Should Regulators Set Rates to Terminate Calls on Mobile Networks
Bibliographic record
Abstract
When a person uses the traditional wireline telephone network to call another person on his cell phone, the fixed network must transfer the call to the mobile network to which the recipient subscribes. The fixed network provides originating access for the call, and the mobile network provides terminating access. This paper provides an economic analysis of the regulation of fixed-to-mobile termination rates. Mobile party pays ("MPP") creates better incentives than calling party pays ("CPP") for mobile network operators to place downward pressure on termination rates. Cellular telephone use in the United States and Canada has continued to increase at a significant pace despite the MPP regime and now far exceeds mobile telephone use in countries with CPP regimes. Multiple factors, including substitution possibilities for the callers of mobile subscribers, constrain the market power of mobile operators in setting mobile termination rates under CPP regimes. It is unrealistic for regulators to attempt to set mobile rates, including termination rates, at marginal cost. If large fixed network costs and customer acquisition costs must be recovered from variable charges, then marginal-cost-based pricing is not feasible. Also, the value to callers of being able to reach mobile subscribers justifies mobile termination charges that exceed marginal cost because of network externalities in mobile telecommunications. Finally, mobile termination rates that exceed marginal cost (or its proxy, long-run average incremental cost) are consistent with Ramsey (quasi-efficient) pricing. To the extent that high termination rates are a problem in countries that have embraced CPP, it is because customers are poorly informed of the charges they pay for their terminating calls. Consumer education would solve the potential market failure without the need to impose price regulation on otherwise competitive markets.
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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 | 0.020 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.016 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".