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Unconditional and Conditional Quantile Treatment Effect: Identification Strategies and Interpretations

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  • M. Fort

Abstract

This paper reviews strategies that allow one to identify the effects of policy interventions on the unconditional or conditional distribution of the outcome of interest. This distiction is irrelevant when one focuses on average treatment effects since identifying assumptions typically do not affect the parameter's interpretation. Conversely, finding the appropriate answer to a research question on the effects over the distribution requires particular attention in the choice of the identification strategy. Indeed, quantiles of the conditional and unconditional distribution of a random variable carry a different meaning even if identification of both these set of parameters may require conditioning on observed covariates.

Suggested Citation

  • M. Fort, 2012. "Unconditional and Conditional Quantile Treatment Effect: Identification Strategies and Interpretations," Working Papers wp857, Dipartimento Scienze Economiche, Universita' di Bologna.
    • Handle: RePEc:bol:bodewp:wp857
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    References listed on IDEAS

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    1. Alberto Abadie & Joshua Angrist & Guido Imbens, 2002. "Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings," Econometrica, Econometric Society, vol. 70(1), pages 91-117, January.
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    3. Abadie, Alberto, 2003. "Semiparametric instrumental variable estimation of treatment response models," Journal of Econometrics, Elsevier, vol. 113(2), pages 231-263, April.
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    6. Giorgio Brunello & Margherita Fort & Guglielmo Weber, 2009. "Changes in Compulsory Schooling, Education and the Distribution of Wages in Europe," Economic Journal, Royal Economic Society, vol. 119(536), pages 516-539, March.
    7. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    8. David Powell, 2010. "Unconditional Quantile Treatment Effects in the Presence of Covariates," Working Papers 816, RAND Corporation.
    9. Sergio Firpo, 2007. "Efficient Semiparametric Estimation of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 75(1), pages 259-276, January.
    10. Markus Frolich & Blaise Melly, 2010. "Estimation of quantile treatment effects with Stata," Stata Journal, StataCorp LP, vol. 10(3), pages 423-457, September.
    11. Chernozhukov, Victor & Hansen, Christian, 2008. "Instrumental variable quantile regression: A robust inference approach," Journal of Econometrics, Elsevier, vol. 142(1), pages 379-398, January.
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    Cited by:

    1. repec:hal:spmain:info:hdl:2441/1dniduq06u8se8q5enfvnorti9 is not listed on IDEAS
    2. Lecoutere, E., 2018. "Making spouses cooperate in rural Ugandan households Experimental evidence of distributional treatment effects," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277160, International Association of Agricultural Economists.
    3. P. Givord & M. Suarez Castillo, 2019. "Excellence for all? Heterogeneity in high-schools’ value-added," Documents de Travail de l'Insee - INSEE Working Papers g2019-14, Institut National de la Statistique et des Etudes Economiques.
    4. repec:hal:journl:hal-03389176 is not listed on IDEAS
    5. repec:spo:wpmain:info:hdl:2441/1dniduq06u8se8q5enfvnorti9 is not listed on IDEAS

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    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General

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