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Robot and Automation, New Insights from Micro Data

Paper Session

Saturday, Jan. 7, 2023 10:15 AM - 12:15 PM (CST)

Hilton Riverside, Grand Salon C Sec 13 & 16
Hosted By: American Economic Association
  • Chair: Erik Brynjolfsson, Stanford University

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

Daron Acemoglu
,
Massachusetts Institute of Technology
Gary Anderson
,
National Center for Science and Engineering Statistics
David Beede
,
U.S. Census Bureau
Catherine Buffington
,
U.S. Census Bureau
Pascual Restrepo
,
Boston University
Eric Childress
,
George Mason University
Emin Dinlersoz
,
U.S. Census Bureau
Lucia Foster
,
U.S. Census Bureau
Nathan Goldschlag
,
U.S. Census Bureau
John Haltiwanger
,
University of Maryland
Zachary Kroff
,
U.S. Census Bureau
Nikolas Zolas
,
U.S. Census Bureau

Abstract

This paper provides a comprehensive description of the adoption of automation technologies by US firms across all economic sectors by leveraging a new technology module introduced in the Census Bureau’s 2019 Annual Business Survey. 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. We document that the adoption of these technologies remains low (especially for AI and robotics), varies substantially across industries, and concentrates on large and younger 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 wages and lower labor shares. The use of these technologies is associated with a 15% increase in labor productivity, which accounts for 20–30% of the higher labor productivity achieved by the largest firms 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.

Robot Hubs: The Skewed Distribution of Robots in U.S. Manufacturing

Erik Brynjolfsson
,
Stanford University
Catherine Buffington
,
U.S. Census Bureau
J. Frank Li
,
Stanford University
Javier Miranda
,
Halle Institute for Economic Research
Nathan Goldschlag
,
U.S. Census Bureau
Robert Seamans
,
New York University

Abstract

In this paper we present results on the distribution of robots in U.S. manufacturing, using new establishment-level microdata collected by the U.S. Census Bureau. We use the data to present a number of facts about the location and use of robots. We find that the distribution of robots is surprisingly skewed across locations, even accounting for the different mix of industry and manufacturing employment across locations. Some locations - which we call "Robot Hubs" - have many more robots than one would expect if distribution of robots was uniform, after accounting for industry and manufacturing employment. We characterize these Robot Hubs along several industry, demographic, and institutional dimensions.

What Are the Labor and Product Market Effects of Automation? New Evidence from France

Philippe Aghion
,
College de France and London School of Economics
Celine Antonin
,
Sciences Po
Simon Bunel
,
National Institute of Statistics and Economic Studies and Paris School of Economics
Xavier Jaravel
,
London School of Economics

Abstract

We use comprehensive micro data in the French manufacturing sector between 1995 and 2017 to document the effects of automation technologies on employment, sales, prices, wages, and the labor share. Causal effects are estimated with event studies and a shift-share IV design leveraging pre-determined supply linkages and productivity shocks across foreign suppliers of industrial equipment. At all levels of analysis — plant, firm, and industry — the estimated impact of automation on employment is positive, even for unskilled industrial workers. We find that automation leads to higher sales, higher profits, and lower consumer prices, while it leaves wages, the labor share and within-firm wage inequality unchanged. Consistent with the importance of business-stealing across countries, the estimated industry-level employment response to automation is stronger in industries that face international competition. These estimates can be accounted for in a simple monopolistic competition model: firms that automate more increase their profits but pass through some of the productivity gains to consumers, inducing higher scale and higher labor demand. The results indicate that automation can increase labor demand and can generate productivity gains that are broadly shared across workers, consumers and firm owners. In a globalized world, attempts to curb domestic automation in order to protect domestic employment may be self-defeating due to foreign competition.

Robots, Job Tasks, and Worker Age: A Production-Unit Analysis of Employment

Liuchun Deng
,
Yale-NUS College
Steffen Müller
,
Halle Institute for Economic Research
Verena Plümpe
,
Halle Institute for Economic Research
Jens Stegmaier
,
Institute for Employment Research

Abstract

We analyze the impact of robot adoption on employment composition using novel micro data on robot use of German manufacturing plants linked with social security records and data on job tasks. Our task-based model predicts more favorable employment effects for the least routine-task intensive occupations and for young workers, the latter being better able to adapt to change. An event-study analysis for 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.

Discussant(s)
Betsey Stevenson
,
University of Michigan
James Bessen
,
Boston University
Gino Gancia
,
Queen Mary University of London
Susan Helper
,
Case Western Reserve University
JEL Classifications
  • O3 - Innovation; Research and Development; Technological Change; Intellectual Property Rights