Bibliographic record
Abstract
Using a dynamic overlapping‐generations model, we show that loyalty rewards robustly facilitate tacit collusion. We compare the sustainability of tacit collusion when uniform prices are used, when loyal customers are rewarded without using commitment, and when loyalty rewards are implemented by committing to offering customers either lower fixed repeat‐purchase prices or fixed repeat‐purchase discounts. We find that, relative to uniform prices, rewarding loyalty without using commitment on the equilibrium path makes tacit collusion easier to sustain, because a deviating firm is unable to steal one period of industry profit before losing all future profits. When loyalty rewards are offered by firms committing to repeat‐purchase prices, collusion is even easier to sustain, because a deviating firm cannot renege on its discounted price for repeat‐purchase customers. When firms commit to repeat‐purchase discounts, they also commit to lowering the price for their repeat‐purchase customers if they undercut the regular price, rendering tacit collusion to be even more readily sustainable. Our results hold whether products are homogeneous or horizontally differentiated as in a Hotelling model.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".