  The literature often employs moment-based earnings risk measures like
variance, skewness, and kurtosis. However, under heavy-tailed distributions,
these moments may not exist in the population. Our empirical analysis reveals
that population kurtosis, skewness, and variance often do not exist for the
conditional distribution of earnings growth. This challenges moment-based
analyses. We propose robust conditional Pareto exponents as novel earnings risk
measures, developing estimation and inference methods. Using the UK New
Earnings Survey Panel Dataset (NESPD) and US Panel Study of Income Dynamics
(PSID), we find: 1) Moments often fail to exist; 2) Earnings risk increases
over the life cycle; 3) Job stayers face higher earnings risk; 4) These
patterns persist during the 2007--2008 recession and the 2015--2016 positive
growth period.
