Assessing the impact of unilateral trade policies EBA and AGOA on African beneficiaries' exports using matching econometrics
Bibliographic record
Abstract
Abstract The existing literature studying the impact of non‐reciprocal preferential trade agreements (NRPTAs) assumes implicitly NRPTAs are non‐randomly assigned without showing the evidence of that. Using a matching methodology, this paper investigates whether the “African Growth and Opportunity Act” (AGOA) and the “Everything But Arms” (EBA) unilateral trade concessions have had an impact, and in what magnitude, on the exports of African beneficiary countries in the light of the evidence of the non‐random nature (endogeneity) of NRPTAs. Methodologically, previous studies using the matching procedure focused on bi or multilateral trade agreements. Our work focuses on NRPTAs that depend only on donors' conditions. Accordingly, we show that for NRPTAs, gravity covariates cannot be used for the matching procedure. We propose to use political variables as determinants for obtaining non‐reciprocal trade preferences in order to address the endogenous nature of NRPTA assignment. Our main results confirm that a country becomes eligible for a NRPTA only when it meets certain conditions, defined by their donors, such as political stability and economic regulation (for AGOA), and freedom of expression and human development (for EBA). Results also show that both AGOA and EBA policies have had a positive impact on African beneficiary countries' exports to NRPTA's providers, even if the magnitude impact of EBA is significantly lower than that of AGOA.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".