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Identifying effects of multivalued treatments

Authors: Sokbae Lee and Bernard Salanie
Date: 08 December 2015
Type: cemmap Working Paper, CWP72/15
DOI: 10.1920/wp.cem.2015.7215

Abstract

Multivalued treatment models have only been studied so far under restrictive assumptions: ordered choice, or more recently unordered monotonicity. We show how marginal treatment effects can be identified in a more general class of models. Our results rely on two main assumptions: treatment assignment must be a measurable function of threshold-crossing rules; and enough continuous instruments must be available. On the other hand, we do not require any kind of monotonicity condition. We illustrate our approach on several commonly used models; and we also discuss the identification power of discrete instruments.

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