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Cited by 1,193Labor market dynamics and wage inequalityDigital Economy and Work TransformationImpact of AI and Big Data on Business and Society

The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index; none of the 38 sources its claims cite is checked for support yet.

Artificial Intelligence and Jobs: Evidence from Online Vacancies

Open access
Acemoglu, Daron, Autor, David, Hazell, Jonathon, Restrepo, Pascual·2022·Journal of Labor Economics
DOI 10.1086/718327
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Abstract

We study the impact of artificial intelligence (AI) on labor markets using establishment-level data on the near universe of online vacancies in the United States from 2010 onward. There is rapid growth in AI-related vacancies over 2010–18 that is driven by establishments whose workers engage in tasks compatible with AI’s current capabilities. As these AI-exposed establishments adopt AI, they simultaneously reduce hiring in non-AI positions and change the skill requirements of remaining postings. While visible at the establishment level, the aggregate impacts of AI-labor substitution on employment and wage growth in more exposed occupations and industries is currently too small to be detectable.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

Using near-universe US online vacancies from Burning Glass, the paper measures establishment AI exposure from baseline occupational task structure and finds a post-2015 surge in AI vacancies, skill churn, and reduced non-AI hiring at AI-exposed establishments. It finds no detectable relationship between AI exposure and employment or wage growth at the industry or occupation level.

Key findings

  • AI vacancy postings grew rapidly after 2015, concentrated in AI-exposed establishments. A one standard deviation increase in Felten et al. AI exposure is associated with 15.96 (SE = 1.73) in the baseline AI-vacancy-growth specification, or 9.19 (SE = 1.21) with three-digit industry fixed effects; the paper describes this as roughly 15–16% more AI vacancy posting.
  • AI exposure predicts both disappearance of previously posted skills and emergence of new skills. With Felten et al., one standard deviation in AI exposure is associated with a 0.83 absolute decline in per-vacancy frequency of previously demanded skills (SE = 0.09; sample mean negative skill change = 4.70) and a 0.95 absolute increase in new or increasingly demanded skills (SE = 0.08; sample mean positive skill change = 6.30).
  • AI-exposed establishments reduce non-AI hiring. With Felten et al., the baseline estimate is −13.80 (SE = 4.22), about a 14% decline in non-AI vacancies; with firm fixed effects it is −4.81 (SE = 1.44), and for 2014–2018 it is −11.94 (SE = 3.80). The implied 2010–2018 non-AI employment decline is 7.2% (coefficient −7.24, SE = 4.66). Webb results are broadly similar but less stable; SML shows no consistent negative relationship.
  • No detectable aggregate relationship: industry-by-commuting-zone employment estimate for 2010–2016 is −0.05 (SE = 0.08), and occupation-level employment and wage regressions also show no differential behavior for more AI-exposed occupations.

Methods

  • Data: Burning Glass online vacancy postings covering the near-universe of US online vacancies from 2010 onward, with 2007 data also used; establishment defined as firm-by-commuting-zone; occupations and skills coded from postings; establishments in NAICS 51 (information) and 54 (professional/business services) excluded.
  • AI exposure: baseline 2010–2012 occupational structure weighted by three occupation-level AI suitability indices—Felten, Raj, and Seamans; Webb; and Brynjolfsson, Mitchell, and Rock’s SML—then standardized.
  • Design: establishment-level regressions of changes from 2010–2012 to 2016–2018 on baseline AI exposure, weighted by baseline vacancies, with controls for firm size decile, commuting zone, three-digit industry, and sometimes firm fixed effects; outcomes include AI vacancies, negative/positive skill change, non-AI vacancies, and industry/occupation employment and wages.

Limitations

  • The paper states that it leaves open the juxtaposition of negative establishment-level impacts and zero aggregate effects; that its task-structure focus may exclude AI impacts from new products or services; that estimates are not informative about AI applications missed by its exposure measures; and that future AI technologies may be more complementary or substitutive.
  • The text makes evident that results are much less robust with the SML measure and less stable with Webb than with Felten et al.; that the AI exposure measure is a shift-share design and standard errors may not be the most conservative; that County Business Patterns analysis ends in 2016, excluding later rapid AI expansion; and that online vacancy postings overrepresent technical and professional jobs relative to blue-collar and personal service jobs.

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Integrity signals

Registry

Crossref · 10.1086/718327

Cites

47 of 47 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

0 of 38 cited sources checked · 0 supported · 0 flagged

Show all 107 claims

  • It may state two separate claims as one. 49
  • Probably not a substantial claim. 20
  • The quote only partly states this claim. 1
64 of 64

  • The authors state this as an assumption.

    The elasticity of substitution between labor and algorithms is infinite (σ=∞) in Propositions 1 and 2.

