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Automation, Firms, and Labor Markets

Paper Session

Tuesday, Jan. 5, 2021 3:45 PM - 5:45 PM (EST)

Hosted By: American Economic Association
  • Chair: James Bessen, Boston University

Technology and the Size and Composition of Workforce in United States Firms: First Evidence from the 2019 Annual Business Survey

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

Abstract

This paper provides initial evidence on the connection between advanced technology presence and the size and composition of the workforce of U.S. firms and industries. Recent research using task-based models of production has demonstrated that advanced technology use by firms can alter both the number and types of workers through the creation of new tasks and the substitution of capital for labor in existing tasks. These effects can induce reallocation of labor and other inputs. Detailed evidence on the extent of these effects has been lacking for the population of U.S. firms, mainly due to the absence of reliable and comprehensive measures of technology use at the firm level. This analysis leverages a new technology module included in Census Bureau’s 2019 Annual Business Survey that collected data from over 300,000 firms on the adoption and use of five advanced technologies (Robotics, AI, Cloud Computing, Specialized Software, and Specialized Equipment), combined with firms’ subjective assessments of how each technology alters their workforce. The survey data is matched at the firm-level with administrative data on employment, revenues, and wages from the Census Bureau’s Longitudinal Business Database. The data is used to document across firms and industries the heterogeneity in technology adoption rates, the intensity of technology use, motivations for technology adoption, and barriers to adoption. The analysis also explores how firm-level technology adoption and use is related to firm and industry-level employment growth, how the adoption of new technologies creates (or destroys) roles for labor, and which type of technologies complement (or substitute) labor and skill.

Displacement and Inequality: Task Structure of Production and Changes in United States Wage Structure

Daron Acemoglu
,
Massachusetts Institute of Technology
Pascual Restrepo
,
Boston University

Abstract

This paper develops a framework that links automation to inequality. At the center of the framework is the substitution of machines for tasks brought about by the automation process. Critically, workers have different competitive advantages and specialize in different industries and occupations. The pattern of automation then creates downward pressure on the wages of different groups of workers and shapes the structure of wages. The theoretical framework characterizes the direct and the general equilibrium (indirect) effects of automation. The general equilibrium effects involve, for example, the fact that once the tasks performed by some workers are automated, these workers compete for jobs previously performed by other workers. We then derive a flexible framework based on this theoretical model for estimating the extent of inequality created by automation. Our empirical framework flexibly includes the standard forces emphasized in the literature, including skill-biased technological change, gender-biased technological change, and changes in experience premia as well as inter-industry wage premia that may be time-varying. We estimate this model on data on the evolution of wages by detailed demographic groups. Our estimates indicate that the majority of the changes in the US wage structure are due to automation. For example, more than 80% of the changes in the college-high school wage premia are explained by automation patterns, and very little is accounted for by standard skill-biased technological change channel.

Robots and Firms

Michael Koch
,
Aarhus University
Ilya Manuylov
,
Aarhus University
Marcel Smolka
,
University of Flensburg

Abstract

We study the microeconomic implications of robot adoption using a rich panel data-set of Spanish manufacturing firms over a 27-year period (1990-2016). We provide causal evidence on two central questions: (1) Which firm characteristics prompt firms to adopt robots? (2) What is the impact of robots on adopting firms relative to non-adopting firms? To address these questions, we look at our data through the lens of recent attempts in the literature to formalize the implications of robot technology. As for the first question, we establish robust evidence for positive selection, i.e., ex-ante better performing firms (measured through output and labor productivity) are more likely to adopt robots. On the other hand, conditional on size, ex-ante more skill-intensive firms are less likely to do so. As for the second question, we find that robot adoption generates substantial output gains in the vicinity of 20-25% within four years, reduces the labor cost share by 5-7%-points, and leads to net job creation at a rate of 10%. These results are robust to controlling for non-random selection into robot adoption through a difference-in-differences approach combined with a propensity score reweighting estimator. To further validate these results, we also offer structural estimates of total factor productivity (TFP) where robot technology enters the (endogenous) productivity process of firms. The results demonstrate a positive causal effect of robots on productivity, as well as a complementarity between robots and exporting in boosting productivity.
Discussant(s)
Christina Patterson
,
Northwestern University
Thomas Lemieux
,
University of British Columbia
Anders Humlum
,
Princeton University
JEL Classifications
  • J2 - Demand and Supply of Labor
  • O3 - Innovation; Research and Development; Technological Change; Intellectual Property Rights