The Effects of Vertical Separation and Competition: Evidence from US Electric Utility Restructuring
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Competition usually increases firm productivity; but in network industries, effective competition requires vertical separation, which might reduce productivity and lead to a potential trade-off. We analyze the combined effect of competition and vertical separation on inefficient costs for US electricity industry restructuring. We estimate firm-level inefficiencies with the use of different nonparametric models of the technology and calculate net benefits with the use of difference-in-differences. The results depend on how we model the production technology and the length of the post-treatment horizon. The more flexible is the production frontier, the greater is the net benefit from divestiture and competition. Across our models the combined effect of divestiture and competition is positive.
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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 | 0.000 | 0.002 |
| 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 it