Arbeitspapier
Skilled and unskilled labor are less substitutable than commonly thought
A key parameter in the analysis of wage inequality is the elasticity of substitution between skilled and unskilled labor. We question the common view that the elasticity exceeds 1. Two biases, publication and attenuation, conspire to pull the mean elasticity reported in the literature to 1.9. After correcting for the biases, the literature is consistent with the elasticity in the US of 0.6-0.9. Our analysis relies on 729 estimates of the elasticity collected from 76 studies as well as 37 controls that reflect the context in which the estimates were obtained. We use recently developed nonlinear techniques to correct for publication bias and employ Bayesian and frequentist model averaging to address model uncertainty. Our results suggest that, first, insignificant estimates of the elasticity are underreported. Second, because researchers typically estimate the elasticity's inverse, measurement error exaggerates the elasticity, and we show the exaggeration is substantial. Third, elasticities are systematically larger for developed countries, translog estimation, and methods that ignore endogeneity.
- Sprache
-
Englisch
- Erschienen in
-
Series: IES Working Paper ; No. 29/2020
- Klassifikation
-
Wirtschaft
Labor Demand
Human Capital; Skills; Occupational Choice; Labor Productivity
Wage Level and Structure; Wage Differentials
- Thema
-
Elasticity of substitution
skill premium
meta-analysis
model uncertainty
publication bias
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Havranek, Tomas
Havránková, Zuzana
Laslopova, Lubica
Zeynalova, Olesia
- Ereignis
-
Veröffentlichung
- (wer)
-
Charles University in Prague, Institute of Economic Studies (IES)
- (wo)
-
Prague
- (wann)
-
2020
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Havranek, Tomas
- Havránková, Zuzana
- Laslopova, Lubica
- Zeynalova, Olesia
- Charles University in Prague, Institute of Economic Studies (IES)
Entstanden
- 2020
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