Revisiting the relationship between economic uncertainty and suicide: An alternative approach
Bibliographic record
Abstract
Previous research provides evidence that economic uncertainty is powerful enough to precipitate suicide. This study examines whether the relationship between economic uncertainty and suicide in the US is (i) context dependent and (ii) asymmetric. To answer these questions, we link US monthly total- age- and gender-specific suicide rates to the US Economic Policy Uncertainty index between 1999 and 2019, controlling for month fixed effects, year fixed effects and unemployment rates. We find that the relationship between economic uncertainty and the suicide rates of males in their mid-career (aged 25-54) and late career (aged 55-64) is context dependent. Our results show that absolute monthly changes in economic uncertainty have no effect on suicide rates of males aged 25-54 and 55-64, whereas when these changes are unexpected - departing from the economic uncertainty regime during which they occur - they precipitate the suicide of these age- and gender-specific groups. Additionally, our findings provide evidence of the presence of negativity bias in these relationships. We show that extreme unexpected increases in economic uncertainty induce suicide of males aged 25-54 and 55-64, while extreme unexpected decreases in economic uncertainty do not significantly decrease suicide rates of these age- and gender-specific groups. Females, with the exception of those aged 65 plus, are perceived to be predominantly insulated from changes in economic uncertainty. Our results suggest that females aged 65 plus are particularly affected by extreme unexpected increases in economic uncertainty, suggesting that the relationship for females aged 65 plus is context dependent and exhibits negativity bias.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".