Myth-Busting? Confronting Six Common Perceptions about Unconditional Cash Transfers as a Poverty Reduction Strategy in Africa

Abstract

This paper summarizes evidence on six perceptions associated with cash transfer programming, using eight rigorous evaluations conducted on large-scale government unconditional cash transfers in sub-Saharan Africa under the Transfer Project. Specifically, it investigates if transfers: 1) induce higher spending on alcohol or tobacco; 2) are fully consumed (rather than invested); 3) create dependency (reduce participation in productive activities); 4) increase fertility; 5) lead to negative community-level economic impacts (including price distortion and inflation); and 6) are fiscally unsustainable. The paper presents evidence refuting each claim, leading to the conclusion that these perceptions—insofar as they are utilized in policy debates—undercut potential improvements in well-being and livelihood strengthening among the poor, which these programs can bring about in sub-Saharan Africa, and globally. It concludes by underscoring outstanding research gaps and policy implications for the continued expansion of unconditional cash transfers in the region and beyond.

Arguments for providing unconditional cash transfers (UCTs) to poor households in low-and-middle income countries (LMICs) to utilize as they wish are numerous. Indeed, cash transfers have been shown to reduce poverty and have widespread human capital development impacts—often larger than traditional forms of assistance; cash also provides recipients with dignity and autonomy over use (Blattman and Niehaus 2014; UNICEF ESARO/Transfer Project 2015; Bastagli et al. 2016; Gentilini 2016). Cash transfers have also been recognized as a promising response in humanitarian crises, as reflected in the high-level commitments at the World Humanitarian Summit, the Grand Bargain, and the High-Level Panel on Humanitarian Cash Transfers (ODI and CGD 2015; WHS 2016). However, cash assistance remains a relatively smaller portion of social safety net programming as compared to in-kind assistance (Honorati, Gentilini, and Yemtsov 2015). In the United States, the discussion on the political economy obstacles to just giving cash dates back at least to the 1960s (Tobin 1970), highlighting the role that misinformation may play in influencing policy debates. Policy makers and other stakeholders often cite anecdotal evidence that beneficiaries do not use cash “wisely”. These stakeholders maintain that beneficiaries spend cash on alcohol or tobacco, or that cash transfers create dependency, thereby thwarting attempts to improve financial standing in order to remain eligible for transfers, and thus cash transfers amount to nothing more than a “handout”. Similarly, doubts have been expressed regarding the costs of financing such programs, along with fears that beneficiary households will decide to increase fertility in an effort to qualify for benefits (particularly in child-grant models). These narratives influence the public perception of cash transfers and can play an important role in the political and social acceptability of financing, piloting, and scaling up such programs. But what does the evidence say about these and other perceptions and claims around cash transfers? Are these anecdotes actually representative of systematic behavior by program recipients within large-scale, representative surveys?

Using eight experimental and quasi-experimental evaluations of large-scale government UCTs in Sub-Saharan Africa (SSA), conducted in collaboration with the Transfer Project, we summarize evidence around six common perceptions associated with cash transfer programs, in resource-poor settings. Specifically, we investigate if transfers: 1) induce higher spending on alcohol or tobacco; 2) are fully consumed (rather than invested); 3) create dependency (reduce participation in productive work); 4) increase fertility; 5) lead to negative community-level economic impacts (including price distortion and inflation); and 6) are fiscally unsustainable. We present evidence refuting each of these claims. We complement our evidence with summaries of other review papers and prominent literature, which has examined these questions—both in SSA, and globally. We conclude that these perceptions are myths, and that they present a distorted picture of the potential benefits of these programs. To the extent that such perceptions are utilized—or inform underlying assumptions—in policy debates, they constrain governments’ policy decisions in the area of poverty reduction. Our review adds value by aggregating evidence from evaluations with similar outcome measures and analysis, focusing on government unconditional programs in SSA, a typology of program and setting less evidenced in the literature. We conclude by suggesting avenues for future research on topics that are still under-studied, and call for implementers, donors, and other stakeholders to draw on the growing evidence base when informing programming and resource allocation, instead of relying on dated studies with little applicability to current programming, and on anecdotes, opinion, or speculation. Efforts are required by all actors to sustain a discourse where ideology does not overcome evidence.

We note several caveats in the narrative and discussion around the findings presented here. First, although we attempt to frame the narrative by investigating the source of each myth—and in some cases can trace this back to evidence (both rigorous and anecdotal) —in many cases it is not entirely clear where the policy narratives originated. We therefore speculate that some myths, or their evolution, are the result of rhetoric and cannot be clearly traced to evidence-based origins. Second, since we examine only UCTs, we cannot clearly say that findings would hold true for conditional cash transfers (CCTs) implemented in SSA or elsewhere. In other words, although in many cases evidence may equally support both conditional and unconditional transfers, the evidence in the Transfer Project cannot support this claim directly. However, we do draw on the broader body of CCT literature to provide complementary evidence where available. Such literature often comes to the same conclusions as we do, albeit largely in a very different context (i.e., Latin America). In addition, existing reviews fail to distinguish between small researcher- or NGO-implemented programs versus large-scale national programs, and so the associated policy implications may be unclear. Therefore, although we focus on UCTs, we recognize the linkages to broader cash transfer typologies and aim to make distinctions throughout the manuscript whenever and wherever specific design components may matter.¹ Finally, although we focus on the narrow(er) set of outcomes as relevant to inform each perception or myth, we recognize that the main objectives of cash transfers largely remain in the realm of poverty and vulnerability reduction, as well as increasing material wellbeing, food security, and human capital. As such, cash transfers and social protection are integral in achieving Sustainable Development Goal (SDG) 1, which seeks to end (extreme) poverty in all its manifestations by 2030.

Cash Transfer Programs, Data, and Methodology

We assess the evidence using data from the suite of evaluations on large-scale government UCTs in SSA conducted in collaboration with the Transfer Project. The Transfer Project is a multi-organizational research initiative of the United Nations Children’s Fund (UNICEF), the “From Protection to Production (PtoP)” project of the UN Food and Agriculture Organization (FAO), Save the Children UK, and the University of North Carolina at Chapel Hill (UNC-CH), in collaboration with national governments, and other national and international researchers. The objectives of the Transfer Project are: 1) to provide evidence on the impacts of national cash transfer programs in SSA; 2) to inform the development, design, and implementation of national cash transfer policy and programs based on evidence, through engagement with governments, donors, and civil society; and 3) to promote learning across Africa on cash transfer implementation, research, and evaluation.

Table 1 summarizes the key components of the suite of eight evaluations across seven countries utilized in this paper: 1) Ethiopia Tigray Social Cash Transfer Pilot Program (SCTPP); 2) Ghana Livelihood Empowerment Against Poverty (LEAP); 3) Kenya Cash Transfers for Orphans and Vulnerable Children (CT-OVC); 4) Lesotho Child Grant Program (CGP); 5) Malawi Social Cash Transfer Program (SCTP); 6) Zambia Child Grant Program (CGP); 7) Zambia Multiple Categorical Targeting Grant (MCTG); and 8) Zimbabwe Harmonized Social Cash Transfer (HSCT).² Although specific program objectives vary, all programs were designed with poverty-related objectives, including the improvement of food security, health and education of children, and household resilience to negative shocks. Columns 3–8 indicate the year the program started, the government implementing agency, the target group of beneficiaries, the transfer size and type, and the approximate coverage at the time of writing (Davis and Handa 2015).

Table 1.

Program Components of Unconditional Cash Transfers Included in Transfer Project.

