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Identifying effects of multivalued treatments

Author

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  • Sokbae (Simon) Lee

    () (Institute for Fiscal Studies and Columbia University and IFS)

  • Bernard Salanie

    () (Institute for Fiscal Studies and Columbia)

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 e?ects can be identi?ed 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 identi?cation power of discrete instruments.

Suggested Citation

  • Sokbae (Simon) Lee & Bernard Salanie, 2015. "Identifying effects of multivalued treatments," CeMMAP working papers CWP72/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:cemmap:72/15
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    References listed on IDEAS

    as
    1. J. P. Florens & J. J. Heckman & C. Meghir & E. Vytlacil, 2008. "Identification of Treatment Effects Using Control Functions in Models With Continuous, Endogenous Treatment and Heterogeneous Effects," Econometrica, Econometric Society, vol. 76(5), pages 1191-1206, September.
    2. Pedro Carneiro & James J. Heckman & Edward Vytlacil, 2010. "Evaluating Marginal Policy Changes and the Average Effect of Treatment for Individuals at the Margin," Econometrica, Econometric Society, vol. 78(1), pages 377-394, January.
    3. J.J. Heckman & E.E. Leamer (ed.), 2007. "Handbook of Econometrics," Handbook of Econometrics, Elsevier, edition 1, volume 6, number 6b, January.
    4. Fricke, Hans & Frölich, Markus & Huber, Martin & Lechner, Michael, 2015. "Endogeneity and non-response bias in treatment evaluation - nonparametric identification of causal effects by instruments," FSES Working Papers 459, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    5. Lewbel, Arthur, 2000. "Semiparametric qualitative response model estimation with unknown heteroscedasticity or instrumental variables," Journal of Econometrics, Elsevier, vol. 97(1), pages 145-177, July.
    6. Charles F. Manski & John V. Pepper, 2000. "Monotone Instrumental Variables, with an Application to the Returns to Schooling," Econometrica, Econometric Society, vol. 68(4), pages 997-1012, July.
    7. J.J. Heckman & E.E. Leamer (ed.), 2007. "Handbook of Econometrics," Handbook of Econometrics, Elsevier, edition 1, volume 6, number 6a, January.
    8. Alexander Torgovitsky, 2015. "Identification of Nonseparable Models Using Instruments With Small Support," Econometrica, Econometric Society, vol. 83(3), pages 1185-1197, May.
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    Cited by:

    1. Christian Dippel & Robert Gold & Stephan Heblich & Rodrigo Pinto, 2017. "Instrumental Variables and Causal Mechanisms: Unpacking the Effect of Trade on Workers and Voters," CESifo Working Paper Series 6816, CESifo Group Munich.
    2. repec:eee:pubeco:v:158:y:2018:i:c:p:79-102 is not listed on IDEAS
    3. Heng Chen & Geoffrey R. Dunbar & Rallye Shen, 2017. "The Mode is the Message: Using Predata as Exclusion Restrictions to Evaluate Survey Design," Staff Working Papers 17-43, Bank of Canada.

    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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