Abstract
We propose an econometric method of estimation and inference for conditional Pareto exponents. Applying this method to an administrative dataset for the U.K., the New Earnings Survey Panel Dataset, we quantify the tail heaviness in the conditional distributions of earnings changes given age, gender, and past earnings. We interpret this measure of conditional tail risk as a novel measure of earnings risk. Our main findings are that: 1) the kurtosis, skewness, and even standard deviation may not exist for the conditional distribution of earnings growth; 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 great recession and the period 2015–2016 of a positive growth despite some minor differences.