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When is Affirmative Action Fair? Answers from a Hypothetical Survey Experiment

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Abstract

In this paper, we provide evidence on attitudes toward indirect past-in-present educational discrimination (i.e., educational discrimination that took place in the past but has a negative impact on the current employment opportunities of the discriminated against workers). We use an original vignette-based hypothetical survey experiment and collect data from a representative sample of the US population. We find that a significant majority of respondents support costly compensation for past educational discrimination. Moreover, we find that respondents are as sensitive to indirect past-in-present educational discrimination as they are to present-day employment discrimination. We point out that the causal effects on attitudes are stronger for the intentionality of discrimination than for its financial consequences for the discriminated group. Finally, attitudes appear to be driven more by respondents' political perspective than by their own actual identity.

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Notes

  1. Students for Fair Admissions, Inc., v. President and Fellows of Harvard College 20 U.S. 1199 (2023) and Students for Fair Admissions, Inc., v. University of North Carolina, et al. 21 U.S. 707 (2023).

  2. The survey was administered between June 2019 and January 2020 on US respondents via the online platform SurveyMonkey and received 788 valid questionnaires. SurveyMonkey is a well-known panelist that is often used to administer online surveys: for recent examples, see Rizzo et al. (2021) and Schomakers et al. (2019).

  3. See Harrison et al. (2006) for a comprehensive typology of affirmative action policies.

  4. The American College Test (ACT) is an equivalent and competing test.

  5. In 2019, a suit was filed against the University of California on the grounds that the SAT-based admission system discriminated against applicants on the basis of race, wealth, disability and mother tongue. In May 2020, the university decided to stop using SAT scores for admissions from the autumn semester of 2021 (Kroichick, 2020).

  6. According to the Census Bureau, real median disposable income in the USA was around USD 2,800 per month in 2018.

  7. A full survey on affirmative action programs is well beyond the scope of this paper. The paper focuses on one of these mechanisms. For an enlightening typology of affirmative action programs, see Harrison et al. (2006). For comprehensive reviews, see Arcidiacono et al., 2013; Hinrichs, 2012, 2014; Holzer & Neumark, 2006; Kellough, 2006; and Page & Scott-Clayton, 2016.

  8. Parents v. Seattle and Meredith v. Jefferson (2007), Fisher I $\&$ II (2013; 2016), Students for Fair Admissions, Inc., v. President and Fellows of Harvard College and the University of North Carolina, et al. (2023). See (Wallace & Allen, 2016) for a discussion of the 2007–2016 rulings.

  9. i.e., the elite selection process described in the survey is simply adapted in order to exactly compensate the effects of past educational discrimination.

  10. Hypothetical vignette-based experimental surveys (following the terminology of Haaland et al., 2023) are also often simply called “factorial surveys” or “vignette surveys” in the literature.

  11. Other approaches, such as testing studies and laboratory experiments, share this same experimental and causal nature, but focus on behaviors, not attitudes. We believe that behavior- and opinion- oriented studies are complementary. Beyer & Liebe (2015) have shown the fruitfulness, to study discrimination, of using vignette-based hypothetical experimental surveys as complements for behavior-oriented experimental protocols. Further, Gutfleisch et al. (2021), Hainmueller et al. (2015) and Petzold & Wolbring (2019) provide evidence that determinants of behaviors might be inferred from behavioral intentions measured with survey experiments. Last but not least, Riach & Rich (2004) argue that deceptive field experiments of discrimination may be questionable from an ethical point of view. All in all, compared to behavior-oriented experiments, we believe that survey experiments are a valuable complement, if not substitute.

  12. To minimize biographical bias, the situation is deliberately decontextualized by locating the scene in a far away and peaceful galaxy where humans and many alien races coexist peacefully (See Appendix 1 for the full version of the vignette).

