Price Competition with Reduced Consumer Switching Costs: The Case of “Wireless Number Portability” in the Cellular Phone Industry
Bibliographic record
Abstract
Wireless number portability (WNP) is a telecommunication regulatory policy that requires cellular phone service providers to allow customers who switch service subscriptions to retain their original phone numbers. The right to retain the number lowers the switching cost for a consumer. Thus, the purpose of the policy is to induce more competition and facilitate the growth of new or small service providers. In this paper, we show that WNP drives market price downward as expected but with a surprising twist—rather than helping the smaller firms grow, the policy may accelerate the process of market concentration. We find that the main contributing factor to this peculiarity is the discriminatory pricing scheme prevalent in the industry—that is, a service provider charges a lower per-minute fee for the calls initiated and received within the same network than for the calls connected across two networks. Under this pricing scheme, a consumer who subscribes to a larger network would benefit more than if subscribing to a smaller network, despite the relatively higher fixed access fee that the former may charge. By lowering the barrier of switching, WNP creates a market condition conducive for a larger network to gain market share. We support our analysis with the empirical evidence gathered from Hong Kong where WNP was adopted in March 1999.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".