Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/309959 
Year of Publication: 
2024
Series/Report no.: 
cemmap working paper No. CWP23/24
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
Multivalued treatments are commonplace in applications. We explore the use of discrete-valued instruments to control for selection bias in this setting. Our discussion revolves around the concept of targeting: which instruments target which treatments. It allows us to establish conditions under which counterfactual averages and treatment effects are point- or partially-identified for composite complier groups. We illustrate the usefulness of our framework by applying it to data from the Head Start Impact Study. Under a plausible positive selection assumption, we derive informative bounds that suggest less beneficial effects of Head Start expansions than the parametric estimates of Kline and Walters (2016).
Subjects: 
Identification
selection
multivalued treatments
discrete instruments
monotonicity
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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