Instrument-Based Estimation of Full Treatment Effects with Partial Compliers
Bibliographic Data
| ID | 19320021 |
|---|---|
| Authors | Didier Nibbering (0000-0003-4398-4321, Department of Econometrics and Business Statistics, Monash University, Melbourne, Australia [email protected]), Matthijs Oosterveen (Department of Economics, Lisbon School of Economics and Management, and Advance/CSG, University of Lisbon, Lisbon, Portugal [email protected]) |
| Year | 2024 |
| Pages | 1-46 |
| Publication date | 2024-08-22 |
| Open Access | No |
| Type | ARTICLE |
| Venue | The Review of Economics and Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0034-6535 • E-ISSN: 1530-9142 |
| Publisher | MIT Press (PUBLISHER • US) |
| DOI | 10.1162/rest_a_01486 |
| OpenAlex | W4401872925 |
| Language | EN |
The effect of the full treatment is a primary parameter of interest in policy evaluation, while often the effect of a subset of treatment is estimated. We partially identify the local average treatment effect of receiving full treatment (LAFTE) using an instrumental variable that may induce individuals into subsets of treatment (partial compliers). We show that partial compliers violate the standard exclusion restriction, necessary conditions on the absence of partial compliers are testable, and partial identification holds under a double exclusion restriction. We identify partial compliers in four applications and estimate informative bounds on the LAFTE in three of them
Econometrics · Identification (biology) · Instrumental variable · Partial derivative · Partial evaluation · Statistics · Advanced Causal Inference Techniques · Computer Science · Health Systems, Economic Evaluations, Quality of Life · Healthcare Policy and Management · Mathematics · Theoretical Computer Science
| Highly cited | No |
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