Financially Fragile Households: Evidence and Implications
Bibliographic record
Abstract
This paper examines households' financial fragility by looking at their capacity to come up with $2,000 in 30 days.Using data from the 2009 TNS Global Economic Crisis survey, we document widespread financial weakness in the United States: Approximately one quarter of Americans report that they would certainly not be able to come up with such funds, and an additional 19% would do so by relying at least in part on pawning or selling possessions or taking payday loans.If we consider the respondents who report being certain or probably not able to cope with an ordinary financial shock of this size, we find that nearly half of Americans are financially fragile.While financial fragility is more severe among those with low educational attainment and no financial education, families with children, those who suffered large wealth losses, and those who are unemployed, a sizable fraction of seemingly "middle class" Americans also judge themselves to be financially fragile.We examine the coping methods people use to deal with shocks.While savings is used most often, relying on family and friends, using formal and alternative credit, increasing work hours, and selling items are also used frequently to deal with emergencies, especially for some subgroups.Household finance researchers must look beyond precautionary savings to understand how families cope with risk.We also find evidence of a "pecking order" of coping methods in which savings appears to be first in the ordering.Finally, the paper compares the levels of financial fragility and methods of coping among eight industrialized countries.While there are differences in coping ability across countries, there is general evidence of a consistent ordering of coping methods
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".