Country	Program	Year program began	Implementing ministry	Target group	Transfer size (% of baseline consumption)	Transfer type	Approximate coverage at writing (2016)
(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Ethiopia	Tigray Social Cash Transfer Program Pilot (SCTPP)	2011	Tigray Bureau of Labour and Social Affairs	Ultra-poor, labor-constrained, female, or child-headed households with elderly or disabled members	~24.8%	Base flat transfer + additional variable transfer by number of eligible HH members	3,800 households
Ghana	Livelihood Empowerment Against Poverty (LEAP)	2008	Ministry of Gender, Children and Social Protection	Extreme poor with elderly, disabled or OVC member	~7%	Variable transfer by number of eligible HH members	213,000 households
Kenya	Cash Transfers for Orphans and Vulnerable Children (CT-OVC)	2004	Ministry of Home Affairs, Department of Children’s Services	Poor households with OVC	~22%	Flat transfer	365,000 households
Lesotho	Child Grant Program (CGP)	2009	Ministry of Social Development	Poor households with a child under 18 years old	16.70%	Flat transfer until April 2013; then variable transfer by number of eligible HH members	26,600 households
Malawi	Social Cash Transfer Program (SCTP) [Expansion]	2009	Ministry of Gender, Children and Social Welfare	Ultra-poor, labor constrained	18%	Variable transfer by number of eligible HH members	170,000 households
Zambia	Child Grant (CG) model of the Social Cash Transfer (SCT) program	2010	Ministry of Community Development, Mother and Child Health	Household with a child under 5 years old	27%	Flat transfer	239,000 households (overall SCT)
	Multiple Categorical Targeting Grant (MCTG) model of the SCT program	2011	same	Poor female- and elderly-headed households with OVC or disabled person	21%	Flat transfer
Zimbabwe	Harmonized Social Cash Transfer (HSCT)	2011	Ministry of Public Service, Labour and Social Welfare	Food poor and labor constrained	~20%	Variable transfer by number of eligible HH members	62,000 households

Sources: Adapted from Davis et al. (2016a). Additional sources: Ethiopia SCTPP (Berhane et al. 2015): Ghana LEAP (Handa and Park 2012: Handa et al. 2014): Kenya CT-OVC (Ward et al. 2010: Asfawet al. 2014): Lesotho CGP (Pellerano et al. 2014): Malawi SCTP (UNC 2016b): Zambia CGP (Seidenfeld and Handa 2011): Zambia MCTG (Seidenfeld. Prencipe. and Handa 2012): Zimbabwe HSCT (AIR 2014a: Dewbre et al. 2015): HH = household. OVC = Orphans and vulnerable children.

Note: HH = household: OVC = Orphans and vulnerable children: Year program began is not necessarily year of program expansion tied to the evaluation. In Lesotho and Malawi, transfer sizes were adjusted up to 2 3% post-April 2013 and May 2015. respectively.

The majority of programs started in the late 2000s (see column 3), and are run by the national ministries overseeing the community development, gender, children, or social welfare portfolios; in one case implementation is at the state level—Tigray state in Ethiopia (see column 4). Although diverse, many of these national programs share some common characteristics in their design and implementation, including the use of vulnerability criteria in targeting, similar beneficiary demographic profiles, and unconditional transfers. A key characteristic of government programming in SSA in general, and among the programs evaluated under the Transfer Project in particular, is the inclusion of vulnerability criteria, in addition to poverty-based targeting criteria (see column 5). Targeting mechanisms vary by program, and typically involve a combination of geographical, categorical, community-based and proxy means testing, with a varying weight for each of the components that identify poor eligible households. In general, UCTs in SSA have emphasized, or included, more community involvement in targeting, whereby local committees either identify and rank or verify eligibility status, based on program guidelines. Among the targeted categories, nearly all countries include components that give priority to labor-constrained households, or households caring for orphans and vulnerable children (OVCs), driven, in part, by the HIV pandemic. This emphasis typically results in a demographic profile of beneficiary households with older household heads and more adolescent and youth-aged members. This is markedly different from the demographic profile in the Latin American CCTs, which typically consists of younger couples with young children.³ Transfer sizes ranged from 7 percent (Ghana) to 27 percent (Zambia CGP model) of baseline household consumption (see column 6) at the time of the study. Three programs give flat transfers (Kenya, and both models in Zambia), while the remaining countries give variable transfers based on household size (see column 7). At the time of writing, the approximate coverage of the programs ranged from 3,800 households (Ethiopia) to 365,000 households (Kenya).⁴

One key component of all programs reviewed here is the fact that they are unconditional; a characteristic shared by the majority of government programs in SSA. This runs in contrast to the majority of programs in Latin America, which tend to be conditional programs and currently represent the bulk of evaluation evidence to date. However, in some cases, the programs were originally conceived as conditional (e.g., Ghana), but never enforced, or are implemented with “soft conditions”, or social messaging around transfers. For example, Kenya attempted but never enforced conditions and care-givers were told at the time of enrollment that the cash was for the care and development of the OVCs. A similar “light-touch” soft-conditionality in the form of messaging was provided to recipients in the Lesotho program. The Malawi SCTP provides a “top-up” benefit for school-aged children, although enrollment status is not a condition nor is it verified. Currently, Tanzania is the only large-scale government-run cash transfer that we are aware of in SSA with enforced, explicit conditions. It is not, however, included here, since the evaluation is ongoing. Initial consultations with stakeholders at the beginning of the Transfer Project suggested interest in studying a wider range of outcomes than had explicitly been studied in Latin America, with particular interest in the productive and economic effects of cash transfers, and adolescent transitions to adulthood. The set of research questions implied by the topic of adolescent transitions to adulthood was driven in part by the large number of OVCs reached by these programs in Southern Africa.

The Transfer Project evaluations incorporate multiple methodologies to answer evaluation questions, including quantitative impact evaluations using experimental or quasi-experimental longitudinal designs, qualitative data, general equilibrium modeling, targeting, operations, and costing studies (see Davis et al. 2016a for a full description of methodologies utilized). The results in this paper come primarily from the quantitative impact evaluations that follow treatment and comparison households over time, largely made possible by phased program expansion. Table 2, columns 3–6 describe the type of evaluation design, identification of counter-factual (control/comparison) group, years of survey data collection, and baseline household sample sizes.

Table 2.

Quantitative Methods for Impact Evaluation Utilized by Transfer Project.

Country	Program	Design	Identification of counterfactual	Years of data collection	Baseline sample size	Household level attrition
(1)	(2)	(3)	(4)	(5)	(6)	(7)
Ethiopia	Tigray Social Cash Transfer Program Pilot (SCTPP)	Ouasi-experimental	Longitudinal Propensity Score Matching	2012, 2014	3,351	8.70%
Ghana	Livelihood Empowerment Against Poverty (LEAP)	Ouasi-experimental	Longitudinal Propensity Score Matching	2010, 2012	1,614	7.6%
Kenya	Cash Transfers for Orphans and Vulnerable Children (CT-OVC)	Experimental	Randomized controlled trial	2007, 2011	1,913	17.4%
Lesotho	Child Grant Program (CGP)	Experimental	Randomized controlled trial	2011, 2013	1,486	6.0%
Malawi	Social Cash Transfer Program (SCTP)	Experimental	Randomized controlled trial	2013, 2015	3,531	6.5%
Zambia	Child Grant (CG) model of the Social Cash Transfer (SCT) program	Experimental	Randomized controlled trial	2010, 2013	3,078	2.0%
	Multiple Categorical Targeting Grant (MCTG) model of the SCT program	Experimental	Randomized controlled trial	2011, 2014	2,519	3.5%
Zimbabwe	Harmonized Social Cash Transfer (HSCT)	Ouasi-experimental	District Matched Case Control	2013, 2014	3,063	14.0%

Sources: Ethiopia SCTPP (Berhaneet al. 2015): Ghana LEAP (Handa and Park 2012: Handa et al. 2014): Kenya CT-OVC (Ward et al. 2010: Asfawet al. 2014): Lesotho CGP (Pellerano et al. 2014): Malawi SCTP (UNC 2016b): Zambia CGP (Seidenfeld and Handa 2011: AIR 2014b): Zambia MCTG (Seidenfeld. Prencipe. and Handa 2012: AIR 2016): Zimbabwe HSCT (AIR 2014a: Dewbre et al. 2015).

Note: Additional rounds of data collection were undertaken in some countries. However, due to comparability we report those which make up the majority of estimates presented here.

In the majority (five) of the evaluations summarized here, randomized controlled trials (RCTs) were implemented (columns 3 and 4). In these cases, randomization of treatment (cash transfer) and comparison (similarly eligible control) groups was done at central forums in a transparent manner, by government personnel. This process increases the probability that treatment is statistically independent from, and un-correlated with, observed and unobserved background characteristics of individuals and households, which may influence outcomes. However, in several cases non-experimental approaches were utilized due to the inability to randomize treatment and comparison groups because of political, institutional/logistical, ethical, or other reasons. For example, in Zimbabwe, operational plans called for immediate scale-up in any district entering the program, ruling out the possibility of random assignment of clusters, or wards, within a district. A variety of quasi-experimental methods are implemented across countries, including the following: longitudinal propensity score matching (PSM) in Ghana and Ethiopia, and district-matched case control in Zimbabwe. These methodologies are well represented in the literature and further information on the specific approach is detailed within country-specific impact evaluation reports. While the preference was for randomized designs, the objective was to implement rigorous and internally valid designs within the parameters of government implementation plans, thus maximizing the external validity and policy applicability of findings (Davis and Handa 2016).

