Bibliographic record
Abstract
The COVID-19 crisis has disrupted traditional trade connections, significantly altering the global trade landscape. This served as a crucial stress test for international trade and regional integration blocs, challenging trade regionalization. Given these new challenges, we propose the concept of regional integration resilience, defined as the capacity of an integration bloc to mitigate the adverse impact of the pandemic on intraregional trade and minimize the immediate reduction of trade within that bloc. With the fortification of supply chains and greater economic interconnection within the integrating economies, our hypothesis is that regional economic integration could serve as a buffer against the negative consequences of the COVID-19 pandemic. Specifically, we have utilized fixed-effects instrumental variable regression applied to the augmented gravity model to analyze quarterly observations from January 2018 to December 2020. To gauge the influence of being in a trade bloc during the COVID-19 crisis, we introduced interaction terms (participation in a regional trade agreement and COVID-19 parameters) into the model. The findings suggest that the pandemic markedly and adversely impacted bilateral trade. Interestingly, the weight of the COVID-19 pandemic had a more pronounced effect on trade flows compared to its severity. Despite the anticipated positive effects of regional integration on intraregional trade due to its static and dynamic effects, overall, we did not observe any stabilizing influence of regional economic integration. There was no evidence that regional integration contributed to alleviating the negative effects on trade during a pandemic or fostering higher trade resilience within regional trade agreements. However, the impact of regional trade integration may vary across different integration blocs. Among the six integration blocs analyzed, two demonstrated a significant positive influence on trade during the pandemic – the European Union and the United States–Mexico–Canada Agreement (formerly the North American Free Trade Agreement).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".