Two Sided Markets: The Case of The Waterbed Effect in OECD Mobile Telecommunications Markets
Bibliographic record
Abstract
Efforts to develop appropriate policies and accurate strategies through the most accurate analyzes highlight the examination of two-sided markets, which differ significantly from one-sided markets. In this study, mobile telecommunications market which has a special place in two-sided market analysis will be studied. In mobile telecommunications market, it is suggested that a reduction in mobile interconnection rates for the interconnection service will show the waterbed effect, resulting in an increase in mobile communication prices that the end user faces in the market. In this context, an empirical analysis was made by Common Correlated Effects Mean Group (CCEMG) estimator using the 41 quarter period data of 21 OECD countries between 2005 and 2015, and the effect of mobile interconnection rates, income and quantity were estimated in determining the mobile communication prices. According to the results, the waterbed effect in mobile telecommunications markets could not be determined in the period analyzed for the related country group and mobile interconnection rates acted as a cost factor and changed the prices in the same direction. The coefficient of income variable was positive and the expectation that the increase in income would increase the price was met. The explanatory variable coefficients of the number of subscribers and the penetration rate representing the quantity were negative and they were evaluated to behave in accordance with economic expectations and two-sided market characteristics.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".