Micro Data on Robots from the IAB Establishment Panel
Jahrbücher für Nationalökonomie und Statistik,
Micro-data on robots have been very sparse in Germany so far. Consequently, a dedicated section has been introduced in the IAB Establishment Panel 2019 that includes questions on the number and type of robots used. This article describes the background and development of the survey questions, provides information on the quality of the data, possible checks and steps of data preparation. The resulting data is aggregated on industry level and compared with the frequently used robot data by the International Federation of Robotics (IFR) which contains robot supplier information on aggregate robot stocks and deliveries.
The Characteristics and Geographic Distribution of Robot Hubs in U.S. Manufacturing Establishments
American Economic Association Papers and Proceedings,
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.
Robots, Occupations, and Worker Age: A Production-unit Analysis of Employment
IWH Discussion Papers,
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.
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On the Employment Consequences of Automation and Offshoring: A Labor Market Sorting View
Lili Yan Ing, Gene M. Grossman (eds), Robots and AI: A New Economic Era. Routledge: London,
We argue that automation may make workers and firms more selective in matching their specialized skills and tasks. We call this phenomenon “core-biased technological change”, and wonder whether something similar could be relevant also for offshoring. Looking for evidence in occupational data for European industries, we find that automation increases workers’ and firms’ selectivity as captured by longer unemployment duration, less skill-task mismatch, and more concentration of specialized knowledge in specific tasks. This does not happen in the case of offshoring, though offshoring reinforces the effects of automation. We show that a labor market model with two-sided heterogeneity and search frictions can rationalize these empirical findings if automation strengthens while offshoring weakens the assortativity between workers’ skills and firms’ tasks in the production process, and automation and offshoring complement each other. Under these conditions, automation decreases employment and increases wage inequality whereas offshoring has opposite effects.
Development of Survey Questions on Robotics Expenditures and Use in U.S. Manufacturing Establishments
Center for Economic Studies (CES) Working Paper Series,
The U.S. Census Bureau in partnership with a team of external researchers developed a series of questions on the use of robotics in U.S. manufacturing establishments. The questions include: (1) capital expenditures for new and used industrial robotic equipment in 2018, (2) number of industrial robots in operation in 2018, and (3) number of industrial robots purchased in 2018. These questions are to be included in the 2018 Annual Survey of Manufactures. This paper documents the background and cognitive testing process used for the development of these questions.