Do trade agreements actually reduce trade volatility?
Bibliographic record
Abstract
Abstract A frequently stated objective of regional and multilateral trade agreements is to provide a more stable and certain trading environment. Does this translate into reduced volatility of trade flows? Using a structural gravity approach, we identify two potential channels through which international trade institutions may influence the volatility of bilateral trade flows: by affecting the variance of trade barriers and by affecting the covariance of economic outcomes between the trading partners. We then use a panel of bilateral industry‐level trade data to empirically examine the effects of regional trade agreements and GATT/WTO membership on export earnings volatility. We find some evidence that joining a multilateral trade agreement such as the GATT makes export earnings less volatile. However, we find even stronger evidence that membership in a regional trade agreement increases the measured volatility in bilateral exports with other regional trading partners, and this rise in volatility increases as the agreement becomes deeper and more integrated. This increased volatility could be due to increased co‐movements of economic outcomes across regional trading partners; we also demonstrate that regional trade agreements increase the industry‐level covariance of importer expenditure and exporter production among member countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".