The impact of unconditional cash transfers on morbidity and health-seeking behaviour in Africa: evidence from Ghana, Malawi, Zambia and Zimbabwe

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. Data used in this study came from five UCT programmes from four countries in Africa. These programmes are the Ghana Livelihood Empowerment Against Poverty (LEAP) 1000 programme, the Malawi Social Cash Transfer Programme (Malawi SCTP), the Zambia Child Grant Programme (Zambia CGP), the Zambia Multiple Categorical Targeting Programme (Zambia MCT) and the Zimbabwe National Harmonized Social Cash Transfer Programme (Zimbabwe HSCT) (American Institutes for Research, 2014a; 2014b; Ghana Leap 1000 Evaluation Team, 2018; Ward et al., 2010). Table 1 summarizes the geographical regions where data were collected, survey years and household sample sizes. In all countries examined, study designs were either cluster randomized controlled trial (RCT) (Malawi and Zambia) or quasi-experimental (Ghana and Zimbabwe). The Ghana evaluation sampled households around an eligibility score cut-off for the programme to construct a treatment and comparison group. In Zimbabwe, comparison districts were selected based on agro-ecological characteristics (they neighbour each other), culture and level of development. Data summary and sample size We provide brief descriptions of these programmes below. The Ghana LEAP 1000 programme is an extension of the country’s flagship anti-poverty programme, LEAP. While LEAP is targeted to extremely poor households with orphans and vulnerable children, elderly with no productive capacity or persons with acute disability, LEAP 1000 was a pilot designed to add a new target group: pregnant women and mothers with infants under 1 year of age. The programme is implemented by the LEAP Management Secretariat (LMS) and the Department of Social Welfare, under the Ministry of Gender, Children and Social Protection. LEAP provides a bimonthly transfer, which ranges from GHc 64 to GHc 106 (approximately USD17–USD28 in 2015) based on the number of eligible beneficiaries in the household. Beneficiary households are targeted through a proxy-means test (PMT) with a sharp cut-off established by the LMS. Those households meeting the poverty criterion (a PMT score below the cut-off) were enrolled in LEAP 1000 from August 2015 onwards. In addition to the CT, eligible households received a fee waiver for enrolment into the National Health Insurance Scheme, providing access to free out-patient and in-patient services, dental services and maternal health services. The impact evaluation exploited the discontinuity at the PMT cut-off to establish a treatment and comparison group. Those falling just below the cut-off comprise the treatment group, and those just above the cut-off were sampled as the comparison group. Households close to the cut-off on both sides are highly similar in terms of their characteristics because they have very similar PMT scores. The baseline sample included 2497 households in 2015 and one follow-up was conducted after 23 months (Ghana Leap 1000 Evaluation Team, 2016; 2018). In Malawi, the beneficiaries include ultra-poor and labour-constrained households for whom the programme aims to reduce poverty and hunger and increase school enrolment rates. The SCTP is operated by the Ministry of Gender, Children, Disability and Social Welfare with support from the Ministry of Finance, Economic Planning and Development as well as UNICEF Malawi. Average transfer amounts are ∼2000 Kwacha per month (∼3 USD). As of the last wave of data collection used in this study (December 2015), the programme was operating in 18 out of 28 districts and reached over 160 000 households. Households were first selected by village-level committees to identify poor households containing individuals with a chronic illness or disability and then a PMT was used to verify poverty status before households were enrolled. Transfers in the Malawi programme are adjusted for household size. There is also a schooling bonus that is based on the number of youths of school age in the household (Abdoulayi et al., 2016; 2014). The evaluation utilized an RCT design with 29 village clusters randomized into treatment and delayed-entry control arms in two traditional authorities (Salima and Mangochi) and four districts (Maganga, Ndindi, Jalasi and M’bwana Nyambi). The evaluation sample consisted of 3531 eligible households at baseline in 2013, and follow-up waves were conducted at 12 months (2014) and 24 months (2015) (Abdoulayi et al., 2016). The Zambia CGP programme commenced in 2010 with the primary objective of reducing extreme poverty and the intergenerational transfer of poverty. Specifically, the programme sought to improve some specific health and education outcomes including (1) improvement in food security, (2) reduction in child mortality and morbidity, (3) reduction in stunting and wasting, (4) increase in school enrolment and attendance and (5) increased asset ownership (American Institutes for Research, 2011). Beneficiaries of the programme included all households with a child under age 5 years. Households with newborn babies were immediately enrolled in the programme through a continuous system. The CGP was operated by the Ministry of Community Development, Mother and Child Health in three districts (Kalabo, Kaputa and Shangombo). These districts were targeted because they face the highest rates of malnutrition, morbidity and mortality. Moreover, ∼95% of beneficiaries in the region were estimated to be living below the extreme poverty line in 2010. Beneficiaries received a flat monthly transfer, which started at 60 Zambian Kwacha (revised to 70 Zambian Kwacha in 2014; ∼11 USD) (American Institutes for Research, 2016a). The total amount translates to ∼12 Kwacha per capita per month and is estimated to be sufficient to provide one additional meal per person per day. The evaluation utilized an RCT design, with 92 communities randomized into treatment and control (delayed entry) arms in three districts (Kalabo, Kaputa and Shangombo). The evaluation sample consisted of 2515 households at baseline in 2010, and follow-up waves