IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/28503.html
   My bibliography  Save this paper

What Marginal Outcome Tests Can Tell Us About Racially Biased Decision-Making

Author

Listed:
  • Peter Hull

Abstract

Marginal outcome tests compare the expected effects of a decision on individuals who are of different races but at the same indifference point of the decision-maker. I present a simple formalization of how such tests can detect racial bias, defined as a deviation from accurate statistical discrimination. Namely, the tests can reject that the decision-maker ranks individuals according to some accurate prediction of a mandated outcome, given some unspecified race-inclusive information set. The frontier of marginal effects can furthermore rule out canonical taste-based discrimination. I relate this analysis to other interpretations of marginal outcome tests, other notions of racial discrimination, and recent identification strategies.

Suggested Citation

  • Peter Hull, 2021. "What Marginal Outcome Tests Can Tell Us About Racially Biased Decision-Making," NBER Working Papers 28503, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:28503
    Note: LS
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w28503.pdf
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Akter, Shahriar & Dwivedi, Yogesh K. & Sajib, Shahriar & Biswas, Kumar & Bandara, Ruwan J. & Michael, Katina, 2022. "Algorithmic bias in machine learning-based marketing models," Journal of Business Research, Elsevier, vol. 144(C), pages 201-216.
    2. Patrick Kline & Evan K Rose & Christopher R Walters, 2022. "Systemic Discrimination Among Large U.S. Employers [“Teachers and Student Achievement in the Chicago Public High Schools,”]," The Quarterly Journal of Economics, Oxford University Press, vol. 137(4), pages 1963-2036.
    3. Domínguez, Patricio & Grau, Nicolás & Vergara, Damián, 2022. "Combining discrimination diagnostics to identify sources of statistical discrimination," Economics Letters, Elsevier, vol. 212(C).
    4. David Arnold & Will Dobbie & Peter Hull, 2022. "Measuring Racial Discrimination in Bail Decisions," American Economic Review, American Economic Association, vol. 112(9), pages 2992-3038, September.
    5. E. Jason Baron & Joseph J. Doyle Jr. & Natalia Emanuel & Peter Hull & Joseph Ryan, 2024. "Unwarranted Disparity in High-Stakes Decisions: Race Measurement and Policy Responses," NBER Chapters, in: Race, Ethnicity, and Economic Statistics for the 21st Century, National Bureau of Economic Research, Inc.
    6. Nicholas Tenev, 2024. "De-Biasing Models of Biased Decisions: A Comparison of Methods Using Mortgage Application Data," Papers 2405.00910, arXiv.org.
    7. Paula Onuchic, 2022. "Recent Contributions to Theories of Discrimination," Papers 2205.05994, arXiv.org, revised Jun 2023.
    8. Jay Euijung Lee & Martina Zanella, 2024. "Learning about women's competence: The dynamic response of political parties to gender quotas in South Korea," CEP Discussion Papers dp2029, Centre for Economic Performance, LSE.
    9. Patrick Kline & Evan K Rose & Christopher R Walters, 2023. "Systemic Discrimination Among Large U.S. Employers," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 137(4), pages 1963-2036.
    10. Patrick Kline & Evan K Rose & Christopher R Walters, 2023. "Systemic Discrimination Among Large U.S. Employers," Journal of Economic Geography, Oxford University Press, vol. 137(4), pages 1963-2036.
    11. Ashesh Rambachan, 2022. "Identifying Prediction Mistakes in Observational Data," NBER Chapters, in: Economics of Artificial Intelligence, National Bureau of Economic Research, Inc.
    12. Will Dobbie & Crystal S. Yang, 2021. "The US Pretrial System: Balancing Individual Rights and Public Interests," Journal of Economic Perspectives, American Economic Association, vol. 35(4), pages 49-70, Fall.
    13. Joshua Grossman & Julian Nyarko & Sharad Goel, 2023. "Racial bias as a multi‐stage, multi‐actor problem: An analysis of pretrial detention," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 20(1), pages 86-133, March.
    14. Patricio Dom'inguez & Nicol'as Grau & Dami'an Vergara, 2022. "Discrimination Against Immigrants in the Criminal Justice System: Evidence from Pretrial Detentions," Papers 2202.10685, arXiv.org.

    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • J15 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination
    • J71 - Labor and Demographic Economics - - Labor Discrimination - - - Hiring and Firing

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:nbr:nberwo:28503. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    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.