Impact of unconditional cash transfers on household livelihood outcomes in Nigeria
Bibliographic record
Abstract
Abstract In 2018, Nigeria began the implementation of a cash transfer programme (CCT) for poor and vulnerable people. We evaluated the impact of cash transfer on household livelihood outcomes in Nigeria. Using multistage cluster sampling methodology, beneficiaries and non-beneficiaries within the same locality were randomly selected to participate in a survey to assess the impact of cash transfer on food security and food diversity. When gender, marital status, educational status, and age were controlled, beneficiaries were about three times more likely than non-beneficiaries to report experiencing little or no hunger. Children 0–59 months of beneficiaries were twice likely to have at least three meals a day compared to children of non-beneficiaries. Difference in differences regression analysis showed that on the average, beneficiaries of the cash transfer significantly consumed more diverse food than non-beneficiaries. Beneficiaries of the CCT experienced fewer episodes of severe hunger, have more meal frequency, and higher household dietary diversity than non-beneficiaries. This shows that the CCT programme is effective and can directly mitigate adverse effects of malnutrition with its long-term negative impact on children and thus must be expanded to more vulnerable people across all states in Nigeria.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".