The impact of unconditional cash transfers on morbidity and health-seeking behaviour in Africa: evidence from Ghana, Malawi, Zambia and Zimbabwe
Bibliographic record
Abstract
Unconditional cash transfers have demonstrated widespread, positive impacts on consumption, food security, productive activities and schooling. However, the evidence to date on cash transfers and health-seeking behaviours and morbidity is not only mixed, but the evidence base is biased towards conditional programmes from Latin America and is more limited in the context of Africa. Given contextual and programmatic design differences between the regions, more evidence from Africa is warranted. We investigate the impact of unconditional cash transfers on morbidity and health-seeking behaviour using data from experimental and quasi-experimental study designs of five government cash transfer programs in Ghana, Malawi, Zambia and Zimbabwe. Programme impacts were estimated using difference-in-differences models with longitudinal data. The results indicate positive programme impacts on health seeking when ill and on health expenditures. Our findings suggest that while unconditional cash transfers can improve health seeking when ill, morbidity impacts were mixed. More research is needed on longer-term impacts, mechanisms of impact and moderating factors. Additionally, taken together with existing evidence, our findings suggest that when summarizing the impacts of cash transfers on health, findings from conditional and unconditional programmes should be disaggregated.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".