Working Paper: NBER ID: w29567
Authors: J. Carter Braxton; Kyle F. Herkenhoff; Jonathan L. Rothbaum; Lawrence Schmidt
Abstract: 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 20% 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.
Keywords: Earnings risk; Persistent earnings; Temporary earnings; Kalman filter; Technology adoption
JEL Codes: E24; J3; J6
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| unemployment duration (J64) | persistent earnings risk (G19) |
| technology adoption (O33) | persistent earnings risk (G19) |
| persistent earnings risk (G19) | welfare losses (D69) |
| persistent earnings risk (G19) | temporary earnings risk (J31) |
| time (C41) | persistent earnings risk (G19) |