Research Note—Price Discrimination After the Purchase: Rebates as State-Dependent Discounts
Bibliographic record
Abstract
Promotional tools such as rebates and coupons are usually seen as different ways of price discriminating among consumers. We focus on a different property of rebates: their ability to price discriminate within a consumer among her postpurchase states. Unlike price discrimination between consumers, this property is unique to rebates because, by design, they are redeemed after the purchase. (Coupons, by contrast, are redeemed with the purchase.) The consumer redeems the rebate only in postpurchase states in which her marginal utility of income is high. This selective redemption behavior provides an opportunity for the seller to “utility arbitrage,” directing discounts to when they matter most, resulting in an increase in the consumer’s up-front willingness to pay. In turn, this enables an increase in the regular price. Of course, rebates can still price discriminate among consumers. Indeed, their ability to deliver state-dependent discounts may enhance their overall price discrimination ability, as we show in an example comparing them to coupons.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".