The effects of cash transfers on adult and child mortality in low

May 14, 2023

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Poverty is an important social determinant of health that is associated with increased risk of death1,2,3,4,5. Cash transfer programmes provide non-contributory monetary transfers to individuals or households, with or without behavioural conditions such as children's school attendance6,7. Over recent decades, cash transfer programmes have emerged as central components of poverty reduction strategies of many governments in low- and middle-income countries6,7. The effects of these programmes on adult and child mortality rates remains an important gap in the literature, however, with existing evidence limited to a few specific conditional cash transfer programmes, primarily in Latin America8,9,10,11,12,13,14. Here we evaluated the effects of large-scale, government-led cash transfer programmes on all-cause adult and child mortality using individual-level longitudinal mortality datasets from many low- and middle-income countries. We found that cash transfer programmes were associated with significant reductions in mortality among children under five years of age and women. Secondary heterogeneity analyses suggested similar effects for conditional and unconditional programmes, and larger effects for programmes that covered a larger share of the population and provided larger transfer amounts, and in countries with lower health expenditures, lower baseline life expectancy, and higher perceived regulatory quality. Our findings support the use of anti-poverty programmes such as cash transfers, which many countries have introduced or expanded during the COVID-19 pandemic, to improve population health.

Poverty has long been recognized as an important social determinant of health. Poverty can negatively influence health outcomes through numerous, often interconnected pathways—food insecurity, access to and quality of healthcare, housing stability, neighbourhood safety, occupational risk, educational attainment, health behaviours, and social well-being, among others15,16,17,18,19. Consequently, living in poverty has been closely linked to a decrease in life expectancies, with a greater risk of mortality among both adults and children1,2,3,4,5.

Despite many years of progress, nearly 10% of the world's population lived on less than US$1.90 per day (extreme poverty) in 2018, and more than 40% lived on less than US$5.50 per day20 (upper middle-income poverty line). The COVID-19 pandemic has markedly worsened these figures—an estimated 97 million more people lived in extreme poverty in 2020 (a 12% increase) and additional increases were seen in low-income countries in 202121. These enduring pandemic-related effects make the assessment and implementation of evidence-based strategies to combat poverty and improve health an even more urgent priority.

Over the past two decades, more than 100 low- and middle-income countries (LMICs) have introduced cash transfer programmes as components of their poverty reduction and social protection strategies6. Cash transfer programmes are defined as those that provide non-contributory monetary transfers to individuals or households. They include unconditional transfers (more common in sub-Saharan Africa), conditional transfers (more common in Latin America), public pensions and enterprise grants (money provided to support income-generating activities).

Cash transfer programmes have become even more common during the COVID-19 pandemic. A World Bank report in February 2022 identified 962 cash transfer programmes in 203 countries—672 of these were newly introduced during the pandemic7. Indeed, it is estimated that cash transfers were distributed to 1.36 billion people—17% of the world's population—during the pandemic period22.

Large-scale, government-run cash transfer programmes have been successful in reducing poverty and improving economic autonomy, school attendance, child nutrition, women's empowerment and health-service use among beneficiaries23,24. A few studies have also documented population-wide effects such as greater economic activity in communities where beneficiaries reside25, and—in the case of infectious diseases such as HIV—reduced new infections following the introduction of cash transfer programmes26. The improvements seen with cash transfers could be driven by the removal of economic and psychological barriers of poverty as a result of receiving cash transfers, as well as spillover effects on non-beneficiaries27,28,29,30,31,32.

Despite the large body of literature on the effects of cash transfer programmes on various outcomes, there is limited evidence about the effect of such programmes on overall, population-level mortality rates, particularly outside of a few conditional cash transfer programmes in Latin America. Several municipal-level analyses have shown a decline in infant mortality associated with the Bolsa Familia programme in Brazil8,9,10. An individual-level analysis found 17% decreased odds of mortality among children aged less than 5 years who were beneficiaries of Bolsa Familia, with stronger associations for children from the poorest communities11. Other single-country municipal-level analyses have suggested reductions in infant mortality associated with conditional cash transfer programmes in Mexico, Ecuador and India33,34,35.

There are even fewer studies of relationships between cash transfer programmes and adult mortality rates. Evaluations of the Mexican conditional cash transfer programme Oportunidades found an 11% decline in maternal mortality and a 4% decline in overall mortality in regions where the programme had been phased in12,13. A municipal-level study of Bolsa Familia similarly found a 10–20% reduction in maternal mortality14. In an analysis of 42 countries, we found that cash transfer programmes were associated with population-wide reductions in AIDS-related deaths that grew larger over time26. Notably, however, most randomized and non-randomized evaluations of cash transfers have lacked adequate sample sizes or study durations to detect differences in adult or child mortality. The design of most country-specific evaluations is typically also focused on estimating programme effects on beneficiaries rather than the entire population. Unlike large-scale, multinational evaluations of major health aid programmes such as the US President's Emergency Plan for AIDS Relief36,37 (PEPFAR), no such multinational studies assess the effectiveness of cash transfer programmes in reducing population-level adult and child mortality rates.

