Social Security and Statistical Prediction Problems

University Of Wisconsin-Madison, Madison WI

Investigators

Abstract

The objective of this project is to investigate the use of longitudinal/panel data methods to combine forecasts for different cohorts and series used in Social Security forecasting. Forecasts of the Social Security system rely on forecasts of two components, demographic variables and economic variables. The demographic variables, for example, include mortality, fertility, and immigration; the economic variables include labor productivity, labor force participation, and inflation. Because they are from different functional areas, the forecasts of each component tend to be done in isolation of the other. Moreover, series often are decomposed into cohorts, generally by age and sex and sometimes marital status, and then forecast individually, that is, each cohort is forecast in isolation of the others. The project will use Bayesian and empirical Bayesian methods to combine, in a disciplined manner, information about neighboring cohorts with expert opinion of the future and currently available data. This modeling strategy, together with data from selected series, will be used to construct and validate the proposed forecasting techniques. As with almost every developed country, the U.S. government maintains a large financial security system that provides partial protection for its constituents in the event of adverse contingencies. The largest portion of the U.S. Social Security system is the Old-Age, Survivors and Disability Insurance (OASDI) program, which provides protection against loss of earnings due to retirement, death, or disability. The 2000 Social Security Trustees' Report forecasts that by the year 2037 revenues to fund this program will be exhausted. The magnitude and timing of this predicted shortfall heavily influences numerous academic and public policy debates concerning program reform. This project will supplement the forecasting methods used by the Social Security Administration and provide insights regarding the reliability of the predicted shortfalls. By combining information from different sources, the research will achieve more reliable (efficient) forecasts and forecasts of components that are integrated and consistent with one another. Moreover, the stochastic prediction methods will allow us to quantify the uncertainty in the forecasts in a probabilistic manner. This is not possible with the current deterministic forecasting system employed by the Social Security Administration. This research is supported by the Methodology, Measurement, and Statistics Program under the Mid-Career Methodological Opportunities Fellowship Announcement.