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Automation and the Workforce: A Firm-Level View from the 2019 Annual Business Survey

Author

Listed:
  • Daron Acemoglu
  • Gary Anderson
  • David Beede
  • Catherine Buffington
  • Eric Childress
  • Emin Dinlersoz
  • Lucia Foster
  • Nathan Goldschlag
  • John Haltiwanger
  • Zachary Kroff
  • Pascual Restrepo
  • Nikolas Zolas
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.

Suggested Citation

  • Daron Acemoglu & Gary Anderson & David Beede & Catherine Buffington & Eric Childress & Emin Dinlersoz & Lucia Foster & Nathan Goldschlag & John Haltiwanger & Zachary Kroff & Pascual Restrepo & Nikolas, 2022. "Automation and the Workforce: A Firm-Level View from the 2019 Annual Business Survey," Working Papers 22-12, Center for Economic Studies, U.S. Census Bureau.
    • Handle: RePEc:cen:wpaper:22-12
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    File URL: https://www2.census.gov/ces/wp/2022/CES-WP-22-12R.pdf
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    References listed on IDEAS

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    Cited by:

    1. Erik Brynjolfsson & Catherine Buffington & Nathan Goldschlag & J. Frank Li & Javier Miranda & Robert Seamans, 2023. "The Characteristics and Geographic Distribution of Robot Hubs in U.S. Manufacturing Establishments," Working Papers 23-14, Center for Economic Studies, U.S. Census Bureau.
    2. Deng, Liuchun & Müller, Steffen & Plümpe, Verena & Stegmaier, Jens, 2023. "Robots, Occupations, and Worker Age: A Production-Unit Analysis of Employment," IZA Discussion Papers 16128, Institute of Labor Economics (IZA).
    3. MORIKAWA Masayuki, 2024. "Use of Artificial Intelligence and Productivity: Evidence from firm and worker surveys," Discussion papers 24074, Research Institute of Economy, Trade and Industry (RIETI).
    4. Mann, Katja & Pozzoli, Dario, 2022. "Automation and Low-Skill Labor," IZA Discussion Papers 15791, Institute of Labor Economics (IZA).

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    More about this item

    JEL classification:

    • E22 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Investment; Capital; Intangible Capital; Capacity
    • J20 - Labor and Demographic Economics - - Demand and Supply of Labor - - - General
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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