An error corrected almost ideal demand system for major cereals in Kenya
Bibliographic record
Abstract
Abstract Despite significant progress in theory and empirical methods, the analysis of food consumption patterns in developing countries, particularly those in Sub‐Saharan Africa (SSA), has received very limited attention. An attempt is made in this article to estimate an Error Corrected Almost Ideal Demand System for four major cereals consumed in Kenya employing annual data from 1963 to 2005. This demand system performs well on both theoretical and empirical grounds. The symmetry and homogeneity conditions are supported by the data and the Le Chatelier principle holds. Empirically, all own‐price elasticities are negative and significant at 5% level and irrespective of the time horizon, maize, wheat, rice, and sorghum may be considered as necessities in Kenya. While the expenditure elasticities of all four cereals are positive, they are inelastic both in the short run and in the long run. Finally, wheat and rice complement maize consumption in Kenya while sorghum acts as a substitute. Since cereal consumers have price and income inelastic responses, a combination of income and price‐oriented policies could improve cereal consumption in Kenya.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".