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Essays in the Economics of Education Essays in the Economics of Education by Brian Christopher Clark Department of Economics Duke University Date: Approved: Peter Arcidiacono, Supervisor Arnaud Maurel V. Joseph Hotz Seth Sanders Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Economics in the Graduate School of Duke University 2016 Abstract Essays in the Economics of Education by Brian Christopher Clark Department of Economics Duke University Date: Approved: Peter Arcidiacono, Supervisor Arnaud Maurel V. Joseph Hotz Seth Sanders An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Economics in the Graduate School of Duke University 2016 Copyright c 2016 by Brian Christopher Clark All rights reserved except the rights granted by the Creative Commons Attribution-Noncommercial Licence Abstract This dissertation is comprised of three essays in the economics of education. In the first essay, I examine how college students' major choice and major switching behavior responds to major-specific labor market shocks. The second essay explores the incidence and persistence of overeducation for workers in the United States. The final essay examines the role that students' cognitive and non-cognitive skills play in their transition from secondary to postsecondary education, and how the effect of these skills are moderated by race, gender, and socioeconomic status. iv To my loved ones: for always supporting me v Contents Abstract iv List of Tables ix List of Figures xi Acknowledgements xii 1 Introduction1 2 Shocked out of Your Major: Do Labor Market Shocks Prompt Ma- jor Switching?4 2.1 Labor market shocks........................... 10 2.1.1 Data requirements........................ 11 2.1.2 Survey of Income and Program Participation.......... 12 2.1.3 Wage and unemployment trends................. 14 2.1.4 Implementation: Students' information set........... 17 2.2 Student beliefs.............................. 20 2.2.1 Forecasts of labor market values................. 21 2.2.2 Predictions with rational expectations............. 23 2.2.3 Additions to beliefs and forecasting process........... 25 2.2.4 Implications for students' major choices............. 27 2.3 Major switching costs........................... 29 2.3.1 Data: University of Texas at Austin............... 30 2.3.2 Direct costs............................ 33 vi 2.3.3 Indirect costs: Grades and probability of graduating...... 36 2.4 Framework model of major choice.................... 40 2.5 Estimation................................. 46 2.5.1 Model constraints......................... 46 2.5.2 Conditional choice probabilities................. 47 2.6 Results................................... 51 2.6.1 Relevance of labor market values................ 52 2.6.2 Role of switching costs...................... 54 2.6.3 Do students deviate from rational expectations?........ 59 2.6.4 Simulations and welfare implications.............. 63 2.7 Conclusion................................. 66 3 The Career Prospects of Overeducated Americans 70 3.1 Data.................................... 75 3.1.1 Measuring required schooling.................. 76 3.1.2 Summary statistics........................ 79 3.2 The determinants of overeducation................... 83 3.3 The dynamics of overeducation..................... 85 3.4 Duration dependence versus dynamic selection............. 91 3.5 Effects of overeducation on wages.................... 96 3.5.1 Wage dynamics.......................... 96 3.5.2 Augmented wage regressions................... 97 3.5.3 Accounting for heterogeneity types............... 101 3.6 Conclusion................................. 104 4 A Factor Analysis of College Major: Explaining Gaps by Race, Gender, and Socioeconomic Status 106 4.1 Data.................................... 113 vii 4.1.1 NCERDC............................. 113 4.1.2 NLSY97.............................. 118 4.2 Reduced form evidence.......................... 118 4.2.1 Educational disparities by subgroup............... 119 4.2.2 Regressions............................ 124 4.3 Factor model............................... 128 4.3.1 Measurement system....................... 130 4.3.2 Student outcomes......................... 134 4.3.3 Estimation............................. 137 4.4 Results................................... 138 4.4.1 High school continuation..................... 139 4.4.2 Major intentions......................... 141 4.4.3 College applications........................ 144 4.5 Conclusion................................. 146 A CPS Data 149 B Factor and Measure Parameter Estimates 151 Bibliography 156 Biography 165 viii List of Tables 2.1 Summary statistics by student...................... 32 2.2 Semester GPA regressions........................ 