Abstract: This paper provides a novel characterization of time-varying heterogeneous earnings risk through a Markov process with heterogeneous transition probabilities. The resulting earnings process allows for a richer notion of earnings risk heterogeneity than previously studied by the literature. Assumptions are derived under which a combination of savings and earnings data can be used to identify the earnings process parameters. Alternatively, a narrower interpretation of earnings risk can be adopted, limiting risk heterogeneity to heterogeneous variances of earnings shocks, such that the earnings process is identifiable from earnings data only. This gives rise to two identification strategies. Applying both strategies to the Survey of Income and Program Participation dataset shows that individuals face considerable inequality of earnings risk. High-risk states are found to be temporary, while low-risk states are persistent. Comparing both strategies shows that only allowing for variance heterogeneity is too restrictive, and a rich notion of risk is required to capture the joint dynamics of individuals' savings and earnings.
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