    “To illustrate how the task structure determines AI adoption, we follow Acemoglu and Autor (2011) and Acemoglu andRestrepo (2018, 2019a) and assume that σ = ∞, so that algorithms and labor are perfectly substitutable within a task.”

  • The authors state this as an assumption.

    The initial cost share of AI is small.

    “We also focus on the realistic case in which the initial cost share of AI, denoted by s A e (= (Rk e (x) + w AI ℓ AI e (x))/total costs), is small.”

  • The authors state this as an assumption.

    AI services are produced by combining AI capital and in-house AI workers in fixed proportions.

    “We assume that AI services are provided by combining AI capital (machinery or algorithms) purchased from the outside, k e (x), and in-house workers operating, programming, or maintaining this capital, ℓ AI e (x), with the following technology:”

  • The authors state this as an assumption.

    All establishments are price takers for production workers, AI workers, and AI capital.

    “We assume throughout that all establishments are price takers for production workers, AI workers, and AI capital, whose respective prices are: w, w AI , and R.”

  • The authors state this as an assumption.

    The set of tasks in the economy is partitioned into tasks performed by distinct occupations.

    “Formally, we assume that the set of tasks in the economy, T , is partitioned into tasks performed by a set of distinct occupations and denote the set of tasks performed in occupation o ∈ O by T o .”

  • The paper relies on this without support.

    AI adoption can be partially identified from the footprints it leaves at adopting establishments as they hire workers specializing in AI-related activities.

    “Our starting point is that AI adoption can be partially identified from the footprints it leaves at adopting establishments as they hire workers specializing in AI-related activities, such as supervised and unsupervised learning, natural language processing, machine translation, or image recognition.”

  • The paper relies on this without support.

    Baseline occupational structure measures AI exposure throughout the analysis.

    “We construct an establishment's AI exposure from its baseline (2010)(2011)(2012) occupational structure according to each one of these indices, and use these baseline measures as proxies for AI exposure throughout our analysis.”

  • The authors state this as an assumption.

    Each establishment faces a downward-sloping demand curve for its product and sets its price to maximize profits.

    “We also assume that each establishment faces a downwardsloping demand curve for its product and will set its price p e to maximize profits (and its problem is separable from the profit-maximization problem of the firm's other establishments in case of multi-establishment firms).”

  • The authors state this as an assumption.

    Each establishment is small in the labor market and takes other prices and aggregate output as given.

    “In this profit-maximization problem, we assume that each establishment is small in the labor market and takes other prices and aggregate output as given.”

  • The authors state this as an assumption.

    The importance or quality of each task in production, α(x), is common across establishments.

    “where T is the set of feasible tasks, a subset T e of which is used in the production process of establishment e, and α(x) ≥ 0 designates the importance or quality of task x in the production process and are common across establishments.”

  • The authors state this as an assumption.

    The task-specific labor and AI productivity parameters γℓ(x) and γa(x) are common across establishments.

    “where σ is the elasticity of substitution between labor and algorithms, and γ ℓ (x) and γ a (x) are assumed to be common across establishments.”

  • The authors state this as an assumption.

    All establishments have constant returns to scale.

    “We impose Te α(x)dx = 1 for all feasible T e , which ensures that all establishments have constant returns to scale.”

  • The authors state this as an assumption.

    1.6% of the increase in AI vacancies were driven by task-level substitution of algorithms for labor.

    “Assuming that 1.6% of these increases-the partial R 2 of our AI exposure measure in Table 1-were driven by task-level substitution of algorithms for labor”

  • The paper relies on this without support.

    AI is intended to supplement, replicate, and in some cases, exceed human-level intelligence in a variety of tasks.

    “AI is intended to supplement, replicate, and in some cases, exceed human-level intelligence in a variety of tasks.”

  • The paper relies on this without support.

    The US BLS Occupational Employment Statistics (OES) data provides more accurate estimates of employment and wages in occupations than is available from household surveys.

    “This establishment-based data series provides more accurate estimates of employment and wages in occupations than is available from household surveys.”

  • The authors state this as an assumption.

    The common technology assumption in (2) and (3) holds.

    “The common technology assumption in (2) and (3) can also be relaxed but is useful for simplifying the exposition by ensuring that differences in factor demands across establishments are driven entirely by task structures, making the link between the model and our empirical approach more transparent.”

  • The authors state this as an assumption.

    The share of AI algorithms in cost is initially small.

    “When σ = ∞ as in Propositions 1 and 2 below and the share of AI algorithms in cost is initially small”

  • The authors state this as an assumption.

    The passthrough rate is less than 1/ε_e.

