A Selective Systematic Review and Bibliometric Analysis of Gender and Financial Literacy Research in Developing Countries
Bibliographic record
Abstract
Disparities in financial literacy between males and females pose significant challenges in the developing world, particularly in terms of banking sector participation and economic engagement. Women, in particular, face greater difficulties in managing personal and household income due to lower financial literacy levels compared to men. This research aims to analyze the causes, effects, and potential measures to address these disparities, situating the discussion within socio-cultural, educational, and economic contexts. A systematic review and bibliometric analysis were conducted to examine relevant studies, with Open Alex serving as the primary database. The search was conducted from 2010 to 2024. Initially, 1620 papers were identified and through stringent inclusion criteria following PRISMA guidelines, 193 studies were selected for the final review. The study employed bibliometric techniques such as co-authorship, keywords analysis, and citation analysis to identify key topics, contributors, and research gaps in the literature. The findings reveal that socio-cultural practices, a lack of resources, and low income levels significantly contribute to women’s financial illiteracy. Furthermore, the research underscores the increasing recognition of the importance of adopting a gender-sensitive approach to financial literacy. These disparities limit women’s decision-making power and exacerbate socio-economic imbalances in developing countries. This study offers valuable implications for policy and practice, advocating for targeted interventions to enhance women’s financial literacy and economic participation. The results emphasize the need for differentiated strategies and provide a foundation for future research focused on closing the gender gap in financial competence and economic empowerment.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.057 | 0.081 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".