Given the growing popularity of cash transfer programmes, evaluating their overall effects on adult and child mortality rates remains an important and policy-relevant gap in the literature. To close this gap, we used multinational longitudinal data generated from sibling and birth histories collected in national household surveys to evaluate the effects of cash transfer programmes on adult and child mortality among more than seven million people from 2000 to 2019. We used a difference-in-differences approach, a quasi-experimental technique that can be used to estimate causal effects from observational data by comparing the differences in outcomes between intervention and comparison groups during pre-intervention and post-intervention periods, under an assumption of parallel trends (that is, that in the absence of cash transfer programmes, trends in outcomes would be similar in intervention and comparison countries). Our primary finding was that these programmes were associated with significant mortality reductions among women and children aged less than 5 years, indicating the important role that these anti-poverty initiatives have had in promoting population health over the last 20 years.

There were 37 LMICs included in our analysis (see Methods, ‘Mortality data’ and ‘Cash transfer programme data’ for selection criteria and Supplementary Table 1 for excluded countries)—29 in sub-Saharan Africa, 3 in Latin America and the Caribbean, 4 in the Asia–Pacific region, and 1 in northern Africa (Fig. 1). Sixteen countries introduced large-scale cash transfer programme(s) during the study period and had mortality data available during their respective cash transfer periods (see Methods, ‘Cash transfer programme data’ and ‘Statistical analysis’ for how we identified programmes and defined the cash transfer programme exposure, and Extended Data Fig. 1 for the country inclusion flow diagram).

The study period (2000–2019) is along the x-axis, included countries (n = 37) are listed on the y-axis, red points represent national demographic and health surveys (n = 84), red lines represent corresponding years with included mortality data generated from sibling and birth histories, blue points represent the first complete year of cash transfer programme(s) covering more than 5% of the impoverished population (n = 20 total; n = 16 with mortality data during the cash transfer period), and blue lines represent the cash transfer period. DHS, demographic and health survey; DRC, Democratic Republic of the Congo.

Within these 16 ‘intervention countries’, there were 29 total cash transfer programmes, 14 (48%) of which were unconditional (Supplementary Tables 2 and 3 show programme-specific details). Intervention countries had a median most recent impoverished population coverage of 27% (interquartile range 16–100%), with a median most recent maximum transfer amount per beneficiary equating to 10% of per capita gross domestic product (GDP) (interquartile range 6.25–13.25%). Six countries had high (above-median) coverage with high (above-median) maximum transfer amounts, two had high coverage with low maximum transfer amounts, two had low coverage with high maximum transfer amounts, and six had low coverage with low maximum transfer amounts.

There were 4,325,484 people included in the adult dataset, with a total of 30,244,277 person-years (6,057,387 (20%) during intervention years) and 126,714 deaths (42 per 10,000 person-years) (Supplementary Table 4; see Methods, ‘Mortality data’ for details about the generation of the mortality datasets). There were 2,867,940 people included in the child dataset, with a total of 16,400,545 person-years (2,943,910 (18%) during intervention years) and 162,488 deaths (99 per 10,000 person-years) (Supplementary Table 5). For both datasets, comparison person-years had lower GDP per capita, lower percentiles for the World Bank Worldwide Governance Indicators, and a greater proportion of person-years from sub-Saharan Africa (Extended Data Tables 1 and 2).

In our primary difference-in-differences analyses, cash transfer programmes were associated with reductions in mortality among women (adult female individuals at least 18 years of age) (adjusted risk ratio (ARR) 0.80, 95% confidence interval 0.67–0.95) and children aged less than 5 years (ARR 0.92, 95% confidence interval 0.85–0.99) (Fig. 2 and Supplementary Tables 6–10; see Methods, ‘Statistical analysis’ for additional details about the models). These reductions are at the higher end of the range of estimates from single-country studies of specific cash transfer programmes8,9,10,11,12,13,14,33,34,35. There were no associations between cash transfer programmes and mortality among men (adult male individuals at least 18 years of age) (ARR 0.87, 95% confidence interval 0.75–1.00), children aged 5–9 years (ARR 0.96, 95% confidence interval 0.86–1.08) or children aged 10–17 years (ARR 0.93, 95% confidence interval 0.78–1.10).

