Did Mandatory Unbundling Achieve Its Purpose? Empirical Evidence from Five Countries
Bibliographic record
Abstract
In this article, we examine the rationales offered by telecommunications regulators worldwide for pursuing mandatory unbundling. We begin by defining mandatory unbundling, with brief descriptions of different wholesale forms and different retail products. Next, we examine four major rationales for regulatory intervention of this kind: (1) competition in the form of lower prices and greater innovation in retail markets is desirable, (2) competition in retail markets cannot be achieved with mandatory unbundling, (3) mandatory unbundling enables future facilities-based investment (‘stepping-stone’ or ‘ladder of investment’ hypothesis), and (4) competition in wholesale access markets is desirable. We proceed by testing empirically the major rationales in the United States, the United Kingdom, New Zealand, Canada, and Germany. For each case study, we review the mandatory unbundling experience with respect to retail pricing, investment, entry barriers, and wholesale competition. We review the lessons learned from the unbundling experience. We also identify which rationales were incorrect in theory and which rationales were correct in theory yet were not satisfied in practice. For the second category of rationales, we attempt to provide alternative explanations for the failure of mandatory unbundling to achieve its goals.
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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".