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Author: Arcidiacono, Peter

Resulting in 4 citations.

1.	Arcidiacono, Peter Aucejo, Esteban M. Maurel, Arnaud Ransom, Tyler
1.	College Attrition and the Dynamics of Information Revelation NBER Working Paper No. 22325, National Bureau of Economic Research, June 2016. Also: http://www.nber.org/papers/w22325 Cohort(s): NLSY97 Publisher: National Bureau of Economic Research (NBER) Keyword(s): Armed Services Vocational Aptitude Battery (ASVAB); Attrition; College Enrollment; College Graduates; College Major/Field of Study/Courses; Grade Point Average (GPA)/Grades; Test Scores/Test theory/IRT; Wages This paper investigates the role played by informational frictions in college and the workplace. We estimate a dynamic structural model of schooling and work decisions, where individuals have imperfect information about their schooling ability and labor market productivity. We take into account the heterogeneity in schooling investments by distinguishing between two- and four-year colleges, graduate school, as well as science and non-science majors for four-year colleges. Individuals may also choose whether to work full-time, part-time, or not at all. A key feature of our approach is to account for correlated learning through college grades and wages, whereby individuals may leave or re-enter college as a result of the arrival of new information on their ability and productivity. Our findings indicate that the elimination of informational frictions would increase the college graduation rate by 9 percentage points, and would increase the college wage premium by 32.7 percentage points through increased sorting on ability.
	Bibliography Citation Arcidiacono, Peter, Esteban M. Aucejo, Arnaud Maurel and Tyler Ransom. "College Attrition and the Dynamics of Information Revelation." NBER Working Paper No. 22325, National Bureau of Economic Research, June 2016.

2.	Arcidiacono, Peter Aucejo, Esteban M. Maurel, Arnaud Ransom, Tyler
2.	College Attrition and the Dynamics of Information Revelation IZA Institute of Labor Economics (2023 November). Also: https://www.jstor.org/stable/resrep57219 Cohort(s): NLSY97 Publisher: IZA Institute of Labor Economics Keyword(s): Academic Development; Achievement; College Degree; College Dropouts; College Education; College Graduates; Higher Education; Income; Informational Friction; Schooling, Post-secondary; Wages Permission to reprint the abstract has not been received from the publisher. We examine how informational frictions impact schooling and work outcomes. To do so, we estimate a dynamic structural model where individuals face uncertainty about their academic ability and productivity, which respectively determine their schooling utility and wages. Our framework accounts for heterogeneity in college types and majors, as well as occupational search frictions and work hours. Individuals learn from grades and wages in a correlated manner and may change their choices as a result. Removing informational frictions would increase the college graduation rate by 4.4 percentage points, which would increase further by 2 percentage points in the absence of search frictions. Providing students with full information about their abilities would also result in large increases in the college and white-collar wage premia, while reducing the college graduation gap by family income.
	Bibliography Citation Arcidiacono, Peter, Esteban M. Aucejo, Arnaud Maurel and Tyler Ransom. "College Attrition and the Dynamics of Information Revelation." IZA Institute of Labor Economics (2023 November).

3.	Arcidiacono, Peter Bayer, Patrick Hizmo, Aurel
3.	Beyond Signaling and Human Capital: Education and the Revelation of Ability American Economic Journal: Applied Economics 2,4 (October 2010): 76-104. Also: http://pubs.aeaweb.org/doi/pdfplus/10.1257/app.2.4.76 Cohort(s): NLSY79 Publisher: American Economic Association Keyword(s): Armed Forces Qualifications Test (AFQT); College Enrollment; Discrimination, Employer; High School Completion/Graduates; Modeling, Fixed Effects; Racial Differences; Test Scores/Test theory/IRT; Wages Permission to reprint the abstract has not been received from the publisher. In traditional signaling models, education provides a way for individuals to sort themselves by ability. Employers in turn use education to statistically discriminate, paying wages that reflect the average productivity of workers with the same given level of education. In this paper, we provide evidence that education (specifically, attending college) plays a much more direct role in revealing ability to the labor market. Using the NLSY79, our results suggest that ability is observed nearly perfectly for college graduates. In contrast, returns to AFQT for high school graduates are initially very close to zero and rise steeply with experience. As a result, from very beginning of the career, college graduates are paid in accordance with their own ability, while the wages of high school graduates are initially completely unrelated to their own ability. This view of ability revelation in the labor market has considerable power in explaining racial differences in wages, education, and the returns to ability. In particular, we find no racial differences in wages or returns to ability in the college labor market, but a 6-10 percent wage penalty for blacks (conditional on ability) in the high school market. These results are consistent with the notion that employers use race to statistically discriminate in the high school market but have no need to do so in the college market.
	Bibliography Citation Arcidiacono, Peter, Patrick Bayer and Aurel Hizmo. "Beyond Signaling and Human Capital: Education and the Revelation of Ability." American Economic Journal: Applied Economics 2,4 (October 2010): 76-104.

4.	Arcidiacono, Peter Bayer, Patrick Hizmo, Aurel
4.	Beyond Signaling and Human: Education and the Revelation of Ability NBER Working Paper No. 13951, National Bureau of Economic Research, 2008. Also: http://www.nber.org/papers/w13951.pdf Cohort(s): NLSY79 Publisher: National Bureau of Economic Research (NBER) Keyword(s): Armed Forces Qualifications Test (AFQT); College Enrollment; Discrimination, Employer; Education; High School Completion/Graduates; Modeling, Fixed Effects; Test Scores/Test theory/IRT; Wages In traditional signaling models, education provides a way for individuals to sort themselves by ability. Employers in turn use education to statistically discriminate, paying wages that reflect the average productivity of workers with the same given level of education. In this paper, we provide evidence that education (specifically, attending college) plays a much more direct role in revealing ability to the labor market. We use the NLSY79 to examine returns to ability early in careers; our results suggest that ability is observed nearly perfectly for college graduates but is revealed to the labor market much more gradually for high school graduates. As a result, from very beginning of the career, college graduates are paid in accordance with their own ability, while the wages of high school graduates are initially completely unrelated to their own ability. This view of ability revelation in the labor market has considerable power in explaining racial differences in wages, education, and the returns to ability. In particular, we find no racial differences in wages or returns to ability in the college labor market, but a 6-10 percent wage penalty for blacks (conditional on ability) in the high school market. These results are consistent with the notion that employers use race to statistically discriminate in the high school market but have no need to do so in the college market. That blacks face a wage penalty in the high school but not the college labor market also helps to explains why, conditional on ability, blacks are more likely to earn a college degree, a fact that has been documented in the literature but for which a full explanation has yet to emerge
	Bibliography Citation Arcidiacono, Peter, Patrick Bayer and Aurel Hizmo. "Beyond Signaling and Human: Education and the Revelation of Ability." NBER Working Paper No. 13951, National Bureau of Economic Research, 2008.

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