Forest plot showing the fully adjusted overall associations between cash transfer programmes and mortality among women (n = 14,994,934 person-years), men (n = 15,249,343 person-years) and children aged less than 5 years (n = 6,757,284 person-years), 5 to 9 years (n = 4,818,370 person-years) and 10 to 17 years (n = 4,824,891 person-years). Effect estimates are ARRs and error bars represent 95% confidence intervals. Estimates were generated using multivariable modified Poisson models with country and year fixed effects, country-level covariates (GDP per capita, PEPFAR funding budgeted, and three Worldwide Governance Indicators: control of corruption, political stability and absence of violence, and voice and accountability), and individual-level covariates (age and rural or urban setting in all models; sex, age of mother and birth order in child analyses). We used robust standard errors clustered at the country level. CI, confidence interval.

We next assessed the temporal patterns in relationships between cash transfer programmes and mortality by creating a series of binary indicators for each year before and after each cash transfer period began. Consistent with our primary analyses, fully adjusted models showed that significant reductions in mortality among adult women and children aged less than 5 years occurred within 2 years of programme introduction (Fig. 3), with even larger reductions detected over time among women. Temporal analyses also suggested reductions in mortality among men over time (Fig. 3). There was no evidence of associations between cash transfer programmes and mortality over time among children aged 5–9 years or 10–17 years (Extended Data Fig. 2).

Temporal plots showing the associations between cash transfer programmes and mortality as a function of the year of the cash transfer period. Effect estimates are ARRs and error bars show 95% confidence intervals. Estimates were generated using multivariable modified Poisson models with country and year fixed effects, country-level covariates (GDP per capita, PEPFAR funding budgeted and three Worldwide Governance Indicators: control of corruption, political stability and absence of violence, and voice and accountability), and individual-level covariates (age and rural or urban setting in all models; sex, age of mother and birth order in child analyses). We used robust standard errors clustered at the country level. Top, estimates for women (n = 14,994,934 person-years). Middle, estimates for men (n = 15,249,343 person-years). Bottom, estimates for children aged less than 5 years (n = 6,757,284 person-years).

We also used these temporal plots to show that there were no differential pre-trends in the years before the introduction of cash transfer programmes (Fig. 3 and Extended Data Fig. 2). The parallel trends assumption was further supported by regression models showing that trends in mortality rates were similar between intervention and comparison countries before the introduction of cash transfers (see Methods, ‘Statistical analysis’ for details of these models, and Supplementary Table 11).

We then explored the heterogeneity of the effects of cash transfer programmes on mortality through subgroup analyses based on individual characteristics, cash transfer programme design features and country characteristics (Tables 1 and 2 show subgroups for women; men and children are shown in Extended Data Figs. 3–6). Although these subgroup analyses should be considered exploratory in the setting of multiple comparisons, there were several notable findings.

There was a significant reduction in mortality among men aged 18–40 years (ARR 0.86, 95% confidence interval 0.77–0.96) (Extended Data Fig. 2), with a possible mortality reduction among some men also supported by findings from our temporal analyses. Among women, there were reductions in both pregnancy-related deaths (ARR 0.74, 95% confidence interval 0.61–0.91) and non-pregnancy-related deaths (ARR 0.81, 95% confidence interval 0.68–0.94).

There were no apparent differences between the effects of unconditional and conditional transfers on mortality. At a minimum this provides reassurance that the mortality benefits of cash transfers were not limited to conditional transfers, which have been the focus of the few country-specific studies that evaluated the effects of cash transfers on mortality8,9,10,11,12,13,14,33,34,35. Conditional cash transfer programmes typically incentivize behaviours surrounding nutrition, education or health services use (commonly focused on children), whereas unconditional cash transfer programmes tend to be more direct anti-poverty approaches, have fewer administrative costs, and are more widely used in sub-Saharan Africa.

We also found that programmes with higher coverage and larger cash transfer amounts were associated with the largest reductions in mortality, with these types of programmes being associated with significant reductions among women (ARR 0.70, 95% confidence interval 0.62–0.79), men (ARR 0.77, 95% confidence interval 0.71–0.84), children aged less than 5 years (ARR 0.86, 95% confidence interval 0.81–0.93) and children aged 10–17 years (ARR 0.80, 95% confidence interval 0.65–0.97), but not for children aged 5–9 years (ARR 0.94, 95% confidence interval 0.83–1.07). This finding further supports a causal relationship between cash transfer programmes and risks of death. It also indicates that programmes with lower coverage or transfer amounts may be less effective or ineffective in reducing population-level mortality rates, although the confidence intervals in these lower-coverage, lower-amount groups were generally too wide to draw firm conclusions.

