A Measurement of Aggregate Trade Restrictions and Their Economic Effects
Bibliographic record
Abstract
Abstract This study develops a new Measure of Aggregate Trade Restrictions (MATR) using data from the IMF's Annual Report on Exchange Arrangements and Exchange Restrictions. MATR is a measure of direct and indirect official government policy related to the international flow of goods and services. MATR is simple, plausible, quantitative, easily updated, based on relevant measures of trade policy and other international restrictions affecting trade (e.g., payment restrictions), and covers an unbalanced sample of up to 157 countries from 1949 to 2019. MATR is strongly correlated with, and—importantly—more comprehensive, in terms of country and time coverage, than existing measures of de jure openness; it is also granular. As such, MATR empowers empirical analysis to increase coverage in research related to trade restrictions and other trade-related openness policies. MATR is used in the study to show that direct and indirect restrictions to trade are associated with significant contractions in output.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".