All evaluations involve longitudinal data collection (column 5), with at least one follow-up—ranging from a four-year panel in Kenya, to a 12-month panel in Zimbabwe. In many cases, multiple or on-going data collection is planned. With very few exceptions, the results compiled in this article come from the most recent publicly available impact evaluation results. Sample sizes at baseline range from 1,486 households in Lesotho, to 3,500 in Malawi. Across the research designs, we employ a simple difference-in-difference (DD) multi-variate approach to account for baseline differences that occur due to attrition, sampling error, or simply by chance (Wooldridge 2002). In the non-experimental studies, more sophisticated modeling is used to strengthen internal validity, such as cluster or household fixed effects models.

The typical DD model includes a vector of characteristics of individuals, households, and communities measured at the baseline to control for observable differences across households at the baseline that could affect the outcome of interest. Although these vary by evaluation, these characteristics typically include the following: pre-treatment indicators such as the age and sex of individuals (if individual level outcome); sex, age, education, and marital status of household head or transfer recipient; household demographic composition and size; geographic fixed effects; and a vector of contemporaneous cluster-level prices. Throughout the tables reported here, we are mainly interested in the double difference estimator, representing the treatment effect. For some outcomes in select countries, information is not collected at the baseline, and therefore impacts are from cross-sectional differences between comparison and treatment groups at follow-up, as indicated in the tables. Standard errors are clustered according to level of randomization, and account for complex survey design or sampling when appropriate. Further details of the exact models implemented by evaluation are available in the corresponding technical reports and publications.

Summarizing the Evidence: The Myths

Perception 1: Transfers Induce Higher Spending on Alcohol and Tobacco

A common argument against the use of cash transfers, particularly unconditional transfers, is the fear that beneficiaries will spend cash on temptation goods or luxury items. Although the list of possible goods is extensive and depends on context, alcohol and tobacco are the most commonly singled out potential expenditure items that beneficiaries could abuse as a result of increased purchasing power. The source of this perception is largely rooted in anecdotal evidence, as well as distrust from policymakers, donors, and stakeholders at large, who fear that poor populations will “waste” funds inappropriately. The claim is most commonly associated with men or male partners of beneficiaries: “If they give it to the man, he goes out and finds some friends and they drink…” (Adato et al. 2000; this quote makes reference to Mexico’s Progresa). Such concerns are often repeated by political figures who oppose such programs: “Husbands were waiting for wives to return in order to take the money and spend it on alcohol,” (Moore 2009; this quote is from a senior government official in reference to Nicaragua’s Red de Protección Social). This debate leads to prioritization of in-kind transfers, or cash transfers that are highly conditioned or restricted in terms of spending behaviors, ultimately reducing the freedom of beneficiaries to utilize cash in the way which is most welfare-improving on an individual basis.

The question of whether or not extra cash might increase expenditure and consumption of goods such as alcohol and tobacco is also interesting from a theoretical perspective, since the direction of the relationship is ambiguous. In particular, we might expect the relationship to vary depending on whether or not alcohol and tobacco are normal goods, potential substitution effects, or intra-households bargaining effects of transfers, and on the degree of social marketing or messaging delivered alongside transfers (Evans and Popova 2017). For example, if alcohol and tobacco are normal goods, then we might expect increases in expenditure as households move up the income distribution. However, if use of alcohol or tobacco are partially a result of poverty-related poor mental health, stress and desperation, and cash transfers decrease poverty, there is potential for decreased consumption of temptation goods by addressing upstream structural factors (Lorant et al. 2003; Lund et al. 2010; Jones and Sumnall 2016).

Table 3 shows comparative baseline means (panel A) and impacts (panel B) on per-capita alcohol and tobacco expenditure across seven Transfer Project evaluations, in six countries (Ghana, Kenya, Lesotho, Malawi, Zambia, and Zimbabwe). These estimates come from standardized consumption modules, which ask respondents to recall consumption and expenditure on specific categories of approximately 11 food groups across over 120 specific food items, including typologies of alcohol and tobacco, typically over the last seven days. Baseline per capita expenditures on alcohol and tobacco (in local currency) are low; representing under 1 to 2 percent of per capita food expenditure across countries (row 4, table 3).⁵

Table 3.

Baseline Means and Impacts of Cash Transfers on Monthly Per Capita Expenditure on Food, and Alcohol/Tobacco.

	Ethiopia SCTPP	Ghana LEAP	Kenya CT-OVC	Lesotho CGP	Malawi SCTP	Zambia CGP	Zambia MCTG	Zimbabwe HSCT
Panel A: Baseline Means (Local currency)
Total per capita expenditure	-	63.29	1441.89#	163.718#	45840.45	40.48	51.35	32.11
Per capita expenditure on food	2628	44.83	849.04	108.764#	35519.83	30.06	39.51	20.41
Per capita expenditure on alcohol/tobacco	-	0.51	7.07	1.747#	87.18	0.40	0.63	0.51
Expenditure on alcohol/tobacco as percentage of food expenditure	-	0.01	0.01	0.02	0.00	0.01	0.02	0.02
Panel B: Impacts (coefficients from multivariate regression models)
Total per capita expenditure	- (-)	−4.37 (0.88)	259.98**# (2.67)	18.155*# (1.68)	10,292.66*** (4.16)	10.44** (4.45)	19.96** (3.90)	2.74** (2.24)
Per capita expenditure on food	150.5*** (47.0)	−1.84 (0.47)	145.394** (2.79)	13.981*# (1.67)	8,475.40*** (4.29)	7.56** (3.86)	16.79** (4.09)	1.56 (1.35)
Per capita expenditure on alcohol/tobacco	- (-)	−0.30 (1.53)	−3.72 (1.59)	−1.227**# (1.99)	−19.50 (−0.86)	0.02 (0.14)	0.11 (0.36)	−0.01 (−0.08)
N	1,598	2,979	3,724	2,696	9,766	7,272	9,056	5,245
Currency	Birr	Cedis	Shilling	Loti	Kwacha	Kwacha	Kwacha	DSD

Sources:

^#indicates estimate is author’s additional calculation: In all other cases, estimates summarized from the following sources: Ethiopia (Berhaneet al. 2015): Ghana LEAP (Handa et al. 2014): Kenya CT-OVC (The Kenya CT-OVC Evaluation Team 2012a): Lesotho CGP (Pellerano et al. 2014): Malawi SCTP (DNC 2016b): Zambia CGP (AIR 2014b): Zambia MCTG (AIR 2016): Zimbabwe HSCT (AIR 2014a: Dewbre et al. 2015)

Note: Asterisks indicate the following:

^* =p < 0.1

^** =p < 0.05; and

^*** =p < 0.01.

Robust t-statistics provided in parentheses when reported, except for Ethiopia SCTPP. which provides standard errors. Zambia CGP and MCTG report indicate significance at the p < 0.05 level or better: SCTPP = [Ethiopia Tigray] Social Cash Transfer Pilot Program: LEAP = [Ghana] Livelihood Empowerment Against Poverty Program: CT-OVC = [Kenya] Cash Transfers for Orphans and Vulnerable Children: CGP = [Lesotho and Zambia] Child Grant Programs: SCTP = [Malawi] Social Cash Transfer Program: MCTG = [Zambia] Multiple Categorical Targeting Grant: HSCT = [Zimbabwe] Harmonized Social Cash Transfer. In Ethiopia, impact is on per capita calorie availability (adult equivalency), rather than food expenditure. In Malawi, estimates are annual measures (in contrast to monthly).

Panel B shows there are no significant positive impacts of transfers on alcohol or tobacco expenditure. In Lesotho, transfers decrease expenditure on alcohol and tobacco. In contrast, five out of the seven evaluations show significant increases in food expenditure and/or total per capita expenditure (in addition to Ethiopia, where alcohol and tobacco expenditure was not reported as a disaggregated measure). Thus, at the same time households showed increases in expenditure allocated to food and other items, they did not increase spending on alcohol and tobacco. Further, cross-country comparative analysis of Transfer Project evaluations show that transfers have a variety of positive impacts on additional food security indicators, including household dietary diversity, consumption of nutritious foods, and hunger-related coping strategies (Hjelm 2016; Tiwari et al. 2016).

One criticism of these findings could be that survey respondents may under-report expenditure on temptation items due to social desirability bias. In our case, this bias would only be a problem if beneficiaries had more incentive to under-report as compared to control individuals. This could perhaps be the case if beneficiaries believed that their reporting of alcohol or tobacco could jeopardize their eligibility for the program, particularly in those with a social messaging component (e.g., Lesotho). However, fieldwork was implemented by independent research institutes or universities, framed as routine health and welfare research, not directly affiliated with the cash transfer program. In addition, the transfer income was never singled out as a separate source of income, thus respondents were asked to report on general expenditures. Due to these factors, in combination with the fact that consumption of alcohol and tobacco items were listed as one of many categories, we believe the likelihood of under-reporting due to social desirability bias is low across studies.