were conducted at 24, 30, 36 and 48 months (American Institutes for Research, 2016a). In Zambia, the MCT programme started in two of the most deprived districts: Luwingu and Serenje and targets: (1) households headed by widows and caring for orphans; (2) households headed by an elderly person and caring for orphans and (3) households that have a member living with some form of disability. The programme began with the broad objective of reducing poverty and the intergenerational transmission of poverty. Specifically, the programme seeks to (1) improve food security among beneficiary households, (2) increase school enrolment and attendance and (3) increase asset ownership. The programme is also operated by the Ministry of Community Development, Mother and Child Health. Flat monthly transfers are provided in the same amount as in the CGP (started at 60 Kwacha per month, revised to 70 Kwacha in 2014—∼11 USD). The evaluation utilized an RCT design, whereby 90 communities (CWACS, or community welfare assistance committees) were randomly assigned to either treatment or control (delayed entry) status, and the total evaluation sample consisted of 3078 households at baseline. Data collection for the baseline survey occurred in 2011, with 24- and 36-month follow-ups (American Institutes for Research, 2016b). The Zimbabwe HSCT targets labour-constrained and food-poor households and seeks to increase households’ consumption and food security. The programme is administered and managed by the Department of Social Services in the Ministry of Public Service, and funding for the programme comes from the Zimbabwe government and external donors. Transfers are distributed bi-monthly and amounts range from 10 to 25 USD per month, varying based on household size. A total of about 55 000 households were benefiting from the programme as of the last data collection utilized in this analysis (2014). The evaluation uses a case–control design, with the treatment arm drawn from three districts (Binga, Mwenzi and Mudzi) and the comparison group drawn from three neighbouring districts (UMP, Chiredzei and Hwange) matched to the treatment districts based on agro-ecological characteristics, culture and level of development. The evaluation sample consists of 3063 households in 90 wards across six districts (60 treatment wards and 30 comparison wards). Baseline data were collected in 2013, and a 12-month follow-up survey was carried out in 2014 (American Institutes for Research, 2014a). In this analysis, we examine the incidence of acute illness (disaggregated into specific illnesses such as fever/malaria, respiratory and diarrhoea), seeking medical care, health spending and self-assessed health (see Supplementary Appendix I for detailed indicator construction by country). Measures of child and adult health outcomes were examined separately, as information collected varied by age, except for the Zambia MCT and Zambia CGP, where the health status of children was reported with the same indicators as for all household members. In all programmes, questions were asked to the main household respondent (generally the main CT recipient or household head) on the health of all individuals in the household, with a recall period of 2–4 weeks preceding the survey (2 weeks in Ghana, Malawi and Zambia and 4 weeks Zimbabwe; Supplementary Appendix 1). Health outcomes for children under age 5 years include seeking preventive care, seeking medical treatment for various illnesses, the incidence of illness (diarrhoea, fever/malaria and cough/respiratory illness) and health spending. Controls included age in years; sex; main respondent characteristics (age, marital status and education); household size, household access to clean water, improved toilet, the experience of shocks, per capita monthly expenditures; and district (more details and availability by country outlined in Supplementary Appendix 2). In Ghana LEAP 1000, education is measured for the household head, not the main questionnaire respondent. We first summarize outcomes by treatment status and assess baseline balance between groups by regressing background characteristics and outcomes (at baseline) on the treatment indicator. Next, we assessed differential attrition between treatment and control groups across survey waves and age groups (sub-samples) by running a regression with an indicator for a respondent being observed at follow-up (i.e. not attritted) as the dependent variable and treatment dummy as the independent variable. A significant difference in our outcome(s) with respect to the treatment dummy in this regression would suggest that there was differential attrition, potentially threatening internal validity. To estimate the impact of UCT programmes on morbidity and health-seeking behaviour, a difference-in-differences (DID) estimation method was used. The estimating equation is as follows: where individual i’s treatment status is represented by T. R denotes the survey round taking the value of 1 for follow-up and 0 for baseline while j represents the health outcome of interest. X is a vector of baseline control variables, and is the error term. The coefficient of the interaction term () indicates the intent-to-treat programme impact. All analyses were conducted separately for each country and by age groups (under 5 years; 5–19 years; 20–59 years; 60 and above). For binary outcomes, linear probability models (LPMs) were estimated with robust standard errors adjusted for clustering at the community level. These models were chosen over logistic regressions for ease of interpretation of the interaction term (Norton et al., 2004), which represents the programme effect. Moreover, LPM indicates marginal effects (percentage point changes), which are meaningful for interpretation. For continuous outcomes (health expenditures), we run ordinary least squares regressions and report coefficient estimates that can be interpreted as the average change in health expenditures in local currency. Data sets for the Ghana LEAP 1000 evaluation are publicly available through the Carolina Population Center (https://data.cpc.unc.edu/projects/13/view). Other data sets are not publicly available.