Intersectional Discrimination and Candidate Selection: Evidence from a Lab in the Field Experiment in Kenya

Jonah Foong, Macartan Humphreys, Kimuli Kasara

2024-02-01

Outline

  • Motivation
  • Experiment hypotheses
  • Study design
  • Results
  • Discussion/Conclusion

Preview of Findings

  • Conducted a lab experiment in Nairobi to understand how gender and ethnic discrimination interact
  • Individuals select a candidate (individually or collectively) to perform a task
  • Three treatments
    • a gender quota
    • information on a candidate’s past performance
    • collective discussions in heterogeneous groups.
  • Findings
    • Baseline discrimination against women and outgroups, but no double-discrimination
    • Moreover no double discrimination in any treatment condition
    • Discussions remove biases for outgroups, but not so much for women

Intersectional discrimination

  • Seminal work by Crenshaw (1989); discrimination is not merely additive but more than the sum of its parts. This was first studied in relation to Black women’s employment and lack of legal recourse. It has since been extended to other areas across the social sciences.
  • Political scientists have focused on
  • Interactive interpretation: People who are discriminated against on one dimension are more or less likely to be discriminated on another dimension.
  • Agents: We focus on discrimination by individual actors, which aggregate to systemic discrimination. This lets us assess intersectional discrimination even on unranked dimensions.
  • A key point is that it is not obvious under what circumstances we would expect interactions.

Burying S.M.: An illustration

  • S.M. Otieno a prominent Luo Kenyan criminal lawyer whose burial dispute became famous in 1987
  • Wambui Otieno, Kikuyu, was taken to court in 1987 over the right right to bury him (and to his estate)
  • Can the fact that she lost the case be attributed to gender discrimination being more powerful than ethnic discrimination? Or to an interaction?

Burying S.M.: Possible counterfactual conditions

Surviving spouse is
Male Female
Surviving spouse is Kikuyu Counterfactual case: surviving spouse wins Actual case: surviving spouse loses
Luo Counterfactual case: surviving spouse wins Counterfactual case: surviving spouse wins

Burying S.M.: What this case taught us

  • Because they study ranked ethnic groups, most scholars tend to assume systemic intersectional biases reflect the preferences of men from dominant groups.
  • What we learn from this case, is that it is unclear who wanted to or felt able to support Wambui.
    • Luo women
    • Kikuyu men (generally)
    • Members of the Kikuyu political elite
  • In this case, systemic biases are harder to identify

Institutional conditions

We focus on conditions to assess the presence of intersectional discrimination and to assess whether double discrimination can be addressed, or exacerbated, by institutional features:

  • Gender quotas
  • Information on a candidate’s likely performance
  • Collective decision making

Quotas: Potential Mechanisms

  • Quotas produce double discrimination through constrained optimization
  • Outgroup biases are proportional in each group
  • No interaction without constraints
  • Intuition: When forced to be gender-egalitarian people are more likely to choose people from their own ethnic group.
Group A Group B Average Difference
Male 0.35 0.25 0.3 -0.1
Female 0.25 0.15 0.2 -0.1
Average 0.3 0.2 0.25 -0.2
Difference -0.1 -0.1 -0.2 Interaction: 0
Group A Group B Average Difference
Male 0.29 0.21 0.25 -0.08
Female 0.31 0.19 0.25 -0.12
Average 0.3 0.2 0.25 -0.02
Difference 0.02 -0.02 0 Interaction: -0.04

Information

Information

  • If double discrimination is statistical, information on quality limits discrimination (Unless updating on an identity dimension is biased)
  • Effects depend on whether we are concerned with overall inequality in treatment or inequality given type

Discussions

Multiple mechanisms

  1. they are informative
  2. they invoke egalitarian norms
  3. they lead to bargaining

Discussions

We focus on (3), in this case between group bargaining can lead to, or relieve, double discrimination

e.g. Male A and Male B bargain. They are both double discriminators. Nash barganining solution yields preferential outcomes for males but removes double discrimination

Table 1: Prejudices for male \(A\) prior to Nash bargaining.
Group A Group B Average Difference
Male 0.4 0.3 0.35 -0.1
Female 0.3 0.0 0.15 -0.3
Average 0.35 0.15 0.25 -0.2
Difference -0.1 -0.3 -0.2 Interaction: -0.2
Table 2: Prejudices for male \(B\) prior to Nash bargaining.
Group A Group B Average Difference
Male 0.3 0.4 0.35 -0.1
Female 0.0 0.3 0.15 -0.3
Average 0.35 0.15 0.25 -0.2
Difference -0.1 -0.3 -0.2 Interaction: -0.2
Table 3: Nash bargaining solution from negotiation between an intersectionally discriminating A man and an intersectionally discriminating B man.
Group A Group B Average Difference
Male 0.35 0.35 0.35 0
Female 0.15 0.15 0.15 0
Average 0.2 0.2 0.2 0
Difference -0.2 -0.2 -0.2 Interaction: 0

Hypotheses

Double discrimination:

  • Exists
  • Is exacerbated by quotas on one dimension
  • Is curbed by information
  • Is curbed by collective decision making procedures

How did we test these hypotheses?

Why a Lab Experiment in Kenya?

  • Ethnicity is highly socially salient and people have in-group biases
  • Gender discrimination occurs
  • Gender quotas are politically salient since 2010 constitutional reforms
  • Many studies (including lab experiments) have explored intersectional discrimination in contexts with ranked ethnic identities. Mostly in the US, but also in India.

Design - treatments

In total there are three fully crossed treatment arms and participants make choices across two vignettes:

  1. T1 Quota: electors are asked to choose at least one woman

  2. T2 Information: vignettes display ability measures of all candidates (test scores)

  3. T3 Discussion: triads are assigned to group discussions in either, neither, or one of the vignettes, after which they will be asked to make a group choice

Design - sample

  • We target a sample of 900 participants distributed equally across ethnic (Luo/Kikuyu/Other) and gender (M/F) dimensions

  • Luo and Kikuyu groups were chosen as these are the most populous in the region; the “other” group is heterogeneous, defined simply as non-Luo and non-Kikuyu

Male Female
Kikuyu 150 150
Luo 150 150
Other 150 150

Design - Individual survey

  • Participants first take individual survey with standard questions about personal characteristics as well as a raven’s test, which we use to generate scores and determine electors’ compensation.

Raven’s matrices

Design - experiment and group discussion

  • All tasks are incentivized; participants are allocated payments the more accurate their ratings were and if they chose a successful candidate. Candidates who are chosen are paid out regardless of whether they’re successful
  • Participants are shown 2 vignettes in total

Vignette in non-information treatment

Lab set-up