Information Asymmetries and Regulatory Decision Costs: An Analysis of U.S. Electric Utility Rate Changes 1980-2000
Bibliographic record
Abstract
We argue that information asymmetries between regulators and firms increase the administrative decision costs of initiating new policies due to the costs of satisfying evidentiary or “burden of proof” requirements. We further contend that regulators with better information about regulated firms—that is, with lower information asymmetries—have lower decision costs, thereby facilitating regulator policy making. To empirically test our predictions, we examine the relationship between regulatory informational environments and changes to regulated rates for all investor-owned electric utilities from 1980 to 2000. We exploit several natural sources of variation in the informational environments of US state utility regulators. These stem from the prior experiences and administrative resources of regulators, observable policy decisions of other regulatory agencies for a given utility, and differences in procedural regulations pertaining to rate increases and decreases. Our results suggest that as regulators acquire more information about utility operations, including from experience in office, they are more likely to enact rate decreases and less likely to implement rate increases.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".