Journal article | Zeitschriftenartikel

Estimating potential outcome distributions using local instrumental variables with an application to changes in college enrollment and wage inequality

This paper extends the method of local instrumental variables developed by Heckman and Vytlacil [Heckman, J. and Vytlacil, E., 1999. Local instrumental variable and latent variable models for identifying and bounding treatment effects. In: Proceedings of the National Academy of Sciences, 96, 4730–4734; Heckman, J. and Vytlacil, E., 2001. Local Instrumental Variables. In: C. Hsiao, K. Morimune, and J. Powells, (Eds.), Nonlinear Statistical Modeling: Proceedings of the Thirteenth International Symposium in Economic Theory and Econometrics: Essays in Honor of Takeshi Amemiya, Cambridge University Press, Cambridge, (2000), pp. 1–46; Heckman, J. and Vytlacil E., 2005. Structural equations, treatment, effects and econometric policy evaluation. Econometrica 73(3), 669–738] to the estimation of not only means, but also distributions of potential outcomes. The newly developed method is illustrated by applying it to changes in college enrollment and wage inequality using data from the National Longitudinal Survey of Youth of 1979. Increases in college enrollment cause changes in the distribution of ability among college and high school graduates. This paper estimates a semiparametric selection model of schooling and wages to show that, for fixed skill prices, a 14% increase in college participation (analogous to the increase observed in the 1980s), reduces the college premium by 12% and increases the 90-10 percentile ratio among college graduates by 2%.

Estimating potential outcome distributions using local instrumental variables with an application to changes in college enrollment and wage inequality

Urheber*in: Carneiro, Pedro; Lee, Sokbae

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Language
Englisch
Extent
Seite(n): 191-208
Notes
Status: Postprint; begutachtet (peer reviewed)

Bibliographic citation
Journal of Econometrics, 149(2)

Classification
Semiparametric and Nonparametric Methods: General
Subject
Wirtschaft
Wirtschaftsstatistik, Ökonometrie, Wirtschaftsinformatik

Event
Geistige Schöpfung
(who)
Carneiro, Pedro
Lee, Sokbae
Event
Veröffentlichung
(where)
Vereinigtes Königreich
(when)
2009

DOI
URN
urn:nbn:de:0168-ssoar-213463
Rights
GESIS - Leibniz-Institut für Sozialwissenschaften. Bibliothek Köln
Last update
21.06.2024, 4:27 PM CEST

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Object type

  • Zeitschriftenartikel

Associated

  • Carneiro, Pedro
  • Lee, Sokbae

Time of origin

  • 2009

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