We use establishment-level data from the US Census Bureau's Annual Survey of Manufactures to study the characteristics and geographic locations of investments in robots. We find that the distribution of robots is highly skewed across locations. Some locations, which we call Robot Hubs, have far more robots than one would expect even after accounting for industry and manufacturing employment. We characterize these Robot Hubs along several industry, demographic, and institutional dimensions. The presences of robot integrators, which specialize in helping manufacturers install robots, and of higher levels of union membership are positively correlated with being a Robot Hub.

We analyze the impact of robot adoption on female employment. Our analysis is based on novel micro data on robot use by German manufacturing establishments linked with social security records. An event study analysis for robot adoption shows increased churning among female workers. Whereas hiring rises significantly at robot adoption, separations increase with a smaller magnitude one year later. Overall, employment effects are modestly positive and strongest for medium-qualified women. We find no adverse employment effects for female workers in any of our broad qualification groups. 

We use data from the Annual Survey of Manufactures to study the characteristics and geography of investments in robots across U.S. manufacturing establishments. We find that robotics adoption and robot intensity (the number of robots per employee) is much more strongly related to establishment size than age. We find that establishments that report having robotics have higher capital expenditures, including higher information technology (IT) capital expenditures. Also, establishments are more likely to have robotics if other establishments in the same Core-Based Statistical Area (CBSA) and industry also report having robotics. The distribution of robots is highly skewed across establishments’ locations. Some locations, which we call Robot Hubs, have far more robots than one would expect even after accounting for industry and manufacturing employment. We characterize these Robot Hubs along several industry, demographic, and institutional dimensions. The presence of robot integrators and higher levels of union membership are positively correlated with being a Robot Hub.

We examine the impact of the COVID-19 pandemic on the credit risk of companies around the world. We find that increased infection rates affect firms more adversely as reflected by the wider increase in their credit default swap (CDS) spreads if they are larger, more leveraged, closer to default, have worse governance and more limited stakeholder engagement, and operate in more highly exposed industries. We observe that country-level determinants such as GDP, political stability, foreign direct investment, and commitment to crisis management (income support, health and lockdown policies) also affect the sensitivity of CDS spreads to COVID-19 infection rates. A negative amplification effect exists for firms with high default probability in countries with fiscal constraints. A direct comparison between global CDS and stock markets reveals that the CDS market prices in a distinct set of corporate traits and government policies in pandemic times.