Bibliographic record
Abstract
This paper examines a key difference between two promotional vehicles, coupons and rebates. Whereas coupons offer deals up front, with the purchase of the product, rebates can be redeemed only after purchase. When consumers experience uncertain redemption costs, this difference translates to a difference in when uncertainty is resolved. With coupons the uncertainty is resolved before purchase; with rebates the uncertainty is resolved after purchase. As a result, we show that rebates are more efficient in surplus extraction but coupons offer more finetuned control over whom to serve. We identify the conditions under which each is optimal, and these conditions turn on the gap between “low” reservation price consumers’ valuations and their highest redemption costs. Rebates are optimal when this gap is large; coupons tend to be optimal otherwise. Risk aversity on the part of consumers reduces the attractiveness of rebates, as does the delay between rebate redemption and rebate payment, but the latter if and only if consumers are more impatient than the seller. These observations match up well with what we know about the use of these promotional vehicles in the real world.
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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 | 0.008 |
| 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 | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".