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020 ▼a 9781085614528
035 ▼a (MiAaPQ)AAI13859681
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 247004
0820 ▼a 300
1001 ▼a Ye, Karen J.
24510 ▼a Understanding Peer Effects in Educational Decisions: Evidence from Theory and a Field Experiment.
260 ▼a [S.l.]: ▼b The University of Chicago., ▼c 2019.
260 1 ▼a Ann Arbor: ▼b ProQuest Dissertations & Theses, ▼c 2019.
300 ▼a 107 p.
500 ▼a Source: Dissertations Abstracts International, Volume: 81-03, Section: B.
500 ▼a Advisor: List, John A.
5021 ▼a Thesis (Ph.D.)--The University of Chicago, 2019.
506 ▼a This item must not be sold to any third party vendors.
506 ▼a This item must not be added to any third party search indexes.
520 ▼a While a large literature documents the presence of peer effects in teenage decision-making, researchers know very little about the underlying mechanisms. In this dissertation, I focus on the decision by high school students to participate in an educational program. I develop a theoretical model based on Brock and Durlauf (2001) with two channels of peer effects: social learning (where a peer's decision is informative about the value of a program) and social utility (where a peer's participation directly changes the benefits or costs of a program). I conduct a field experiment in three Chicago high schools to disentangle the two channels. In the experiment, I measure students' sign-up rates for a college application assistance program where I randomize (a) whether a student sees a peer's decision, and (b) which type of peer's decision they see. I find large peer effects in the participation decision that are entirely driven by seeing a peer choose not to participate - seeing a peer choose "No" decreases the sign-up rate by 26.9 percentage points. The peer effects are driven by social utility, and seeing a peer choose "No" informs students about the social norms of participation. In this context, smart students' decisions are especially influential. Further, while students want to conform to the social norm, they have very biased beliefs about (they drastically underestimate) their peers' participation. I estimate my model and combine the structural estimates with collected school social network data to run a policy counterfactual. I find that when there are negative peer effects and costly initial adoption, programs targeting smart students may have lower sign-up rates compared to programs targeting highest need students.
590 ▼a School code: 0330.
650 4 ▼a Economics.
650 4 ▼a Educational psychology.
650 4 ▼a Developmental psychology.
650 4 ▼a Secondary education.
650 4 ▼a Economic theory.
650 4 ▼a Behavioral psychology.
650 4 ▼a Social psychology.
650 4 ▼a Social research.
690 ▼a 0501
690 ▼a 0451
690 ▼a 0511
690 ▼a 0384
690 ▼a 0620
690 ▼a 0533
690 ▼a 0344
690 ▼a 0525
71020 ▼a The University of Chicago. ▼b Economics.
7730 ▼t Dissertations Abstracts International ▼g 81-03B.
773 ▼t Dissertation Abstract International
790 ▼a 0330
791 ▼a Ph.D.
792 ▼a 2019
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T15490893 ▼n KERIS ▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다.
980 ▼a 202002 ▼f 2020
990 ▼a ***1008102
991 ▼a E-BOOK