Method. However, even with controlling we find the Hamilton–Perry projections by age to be biased upward. Finally, we use MAPE-R (MAPE- Revised) to evaluate the effect of extreme outliers and find that high MAPEs in the uncontrolled projections are largely driven by extreme errors (outliers) found in less than 1 percent of the 65,221 census tracts used in the study.

Suggested Citation

  • Jack Baker & David Swanson & Jeff Tayman, 2021. "The Accuracy of Hamilton–Perry Population Projections for Census Tracts in the United States," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 40(6), pages 1341-1354, December.
  • Handle: RePEc:kap:poprpr:v:40:y:2021:i:6:d:10.1007_s11113-020-09601-y
    DOI: 10.1007/s11113-020-09601-y
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    References listed on IDEAS

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    1. Jack Baker & Adelamar Alcantara & Xiaomin Ruan & Kendra Watkins & Srini Vasan, 2013. "A Comparative Evaluation of Error and Bias in Census Tract-Level Age/Sex-Specific Population Estimates: Component I (Net-Migration) vs Component III (Hamilton–Perry)," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 32(6), pages 919-942, December.
    2. Tayman, Jeff & Swanson, David A., 2016. "New insights on the impact of coefficient instability on ratio-correlation population estimates," Journal of Economic and Social Measurement, IOS Press, issue 2, pages 121-143.
    3. Aude Bernard & Martin Bell & Elin Charles-Edwards, 2014. "Life-Course Transitions and the Age Profile of Internal Migration," Population and Development Review, The Population Council, Inc., vol. 40(2), pages 213-239, June.
    4. Jeff Tayman & Stanley Smith & Jeffrey Lin, 2007. "Precision, bias, and uncertainty for state population forecasts: an exploratory analysis of time series models," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 26(3), pages 347-369, June.
    5. Jeff Tayman & Stanley Smith & Stefan Rayer, 2011. "Evaluating Population Forecast Accuracy: A Regression Approach Using County Data," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 30(2), pages 235-262, April.
    6. David A. Swanson, 2015. "On the Relationship among Values of the Same Summary Measure of Error when it is used across Multiple Characteristics at the Same Point in Time: An Examination of MALPE and MAPE," Review of Economics & Finance, Better Advances Press, Canada, vol. 5, pages 1-14, August.
    7. Stefan Rayer & Stanley Smith, 2014. "Population Projections by Age for Florida and its Counties: Assessing Accuracy and the Impact of Adjustments," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 33(5), pages 747-770, October.
    8. Tom Wilson, 2016. "Evaluation of Alternative Cohort-Component Models for Local Area Population Forecasts," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 35(2), pages 241-261, April.
    9. David Swanson & Alan Schlottmann & Bob Schmidt, 2010. "Forecasting the Population of Census Tracts by Age and Sex: An Example of the Hamilton–Perry Method in Action," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 29(1), pages 47-63, February.
    10. Ruben Hernandez-Murillo & Lesli S. Ott & Michael T. Owyang & Denise Whalen, 2011. "Patterns of interstate migration in the United States from the survey of income and program participation," Review, Federal Reserve Bank of St. Louis, vol. 93(May), pages 169-186.
    11. repec:mpr:mprres:6984 is not listed on IDEAS
    12. Stanley Smith & Jeff Tayman, 2003. "An evaluation of population projections by age," Demography, Springer;Population Association of America (PAA), vol. 40(4), pages 741-757, November.
    13. David A. Swanson & Jeff Tayman & T.M. Bryan, 2018. "A Note on Rescaling the Arithmetic Mean for Right-skewed Positive Distributions," Review of Economics & Finance, Better Advances Press, Canada, vol. 14, pages 17-24, November.
    14. Jeff Tayman & David A. Swanson, 2017. "Using modified cohort change and child-woman ratios in the Hamilton–Perry forecasting method," Journal of Population Research, Springer, vol. 34(3), pages 209-231, September.
    15. David Swanson & George Hough, 2012. "An Evaluation of Persons per Household (PPH) Estimates Generated by the American Community Survey: A Demographic Perspective," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 31(2), pages 235-266, April.
    16. Green, Kesten C. & Armstrong, J. Scott, 2015. "Simple versus complex forecasting: The evidence," Journal of Business Research, Elsevier, vol. 68(8), pages 1678-1685.
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