Do Labor Demand Shifts Occur Within Firms or Across Them? Non-Routine-Biased Technological Change, 2000-2016

29 Pages Posted: 30 Jul 2019

See all articles by Seth Benzell

Seth Benzell

Chapman University - The George L. Argyros School of Business & Economics; MIT Initiative on the Digital Economy; Stanford University, Human-Centered Artificial Intelligence Digital Economy Lab

Guillermo Lagarda

Global Development Policy Center Boston University

Daniel Rock

University of Pennsylvania - Operations & Information Management Department

Date Written: July 16, 2019

Abstract

A large literature has documented occupational shifts in the US away from routine intensive tasks. Theories of skill-biased technological change differ in whether they predict changes in occupational mix within firms, or merely across different firms or industries. Using LinkedIn resume records, BLS OES data, and Compustat employee counts, we estimate occupational employment for publicly traded US firms from 2000 through 2016. We find that faster employment growth among firms that disproportionately employ non-routine workers is the most important cause of SBTC, followed by within firm occupational mix rebalancing. The entry of new firms also plays a role, although firm exit is slightly routine-worker biased. R&D leads firms to have a larger share of routine workers. These results are most consistent with a theory of routine task demand reduction caused by the diffusion of infra-marginally implemented new technologies. We also introduce a new measure of business labor dynamism, capturing the frequency with which firms change their occupational mix. Consistent with trends in productivity and other measures of business and labor market dynamism, this measure has decreased steadily since 2000.

Keywords: Skill-Biased Technological Change, Labor Demand, Automation, Business Dynamism

JEL Classification: D24, E32, J23, M21

Suggested Citation

Benzell, Seth and Lagarda, Guillermo and Rock, Daniel, Do Labor Demand Shifts Occur Within Firms or Across Them? Non-Routine-Biased Technological Change, 2000-2016 (July 16, 2019). Available at SSRN: https://ssrn.com/abstract=3427396 or http://dx.doi.org/10.2139/ssrn.3427396

Seth Benzell (Contact Author)

Chapman University - The George L. Argyros School of Business & Economics ( email )

333 N. Glassell
Orange, CA 92866
United States

MIT Initiative on the Digital Economy ( email )

245 First Street
Cambridge, MA 02142
United States

Stanford University, Human-Centered Artificial Intelligence Digital Economy Lab ( email )

Stanford, CA 94305
United States

Guillermo Lagarda

Global Development Policy Center Boston University ( email )

53 Bay State Road
Boston, MA 02215
United States

Daniel Rock

University of Pennsylvania - Operations & Information Management Department ( email )

Philadelphia, PA 19104
United States

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