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Unconditional Partial Effects of Binary Covariates

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  • Rothe, Christoph

Abstract

In this paper, we study the effect of a small ceteris paribus change in the marginal distribution of a binary covariate on some feature of the unconditional distribution of an outcome variable of interest. We show that the RIF regression techniques recently proposed by Firpo, Fortin, and Lemieux (2009) do not estimate this quantity. Moreover, we show that such parameters are in general only partially identified, and derive straightforward expressions for the identified set. The results are implemented in the context of an empirical application that studies the effect of union membership rates on the distribution of wages.

Suggested Citation

  • Rothe, Christoph, 2009. "Unconditional Partial Effects of Binary Covariates," TSE Working Papers 09-79, Toulouse School of Economics (TSE).
    • Handle: RePEc:tse:wpaper:22190
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    References listed on IDEAS

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    1. Victor Chernozhukov & Iván Fernández‐Val & Blaise Melly, 2013. "Inference on Counterfactual Distributions," Econometrica, Econometric Society, vol. 81(6), pages 2205-2268, November.
    2. DiNardo, John & Fortin, Nicole M & Lemieux, Thomas, 1996. "Labor Market Institutions and the Distribution of Wages, 1973-1992: A Semiparametric Approach," Econometrica, Econometric Society, vol. 64(5), pages 1001-1044, September.
    3. Victor Chernozhukov & Sokbae Lee & Adam M. Rosen, 2013. "Intersection Bounds: Estimation and Inference," Econometrica, Econometric Society, vol. 81(2), pages 667-737, March.
    4. Trivedi, Pravin K. & Zimmer, David M., 2007. "Copula Modeling: An Introduction for Practitioners," Foundations and Trends(R) in Econometrics, now publishers, vol. 1(1), pages 1-111, April.
    5. Stephen G. Donald & David A. Green & Harry J. Paarsch, 2000. "Differences in Wage Distributions Between Canada and the United States: An Application of a Flexible Estimator of Distribution Functions in the Presence of Covariates," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 67(4), pages 609-633.
    6. Sergio Firpo & Nicole M. Fortin & Thomas Lemieux, 2009. "Unconditional Quantile Regressions," Econometrica, Econometric Society, vol. 77(3), pages 953-973, May.
    7. Vaart,A. W. van der, 2000. "Asymptotic Statistics," Cambridge Books, Cambridge University Press, number 9780521784504.
    8. José Mata & José A. F. Machado, 2005. "Counterfactual decomposition of changes in wage distributions using quantile regression," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(4), pages 445-465.
    9. Rothe, Christoph, 2010. "Nonparametric estimation of distributional policy effects," Journal of Econometrics, Elsevier, vol. 155(1), pages 56-70, March.
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    Cited by:

    1. Leo Kaas & Georgi Kocharkov & Edgar Preugschat, 2019. "Wealth Inequality and Homeownership in Europe," Annals of Economics and Statistics, GENES, issue 136, pages 27-54.
    2. Nicoletti, Cheti & Best, Nicky, 2012. "Quantile regression with aggregated data," Economics Letters, Elsevier, vol. 117(2), pages 401-404.

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    More about this item

    Keywords

    unconditional partial effect; partial identification; unconditional quantile regression;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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