The Gender Wealth Gap: Structural and Material Constraints and Implications for Later Life
Bibliographic record
Abstract
Wealth is an important measure of economic well-being, because while income captures the current state of inequality, wealth has the potential for examining accumulated and historically structured inequality. This presentation documents the extent of gender inequality in wealth for Canadian women and men aged 45 and older. The analysis uses data from the 1999 Canadian Survey of Financial Security, a large nationally representative survey of household wealth in Canada. Wealth is measured by total net worth as measured by total assets minus debt. We test two general hypotheses to account for gender differences in wealth. The differential exposure hypothesis suggests that women report less wealth accumulation because of their reduced access to the material and social conditions of life that foster economic security. The differential vulnerability hypothesis suggests that women report lower levels of wealth because they receive differential returns to material and social conditions of their lives. Support is found for both hypotheses. Much of the gender differences in wealth can be explained by the gendering of work and family roles that restricts women's ability to build up assets over the life course. But beyond this, there are significant gender interaction effects that indicate that women are further penalized by their returns to participation in family life, their health and where they live. When women do work, net of other factors, they are better able to accumulate wealth than their male counterparts.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".