Bibliographic record
Abstract
This paper examines the phenomenon of profit-increasing consumer exit and the related phenomenon of profit-decreasing consumer entry. We demonstrate that firms can be better off in shrinking markets and worse off in growing markets, even in the absence of competitive entry or exit. Specifically, firms may benefit if a segment of consumers who are relatively indifferent about consuming any product in the category leave the market. Profits can increase for all firms even if the exiting consumers have strong preferences for only one of the products in the market. In shrinking markets, it is reasonable to assume that the people who are likely to exit the market first are people who are “least committed” to the category. In particular, people who are the least satisfied with the existing offers are the most likely to change their behavior by finding an alternative or adopting a new technology. Similarly, in growing markets, consumers who enter the market late are generally the least committed to the category. Such exiting can relax the competitive pressure between firms and lead to increased profitability. Our findings provide an explanation for profit growth that has been observed in product industries exhibiting slow and predictable declines over time, including vacuum tubes, cigarettes, and soft drinks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".