Inequality over the business cycle: Estimating income risk using micro-data on consumption

We use CEX repeated cross-section data on consumption and income, to evaluate the nature of increased income inequality in the 1980s and 90s. We decompose unexpected changes in family income into transitory and permanent, and idiosyncratic and aggregate components, and estimate the contribution of each component to total inequality. The model we use is a linearized incomplete markets model, enriched to incorporate risk-sharing while maintaining tractability. Our estimates suggest that taking risk sharing into account is important for the model fit; that the increase in inequality in the 1980s was mainly permanent; and that inequality is driven almost entirely by idiosyncratic income risk. In addition we find no evidence for cyclical behavior of consumption risk, casting doubt on Constantinides and Duffie s (1995) explanation for the equity premium puzzle.

Heterogeneous life-cycle profiles, income risk and consumption inequality

Was the increase in income inequality in the US due to permanent shocks or merely to an increase in the variance of transitory shocks? The implications for consumption and welfare depend crucially on the answer to this question. We use CEX repeated cross-section data on consumption and income to decompose idiosyncratic changes in income into predictable life-cycle changes, transitory and permanent shocks and estimate the contribution of each to total inequality. Our model fits the joint evolution of consumption and income inequality well and delivers two main results. First, we find that permanent changes in income explain all of the increase in inequality in the 1980s and 90s. Second, we reconcile this finding with the fact that consumption inequality did not increase much over this period. Our results support the view that many permanent changes in income are predictable for consumers, even if they look unpredictable to the econometrician, consistent withmodels of heterogeneous income profiles.

A sharp concentration inequality with applications

We present a new general concentration-of-measure inequality and illustrate its power by applications in random combinatorics. The results find direct applications in some problems of learning theory.