Social security and the retirement and savings behavior of low-income households
Section snippets
Executive summary
With the oldest cohort of the baby-boom generation just beginning to reach retirement age, the impending wave of retirements during the next two decades is expected to place mounting budgetary pressures on the federal government. Consequently, the reform of Social Security and Medicare has become a central policy issue, with policy makers facing the difficult task of choosing from among a large set of alternative policy proposals. To help this debate, we develop and estimate a rich dynamic
Model
The model represents the decision problem of an individual of given gender or a married couple. The optimization problem, consistent with the data available for estimation, begins at a point in the middle of the household’s life cycle. Initial conditions are those that prevail at that life cycle point; variation among agents in initial conditions are not explicitly considered until the model’s solution and estimation method are discussed.
Solution method and data
The model is numerically solved by backward recursion. However, because the state space consists of elements that are continuous variables, e.g., the current level of household assets and the current level(s) of the AIME, it is not possible to obtain “exact” solutions. Instead, we adopt an approximation method due to Keane and Wolpin, 1994, Keane and Wolpin, 1997.22
Estimation method
The model represents the decision process of one household. Differences in the behavior of households with the same initial state variables arise solely due to iid shocks to preferences, shocks to health and health insurance coverage if changing jobs, and shocks to wages (conditional on the employer-specific unobservable for those who are working). However, behaviors tend to be more persistent than can be captured by observable state variables. We, therefore, allow for households to differ in
The auxiliary statistical model
The set of auxiliary models used in estimation are (the number of parameters is in parentheses, including missing value dummies32)33
Simulating the data
Having solved the optimization problem, at given parameter values simulating one-step ahead decisions would be straightforward if all of the state variables were observed in each period. For example, consider a hypothetical individual who is 58 years old and unmarried as of the 1992 interview, and who is observed for five 12-month periods, i.e., through age 62. Given the state variables at the 1992 interview, a simulation of the decision at age 58 for that individual would be obtained by
Parameter estimates
The functional forms of the model’s structure are provided in Appendix (Eqs. (A.1), (A.2), (A.3), (A.4), (A.5), (A.6), (A.7), (A.8), (A.9)). Table A.1 provides parameter estimates and associated standard errors. A number of the parameters are worth highlighting and provide some evidence about the credibility of the model. The coefficient of relative risk aversion is estimated to be 1.678 for type 1 individuals and 1.591 for type 2 individuals, within the range of many other findings in
Policy experiments
In this section, we first consider the effects on labor supply and savings of social security reforms that have been suggested as a means of bringing the system into financial balance. In this analysis we focus on a sample of households who would be most at risk from the types of changes in the social security program that have been proposed: households whose members are not currently, or anticipating, collecting on a defined benefit pension plan from a previous job.54
Conclusions
In this paper, we have specified and estimated a dynamic model of retirement and savings decisions for a low-income subsample of households from the Health and Retirement Study. The model incorporated a discrete employment decision (non-employment, part-or full-time employment) and a continuous consumption decision for both unmarried men and women and for married couples. Additional features of the model included a detailed specification of social security rules, limited borrowing,
Acknowledgements
We are grateful for support from the National Institute on Aging grant AG14862. The views and opinions offered in this article do not necessarily reflect those of the Federal Reserve Bank of New York or the Federal Reserve System as a whole. We thank the two anonymous referees for their extensive and helpful comments.
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