Do <scp>regional trade agreements</scp> affect agri‐food trade? Evidence from a meta‐analysis
Bibliographic record
Abstract
Abstract Regional trade agreements (RTAs) have experienced significant growth worldwide, leading to an increase in studies assessing their impact on bilateral trade flows. With the availability of disaggregated trade data, numerous studies have examined the influence of these agreements specifically on agri‐food trade. However, the results of these studies exhibit heterogeneity, posing challenges for policymakers seeking to understand the effects of RTAs on agri‐food trade. To address this issue, we conducted a meta‐analysis of 61 studies investigating the effects of various RTAs on agri‐food trade. Using funnel asymmetric testing, our analysis reveals the presence of publication bias in the existing literature. By accounting for this bias, we found robust evidence that RTAs positively and significantly promote agri‐food trade. Notably, the extent of this effect depends on the depth of economic integration within the RTA, distinguishing between customs unions and free trade agreements, as well as the classification of agri‐food products as primary or processed. The ex‐post effects of RTAs on agri‐food trade are less pronounced when we control for both publication bias and heterogeneity, compared to controlling only for publication bias.
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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 | 0.034 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.028 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".