2023 OASDI Trustees Report

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E. STOCHASTIC PROJECTIONS AND UNCERTAINTY
Significant uncertainty surrounds the estimates under the intermediate assumptions, especially for a period as long as 75 years. This appendix presents stochastic projections, a way to illustrate the uncertainty of these estimates. The stochastic projections supplement the traditional methods of examining such uncertainty.
1. Background
The Trustees have traditionally shown estimates using the low-cost and high-cost sets of specified assumptions to illustrate the potential implications of uncertainty. These low-cost and high-cost estimates provide a range of possible outcomes for the projections. However, they do not provide an indication of the probability that actual future experience will be inside or outside this range. This appendix presents the results of a stochastic model that estimates a probability distribution of future outcomes of the financial status of the combined OASI and DI Trust Funds. This model was introduced in the 2003 report and enhanced in the 2021 report to include parameter uncertainty for the expected mean for the key variables described in the next section.
2. Stochastic Methodology
Other sections of this report provide estimates of the financial status of the combined OASI and DI Trust Funds using a scenario-based model. For the scenario-based model, the Trustees use three alternative scenarios (low-cost, intermediate, and high-cost) that use specific assumptions for key variables. In general, the Trustees assume that each of these variables will reach an ultimate value at a specific point during the long-range period, and will maintain that value throughout the remainder of the period. The three alternative scenarios assume separate, specified values for each of these variables. Chapter V contains more details about each of these assumptions.
This appendix presents estimates of the probability that key measures of OASDI solvency will fall in certain ranges, based on 5,000 independent stochastic simulations. Each simulation allows key variables to vary throughout the long-range period. These key variables include total fertility rates, changes in mortality rates, new arrival lawful permanent resident (LPR) and other-than-LPR immigration levels, rates of adjustment of status (from other-than-LPR to LPR), rates of legal emigration (from the population of citizens and LPRs), changes in the Consumer Price Index, changes in average real wages, unemployment rates, trust fund real yield rates, and disability incidence and recovery rates. The fluctuation of each variable over time is simulated using historical data and standard time-series techniques. Generally, each variable is modeled using an equation that: (1) captures a relationship between current and prior years’ values of the variable, and (2) introduces random variation based on variation observed in the historical period. For some variables, the equations also reflect relationships with other variables. The equations contain parameters that are estimated using historical data for periods from about 20 years to over 100 years, depending on the nature and quality of the available data. Each time-series equation is designed so that, in the absence of random variation over time, the value of the variable for each year equals its value for the intermediate scenario.1
For each equation in a given simulation, the stochastic model assigns random variation to (1) year-by-year error term values and (2) simulation-specific mean term levels that provide variation in the central tendency across simulations. Each simulation produces estimates for all key variables and for the overall financial status of the combined OASI and DI Trust Funds. This appendix shows the distribution of results from 5,000 simulations of the model.
Readers should interpret the results from this model with an understanding of the model’s limitations. Results are sensitive to equation specifications, degrees of interdependence among variables, and the historical periods used for estimating model coefficients. For some variables, recent historical variation may not provide a realistic representation of the potential variation for the future. Also, results would differ if additional variables (such as labor force participation rates, retirement rates, marriage rates, and divorce rates) were also allowed to vary randomly. Time-series modeling reflects only what occurred in the historical period. Future uncertainty exists not only for the underlying central tendency but also for the frequency and size of occasional longer-term shifts in the central tendency. Many experts predict, and history suggests, that the future will likely bring substantial shifts that are not fully reflected in the historical period used for the current model. As a result, readers should understand that the true range of uncertainty might be larger than indicated in this appendix.
3. Stochastic Results
This section illustrates the results for the stochastic simulations of two fundamental measures of actuarial status: annual cost rates and trust fund ratios. The latter measure is highlighted in section II.D of this report. Section 4 of this appendix follows with a comparison of stochastic results to results from the alternative scenarios for these and other measures, and an analysis of the differences.
Figure VI.E1 displays the probability distribution of the year-by-year OASDI cost rates (that is, cost as a percentage of taxable payroll). The range of the annual cost rates widens as the projections move further into the future, which reflects increasing uncertainty. Because there is relatively little variation in income rates across the 5,000 stochastic simulations, the figure includes only the income rate for the intermediate scenario. The two outermost cost rate lines in this figure indicate the range within which future annual cost rates are projected to occur 95 percent of the time (i.e., a 95-percent confidence interval). In other words, the current model estimates that there is a 2.5 percent probability that the cost rate for a given year will exceed the upper end of this range and a 2.5 percent probability that it will fall below the lower end of this range. Other lines in the figure delineate the 80‑percent confidence interval and the median cost rate. The median (50th percentile) cost rate for each year is the rate for which half of the simulated outcomes are higher and half are lower for that year. These lines do not represent the results of individual stochastic simulations. Instead, for each given year, they represent the percentile distribution of annual cost rates based on all stochastic simulations for that year.
