Effects of COVID-19 on Food Demand in Rural Indonesia: The Case of Bengkulu Province
Bibliographic record
Abstract
This paper examines food demand before and after the outbreak of COVID-19 and studies the effects of the situation on households’ demand for food in rural Indonesia, in the case of Bengkulu Province. The research data is taken from the Indonesia Socio-Economic Survey (SUSENAS) as a microdata set which is collected annually by Indonesia Central Statistics Agency (BPS) from 2017 to 2021. The effect of COVID-19 on food demand estimates using the Quadratic Almost Ideal Demand System (QUAIDS). The results demonstrate that prepared food expenditure is the largest portion of household expenditure on food in the Bengkulu rural area. Before the outbreak of COVID-19, animal source food is the most sensitive to food expenditure, but after the outbreak, prepared food is the most sensitive. Staple food is the most expenditure-inelastic before and after the outbreak. Expenditure for animal source food, vegetables & fruits, and prepared food have significant differences between before and after the outbreak. All the food groups substitute for each other before the outbreak, whereas staple food and prepared food cannot be regarded as a substitute for each other after the outbreak. There are eleven of the compensated price elasticities whose differences between before and after the outbreak are significant, whereas as a set the compensated price elasticities are significantly different between before and after the outbreak. Other food is the easiest to be substituted for both phases. Prepared food is the most difficult to be substituted before the outbreak, but the staple food is the most difficult to be substituted after the outbreak. After the outbreak of COVID-19, the demand for vegetables & fruits increases, but the demand for staple food and prepared food decreases, ceteris paribus. Family size, children, gender, age, and other demographics variables have an impact on household food demand. These findings imply that after the outbreak, the supply of vegetables & fruits should be increased and that government support for suppliers of staple food and prepared food will be preferable.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".