Traceability, Value, and Trust in the Coffee Market: A Natural Experiment in Ethiopia
Bibliographic record
Abstract
This study measures the impact of traceability attributes on international buyers’ willingness to pay for coffee produced in Ethiopia and the impact of accurate information on the production location of the coffee on the pricing according to its type and grade. Two sets of regression models were used to investigate the important determinant factors affecting the export prices of trader and producer coffee, one each for trader and producer coffee, to measure the impact of the Ethiopian Commodity Exchange (ECX) on the prices and to evaluate the effect of the coffee types and grades on the prices. The results show that after coffee was forced to be traded via the (ECX), traceable coffee export prices increased more than the reported price of nontraceable coffee. We also found that after the introduction of the ECX, the reported export prices of coffee were much more closely aligned to the movements in the international prices of coffee than before the ECX. Furthermore, we also found evidence that exporters and overseas buyers do not trust the results of the inspection and grading of coffee by the ECX unless traceability is also present. This is the first study to evaluate foreign buyers’ willingness to pay for the attribute of traceability of Ethiopian coffee and to see how traceability has affected buyers’ trust in the grades given by the ECX for the coffee it grades.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".