  13. In the vignette, the members of the discriminated group miss the extra training opportunity because they are culturally obliged to stay at home for ritual periods during their adolescence. Note that the Manta survey was designed in the spring of 2019 and conducted between September 2019 and January 2020. It precedes the COVID-19 epidemic, which forced a large share of the world population to experience confinement first-hand. It also precedes the #BlackLivesMatter civil rights protests that took place in 2020 in the United States and elsewhere in the world.

  14. We chose to place the vignette in a neutral context with regard to the respondents' own experience (a faraway planet with an explicitly alien population) to maximize this distinction.

  15. Sauer et al. (2011) showed that hypothetical vignette-based experimental surveys are applicable in general population samples, provided that a limited number of vignettes and dimensions per respondent were used.

  16. https://fr.surveymonkey.com.

  17. “In the next page, you'll find a story where a character needs to make a decision. You'll be asked which choice is, in YOUR opinion, the best from a moral point of view. Please read carefully the story in order to make YOUR decision: there are no good or bad answers! After, there are very brief questions about you. These will allow us to compare the answers of all the people that will complete the survey. All in all, the survey takes about 3–4 min to complete.".

  18. SurveyMonkey’s pool of respondents was stratified by age, gender, income, US region and device used to complete the survey. Table 2 shows that our working sample is not representative of the 18 years old + US population. To mitigate this issue, results presented in Sect. "Results" were obtained using sampling weights stratified by gender, age and race, using data from the 2019 US Census.

  19. White = self-declared White or Caucasian; non-White = self-declared American Indian or Alaskan Native, Asian or Pacific Islander, Black or African American, Hispanic or Latino.

  20. Some higher education = some college, no degree; a college degree or equivalent; a bachelor's degree; a master's degree; a professional degree or doctorate (e.g., MD, DDS, DVM); a doctorate (PhD, EdD).

  21. See Models 1 and 2 of Table 4 in Appendix 3 for details. A comparison of models 2 and 3 in Table 4 of Appendix 3 shows that results do not differ according to whether we include only sociodemographic covariates without the point-of-view variable / sociodemographic and point of view covariates.

  22. See Models 3 to 7 in Table 4 of Appendix 3 for detailed results.

  23. See Models 4 to 7 in Table 4 of Appendix 3 for detailed results.

  24. See Models 4 and 5 in Table 4 of Appendix 3 for detailed results.

  25. Insignificant effects and large confidence intervals could be due to sample size issues.

  26. See Models 4 and 5 in Table 4 of Appendix 3 for detailed results.

  27. See models 4 and 5 in Table 4 in Appendix 3 for detailed results.

  28. We found not significant results when interacting directly the respondents’ identity and ideology characteristics with the “point of view” variables in the main regressions presented above (but this could be due to sample size issues). Detailed results are available upon request.

  29. See Table 5 of Appendix 4 for detailed results.

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Acknowledgement

This paper benefited from an ANR JCJC grant (ANR JCJC JEM 2015) and from funding from the CEPREMAP.

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  1. Ledi, EconomiX and TEPP, Esplanade Erasme, Université de Bourgogne-Franche-Comté, 21000, Dijon, France

    Mathieu Bunel

  2. Paris Nanterre University and EconomiX, 200, Avenue de la République, 92000, Nanterre, France

    Élisabeth Tovar

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Appendix

Appendix

Appendix 1. Full Text of the Survey’s Vignette

Imagine a far away galaxy where Humans and many alien races live peacefully.

Tom is the Human manager of a recruitment agency. This job is Tom’s only source of income. This year, SpaceTravel, a big intergalactic firm, is Tom's only client. SpaceTravel asks Tom to run a selection process to find qualified navigators for its spaceships.

On average, half of the candidates are successful and become navigators for SpaceTravel.

[Financial Consequences for the Discriminated Group]

  • [None: #1 and 4] Tom knows that the candidates who fail the selection process will be quickly hired by other firms and will earn the same income as candidates who were successful.

  • [× 2 wage loss: #2, 3, 5 and 6] Tom knows that a SpaceTravel navigator earns twice the income of a candidate who failed the selection process.

