On the Role of Gender and Age in the Use of Digital Financial Services in Zimbabwe
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Women and youth in developing countries remain unserved or underserved by formal financial services. The rise of digital financial services (DFS), including mobile money, provides a promise to accelerate financial and economic inclusion to these population segments. As a result, both academic researchers and policy makers are increasingly interested in understanding the role of gender and age in the use of DFS across use cases. To nuance this, the current study analyses data from a sample of 3000 respondents collected during the second quarter of 2022 from the ten provinces of Zimbabwe. Results from multivariate logit models, controlling for some socio-economic factors, show that in Zimbabwe, gender is not a significant predictor of receiving income through digital means, making payments for goods and services digitally, or for the frequency of DFS use. On the other hand, youth lag in the use of DFS, especially for making payments for goods and services, and in the frequency of use. Besides the findings on gender and age, the study reveals that the level of education, the source of income, locality, and the level of income are important determinants of how individuals use DFS in Zimbabwe.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it