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Testing for Rank Invariance or Similarity in Program Evaluation

Author

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  • Yingying Dong

    (University of California, Irvine)

  • Shu Shen

    (University of California, Davis)

Abstract

This paper discusses testable implications of rank invariance or rank similarity, assumptions that are common in program evaluation and in the quantile treatment effect (QTE) literature. We nonparametrically identify, estimate, and test the counterfactual distribution of potential ranks, or features of the distribution. The proposed tests allow treatment to be endogenous, with exogenous treatment following as a special case. The tests essentially do not require any additional assumptions other than those to identify and estimate QTEs. We apply the proposed tests to investigate whether the Job Training Partnership Act training causes trainees to systematically change their ranks in the earnings distribution.

Suggested Citation

  • Yingying Dong & Shu Shen, 2018. "Testing for Rank Invariance or Similarity in Program Evaluation," The Review of Economics and Statistics, MIT Press, vol. 100(1), pages 78-85, March.
    • Handle: RePEc:tpr:restat:v:100:y:2018:i:1:p:78-85
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    Citations

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    Cited by:

    1. Callaway, Brantly, 2021. "Bounds on distributional treatment effect parameters using panel data with an application on job displacement," Journal of Econometrics, Elsevier, vol. 222(2), pages 861-881.
    2. Battistin, Erich & Lamarche, Carlos & Rettore, Enrico, 2020. "Quantiles of the Gain Distribution of an Early Childhood Intervention," IZA Discussion Papers 13101, Institute of Labor Economics (IZA).
    3. Battistin, Erich & Lamarche, Carlos & Rettore, Enrico, 2020. "Quantiles of the Gain Distribution of an Early Child Intervention," CEPR Discussion Papers 14721, C.E.P.R. Discussion Papers.
    4. Minna Tuominen & Leena Haanpää, 2022. "Young People’s Well-Being and the Association with Social Capital, i.e. Social Networks, Trust and Reciprocity," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 159(2), pages 617-645, January.
    5. Jad Beyhum & Jean-Pierre Florens & Ingrid Van Keilegom, 2021. "A nonparametric instrumental approach to endogeneity in competing risks models," Papers 2105.00946, arXiv.org.
    6. Liao, Wen-Chi & Zhao, Daxuan, 2019. "The selection and quantile treatment effects on the economic returns of green buildings," Regional Science and Urban Economics, Elsevier, vol. 74(C), pages 38-48.
    7. Lars Kunze & Nicolai Suppa, 2020. "Who Is Bowling Alone? Quantile Treatment Effects of Unemployment on Social Participation," SOEPpapers on Multidisciplinary Panel Data Research 1077, DIW Berlin, The German Socio-Economic Panel (SOEP).
    8. Kaspar Wüthrich, 2020. "A Comparison of Two Quantile Models With Endogeneity," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 443-456, April.
    9. Hsu, Yu-Chin & Shen, Shu, 2019. "Testing treatment effect heterogeneity in regression discontinuity designs," Journal of Econometrics, Elsevier, vol. 208(2), pages 468-486.
    10. Yingying DONG & Ying-Ying LEE & Michael GOU, 2019. "Regression Discontinuity Designs with a Continuous Treatment," Discussion papers 19058, Research Institute of Economy, Trade and Industry (RIETI).
    11. Masayuki Sawada, 2019. "Noncompliance in randomized control trials without exclusion restrictions," Papers 1910.03204, arXiv.org, revised Jun 2021.
    12. David Powell, 2020. "Quantile Treatment Effects in the Presence of Covariates," The Review of Economics and Statistics, MIT Press, vol. 102(5), pages 994-1005, December.

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