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Identifying effects of multivalued treatments

Author

Listed:
  • Sokbae (Simon) Lee

    (Institute for Fiscal Studies and Columbia University)

  • Bernard Salanie

    (Institute for Fiscal Studies and Columbia)

Abstract
Multivalued treatment models have typically been studied under restrictive assumptions: ordered choice, and more recently unordered monotonicity. We show how treatment effects can be identi fied in a more general class of models that allows for multidimensional unobserved heterogeneity. 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. We illustrate our approach for several classes of models.

Suggested Citation

  • Sokbae (Simon) Lee & Bernard Salanie, 2018. "Identifying effects of multivalued treatments," CeMMAP working papers CWP34/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:34/18
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Identi fication; selection; multivalued treatments; instruments; monotonicity; multidimensional unobserved heterogeneity;
    All these keywords.

    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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