Assessing the impact of unilateral trade policies EBA and AGOA on African beneficiaries' exports using matching econometrics
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it