Financial Literacy as a Catalyst for Women’s Economic Empowerment in the MENA Region: Evidence from a Structural Equation Model
Bibliographic record
Abstract
This study examines the role of financial literacy as a catalyst for women’s economic empowerment in the MENA region, focusing on its impact on financial performance through the mediating effects of autonomy and family support, as well as the moderating effects of male partners and employment type. Drawing on data from 515 women professionals across five MENA countries, the research employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine both direct and indirect relationships among key variables. The findings reveal that financial literacy significantly enhances financial performance, primarily by fostering greater autonomy in financial decision-making. While parental and spousal support also contribute, their mediating effects are comparatively weaker. Moreover, the relationship between financial literacy and autonomy is moderated by employment type and the presence of male partners, with employed women and those in collaborative environments experiencing stronger gains in autonomy. These results underscore the importance of targeted financial education and autonomy-enhancing policies to support women’s economic advancement in culturally complex and economically volatile contexts. The study contributes to the literature on gender and development economics by offering empirical evidence from an under-researched region and provides actionable insights for policymakers, educators, and organizations aiming to promote inclusive economic growth.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".