Gender Roles, Gender (In)equality and Fertility: An Empirical Test of Five Gender Equity Indices
Bibliographic record
Abstract
The division of gender roles in the household and societal level gender (in)equality have been situated as one of the most powerful factors underlying fertility behaviour. Despite continued theoretical attention to this issue by demographers, empirical research integrating gender roles and equity in relation to fertility remains surprisingly sparse. This paper first provides a brief review of previous research that has examined gender roles and fertility followed by a comparison of six prominent gender equality indices: Gender-related Development Index (GDI), Gender Empowerment Measure (GEM), Gender Gap Index (GGI), Gender Equality Index (GEI), the European Union Gender Equality Index (EU-GEI) and the Social Institutions and Gender Index (SIGI). The paper then tests how five of these indices impact fertility intentions and behaviour using a series of multilevel (random-coefficient) logistic regression models, applying the European Social Survey (2004/5). The GDI, with its emphasis on human development, adjusted for gender, has the strongest and significant effect on fertility intentions. The EU-GEI, which focuses on the universal caregiver model, uncovers that more equity significantly lowers fertility intentions, but only for women. The remaining indicators show no significant impact. The paper concludes with a reflection and suggestions for future research.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 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".