An Empirical Analysis of Price Discrimination Mechanisms and Retailer Profitability
Bibliographic record
Abstract
Retailers typically engage in some form of price discrimination to increase profitability. In this article, the authors compare the impact on retailer profitability of two price discrimination mechanisms: quantity discounts based on package size (second-degree price discrimination) and store-level pricing or micromarketing (third-degree price discrimination). Whereas the latter has been well addressed in the marketing literature, there is limited empirical research on the use of quantity discounts for price discrimination. Using store-level sales data, the authors estimate a structural demand model, accounting for parameter heterogeneity and price endogeneity. They combine the parameter estimates with a model of retailer pricing to conduct optimal pricing and profitability simulations under several scenarios, ranging from constraining the retailer not to engage in any form of price discrimination to the least restrictive scenario of setting nonlinear price schedules specific to each store. The pricing simulations enable the decomposition of profitability as a result of the different forms of price discrimination. Profits are greatest when retailers combine second- and third-degree price discrimination. The authors find that the ability to engage in second-degree price discrimination contributes more to retailer profitability than does third-degree price discrimination.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".