Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses

Edward Felten, Manav Raj, Robert Seamans

Research output: Contribution to journalArticlepeer-review

Abstract

Research Summary: We create and validate a new measure of an occupation's exposure to AI that we call the AI Occupational Exposure (AIOE). We use the AIOE to construct a measure of AI exposure at the industry level, which we call the AI Industry Exposure (AIIE) and a measure of AI exposure at the county level, which we call the AI Geographic Exposure (AIGE). We also describe several ways in which the AIOE can be used to create firm level measures of AI exposure. We validate the measures and describe how they can be used in different applications by management, organization and strategy scholars. Managerial Summary: Although artificial intelligence (AI) promises to spur economic growth, there is widespread concern that it could displace workers, alter industry trajectories, and reshape organizations. Despite the interest in this area, we have limited ability to study the effects of AI on occupations, firms, industries, and geographies because of limited availability of data that measures exposure to AI. To address this limitation, we create and validate a new measure of an occupation's exposure to AI that we call the AI Occupational Exposure (AIOE). We use the AIOE to construct a measure of AI exposure at the industry level (AIIE) and county level (AIGE). We describe how our measures can be useful to scholars and policy-makers interested in identifying the effect of AI on markets.

Original languageEnglish (US)
JournalStrategic Management Journal
DOIs
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Business and International Management
  • Strategy and Management

Keywords

  • artificial intelligence
  • industry
  • innovation
  • occupation
  • technology

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