Courageous but Indebted? Regional Courage is Associated With Higher Debt-to-Income Ratio in the United States
Bibliographic record
Abstract
Geographic disparities in household indebtedness present an economic puzzle that traditional models inadequately explain. We examine whether regional psychological traits-specifically courage-help explain these differences. Analyzing data from 836,184 individuals across 1,220 U.S. counties, we tested whether areas with higher collective courage (willingness to act despite fear) exhibit higher debt-to-income ratios. Using spatial regression techniques to account for geographic clustering and controlling for sociodemographic factors and Big Five personality traits, we found that courage significantly predicted county-level debt-to-income ratios. A one standard deviation increase in regional courage was associated with a 0.22 standard deviation increase in debt-to-income-an effect that persisted across different geographic scales and modeling approaches. Courage hotspots in western and southern regions showed corresponding patterns of higher indebtedness. These findings reveal that psychological traits traditionally viewed as virtuous may have unintended economic consequences, highlighting the importance of considering regional psychology when designing financial policies and interventions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".