Impact of Mobile Money on Resilience to Health Shocks in Sub-Saharan Africa: Evidence from Togo
Bibliographic record
Abstract
Mobile money has transformed access to financial services in many countries in sub-Saharan Africa and has helped to reduce gaps in the financial inclusion of the unbanked poor. This paper analyzes the impact of the use of mobile money on household resilience to health shocks. Using the propensity score matching method and the probit model with instrumental variables, the results show that the use of mobile money reduces the vulnerability of households to health shocks. We also find that women use mobile money more frequently to alleviate the adverse effects of a health shock. Moreover, we find that mobile money has a greater impact on health shock in rural areas than in non-rural areas. The results also show that the use of mobile money’s impact magnitude on health shock resilience increases with age and education level up to a threshold. Based on the paper’s findings, we have highlighted some economic policies to improve household resilience to shocks. First, the expansion of mobile network coverage, particularly in rural areas, is essential to ensure widespread access to mobile money services. Second, reducing mobile money transaction and service costs is necessary to make these services accessible to low-income households. Third, training and awareness-raising programs on responsible financial management and the effective use of mobile money must be implemented to ensure that all population segments benefit fully from these services, particularly women and older people. Fourth, tax incentives could be offered to mobile operators expanding into rural areas and offering reduced rates for emergency transactions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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, 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".