Effect of the contextual (community) level social trust on women’s empowerment: an instrumental variable analysis of 26 nations
Bibliographic record
Abstract
Abstract The purpose of this paper is to examine the effect of contextual (community) level social trust on women’s empowerment. The specific knowledge gap explored in this study is that the previous studies theorized that community trust has a positive impact on women’s empowerment. Thus, an increase in trust in the community will empower women. However, such an assumption has been never empirically tested and confirmed. Against this backdrop, the present paper develops a theoretical argument on why the increase in community trust should lead to a higher level of women empowerment. Then, a cross-country survey was used as a data source to test the effect of community trust on women’s empowerment. A traditional single-stage OLS and instrumental variable regressions are estimated to test the effect of community trust on women’s empowerment and quantify the magnitude of such impact. The key finding of this paper is that community trust indeed significantly strengthens the empowerment of women by increasing women’s ownership of assets and improving the decision-making authority of women in the family. Importantly, our findings are robust for the separate rural and urban samples, as well as the samples of younger and older women. Equally, our findings are robust for an alternative set of instruments. The main implication of these findings is that policymakers, social administrators, and government authorities who are working on promoting gender equality should give priority to promoting community-based interventions that nurture and maintain women’s trust.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".