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An Empirical Model of Life-Cycle Earnings- and Mobility Dynamics

CIREQ-McGill Seminar 2017-2018
joint with the Department of Economics, McGill University

Leacock 927 (McGill University, 855 Sherbrooke West)



Estimates of Dynamic Discrete Choice (DDC) Models of life-cycle career dynamics with human capital accumulation, such as those in Keane and Wolpin’s seminal study, suggest that up to 90 percent of earnings inequality is driven by heterogeneity in unobserved pre-labor market skills. This is in sharp contrast with findings from statistical earnings processes which associate only about half of these inequalities to pre-labor market skills. Explaining the discrepancy between these findings is important for gaining a deeper understanding of the sources of life-cycle inequality since DDC models feature multi-dimensional skills, endogenize the part of earnings risk that is associated with worker mobility, and are attractive for counterfactual analysis. In this study I argue that this discrepancy can be fully accounted for by specification error in DDC-models without persistent earnings risk. In particular, common specifications of DDC-models only allow for a permanent and a fully transitory component in the earnings equation, and a large role of pre-labor market skills becomes hard-wired into the model. Guided by an extensive descriptive analysis of earnings- and mobility dynamics of workers, I enrich the Keane-Wolpin framework to reach a model that is explicitly built for studying the sources of labor market inequalities. The primary additions are a frictional search process for better matches between employers and firms that ties worker mobility to earnings dynamics, and an earnings process with persistence. I develop a computationally feasible estimation procedure that calibrates a number of parameters, pre-estimates skill prices using a flat-spot” method and structurally estimates the remaining parameters. Expected value functions are shown to be continuously differentiable, which helps using modern Sparse-Grid approximation methods that are uniformly convergent and have near-optimal rates of convergence. The model is estimated on administrative worker-level data from Germany that follow workers from labor market entry until up to 35 years into their career. Although I use different data like Keane-Wolpin, I reach at an almost identical estimated role of pre-labor market skills for life-cycle earnings inequality when estimating their specification. In particular, 91 percent of the variation of life-cycle earnings is explained by type heterogeneity. However, once I introduce persistent shocks to skills, match heterogeneity and search, my conclusions change dramatically. The estimated role of pre-labor market skills decreases from 91 to only 41 percent. Furthermore, while the more restrictive specification cannot match the covariance structure of earnings, my preferred model is successful in doing so. Since I explicitly model and parameterize the income-tax- and unemployment insurance systems, my framework is well suited for studying how and whether results from counterfactual experiments depend on model misspecification.

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