Wage-Setting Policies, Employment, and Food Insecurity: A Multilevel Analysis of 492 078 People in 139 Countries
Bibliographic record
Abstract
Objectives. To examine the association between wage-setting policy and food insecurity.Methods. We estimated multilevel regression models, using data from the Gallup World Poll (2014–2017) and UCLA’s World Policy Analysis Center, to examine the association between wage setting policy and food insecurity across 139 countries (n = 492 078).Results. Compared with countries with little or no minimum wage, the probability of being food insecure was 0.10 lower (95% confidence interval = 0.02, 0.18) in countries with collective bargaining. However, these associations varied across employment status. More generous wage-setting policies (e.g., collective bargaining or high minimum wages) were associated with lower food insecurity among full-time workers (and, to some extent, part-time workers) but not those who were unemployed.Conclusions. In countries with generous wage-setting policies, employed adults had a lower risk of food insecurity, but the risk of food insecurity for the unemployed was unchanged. Wage-setting policies may be an important intervention for addressing risks of food insecurity among low-income workers.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".