Differentiation Strategy and Market Deregulation: Local Telecommunication Entry in the Late 1990s
Bibliographic record
Abstract
The authors examine the role of differentiation strategies for entry behavior in markets for local telecommunication services in the late 1990s. Whereas the prior literature has used models of interaction among homogenous firms, this research is motivated by the claim of entrants that they differ substantially in their product offerings and business strategies. Exploiting a new, detailed data set of Competitive Local Exchange Carriers (CLECs) entry into over 700 U.S. cities, the authors take advantage of recent developments in the analysis of entry and competition among differentiated firms. They test and reject the null hypothesis of homogeneous competitors. They also find strong evidence that CLECs account for both potential market demand and the business strategies of competitors when making their entry decisions. This suggests that firms' incentives to differentiate their services should shape the policy debate for competitive local telecommunications.
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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 | 0.003 | 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 | 0.001 |
| 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".