Candidates can only apply to the SpaceTravel selection process once in their life, just after high school. All the high schools organize short training courses where their pupils can get familiar with the very specific flight simulators used by SpaceTravel during its selection process.

Among all the galactic races that apply to the selection process, 100 candidates are Tenka aliens. The Tenka have a specific tradition: The young Tenka must stay at home during short ritual periods.

[Discrimination Intent]

  • [Deliberate: #1, 2, and 3] Knowing this, the school administrators have deliberately set the training courses at times when the Tenka pupils could not attend. This way, they want to make sure that very few Tenka will successfully complete the selection process.

  • [Involuntary: #4, 5, and 6] This cultural specificity prevents them from enrolling in the specific flight simulator training.

SpaceTravel asks Tom to start the selection immediately after the high school year. Tom knows that, contrary to all the other candidates, the Tenka candidates could not get familiar with the flight simulators. Personally, Tom equally cares about all the alien races. In his opinion, they are all equally capable of being efficient navigators for SpaceTravel.

[Sanction of the Firm for Discriminating]

  • No sanction [#1, 2, 4 and 5] < no additional text > 

  • Government sanction [#3 and 6] The galactic government makes sure that all firms provide the same job opportunities to everyone. The government financially sanctions the firms that do not respect this principle.

In your opinion, from a moral point of view, what should Tom do? Knowing your opinion will not affect Tom's decision.

Scenarios without any Sanction of the Firm for Discriminating [#1, 2, 4 and 5]

  • [Discrimination] Start the selection process immediately after the high school year for everybody, knowing that the Tenka will not have had any opportunity to learn to use the flight simulators. Because of that, no Tenka candidate will successfully become a navigator. In this case, Tom will not lose any income.

  • [Compensation] Delay the selection process to give the Tenka enough time to learn how to use the flight simulators. In this case, 50 Tenka will become navigators. But, because of the delay, Tom will have to pay a penalty and lose half of his income.

Scenarios with a Sanction of the Firm for Discriminating [#3 and 6]

  • [Discrimination] Start the selection process immediately after the high school year for everybody, knowing that the Tenka will not have had any opportunity to learn to use the flight simulators. Because of that, no Tenka candidate will successfully become a navigator. In this case, SpaceTravel will pay Tom the full amount of income due. Also, the government will impose a heavily financial penalty to SpaceTravel because of the high failure rate of the Tenka.

  • [Compensation] Delay the selection process to give the Tenka enough time to learn how to use the flight simulators. In this case, 50 Tenka will become navigators. But, because of the delay, Tom will have to pay a penalty and lose half of his income. In this case, the government will not sanction SpaceTravel.

Appendix 2. Subsample Structure

The tests presented in Table 3 check the comparability of the subsamples of respondents who were shown the different versions of the vignette.

Table 3 Chi-square statistics and p values associated with tests of independence between the scenarios and the characteristics of the respondents
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Appendix 3. Marginal Effects of Covariates (Linear Probability Model)

Table 4 below shows the results obtained using a linear probability model. The discrete explanatory variable is the respondent's opinion on whether the IPP educational discrimination should be compensated by delaying the selection at the expense of the recruiter or by going ahead with the original schedule (variable compensate = yes (reference), no).

Models 1 and 2 are run for respondents who received scenarios 2 and 7 to explore the causal impact of the temporality of the discrimination on their choices. In Model 1, the only explanatory variable is the temporality of the discrimination (variable past-in-present = IPP discrimination (reference factor), discrete discrimination in the present).

Model 2 adds individual covariates on identity, ideology and identification of the respondents:

  • Identity covariates: gender (= male (ref), female), race (= white (ref), non-white), age (= under 30, between 30 and 60 (ref), over 60), education (= no higher education (ref), higher education), income (= less than $50,000/year, between $50,000-$149,999/year (ref), more than $150,000/year).

