DP15092 Filtered and Unfiltered Treatment Effects with Targeting Instruments

Author(s): Sokbae Lee, Bernard Salanié
Publication Date: July 2020
Keyword(s): discrete instruments, factorial design, identification, multivalued treatments, selection, unordered monotonicity
JEL(s):
Programme Areas: Labour Economics
Link to this Page: cepr.org/active/publications/discussion_papers/dp.php?dpno=15092

Multivalued treatments are commonplace in applications. We explore the use of discrete-valued instruments to control for selection bias in this setting. We establish conditions under which counterfactual averages and treatment effects are identified for heterogeneous complier groups. These conditions require a combination of assumptions that restrict both the unobserved heterogeneity in treatment assignment and how the instruments target the treatments. We introduce the concept of filtered treatment, which takes into account limitations in the analyst's information. Finally, we illustrate the usefulness of our framework by applying it to data from the Student Achievement and Retention Project and the Head Start Impact Study.