Acquisitions as a Response to Deregulation: Evidence from the Cable Television Industry
Bibliographic record
Abstract
This paper studies the dynamics of an industry that is subject to exclusive geographical licensing. I develop a model of license ownership that predicts the evolution of profit-maximizing entry and acquisition decisions by firms over time, starting from an initial allocation of licenses. The entry and acquisition process is modeled as a one-sided coalition-formation game as in Farrell and Scotchmer (1988), where acquisition payoffs depend on economies of scale and agglomeration (economies of density). I estimate the model for the cable television industry in Canada using a panel that I have constructed from 1990 to 1996. The dataset builds up from the national regulator’s license-ownership decision files, and contains license-level information on acquisition decisions, subscribership, and subscription profits. The model is estimated in two steps. I first estimate firms’ license-level profit functions, and then estimate the parameters of the fixed, merger and entry cost functions by Simulated Maximum Likelihood. Through counterfactual simulations, I use the estimated model to quantify the extent to which economies of scale and density drive acquisition behaviour, and to evaluate how merger activity reacts to a partial deregulation that occurs in 1994. Counterfactual experiments are also used to evaluate policies that stimulate entry or reduce acquisitions in the early years of the sample. The main finding is that these policies can lead to more productive dominant firms in the long-run as the industry consolidates.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".