Banning Controversial Sponsors: Understanding Equilibrium Outcomes When Sports Sponsorships Are Viewed as Two-Sided Matches
Bibliographic record
Abstract
This article applies a two-sided matching model to investigate the consequences of banning controversial sponsors. Using a data set containing the shirt sponsorships from 43 English football clubs between 1990 and 2010, the authors' estimates suggest assortative matching between a club's attendance and a sponsor's revenue. In addition, sponsorships become less valuable as the distance between the club and the sponsor's head office grows, particularly for low-performing clubs and smaller domestic sponsors. The authors use these estimates to simulate the consequences of banning alcohol and gambling sponsors. Their estimates of counterfactual outcomes suggest that such bans may not have the largest impact on the clubs (particularly the relatively successful clubs) that currently have alcohol and gambling sponsors. Instead, clubs with low attendance and clubs in low-income areas will be most affected by a ban. More generally, the results demonstrate that when marketing relationships are viewed as the result of a matching process, actions that affect only some marketers may have substantial indirect effects on a variety of players in the market.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".