Gender Differences in Risk-Taking Investment Strategies in Defined Contribution Plans
Bibliographic record
Abstract
We study gender differences in risk-taking investment strategies in Defined Contribution (DC) Plans with the help of data from the US Federal Reserve Board’s Survey of Consumer Finances (SCF). By DC plans, we refer not only to employer-sponsored plans such as 401(k)s and 403(b)s, but also to Individual Retirement Accounts (IRAs) and Roth and Keogh accounts. We suggest our own split of the SCF DC plans into risk-free and risky ones, and we build risky shares of total DC plans. We compare the risky shares of females and males in two different settings. In the first setting, we work with two samples of single people, and in the second setting we work with an extended SCF sample. In both settings, we conclude that there are no significant differences in the risky shares of total DC plans between (single) women and (single) men but that there are significant gender differences in risky IRAs and 401(k)s between (single) women and (single) men. We conclude with policy implications.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".