Unintended and Persistent Consequences of Regulation: The Case of Cable Television Provision in Canada
Bibliographic record
Abstract
Economic regulation can have unintended consequences that are detrimental to subsequent policy change if the regulatory regime has institutionalized certain aspects of firm behaviour. For example, in a post-deregulation environment, unforeseen scalerelated phenomenon may persist in an industry that was formerly regulated according to firm size. Unfortunately, in many industries such effects can be difficult to identify. We examine measures of efficient scale for annual cross-sectional data from the cable television industry in Canada from 1992–1996, a time when the industry was being partially deregulated. Due to sample size problems among some of the size categories of interest, we estimate efficient scale in the industry using both a translog cost function and a non-parametric efficiency estimation method. We find that well after partial deregulation, the points of efficient scale in the industry can still be found at those size categories specified in the repealed rules. We label this phenomenon “regulatory persistence” and offer an explanation specific to this industry: the technology of cable television provision encouraged optimal firm sizes corresponding to the size categories found in the regulatory regime. These findings are further evidence of the existence of complex incentive structures between firms and regulators in the cable television industry.JEL Classification: C61, L82
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".