Abstract/Details

Evidence Aggregation in Development Economics via Bayesian Hierarchical Models

Meager, Rachael. Massachusetts Institute of Technology, ProQuest Dissertations Publishing, 2017. 10696949.

Abstract (summary)

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. (Copies available exclusively from MIT Libraries, libraries.mit.edu/docs - [email protected])

Indexing (details)


Subject
Statistics;
Economics;
Public health
Classification
0463: Statistics
0501: Economics
0573: Public health
Identifier / keyword
Pure sciences; Social sciences; Health and environmental sciences; External Validity; Heterogeneous; Meta-Analysis; Quantiles; Treatment Effects
Title
Evidence Aggregation in Development Economics via Bayesian Hierarchical Models
Author
Meager, Rachael
Number of pages
0
Publication year
2017
Degree date
2017
School code
0753
Source
DAI-A 79/02(E), Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
Advisor
Duflo, Esther
University/institution
Massachusetts Institute of Technology
University location
United States -- Massachusetts
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
10696949
ProQuest document ID
1957879711
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
https://www.proquest.com/docview/1957879711