Using spectrum set-asides to address distributional objectives: Lessons from Canada, New Zealand, South Africa and the United States
Bibliographic record
Abstract
This paper critically examines the effectiveness of spectrum set-asides as a policy tool to address distributional objectives in telecommunications across four diverse national contexts: Canada, New Zealand, South Africa, and the United States. Spectrum allocation is a crucial factor for the provision of telecommunications services and by extension, for citizens’ participation in the digital economy. While economic theory supports auction-based allocations to maximise market efficiency, set-asides aim to facilitate access for disadvantaged groups or to stimulate competition. This study employs case studies from the selected countries to evaluate the impact of these set-asides on market efficiency, competition, and economic development. In Canada, set-asides intended to encourage new market entrants have led to higher spectrum costs and inefficiencies due to speculative behaviour. In New Zealand, allocations to the indigenous Māori population have raised concerns over long-term sector efficiency and capital accessibility. South Africa’s policy mandates spectrum allocations to entities with significant ownership by historically disadvantaged persons, with mixed outcomes on market dynamics and social equity. Meanwhile, the United States’ approach includes grants rather than direct spectrum set-asides, offering a potentially less distortive model. The findings suggest that while set-asides can support social objectives, they often introduce inefficiencies and fail to achieve the desired economic outcomes. The paper concludes by discussing the implications for future spectrum policy, advocating for careful consideration of the trade-offs between equity and efficiency in spectrum management. • Examines spectrum set-asides in Canada, NZ, SA, and the US for equity goals. • Canada’s set-asides raised costs, failing to foster new competition effectively. • NZ’s Māori spectrum allocation risks efficiency; access to capital is a challenge. • SA’s ownership mandates yield mixed results in equity and market dynamics. • US model with grants may better align spectrum policy with economic objectives.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".