A Data-Driven Exploration of the Race between Human Labor and Machines in the 21st Century

Communications of the ACM (Forthcoming)

32 Pages Posted: 20 Sep 2021 Last revised: 15 Feb 2022

See all articles by Jiyong Park

Jiyong Park

Terry College of Business, the University of Georgia

Jongho Kim

Cornell University - Samuel Curtis Johnson Graduate School of Management

Date Written: September 16, 2021

Abstract

A full picture of automation and the future of work requires an understanding of not only technological change—to what extent a human task is automated—but also labor demand for occupations performing a task. This study explores how human labor competes, or cooperates, with the machine in performing a range of tasks. By applying a data-driven methodology, we reveal 15 endogenous task types performed by representative occupations, and measure the task-level degree of automation. Taking into account the changes in both degree of automation and employment share for each task from 2008 to 2020, task types are classified into five distinct categories. Our findings highlight that the increase in automation level does not always lead to a decrease in labor demand for the task. Finally, we construct task-level automation indexes for representative occupations and U.S. cities, which are found to be significantly correlated with occupational characteristics and regional innovation capacity, respectively. We develop a website for the automation indexes to facilitate discussion and communications regarding the impacts of automation technology: www.jobautomationindex.com.

Keywords: Automation, Future of Work, Skill Network

Suggested Citation

Park, Jiyong and Kim, Jongho, A Data-Driven Exploration of the Race between Human Labor and Machines in the 21st Century (September 16, 2021). Communications of the ACM (Forthcoming), Available at SSRN: https://ssrn.com/abstract=3924789

Jiyong Park (Contact Author)

Terry College of Business, the University of Georgia ( email )

Athens, GA 30602-6254
United States

Jongho Kim

Cornell University - Samuel Curtis Johnson Graduate School of Management ( email )

Ithaca, NY 14853
United States

HOME PAGE: http://jonghkim.github.io

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