Nevertheless, in three countries we explored an alternative approach to see if transfers increased alcohol consumption. In follow-up surveys in Malawi and Zimbabwe we asked the main respondent if alcohol consumption in the community over the past 12 months had increased, decreased, or stayed the same. In addition, in follow-up surveys of the Zambia CGP and MCTG we provided the main respondent with the statements “Alcohol consumption in this community has increased over the past year”, and “Alcohol consumption is a problem in this community”, and asked for a response on a Likert five-point scale, ranging from strongly agree to strongly disagree. These questions were asked in a section that also covered topics on expectations about risk, savings, household decision-making, time preference, and intimate partner violence (IPV). Hence, the questions on alcohol were unlikely to stand out as particularly noteworthy or sensitive. In addition, since respondents were not asked directly about their own alcohol consumption, these questions are not subject to the same critiques around social desirability bias.

Table 4 reports cross-sectional t-tests for differences in means across treatment and control groups since these questions were only asked at follow-up waves. The results are in line with those from the consumption module. We do not see any evidence that respondents in treatment communities report a higher prevalence of alcohol consumption or larger increases over the past year. In fact, the few differences that are statistically significant go the other way (Zambia (CGP) and Zimbabwe (HSCT)), showing significantly lower rates of perceived alcohol “problems” and fewer increases in treatment communities.

Table 4.

Means and Differences of Alternative Alcohol Measures (Community Perceptions).

	Malawi SCTP	Zambia CGP	Zambia MCTG	Zimbabwe HSCT
Panel A: Alcohol consumption has increased in this community in the past year (agree or strongly agree)
Treatment mean	0.36	0.59	0.63	0.38
Control mean	0.38	0.63	0.63	0.44
P-value (difference treatment and control)	0.200	0.024	0.750	0.021
Panel B: Alcohol consumption is a problem in the community (agree or strongly agree)
Treatment mean	-	0.68	0.71	-
Control mean	-	0.73	0.69	-
P-value (difference treatment and control))	-	0.018	0.230	-
N	3,145	2,407	2,840	1,415

Sources: All estimates from authors own calculations based on the Malawi Social Cash Transfer Program (SCTP); Zambia Child Grant Program (CGP); Zambia Multiple Categorical Targeting Grant (MCTG); Zimbabwe Harmonized Social Cash Transfer (HSCT).

Note: For Malawi and Zimbabwe, response options include yes/no/stayed the same, instead of agree or strongly agree

Our results and conclusions are in line with a systematic review and meta-analysis conducted by Evans and Popova (2017), which examined 50 estimates from 19 experimental and quasi-experimental studies linking both conditional and unconditional cash transfers to temptation goods in LMICs. Across studies, none of which included the Transfer Project evaluations reviewed here, they found either no significant impacts, or significant, negative impacts of transfers on temptation goods, with two exceptions: In Indonesia, results of a UCT showed mixed results (first disbursement had a negative and highly significant impact, while the second disbursement had a positive and weakly significant impact—almost identical to the coefficient on prepared food). In Peru mixed results are found from Juntos based on modeling choice (matching models versus instrumental variables). Their main results are robust to a number of different sensitivity analyses, including the use of alternative outcome measures and sample exclusions (e.g., including only RCTs, excluding outliers). Evidence clearly shows that on average, the misuse of transfers for temptation goods, specifically alcohol and tobacco, is not supported by data. However, it should be noted that evaluations will typically not have been powered to detect significant effects on these outcomes explicitly, so low power may be a limitation of the findings reviewed here.

To test whether cash transfers change household preferences in terms of their consumption behavior, and shift their total expenditure Engel curve, the Kenya CT-OVC Evaluation Team (2012a) compares ex post impact estimates with ex ante expected effects given baseline expenditure elasticities. These authors find that in about half the budget items, ex ante and ex post effects are significantly different, implying that preferences may have changed among program recipients or that transfer income is spent differently. With respect to alcohol and tobacco, actual program impacts were lower than expected. Further, the same authors directly test whether the program has induced significant changes in expenditure elasticities (as implied by their associated marginal propensities to consume), and find evidence of this for alcohol and tobacco, and to a lesser extent for food, health and transportation and communication. This evidence suggests that transfer income might be spent differently from other income, perhaps due to messaging around the use of funds.

Perception 2: Transfers Are Fully Consumed (Rather than Invested)

Particularly when cash transfers are unconditional and not tied to specific human capital-related co-responsibilities, a perception is often voiced that cash will be utilized for short-term consumption only and not invested (either in human capital or productive activities). In other words, there are concerns that cash is a “hand out” or “charity”, with opponents citing the age-old need to “teach a man to fish” through training or investment, rather than giving an entitlement (Ferguson 2015). The possibility of transfers being fully consumed is certainly valid, as the average beneficiary household in the Transfer Project evaluations is well below the poverty line and faces chronic food insecurity. For example, in the Zambia MCTG, 91 percent of eligible households are below the national poverty line, compared to 62 percent of households living in rural areas of the same districts (Seidenfeld, Prencipe, and Handa 2012). Thus, we might expect that the households would spend the bulk of the transfer on meeting immediate basic needs, including food and shelter, rather than making longer-term investments. The proposition that cash is allocated to direct expenditures is also supported by a synthesis review of cash transfers in LMICs that finds 35 studies which measure impacts on household expenditure, 25 of which (or 71 percent) find significant impacts on total household expenditure (Bastagli et al. 2016). Of course, the use of transfers for short-term basic needs may still be considered investments if they help maintain the human capital of children through better nutrition and increased capacity to learn. However, the basic perception that cash is used for short-term consumption needs, instead of invested in productive and human capital, has implications for both the sustainability of impacts as well as the overall objectives assigned to programs.

In Transfer Project evaluations, a defining characteristic of most beneficiary households is that they are not wage workers, but rather depend on their own efforts in smallholder agriculture or family-run businesses to assure enough income and food for survival. Moreover, most beneficiary households live and work in a context of poorly functioning or non-existent input, output, labor, insurance, and/or credit markets. This has profound implications for household decision-making on consumption and production activities. In practice, the decisions become inseparable (Singh, Squire, and Strauss 1986). For example, the choice of crops to produce may be made on which is most profitable, or which lowers the risk of going hungry. Households may rely on casual agricultural wage employment (including for children), not because it is the highest return on labor but because they have an urgent need for liquidity in order to buy food.

We first examine whether cash transfer programs can help households overcome, at least partially, some of these constraints, particularly in credit and insurance markets, by investing in productive activities. We present means (panel A) and impact estimates (panel B) on a range of productive indicators for all eight evaluations (table 5). These indicators are divided into three categories: 1) livestock ownership (measured in both Tropical Livestock Units and any ownership); 2) ownership of agricultural assets (axe, hoe, pick, and others); and 3) agricultural inputs/outputs (seed expenditure, fertilizer use, etc., and value of harvest). Tropical Livestock Units are livestock numbers converted to a common unit, reflecting weight and feed requirements. Reported estimates follow conversion factors for a unit equivalent to a tropical cow, with a weight equivalent of one unit of 250 kg (Daidone et al. 2017).

Table 5.

Baseline Means and Impacts on Productive Indicators.

	Ethiopia SCTPP	Ghana LEAP	Kenya CT-OVC	Lesotho CGP	Malawi SCTP	Zambia CGP	Zambia MCTG	Zimbabwe HSCT
Panel A: Baseline Means
Tropical Livestock Units (TLU)	0.354	0.310#	1.465#	2.703#	0.039	0.34#	0.092#	1.37
Any livestock owned (share of HHs)	0.276	0.463#	0.810#	0.594	0.288	0.47#	0.513#	0.779
Any agricultural asset (share of HHs)	0.473	0.784#	0.879#	0.5 39#	0.882	0.96*	0.941#	0.955#
Expenditure on crop inputs (LCU)	-	-	971.165*	61.271	-	21.78	53.9	4.387
Value of harvest (LCU)	1,428.11	231#	-	304.71*	29,280	393.88	1,080.44	-
Panel B: Impacts (program coefficients from multivariate regression models)
Tropical Livestock Units (TLU)	0.031 (0.084)	− 0.118*# (−1.95)	0.123# (0.84)	0.067# (0.21)	0.051*** (3.73)	0.076# (0.70)	0.118***# (5.08)	−0.022 (−0.18)
Any livestock owned (share of HHs)	−0.174* (0.101)	−0.041# (−0.90)	−0.013* (−0.41)	0.03 (0.8)	0.220*** (5.15)	0.1.78***# (4.40)	0.306***# (8.25)	0.047* (1.88)
Any agricultural asset (share of HHs)	0.062***# (0.018)	− 0.019# (−0.47)	0.006# (0.18)	0.006# 0.13	0.065*** (3.21)	−0.007# (−0.72)	0.0239# (1.59)	−0.011# (−0.71)
Expenditure on crop inputs (LCU)	-	- (-)	− 365.082* (−1.52)	15.085 (0.89)	- (-)	−1.754# (−0.18)	66.22*** (3.57)	1.093 (0.75)
Value of harvest (LCU)	256.68** (129.551)	−46.4# (−0.69)	-	425.2 3*# (1.76)	12.175*** (3.8)	283.9** (2.21)	355.54*** (3.57)	- (-)
N	2,923	2,978	3,412	2,706	9,902	7,276	9,056	5,260
Currency	Birr	Cedis	Shilling	Loti	Kwacha	Kwacha	Kwacha	USD

