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Statistical Security for Social Security

Demography

Abstract

The financial viability of Social Security, the single largest U.S. government program, depends on accurate forecasts of the solvency of its intergenerational trust fund. We begin by detailing information necessary for replicating the Social Security Administration’s (SSA’s) forecasting procedures, which until now has been unavailable in the public domain. We then offer a way to improve the quality of these procedures via age- and sex-specific mortality forecasts. The most recent SSA mortality forecasts were based on the best available technology at the time, which was a combination of linear extrapolation and qualitative judgments. Unfortunately, linear extrapolation excludes known risk factors and is inconsistent with long-standing demographic patterns, such as the smoothness of age profiles. Modern statistical methods typically outperform even the best qualitative judgments in these contexts. We show how to use such methods, enabling researchers to forecast using far more information, such as the known risk factors of smoking and obesity and known demographic patterns. Including this extra information makes a substantial difference. For example, by improving only mortality forecasting methods, we predict three fewer years of net surplus, $730 billion less in Social Security Trust Funds, and program costs that are 0.66% greater for projected taxable payroll by 2031 compared with SSA projections. More important than specific numerical estimates are the advantages of transparency, replicability, reduction of uncertainty, and what may be the resulting lower vulnerability to the politicization of program forecasts. In addition, by offering with this article software and detailed replication information, we hope to marshal the efforts of the research community to include ever more informative inputs and to continue to reduce uncertainties in Social Security forecasts.

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Acknowledgments

We thank Robert Aronowitz, Jon Bischof, David Asch, Federico Girosi, David Grande, James Greiner, Kosuke Imai, Valerie Lewis, Scott Lynch, Doug Massey, John Sabelhaus, three anonymous reviewers, and a Deputy Editor for helpful comments and suggestions; Felicitie Bell, Michael Morris, Alice Wade, and John Wilmoth for help reconstructing current Social Security Administration forecast procedures; Martin Holmer for modifying his Social Security simulation program, SSASIM, so that we could measure the effects of alternative mortality forecasts; and the Robert Wood Johnson Foundation Health & Society Scholars program, the National Institute of Child Health and Human Development (NIH 5T32 HD07163), the National Cancer Institute (RC2CA148259) and Harvard's Institute for Quantitative Social Science for research support. Earlier versions of this article were presented at the 2008 Population Association of America annual meeting and at the Harvard Center for Population and Development.

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Correspondence to Samir Soneji.

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Soneji, S., King, G. Statistical Security for Social Security. Demography 49, 1037–1060 (2012). https://doi.org/10.1007/s13524-012-0106-z

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