Competition Law and Europe's Open Borders: The Case of Motor Vehicle Distribution in Switzerland
Bibliographic record
Abstract
This paper contains an independent empirical analysis of the effect of a Notice, issued by the Swiss Competition Commission in 2002 concerning vertical agreements between manufacturers and distributors of motor vehicles, on the degree to which the subsequent prices of cars in Switzerland exceeded those charged on the same models in neighbouring countries. Evidence presented here implies a non-transitory reduction in the degree of price discrimination against Swiss customers of medium- and large-sized cars in the years after the Notice came into effect. The total gain to Swiss buyers of cars is very conservatively estimated to be six times the total cumulative budget of the Swiss Competition Commission during the years 2003-2006; the best estimate of those gains exceed a quarter of a billion Swiss Francs during the same period. By 2006 the cumulative price reduction of the Swiss Competition Commission's action resulted in average savings per medium- and large-sized car that are estimated to be 929 and 2113 Swiss Francs, respectively. Moreover, the recurring annual gain to Swiss consumers of this measure by the Swiss Competition Commission is conservatively estimated to exceed ten times the latter's current annual budget, providing some indication of the "value for money" that effective competition law can have, even in economies with ostensibly open borders.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".