Bibliographic record
Abstract
Abstract Trade restrictiveness indexes (TRIs) have become a staple for practitioners and policy‐makers to summarize international trade barriers. TRIs theoretically found a measure of trade restrictiveness by calculating the uniform tariff that is welfare equivalent to the observed distribution of applied tariffs within a country. Here we incorporate importer market power and exporter heterogeneity into calculations of TRIs and welfare globally. To do so, we structurally estimate a quantitative model of international trade. The structure of the model allows tractable estimation of importer and exporter welfare and TRIs for every country in the world from 1990 to 2007. Canonical estimates, which ignore exporter heterogeneity and importer market power, are shown to overstate efficiency losses from tariffs by a factor of 5 for the average importer. Additionally, by not accounting for importer market power canonical methods fail to measure substantial welfare losses to exporters that are captured by importers through tariffs. These channels are shown to significantly impact the measurement and interpretation of TRIs. To conclude, we employ the methodology to evaluate China's WTO accession and a counterfactual renegotiation of NAFTA.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".