IDEAS home Printed from https://ideas.repec.org/a/oup/qjecon/v139y2024i1p305-358..html

Inference on Winners

Author

Listed:
  • Isaiah Andrews
  • Toru Kitagawa
  • Adam McCloskey

Abstract

Policy makers, firms, and researchers often choose among multiple options based on estimates. Sampling error in the estimates used to guide choice leads to a winner’s curse, since we are more likely to select a given option precisely when we overestimate its effectiveness. This winner’s curse biases our estimates for selected options upward and can invalidate conventional confidence intervals. This article develops estimators and confidence intervals that eliminate this winner’s curse. We illustrate our results by studying selection of job-training programs based on estimated earnings effects and selection of neighborhoods based on estimated economic opportunity. We find that our winner’s curse corrections can make an economically significant difference to conclusions but still allow informative inference.

Suggested Citation

  • Isaiah Andrews & Toru Kitagawa & Adam McCloskey, 2024. "Inference on Winners," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 139(1), pages 305-358.
    • Handle: RePEc:oup:qjecon:v:139:y:2024:i:1:p:305-358.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/qje/qjad043
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or

    for a different version of it.

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Will Davis & Alexander Gordan & Rusty Tchernis, 2021. "Measuring the spatial distribution of health rankings in the United States," Health Economics, John Wiley & Sons, Ltd., vol. 30(11), pages 2921-2936, November.
    2. Isaiah Andrews & Jiafeng Chen, 2025. "Certified Decisions," Papers 2502.17830, arXiv.org.
    3. Dominic Coey & Kenneth Hung, 2022. "Empirical Bayes Selection for Value Maximization," Papers 2210.03905, arXiv.org, revised May 2025.
    4. Emily Breza & Arun G. Chandrasekhar & Davide Viviano, 2025. "Generalizability with ignorance in mind: learning what we do (not) know for archetypes discovery," Papers 2501.13355, arXiv.org, revised Jul 2025.
    5. Bazylik, Sergei & Mogstad, Magne & Romano, Joseph P. & Shaikh, Azeem M. & Wilhelm, Daniel, 2025. "Finite- and large-sample inference for ranks using multinomial data with an application to ranking political parties," Journal of Econometrics, Elsevier, vol. 250(C).
    6. Andrews, Isaiah & Kitagawa, Toru & McCloskey, Adam, 2021. "Inference after estimation of breaks," Journal of Econometrics, Elsevier, vol. 224(1), pages 39-59.
    7. Kohei Yata, 2021. "Optimal Decision Rules Under Partial Identification," Papers 2111.04926, arXiv.org, revised Mar 2025.
    8. Aparajithan Venkateswaran & Anirudh Sankar & Arun G. Chandrasekhar & Tyler H. McCormick, 2024. "Robustly estimating heterogeneity in factorial data using Rashomon Partitions," Papers 2404.02141, arXiv.org, revised Aug 2025.
    9. Magne Mogstad & Joseph P Romano & Azeem M Shaikh & Daniel Wilhelm, 2024. "Inference for Ranks with Applications to Mobility across Neighbourhoods and Academic Achievement across Countries," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 91(1), pages 476-518.
    10. Oguzhan Akgun & Ryo Okui, 2025. "Robust Inference Methods for Latent Group Panel Models under Possible Group Non-Separation," Papers 2511.18550, arXiv.org.
    11. repec:arx:papers:2411.16552 is not listed on IDEAS
    12. Davide Viviano & Jelena Bradic, 2020. "Fair Policy Targeting," Papers 2005.12395, arXiv.org, revised Jun 2022.
    13. Davide Viviano & Jess Rudder, 2020. "Policy design in experiments with unknown interference," Papers 2011.08174, arXiv.org, revised May 2024.
    14. Hsieh, Yu-Wei & Shi, Xiaoxia & Shum, Matthew, 2022. "Inference on estimators defined by mathematical programming," Journal of Econometrics, Elsevier, vol. 226(2), pages 248-268.
    15. David J. Deming, 2021. "The Growing Importance of Decision-Making on the Job," NBER Working Papers 28733, National Bureau of Economic Research, Inc.
    16. Anya Shchetkina, 2025. "Blind Targeting: Personalization under Third-Party Privacy Constraints," Papers 2507.05175, arXiv.org.
    17. Isaiah Andrews & Jonathan Roth & Ariel Pakes, 2023. "Inference for Linear Conditional Moment Inequalities," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(6), pages 2763-2791.
    18. Reca Sarfati & Vod Vilfort, 2025. ""Post" Pre-Analysis Plans: Valid Inference for Non-Preregistered Specifications," Papers 2510.02507, arXiv.org.
    19. Wei, Waverly & Zhou, Yuqing & Zheng, Zeyu & Wang, Jingshen, 2024. "Inference on the best policies with many covariates," Journal of Econometrics, Elsevier, vol. 239(2).
    20. Jiafeng Chen, 2021. "Nonparametric Treatment Effect Identification in School Choice," Papers 2112.03872, arXiv.org, revised Dec 2025.

    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

    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:oup:qjecon:v:139:y:2024:i:1:p:305-358.. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/qje .

    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.