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Stochastic Infinite Horizon Forecasts for US Social Security Finances

Published online by Cambridge University Press:  26 March 2020

Ronald Lee  and
Michael Anderson
Ronald Lee*
Affiliation:
Demography and Economics, University of California, Berkeley
Michael Anderson*
Affiliation:
Centre for the Economics and Demography of Aging, University of California at Berkeley, 2232 Piedmont Avenue Berkeley, CA, 94720-2120
*
e-mail: rlee@demog.berkeley.edu
e-mail: mikeand1@comcast.net
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Abstract

Even over a 75-year horizon, forecasts of PAYGO pension finances are misleadingly optimistic. Infinite horizon forecasts are necessary, but are they possible? We build on earlier stochastic forecasts of the US Social Security trust fund which model key demographic and economic variables as historical time series, and use the fitted models to generate Monte Carlo simulations of future fund performance. Using a 500-year stochastic projection, effectively infinite with discounting, we find a fund balance of −5.15 per cent of payroll, compared to the −3.5 per cent of the 2004 Trustees‘ Report, probably reflecting different mortality projections. Our 95 per cent probability bounds are −10.5 and −1.3 per cent. Such forecasts, which reflect only ‘routine’ uncertainty, have many problems but nonetheless seem worthwhile.

Keywords

Social securitysustainableinfinite horizonstochasticforecastH55J11C15
Type
Articles
Information
National Institute Economic Review , Volume 194 , October 2005 , pp. 82 - 93
Copyright
Copyright © 2005 National Institute of Economic and Social Research

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Footnotes

This research was supported by the US Social Security Administration through grant #10-P-98363-1 to the National Bureau of Economic Research as part of the SSA Retirement Research Consortium. The opinions and conclusions expressed are solely those of the authors and do not represent the opinions or policy of SSA, any agency of the Federal Government, or the NBER.

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