Sources: The # symbol indicates the estimate is the authors’ additional calculation. In all other cases, estimates are summarized from the following sources: Ethiopia SCTPP (Afsaw et al. 2016): Ghana LEAP (Handa et al. 2014): Kenya CT-OVC (Ward et al. 2010): Lesotho CGP (FAO 2014): Malawi SCTP (UNC 2016a): Zambia CGP (AIR 2014c): Zambia MCTG (AIR 2016): Zimbabwe HSCT (Dewbre et al. 2015).

Note: Asterisks indicate the following:

^*= p < 0.1

^**=p < 0.05, and

^***=p < 0.01.

Robust t-statistics provided in parentheses when reported, except for Ethiopia SCTPP. which provides standard errors: SCTPP = [Ethiopia Tigray] Social Cash Transfer Pilot Program: LEAP = [Ghana] Livelihood Empowerment Against Poverty Program: CT-OVC = [Kenya] Cash Transfers for Orphans and Vulnerable Children: CGP = [Lesotho and Zambia] Child Grant Programs: SCTP = [Malawi] Social Cash Transfer Program: MCTG = [Zambia] Multiple Categorical Targeting Grant: HSCT = [Zimbabwe] Harmonized Social Cash Transfer: HHs = households: LCD = Local currency units: TLD = Tropical Livestock Dnits. which are livestock numbers converted to a common unit, equivalent to a tropical cow. weighting 2 50 kg: N may vary depending on exact indicator and reflects the most common sample size for estimates. Expenditures in Kenya are per acre, and follow-up (instead of baseline) means are provided for agricultural assets. Expenditures in Zimbabwe do not include seed expenditure.

Results show that in every evaluation, with the exception of Kenya CT-OVC, there are significant, positive impacts on at least one productive indicator, with the magnitude and type of investment varying across countries.⁶ Further, it should be noted that while impacts were not always statistically significant overall, in all countries positive and significant results were observed for population subgroups or for selected items (such as by type of animal) for livestock indicators (Daidone et al. 2017). The strongest impacts are found for Zambia MCTG, where there are significant positive effects of transfers across the majority of productive domains measured. Since households choose how and what to invest in, even under a diversification strategy we would not necessarily expect to find impacts across all productive domains, but rather those reflecting the productive activities in which the household engages. For example, households in Lesotho and Ethiopia are more reliant on livestock production compared to those in Zambia or Malawi, and thus we may expect impacts on livestock in the former, rather than the latter countries. These results are confirmed by other literature, including a recent meta-analysis that examines the impacts of social protection (including conditional and unconditional cash transfers) on household assets and livestock, among others, and finds overall significant effects (Gertler, Martinez, and Rubio-Codina 2012; Hidrobo et al. 2018).

To complement these findings, we summarize the impacts on children’s education (table 6), focusing on secondary school age enrollment, as the largest financial barrier to schooling occurs at the secondary level, and drop-outs begin at exactly the transition from primary to secondary levels of education.⁷ Across the eight evaluations included in this report, the impacts on secondary schooling enrollment were significant in six evaluations, ranging from 6.5 percentage points (Lesotho CGP) to 15.7 percentage points (Malawi SCTP). These impacts are in line with and often greater than those found in the conditional programs in Latin America (Baird et al. 2014; Garcia and Saavedra 2017). In cases where impacts were not significant in the full samples (Ethiopia SCTPP and Zimbabwe HSCT), there were clear operational reasons why this was expected, or significant impacts in sub-groups.⁸ Although enrollment is only one indicator of schooling investment, a systematic review of the relative effectiveness of conditional and unconditional cash transfers for schooling outcomes in developing countries showed significant impacts on a range of schooling outcomes in both types of program (Baird et al. 2014). Combined with the productive impacts reported in table 5, it is clear that households are not only utilizing transfers for immediate subsistence needs, but also using the transfer for investment in productive activities and human capital for their children.

Table 6.

Baseline Means and Impacts on Secondary School-Age Enrollment.

	Ethiopia SCTPP	Ghana LEAP	Kenya CT-OVC	Lesotho CGP	Malawi SCTP	Zambia CGP	Zambia MCTG	Zimbabwe HSCT
Panel A: Baseline Means (age group)	6–16	13–17	13–17	13–17	14–17	11–14	11–14	13–17
School enrollment	0.83	0.875	0.855	0.837	0.540	0.884	0.809	0.71
Panel B: Impacts (coefficients from multivariate regression models)
School enrollment	0.026 (0.02)	0.081** (2.44)	0.078** (3.38)	0.0648* NR	0.157*** (5.31)	0.0688** (2.09)	0.074*** (3.03)	0.03 (0.95)
N	1,751	1,483	4,175	2223	5,630	2,724	5,078	4,828

Sources: Estimates summarized from the following sources: Ethiopia (Berhane et al. 2015); Ghana LEAP (de Groot et al. 2015); Kenya CT-OVC (Kenya CT-OVC Evaluation Team 2012b); Lesotho CGP (Pellerano et al. 2014); Malawi SCTP (UNC 2016b); Zambia CGP (Handa et al. 2016) and Zambia MCTG (AIR 2016); Zimbabwe HSCT (AIR 2014a).

Note: Asterisks indicate the following:

^*= p < 0.1

^**= p < 0.05, and

^***= p < 0.01.

Robust t-statistics provided in parentheses when reported, except for Ethiopia SCTPP, which provides standard errors. Kenya CT-OVC and Zambia CGP indicate significance at the p < 0.05 level or better; SCTPP = [Ethiopia Tigray] Social Cash Transfer Pilot Program; LEAP = [Ghana] Livelihood Empowerment Against Poverty Program; CT-OVC = [Kenya] Cash Transfers for Orphans and Vulnerable Children; CGP = [Lesotho and Zambia] Child Grant Programs; SCTP = [Malawi] Social Cash Transfer Program; MCTG = [Zambia] Multiple Categorical Targeting Grant; HSCT = [Zimbabwe] Harmonized Social Cash Transfer. In Ethiopia, ages include both primary and secondary range. In Malawi, indicator is for attending school regularly (instead of enrollment).

Perception 3: Cash Creates Dependency (Reduces Participation in Productive Work)

A common perception among many policy makers, the media, and stakeholders in general is that cash transfers foster dependency. That is, poor families who receive financial support will work less and become lazy, leading to dependency on the transfer for their well-being. In a recent paper on the political economy of CCTs, Lindert and Vincensini (2010) analyzed perceptions about these social policy instruments as portrayed and debated in free and independent press in Brazil. These authors find that the press played an important role in perpetuating allegations of welfare dependency. For instance, the newspaper O Globo published a special multi-page issue on the topics of welfare dependency and welfarism on August 12, 2006, including 27 articles with headlines such as “Programme Generates Dependency and Disincentives to Work”, and with reference to cash transfers, “Bad With Them, Worse Without Them” and “The Promises to Teach How to Fish.” Similarly, as reported in an institutional analysis in Malawi, elites believe that strategies such as cash transfers lead to dependence amongst the poor and reduce the incentive to work hard. As a media leader said: “If you keep giving the poor programs that involve giving cash, food, or subsidies, you end up breaking the hardworking nature of Malawians. At the end of the day we will achieve laziness. People will get used and become dependent on handouts,” (Kalebe-Nyamongo and Marquette 2014). Further, respondents’ ideas about the poor (that they are uneducated, passive, dependent, and have a fatalistic mentality), provided a sense of moral grounding for policies which target those that respondents consider productively active (rather than inactive) poor. These perceptions help explain the popularity of public works programs in many countries, and the aversion to giving away money for “free”: “Public works programs are good because they ensure ownership, especially because you are doing things that improve your own area, such as roads. The money earned can be used to buy food and farming inputs…With cash transfers someone can decide not to take a bath for three days, puts on torn clothes, comes and present themselves as poor to receive this money and do so the following month as long as the project is there. This is not sustainable and breeds laziness,” (Church and Society Organization respondent cited in Kalebe-Nyamongo and Marquette 2014).

