Cost efficiency in regional bus companies: an application of new stochastic frontier models
Bibliographic record
Abstract
anonymous referee for their helpful suggestions. We would also like to thank participants at the North American Productivity workshop (Toronto, 2004) for their comments on an earlier version of the paper. Any remaining errors are solely the responsibility of the authors. This paper evaluates cost and scale efficiencies of Switzerland’s regulated rural bus companies operating in regional networks. The adopted methodology can be used in benchmarking analyses applied to incentive regulation systems. Moreover, the estimations can be used to evaluate the bidding offers for the tendering processes predicted by the ongoing reform policies. Since these companies operate in different regions with various characteristics that are only partially observed, it is crucial for the regulator to distinguish between inefficiency and exogenous heterogeneity that influences the costs. A number of stochastic cost frontier models are applied to a panel of 94 companies over a 12-year period from 1986 to 1997. The main focus lies on the ability of these models to distinguish inefficiency from the unobserved firm-specific heterogeneity in a network industry. The estimation results are compared and the effect of unobserved heterogeneity on inefficiency estimates is analyzed. 1
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".