An empirical investigation of the relationship between business performance and suicide in the US
Bibliographic record
Abstract
Previous research suggests that mortality rates behave pro-cyclically with respect to economic growth, with suicides representing a notable exception that consistently increase in economic downturns. Over recent years, there is ample evidence in the literature that the working environment in the US has deteriorated significantly, suggesting that suicide rates may not necessarily behave in a counter-cyclical manner with business performance. Utilising recent suicide data, this study empirically tests the hypothesis that adverse working conditions over recent years may have resulted in a pro-cyclical relationship between business performance and suicide. Unlike previous studies, we use a stock market index, a leading macroeconomic indicator, to measure economic conditions from a business perspective. We employ the Autoregressive Distributed Lag (ARDL) co-integration methodology to study the long-run relationship between monthly S&P500 stock market data and age and gender-specific suicide rates during the period January 1999 to July 2017. Our results highlight substantial differences in age groups responses to fluctuations in business performance. We find a clear positive association between business performance and suicide rates for the youngest males and females aged 15-34 years, indicating that there is a human cost associated with improved business performance. Additionally, we investigate the association between economic insecurity, a unique aspect of the recent deterioration in the working environment, using the Implied Volatility Index "VIX" and age and gender-specific suicide rates. Our findings do not support a population-wide adverse impact of economic insecurity on suicide incidences. The exception was males aged 15-24, and females aged 55-64 for whom we find a significant positive association. Teaching work-life management and problem-solving skills to manage everyday work stressors may be important strategies to mitigate the psychological cost of business successes.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".