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Author: Augurzky, Boris
Resulting in 2 citations.
1. Augurzky, Boris
What Are College Degrees Worth? Evidence from the NLSY79 Using Matching Methods
Discussion Paper Series No. 299, Department of Economics, University of Heidelberg, Germany, August 1999.
Also: http://www.uni-heidelberg.de/institute/fak18/publications/papers/dp299.pdf
Cohort(s): NLSY79
Publisher: Faculty of Economics, University of Heidelberg
Keyword(s): College Education; Earnings; Women

Permission to reprint the abstract has not been received from the publisher.

This paper makes use of the method of matching to estimate the causal effect of the college education on future earnings for men and women. In contrast to regression models, the method of matching does not rely on functional form assumptions. Yet in this study a simple linear model is added to examine heterogeneity and an exogenous time trend in the return to college. The data are taken from the National Longitudinal Survey of Youth 1979. In particular, these data comprise ability measures that can be used to assess the ability bias in the estimated returns to college. Furthermore, the development of the returns is examined for the first ten years after college. An optimal full matching procedure based on propensity score calipers is used to stratify the whole sample of college graduates and non-graduates implying only small loss of observations and minimization of the distance across treatment and control groups over the complete sample.
Bibliography Citation
Augurzky, Boris. "What Are College Degrees Worth? Evidence from the NLSY79 Using Matching Methods." Discussion Paper Series No. 299, Department of Economics, University of Heidelberg, Germany, August 1999.
2. Kluve, Jochen
Augurzky, Boris
Assessing the Performance of Matching Algorithms when Selection into Treatment is Strong
Journal of Applied Econometrics 22,3 (2007): 533-557.
Also: http://onlinelibrary.wiley.com/doi/10.1002/jae.919/abstract
Cohort(s): NLSY79
Publisher: Wiley Online
Keyword(s): College Education; Earnings; Heterogeneity

Permission to reprint the abstract has not been received from the publisher.

This paper investigates the method of matching regarding two crucial implementation choices: the distance measure and the type of algorithm. We implement optimal full matching—a fully efficient algorithm—and present a framework for statistical inference. The implementation uses data from the NLSY79 to study the effect of college education on earnings. We find that decisions regarding the matching algorithm depend on the structure of the data: In the case of strong selection into treatment and treatment effect heterogeneity a full matching seems preferable. If heterogeneity is weak, pair matching suffices. Copyright © 2007 John Wiley & Sons, Ltd.
Bibliography Citation
Kluve, Jochen and Boris Augurzky. "Assessing the Performance of Matching Algorithms when Selection into Treatment is Strong." Journal of Applied Econometrics 22,3 (2007): 533-557.