Statistical security for Social Security.

Area of Science:

• Demographic analysis
• Actuarial science
• Public finance

Background:

• Social Security's financial solvency is critical for its function as the largest U.S. government program.
• Accurate forecasting of the Social Security Trust Fund's solvency is essential for long-term financial planning.
• Current Social Security Administration (SSA) forecasting relies on outdated mortality projection methods.

Purpose of the Study:

• To provide transparent and replicable methods for Social Security forecasting.
• To improve the accuracy of Social Security solvency forecasts by enhancing mortality prediction models.
• To incorporate modern statistical techniques and additional risk factors into demographic projections.

Main Methods:

• Detailed replication of existing Social Security Administration (SSA) forecasting procedures.
• Development and application of age- and sex-specific mortality forecasts using modern statistical methods.
• Inclusion of known risk factors (e.g., smoking, obesity) and demographic patterns in mortality models.

Main Results:

• Improved mortality forecasting alone predicts three fewer years of net surplus compared to SSA projections.
• Estimated reduction of $730 billion in Social Security Trust Funds by 2031 due to enhanced mortality forecasts.
• Projected program costs are 0.66% greater for taxable payroll by 2031 when using improved mortality methods.

Conclusions:

• Modern statistical methods offer significant advantages over linear extrapolation for mortality forecasting.
• Enhanced mortality forecasts increase transparency, replicability, and reduce uncertainty in Social Security solvency predictions.
• The study provides software and data to encourage community efforts in refining Social Security financial forecasts.