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Uncertainty in Hiring Does Not Justify Affirmative Action

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Abstract

Luc Bovens has recently advanced a novel argument for affirmative action, grounded in the plausible idea that it is hard for an employer to evaluate the qualifications of candidates from underrepresented groups. Bovens claims that this provides a profit-maximizing employer with reason to shortlist prima facie less-qualified candidates from underrepresented groups. In this paper, I illuminate three flaws in Bovens’s argument. First, it suffers from model error: A rational employer does not incur costs to scrutinize candidates when it knows their qualifications with perfect certainty, nor does it refuse to hire better-qualified candidates just because they did not require extra scrutiny. Second, Bovens’s core premise--that there is greater variance in the evaluation of underrepresented candidates than there is the evaluation of other candidates--hurts underrepresented candidates rather than helps them. Third, candidates who are not shortlisted for the reasons Bovens gives have a plausible complaint about unfairness in the hiring process.

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Notes

  1. Whether this is in fact a libertarian ideal is debatable, as many libertarians will be loath to put any restrictions on owners’ liberty to manage their businesses however they desire (see, e.g., Narveson 1993).

  2. At least as far as mathematical ability is concerned, Summers presented the empirical evidence fairly: There is no mean difference in ability between men and women, but men do display greater variance than women (see, e.g., Hedges and Nowell 1995 and Hyde et al. 2008). Whether this explains, even in part, the underrepresentation of women is unclear.

  3. An optimal shortlist size will in fact be a function of both the costs involved in shortlisting and the uncertainty over the qualifications of the candidates who are being considered for inclusion on the shortlist.

  4. The probability that the selector gets a score 7 candidate is the probability that D, E, and F are all 7 s. This is 0.203 = 0.008. The probability that the selector gets a score 9 candidate is the probability that at least one of D, E, and F is a 9. This is 1–0.93 = 0.271. Therefore, the probability that the selector gets a score 8 candidate is 1–0.008 – 0.271 = 0.721. The expected qualification is thus (7 × 0.008) + (8 × 0.721) + (9 × 0.271) = 8.263.

  5. “This is precisely why we are shortlisting: We need to get a better view of the candidates in order to reduce the variance in our assessment.” (Bovens 2016: 424).

  6. I set aside the possibility of having to enter into battle with Human Resources, which might object—as is its wont—to the selection of a candidate not on the shortlist.

  7. Recall from n. 4 that the probability that the selector gets a score 9 candidate is 0.271. This time, if the selector doesn’t get a score 9 candidate, it is guaranteed to get a score 8 candidate. So the expectation is (8 × 0.729) + (9 × 0.271) = 8.271.

  8. See also Finnigan and Corker 2016 and Ganley et al. 2013.

  9. See, e.g., Arkes and Tetlock 2004, Blanton et al. 2009, Forscher et al. 2017, Manuscript, and Oswald et al. 2013.

  10. Here I shall follow the models of Phelps and Aigner and Cain (1977).

  11. Although this is a weak assumption, obviously satisfied in the real world, it is an important one, since everything gets reversed for candidates below the mean: Var(ε B) > Var(ε W) helps rather than hurts blacks, and Var(q B) > Var(q W) hurts rather than helps them. With equation (2) in mind, think of it this way: If you’re above the mean, you want the signal to be as accurate as possible. If you’re below the mean, you’re aided by noise in the signal, since selectors will place greater weight on the mean, which exceeds your actual qualifications.

  12. Cf. Aigner and Cain: “Blacks confront environmental restrictions on fulfilling their capacities, and this may lead to a smaller variance of [q B].” (1977: 180 n. 13).

  13. I thank an anonymous referee for the evocative example.

  14. I thank an anonymous referee for suggesting this interpretation of promise to me.

  15. See, e.g., Ewens, Tomlin, and Choon Wang 2014 and List 2004.

  16. See, e.g., Baldi 1995, Burris 2004, Clauset, Arbesman, and Larremore 2015, Jacobs 1999 and 2004, Keith and Babchuk 1998, McGinnis and Long 1997, and Oprisko, Dobbs, and DiGrazia 2013.

  17. These are cases of taste discrimination (Becker 1957).

  18. I discuss these issues of justice in hiring in detail in my Mulligan 2017.

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Acknowledgements

I thank two anonymous referees for their many helpful suggestions on this article.

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  1. McDonough School of Business, Georgetown University, 37th and O Streets NW, Washington, DC, 20057, USA

    Thomas Mulligan

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Mulligan, T. Uncertainty in Hiring Does Not Justify Affirmative Action. Philosophia 45, 1299–1311 (2017). https://doi.org/10.1007/s11406-017-9877-1

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