Working Paper: NBER ID: w24239
Authors: Robert Seamans; Manav Raj
Abstract: We summarize existing empirical findings regarding the adoption of robotics and AI and its effects on aggregated labor and productivity, and argue for more systematic collection of the use of these technologies at the firm level. Existing empirical work primarily uses statistics aggregated by industry or country, which precludes in-depth studies regarding the conditions under which robotics and AI complement or are substituting for labor. Further, firm-level data would also allow for studies of effects on firms of different sizes, the role of market structure in technology adoption, the impact on entrepreneurs and innovators, and the effect on regional economies amongst others. We highlight several ways that such firm-level data could be collected and used by academics, policymakers and other researchers.
Keywords: AI; Labor Productivity; Robotics; Firm-level Data
JEL Codes: B40; O40
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Robotics adoption (L63) | GDP growth (O49) |
| Robotics adoption (L63) | Labor productivity (O49) |
| Industrial robot adoption (L63) | Employment (J68) |
| Industrial robot adoption (L63) | Wages (J31) |
| Positive technology shocks (O49) | Job opportunities in other sectors (J69) |
| Robots (L63) | Employment effects (J68) |
| Robots (L63) | Productivity effects (O49) |
| Automation (L23) | High risk of job loss (J63) |