Price Discrimination and Social Network : Evidence from North American Auto Dealership Transaction Data
Bibliographic record
Abstract
Using personnel and transaction data obtained from two auto dealerships located in a large city in Canada, we examine whether same or different ethnic matches between salespersons and customers affect the prices and quantities of transactions. First, compared with White-White matches, we find little evidence of price discrimination for different ethnicity matches (such as White vs. Middle East), and we detect neither premium price setting nor discounting among same ethnicity matches (such as Asian vs. Asian) relative to different ethnicity matches. Regarding quantity, however, sales ratios to ethnically-same customers are substantially higher than is the case for ethnically dissimilar customers. For example, East Asian salespersons concluded more than 30% of their sales with East Asian customers. Moreover, we find that high-performing salespersons skillfully utilize social networks to conclude transactions with customers of the same ethnicity, especially when business conditions are unfavorable. This finding suggests that social networks are important to understanding the nature of auto retail markets.
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How this classification was reachedexpand
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".