Abstract
We develop point-identification for the local average treatment effect when the binary treatment contains a measurement error. The standard instrumental variable estimator is inconsistent for the parameter since the measurement error is non-classical by construction. We correct the problem by identifying the distribution of the measurement error based on the use of an exogenous variable that can even be a binary covariate. The moment conditions derived from the identification lead to generalized method of moments estimation with asymptotically valid inferences. Monte Carlo simulations and an empirical illustration demonstrate the usefulness of the proposed procedure.
Highlights
The local average treatment effect (LATE) is a popular causal parameter in the microeconometric literature (e.g., Angrist and Pischke, 2008, Chapter 4)
Our proposed analysis corrects the problem by identifying the distribution of the measurement error based on the use of an exogenous variable such as a covariate or instrument
This study presents novel point-identification and estimation methods for the LATE when the binary endogenous treatment may contain a measurement error
Summary
The local average treatment effect (LATE) is a popular causal parameter in the microeconometric literature (e.g., Angrist and Pischke, 2008, Chapter 4). It represents the average causal effect of binary endogenous treatment T ∗ on outcome Y for the unit whose treatment status changes depending on the value of binary instrument Z. As shown by Imbens and Angrist (1994), the instrumental variable (IV) estimator identifies the LATE under suitable identification conditions. While the identification of the LATE needs the precise measurement of true treatment T ∗ in addition to the identification conditions, in practice, observed binary treatment T may be a mismeasured variable of T ∗. While econometricians are not aware of the presence of measurement errors in their data, as we discuss below, ignoring such measurement errors may lead to drawing misleading economic implications
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
