How does food price increase affect Ugandan households?
Bibliographic record
Abstract
"Almost unaffected by the 2008 wave of soaring world food prices, Ugandan local market prices exhibit signs of high price volatility in the first quarter of 2009. At the household level, while net producers may reap some benefits from this increase in food prices, net consumers are more likely to suffer from it. However, the net consumption impact of food price increase is not as straightforward as reported in previous studies. In this paper, we extend Singh et al. (1986) multimarket model by adding demand elasticities from the Almost Ideal Demand System (AIDS). We use the integrated Ugandan National Household Survey (UNHS) 2005/2006 to estimate a measure of net consumption impact that includes both price and profit effects. Overall, we found that household welfare is expected to decrease with loss in consumption and increase with income gain as a result of higher food prices for the cereals producers. Simulating change in cereals consumption induced by a 50 percent increase in cereals price and taking into account the profit effect, our results predict a 23 percent decrease in food consumption for net sellers, compared with 44 percent when using the consumption approach alone. Accounting for such substitution effects, our results suggest that the impact of rising food prices may be mitigated because some households will attempt to substitute more expensive food items with cheaper ones; however, this apparent coping strategy often leads to a much poorer diet. The results suggest that the majority of households with expected positive income impact, the gainers, live in rural areas. These households also tend to have better access to agricultural services than the nongainers." --from authors' abstract
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".