Impact of AGOA on Agricultural Exports Growth of Member Countries: A Dynamic Shift-Share Analysis
Bibliographic record
Abstract
Since the commencement of AGOA, U.S. exports to Sub-Saharan Africa (SSA) have grown by 23% attaining $21 billion while the exports from the U.S. to the rest of the world increased by only 15%. Total bilateral trade between the U.S. and SSA also increased by 5.8%, up from $36.9 billion in 2015 to $39 billion in 2017. U.S. imports from SSA region have also increased more than three times reaching $26.7billion in 2014. However, others have argued that AGOA has failed to enhance member countries’ agricultural exports to the U.S. But these studies only focused on overall export growth. Using dynamic shift-share analysis, this study evaluates potential impact of AGOA on U.S. export growth for four major aggregate commodity groups – bulk, consumer, intermediate and ag-related. Export performance is empirically examined by comparing pre-AGOA (1980-200), post-AGOA (2000-2019) and complete time-period (1980- 2019). The results suggest member countries’ exports have grown from a deficit of $436 million pre-AGOA to $1,487 million in Post-AGOA with bulk commodities contributing close to 50%.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".