Family policy and food insecurity: an observational analysis in 142 countries
Bibliographic record
Abstract
BACKGROUND: Levels of child malnutrition and hunger across the world have decreased substantially over the past century, and this has had an important role in reducing mortality and improving health. However, progress has stalled. We examined whether family policies (eg, cash transfers from governments that aim to support households with children) are associated with reduced food insecurity. METHODS: In this observational analysis, we used a dataset of individual-level data that captured experience-based measures of food insecurity and sociodemographic characteristics collected by the Gallup World Poll in 142 countries for 2014-17. We then combined this dataset with indicators of the type and generosity of family policies in these countries, taken from the University of California, Los Angeles' World Policy Analysis Center. We used multilevel regression models to examine the association between the presence of family policies for households with children and the probability of reporting moderate or severe food insecurity or severe food insecurity (moderate or severe food insecurity was defined as a "yes" response to at least four of eight questions on the Gallup Food Insecurity Experience Scale, and severe food insecurity was defined as a "yes" response to at least seven questions). We controlled for multiple covariates, including individual-level measures of social position and country-level measures, such as gross domestic product. We further examined whether this association varied by household income level. FINDINGS: Using data from 503 713 households, we found that, on average, moderate or severe food insecurity is 4·09 percentage points (95% CI 3·50-4·68) higher in households with at least one child younger than 15 years than in households with no children and severe food insecurity is 2·20 percentage points (1·76-2·63) higher. However, the additional risk of food insecurity among households with children is lower in countries that provide financial support (either means-tested or universal) for families than for countries with little or no financial assistance. These policies not only reduce food insecurity on average, but they also reduce inequalities in food insecurity by benefiting the poorest households most. INTERPRETATION: In some countries, family policies have been cut back in the past decade and such retrenchment might expose low-income households to increased risk of food insecurity. By increasing investment in family policies, progress towards Sustainable Development Goal 2, zero hunger, might be accelerated and, in turn, improve health for all. FUNDING: Wellcome Trust.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".