The Division and Size of Gains from Liberalization in Service Networks*
Bibliographic record
Abstract
Abstract If two disjoint country service networks involving a small and large country are connected as part of international liberalization in the presence of network externalities, the per capita gain for the small country from access to a large network will be large, and the per capita gain for the large country will be small. In contrast to goods, the benefits of liberalization in network‐related services are more likely to be approximately equally divided between large and small countries than is true of trade in goods, where benefits accrue disproportionately to the small country. We also argue that non‐cooperation in network‐related services trade may involve more extreme retaliation than suggested for trade in goods by the optimal tariff literature, so that relative to a non‐cooperative outcome, gains from liberalization in network‐related services become larger than from liberalization in goods. We develop simple models which we use for numerical examples showing these points, along with an empirical implementation for global telecoms liberalization for the US, Europe, Canada, and the rest of the world using the framework developed in the paper. This shows similar proportional gains to regions, consistent with the theme of the paper that goods and services liberalization differ.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".