Rural Household Food Consumption in Bengkulu, Indonesia: Estimating a Demand System Based on SUSENAS Microdata
Bibliographic record
Abstract
The paper aims to estimate the food demand of rural households in Bengkulu Province, Indonesia, using the Quadratic Almost Ideal Demand System (QUAIDS) and microdata from the SUSENAS. We aggregate food into five groups: staple food, animal food, vegetables & fruits, prepared food, and other food. The results show that demand for animal food is the most sensitive to food expenditure, whereas the demand for staple food is the most expenditure-inelastic. Staple food, animal food, vegetables & fruits, and other food are substitutes for each other. On the other hand, prepared food and staple food complement each other. Other food is the easiest to be substituted, and staple food is the most difficult to be substituted. The demographic variables, as well as prices and expenditures, impact household demand. For example, as family size increases, the demand for staple food increases, while the demand for animal food, vegetables & fruits decreases. The number of children under five years old has a positive impact on animal food demand but a negative impact on staple food and other food demand. Staple farmer households have a higher need for staple food than non-agricultural households. Due to being unmarried, divorced or bereaved, single households have a lower demand for staple food but a higher demand for prepared food. We mainly imply that the food price stabilization policy should emphasize animal food, especially beef and poultry, without increasing prices.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".