Countries with higher regulatory quality ratings within the Worldwide Governance Indicators generally showed greater reductions in mortality, with significant reductions seen among women (ARR 0.71, 95% confidence interval 0.63–0.80), men (ARR 0.80, 95% confidence interval 0.73–0.88) and children aged less than 5 years (ARR 0.89, 95% confidence interval 0.83–0.94). Findings related to voice and accountability ratings were less intuitive, with significant mortality reductions seen in countries with lower ratings among women (ARR 0.74, 95% confidence interval 0.66–0.85), men (ARR 0.81, 95% confidence interval 0.73–0.90) and children aged less than 5 years (ARR 0.89, 95% confidence interval 0.83–0.95), but not in countries with higher ratings. Subgroup analyses based on these indicators are therefore of unclear overall significance and should be interpreted with caution.

We also found greater effect sizes in countries with lower health expenditures per capita and in those with lower life expectancies (a finding that has been noted elsewhere10), indicating that people living in countries with little healthcare infrastructure or substantial public health challenges may especially benefit from cash transfer programmes.

Stratification by region showed a stronger association between cash transfers and mortality among adult women in sub-Saharan Africa (ARR 0.77, 95% confidence interval 0.62–0.95) relative to outside sub-Saharan Africa (ARR 0.93, 95% confidence interval 0.79–1.11).

Despite the heterogeneities noted above, country-specific estimates for the 16 individual intervention countries were largely similar to our primary analyses, indicating benefits of cash transfer programmes across broadly diverse contexts (Extended Data Fig. 7). There were a few noteworthy exceptions—the Dominican Republic (ARR 1.20, 95% confidence interval 0.85–1.68), Indonesia (ARR 1.04, 95% confidence interval 0.82–1.34) and Lesotho (ARR 1.25, 95% confidence interval 1.11–1.41). The Dominican Republic had only one year of mortality data available post-intervention, and Lesotho was the only intervention country in our dataset that had a cash transfer period during which only older adults were targeted by cash transfer programmes. Country-specific estimates may also be more vulnerable to an important confounding factor specific to that country, such as a positive or negative shock or policy changes other than the cash transfer programme occurring approximately simultaneously with its introduction. We therefore caution against placing too much weight on estimates for any single country.

Our findings were generally robust to a variety of additional sensitivity analyses (detailed in Methods, ‘Statistical analysis’). Fully adjusted logistic regression models (rather than modified Poisson models) yielded identical results for all outcomes to two decimal places. Fully adjusted linear models were consistent with those from our primary analyses except that there were overall associations between cash transfer programmes and mortality among men, and there were no longer any associations among children aged less than 5 years (Supplementary Table 12). Recent advances in difference-in-differences analyses with variation in intervention timing have shown that estimates may be biased, particularly if there is heterogeneity in intervention effects over time38,39,40. Use of an alternate fully adjusted linear estimator that is not vulnerable to this bias showed highly similar results to standard fully adjusted linear models, which provides reassurance that bias resulting from heterogeneous intervention effects over time is minimal41 (Supplementary Table 13). This bias also tends to be influenced by later country-years during the intervention period, and excluding intervention years after year five did not substantially influence our effect estimates—although, as with some other modelling approaches, there were now overall associations between cash transfer programmes and mortality among men, and there were no longer any associations among children aged less than five years (Supplementary Table 14). Repeating the adult female analysis with the exclusion of individual countries did not reveal possible outlier countries (Supplementary Table 15). The addition of the survey respondent's wealth quintile and educational attainment to our primary models resulted in minimal changes to our estimates (Supplementary Table 16).

Although our analytic approach has previously been used to evaluate the relationship between health aid programmes and mortality36,37, to our knowledge, this is the first study to use it to examine the effects of government-led anti-poverty programmes on population-level mortality rates. Our results were consistent with previous single-country studies predominantly of conditional cash transfer programmes in Latin America8,9,10,11,12,13,14,33,34,35, although notably we focus on many LMICs outside of Latin America that have higher underlying poverty and mortality. We also study effects on the entire populations rather than on beneficiaries alone. The results are also consistent with our previous multi-country study showing associations between cash transfer programmes and reductions in AIDS-related deaths in a subset of countries with generalized HIV epidemics26.

The largest and most convincing mortality reductions were among women. This adds to previous evidence that cash transfers may disproportionately benefit women, or be more effective when women are the primary beneficiaries23,42,43,44,45. Reflecting this, many of the cash programmes that we identified either targeted women directly or were designed in ways that favoured women (for example, minimum age-based eligibility will tend to benefit women, who live longer). Much of the sex-specific mortality reductions were driven by large decreases in pregnancy-related deaths, defined as deaths while pregnant or within two months following pregnancy termination. In part, this may relate to improved engagement in antenatal care and skilled birth attendance46. Together with the mortality reduction seen among young children, this suggests that poverty reduction may have had particularly important effects on young families. Indeed, a number of high-profile, government-led cash transfer programmes have focused on pregnant women and young children, either with or without conditions for behaviours such as facility-based delivery47.

