Cash transfers: systematic review and meta-analysis
Updates
January 2022: Published in Nature Human Behaviour
The final version of this paper was published in Nature Human Behaviour on 20 January 2022. This version is an earlier working paper published in November 2020.
Abstract
Background: A large body of evidence evaluates the impact of cash transfers (CTs) on physical health and economic indicators in low- and middle-income countries (LMICs). A growing amount of research on CTs contains measures of subjective wellbeing (SWB) and mental health (MH) but no attempt has been made to systematically synthesize this work.
Objective: To evaluate whether CTs improve the SWB and MH of recipients in LMICs.
Methods/design: We undertook a systematic review and meta-analysis of randomised controlled trials (RCTs) and quasi-experimental studies, including peer-reviewed publications and grey literature (e.g. reports, pre-prints, and working papers), conducted over the period 2000-2020, examining the impact of CTs on self-reported SWB and MH outcomes. A protocol for this review was prospectively registered with Prospero (CRD42020175464).
Results: Thirty-seven studies were included in our meta-analysis, covering 100 outcomes, and a total sample of 112,245 individuals. After an average follow-up time of two years, the average effect size on MH and SWB is estimated to be 0.10 standard deviations (SDs). CT value, both in absolute terms (\(\hat{\beta}\)=0.08 SDs per $100 PPP) and relative to previous income (\(\hat{\beta}\)=0.10 SDs for each doubling), are strong predictors of the effect size. Moreover, unconditional CTs have a larger impact than conditional CTs (\(\hat{\beta}\)=0.04). The impact of CTs diminishes marginally over time (\(\hat{\beta}\)=-0.02 SDs per year). We find no significant evidence of negative spillover effects to non-recipients.
Discussion: Cash transfers significantly increase MH and SWB in LMICs. More research on longitudinal (5+ years) and spillover effects is needed. Future impact evaluations should collect data on MH and SWB to enable comparisons of the relative cost-effectiveness of development interventions at improving people’s wellbeing.
1 Introduction
Cash transfers (CTs) - commonly understood as direct payments made to people in poverty - are among the most extensively studied and implemented interventions in low- and middle-income countries (LMICs) (Vivalt, 2015). Previous systematic reviews and meta-analyses of CTs found improvements on several outcomes. These outcomes include material poverty (Kabeer & Waddington, 2015), human capital (Baird et al., 2013b; Millán et al., 2019), social capital (Owusu-Addo et al., 2018), health (Lagarde et al., 2007; Behrman & Parker, 2010; Crea et al., 2015), intimate partner violence (Baranov et al., 2020; Buller et al., 2018), child labor (Kabeer & Waddington, 2015), the spread of HIV (Pettifor et al., 2013), spending on tobacco and alcohol (Evans & Poponova, 2014; Handa et al, 2018), and labor supply (Baird et al., 2018; Banerjee et al., 2017).
Although these factors are relevant to wellbeing, measures of mental health (MH) and subjective wellbeing (SWB), which probe how individuals themselves assess the quality of their lives, are often thought to track wellbeing more accurately. Indeed, measures of SWB are increasingly considered to be essential components in applied policy analyses (Benjamin et al., 2020; Frijters et al., 2020). It therefore seems pertinent to evaluate the effectiveness of CTs with respect to these measures.
Individual income and SWB are known to be positively associated (Powdthavee, 2010; Stevenson & Wolfers, 2013; Jebb et al., 2018), especially for those at low income levels (Clark, 2017; Deaton, 2008). A similar relationship is observed in the MH literature (Karimli et al., 2019; Tampubolon & Hanandita, 2014; Schilbach et al., 2016; Ridley et al., 2020). Moreover, mental health problems may engender and perpetuate poverty (Haushofer & Fehr, 2014). Unfortunately, the literature on the link between income and SWB and MH in LMICs has long lacked causal evidence, which the growing body of primary research on CTs may address.
While CTs may improve the SWB and MH of recipients, these interventions could also have negative psychological consequences on non-recipients. Qualitative research suggests the presence of negative psychological spillovers (Fisher et al., 2017; MacAuslan & Riemenschneider, 2011), and some recent quantitative work echo this worry (Haushofer et al., 2019). For example, envy among non-recipients may be a concern (Ellis, 2012). Community disruptions and crime rates may also increase if CTs are mistargeting to formally ineligible recipients (Agbenyo et al., 2017; Fisher et al., 2017). However, there is also some evidence of positive spillovers. For example, CTs have been found to decrease the intergenerational transmission of depression (Eyal & Burns, 2019) and to lead to decreased suicide rates in the areas they are implemented (Alves et al, 2018).
We know of no previous systematic reviews on this subject. A non-systematic meta-analysis by Ridley et al. (2020), which evaluates the impact of CTs on MH, is closest to our work.Also see the systematic review by Owusu-Addo et al. (2018). They focus on determinants of health inequalities in sub-Saharan Africa and include a descriptive section on MH. We build on their work in four directions. First, we conducted a full systematic review and search of the existing literature in accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidance (Moher, Liberati, Tetzlaff, & Altman, 2010). Second, we consider SWB measures alongside MH measuresUnlike Ridley et al. (2020), we focus on measures of affective or mood disorders and exclude measures of stress or other psychological disorders. An affective or mood disorder refers to depression or anxiety. Mental health issues we do not consider are disorders relating to addiction or personality.. Third, we consider quasi-experimental designs (in addition to randomised controlled trials (RCTs)). Fourth, we evaluate the quality of included studies, assess publication bias, and perform a moderator analyses across (1) outcome type (MH and SWB), (2) CT value, and (3) duration of the transfer.