‘When my husband died, I collected debt of N20,000……were going to take me to the court, but since the money came, I have cleared my debt and bought cattle’: intended and unintended socioeconomic impact of cash transfer program in Nigeria
Bibliographic record
Abstract
The Nigerian government commenced large scale cash transfer program in 2017. We evaluated the socioeconomic impact of the cash transfer program (CTP) in Nigeria. Across six randomly selected states that had implemented the CTP for at least six months, qualitative inquiries were conducted among beneficiaries and program implementers. We utilized a program impact theory to explore the interaction of cash transfer on socioeconomic outcomes. Data were analysed using deductive and inductive thematic analysis. The CTP in Nigeria showed positive impact on reduction of poverty through new income generation or expansion of existing businesses. Food security was improved by promoting increased food expenditure. CTP increased utilization of health services including facility delivery of pregnancies. The CTP also promoted education by increasing attendance at school while also promoting opportunities for savings and investments. Though the majority of the beneficiaries were women, expenditure decision making on the cash was by men and in a few cases jointly. With the large number of poor and vulnerable persons in Nigeria the findings of the CTP in Nigeria show promise in improving key socioeconomic outcomes across poverty, health and nutrition, education, savings, and investment. Our findings justify the need for expansion of the CTP to more poor and vulnerable households. CTPs in Nigeria should consider implementing educational programs to enhance women’s financial literacy or adjusting the structure of the CTPs to incentivize shared decision-making. Future studies on CTP and its socioeconomic impact should include key metrics to measure the size of the impact.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".