  • Ideology covariate: political opinion (= liberal, conservative (ref), neither liberal nor conservative)

  • Identification covariate: viewpoint (= all agents (ref), candidates from the discriminated group, other agents).

Models 3 to 7 are run for respondents who received scenarios 1 to 6 to explore the causal impact of intention, financial consequences and government sanctions on respondents' choices. They're also used to explore the relationship between individual characteristics and opinions on compensation for IPP discrimination.

Model 3 includes a variable combining the intention to discriminate (intentional or unintentional) and the financial consequences of discrimination for the discriminated group (yes, no) (reference factor = unintentional discrimination with no financial loss), as well as a dummy variable for the existence of government sanctions against companies that discriminate (= no, yes (ref)). Model 4 adds identity and ideology covariates. Model 5 adds an identification covariate.

Models 6 and 7 include a variable combining the intention to discriminate (intentional or unintentional) and the existence of government sanctions on firms that discriminate (yes, no) (reference factor = unintentional discrimination without government sanction), and a dummy variable for the financial impact of discrimination on the discriminated group (= no (ref), yes). Model 6 includes no other covariates and Model 7 includes both identity, ideology and identification covariates.

Table 4 Marginal effects of covariates (explained variable: opinion on delaying the selection process to compensate for IPP discrimination, linear probability model)
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Appendix 4. Respondent Characteristics and Point of View

To explore whether identity variables are correlated with the point of view chosen by the respondent to make their choice on the compensation of IPP, we use a multinomial logit model.

Let \(Y\) be a nominal outcome variable equal to.

  • 0 if the respondent stated that they identified the most with applicants from the discriminated group

  • 1 if they identified the most with all the characters of the vignette

  • 2 if they identified the most with other characters (the hiring agent, the hiring firm, or applicants from non-discriminated groups)

Let \({\text{Pr}}\left(Y=j \mid X\right)\) with \(j=0,...,2\) be the probability that the respondent is equal to \(j\) conditional of covariates \(X\).

$$\Pr \left( {Y = 0 \mid X} \right) = \frac{1}{{1 + \mathop \sum \nolimits_{j = 1}^{2} \exp \left( {\beta_{0j} + \mathop \sum \nolimits_{k = 1}^{K} \beta_{{{\text{kj}}}} } \right)}}\;{\text{if}}\;\;j = 0$$
$$\Pr \left( {Y = j \mid X} \right) = F_{j} \left( {\beta_{j} X} \right) = \frac{{\exp \left( {\beta_{0j} + \mathop \sum \nolimits_{k = 1}^{K} \beta_{{{\text{kj}}}} X_{k} } \right)}}{{1 + \mathop \sum \nolimits_{j = 1}^{2} \exp \left( {\beta_{0j} + \mathop \sum \nolimits_{k = 1}^{K} \beta_{{{\text{kj}}}} } \right)}}\;{\text{if}}\;\;j = 1,2$$

We compute marginal effects to identify the socio-economic variables that influence the point of view chosen by respondents when making their choice.

Since we have only discrete covariates, the marginal effect is computed as the difference in predicted probabilities.

For instance, in the case of a dichotomic covariate, the marginal effect of covariate Xk would be computed as:

$$\Pr \left( {Y = j \mid X_{ - k} ,X_{k} = 1} \right) - \Pr \left( { Y = j \mid X_{ - k} ,X_{k} = 0 }\right) = F_{j} \left( {\beta_{ - k} X_{ - k} + \beta_{k} }\right) - F_{j} \left( {\beta_{ - k} X_{ - k} } \right)$$

With X-k the other covariates. We find significant effects for gender, age, educational level and political opinions (see Table 5 over).

Table 5 Marginal effects of the covariates associated to the probability of choosing specific viewpoint (multinomial logit model)
Full size table

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Bunel, M., Tovar, É. When is Affirmative Action Fair? Answers from a Hypothetical Survey Experiment. Soc Just Res 37, 25–56 (2024). https://doi.org/10.1007/s11211-023-00429-3

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