Figure VI.E2 presents the simulated probability distribution of the annual trust fund ratios for the combined OASI and DI Trust Funds. The lines in this figure display the median set (50th percentile) of estimated annual trust fund ratios and delineate the 95‑percent and 80‑percent confidence intervals estimated for future annual trust fund ratios. Again, none of these lines represent the path of a single simulation. For each given year, they represent the percentile distribution of trust fund ratios based on all stochastic simulations for that year.
Figure VI.E2 shows that the 95‑percent confidence interval for the trust fund reserve depletion year ranges from 2031 to 2040, relatively early in the 75-year projection period. The figure also shows that there is a 50‑percent probability of trust fund reserve depletion by the end of 2033 (the median reserve depletion year). The median reserve depletion date is late in 2033; the reserve depletion date for the intermediate scenario is in mid-2034.
4. Comparison of Results: Stochastic to Low-Cost, Intermediate, and High-Cost Alternative Scenarios
This section compares results from two different approaches for illustrating ranges of uncertainty in measures of trust fund actuarial status. One approach uses results from the low-cost, intermediate, and high-cost alternative scenarios. The other approach uses distributions of results from the stochastic simulations. Each of these approaches provides insights into uncertainty. Comparing the results requires an understanding of fundamental differences in the approaches.
One fundamental difference relates to the presentation of distributional results. Figure VI.E3 shows projected OASDI annual cost rates for the low-cost, intermediate, and high-cost alternative scenarios along with the annual cost rates at the 2.5th percentile, 50th percentile, and 97.5th percentile for the stochastic simulations. While all values on each line for the alternative scenarios are results from a single specified scenario, the values on each stochastic line may be results from different simulations for different years. The one stochastic simulation (from the 5,000 simulations) that yields results closest to a particular percentile for one projected year may yield results that are distant from that percentile in another projected year. Because each stochastic simulation shows substantial variability from year to year, the range shown between the 2.5th and 97.5th percentiles is broader than would be seen if simulations followed a smooth trend like in the alternative scenarios.
Both the alternative scenarios and the stochastic results suggest that the range of potential cost rates above the central levels (those for the intermediate scenario and for the stochastic median, respectively) is larger than the range below these central results. The difference between the central results and the higher cost levels (the high-cost alternative scenario and the upper end of the 95-percent confidence range, respectively) is about 1.6 to 1.8 times as large as the difference between the central and lower cost levels for both models by the end of the projection period.
Another fundamental difference between the alternative scenarios and the stochastic simulations is the method of assigning values for assumptions. For the alternative scenarios, specific values are assigned for each of the key demographic, economic, and program-specific variables. The high-cost alternative scenario uses parameter values that increase estimated annual cost as a percent of payroll, while the low-cost alternative scenario uses parameter values that decrease annual cost as a percent of payroll. (One parameter, the interest rate, has no effect on annual cost as a percent of payroll for either the alternative scenarios or the stochastic simulations.) In contrast, the stochastic method independently assigns random variation to each of the key demographic and economic variables for each year in each of the 5,000 stochastic simulations. For each of the stochastic simulations, the assigned values for different variables result in varying and often offsetting effects on projected cost as a percent of payroll, with some tending toward higher cost and some tending toward lower cost. This difference tends to narrow the range of cost as a percent of payroll across the 95-percent confidence interval.
It is important to understand that the stochastic model’s 95-percent confidence intervals for any summary measure of trust fund finances would tend to be narrower than the range produced for the low-cost and high-cost alternative scenarios, even if the stochastic model’s 95-percent confidence interval for annual cost rates were identical to the range defined by the low-cost and high-cost scenarios. This is true because summary measures of trust fund finances depend on cost rates for many years, and the probability that annual cost rates, on average for individual stochastic simulations, will be at least as low (high) as the 2.5 (97.5) percentile line is significantly lower than 2.5 percent. As a result, the relationship between the ranges presented for annual cost rates and summary measures of trust fund finances is fundamentally different for the stochastic model than it is for the low-cost and high-cost alternative scenarios.
Figure VI.E4 compares the ranges of trust fund (unfunded obligation) ratios for the alternative scenarios to the 95-percent confidence interval of the stochastic simulations. This figure extends figure VI.E2 to show unfunded obligation ratios, expressed as negative values below the zero percent line. An unfunded obligation ratio is the ratio of the unfunded obligation accumulated through the beginning of the year to the cost for that year.
Figure VI.E4.—OASDI Trust Fund (Unfunded Obligation) Ratios: Comparison of Stochastic to Low-Cost, Intermediate, and High-Cost Alternative Scenariosa

a
An unfunded obligation, shown as a negative value in this figure, is equivalent to the amount the trust funds would need to have borrowed to date in order to pay all scheduled benefits (on a timely basis) after trust fund reserves are depleted. Note that current law does not permit the trust funds to borrow.
 

