Rural Household Food Consumption in Bengkulu, Indonesia: Estimating a Demand System Based on SUSENAS Microdata
Why this work is in the frame
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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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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 | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it