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Replication data for: What Can Machines Learn, and What Does It Mean for Occupations and the Economy?
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.
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For the SML measure, the coefficient of establishment AI exposure in 2010 on growth of AI vacancies is -2.21 (SE 0.96).
“Coefficient is -2.21,SE is .96”
From Artificial Intelligence and Jobs: Evidence from Online Vacancies (2022) · cites this paper as Brynjolfsson et al. (2019)
Establishment AI exposure in 2010 is positively associated with growth of establishment broad AI vacancies between 2010 and 2018, using the SML measure (coefficient 4.14, SE 1.43, column 1).
“Establishment AI Exposure, 2010 4.14 2.18 -2.22 -2.90 1.92 4.90 (1.43) (1.27) (0.89) (1.33) (0.88) (1.34)”
From Artificial Intelligence and Jobs: Evidence from Online Vacancies (2022) · cites this paper as Brynjolfsson et al. (2019)
Establishment AI exposure in 2007 is positively associated with growth of establishment AI vacancies between 2010 and 2018, using the SML measure (coefficient 7.46, SE 1.99, column 1 of Table A4).
“Establishment AI Exposure, 2007 7.46 5.44 -1.66 -3.39 1.78 -0.68 (1.99) (1.78) (1.57) (1.82) (1.44) (1.64)”
From Artificial Intelligence and Jobs: Evidence from Online Vacancies (2022) · cites this paper as Brynjolfsson et al. (2019)
Establishment AI exposure in 2010 is positively associated with growth of establishment AI vacancies between 2010 and 2018, using the SML measure of occupation AI exposure (coefficient 3.76, SE 2.72, column 1 of Table A2).
“Establishment AI Exposure, 2010 3.76 2.30 -2.21 -3.04 (2.72) (2.09) (1.67) (4.21)”
From Artificial Intelligence and Jobs: Evidence from Online Vacancies (2022) · cites this paper as Brynjolfsson et al. (2019)
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Artificial Intelligence and Jobs: Evidence from Online Vacancies
Acemoglu, D., Autor, D., Hazell, J., & Restrepo, P. (2022) · Journal of Labor Economics · doi:10.1086/718327 · Cited by 1,193
Metadata
- Authors
- Brynjolfsson, E., Mitchell, T., & Rock, D.
- Venue
- ICPSR - Interuniversity Consortium for Political and Social Research
- Publisher
- ICPSR - Interuniversity Consortium for Political and Social Research
- Published
- 2018
- References
- 0
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