Reparative Consumption: The Role of Racial Identity and White Guilt in Consumer Preferences
Bibliographic record
Abstract
Abstract In light of recent social and political movements advocating for racial equity and calls for more research on interracial marketplace interactions, this research explores the role racial identity plays in the consumption domain. Specifically, we investigate the marketplace consequences of U.S.-based White consumers’ feelings about their own racial identity by measuring and manipulating white guilt, defined as the sense of guilt and remorse experienced by White consumers who hold their racial ingroup responsible for historical and ongoing racial injustices and perceive that Whites, as a racial group, benefit from unearned privileges. Consistent with the reparation-oriented action profile of guilt, six studies (all pre-registered, two with incentive-compatible designs) show that white guilt motivates reparative behaviors toward Black-owned businesses in various service contexts: Consumers with high white guilt express greater willingness to patronize and promote a business when it is Black-owned (vs. White-owned, family-owned, or when there is no information on ownership) and feel more moral for doing so, whereas this effect is non-existent, or sometimes reversed, for those low in white guilt. Our findings reveal the complex dynamics of race, identity, and intergroup relations in the marketplace, and demonstrate a contemporary exception to ingroup favoritism among some White consumers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".