IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v83y2013i3p677-679.html

Sharp bounds for complier average potential outcomes in experiments with noncompliance and incomplete reporting

Author

Listed:
  • Aronow, Peter M.
  • Green, Donald P.

Abstract

Published reports of experiments with noncompliance often fail to report information necessary for recovering average potential outcomes for compliers. We derive sharp bounds on the average potential outcomes for compliers, when given only average outcomes for units assigned to treatment, average outcomes for units assigned to control, and the difference in average take-up between assignment statuses.

Suggested Citation

  • Aronow, Peter M. & Green, Donald P., 2013. "Sharp bounds for complier average potential outcomes in experiments with noncompliance and incomplete reporting," Statistics & Probability Letters, Elsevier, vol. 83(3), pages 677-679.
    • Handle: RePEc:eee:stapro:v:83:y:2013:i:3:p:677-679
    DOI: 10.1016/j.spl.2012.11.012
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715212004282
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2012.11.012?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Guido W. Imbens & Donald B. Rubin, 1997. "Estimating Outcome Distributions for Compliers in Instrumental Variables Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 555-574.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mao, Lu, 2022. "Identification of the outcome distribution and sensitivity analysis under weak confounder–instrument interaction," Statistics & Probability Letters, Elsevier, vol. 189(C).
    2. Wang, Xintong & Flores, Carlos A. & Flores-Lagunes, Alfonso, 2025. "The effects of Vietnam-era military service on the long-term health of veterans: A bounds analysis," Journal of Health Economics, Elsevier, vol. 101(C).
    3. Plamen Nikolov & Steve Yeh, 2021. "Reaping the Rewards Later: How Education Improves Old-Age Cognition in South Africa," Papers 2109.02177, arXiv.org, revised Oct 2021.
    4. Alberto Abadie, 2000. "Semiparametric Estimation of Instrumental Variable Models for Causal Effects," NBER Technical Working Papers 0260, National Bureau of Economic Research, Inc.
    5. DiTraglia, Francis J. & García-Jimeno, Camilo, 2019. "Identifying the effect of a mis-classified, binary, endogenous regressor," Journal of Econometrics, Elsevier, vol. 209(2), pages 376-390.
    6. Manuel Arellano & Stéphane Bonhomme, 2017. "Quantile Selection Models With an Application to Understanding Changes in Wage Inequality," Econometrica, Econometric Society, vol. 85, pages 1-28, January.
    7. repec:ces:ceswps:_10944 is not listed on IDEAS
    8. Denis Conniffe & Vanessa Gash & Philip J. O'Connell, 2000. "Evaluating State Programmes - “Natural Experiments” and Propensity Scores," The Economic and Social Review, Economic and Social Studies, vol. 31(4), pages 283-308.
    9. Nikolov, Plamen & Jimi, Nusrat & Chang, Jerray, 2020. "The Importance of Cognitive Domains and the Returns to Schooling in South Africa: Evidence from Two Labor Surveys," Labour Economics, Elsevier, vol. 65(C).
    10. Steven Lehrer & Weili Ding, 2004. "Estimating Dynamic Treatment Effects from Project STAR," Econometric Society 2004 North American Summer Meetings 252, Econometric Society.
    11. Patrick Kline & Christopher R. Walters, 2019. "On Heckits, LATE, and Numerical Equivalence," Econometrica, Econometric Society, vol. 87(2), pages 677-696, March.
    12. Lars Kirkebøen & Edwin Leuven & Magne Mogstad, 2014. "Field of Study, Earnings, and Self-Selection," NBER Working Papers 20816, National Bureau of Economic Research, Inc.
    13. Bruno Crépon & Florencia Devoto & Esther Duflo & William Parienté, 2015. "Estimating the Impact of Microcredit on Those Who Take It Up: Evidence from a Randomized Experiment in Morocco," American Economic Journal: Applied Economics, American Economic Association, vol. 7(1), pages 123-150, January.
    14. Gregory J. Wawro & Ira Katznelson, 2020. "American political development and new challenges of causal inference," Public Choice, Springer, vol. 185(3), pages 299-314, December.
    15. Michael E. Sobel & Bengt Muthén, 2012. "Compliance Mixture Modelling with a Zero-Effect Complier Class and Missing Data," Biometrics, The International Biometric Society, vol. 68(4), pages 1037-1045, December.
    16. Lisa Sanbonmatsu & Jeffrey R. Kling & Greg J. Duncan & Jeanne Brooks-Gunn, 2006. "Neighborhoods and Academic Achievement: Results from the Moving to Opportunity Experiment," Journal of Human Resources, University of Wisconsin Press, vol. 41(4).
    17. Huber Martin & Wüthrich Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-27, January.
    18. C de Chaisemartin & X D’HaultfŒuille, 2018. "Fuzzy Differences-in-Differences," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 85(2), pages 999-1028.
    19. Clément de Chaisemartin, 2017. "Tolerating defiance? Local average treatment effects without monotonicity," Quantitative Economics, Econometric Society, vol. 8(2), pages 367-396, July.
    20. Hanley S. Chiang & Melissa A. Clark & Sheena McConnell, "undated". "Supplying Disadvantaged Schools with Effective Teachers: Experimental Evidence on Secondary Math Teachers from Teach For America," Mathematica Policy Research Reports 4623b3b72e154403be21e3878, Mathematica Policy Research.
    21. Plamen Nikolov & Steve Yeh, 2021. "Reaping the Rewards Later: How Education Improves Old-Age Cognition in South Africa," Working Papers 2021-045, Human Capital and Economic Opportunity Working Group.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:stapro:v:83:y:2013:i:3:p:677-679. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.