Working Paper: NBER ID: w29552
Authors: Leonid Kogan; Dimitris Papanikolaou; Lawrence D. W. Schmidt; Bryan Seegmiller
Abstract: We develop a measure of workers’ technology exposure that relies only on textual descriptions of patent documents and the tasks performed by workers in an occupation. Our measure appears to identify a combination of labor-saving innovations but also technologies that may require skills that incumbent workers lack. Using a panel of administrative data, we examine how subsequent worker earnings relate to workers’ technology exposure. We find that workers at both the bottom but also the top of the earnings distribution are displaced. Our interpretation is that low-paid workers are displaced as their tasks are automated while the highest-paid workers face lower earnings growth as some of their skills become obsolete. Our calibrated model fits these facts and emphasizes the importance of movements in skill quantities, not just skill prices, for the link between technology and inequality.
Keywords: Technology Exposure; Labor Displacement; Earnings; Inequality; Human Capital
JEL Codes: J01; J24; J3; N3; N6; O3; O4
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Increase in technology exposure (O39) | Decline in worker earnings (J31) |
| Task automation (L23) | Displacement of low-paid workers (F66) |
| Skill obsolescence (J24) | Lower earnings growth for high-paid workers (J31) |
| Job separations (J63) | Earnings losses for lower-paid workers (J31) |
| Technological improvements (O33) | Skill loss (J24) |
| Skill loss (J24) | Increased income inequality (D31) |
| Increase in technology exposure (O39) | Heterogeneity in wage responses (J31) |