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

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  • 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
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    Citations

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    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. Paula Onuchic, 2022. "Recent Contributions to Theories of Discrimination," Papers 2205.05994, arXiv.org, revised Jun 2023.
    7. 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.
    8. 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.
    9. Ashesh Rambachan, 2022. "Identifying Prediction Mistakes in Observational Data," NBER Chapters, in: Economics of Artificial Intelligence, National Bureau of Economic Research, Inc.
    10. 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.
    11. 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.
    12. 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

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