We were not able to differentiate whether people were beneficiaries of a given cash transfer programme because this was not generally elicited in the survey questionnaires, and thus we evaluated changes in mortality for entire populations. Although this might underestimate the effects of cash transfers on direct beneficiaries, our approach has the advantage that it captures spillover effects among non-beneficiaries. For example, cash transfers are often pooled within households, families and even communities48,49. Large-scale cash transfer programmes may also affect local and regional economies in favourable ways25. This may in part explain why we found population-wide mortality reductions despite many included programmes targeting specific groups (such as older adults or poor families).

This study has several limitations. Because the surveys focus on women of childbearing age, adults over the age of 60 made up only 1% of our adult dataset. Our findings may therefore not apply to these older age groups. Additionally, we were unable to include several populous countries with sizable cash transfer programmes such as Mexico, Brazil and India.

Although we were able to assess heterogeneity across some individual, programme and country factors, the primary contribution of this study remains the overall assessment of the effects of cash transfer programmes across many countries, and the heterogeneity analyses should be considered to be exploratory. In addition, there were other important factors that we were unable to assess that may influence the effectiveness of cash transfer programmes. For example, our study does not address the possibility of implementation quality (programme outreach, enrolment procedures and ‘leakage’ of funds due to corruption, among others) influencing the success or failure of individual programmes. In India, implementation challenges have been cited as a major reason for the failure of some anti-poverty programmes in the past and recent advances in the ability to make secure payments have led to improvements in implementation50. The effect of these factors (and other unrelated, granular characteristics) is better assessed through more detailed, programme-specific evaluations, particularly given the lack of comparable implementation data across many countries. Indeed, an important challenge facing many countries is to determine how to improve the design of cash transfer programmes51,52, including through differing coverage and transfer amounts. For example, recent experimental evidence supports the usefulness of accompanying capital, educational and psychosocial interventions51. We did not make cost estimates, so we do not know from our study alone whether the benefits relative to costs of these programmes exceed those of alternative programmes.

Finally, although we attempted to control for confounding through the inclusion of fixed effects and other time-varying covariates, as with any observational study, the possibility of residual confounding remains. Recent advances in the difference-in-differences approach have highlighted instances where findings may be biased, but our use of alternate approaches that are not vulnerable to these biases yielded similar results in our study38,39,41.

In conclusion, we found that cash transfer programmes were associated with important reductions in the risk of death among adult women and young children across many LMICs. Our findings support the use of such anti-poverty programmes, which many countries have introduced or expanded during the COVID-19 pandemic, to improve population health and reduce mortality.

We performed analyses of changes in adult and child mortality associated with implementation of cash transfer programmes between 2000 to 2019, a study period when many countries introduced cash transfer programmes.

To estimate mortality, we generated two individual-level longitudinal datasets—one for adults aged ≥18 years and one for children aged <18 years—using demographic and health surveys (DHS)36,37,53. The DHS are conducted in many LMICs about every five years. They use a two-stage cluster sampling design to produce national and sub-national estimates for a variety of indicators that are representative of their target populations54. The first stage involves systematic selection of enumeration areas drawn from census files with probability proportional to population size, and the second stage involves a random sampling of households from each enumeration area. Primary respondents were all female household members of reproductive ages (15–49 years). Procedures and questionnaires for DHS have been reviewed and approved by the ICF Institutional Review Board. All analysed data were anonymized. In accordance with standard procedures for secondary data analysis, the University of Pennsylvania Institutional Review Board waived ethical review.

We used surveys that included a maternal mortality module to create the adult dataset. This module collects information from all primary respondents about every sibling born to her biological mother—sex, current vital status, year of death if deceased, current age (or age at death), and for female siblings whether the death was pregnancy-related (death while pregnant or within two months following termination of pregnancy, irrespective of the cause). As there are limited, heterogeneous, and inconsistent data available about other causes of death, we focus on mortality from all causes. Using previously established methodology36,37,53, we first restructured the dataset such that there was one observation per sibling, and then again such that each observation corresponded to one person-year from one sibling. Each observation included a binary variable indicating the sibling's survival status during that person-year. We excluded observations from incomplete years (that is, the year of the survey). To minimize recall bias, we excluded observations earlier than ten years before the survey. We excluded person-years during which a sibling was aged <18 years for the purposes of this adult dataset. Of note, because primary respondents in the DHS were 15–49 years of age, older adults were underrepresented.

We created a child dataset from the same set of surveys using the birth history module, which asked female respondents for information about all births—sex, birth date, survival status, and death date. As above, we constructed a longitudinal dataset with observations at the level of the person-year, including an indicator variable for survival and excluding incomplete years and observations earlier than ten years prior to the survey. We excluded person-years during which a child was >17 years old.

