Determinants of Domestic Food Price Differentials: Constraints for Intra-Uganda Trade
Bibliographic record
Abstract
The paper estimates the determinants of price differentials across 79 districts in Uganda. In the framework of the law of one price, we examine the hypothesis that the spatial price differentials are at least partly influenced by transportation and other transaction costs, infrastructural constraints, productivity and commodity output shocks and the purchasing power of households. The study notes the wide range of price differences across the country, which to a large extent can be attributed to the interaction between remoteness and the quality of physical infrastructure. The effect of income <em>per capita</em> on price differentials is relatively uniform across commodities. The findings point towards the importance of strengthening the capacities of farmers and their productivity as a means to improve their livelihoods and foster more efficient markets with faster supply responses to changes in prices. The findings further emphasize the significance of spatial dimension and infrastructure conditions in Uganda, suggesting that infrastructural development must be a core area to reduce price differences in the country.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".