The Effect of Minimum Wage on Suicide Rates
Bibliographic record
Abstract
The main hypothesis of this study is that minimum wages may influence suicide mortality. The dataset consists of annual observations for 30 OECD countries with statutory minimum wage systems—Australia, Belgium, Canada, Chile, Colombia, Costa Rica, Czechia, Estonia, France, Germany, Greece, Hungary, Ireland, Israel, Japan, Korea, Latvia, Lithuania, Luxembourg, Mexico, the Netherlands, New Zealand, Poland, Portugal, the Slovak Republic, Slovenia, Spain, the United Kingdom, Turkiye, and the United States. It includes key socioeconomic indicators such as suicide mortality, minimum wages, unemployment rates, GDP, public social expenditure, health expenditure, fertility, divorce rates andalcohol consumption. The dataset integrates multiple thematic databases, including OECD Health Statistics, Labour Market Statistics, National Accounts Statistics, the Social Expenditure Database (SOCX), and the OECD Family Database. Suicide mortality data are derived from age-standardised death rates from intentional self-harm, adjusted to the 2015 OECD standard population and based on the WHO Mortality Database. Minimum wage data use the real hourly statutory minimum wage converted into 2024 USD PPPs. Economic indicators include unemployment rates—standardised using constant PPP-based measures—along with expenditure-approach GDP and GDP per capita, both reported in chain-linked volume terms and deflated to constant 2020 PPPs. Social indicators include demographic and behavioural measures such as total fertility rates, crude divorce rates, and female labour force participation. Fiscal expenditure indicators consist of per-capita public social expenditure—measured as spending on public programmes from the SOCX database—and per-capita health expenditure under government or compulsory schemes, both reported in constant PPPs. Health-related indicator includes per-capita alcohol. For ease of coefficient interpretation, minimum wage, GDP, public social expenditure per capita, health expenditure per capita, and alcohol consumption were log-transformed. This dataset consists of annual indicators for 30 OECD countries and allows researchers to examine both cross-country heterogeneity and temporal dynamics.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".