Economic policy and the double burden of malnutrition: cross-national longitudinal analysis of minimum wage and women’s underweight and obesity
Bibliographic record
Abstract
OBJECTIVE: To examine changes in minimum wage associated with changes in women's weight status. DESIGN: Longitudinal study of legislated minimum wage levels (per month, purchasing power parity-adjusted, 2011 constant US dollar values) linked to anthropometric and sociodemographic data from multiple Demographic and Health Surveys (2000-2014). Separate multilevel models estimated associations of a $10 increase in monthly minimum wage with the rate of change in underweight and obesity, conditioning on individual and country confounders. Post-estimation analysis computed predicted mean probabilities of being underweight or obese associated with higher levels of minimum wage at study start and end. SETTING: Twenty-four low-income countries. SUBJECTS: Adult non-pregnant women (n 150 796). RESULTS: Higher minimum wages were associated (OR; 95 % CI) with reduced underweight in women (0·986; 0·977, 0·995); a decrease that accelerated over time (P-interaction=0·025). Increasing minimum wage was associated with higher obesity (1·019; 1·008, 1·030), but did not alter the rate of increase in obesity prevalence (P-interaction=0·8). A $10 rise in monthly minimum wage was associated (prevalence difference; 95 % CI) with an average decrease of about 0·14 percentage points (-0·14; -0·23, -0·05) for underweight and an increase of about 0·1 percentage points (0·12; 0·04, 0·20) for obesity. CONCLUSIONS: The present longitudinal multi-country study showed that a $10 rise in monthly minimum wage significantly accelerated the decline in women's underweight prevalence, but had no association with the pace of growth in obesity prevalence. Thus, modest rises in minimum wage may be beneficial for addressing the protracted underweight problem in poor countries, especially South Asia and parts of Africa.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".