We use data from the Annual Survey of Manufactures to study the characteristics and geographic distribution of investments in robots across US manufacturing establishments. Robotics adoption and robot intensity (the number of robots per employee) cluster in "robot hubs." Establishments that report having robotics are larger and have a larger production worker share, lower pay per worker, lower labor share, and higher capital expenditures, including higher IT capital expenditures. Notably, establishments are more likely to have robots if other establishments in the same core-based statistical area and industry also report having robotics, suggestive of agglomeration and peer effects.

We analyse the impact of robot adoption on employment composition using novel micro data on robot use in German manufacturing plants linked with social security records and data on job tasks. Our task-based model predicts more favourable employment effects for the least routine-task intensive occupations and for young workers, with the latter being better at adapting to change. An event-study analysis of robot adoption confirms both predictions. We do not find adverse employment effects for any occupational or age group, but churning among low-skilled workers rises sharply. We conclude that the displacement effect of robots is occupation biased but age neutral, whereas the reinstatement effect is age biased and benefits young workers most.