As mentioned above, a summary measure that accumulates annual values tends to smooth the kind of annual fluctuations that occur in stochastic simulations. Therefore, one might expect the range across the stochastic confidence interval for trust fund (unfunded obligation) ratios to be narrower and fall within the range seen across the high-cost and low-cost alternative scenarios, as it does for the actuarial balance measure. But this is not the case, largely due to the way interest rates are assigned.
For the stochastic model, real interest rates for each simulation are assigned to be essentially independent of other variables, so the rate for compounding of trust fund reserves (unfunded obligations) is essentially uncorrelated with the level of cost as a percent of payroll. On the other hand, real interest rates are assigned to be higher for the low-cost alternative scenario and lower for the high-cost alternative scenario. High interest rates raise the level of the positive trust fund ratio in the low-cost alternative scenario somewhat, but this effect is limited because the magnitude of reserves is small. However, low interest rates substantially reduce the magnitude of the unfunded obligation ratio for the high-cost alternative scenario because the magnitude of unfunded obligations is relatively large. As a result, the trust fund (unfunded obligation) ratios are shifted, albeit unevenly, higher (or less negative) for both the high-cost and low-cost alternative scenarios relative to those of the stochastic simulations.
This interest rate effect on the alternative scenarios is not as evident for some other summary measures of actuarial status, such as the actuarial balance. Because the actuarial balance reflects the cumulative effects of interest in both its numerator and denominator, the interest rate effect is much less pronounced. In contrast, cumulative interest affects only the numerator of the trust fund (unfunded obligation) ratio. There is also no significant interest rate effect on the trust fund depletion date.
Other factors also contribute, to varying degrees, to the difference in ranges between the results of the alternative scenarios and the stochastic simulations. The contrasts in results and methods do not mean that either approach to illustrating ranges of uncertainty is superior to the other. The ranges are different and explainable.
Table VI.E1 displays long-range actuarial estimates for the combined OASDI program using the two methods of illustrating uncertainty: alternative scenarios and stochastic simulations. The table shows scenario-based estimates for the intermediate, low-cost, and high-cost assumptions. It also shows stochastic estimates for the median (50th percentile) and for the 80‑percent and 95‑percent confidence intervals. Each individual stochastic estimate in the table is the level at that percentile from the distribution of the 5,000 simulations. For each given percentile, the values in the table for each long-range actuarial measure are generally from different stochastic simulations.
The median stochastic estimates displayed in table VI.E1 are similar to the intermediate scenario-based estimates. The median estimate of the long-range actuarial balance is -3.69 percent of taxable payroll, about 0.08 percentage point lower (more negative) than projected in the intermediate scenario. The median estimate for the open-group unfunded obligation is $22.8 trillion, about $0.4 trillion larger than the $22.4 trillion estimate in the intermediate scenario. The median first projected year for which cost exceeds non-interest income (as it did in 2010 through 2022), and remains in excess of non-interest income throughout the remainder of the long-range period, is 2023. This is the same year as projected in the intermediate scenario. The median projected date at which trust fund reserves first become depleted is late in 2033; the reserve depletion date for the intermediate scenario is mid-2034. The median estimates of the annual cost rate for the 75th year of the projection period are 18.25 percent of taxable payroll and 6.14 percent of gross domestic product (GDP). The comparable estimates in the intermediate scenario are 17.75 percent of payroll and 5.98 percent of GDP.
For three measures in table VI.E1 (the actuarial balance, the first projected year cost exceeds non-interest income and remains in excess through 2097, and the first year trust fund reserves become depleted), the 95‑percent stochastic confidence interval falls within the range defined by the low-cost and high-cost scenarios. For the remaining three measures (the open-group unfunded obligation, the annual cost in the 75th year as a percent of taxable payroll, and the annual cost in the 75th year as a percent of GDP), one or both of the bounds of the 95‑percent stochastic confidence interval fall outside the range defined by the low-cost and high-cost scenarios.
First year trust fund reserves become depleted b

a
Cost is projected to exceed non-interest income for a temporary period, before falling below non-interest income by the end of the projection period.

b
For the low-cost scenario and for some stochastic simulations, the first year in which trust fund reserves become depleted does not indicate a permanent depletion of reserves.


1
More detail on this model, and stochastic modeling in general, is available at www.ssa.gov/OACT/stochastic/index.html.


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