Behavioral consequences and intervention of financial shocks
Bibliographic record
Abstract
An increasing number of individuals report hardship to cover financial shortfalls, but most research to date examines expense shocks (e.g., a car repair) rather than income shocks (e.g., a one-time pay cut). Here we explore the behavioral consequences of expense and income shocks and propose a self-affirmation intervention to mitigate the psychological toll posed by financial shocks. In three experiments, participants were presented with a hypothetical financial emergency (i.e., a one-time income shock or expense shock) and answered questions afterwards. We found that income shocks evoked more methods of coping, were harder to cope with, more impactful on daily life, and perceived as more of a loss than expense shocks of the same amount. Self-affirmation as a behavioral intervention successfully mitigated some of the deleterious effects of the shocks. The findings contribute a more nuanced understanding of decision making in response to shortfalls by differentiating income and expense shocks. Our study suggests that there are psychological distinctions in how different financial shocks are perceived. This evidence can inform the strategies used to prevent and cope with financial emergencies and inform public policy to support household financial management.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".