We extracted additional data about the primary respondent (sibling in the adult dataset, mother in the child dataset)—age, rural or urban setting, wealth quintile, and schooling attainment (categorized as none, primary, secondary, or greater than secondary). Respondents were classified into wealth quintiles using the DHS Wealth Index, a composite measure of households’ cumulative living standard generated using a principal components analysis based on ownership of certain assets, materials used for housing construction, and types of water access and sanitation facilities55.

We identified all major, government-led cash transfer programmes within included countries using previously established methods26. We manually searched a variety of sources to identify the programmes as well as the years in which they were implemented, the population targeted by the programmes (for example, older adults, families with young children), whether the programmes had behavioural conditionalities, amounts of annual cash transfers, and most recently available number of beneficiaries56,57,58,59,60. Data sources included social protection databases from the World Bank, United Nations, and non-governmental organizations, as well as primary documentation and reporting from individual programmes. We excluded countries with pre-existing cash transfer programmes at the start of the study period.

We calculated the impoverished population coverage for each programme as the most recent estimate of the number of programme beneficiaries divided by the number of individuals in a country with income less than the international poverty line of US$1.90 per day (2011 purchasing power parity). To do this, we divided the most recent estimate of total household beneficiaries by the impoverished population size. If estimates for total beneficiaries were not available, we multiplied direct beneficiaries by the average household size to estimate total beneficiaries61. In general, the number of beneficiaries was available during only a limited number of years. Impoverished population sizes were calculated by multiplying the percentages of the populations with income less than the international poverty line (that is, the poverty headcount) prior to programme implementation by the mid-year population from the year of the total beneficiaries estimate62. We used the poverty headcount prior to programme implementation because poverty headcount estimates after programme implementation may be decreased by the programmes themselves. For example, if a cash transfer programme began in 2012, we divided the most recent estimate of beneficiaries (numerator) by the poverty headcount in 2012 (denominator) to calculate the impoverished population coverage.

We also calculated the maximum transfer amounts as percentages of GDP per capita in the most recent year the maximum transfer amounts were reported.

We obtained additional time-varying covariates for each country and year that are known to be or are likely to be associated with changes in cash transfer programmes and mortality: GDP per capita62, total health expenditures per capita62, life expectancies at birth62, PEPFAR funding budgeted63, and six Worldwide Governance Indicators from the World Bank that are composite indicators based on 30 data sources: voice and accountability, political stability and absence of violence, government effectiveness, regulatory quality, rule of law, and control of corruption62.

We used a difference-in-differences approach, a quasi-experimental technique that can be used to estimate causal effects from observational data by comparing the differences in outcomes between intervention and comparison groups during pre-intervention and post-intervention periods, under an assumption of parallel trends (that is, that in the absence of cash transfer programmes, trends in outcomes would be similar in intervention and comparison countries). To do this, we estimated multivariable modified Poisson regression models with the unit of observation being the person-year and a binary outcome variable indicating whether an individual died in a given year64.

Our primary explanatory variable was a binary variable set to 1 if a cash transfer programme (or combination of programmes) with total impoverished population coverage greater than 5% was active in the respondent's country during that year. We were prevented from considering coverage as a continuous, time-varying exposure because beneficiary data were available only during a limited number of years for most programmes. We chose 5% based on our prior analyses showing this threshold was associated with improvements in HIV-related outcomes26, but conducted subgroup analyses (described below) to explore the association with different levels of coverage. We excluded intervention countries that lacked at least two years of mortality data prior to the cash transfer period.

To optimize our comparison country-years, we excluded from our analysis country-years during which cash transfer programmes (or combination of programmes) were implemented with coverage between 2% and 5%. Comparison country-years were therefore defined as those during which there were no active cash transfer programmes, or cash transfer programmes (or combination of programmes) had coverage <2%.

Our effect measure of interest was the risk ratio denoting the association between the cash transfer programme exposure and mortality. In addition to overall estimates, we also evaluated the temporal relationship between cash transfer programmes and mortality by creating a series of binary indicators for each year before and after the cash transfer period began.

We included in the models country- and individual-level covariates that were likely to confound relationships between cash transfer programmes and mortality. For country-level covariates, we included GDP per capita, PEPFAR funding budgeted, and three Worldwide Governance Indicators: control of corruption, political stability and absence of violence, and voice and accountability. The other three Worldwide Governance Indicators were left out of the models because they displayed substantial multicollinearity with the other covariates as evidenced by variance inflation factors >5. We also considered inclusion of health expenditures per capita, but this variable was not available for all years and adding it to the models minimally impacted the effect estimates.

