Revisiting Retirement and Social Security Claiming Decisions
By Neha Bairoliya, Kathleen McKiernan
Why do individuals retire and claim their Social Security benefits at the age they do? Understanding the key drivers of these decisions has been an important topic of research as it can help guide policy discussions on the impact of potential reforms to the Social Security program. We revisit this crucial question by exploring new sources of heterogeneity in these decisions as well as novel mechanisms governing these trade-offs. Using data from the Health and Retirement Study and the Understanding America Survey, we first document (1) important heterogeneities in social security claiming behavior of men by their education and marital status, (2) strong correlations between health, labor supply and benefit claiming decisions and (3) significant misinformation related to Social Security program knowledge and survival chances at older ages. We then build a life-cycle model of consumption, savings, labor supply, and Social Security application decisions as well as heterogeneity in education, marital status and SS program knowledge. The model includes uncertainty in health, subjective survival, wages, and job separation as well as rich details of the U.S. Social Security program to understand why a majority of individuals claim Social Security benefits prior to their normal retirement age, despite large penalties associated with these early benefit claims. We show that the estimated model can closely match the claiming behavior as seen in the data and also produce differences in SS claims along the dimensions of heterogeneity considered. Counterfactual experiments indicate that precautionary motives, misinformation, and preferences governing future discounting as well as altruism, together, go a long way in explaining overall claiming behavior. Together, these forces can explain a third of the overall early benefit claims and two-thirds of age 62 claims– with varying intensities across education and marital groups.
Source: SSRN
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