Earnings Dynamics, Changing Job Skills, and STEM Careers*
By David J. Deming, K. Noray. Quarterly Journal of Economics, 2020
270 citations recorded by the source corpus.
This article studies the impact of changing job skills on career earnings dynamics for college graduates. We measure changes in the skill content of occupations between 2007 and 2019 using detailed job descriptions from a near universe of online job postings. We then develop a simple model where the returns to work experience are a race between on-the-job learning and skill obsolescence. Obsolescence lowers the return to experience, flattening the age-earnings profile in faster-changing careers. We show that the earnings premium for college graduates majoring in technology-intensive subjects such as computer science, engineering, and business declines rapidly, and that these graduates sort out of faster-changing occupations as they gain experience.
Subfields: Economics and Econometrics, Education, Management of Technology and Innovation
View Paper: https://www.semanticscholar.org/paper/daf31abf0d7a5e356b0b364568c43b8b2941709c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- P. Restrepo
- David H. Autor
- Daron Acemoglu
- A. Salomons
- B. Seegmiller
- Caroline Chin
- Lawrence D. W. Schmidt
- David Hémous
In Education
- Caroline Chin
- B. Seegmiller
- P. Restrepo
- Daron Acemoglu
- Lawrence D. W. Schmidt
- David H. Autor
- A. Salomons
In Management of Technology and Innovation
- Caroline Chin
- B. Seegmiller
- P. Restrepo
- Daron Acemoglu
- Lawrence D. W. Schmidt
- David H. Autor
- A. Salomons
Citations in the corpus
- New Frontiers: The Origins and Content of New Work, 1940–2018 (2022, methodology): “Complementing work by Lin (2011); Acemoglu and Restrepo (2018, 2019); Atalay et al. (2020); Deming and Noray (2020), we construct a novel and detailed inventory of new labor-using tasks that allows us to study the evolution and origins of new work over 1940–2018 using representative U.S. Census and…”
- AI and Jobs: Evidence from Online Vacancies (2020, methodology): “The first measure is from Felten et al. (2019). It is based on data from the AI Progress Measurement project, from the Electronic Frontier Foundation.”
- Technology, Vintage-Specific Human Capital, and Labor Displacement: Evidence from Linking Patents with Occupations∗ (2022, background): “Unlike much of this literature, we do not consider worker skill to be a fixed characteristic of the worker: our model allows for the possibility that gains from new technologies can displace the demand for specific expertise of workers skilled at tasks associated with older vintages—similar to the l”