Multidimensional Skill Mismatch

By Fatih Guvenen, Burhanettin Kurusçu, Satoshi Tanaka, David G. Wiczer. American Economic Journal: Macroeconomics, 2020

162 citations recorded by the source corpus.

What determines the earnings of a worker relative to his peers in the same occupation? What makes a worker fail in one occupation but succeed in another? More broadly, what are the factors that determine the productivity of a worker-occupation match? To help answer questions like these, we propose an empirical measure of multidimensional skill mismatch that is based on the discrepancy between the portfolio of skills required by an occupation and the portfolio of abilities possessed by a worker for learning those skills. This measure arises naturally in a dynamic model of occupational choice and human capital accumulation with multidimensional skills and Bayesian learning about one’s ability to learn skills. Not only does mismatch depress wage growth in the current occupation, it also leaves a scarring effect—by stunting skill acquisition—that reduces wages in future occupations. Mismatch also predicts different aspects of occupational switching behavior. We construct the empirical analog of our skill mismatch measure from readily available US panel data on individuals and occupations and find empirical support for these implications. The magnitudes of these effects are large: moving from the worst- to best-matched decile can improve wages by 11 percent per year for the rest of one’s career. (JEL E24, J24, J31, J41)

Subfields: Demography, Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/88b6b8c309ed8176b0b0b53c26c73818ed51b206

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Demography

1. F. Postel-Vinay
2. Jeremy Lise

In Economics and Econometrics

1. Jeremy Lise
2. F. Postel-Vinay
3. J. Robin
4. S. Bonhomme
5. Theodore Papageorgiou
6. Pierre Cahuc

Citations in the corpus

• Multidimensional Skills, Sorting, and Human Capital Accumulation (2020, methodology): “We retain descriptors from the skills, abilities, knowledge, work activities, and work context O*NET files, as descriptors contained in the other files (job interests, work values, and work styles) are less directly interpretable in terms of skill requirements, and merge those files with our NLSY…”