For individual-level covariates, we included age and rural or urban setting in all analyses. In the child analyses, we also included sex, mother's age, and birth order. We did not include other individual-level variables that were likely to be affected by receipt of cash transfers and/or potentially mediate relationships between cash transfer programmes and mortality (for example, wealth quintile, schooling attainment).

We included country fixed effects to control for time-invariant differences among countries, and year fixed effects to control for secular trends in mortality. We used robust standard errors clustered at the country level to relax the assumption of independently and identically distributed error terms65,66.

We stratified the adult mortality analysis by sex because of previously identified sex-specific effects of cash transfers26,43,44,56, and the child mortality analysis by age (<5 years, 5–9 years, 10–17 years) because of highly varying mortality rates by child age67.

We explored heterogeneity of the effect of cash transfer programmes using subgroup analyses based on the beneficiary, cash transfer programme design, and country factors. For the beneficiary, we considered wealth quintile (of the sibling for the adult analysis and the mother for the child analysis), age (for the adult analysis, categorized as 18–40, 41–60, and >60 years), educational attainment (of the sibling for the adult analysis, and the mother for the child analysis), rural or urban setting, and cause of death among women (pregnancy-related versus not pregnancy-related). For cash transfer design features, we considered conditionality (unconditional, mixed, or conditional), and four subgroups characterized by most recent impoverished population coverage above or below the median (30%) and maximum annual transfer above or below the median (11% of GDP per capita). For country factors, we considered subgroups characterized by being above or below the median at the start of the cash transfer period for the following: each of the Worldwide Governance Indicators, current annual healthcare expenditures per capita (US$118 purchasing power parity), and life expectancies at birth (62 years). We also stratified by region (sub-Saharan Africa versus outside of sub-Saharan Africa). Finally, we generated country-specific estimates for adult women to allow for informal evaluations of heterogeneity across a range of dimensions.

We also conducted additional sensitivity analyses. First, we assessed the validity of the parallel trends assumption in two ways. We used the previously described temporal analysis to visualize pre-trends, and we estimated regression models using only data prior to the cash transfer period in each country and including an interaction term between an indicator of whether the country was in the intervention group and a linear time trend.

Second, while we used modified Poisson regression models based on conceptual justifications and to be consistent with prior literature assessing changes in mortality using DHS datasets36,37,53, we assessed for robustness of the results when using logistic and linear models.

Third, recent advances in difference-in-differences analyses with variation in intervention timing have shown that estimates may be biased particularly if there is heterogeneity in intervention effects over time38,39,63. When there is effect heterogeneity only in time since the intervention, this concern can be mitigated through use of temporal analysis with dynamic effect estimates (as described above), although there can still be bias present if there are heterogeneous treatment effects over overall calendar time68. To address this, we assessed whether a proposed alternative linear estimator not vulnerable to this bias was consistent with our primary findings41. In addition, this bias tends to be influenced by later country-years during the intervention period, so to assess the possible magnitude of this bias we conducted a sensitivity analysis by repeating the primary analysis after excluding country-years after year 5 of the cash transfer programme69.

Fourth, we assessed whether individual countries might be outliers for key outcomes by assessing whether estimates for women changed substantially after excluding each country individually.

Fifth, we repeated our primary analyses with inclusion of the respondent's wealth quintile and educational attainment.

We did not use statistical methods to predetermine sample size. We performed statistical analyses using SAS V.9.4, R V.3.5.2 using the ggplot2 and forester packages, and STATA V.17 using the did2s package.

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

The analysed data can be requested from the DHS Program website (individual recode datasets from the included countries from https://www.dhsprogram.com/Data/) or are publicly available from the World Bank (GDP per capita, total health expenditures per capita, life expectancies at birth, and Worldwide Governance Indicators datasets from https://data.worldbank.org/data-catalog/) or PEPFAR (PEPFAR Operating Unit Budgets by Financial Classifications FY04-FY20 dataset from https://data.pepfar.gov/financial). The cash transfer programme dataset is available in the Supplementary Information.

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A.R. was supported by the National Institute of Mental Health of the National Institutes of Health under Award Number K23MH131464.

Division of Infectious Diseases, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Aaron Richterman

Partners in Health, Mirebalais, Haiti

Christophe Millien

Department of Medical Ethics and Health Policy, University of Pennsylvania, Philadelphia, PA, USA

Elizabeth F. Bair & Harsha Thirumurthy

Partners in Health, Kono, Sierra Leone

Gregory Jerome

Partners in Health, Neno, Malawi

Jean Christophe Dimitri Suffrin

Departments of Economics and Sociology, University of Pennsylvania, Philadelphia, PA, USA

Jere R. Behrman

Population Studies Center, University of Pennsylvania, Philadelphia, PA, USA

Jere R. Behrman & Harsha Thirumurthy

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The study was conceptualized by A.R., H.T., C.M., G.J., J.C.D.S. and J.R.B. Methodology design was led by A.R., H.T., E.F.B. and J.R.B., and the data curation and formal analyses were conducted by A.R. under the supervision of H.T. Figures were created by A.R. The first draft of the manuscript was written by A.R. and all authors provided critical inputs into the final draft.

