Determinants of Household Food Insecurity in Developing Countries Evidences From a Probit Model for the Case of Rural Households in Rwanda
Bibliographic record
Abstract
This study uses probit model to identify determinants of food insecurity among rural households in developing countries. The model used in this study, that allowed us to estimate coefficient and marginal effect for each independent variable vis-à-vis dependent variable, guarantees large applications among food security actors and policymakers to find out factors that significantly explain food insecurity and the level of their predictability. The ability of the model used to correctly classify food insecure and food secure households is good for the overall model and for households headed by males while it is fair for households headed by females. The empirical results show that rural households are more exposed to food insecurity than urban households. Gender disaggregation by the head of households shows that among food insecure rural households, the majority of them are headed by females. It also shows that the mean and median of predicted probability of becoming food insecure among rural households headed by males and females is 0.21 and 0.28 for mean and 0.15 and 0.24 for median respectively. This indicates that households headed by females are more likely exposed to food insecurity than those headed by males. However, as the majority of rural households in developing countries depend on agriculture, this study found that it is worthwhile for developing countries to adopt new agricultural technologies to urgently increase productivity and to implement and facilitate programs supporting rural households pathways to increase households’ livelihood capacities.
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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 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".