AGENCY COSTS OF THIRD‐PARTY FINANCING AND THE EFFECTS OF REGULATORY CHANGE ON UTILITY COSTS AND FACTOR CHOICES
Bibliographic record
Abstract
ABSTRACT ** : Beginning in 1999, the Canadian Province of Ontario undertook restructuring and tried to implement performance based regulation for local electricity distribution utilities. Regulatory parameters were based on productivity research covering 1988–1997 that found little productivity difference by size, but wide variations in costs, factor mix, financing, and returns to capital among utilities. While some utilities questioned their ability to improve efficiency, other observers maintained many utilities were over‐capitalized, especially from third‐party financing paid by customers for connection/development charges; these observers noted that rates, profits, and valuations would be inflated. Despite its pervasive use, we can find no literature dealing with the implications of third‐party funding. We assess the effects and adjustment dynamics of regulatory and financing changes on costs, factor mix, and performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".