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Cited by 574Labor market dynamics and wage inequalityImpact of AI and Big Data on Business and SocietyArtificial Intelligence Applications

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

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Felten, Edward, Raj, Manav, Seamans, Robert·2021·Strategic Management Journal
DOI 10.1002/smj.3286
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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.

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Crossref · 10.1002/smj.3286

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6 of 6 references verified0 citation errors · 0 not checkable

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  • Felten, Raj, and Seamans (2021) map advances in AI capabilities to O*NET abilities, and then link these to occupational groups.

    “Felten, Raj, and Seamans (2021) map advances in AI capabilities to O*NET abilities, and then link these to occupational groups.”

    From Labor Market Consequences of Generative AI: Early Evidence from Norway (2026) · cites this paper as Felten et al., 2021

  • The four occupational AI exposure indices are measured on different scales, ranging from a standardized score with mean near zero and standard deviation near one to a right-skewed usage-weighted share.

    “The four indices live on different scales -Felten et al. (2021) delivers a standardized score with mean roughly zero and standard deviation roughly one; Pizzinelli et al. (2023) delivers a positive, bounded score on a wider range;”

    From Labor Market Consequences of Generative AI: Early Evidence from Norway (2026) · cites this paper as Felten et al. (2021)

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Authors
Felten, E., Raj, M., & Seamans, R.
Affiliations
  • Princeton University (US)
  • New York University (US)
Corresponding author
Seamans, Robert
ORCID
Venue
Strategic Management Journal
Publisher
Wiley
Published
2021-05-08
Volume
42
Issue
12
Pages
2195-2217
ISSN
  • 0143-2095 (ISSN-L)
  • 1097-0266
DOI
10.1002/smj.3286
OpenAlex
W3159104979
Cited by
574
References
6
Citations by year
2026: 254 · 2025: 136 · 2024: 96 · 2023: 62 · 2022: 16 · 2021: 7 · 2020: 1 · 2018: 1
FWCI
34.55
Type
journal article
Language
en
Open access
Licence
Locations
Strategic Management Journal (publishedVersion, cc-by)
Topics
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Labor market dynamics and wage inequality
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords
artificial intelligence

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