Flexibility-based price discrimination in a competitive context considering consumers’ socioeconomic status
Bibliographic record
Abstract
This study examines the impact of flexibility-based price discrimination (FBPD) on the pricing and quality strategy of the adopting firm and its competitor, as well as the impact on the welfare of consumers. We assume that the inflexible consumers being targeted for price discrimination can be either high-income consumers or low-income consumers, and the high-income consumers are more sensitive to product quality. We show that depending on who the targeted inflexible consumers are, the impact of FBPD on all firms and consumers can be either negative or positive. If an FBPD is to exploit the inflexibility of low-income consumers, it will not only make the vulnerable group even more disadvantaged but also lower the firms’ incentive to produce high-quality products. On the contrary, if an FBPD is to exploit the inflexibility of high-income consumers, it will increase the firms’ incentive to produce high-quality products, and the targeted consumers will be compensated by having higher quality products. However, the firms might engage in excessive quality enhancement, leading to a situation where the competition between the firms falls into a prisoner’s dilemma. Our research results suggest that the application of FBPD could necessitate a comprehensive regulatory framework to ensure ethical implementation while safeguarding consumer welfare, particularly that of vulnerable groups.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".