Bibliographic record
Abstract
We study the role of switching costs in a dynamic buyer‐seller relationship where quality is not contractible and the sellers retain private information about their quality‐relevant abilities. In this environment buyer switching costs increase the seller's bargaining power in negotiations for the second contract, but they also induce the seller to improve quality during the first ontract, in signalling his type. The overall effect is to enhance efficiency and ncrease the buyer's welfare. This beneficial effect stems from the link between quality, the buyer's posterior beliefs, and ex post distribution of bargaining power as a function of the switching cost. JEL Classification: L14 L'auteur étudie le rôle des coûts de commutation dans une relation dynamique acheteur‐vendeur où la qualité de la prestation ne peut être partie du contrat, et où les vendeurs peuvent rationner l'information quant à leurs compétences à livrer un produit de qualité. Dans ce contexte, les coûts de commutation de l'acheteur accroissent le pouvoir de marchandage du vendeur dans la négociation du second contrat, mais ils peuvent aussi inciter le vendeur à améliorer la qualité au cours du premier contrat pour signaler ses compétences à l'acheteur. L'effet global est d'améliorer l'efficacité et d'accroître le bien‐être de l'acheteur. Cet effet bénéfique émerge du lien entre la qualité, les croyances de l'acheteur a posteriori, et la répartition du pouvoir de négociation ex post en tant que fonction des coûts de commutation.
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 | 0.015 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".