The Gender Gap in the Care Economy is Larger in Highly Developed Countries: Socio-cultural Explanations for Paradoxical Findings
Bibliographic record
Abstract
Despite the growing demand for care economy employees (e.g., nurses, teachers, and social workers), men remain underrepresented in these careers. While economically developed countries support more equal rights for women and men, their labor markets are highly gender segregated (Charles 1992, 2003). We conducted a focused investigation of this paradoxical pattern in the care economy, testing whether gender gaps in care economy career interest are larger in more economically developed countries, and if so, what psychological and cultural factors underlie these patterns. We examined these questions with labor data from 70 countries (Study 1) and a pre-registered study of career interests among 19,240 university students from 49 countries (Study 2). Although more economically developed countries tend to promote greater gender equality, our results reveal the gender gap in care economy representation (Study 1) and interest (Study 2) is especially large in such countries. We did not observe parallel patterns for STEM representation or interest. Results from Study 2 supported an integrated theoretical account of this development paradox in care economy interest: Cross-national variation in the gender gap in care economy interest was predicted by country-level variation in economic development and individualism/collectivism but not by self-expression values or country-level gender equality, countering prior (controversial) claims of a gender equality paradox. Furthermore, larger gender gaps in communal values (e.g., men’s lower valuing of helping and caring for others) were a proximal predictor of larger gender gaps in care economy interest in highly economically developed countries.
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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 | 0.013 | 0.025 |
| 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 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".