Correspondence to Aaron Richterman.

The authors declare no competing interests.

Nature thanks Till Bärnighausen, Davide Rasella and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Flow Diagram showing selection of intervention (N = 16) and comparison (N = 21) countries during our study period of 2000–2019, and reasons for exclusion (red boxes).

Temporal plots showing the associations between cash transfer programs and mortality as a function of the year of the cash transfer period. Effect estimates are adjusted risk ratios and error bars are 95% confidence intervals. Estimates were generated using multivariable modified Poisson models with country and year fixed effects, country-level covariates (GDP per capita, PEPFAR funding budgeted, and three Worldwide Governance Indicators: Control of Corruption, Political Stability and Absence of Violence, and Voice and Accountability), and individual-level covariates (age and rural/urban setting in all models; sex, age of mother, and birth order in child analyses). We used robust standard errors clustered at the country level. The top panel shows estimates for children aged 5 to 9 years (N = 4,818,370 person-years), the bottom panel shows estimates for children aged 10 to 17 years (N = 4,824,891 person-years).

Forest plot showing subgroup analyses among adult males (N = 15,249,343 person-years), with fully adjusted risk ratios of mortality with 95% confidence intervals generated using multivariable modified Poisson models with country and year fixed effects, country-level covariates (GDP per capita, PEPFAR funding budgeted, and three Worldwide Governance Indicators: Control of Corruption, Political Stability and Absence of Violence, and Voice and Accountability), and individual-level covariates (age and rural/urban setting). We used robust standard errors clustered at the country level. Effect estimates are adjusted risk ratios and error bars are 95% confidence intervals.

Forest plot showing subgroup analyses among children aged <5 years (N = 6,757,284 person-years), with fully adjusted risk ratios of mortality with 95% confidence intervals generated using multivariable modified Poisson models with country and year fixed effects, country-level covariates (GDP per capita, PEPFAR funding budgeted, and three Worldwide Governance Indicators: Control of Corruption, Political Stability and Absence of Violence, and Voice and Accountability), and individual-level covariates (age and rural/urban setting). We used robust standard errors clustered at the country level. Effect estimates are adjusted risk ratios and error bars are 95% confidence intervals.

Forest plot showing subgroup analyses among children aged 5 to 9 years (N = 4,818,370 person-years), with fully adjusted risk ratios of mortality with 95% confidence intervals generated using multivariable modified Poisson models with country and year fixed effects, country-level covariates (GDP per capita, PEPFAR funding budgeted, and three Worldwide Governance Indicators: Control of Corruption, Political Stability and Absence of Violence, and Voice and Accountability), and individual-level covariates (age and rural/urban setting). We used robust standard errors clustered at the country level. Effect estimates are adjusted risk ratios and error bars are 95% confidence intervals.

Forest plot showing subgroup analyses among children aged 10 to 17 years (N = 4,824,891 person-years), with fully adjusted risk ratios of mortality with 95% confidence intervals generated using multivariable modified Poisson models with country and year fixed effects, country-level covariates (GDP per capita, PEPFAR funding budgeted, and three Worldwide Governance Indicators: Control of Corruption, Political Stability and Absence of Violence, and Voice and Accountability), and individual-level covariates (age and rural/urban setting). We used robust standard errors clustered at the country level. Effect estimates are adjusted risk ratios and error bars are 95% confidence intervals.

Forest plot showing country-specific effects of cash transfers on mortality among adult females (N = 14,994,934 person-years). Estimates were generated using multivariable modified Poisson models with country and year fixed effects, country-level covariates (GDP per capita, PEPFAR funding budgeted, and three Worldwide Governance Indicators: Control of Corruption, Political Stability and Absence of Violence, and Voice and Accountability), and individual-level covariates (age and rural/urban setting in all moels; sex, age of mother, and birth order in child analyses). We used robust standard errors clustered at the country level.

This file contains Supplementary Tables 1–16 and references.

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Richterman, A., Millien, C., Bair, E.F. et al. The effects of cash transfers on adult and child mortality in low- and middle-income countries. Nature (2023). https://doi.org/10.1038/s41586-023-06116-2

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Received: 22 September 2022

Accepted: 21 April 2023

Published: 31 May 2023

DOI: https://doi.org/10.1038/s41586-023-06116-2

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