The Division and Size of Gains from Liberalization in Service Networks*
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it