Public Data and Code for Changing Income Risk Across the US Skill Distribution

Public data and code repository for "Changing Income Risk across the US Skill Distribution: Evidence from a
Generalized Kalman Filter." In this paper we examine for whom has earnings risk changed, and why? We answer these questions by combining the Kalman filter and EM-algorithm to estimate persistent and temporary earnings for every individual at every point in time. We apply our method to administrative earnings linked with survey data. We show that since the 1980s, persistent earnings risk rose by 12.5% for both employed and unemployed workers and the scarring effects of unemployment doubled. At the same time, temporary earnings risk declined. Using education and occupation codes, we show that rising persistent earnings risk is concentrated among high-skill workers and related to technology adoption.