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Record W3124809076 · doi:10.55016/ojs/sppp.v6i1.42439

Wireless Competition in Canada: An Assessment

2013· article· en· W3124809076 on OpenAlexafffundabout
Jeffrey Church, Andrew Wilkins

Bibliographic record

VenueThe School of Public Policy Publications · 2013
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsUniversity of Calgary
FundersIndustry Canada
KeywordsCompetition (biology)WirelessTelecommunicationsBusinessComputer scienceBiologyEcology

Abstract

fetched live from OpenAlex

If there’s one thing Canadians agree on, it’s that Canada’s wireless industry can and should be more competitive. The federal government is on side with the policy objective of having four carriers in every region and has responded with policies that provide commercial advantages to entrants. But, the rub is that there has not been a study that actually assesses the state of competition in wireless services in Canada, until now. Those in favour of policies that will promote and sustain entry point to Canada’s high average revenue per user and low wireless penetration rate (mobile connections per capita) as evidence that there is insufficient competition. The difficulty is that the facts are not consistent with this simplistic analysis. Measurements of wireless penetration are skewed toward countries that maintain the Calling Party Pays Protocol and favour pay-as-you-go plans, both of which encourage inflated user counts. Canada’s participation per capita on monthly plans and minutes of voice per capita are not outliers. Moreover, in terms of smartphone adoption and smartphone data usage, Canada is a global leader, contributing to high average revenue per user. Consistent with being world leaders in the rollout of high speed wireless networks, Canada lead its peer group in capital expenditures per subscriber in 2012: the competition of importance to Canadians is not just over price, but also over the quality of wireless networks. In any event, none of the measures typically used in international comparisons are relevant to assessing the competitiveness of Canadian wireless services. The appropriate competitive analysis recognizes two relevant features of the technology of wireless services: (i) high fixed and sunk capital costs; and (ii) economies of scale and scope. The implications of these are that profitability requires mark ups over short run measures of cost — high gross margins — and that there will be a natural, upper limit on the number of wireless carriers. The key issue in terms of competition is whether prices track long run average costs, namely, whether wireless providers make monopoly profits. An examination of the leading firm’s cash flow over the life cycle of the wireless industry suggests internal rates of return well below the likely ex ante, pre-tax, cost of capital that reflects the risk of its investments. This is not consistent with either the inefficient exercise of market power or monopoly profits. Moreover, international comparisons of structural indicators of competition indicate that, if anything, wireless services in Canada are more competitive than in many of its peers. There is no evidence that there is a competition problem in wireless services in Canada. Efforts to create competition in the short run, that increase the number of carriers, will simply squeeze margins in the short run and likely will not be sustained in the long run, as carriers exit and consolidate to reduce competition and restore margins consistent with profitability and the natural limit. And, while consumers might gain in the short run from lower prices, everyone is likely made worse off in the long run from the misallocation of spectrum, reduction in scale of carriers, and reduction in incentives to invest from such intervention. The AWS set asides allocated spectrum to carriers whose focus was on talk and text, not carriers whose focus is on data, resulting in an inefficient mix of networks and a suboptimal allocation of spectrum.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.892
Threshold uncertainty score0.678

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.022
GPT teacher head0.288
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2013
Admission routes3
Has abstractyes

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