(Un)Informed College and Major Choice: Evidence from Linked Survey and Administrative Data
Christopher Neilson (),
Anely Ramirez and
No 21330, NBER Working Papers from National Bureau of Economic Research, Inc
We use large-scale surveys of Chilean college applicants and college students to explore the way students form beliefs about earnings and cost outcomes at different institutions and majors and how these beliefs relate to degree choice and persistence. Linking our survey records to administrative education and earnings data, we compare earnings and cost expectations to observed values for past students and follow survey participants forward to see how beliefs relate to matriculation and dropout outcomes. We find that students have correctly centered but noisy cost expectations, and appear to systematically overestimate earnings outcomes for past graduates. Students who overestimate costs are less likely to matriculate in any degree program and in their stated first-choice program, and are more likely to drop out. Students who overestimate earnings matriculate at similar rates to other students, but choose degree programs where past students have been less likely to graduate, have earned less early in their careers, and have been more likely to default on student loans. Consistent with an informal model of enrollment choice, students with a stated preference for labor market-related degree characteristics are less likely to overestimate earnings outcomes and choose degrees where past students have gone on to earn more, while the opposite is true for students with a stated preference for enjoyment of the curriculum.
JEL-codes: H52 I2 I24 I26 I28 J24 (search for similar items in EconPapers)
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Published as Justine S. Hastings & Christopher A. Neilson & Anely Ramirez & Seth D. Zimmerman, 2015. "REMOVED: (Un)informed college and major choice: Evidence from linked survey and administrative data," Economics of Education Review, .
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Journal Article: (Un)informed college and major choice: Evidence from linked survey and administrative data (2016)
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