In the previous section, we found that cash transfer programs have important impacts on different aspects of household livelihoods, particularly in terms of crop and livestock activities. In this context, it is very likely that cash transfers could affect household decision-making on labor allocation—either inducing a switch among income-generating activities, or shifts between labor, household domestic tasks, and leisure. Table 7 summarizes the study results of adult labor force participation from all eight evaluations, aggregated to the household level, in: 1) any wage occupation; 2) agricultural (casual) wage labor; 3) non-agricultural wage labor; 4) own farm activities; and 5) own non-farm enterprise participation. The first three represent paid/wage labor indicators, which in the context of highly informal rural labor markets where subsistence farming is the norm, is often the least desired form of work, such as agricultural piecework, heavy labor on larger land-holder farms, or public works manual labor. The latter two represent household-driven activities, such as own farm, livestock, and small business activities, and are more desirable in these study settings, as they reflect investment in the household economy, and individuals have control over working conditions.

Table 7.

Baseline Means and Impacts on Livelihood Work Effort (Last 12 months, Unless Specified).

	Ethiopia SCTPP	Ghana LEAP	Kenya CT-OVC	Lesotho CGP	Malawi SCTP	Zambia CGP	Zambia MCTG	Zimbabwe HSCT
Panel A: Baseline Means
At least one adult member in wage labor (all occupations, share of HHs)	0.132	0.068#	0.453#	0.651#	-	0.691#	0.553#	0.096
Agricultural wage labor (casual)	-	0.013#	0.164#	-	7.376	0.509#	0.499#	0.02
Non-agricultural wage labor	-	0.057#	0.31#	-	0.747	0.239#	0.105#	0.079
At least one adult member in any farming activities (share of HHs)	-	0.498#	0.742#	0.809#	86.949	0.975#	0.878#	0.876
Operating non-farm enterprise (share of HHs)	0.132	0.305#	0.302#	0.21	0.238	0.307	0.08	0.116
Panel B: Impacts (program coefficients from multivariate regression models)
At least one adult member in wage labor (all occupations, share of HHs)	−0.033** (0.016)	−0.029# (−1.10)	−0.051# (1.300)	−0.075*# (−1.90)	- (-)	0.130***# (−3.73)	−0.0729**# (−2.58)	0.005 (0.240)
Agricultural wage labor (casual)	- (-)	−0.015# (−0.90)	−0.019# (−0.67)	- (-)	−3.307** (−2.75)	0.001***# (−3.06)	−0.0673**# (−2.29)	−0.002 (−0.14)
Non-agricultural wage labor	- (-)	−0.015# (−0.63)	−0.026# (−0.77)	- (-)	1.003** (2.07)	−0.0377# (−1.63)	−0.0061# (−0.41)	0.017 (1.12)
At least one adult member in any farming activities (share of HHs)	- (-)	−0.063*# (−1.69)	0.007# (0.14)	0.058# (1.63)	6.995 (0.85)	−0.0044# (−0.37)	0.0506**# (2.59)	−0.022 (−0.84)
Operating non-farm enterprise (share of HHs)	−0.003 (0.019)	−0.002# (−0.06)	0.016# (0.47)	−0.038 (−1.04)	0.01 (0.2S)	0.121*** (2.708)	0.03** (2.03)	0.048** (2.3)
N	2,922	2,978	1,706	2,706	9,906	2,518	2,969	5,260

Sources: The # symbol indicates the estimate is the authors’ additional calculation. In all other cases, estimates are summarized from the following sources: Ethiopia SCTPP (Asfaw et al. 2016): Lesotho CGP (FAO 2014): Malawi SCTP (UNC 2016a,b): Zambia CGP (AIR 2014b): Zambia MCTG (AIR 2016): Zimbabwe HSCT (Dewbre et al. 2015).

Notes: Asterisks indicate the following:

^*= p < 0.1

^**= p< 0.05, and

^***= p< 0.01.

Robust t-statistics provided in parentheses when reported, except for Ethiopia SCTPP. which provides standard errors: N may vary depending on exact indicator and reflects the most common sample size for estimates: HH = Household: SCTPP = [Ethiopia Tigray] Social Cash Transfer Pilot Program: LEAP = [Ghana] Livelihood Empowerment Against Poverty Program: CT-OVC = [Kenya] Cash Transfers for Orphans and Vulnerable Children: CGP = [Lesotho and Zambia] Child Grant Programs: SCTP = [Malawi] Social Cash Transfer Program: MCTG = [Zambia] Multiple Categorical Targeting Grant: HSCT = [Zimbabwe] Harmonized Social Cash Transfer: In Ethiopia. HH wage participation refers to any time during March. April, or May: In Ghana. HH wage participation refers to the last seven days. In Kenya, follow-up (instead of baseline) means are provided: In Malawi, agricultural wage labor (casual) refers to “Number of total months any member worked in ganyu in the past 12 months.” non-agricultural wage labor refers to “Total hours any member spent on work outside household (excluding ganyu) in the past seven days.” and at least one adult member in any farming activity refers to “Total number of days any member spent in past season on own farm activities.” In Zambia, follow-up control means (instead of baseline) are provided and estimates use single-difference modeling. In Zimbabwe, estimates are among HHs with at least one adult member involved in any farming activity, and refers to the last rainy season.

Results from multivariate models (panel B) show that for the majority of indicators there are no significant impact of transfers on labor supply. However, there are exceptions: labor supply for wage work decreases in four cases (in Ethiopia SCTPP, Lesotho CGP, Zambia CGP, and MCTG), and these are largely driven by engagement in casual labor, the least desirable form of labor in this context. At the same time, labor supply for own farm and non-farm enterprises increases in three cases (Zambia CGP, MCTG, and Zimbabwe HSCT). In Malawi, although there are no overall impacts, we find similar patterns where households are substituting out of agricultural casual wage labor and into other forms of non-agricultural wage labor. This switch from causal agricultural labor to on-farm activities indicates that households are able to transfer from less to more preferred labor activities.

Moreover, disaggregating by gender and age reveals a more complex pattern of the switch from agricultural wage labor to on-farm activities in a number of countries (Daidone et al. 2017). For example, while the Malawi SCTP led to a large reduction in participation in casual labor (or “ganyu”), overall there was not a corresponding positive impact on on-farm activities. However, when disaggregated by gender, adult males are more likely to work on-farm compared to adult females. Both male and female elderly household members are also more likely to carry out on-farm labor. The gender of the household head was also relevant, with a lower likelihood of female participation among male-headed households. The pattern of switching from agricultural wage labor of last resort to on-farm activities also emerged from the qualitative work across five countries (Kenya, Ghana, Lesotho, Malawi, and Zimbabwe; Fisher et al. 2017). For example, as recounted by an elderly beneficiary, “I used to be a slave to ganyu, but now I’m a bit free,” (Fisher et al. 2017). While casual labor remained a relevant coping strategy in all countries, beneficiaries reported more flexibility regarding when to resort to agricultural wage labor. Overall, the results do not indicate a reduction in work effort—rather, they show that beneficiary households have increased their autonomy over productive activities and have more flexibility in how they allocate their time—often choosing to work on their own farms instead of agricultural wage labor. This is particularly notable as the majority of beneficiary households within these programs comprise adults who may have a good reason to work less—particularly the elderly, disabled, and women responsible for care-giving.

A key point to remember related to age is the targeting demographic for these programs. The typical beneficiary household is labor-constrained at the start of the program, containing children and youth as well as elderly members and very few prime-age household members. Figure 1 shows the age distribution by sex of beneficiary households in the programs reviewed in this paper (males in blue [left], females in red [right]). In all but the Zambia CGP, we see an atypical age structure of beneficiary households with respect to nationally representative demographics, with comparatively large proportions of adolescents and elderly persons, and relatively low proportions of able-bodied household members. Thus, we expect the impacts and constraints on work effort to contrast to households who, given capital, may be more able to absorb rural labor activities.

Figure 1. Age Pyramids of Household Members at Baseline, by Cash Transfer Program.

Source: Baseline data from the Ethiopia Tigray Social Cash Transfer Pilot Program (SCTPP); Ghana Livelihood Empowerment Against Poverty (LEAP) Program; Kenya Cash Transfers for Orphans and Vulnerable Children (CT-OVC); Lesotho Child Grant Program (CGP); Malawi Social Cash Transfer Program (SCTP); Zambia Child Grant Program (CGP); Zambia Multiple Categorical Targeting Grant (MCTG); Zimbabwe Harmonized Social Cash Transfer (HSCT).

