The empirical effects of competition on third‐degree price discrimination in the presence of arbitrage
Bibliographic record
Abstract
Abstract Since Borenstein and Holmes, a theoretical and empirical literature has emerged that examines the effects of competition on third‐degree price discrimination. Since transaction costs involved in conducting arbitrage are typically unobserved, empirical investigations in this area have largely been restricted to markets such as for air travel where arbitrage is difficult, if not impossible. Using an entirely novel dataset, this paper documents the effect of competition on price discrimination in the presence of arbitrage in the Canadian online sports betting market where prices for Canadian teams are higher than in the world market. I observe how the prices of Canadian teams change in real time in response to the presence of arbitrageurs that establish Canadian sportsbooks’ observable marginal opportunity costs. I exploit the existence of government betting outlets not subject to arbitrage to obtain reduced form counterfactual estimates of the extent to which competition affects price discrimination in the presence of arbitrage. In this new empirical environment, I find results consistent with the airline literature: competition reduces overall price dispersion and markups, but dispersion and markups shrink more for those in the “strong” market than the “weak” 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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".