Generalizing the Results from Social Experiments
Theory and Evidence from India
Bibliographic Data
| ID | 19418145 |
|---|---|
| Authors | Michael Gechter (Department of Economics, The Pennsylvania State University, University Park, PA, corresponding author) |
| Year | 2024 |
| Volume | 42 |
| Issue | 2 |
| Pages | 801-811 |
| Publication date | 2024-04-02 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2023.2241529 |
| OpenAlex | W4385298136 |
| Language | EN |
| Citations received | 3 |
| References cited | 41 |
How informative are treatment effects estimated in one region or time period for another region or time? In this article, I derive bounds on the average treatment effect in a context of interest using experimental evidence from another context. The bounds are based on (a) the information identified about treatment effect heterogeneity due to unobservables in the experiment and (b) using differences in outcome distributions across contexts to learn about differences in distributions of unobservables. Empirically, using data from a pair of remedial education experiments carried out in India, I show the bounds are able to recover average treatment effects in one location using results from the other while the benchmark method cannot
Average treatment effect · Econometrics · Economics · Geography · Mathematical economics · Mathematics education · Propensity score matching · Remedial education · Statistics · Treatment effect · Advanced Causal Inference Techniques · Mathematics · Poverty, Education, and Child Welfare · School Choice and Performance
Convex Optimization
Observational Studies
Sensitivity to Exogeneity Assumptions in Program Evaluation
Site Selection Bias in Program Evaluation
Instruments, Randomization, and Learning about Development
Identification and Inference in Nonlinear Difference-in-Differences Models
Remedying Education
Making The Most Out Of Programme Evaluations and Social Experiments
What Mean Impacts Miss
Unobservable Selection and Coefficient Stability
From Local to Global
Generalizing the Results from Social Experiments
Was There a Riverside Miracle? A Hierarchical Framework for Evaluating Programs With Grouped Data
Comparing Treatments across Labor Markets
Revealed Preferences in a Heterogeneous Population
Selection on Observed and Unobserved Variables
| Citations per year | 1.5 |
|---|---|
| Citation span | 2024 - 2025 (2) |
| Highly cited | No |