Competition and Consumer Discrimination
Bibliographic record
Abstract
This paper studies consumer discrimination while taking into consideration the role of competition between firms, providing one of the first large-scale comprehensive analyses of consumer discrimination and market forces. We formally model consumer discrimination, where some majority-group members dislike consuming alongside minorities. In equilibrium, the non-discriminatory-to-discriminatory firm ratio is proportional to the minority-to-majority consumer ratio. Empirically, we examine how local changes in the composition of consumers altered business incentives to discriminate during the decades leading up to the passage of the Civil Rights Act of 1964. Using a nationwide data source of non-discriminatory businesses in three different industries and a research design that leverages two sources of exogenous variation in the ratio of Black-to-White consumers, we find that increases in non-discrimination were concentrated in the least competitive markets, where the threat of defection by White consumers to competing firms was lowest. We assemble new data on over 25,000 prices charged at establishments by discriminatory status and show that non-discriminatory firms charged higher prices than discriminatory firms in the same local market. Consistent with our theoretical model, this finding arises because the effects of greater competition among the more numerous discriminatory firms outweighed the discrimination markup. The results imply that monopoly power blunted the influence of consumer preferences and that Black consumers were harmed through higher prices in the non-discriminatory market. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Economics working paper on competition and consumer discrimination with no abstract; the object is a market question.
The title concerns an economics topic rather than research practice.
Economics title on market competition and consumer discrimination; empty abstract, no metaresearch object indicated.
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".