The effect of job loss on risky financial decision-making
Bibliographic record
Abstract
Job loss is a common and disruptive life event. It is known to have numerous long-term negative effects on financial, health, and social outcomes. While the negative effects of becoming unemployed on health and well-being are well understood, the influence of job loss on financial decisions has received little attention. Across a large-scale survey ([Formula: see text]), spending data from a bank ([Formula: see text]), and two online experiments (total [Formula: see text]), we find that job loss increases financial risk-taking. First, in survey data, job loss is associated with elevated levels of self-reported financial risk-taking and lottery ticket purchases. Next, using administrative data from a large bank, we find consistent causal evidence of the influence of job loss on gambling spending. Although total spending decreases after job loss, gambling spending is less affected than our control categories. Finally, we turn to two incentive-compatible manipulations of job loss operationalized in a lab setting. We find that this experimental manipulation increases the take-up of financial risks. The current finding that job loss increases financial risk-taking could accentuate long-term negative financial effects of job loss.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".