Profiling Compliers and Non-compliers for Instrumental Variable Analysis

22 Pages Posted: 3 Jun 2019 Last revised: 20 Dec 2019

See all articles by Moritz Marbach

Moritz Marbach

ETH Zürich - Department of Humanities, Social and Political Sciences (GESS)

Dominik Hangartner

London School of Economics & Political Science (LSE); Stanford - Zurich Immigration Policy Lab; Public Policy Group

Date Written: October 29, 2019

Abstract

Instrumental-variable (IV) estimation is an essential method for applied researchers across the social and behavioral sciences who analyze randomized control trials marred by non-compliance or leverage partially exogenous treatment variation in observational studies. The potential outcomes framework is a popular model to motivate the assumptions underlying the identification of the local average treatment effect (LATE), and to stratify the sample into compliers, always-takers, and never-takers. However, applied research has thus far paid little attention to the characteristics of compliers and non-compliers. Yet profiling compliers and non-compliers is necessary to understand what subpopulation the researcher is making inferences about, and an important first step in evaluating the external validity (or lack thereof) of the LATE estimated for compliers. In this letter, we discuss the assumptions necessary for profiling, which are weaker than the assumptions necessary for identifying the LATE if the instrument is randomly assigned. We introduce a simple and general method to characterize compliers, always-takers and never-takers in terms of their covariates, and easy-to-use software in R and STATA that implements our estimator. We hope that our method and software facilitate the profiling of compliers and non-compliers as standard practice accompanying any IV analysis.

Keywords: instrumental variable, encouragement design, two-stage least squares, 2SLS, complier

JEL Classification: C26, C36

Suggested Citation

Marbach, Moritz and Hangartner, Dominik, Profiling Compliers and Non-compliers for Instrumental Variable Analysis (October 29, 2019). Available at SSRN: https://ssrn.com/abstract=3380247 or http://dx.doi.org/10.2139/ssrn.3380247

Moritz Marbach

ETH Zürich - Department of Humanities, Social and Political Sciences (GESS) ( email )

Haldeneggsteig 4
Zurich, Zurich 8006
Switzerland

Dominik Hangartner (Contact Author)

London School of Economics & Political Science (LSE) ( email )

Departments of Government and Methodology
Houghton Street
London, WC2A 2AE
United Kingdom

Stanford - Zurich Immigration Policy Lab

30 Alta Road
Stanford, CA 94305
United States

Public Policy Group ( email )

Rämistrasse 101
ZUE F7
Zürich, 8092
Switzerland

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