Trade and Infrastructure in the Belt and Road Initiative: A Gravity Analysis Based on Revealed Trade Preferences
Bibliographic record
Abstract
This paper aims at investigating whether and how the intensity of trade between a pair of countries changes when they experience improvements in their infrastructural systems. We carry out our analysis considering countries participating in the Belt and Road Initiative (BRI), a project specifically designed to promote infrastructural connectivity and therefore boost trade among the countries involved. Our empirical strategy relies on a particular specification of the gravity model, in which the dependent variable consists in an index of revealed trade preferences, calculated by comparing the actual value of trade flows between two countries with their expected value, proportional to the two countries’ total trade. Such methodology allows us to estimate bilateral trade intensity without resorting to the traditional “size” variables of the gravity model, taking the entire network of multilateral trade into account. We then study the possible impact of an improvement in infrastructure on a ‘gravity-adjusted’ measure of trade preferences, given by the residuals of our first estimations. Our results indicate that bilateral preferences among BRI countries will intensify inasmuch as they succeed in coordinating their infrastructural projects.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".