Abstract
We provide a new estimator, MR-LATE, that consistently estimates local average treatment effects when treatment is missing for some observations, not at random. If instead treatment is mismeasured for some observations, then MR-LATE usually has less bias than the standard LATE estimator. We discuss potential applications where an endogenous binary treatment may be unobserved or mismeasured. We apply MR-LATE to study the impact of women’s control over household resources on health outcomes in Indian families. This application illustrates the use of MR-LATE when treatment is estimated rather than observed. In these situations, treatment mismeasurement may arise from model misspecification and estimation errors.
Original language | English |
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Pages (from-to) | 1701-1717 |
Number of pages | 17 |
Journal | Journal of Business and Economic Statistics |
Volume | 40 |
Issue number | 4 |
DOIs | |
Publication status | Published - 2022 |
Keywords
- Collective model
- Health
- LATE
- Measurement error
- Misclassification
- Missing treatment
- Resource shares