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Author: Flores-Lagunes, Alfonso
Resulting in 4 citations.
1. Flores-Lagunes, Alfonso
Light, Audrey L.
Identifying Sheepskin Effects in the Returns to Education
Working Paper, Department of Economics, University of Arizona, Tucson AZ, April 2004.
Also: http://gsbwww.uchicago.edu/labor/flores2004.pdf
Cohort(s): NLSY79
Publisher: Graduate School of Business, University of Chicago
Keyword(s): Education; Educational Attainment; Educational Returns; Schooling

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

[Exerpt from Introduction] In this study, we ask how to interpret the range of estimated sheepskin effects that arise from different model specifications. We begin with the observation that sheepskin effects are identified because individuals with a given amount of schooling differ in their degree attainment or, stated differently, because S varies among individuals within a given degree category. It is important to recognize that the variation needed for identification can represent 'signal,' 'noise,' or a combination of both, and that the source of variation determines which parameters are identified and how we should interpret the estimates. To illustrate the identification issues, suppose we observe variation in S among individuals who hold a bachelor's degree. This variation might accurately reflect the underlying behavioral process if, for example, some college students choose full-time (or even part- time) employment at the expense of more rapid progress toward a degree. The variation might also reflect measurement error: data that cross-classify individuals as college graduates with only 12, 13 or 14 years of school should certainly be met with suspicion, and even reports that appear more logical can be error-ridden. Variation in S within degree category is essential if we wish to identify S-D interactions in a wage model, but it is equally important to consider the source of variation. Under the first scenario, we might predict that the sheepskin effect increases with S because S is positively correlated with omitted in-school work experience, however, the estimate should be interpreted as a reward for work experience rather than evidence that employers use degrees to screen for unobserved traits. If the variation arises primarily from measurement error, it might pay to restrict the functional form of our wage model rather than rely on 'noise' for identification.
Bibliography Citation
Flores-Lagunes, Alfonso and Audrey L. Light. "Identifying Sheepskin Effects in the Returns to Education." Working Paper, Department of Economics, University of Arizona, Tucson AZ, April 2004.
2. Flores-Lagunes, Alfonso
Light, Audrey L.
Interpreting Degree Effects in the Returns to Education
Discussion Paper No. 4169, Institute for the Study of Labor (IZA) (May 2009).
Also: Working Paper 20 (December 2009), China Center for Human Capital and Labor Market Research, Central University of Finance and Economics.
Cohort(s): NLSY79
Publisher: Institute for the Study of Labor (IZA)
Keyword(s): College Dropouts; College Graduates; Educational Returns; GED/General Educational Diploma/General Equivalency Degree/General Educational Development; High School Diploma; Modeling; Schooling, Post-secondary; Wage Equations

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

Researchers often identify degree effects by including degree attainment (D) and years of schooling (S) in a wage model, yet the source of independent variation in these measures is not well understood. We argue that S is negatively correlated with ability among degree holders because the most able graduate the fastest, while a positive correlation exists among dropouts because the most able benefit from increased schooling. Using data from the NLSY79, we find support for this explanation, and we reject the notion that the independent variation in S and D reflects reporting error. [ABSTRACT FROM AUTHOR]
Bibliography Citation
Flores-Lagunes, Alfonso and Audrey L. Light. "Interpreting Degree Effects in the Returns to Education." Discussion Paper No. 4169, Institute for the Study of Labor (IZA) (May 2009).
3. Flores-Lagunes, Alfonso
Light, Audrey L.
Interpreting Degree Effects in the Returns to Education
Journal of Human Resources 45,2 (March 2010): 439-467.
Also: http://jhr.uwpress.org/content/45/2/439.abstract
Cohort(s): NLSY79
Publisher: University of Wisconsin Press
Keyword(s): College Dropouts; College Graduates; Educational Returns; GED/General Educational Diploma/General Equivalency Degree/General Educational Development; High School Completion/Graduates; High School Dropouts; Modeling; Schooling, Post-secondary; Wage Equations

Researchers often identify degree effects by including degree attainment (D) and years of schooling (S) in a wage model, yet the source of independent variation in these measures is not well understood. We argue that S is negatively correlated with ability among degree-holders because the most able graduate the fastest, but positively correlated among dropouts because the most able benefit from increased schooling. Using NLSY79 data, we find support for this argument; our findings also suggest that highest grade completed is the preferred measure of S for dropouts, while age at school exit is a more informative measure for degree-holders. [ABSTRACT FROM AUTHOR]

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Bibliography Citation
Flores-Lagunes, Alfonso and Audrey L. Light. "Interpreting Degree Effects in the Returns to Education." Journal of Human Resources 45,2 (March 2010): 439-467.
4. Germinario, Giuseppe
Amin, Vikesh
Flores, Carlos A.
Flores-Lagunes, Alfonso
What Can We Learn About the Effect of Mental Health on Labor Market Outcomes Under Weak Assumptions? Evidence from the NLSY79
Labour Economics published online (18 September 2022): 102258.
Also: https://www.sciencedirect.com/science/article/pii/S0927537122001488
Cohort(s): NLSY79
Publisher: Elsevier
Keyword(s): Depression (see also CESD); Earnings; Employment; Health, Mental/Psychological; Labor Market Outcomes

We employ a nonparametric partial identification approach to bound the causal effect of poor mental health on employment and earnings using the National Longitudinal Study of Youth 1979. Our approach allows us to provide bounds on the population average treatment effect based on relatively weak, credible assumptions. We find that being categorized as depressed decreases employment by 10% and earnings by 27% at most, but we cannot statistically rule out a zero effect. We also provide insights into the heterogeneity of the effects on labor market outcomes at different levels of adverse mental health experienced (no, little, mild, moderate, and severe depressive symptoms). We find that going from having no (little) to severe depressive symptoms reduces employment by 3-18% (3-16%) and earnings by 11-44% (12-36%). The estimated bounds statistically rule out null effects for earnings but not for employment.
Bibliography Citation
Germinario, Giuseppe, Vikesh Amin, Carlos A. Flores and Alfonso Flores-Lagunes. "What Can We Learn About the Effect of Mental Health on Labor Market Outcomes Under Weak Assumptions? Evidence from the NLSY79." Labour Economics published online (18 September 2022): 102258.