HIP, RIP, and the Robustness of Empirical Earnings Processes
Florian Hoffmann
Quantitative Economics,
Nr. 3,
2019
Abstract
The dispersion of individual returns to experience, often referred to as heterogeneity of income profiles (HIP), is a key parameter in empirical human capital models, in studies of life‐cycle income inequality, and in heterogeneous agent models of life‐cycle labor market dynamics. It is commonly estimated from age variation in the covariance structure of earnings. In this study, I show that this approach is invalid and tends to deliver estimates of HIP that are biased upward. The reason is that any age variation in covariance structures can be rationalized by age‐dependent heteroscedasticity in the distribution of earnings shocks. Once one models such age effects flexibly the remaining identifying variation for HIP is the shape of the tails of lag profiles. Credible estimation of HIP thus imposes strong demands on the data since one requires many earnings observations per individual and a low rate of sample attrition. To investigate empirically whether the bias in estimates of HIP from omitting age effects is quantitatively important, I thus rely on administrative data from Germany on quarterly earnings that follow workers from labor market entry until 27 years into their career. To strengthen external validity, I focus my analysis on an education group that displays a covariance structure with qualitatively similar properties like its North American counterpart. I find that a HIP model with age effects in transitory, persistent and permanent shocks fits the covariance structure almost perfectly and delivers small and insignificant estimates for the HIP component. In sharp contrast, once I estimate a standard HIP model without age‐effects the estimated slope heterogeneity increases by a factor of thirteen and becomes highly significant, with a dramatic deterioration of model fit. I reach the same conclusions from estimating the two models on a different covariance structure and from conducting a Monte Carlo analysis, suggesting that my quantitative results are not an artifact of one particular sample.
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Innovation and Top Income Inequality
Philippe Aghion, Ufuk Akcigit, Antonin Bergeaud, Richard Blundell, David Hemous
Review of Economic Studies,
Nr. 1,
2019
Abstract
In this article, we use cross-state panel and cross-U.S. commuting-zone data to look at the relationship between innovation, top income inequality and social mobility. We find positive correlations between measures of innovation and top income inequality. We also show that the correlations between innovation and broad measures of inequality are not significant. Next, using instrumental variable analysis, we argue that these correlations at least partly reflect a causality from innovation to top income shares. Finally, we show that innovation, particularly by new entrants, is positively associated with social mobility, but less so in local areas with more intense lobbying activities.
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Endogenous Selection of Comparison Groups, Human Capital Formation, and Tax Policy
Oded Stark, Walter Hyll, Y. Wang
Economica,
Nr. 313,
2012
Abstract
We consider a setting in which the acquisition of human capital entails a change of location in social space that causes individuals to revise their comparison groups. Skill levels are viewed as occupational groups. Moving up the skill ladder by acquiring additional human capital, in itself rewarding, leads to a shift in the individual’s inclination to compare himself with a different, and on average better-paid, comparison group, in itself penalizing. We shed new light on the dynamics of human capital formation, and suggest novel policy interventions to encourage human capital formation in the aggregate and reduce inter-group income inequality.
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