DYNAMIC STRUCTURAL MODELS OF RETIREMENT AND DISABILITY

Rand Corporation, Santa Monica CA

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Abstract

We propose to use all available waves of the Health and Retirement Survey (HRS) and AHEAD Survey to estimate a comprehensive dynamic programming (DP) model of behavior at the end of the life cycle. This model provides a detailed treatment of the Social Security Administration's (SSA) Old Age and Survivors Income (OASI), Supplemental Security Income (SSI) and Disability Insurance (DI) programs. Particular attention will be paid to developing, estimating, and testing a multi-stage dynamic programming (DP) model of the SSI and DI applications, appeal, and award process, for (possibly) heterogeneous agents. The resulting model will allow us to derive predictions of the behavioral and welfare implications of policy changes. The DP model we propose will circumvent the shortcomings associated with the commonly used reduced-form and static structural models, which suffer from two major shortcomings: (a) they cannot be used for welfare analysis or to predict behavior responses to policy changes; and (b) they do not accurately reflect the level of complexity and uncertainty facing individual decision makers, nor do they capture the important dynamic elements of the decision processes. Our model will provide a tractable framework for analyzing individual behavior and welfare, and forecasting their response to a wide range of policy changes. Particularly, the model could provide new insights into a number of puzzling aspects about disability in the United States. Specifically, it will help in better understanding why the fraction of Americans receiving SSI and DI benefits continues to increase despite overwhelming epidemiological evidence of steady improvements in health status. We will use detailed health and functional status indicators from the HRS to evaluate whether or not alternative screening rules by the SSA can reduce the level of classification errors in the DI award process. Our estimated DP model will produce detailed predictions of the behavioral and welfare effects of changes in benefit levels, delays, award probabilities, and audit probability of DI beneficiaries.