38 2.3 Logit model of graduation........................ 39 2.4 Structural utility parameter estimates.................. 53 2.5 Structural utility switching cost parameter estimates......... 55 2.6 Logit model of dropout.......................... 56 2.7 Model fit: Fraction switching out (by prior major)........... 61 2.8 Model AIC by labor market specification................ 63 3.1 Summary statistics for pooled cross section (1982-1994)........ 80 3.2 Overeducation status proportions by education level.......... 81 3.3 Ten most frequent occupations among overeducated workers..... 82 3.4 Probit model of overeducation status.................. 84 3.5 Transition matrix............................. 88 3.6 Transition matrix by gender....................... 88 3.7 Transition matrix by race........................ 89 3.8 Transition matrix by AFQT quartile.................. 90 3.9 Duration models (first overeducation spell)............... 94 3.10 Augmented log-wage regressions..................... 99 3.11 Augmented log-wage regressions with heterogeneity types....... 102 4.1 NCERDC: Summary statistics...................... 122 ix 4.2 NLSY97: Summary statistics...................... 124 4.3 Linear probability model: Complete 12th grade............ 126 4.4 Linear probability model: Intend to major in STEM.......... 127 4.5 Linear probability model: Apply to at least four institutions..... 129 4.6 Structural high school continuation parameter estimates....... 140 4.7 Structural major choice parameter estimates.............. 143 4.8 Structural college application parameter estimates........... 145 B.1 Structural factor parameter estimates.................. 152 B.2 Structural cognitive measure parameter estimates........... 153 B.3 Structural non-cognitive measure parameter estimates......... 154 B.4 Structural cognitive measure parameter estimates (homework time /week)................................... 155 x List of Figures 2.1 Unemployment rate by year....................... 15 2.2 Log wages by year............................ 16 2.3 Log wages by year (0-5 yrs. potential experience)........... 17 2.4 Unemployment rate by year (0-5 yrs. potential experience)...... 18 2.5 Expected LM value by year at t “ 4 (Log, rational ρ)......... 24 2.6 Expected LM value by year at t “ 4 (Log, persistent ρ)........ 26 2.7 Expected LM value by year at t “ 4 (CRRA ra “ 1:8, persistent ρ). 28 2.8 Switching rate out of Engineering by semester and cohort....... 33 2.9 Switching rate out of the Sciences by semester and cohort....... 35 2.10 Fraction switching out of Engineering by t “ 4 (by LM type)..... 64 2.11 Fraction switching out of Engineering by t “ 6 (by LM type)..... 65 3.1 Distribution of overeducation...................... 79 3.2 Composition of overeducation (fraction of respondents)........ 86 3.3 Composition of overeducation (14 years of education or more).... 87 3.4 Hazard out of overeducation....................... 91 3.5 Path of median hourly wage....................... 97 xi Acknowledgements First, I must thank my advisors, Peter Arcidiacono and Arnaud Maurel, for their invaluable guidance throughout my time at Duke, and the opportunity to learn from their example. I would also like to thank the other two members of my disserta- tion committee, V. Joseph Hotz and Seth Sanders for their crucial help, insightful comments, and encouragement. My work has also benefitted from the frequent help and comments of seminar participants in the Duke Labor Lunch Group, and seminar participants at the Federal Trade Commission and the United States Census Bureau, along with the opportunity to present my work through Duke Gradx and the North Carolina Council of Graduate Schools. This research would also not be possible without access to data provided by the Office of Population Research at Princeton University in Chapter3, and the North Carolina Education Research Data Center in Chapter4. For Chapter3, my co-authors and I gratefully acknowledge financial support from the W.E. Upjohn Institute for Employment Research through the Early Career Research Grant No. 13-141-02B. We also thank seminar participants at Duke, John Hopkins and UNC - Chapel Hill; as well as the attendees of the 2013 North American Summer Meeting of the Econometric Society (Los Angeles), the workshop \Empir- ical Research in the Economics of Education" (Rovira i Virgili University, October 2013), the workshop \Skill Mismatch: Microeconomic Evidence and Macroeconomic Relevance" (Mannheim, April 2014), the 2014 Annual Meeting of the Society for xii Economic Dynamics (Toronto), the 2014 Annual Congress of the European Eco- nomic Association (Toulouse), and the 2015 Econometric Society World Congress (Montreal)
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