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Systematic Bias and Nontransparency in US Social Security Administration Forecasts

Author

Listed:
  • Konstantin Kashin
  • Gary King
  • Samir Soneji

Abstract

We offer an evaluation of the Social Security Administration demographic and financial forecasts used to assess the long-term solvency of the Social Security Trust Funds. This same forecasting methodology is also used in evaluating policy proposals put forward by Congress to modify the Social Security program. Ours is the first evaluation to compare the SSA forecasts with observed truth; for example, we compare forecasts made in the 1980s, 1990s, and 2000s with outcomes that are now available. We find that Social Security Administration forecasting errors—as evaluated by how accurate the forecasts turned out to be—were approximately unbiased until 2000 and then became systematically biased afterward, and increasingly so over time. Also, most of the forecasting errors since 2000 are in the same direction, consistently misleading users of the forecasts to conclude that the Social Security Trust Funds are in better financial shape than turns out to be the case. Finally, the Social Security Administration's informal uncertainty intervals appear to have become increasingly inaccurate since 2000. At present, the Office of the Chief Actuary, at the Social Security Administration, does not reveal in full how its forecasts are made. Every future Trustees Report, without exception, should include a routine evaluation of all prior forecasts, and a discussion of what forecasting mistakes were made, what was learned from the mistakes, and what actions might be taken to improve forecasts going forward. And the Social Security Administration and its Office of the Chief Actuary should follow best practices in academia and many other parts of government and make their forecasting procedures public and replicable, and should calculate and report calibrated uncertainty intervals for all forecasts.

Suggested Citation

  • Konstantin Kashin & Gary King & Samir Soneji, 2015. "Systematic Bias and Nontransparency in US Social Security Administration Forecasts," Journal of Economic Perspectives, American Economic Association, vol. 29(2), pages 239-258, Spring.
  • Handle: RePEc:aea:jecper:v:29:y:2015:i:2:p:239-58
    Note: DOI: 10.1257/jep.29.2.239
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    Citations

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    Cited by:

    1. Magali Barbieri, 2018. "Investigating the Difference in Mortality Estimates between the Social Security Administration Trustees' Report and the Human Mortality Database," Working Papers wp394, University of Michigan, Michigan Retirement Research Center.
    2. Kajal Lahiri & Junyan Zhang & Yongchen Zhao, 2023. "Inefficiency in social security trust funds forecasts," Applied Economics Letters, Taylor & Francis Journals, vol. 30(10), pages 1353-1357, June.
    3. James Gorry & Dean Scrimgeour, 2018. "Using Engel Curves To Estimate Consumer Price Index Bias For The Elderly," Contemporary Economic Policy, Western Economic Association International, vol. 36(3), pages 539-553, July.
    4. Li Tan & Cory Koedel, 2019. "The Effects of Differential Income Replacement and Mortality on U.S. Social Security Redistribution," Southern Economic Journal, John Wiley & Sons, vol. 86(2), pages 613-637, October.
    5. Kajal Lahiri & Jianting Hu, 2021. "Productive efficiency in processing social security disability claims: a look back at the 1989–95 surge," Empirical Economics, Springer, vol. 60(1), pages 419-457, January.
    6. Alesina, A. & Passalacqua, A., 2016. "The Political Economy of Government Debt," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 2599-2651, Elsevier.
    7. Carlos Patrick Alves da Silva & Claudio Alberto Castelo Branco Puty & Marcelino Silva da Silva & Solon Venâncio de Carvalho & Carlos Renato Lisboa Francês, 2017. "Financial forecasts accuracy in Brazil’s social security system," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-20, August.

    More about this item

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • H55 - Public Economics - - National Government Expenditures and Related Policies - - - Social Security and Public Pensions

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