Evidence aggregation in development economics via Bayesian hierarchical models
Name
1003291099-MIT.pdf
Description
Full printable version
Size
25.18 MB
Format
Adobe PDF
Checksum (MD5)
0095053bc6f4454b7cf29a9b735a8651
Author(s)
Meager, Rachael
Advisor(s)
Esther Duflo.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
It is increasingly recognized that translating research into policy requires aggregating evidence from multiple studies of the same economic phenomenon. This translation requires not only an estimate of the impact of an intervention across different contexts, but also an assessment of the generalizability of the evidence and hence its applicability to policy decisions in other settings. This thesis performs evidence aggregation using Bayesian hierarchical models, which both aggregate evidence and assess the true underlying heterogeneity across settings, for applications in development economics. Where necessary, the thesis develops new methods to aggregate evidence on certain measures of evidence currently neglected in the aggregation literature such as distributional treatment effects or risk ratios. The applications considered are randomized controlled trials of expanding access to microcredit and randomized access to vitamin A supplementation in developing nations.
Description
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 185-193).
Subjects
Economics.
MIT Department
Massachusetts Institute of Technology. Department of Economics
Terms of Use
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
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