What Explains the Gender Gap in Financial Literacy? The Role of Household Decision Making
Bibliographic record
Abstract
Research has shown that financial illiteracy is widespread among women, and that many women are unfamiliar with even the most basic economic concepts needed to make saving and investment decisions. This gender gap in financial literacy may contribute to the differential levels of retirement preparedness between women and men. However, little is known about the determinants of the gender gap in financial literacy. Using data from the RAND American Life Panel, the authors examined potential explanations for the gender gap including the role of marriage and division of financial decision-making among couples. They found that differences in the demographic characteristics of women and men did not explain much of the financial literacy gap, whereas education, income and current and past marital status reduced the observed gap by around 25%. Oaxaca decomposition revealed the great majority of the gender gap in financial literacy is not explained by differences in covariates – characteristics of men and women – but due to coefficients, or how literacy is produced. They did not find strong support for specialization in financial decision-making within couples by gender. Instead, they found that decision-making within couples was sensitive to the relative education level of spouses for both women and men.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".