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Replication data for: What Can Machines Learn, and What Does It Mean for Occupations and the Economy?

Version
1
Resource Type
Dataset
Creator
  • Brynjolfsson, Erik
  • Mitchell, Tom
  • Rock, Daniel
Publication Date
2018-01-01
Description
  • Abstract

    Advances in machine learning (ML) are poised to transform numerous occupations and industries. This raises the question of which tasks will be most affected by ML. We apply the rubric evaluating task potential for ML in Brynjolfsson and Mitchell (2017) to build measures of "Suitability for Machine Learning" (SML) and apply it to 18,156 tasks in O*NET. We find that (i) ML affects different occupations than earlier automation waves; (ii) most occupations include at least some SML tasks; (iii) few occupations are fully automatable using ML; and (iv) realizing the potential of ML usually requires redesign of job task content.
Availability
Download
Relations
  • Is supplement to
    DOI: 10.1257/pandp.20181019 (Text)
Publications
  • Brynjolfsson, Erik, Tom Mitchell, and Daniel Rock. “What Can Machines Learn, and What Does It Mean for Occupations and the Economy?” AEA Papers and Proceedings 108 (2018): 43–47. https://doi.org/10.1257/pandp.20181019.
    • ID: 10.1257/pandp.20181019 (DOI)

Update Metadata: 2020-03-02 | Issue Number: 1 | Registration Date: 2020-03-02

Brynjolfsson, Erik; Mitchell, Tom; Rock, Daniel (2018): Replication data for: What Can Machines Learn, and What Does It Mean for Occupations and the Economy?. Version: 1. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset. https://doi.org/10.3886/E114436V1-22731