Abstract
In the United States, a form called the Free Application for Federal Student Aid, or FAFSA, is used to determine eligibility for federal aid. The FAFSA collects extensive financial information, checks it for accuracy against several government databases, including the IRS, and then shares the information with colleges. I demonstrate that sharing the FAFSA with colleges enables them to engage in substantial price discrimination with widespread repercussions for the cost of a college education as well as the equilibrium sorting of students into colleges. I build a structural model of college pricing and price discrimination, and show that the model is identified from studentlevel data on prices and student characteristics. Reduced form estimates are consistent with several predictions of the model. According to my structural estimates, on average elite colleges capture 70% of the student-college match surplus through their student-specific prices. Withholding FAFSA information would lower prices for middleand high-income students while raising them for lowincome students. On average prices would fall by $825, and the within-college price variance would also drop by 17%. By using the FAFSA to price discriminate, elite colleges effectively levy a 1.9% tax on adjusted gross income coupled with a $709 lump sum rebate. However, with less information to use when price discriminating, they would inefficiently price up to 12% of students out of the elite market. My findings highlight a policy tradeoff between increasing the welfare of middleand highincome students at the expense of total efficiency, college welfare, and the welfare of low-income students. *Contact Information: ianfillmore@uchicago.edu †I would like to thank Derek Neal, Brent Hickman, Steve Levitt, and Chad Syverson for their helpful input throughout the project. I am also grateful for comments from Kris Hult, Trevor Gallen, Xan Vangsathorn, Jonathan Hall, Devin Pope, Ali Hortacsu, Stephen Raudenbush, Lars Lefgren, Brennan Platt and workshop participants at the University of Chicago and Brigham Young University. Kevin Brown and NORC at the University of Chicago provided indispensable help in obtaining the restricted-use data. I also gratefully acknowledge financial support from the University of Chicago Predoctoral Training Grant in Education funded by the Institute of Education Sciences (grant number R305 B090025).
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