Enhancing Coffee Quality in Rwanda: A Cost Benefit Analysis of Government Policies
Bibliographic record
Abstract
Over the past two decades, Rwanda has positioned itself as a leading producer of specialty coffee. The strategic move from ordinary to specialty coffee has overall been economically beneficial to the country. However, the multitude of incentives provided by both the Government and international donors spawned a rush to build a large number of coffee washing stations (CWS) throughout Rwanda. This trend gave rise to an oversupply of these plants, with most operating below their processing capacity. Our study uses cost benefit analysis to estimate the economic welfare loss that Rwanda has suffered owing to the combined effect of the oversupply of CWS, the coffee zoning policy, and the government regulated cherry coffee prices. Our results reveal that, if the coffee industry were rendered more competitive by dint of a reduction in the number of CWS, then the annual savings to Rwanda would be substantial. Furthermore, farmers could potentially receive prices that are 150% higher than the mandated fixed prices they are currently been paid. Our analysis could potentially be beneficial to Rwandese policy makers in devising fairer incentives to keep farmers interested in coffee farming, thus ensuring the sustainability of the coffee value chain in the long term.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".