Working Paper: NBER ID: w31009
Authors: Daron Acemoglu; Hans R. A. Koster; Ceren Ozgen
Abstract: We estimate the effects of robot adoption on firm-level and worker-level outcomes in the Netherlands using a large employer-employee panel dataset spanning 2009-2020. Our firm-level results confirm previous findings, with positive effects on value added and hours worked for robot-adopting firms and negative outcomes on competitors in the same industry. Our worker-level results show that directly-affected workers (e.g., blue-collar workers performing routine or replaceable tasks) face lower earnings and employment rates, while other workers indirectly gain from robot adoption. We also find that the negative effects from competitors' robot adoption load on directly-affected workers, while other workers benefit from this industry-level robot adoption. Overall, our results highlight the uneven effects of automation on the workforce.
Keywords: robot adoption; firm-level outcomes; worker-level outcomes; automation; Netherlands
JEL Codes: D63; E22; E23; E24; J24; O33
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Robot adoption (L23) | Lower earnings for directly affected workers (J31) |
| Robot adoption (L23) | Lower employment rates for directly affected workers (J68) |
| Robot adoption (L23) | Productivity gains for indirectly affected workers (J29) |
| Robot adoption (L23) | Increase in output (E23) |
| Robot adoption (L23) | Increase in hours worked (J29) |
| Increase in robot adoption by competitors (L63) | Decline in hours worked for non-adopting firms (J29) |