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Record W4398175790 · doi:10.1057/s41599-024-03123-0

Effect of the contextual (community) level social trust on women’s empowerment: an instrumental variable analysis of 26 nations

2024· article· en· W4398175790 on OpenAlexaff
Alena Auchynnikava, Nazim Habibov, Yunhong Lyu, Lida Fan

Bibliographic record

VenueHumanities and Social Sciences Communications · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsLakehead UniversityUniversité de MontréalUniversity of Windsor
Fundersnot available
KeywordsInstrumental variableEmpowermentVariable (mathematics)PsychologySociologySocial psychologyEconometricsEconomic growthEconomicsMathematics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.782
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.133
GPT teacher head0.312
Teacher spread0.179 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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