On the Role of Gender and Age in the Use of Digital Financial Services in Zimbabwe
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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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".