Minimum Wage and Overweight and Obesity in Adult Women: A Multilevel Analysis of Low and Middle Income Countries
Bibliographic record
Abstract
OBJECTIVES: To describe the relationship between minimum wage and overweight and obesity across countries at different levels of development. METHODS: A cross-sectional analysis of 27 countries with data on the legislated minimum wage level linked to socio-demographic and anthropometry data of non-pregnant 190,892 adult women (24-49 y) from the Demographic and Health Survey. We used multilevel logistic regression models to condition on country- and individual-level potential confounders, and post-estimation of average marginal effects to calculate the adjusted prevalence difference. RESULTS: We found the association between minimum wage and overweight/obesity was independent of individual-level SES and confounders, and showed a reversed pattern by country development stage. The adjusted overweight/obesity prevalence difference in low-income countries was an average increase of about 0.1 percentage points (PD 0.075 [0.065, 0.084]), and an average decrease of 0.01 percentage points in middle-income countries (PD -0.014 [-0.019, -0.009]). The adjusted obesity prevalence difference in low-income countries was an average increase of 0.03 percentage points (PD 0.032 [0.021, 0.042]) and an average decrease of 0.03 percentage points in middle-income countries (PD -0.032 [-0.036, -0.027]). CONCLUSION: This is among the first studies to examine the potential impact of improved wages on an important precursor of non-communicable diseases globally. Among countries with a modest level of economic development, higher minimum wage was associated with lower levels of obesity.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".