LATE with missing or mismeasured treatment

Rossella Calvi, Arthur Lewbel, Denni Tommasi

Research output: Contribution to journalArticleResearchpeer-review

8 Citations (Scopus)

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 languageEnglish
Pages (from-to)1701-1717
Number of pages17
JournalJournal of Business and Economic Statistics
Volume40
Issue number4
DOIs
Publication statusPublished - 2022

Keywords

  • Collective model
  • Health
  • LATE
  • Measurement error
  • Misclassification
  • Missing treatment
  • Resource shares

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