Financial Literacy, Financial Knowledge, and Financial Behaviors in OECD Countries
Bibliographic record
Abstract
As an integral part of financial inclusion, adequate and correct financial knowledge provides individuals with tools to achieve better financial performance throughout their lives. Financial knowledge also contributes to agents exhibiting financial behaviors. As there is consensus in the literature regarding the benefits of financial literacy, we decided to investigate the importance of several indicators that generally appear to explain this literacy in a set of twenty OECD countries, considering financial literacy, financial knowledge, and financial behavior. Using estimation through corrected heteroscedasticity, the results show that the completion of higher education contributes positively and significantly to financial literacy and financial knowledge and behaviors. Inequality in access to health and education, as well as the level of household debt, negatively impacts financial literacy and knowledge. Still, on the other hand, progression in human development contributes to progression in literacy and financial behavior. In terms of average income, it can be seen that it contributes to literacy and financial behavior, but surprisingly, public spending on education does not impact financial literacy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".