The effect of cash transfers on health in high-income countries: A scoping review
Bibliographic record
Abstract
High-income countries use cash transfer programs to mitigate poverty, in part to improve the health of low-income populations and potentially reduce their use of public health care. This review synthesizes evidence from studies that employed experimental or quasi-experimental designs to evaluate the effect of cash transfer interventions on health outcomes or health care utilization in high-income countries. We excluded interventions if they required prior contributions for eligibility, substituted cash transfers for in-kind services, or were contingent on specific health behaviours, and excluded studies published before 1970. We searched 14 academic databases on May 13, 2022 and April 18, 2023, identifying 20,978 unique records. After screening, 164 studies were included. These studies covered interventions in 14 countries, with the largest share from the United States. The most common health outcomes examined were fertility, birth weight, self-rated health, tobacco use, and depression. We classified studies into seven intervention categories and eight health outcome domains, and identified where systematic reviews may be possible. We found relatively few studies examining health care utilization as an outcome and identify this as a knowledge gap. We categorized effects as beneficial or harmful, except for fertility and health care utilization where effects were categorized as increase or decrease. With insufficient consistency of outcomes for meta-analysis, we employed a vote count and sign test to assess the presence of any effect. Across the six relevant health domains, 98 of 130 studies (.75; 95% CI: .67, .82) reported a beneficial median effect, significantly different from the null value of 50% (p = .000). Of 37 studies examining fertility, 23 showed increases (.62; 95% CI: .46, .76) in fertility, which did not clear our threshold for statistical significance using conservative assumptions (p = .094). However, a larger share of studies reported increased fertility for child/family benefits (.69, n = 26) than for employment-related cash transfers (.44, n = 9). Results for health care utilization were evenly distributed (5 increase, 4 inconsistent, 6 decrease), but these are difficult to interpret as outcomes include both preventive and acute care. Our study provides replicable methods to enable future meta-analyses.
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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 | 0.018 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".