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Identifying effects of multivalued treatments

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  • Sokbae (Simon) Lee
  • Bernard Salanie

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.

Suggested Citation

  • Sokbae (Simon) Lee & Bernard Salanie, 2015. "Identifying effects of multivalued treatments," CeMMAP working papers 72/15, Institute for Fiscal Studies.
    • Handle: RePEc:azt:cemmap:72/15
    DOI: 10.1920/wp.cem.2015.7215
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    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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