    “when the passthrough rate is less than 1/ε e”

  • The authors state this as an assumption.

    Establishment employment follows the law of motion ℓ_{e,t+1} = f v_{e,t} + (1 - s) ℓ_{e,t}.

    “we assume that establishment employment, ℓ e,t , follows the law of motion ℓ e,t+1 = f v e,t + (1 -s) ℓ e,t , where v e,t denotes the establishment's vacancies, f is the vacancy fill rate and s is the separation rate.”

  • The authors state this as an assumption.

    The establishment is in steady state initially in 2010.

    “We calculate employment in 2010 by assuming that the establishment is in steady state initially”
20 of 20

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • AI-vacancy-share effect is described as 19 percentage points instead of about 0.18

    Major

    The stated column-6 estimate for the change in AI-vacancy share is 0.18, and the table defines that outcome as the change in share multiplied by 100. It therefore represents about 0.18 percentage points, not 19 percentage points.

  • Claim of no occupational wage effect conflicts with Table 8

    Major

    The paper says it detects no wage effect in more AI-exposed occupations after 2010. Table 8 reports a post-2010 occupational wage-growth estimate of -0.17 with a standard error of 0.06 for the Felten measure, an estimate approximately 2.8 standard errors from zero.

  • Figure 6 caption interchanges the Webb and SML panels

    Minor

    The plotted panels are labeled Felten, Webb, then SML, but the caption identifies the second as SML and the third as Webb.

  • Hiring-increase condition has the wrong threshold

    Minor

    Equation (5) makes the employment effect proportional to -1 plus the productivity term. Hiring increases only when that term exceeds 1, whereas the text says it need only exceed -1.

  • Table 4 specifies a sample posting in 2019 despite an end date of 2018

    Minor

    The table labels its outcome and sample period as ending in 2018, and the described source data run only through October 2018, but its caption says the establishments posted in both 2010 and 2019.

  • Skill-family descriptions reverse the signs of the plotted outcomes

    Minor

    Figure 7 is identified as negative skill change, yet the text describes its estimates as increasing demand. Figure 8 is identified as positive skill change, yet the text describes its estimates as lower demand.

6 of 6

Citations

Cites

Flagged first, then by citations

Show all 29 references

Research works printed but not linked: 10

  1. Babina Tania, 2020, Alex Xi He, and James Hodson
  2. Bessen James, 2018, Lydia Reichensperger, and Robert Seamans.
  3. Brynjolfsson Erik, Machine learning and occupational change. Unpublished manuscript
  4. Carnevale Anthony, Understanding job ads data: A technical report. Unpublished manuscript
  5. Felten Edward, 2019, Academy of Management Proceedings 1, https://doi.org/10.5465/AMBPP., 2019, 140 · DOI
  6. Ford Martin, The rise of the robots
  7. Grennan Jillian, Artificial intelligence and high-skilled work: Evidence from analysts. Unpublished manuscript
  8. Humlum Anders, Robot adoption and labor market dynamics. Unpublished manuscript
  9. McKinsey Global Institute, Artificial intelligence: The next digital frontier? Unpublished manuscript
  10. West Darrell, AI, and automation.

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Metadata

Authors
Acemoglu, D., Autor, D., Hazell, J., & Restrepo, P.
Affiliations
  • Massachusetts Institute of Technology (US)
  • London School of Economics and Political Science (GB)
  • Boston University (US)
ORCID
Autor, David
Venue
Journal of Labor Economics
Publisher
University of Chicago Press
Published
2022-04-01
Volume
40
Issue
S1
Pages
S293-S340
ISSN
  • 0734-306X (ISSN-L)
  • 1537-5307
DOI
10.1086/718327
OpenAlex
W4224990248
Cited by
1,193
References
29
Citations by year
2026: 395 · 2025: 324 · 2024: 317 · 2023: 91 · 2022: 41
FWCI
31.58
Type
journal article
Language
en
Open access
Locations
  • Journal of Labor Economics (publishedVersion)
  • SSRN Electronic Journal (publishedVersion)
  • London School of Economics and Political Science Theses Online (London School of Economics and Political Science) (acceptedVersion)
  • London School of Economics and Political Science Research Online (London School of Economics and Political Science) (acceptedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • SSRN Electronic Journal (submittedVersion)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
Funders
  • National Science Foundation
  • Alfred P. Sloan Foundation
  • Smith Richardson Foundation
  • Sid W. Richardson Foundation
  • Washington Center for Equitable Growth
  • Economic and Social Research Council (ES/T014431/1)
Topics
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Labor market dynamics and wage inequality
  • Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
Keywords
artificial intelligence, labor market outcomes, online job postings, labor substitution, skill requirements, personnel selection, wage growth, occupation, industry

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