Our results add to a variety of other studies that have come to similar conclusions: cash transfers in resource-poor settings have not been found to reduce the labor supply of beneficiary households in a meaningful way. For example, Banerjee et al. (2017) examined large-scale (primarily conditional) cash transfers from six countries (Honduras, Indonesia, Morocco, Mexico, Nicaragua, and the Philippines), evaluated through RCTs, and found no systematic evidence that cash transfers discourage work. Dependency and labor market engagement were also investigated among several types of grants in Mexico and South Africa using both quantitative and qualitative methods (Surender et al. 2010; Alzúa, Cruces, and Ripani 2013; Samson et al. 2013). Although results are likely to vary by the structure of labor markets in each setting, these authors come to the same conclusion, namely that grants do not create dependency. Thus, the idea of a “lazy welfare recipient,” is simply not borne out by hard evidence in LMIC settings.

Perception 4: Transfers Targeted to Households with Young Children will Increase Fertility

Policymakers often fear that cash transfers targeted to households with young children will have the unintended consequence of increasing fertility in an effort to obtain increased benefits, or to maintain eligibility. This concern is understandable in SSA, which was the last region globally to start experiencing the demographic transition (the phenomenon whereby countries transition from high to low birth and death rates). Indeed, declines in fertility rates have occurred more slowly than other regions, and the transition has even stalled in some countries with total fertility rates (TFRs) over five (Bongaarts and Casterline 2013). It is likely that these claims can be traced back to the first generation of Latin American CCTs, which were often heavily targeted to households with young children. In particular, an evaluation of three CCTs published in 2007 highlighted the potential for “unintended consequences”, finding increases in fertility between two and four percentage points in Honduras, yet no impacts in Mexico and Nicaragua (Stecklov et al. 2007). Some authors hypothesize that the increases in Honduras could be due to an administrative loophole (subsequently closed), which allowed transfer size to be increased immediately after the birth of a child (Palermo et al. 2016). In fact, evidence from longer-term impacts in Honduras show no persistent fertility effects (Li 2016). However, despite little concrete evidence from Latin American programming, the proposition is certainly not outside the realm of possibility, particularly since cash benefits—including childcare support, tax credits, and paid leave—are some of the most popular pro-natal policies in OECD countries (Kim 2014).

We summarize Transfer Project findings on fertility-related impacts from three countries (Kenya, Malawi, and Zambia) in table 8.⁹. Data come from household roster information which includes children aged zero to four years in Kenya, and from in-depth fertility modules requested for all women of reproductive age (15–49 years) in Malawi and Zambia (all reported by the main survey respondent). As methodology varies by country, we include model specifications at the bottom of table 8.

Table 8.

Baseline Means and Impacts of Cash Transfers on Fertility among Females.

	Kenya CT-OVC	Malawi SCTP	Zambia CGP	Zambia MCTG
Panel A: Baseline Means
Woman-level indicators
Total fertility (# children ever born alive)	-	-	3.24	-
Ever pregnant (15–49 years)	-	-	0.83	-
Ever pregnant (< 25years)	0.00	-	0.59	0.00
Currently pregnant	-	-	0.11	-
Ever had miscarriage/stillbirth/abortion	-	-	0.12	-
Household-level indicators
Total children aged 0–1 years in HH	0.16	0.11	0.77	-
Total children aged 2–4 years in HH	-	-	0.91	-
Panel B: Impacts (program coefficients from multivariate regression models)
Woman-level indicators
Total fertility (# children ever born alive) t-statistic	-	-	0.005 (0.28)	-
Ever pregnant (15–49 years) t-statistic	-	-	−0.002 (0.16)	-
Ever pregnant (< 25years) t-statistic/z-statistic	−0.049** (2.42)	-	0.011 (0.41)	0.016 (0.923)
Currently pregnant t-statistic	-	-	0.001 (0.09)	-
Ever had miscarriage/stillbirth/abortion t-statistic	-	-	−0.021** (2.54)	-
Household-level indicators
Total children in aged 0–1 years in HH Standard error	−0.055 (0.113)	0.18 (0.155)	0.02 (0.06)	-
Total children in aged 2–4 years in HH (Zambia CGP)/2–5 years (Kenya/Malawi) Standard error	−0.031 (0.091)	−0.151 (0.097)	−0.01 (0.062)
Methodology (model)	Individual level: cross-sectional probit; HH-level: DD Poisson	DD negative binomial	Individual level: Poisson/LPM; HH-level: DD Poisson	Cross-sectional probit
N	1,547 individuals (pregnancy analysis); 1,906 HHs	751 HHs	3,025 individuals; 2,519 HHs	2,612 individuals

Sources: Estimates summarized from the following sources: Kenya CT-OVC (Stecklov and Winters 2011; Handa et al. 2015); Malawi (Stecklov and Winters 2011); Zambia CGP (Palermo et al. 2016); Zambia MCTG (AIR 2016).

Note: Asterisks indicate the following:

^*= p < 0.1

^**= p < 0.05, and

^***= p < 0.01.

For Malawi, SCTP baseline means refer to Treatment Group only; LPM = linear probability model; DD = difference-in-differences; HH = household; T = treatment; CT-OVC = [Kenya] Cash Transfers for Orphans and Vulnerable Children; CGP = [Zambia] Child Grants Program; SCTP = [Malawi] Social Cash Transfer Program; MCTG = [Zambia] Multiple Categorical Targeting Grant. Analyses for outcomes of ever pregnant in Kenya and Zambia MCTG were conducted on the sample which had never been pregnant at baseline. In Kenya and Malawi, household-level means are for treatment group only. In Zambia, CGP, women-level indicators are for 24-month follow-up (instead of baseline), and examines all women aged 1249 years living in evaluation sample households over 48 months; In Zambia MCTG, analysis is among females aged 13–24 never pregnant at baseline.

We first summarize household-level composition impacts. Stecklov and Winters (2011) examined households in the Kenya CT-OVC evaluation (2007–2009) and in the Malawi SCTP evaluation in Mchinji (2007–2008) and found no impacts on the probability of having a child aged 0–1 years or 2–5 years (pooled, or by gender) in the households studied using DD probit regression analyses. Further, the authors examined the total count of children aged 0–17 in households using DD Poisson (or negative binomial) and PSM models for count data, and found no program impacts. The same methodology was replicated for the Zambia CGP, examining the total count of children aged 0–1 and 2–4 years (combined and by gender) using DD Poisson models, and again, no increases in fertility were found (Palermo et al. 2016).

Turning to individual woman-level outcomes, the Zambia CGP examined pregnancies, live births, stillbirths, and children born alive, currently living, or dead, for all women aged 15–49 in evaluation households, as reported by main respondents. The average number of total children ever born alive to women in the CGP evaluation was 3.24, and there were no overall positive impacts found on any of the afore-mentioned outcomes over a four-year period. The program decreased the probability by 2–3 percentage points that women reported ever having a stillbirth, miscarriage, or abortion at 24 and 48 (but not 36) months. Additionally, after 36 months, there was a decrease in fertility among women aged 15–24 in treatment households (p < 0.10; results not shown). However, this impact disappeared after 48 months. Further, in Kenya, an analysis of females between the ages of 12 and 24 who lived in households receiving the CT-OVC were approximately five percentage points more likely to delay their first pregnancy compared to females in control households (Handa et al. 2015).

The published evidence to date on fertility impacts among adult women of government-run UCT programs in SSA support our findings. The South Africa CSG was found to increase birth spacing among women receiving the grant: among women with a first birth, those receiving the CSG had a hazard ratio of 0.66 (CI: 0.58, 0.75) for a second pregnancy compared to women not receiving the CSG (Rosenberg et al. 2015). Further, women whose children aged out of the grant at the age of 7–8 years in April 2002 to March 2003 (prior to expansion of eligible age) had similar second pregnancy rates compared to women with children aged 7–8 years old between April 2003 and March 2004 (i.e., those whose children remained eligible due to expansion), indicating no evidence that women increased fertility in an attempt to re-qualify for the program (Rosenberg et al. 2015). Decreased childbearing was also found among younger women, namely those under 21 years. Females who received the CSG in adolescence were 10.5 percentage points less likely to have ever been pregnant compared to adolescents who did not receive the CSG (Heinrich, Hoddinott, and Samson 2017). These studies in South Africa used quasi-experimental matching methodologies, as the CSG was at scale nationally at the time of analysis and no RCT-design evaluation exists. In summary, in no instance has a government UCT increased fertility in SSA. To the contrary, existing evidence indicates that programs have increased birth spacing among women in South Africa and delayed pregnancies among youth in South Africa and Kenya while inducing no fertility impacts in Zambia and Malawi. Further, a recent systematic review of what works to prevent unintended and repeat pregnancies among adolescents in LMICs included 21 rigorous studies and highlighted cash transfers (both conditional and unconditional) as the most evidenced intervention to reduce pregnancy (Hindin et al. 2016). Together, these studies show that fears that cash transfers will incentivize increased fertility in Africa do not stand up to rigorous evaluation.

