This project focuses on the sources of life-cycle inequality. Its aim can be summarized as an attempt to answer two questions. First, how important are initial conditions versus shocks for life-cycle inequality? Second, among initial conditions, which are most important for life-cycle inequality? To address these questions, the project offers a quantitative theory of life-cycle earnings by integrating a risky human capital framework into a standard life-cycle, permanent-income model. Three initial conditions are considered: financial wealth, initial human capital, and learning ability. Human capital is risky as human capital is subject to idiosyncratic shocks each period. To make the model empirically operative, the project offers a methodology to infer human capital shocks from data. Parameters governing the distribution of initial conditions are selected so that the model is consistent with key earnings facts for a cohort of individuals: mean earnings and the rise in earnings dispersion with age.
Current work in macroeconomics uses dynamic models in which earnings or wages follow exogenous, stochastic processes. This project goes beyond current work by putting forward a model in which labor earnings are endogenous, as labor earnings are determined by optimal human capital investments in response to initial conditions, and stochastic shocks. The framework therefore permits a natural decomposition of life-cycle inequality in earnings and discounted utility into their sources. The framework offered is also appropriate to conduct analyses of policy for which the presence of human capital risk is critical.
Broader Impacts: Few topics have received more attention in recent economic research than income, earnings and wealth inequality and their changes over time. This research has spanned several fields in economics, including macroeconomics, labor and international trade. By decomposing life-cycle inequality into its sources, this research provides a complimentary and key perspective for the study of inequality observations. Understanding the determinants of life-cycle inequality is arguably fundamental. Its importance stems from the fact that a better, quantitatively oriented understanding is crucial for the assessment of the relative merits of policies. The policies in question are those directed to initial conditions, e.g., public education, and those that provide insurance over the life cycle, e.g., unemployment insurance. Such an understanding is also vital to highlight the most important margins for future research, both at the theoretical and empirical level.
