Research

Abstract: The literature often relies on moment-based measures of earnings risk, such as the variance, skewness, and kurtosis (e.g., Guvenen, Karahan, Ozkan, and Song, 2019, Econometrica). However, such moments may not exist in the population under heavy-tailed distributions. We empirically show that the population kurtosis, skewness, and even variance often fail to exist for the conditional distribution of earnings growths. This evidence may invalidate the moment-based analyses in the literature. In this light, we propose conditional Pareto exponents as novel measures of earnings risk that are robust against non-existence of moments, and develop estimation and inference methods.

Using these measures with an administrative data set for the UK, the New Earnings Survey Panel Dataset (NESPD), and the US Panel Study of Income Dynamics (PSID), we quantify the tail heaviness of the conditional distributions of earnings changes given age, gender, and past earnings. Our main findings are that: 1) the aforementioned moments fail to exist; 2) earnings risk is increasing over the life cycle; 3) job stayers are more vulnerable to earnings risk, and 4) these patterns appear in both the period 2007–2008 of the great recession and the period 2015–2016 of positive growth among others.

Abstract: We propose a method for forecasting individual outcomes and estimating random effects in linear panel data models and value-added models when the panel has a short time dimension. The method is robust, trivial to implement and requires minimal assumptions. The idea is to take a weighted average of time series- and pooled forecasts/estimators, with individual weights that are based on time series information. We show the forecast optimality of individual weights, both in terms of minimax-regret and of mean squared forecast error. We then provide feasible weights that ensure good performance under weaker assumptions than those required by existing approaches. Unlike existing shrinkage methods, our approach borrows the strength - but avoids the tyranny - of the majority, by targeting individual (instead of group) accuracy and letting the data decide how much strength each individual should borrow. Unlike existing empirical Bayesian methods, our frequentist approach requires no distributional assumptions, and, in fact, it is particularly advantageous in the presence of features such as heavy tails that would make a fully nonparametric procedure problematic.

Abstract: This paper presents empirical evidence on the nature of idiosyncratic shocks to firms and discusses its role for firm behavior and aggregate fluctuations. We document that firm-level sales and productivity are hit by heavy-tailed shocks and follow a nonlinear stochastic process, thus departing from the canonical linear AR(1). We estimate a state-of-the-art model to flexibly capture the rich dynamics uncovered in the data and characterize the drivers of nonlinear persistence and non-Gaussian shocks. We show that these features are crucial to get empirically plausible volatility and persistence of micro-originated (granular) aggregate fluctuations.

Abstract: I propose the use of state-space methods as a unified econometric framework for studying heterogeneity and dynamics in micropanels (large N, medium T), which are typical of administrative data. I formally study identification and inference in models with pervasive unobservable heterogeneity. I show how to consistently estimate the cross-sectional distributions of unobservables in the system and uncover how such heterogeneity has changed over time. A mild parametric assumption on the standardized error term offers key advantages for identification and estimation, and delivers a flexible and general approach. Armed with this framework, I study the relationship between job polarization and earnings inequality, using a novel dataset on UK earnings, the New Earnings Survey Panel Data (NESPD). I analyze how the distributions of unobservables in the earnings process differ across occupations and over time, and separate the role played on inequality by workers’ skills, labor market instability, and other types of earnings shocks.