Working Paper: NBER ID: w31846
Authors: Leonid Kogan; Dimitris Papanikolaou; Lawrence DW Schmidt; Bryan Seegmiller
Abstract: We develop measures of labor-saving and labor-augmenting technology exposure using textual analysis of patents and job tasks. Using US administrative data, we show that both measures negatively predict earnings growth of individual incumbent workers. While labor-saving technologies predict earnings declines and higher likelihood of job loss for all workers, labor-augmenting technologies primarily predict losses for older or highly-paid workers. However, we find positive effects of labor-augmenting technologies on occupation-level employment and wage bills. A model featuring labor-saving and labor-augmenting technologies with vintage-specific human capital quantitatively matches these patterns. We extend our analysis to predict the effect of AI on earnings.
Keywords: technology; labor displacement; patents; earnings growth; job loss
JEL Codes: E0; E01; J01; J23; J24; O3; O4
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| exposure to labor-saving technologies (F66) | earnings growth (O49) |
| exposure to labor-saving technologies (F66) | job loss likelihood (J63) |
| labor-augmenting technologies (J89) | earnings growth (O49) |
| labor-augmenting technologies (J89) | job loss likelihood (J63) |
| technology exposure (L63) | worker outcomes (J28) |