Barriers to women’s financial inclusion in Burkina Faso
Bibliographic record
Abstract
This paper investigates the barriers to women’s financial inclusion in Burkina Faso. We focus on both demand and supply side factors. Data from Findex for the years 2014, 2017 and 2021 are used. Statistical analysis show that lack of money and distance are the main barriers to women’s financial inclusion in 2014, 2017 and 2021. Interviews with women during the focus discussion groups revealed that access to formal financial products remains a difficulty for these women working in the informal sector; social pressure, particularly the behavior of husbands against the economic emancipation of women, are barriers to the financial inclusion of these women; poor information sharing and financial education are also barriers to financial inclusion for these women. To promote women’s financial inclusion, policies should focus on reducing barriers. To do this, education and access to the decent labor market are necessary to overcome the constraint of lack of money. Regarding distance, the strengthening of mobile banking must continue. To achieve women’s financial inclusion, banks need to create products tailored to women’s needs.
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How this classification was reachedexpand
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".