Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-28T00:54:51.207Z Has data issue: false hasContentIssue false
  • English
  • Français

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: [email protected]
e-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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.

References

Board of Trustees (2003), ‘Federal old-age and survivors insurance and disability insurance trust funds’, The 2003 Annual Report of the Board of Trustees of the Federal Old-Age and Survivors Insurance and Disability Insurance Trust Funds, Washington, D.C., US Government Printing Office.Google Scholar
Board of Trustees (2004), ‘Federal old-age and survivors insurance and disability insurance trust funds’, The 2004 Annual Report of the Board of Trustees of the Federal Old-Age and Survivors Insurance and Disability Insurance Trust Funds, Washington, D.C., US Government Printing Office.Google Scholar
Board of Trustees (2005), ‘Federal old-age and survivors insurance and disability insurance trust funds’, The 2004 Annual Report of the Board of Trustees of the Federal Old-Age and Survivors Insurance and Disability Insurance Trust Funds, Washington, D.C., US Government Printing Office.Google Scholar
Burdick, C. and Manchester, J. (2003), ‘Stochastic models of the social security trust funds’, Research and Statistics Note, posted on the website of the Office of the Actuary.Google Scholar
Congressional Budget Office (2001), ‘Uncertainty in social security's long-term finances: a stochastic analysis’, a paper posted on the CBO website, www.cbo.gov, December.Google Scholar
Goss, S.C. (1999), ‘Measuring solvency in the social security system’, in Mitchell, O.S., Myers, R.J. and Young, H. (eds), Prospects for Social Security Reform, Philadelphia: University of Pennsylvania Press, pp. 16–36.Google Scholar
Holmer, M.R. (2003), ‘Methods for stochastic trust fund projection’, unpublished report commissioned by the Social Security Administration, and accessible through the website of the Office of the Actuary.Google Scholar
Lee, R.D., Anderson, M.W. and Tuljapurkar, S. (2003), ‘Stochastic forecasts of the social security trust fund’, Report for the Social Security Administration, January.CrossRefGoogle Scholar
Lee, R. and Carter, L. (1992), ‘Modeling and forecasting US mortality’, Journal of the American Statistical Association, 87, 419, September, pp. 659–671,Google Scholar
and ‘Rejoinder’, same issue, pp. 674–675.Google Scholar
Lee, R.D. and Miller, T. (2001), ‘Assessing the performance of the Lee-Carter approach to modeling and forecasting mortality’, Demography, 38, 4, November, pp. 537–549.CrossRefGoogle Scholar
Lee, R., Miller, T. and Anderson, M. (2004), ‘Stochastic infinite horizon forecasts for social security and related studies’, National Bureau of Economic Research Working Paper No. 10918.Google Scholar
Lee, R. and Tuljapurkar, S. (1998a), ‘Stochastic forecasts for social security’, in Wise, D. (ed.), Frontiers in the Economics of Aging, Chicago, University of Chicago Press, pp. 393–420.Google Scholar
Lee, R. and Tuljapurkar, S. (1998b), ‘Uncertain demographic futures and social security finances’, American Economic Review, Papers and Proceedings, May, pp. 237–241.Google Scholar
Lee, R. and Tuljapurkar, S. (2000), ‘Population forecasting for fiscal planning: issues and innovations’, in Auerbach, A. and Lee, R. (eds), Demography and Fiscal Policy, Cambridge, Cambride University Press, pp. 7–57.Google Scholar
Lee, R. and Yamagata, H. (2003), ‘Sustainable social security: what would it cost?’, National Tax Journal, 56, 1, part 1, pp. 27–43.CrossRefGoogle Scholar
Myers, J.R. (1959), ‘Methodology involved in developing long-range cost estimates for the old-age, survivors, and disability insurance system’, Actuarial Study No. 49, Division of the Actuary, Social Security Administration, US Department of Health, Education, and Welfare, Washington, D.C.Google Scholar
Oeppen, J. and Vaupel, J.W. (2002), ‘Broken limits to life expectancy’, Science, 296, pp. 1029–1031.CrossRefGoogle ScholarPubMed
Wilmoth, J.R. (1993), ‘Computational methods for fitting and extrapolating the Lee-Carter model of mortality change’, Technical Report, Department of Demography, University of California, Berkeley. http://www.demog.berkeley.edu/%7Ejrw/Papers/LCtech.pdfGoogle Scholar