## Abstract

### Trust Fund Balance

As discussed earlier, the SSA increased OASI and SSDI payroll taxes in 1983 to build considerable surplus funds in preparation for an aging population. The excess revenue, held in interest-bearing Treasury bonds, had grown to $1.8 trillion for the OASI Trust Fund and$0.20 trillion for the SSDI Trust Fund in 2006. We estimate considerably smaller OASI and SSDI Trust Funds than does the SSA (Fig. 7, upper-right panel). We forecast steady gains in combined trust fund balance until a peak in the year 2020 between $3.43 and$3.47 trillion. The combined trust fund balance then declines. By 2031, we estimate it will decrease beyond its 2006 level and equal between $1.79 and$2.00 trillion. Under SSA projections, the combined trust fund balance is projected to increase until 2022 to between $3.68 and$3.75 trillion. The subsequent decline is much less pronounced. By 2031, the SSA combined trust fund balance is projected to be between $2.42 and$2.87 trillion, based on its intermediate-cost assumptions (see Table 2).

### Cost Rate

The cost rate is the ratio of OASI and SSDI program costs to taxable payroll. We forecast an increase in the combined OASI and SSDI program costs, as a percentage of taxable payroll, from 11.11% in 2006 to between 17.53% and 17.79% in 2031 (Fig. 7, lower-left panel). In comparison, the increase is projected to be less under the SSA assumptions and to equal between 16.71% and 17.49% in 2031 (see Table 2).

### Annual Income and Cost

An additional way to measure future solvency is to compare payroll tax revenue and program costs as a share of U.S. economic output. Annual payroll tax revenues vary little between mortality forecasts (Fig. 7, lower-right panel). Yet we estimate combined OASI and SSDI program costs, as a share of GDP, will rise to between 6.63% and 6.73% in 2031, compared with between 6.32% and 6.61% under the SSA projections (see Table 2).

### Balanced Budget Payroll Tax

The balanced budget payroll tax rate is the tax rate required to offset annual benefit payments with the revenue generated from payroll taxes and interest from the trust fund (Lee and Tuljapurkar 1997). Although it has never been proposed to address future deficits, the balanced budget payroll tax rate is useful in assessing the effect of different mortality forecasts on Social Security finances (Lee and Skinner 1999; Lee and Tuljapurkar 1997). We calculate the 2006–2031 balanced budget payroll tax rates for both the OASI and SSDI programs under our mortality forecasts, as well as the SSA forecasts, as shown in Fig. 8.

A balanced budget provision hypothetically imposed in 2006 for the OASI and SSDI programs would, by definition, preserve solvency at 2006 levels, less loss from inflation. Initially, the combined OASI and SSDI balanced budget payroll tax rate would be much lower than the current total tax rate of 12.4% because of the surplus discussed previously. We estimate that the combined OASI and SSDI balanced budget payroll tax rate would exceed the current payroll tax rate in 2020. By 2031, the combined balanced budget payroll tax rate would be between 15.96% and 16.21%. In comparison, under SSA projections, the combined balanced budget payroll tax rate would exceed the current payroll tax rate in 2021 and would rise to only between 15.19% and 15.94% in 2030. This difference represents an additional $31.9 to$45.6 billion to be generated from payroll taxes. Higher payroll taxes may present a significant burden to future workers and their employers who split the contribution, and especially to the self-employed, who must contribute the full tax themselves.

## Concluding Remarks

Demographic shifts in the twentieth century are at the heart of current concerns over Social Security solvency. Indeed, uncertainty in demographic processes (e.g., fertility and mortality) becomes an increasingly important component in the uncertainty of Social Security Trust Fund balances over time (Lee and Tuljapurkar 1998). We apply Bayesian methodology, which emphasizes the empirical smoothness in age-specific mortality and utilizes potentially informative covariates, to improve the quality and accuracy of SSA all-cause and cause-specific mortality forecasts. We also describe many more details of the procedures the SSA uses to forecast the long-term financial viability of the Social Security program than previously available in the public domain. This information will make it possible for researchers to evaluate the sensitivity of each of the SSA’s assumptions and ultimately to marshal the power of the academic community to help ensure the future of America’s largest governmental program.

The specific assumptions we focused on in this article concern one of the largest components of uncertainty—namely, age- and sex-specific mortality forecasts. We attempt to improve the accuracy and quality of mortality forecasts by introducing new methods that directly and transparently incorporate risk factors with known and emerging biological consequences and maintain long-standing demographic patterns, so long as they are consistent with the data, and emphasize the smoothness in age-specific mortality rates. Uncertainty resulting from model dependence in analyzing mortality data is reflected in our forecast intervals. We predict higher life expectancy and an older age distribution of death, when considering the steady decline in smoking and rapid rise in obesity, than do the SSA projections, which use no covariates except implicitly. The result indicates that Social Security, especially the OASI program, may be in a considerably more precarious position than officially thought. Maintaining the same set of assumptions regarding future economic and demographic growth, and changing only the mortality forecasts to more informed methods, we predict three fewer years of net surplus, \$730 billion less in the OASI and SSDI Trust Funds, program costs 0.66% greater of projected taxable payroll, and expenditures 0.25% greater of projected GDP by 2031.

Recently, Olshansky et al. (2009) reached a similar set of demographic and policy conclusions by using a cohort-components methodology. Analysis performed by the 2007 Technical Panel of Assumptions and Methods, Social Security Advisory Board, found similar solvency results based on its recommended mortality projections. When considering the impact of revised and more realistic immigration assumptions, finances of the system improved (SSABTP 2007). Our findings, and those of Lee and Tuljapurkar (1997), SSABTP (2007), and Olshansky et al. (2009), emphasize the importance of continual assessment of projection methods, underscore the need to introduce more rigorous tools and techniques, and argue for greater study on the complex interrelationship of demographic, economic, and policy processes.

We encourage other academic and government researchers to use the information we provide about SSA’s methods and our models, data, methods, and software to suggest further improvements in these forecasts. Our mortality forecasting methods make it easy to systematically alter prior beliefs about demographic patterns. They also make including additional covariates based on other risk factors straightforward, when they become available. Although we include time as a simple proxy for technological change, other more specific measures may be relevant, including large-scale public health initiatives, medical advancements in detection and treatment, and pharmaceutical breakthroughs. Alternative modeling specifications are also easy to implement in our proposed forecasting framework, as are other components of uncertainty. With all these tools, we hope the scholarly research community will find forecasting more feasible and continue to reduce uncertainty and improve the quality, accuracy, and transparency of Social Security Trust Fund projections.

## 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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