Impact of Community-Level Infrastructure on Household Food Insecurity in Africa
Bibliographic record
Abstract
Although income levels play a critical role in determining a household's food security status, this alone cannot fully explain the variations in the phenomenon. To fully explore food insecurity, it is essential to consider non-household factors such as social and physical infrastructure, which directly impact people's quality of life. In this study, we used data from rounds 6, 7, and 8 of the Afrobarometer survey conducted between 2014 and 2021 to investigate the relationship between infrastructure deficit and household food insecurity vulnerability in Africa. Our findings from multilevel logistic regression showed that access to social and physical infrastructure can alleviate household food insecurity vulnerability to varying degrees. For instance, having an electricity grid and a public water supply system can reduce the likelihood of food insecurity by 15% and 13%, respectively. Similarly, having a bank and a health clinic in a community can reduce the possibility of food insecurity by 6% and 3%, respectively. These findings revealed that physical infrastructure has a more substantial impact on reducing food insecurity than social infrastructure. Nevertheless, African governments should focus on investing in both types of infrastructure and ensuring that it is distributed fairly and equitably to benefit all.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".