Financial Capability in the United States: Consumer Decision-Making and the Role of Social Security
Bibliographic record
Abstract
This paper analyzes new data from the 2009 National Financial Capability Study. This survey provides information to assess how American households make financial decisions, how they are faring under current economic conditions, and in what ways financial knowledge contributes to financial capability. In addition, it includes data about the information that the Social Security Administration (SSA) provides to consumers. The paper finds that the majority of individuals do not plan for retirement or make provisions against shocks. Debt management often results in sizable interest payments and fees and it is notable how many individuals have used high-cost methods of borrowing in the past five years. Levels of financial knowledge are strikingly low and many respondents do not possess knowledge of basic concepts. Social Security has taken steps to provide information about what individuals will expect to receive when they retire. The self-reported evidence provided in the survey shows that the information has been used by about a quarter of the population who acknowledge receiving the statement. Moreover, there are large differences among use in demographic groups and some of the more vulnerable populations, such as African-Americans, those hit by shocks, and single and separated individuals are more likely to use the statement.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".