Automation and the Workforce: A Firm-Level View from the 2019 Annual Business Survey
April 2022
Working Paper Number:
CES-22-12R
Abstract
This paper describes the adoption of automation technologies by US firms across all economic sectors by leveraging a new module introduced in the 2019 Annual Business Survey, conducted by the US Census Bureau in partnership with the National Center for Science and Engineering Statistics (NCSES). The module collects data from over 300,000 firms on the use of five advanced technologies: AI, robotics, dedicated equipment, specialized software, and cloud computing. The adoption of these technologies remains low (especially for AI and robotics), varies substantially across industries, and concentrates on large and young firms. However, because larger firms are much more likely to adopt them, 12-64% of US workers and 22-72% of manufacturing workers are exposed to these technologies. Firms report a variety of motivations for adoption, including automating tasks previously performed by labor. Consistent with the use of these technologies for automation, adopters have higher labor productivity and lower labor shares. In particular, the use of these technologies is associated with a 11.4% higher labor productivity, which accounts for 20'30% of the difference in labor productivity between large firms and the median firm in an industry. Adopters report that these technologies raised skill requirements and led to greater demand for skilled labor, but brought limited or ambiguous effects to their employment levels.
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manufacturing,
enterprise,
industrial,
technology,
technical,
technological,
tech,
manufacturer,
innovation,
patent,
technology adoption,
workforce,
industrialized,
occupation
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:
Annual Survey of Manufactures,
National Science Foundation,
Total Factor Productivity,
Labor Productivity,
Survey of Manufacturing Technology,
Longitudinal Business Database,
North American Industry Classification System,
Computer Network Use Supplement,
Information and Communication Technology Survey,
Business Register,
Census Bureau Disclosure Review Board,
Disclosure Review Board,
National Center for Science and Engineering Statistics
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