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Title: Essays on the economics of information and education
Author: Herresthal, Claudia
ISNI:       0000 0004 6500 6227
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2016
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This thesis consists of three self-contained chapters which study different topics in the economics of information and education. The first chapter studies what families can infer about schools' relative quality from rankings, when schools' performance depends not only on their quality but also on their student intake. I develop a dynamic framework in which each cohort of families has access to the most recent ranking, but can neither observe application choices nor the allocation of past students. I study a steady-state Bayesian-Nash equilibrium, and find that a performance-based ranking is more informative about school quality if oversubscribed schools select a larger share of their intake based on ability. I also find that such a ranking is more informative if it is less costly for families to attend a non-local school. The second chapter studies a decision maker (DM) who can consult an advisor before choosing whether or not to switch from the status quo. Both agree that switching is optimal if and only if a certain hypothesis is true. But the advisor may be biased in how he trades off falsely accepting against falsely rejecting the hypothesis. The advisor has access to a testing technology which generates noisy but informative binary signals. Over two periods, the advisor can sequentially carry out costly tests before reporting his findings to the DM. I contrast the setting in which the DM observes all test outcomes with a setting in which testing itself is hidden and the advisor verifiably discloses outcomes. I fully characterise the circumstances in which the DM is strictly better off with hidden testing than with observable testing. Finally, I study what my findings imply about the bias of the ideal advisor. The third chapter studies the influence of peers on educational performance. I estimate a linear-in-means model of peer effects using a methodology adopted from [DeGiorgi2010]. For identification, I exploit the fact that students have different classmates in different subjects. As instruments for classmates' performance I use the characteristics of students who share common classmates with the individual but who are not themselves the individual's classmates. I find that students are significantly influenced by their classmates' average performance but not by their classmates' average characteristics: a student's average test score in maths and English increases by 0.677 of a standard deviation if the average performance of his peer group increases by one standard deviation.
Supervisor: Meyer, Margaret Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available