Canadian Gender Gap in Financial Literacy: Confidence Matters
Bibliographic record
Abstract
We construct a financial literacy index as well as a financial confidence index in order to evaluate the effect of confidence on financial literacy, and more specifically, on the gender gap in financial literacy. Results confirm the existence of a gender gap in financial literacy in Canada, and show that having a higher con¬fidence in one’s financial skills and knowledge is indeed a factor that increases one’s financial literacy. Financial confidence is found not to track actual financial skills very closely across different ages, espe¬cially for women, and at older ages. We also find evidence that financial literacy and decision making are related to the relative education level of spouses. Using the Oaxaca-Blinder decomposition, confidence is also found to explain 14.15% of the gender gap in financial literacy, while being self-employed explains 19% of the gap, and taking part in the financial planning accounts for 16.76% of the gender gap difference. We find that most of the gap remains unexplained by differences in coefficients of men and women.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".