Does Citizens’ Financial Literacy Relate to Bank Financial Reporting Transparency?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this study, we examine the relationship between financial literacy and bank financial reporting transparency for a sample of banks from the U.S. Following prior literature, we employ discretionary loan loss provisions (DLLP) as our primary measure of bank reporting transparency. We argue that the financial literacy of their customers can influence bank managers’ behaviors with respect to both the mechanics of the loan loss provisioning and their opportunistic actions. Financially literate customers represent more stable sources of funding and have more predictable loan loss provisioning that contributes to more persistent earnings. Financial literacy could also enhance customers’ ability to indirectly follow and monitor bank performance and risk-taking. Therefore, bank managers will be less likely to engage in opportunistic earnings manipulation. Following these arguments, we predict that citizens’ financial literacy is positively associated with bank financial reporting transparency. Consistent with our prediction, we find that the magnitude of bank DLLP is negatively related to state-level financial literacy. Moreover, the association between financial literacy and DLLP is higher for banks with more retail deposits and larger consumer loans, the two channels through which financial literacy could influence bank transparency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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 it