Understanding Earnings, Labor Supply, and Retirement Decisions

Published: 2017


We develop and estimate a model in which individuals make decisions on consumption, human capital investment, labor supply, and retirement. Unlike all previous work, our model allows both an endogenous wage process (which is typically assumed exogenous in the human capital and earnings dynamics literature). In addition, we introduce health shocks. We estimate the model and match the life-cycle profiles of wages, hours and retirement from SIPP data. We analyze the impact of health shocks on retirement, as well as the effect of changes in payroll taxes and increases in the Normal Retirement Age on labor force participation of older Americans.

Key Findings

    • This paper develops and estimates a rich life-cycle model that merges Ben-Porath human capital, neoclassical, endogenous labor supply, and retirement frameworks. Each individual makes decisions on consumption, human capital investment, labor supply and retirement. Investment in human capital generates wage growth over the life-cycle, while depreciation of human capital is the main force generating retirement.
    • We use the estimated model to simulate the impacts of various policy changes:
    • The model shows that less generous Social Security benefits result in higher labor supply later in the life cycle, as workers adjust their investment over the life cycle. This results in a higher human capital level as well as higher labor supply earlier in the life cycle.
    • Modeling labor supply and human capital decisions jointly is critical in an analysis of the effect of policy changes.
    • While presumably other factors would be important for explaining other features of labor markets, endogenous labor supply is critical for understanding life-cycle human capital investment, and retirement choices.


Fan, Xiaodong, Ananth Seshadri, and Christopher Taber. 2017. “Understanding Earnings, Labor Supply, and Retirement Decisions.” University of Michigan Retirement Research Center (MRRC) Working Paper, WP 2017-367. Ann Arbor, MI. https://mrdrc.isr.umich.edu/publications/papers/pdf/wp367.pdf