Courts ruled that General Motors had not discriminated against Black women because the company hired women (white women in clerical roles) and hired Black employees (Black men on factory floors). Crenshaw's intersectional analysis showed this reasoning failed because:
AThe court applied the wrong legal standard and should have used disparate impact rather than disparate treatment analysis
BSingle-axis analysis treated race and gender as separate categories, making the court unable to detect discrimination that fell specifically at their intersection — the systematic exclusion of Black women from both sets of positions
CThe court failed to gather sufficient statistical evidence of individual prejudice against Black women
DExisting anti-discrimination law explicitly excluded intersectional claims, so the court correctly applied law as written
The General Motors case is Crenshaw's foundational example because it shows how single-axis analysis actively generates false negatives. The court found no race discrimination (Black people were hired) and no gender discrimination (women were hired), but this reasoning was blind to the specific combination: Black women were excluded from both the positions that went to other Black employees and the positions that went to other women. The discrimination was not race-plus-gender additively; it was a qualitatively distinct form invisible to each single-axis lens.
Question 2 Multiple Choice
A study of the gender wage gap compares all men to all women and finds a 16% average gap. A follow-up study disaggregates by race and gender simultaneously, finding gaps ranging from 10% to 35% across different groups. Intersectionality explains this discrepancy as:
AMeasurement error in the original study that inflated the average gap
BIndividual variation in education and experience that the first study failed to control for
CEvidence that race and gender interact to produce distinct wage structures that single-axis analysis averages out — potentially misleading researchers about the causes and magnitude of inequality
DA statistical artifact of smaller sample sizes producing unstable estimates in disaggregated subgroups
Intersectionality predicts exactly this finding: aggregating across race to get 'the gender gap' hides that the gap operates differently for different groups of women — some experience large gaps, others smaller ones, and the mechanisms (industry sorting, discrimination, education returns) may differ. The 16% average is not an error — it is real — but it represents an average over structurally distinct situations, misleading analysts who treat it as a uniform quantity. Disaggregation reveals the interaction that single-axis analysis conceals.
Question 3 True / False
According to the intersectionality framework, a person who experiences racial disadvantage can seldom simultaneously benefit from privilege along another dimension such as class, education, or gender.
TTrue
FFalse
Answer: False
Intersectionality explicitly holds that privilege and oppression are not mutually exclusive. A person can face disadvantage along one dimension (race) while benefiting from privilege along another (class, education, gender, sexuality). This is one of intersectionality's key correctives to additive thinking: social position is not a sum of pure advantages and disadvantages, but a complex location where multiple systems overlap, sometimes producing contradictory effects. Recognizing this complexity prevents both oversimplification and the erasure of intra-group differences.
Question 4 True / False
Intersectionality argues that analyzing social categories like race and gender separately does not just produce an incomplete picture of inequality — it can actively generate misleading conclusions about its causes and character.
TTrue
FFalse
Answer: True
This is one of intersectionality's most important methodological claims. The General Motors case illustrates the point: single-axis analysis did not merely miss part of the picture — it produced an actively false conclusion (no discrimination found) from the same facts that revealed discrimination when analyzed intersectionally. Similarly, studies that average the gender wage gap across racial groups may conclude that wage discrimination is moderate when it is actually severe for some groups and mild for others, with different causes in each case. The misleading conclusion is worse than incomplete — it misdirects policy.
Question 5 Short Answer
Why, according to the intersectionality framework, can the experience of a Black woman facing workplace discrimination not be understood simply as the sum of discrimination against Black men plus discrimination against white women?
Think about your answer, then reveal below.
Model answer: Because the intersection of race and gender produces a qualitatively distinct social location, not an additive combination of two separable disadvantages. Black women face forms of discrimination that are not identical to what Black men experience (which is shaped by gender as well as race) nor what white women experience (which is shaped by race as well as gender). In Crenshaw's foundational example, the specific exclusion of Black women from both Black male-dominated positions and white female-dominated positions was a distinct pattern that neither single-axis lens could detect. Adding the two single-axis analyses together would still miss this intersection-specific form of disadvantage.
The additive model assumes that race disadvantage and gender disadvantage are independent effects that stack — that being a Black woman means experiencing 'Black person disadvantage' plus 'woman disadvantage.' Intersectionality shows that the categories modify each other: what it means to be a woman is shaped by race, and what it means to be Black is shaped by gender. The result is a structural location that has its own specific vulnerabilities, forms of discrimination, and access to resources — qualitatively different from any single-axis analysis can reveal.