Perception 5: Transfers Will Lead to Negative Community-level Economic Impacts (Including Price Distortion and Inflation)

There is a fear that transfers injected into small, isolated communities may lead to negative community-level economic impacts, including inflation. These negative impacts could result if a concentrated cash injection raises demand without an associated increase in supply, or if markets are constrained or isolated. The resulting inflation may both devalue the transfer itself (attenuating the impacts of the cash) and also affect non-beneficiaries who may face higher local market prices. For example, cases of inflation attributed to cash transfers have been documented, particularly in humanitarian and post-conflict settings where markets are weak or constrained, and where transfers tend to be large and lumpy (Creti 2010; IPC-IG 2015). Aggregate local economic impacts have been less studied; however, they are increasingly important in understanding overall impacts and in making the investment case for cash transfers. In theory, if cash can be used to overcome market failures facing poor populations in rural economies (e.g., credit, insurance), there is good potential for cash transfers to not only stimulate pro-poor productivity, but also have net positive impacts on local economies (Alderman and Yemtsov 2014; IPC-IG 2015).

We start by investigating the possibility of inflationary impact by presenting information on changes in a vector of community-level prices in three countries across five evaluations (Lesotho, Malawi, Zambia CGP, and Zambia MCTG and Zimbabwe). Prices are collected from community-level surveys administered at local markets and shops (n = 254 in Lesotho; n = 85 in Malawi; n = 270 in Zambia CGP; n = 275 in Zambia MCTG, n = 178 in Zimbabwe). Table 9 shows that across ten items that comprise a standard basket of goods, there are no significant increases among treatment communities, with one exception—a weakly significant impact on the community-level price of beef in Lesotho. Similar analysis of additional prices not uniformly collected across countries are consistent with no impacts reported in table 9 (including the following items: wheat, sorghum, milk, candles, cassava, charcoal, foam mattresses, onions, plantains, tomatoes, yam, dry fish, laundry soap, and secondary school fees). The lack of evidence for inflationary effects of the program can be explained by three factors: 1) coverage is approximately 20 percent of households in communities; 2) beneficiaries are comparatively the poorest households in communities and therefore although the average transfer is substantial for the recipient, it represents a small injection to total community cash flows; and 3) even in rural areas, there is enough market inter-connectivity such that supply is not completely rigid, that is, market failures and constraints to production do not limit producers’ ability to meet increases in demand with adequate supply (Thome et al. 2016).¹⁰

Table 9.

Baseline Means and Impacts on Community-Level Prices.

	Lesotho CGP#	Malawi SCTP	Zambia CGP	Zambia MCTG	Zimbabwe HSCT
Panel A: Baseline Means
Maize grain	3.95	155.98	28.15	18.51	8.36
Rice	14.83	328.96	4.16	5.19	2.61
Beans	14.74	431.15	4.24	4.17	1.47
Beef	96.90	1160.93	-	-	4.95
Salt	8.02	22.77	4.86	3.34	1.00
Sugar	10.21	399.37	9.07	7.6	1.47
Cooking/edible oil/fat	20.57	44.74	12.77	10.92	2.41
Bar soap	43.21	71.91	5.88	4.49	2.19
Panadol	-	20.06	3.6	3.18	-
Panel B: Impacts (coefficients from multivariate regression models)
Maize grain	0.22 (0.69)	5.49 (0.11)	−5.50 (−1.78)	1.21 (0.68)	−0.32 (−0.46)
Rice	2.50 (0.86)	9.07 (0.34)	−0.45 (−0.94)	−0.60 (−1.13)	−0.07 (−0.51)
Beans	−1.57 (−1.49)	−25.65 (−0.33)	−0.68 (−0.79)	0.02 (0.06)	0.14 (0.83)
Beef	151.084* (1.92)	−99.50 (−0.85)	- (-)	- (-)	−0.29 (−0.71)
Salt	0.20 (0.29)	6.26 (0.74)	0.45 (0.50)	0.05 (0.14)	−0.03 (−0.19)
Sugar	0.28 (0.51)	−36.42 (−0.60)	−0.21 (−0.41)	−0.08 (−0.18)	−0.03 (−0.32)
Cooking/edible oil/fat	−0.73 (−0.52)	13.14 (1.34)	−0.40 (−1.24)	−0.21 (−0.36)	−0.11 (−1.02)
Bar soap	−19.75 (−0.97)	−0.98 (−0.11)	0.33 (1.22)	0.26 (0.98)	0.02 (0.12)
Panadol	- (-)	−5.16* (−1.67)	0.98 (1.57)	−0.24 (−0.69)	- (-)
N (communities)	254	85	270	275	178
Currency	Loti	Kwacha	Kwacha	Kwacha	USD

Sources: The # symbol indicates the estimate is the authors’ additional calculation. In all other cases, estimates summarized from the following sources: Malawi SCTP (UNC 2016b); Zambia CGP (AIR 2014b); Zambia MCTG (AIR 2016); Zimbabwe HSCT (AIR 2014a).

Note: Asterisks indicate the following:

^*= p < 0.1

^**= p < 0.05, and

^***= p < 0.01.

Prices are reported in standardized units, which vary across items (majority representing kilograms). Robust t-statistics are provided in parentheses when reported. Zambia CGP and MCTG reports do not provide p-values and only indicate significance at thep < 0.05 level or better. CGP= [Lesotho and Zambia] Child Grant Programs; SCTP =[Malawi] Social Cash Transfer Program; MCTG = [Zambia] Multiple Categorical Targeting Grant; HSCT = [Zimbabwe] Harmonized Social Cash Transfer. Additional indicators include wheat, sorghum, milk, candles, cassava, charcoal, foam mattress, onions, plantains, tomatoes, yam, dry fish, laundry soap, and secondary school fees, but all were not included in every country. No other reported indicators were significant across all countries with data

The evidence to date on inflationary impacts of cash transfers is thin. However, supporting studies from other regions and in varied contexts show similar results. For example, Cunha, De Giorgi, and Jayachandran (2017) examine differences in local prices within villages randomized to food or cash transfers, as compared to a control group in Mexico. Findings suggest no significant differences in cash transfer villages, while in food transfer villages, prices of the goods in the food transfer basket decrease by 3.7 percent. In addition, Aker et al. (2016) found no price effects on a basket of common market goods in weeks where cash was dispersed in rural Niger, for both mobile and manual transfers.

The potential for a supply response to increased demand for goods and services, coupled with increased productive investment and output by beneficiary households, leads to the possibility of a local economy “spillover” effect. Specifically, if beneficiaries spend transfers in the local economy, demand for goods and services may increase, and if local production increases to meet this demand, a local multiplier effect could generate positive impacts of the transfer for suppliers of goods and services who are typically non-beneficiaries. In seven countries, specialized sampling and application of general equilibrium modeling was applied to study local economy-wide impact evaluation (LEWIE). This approach included data collection on households not eligible for the cash transfer programs, administration of community-level price modules and business enterprise surveys to identify local spillover effects, through linking agricultural household models to general-equilibrium models of villages (clusters; see Taylor and Filipski 2014; Taylor, Thome, and Filipski 2016; Thome et al. 2016 for detailed methodology).¹¹

Local economy simulations indicate that rather than having no effect at all—or making everyone worse off through inflation—the programs generate substantial impacts for non-beneficiaries (figure 2). Nominal multiplier effects range from 1.27 in Malawi to 2.52 in Ethiopia (Hintalo area). This means that every dollar transferred in Hintalo generated an additional $1.52 of benefits for the local economy through the multiplier effect. As transfers are non-inflationary, nominal values are appropriate. Moreover, these multiplier effects largely accrue to non-beneficiaries, who are local shopkeepers and service providers. In a detailed cross-country paper exploring LEWIE models, Thome et al. (2016) link the variation in these positive multiplier effects to differences in the size of the local economy, the size of the transfer, and features of the local economy. Consequently, evidence across the countries studied here clearly shows that communities receiving cash transfers are not subject to price inflation or distortion, and that in fact transfers tend to generate important multiplier effects in the local economy, which primarily accrue to non-beneficiaries.

Figure 2. Nominal Income Multipliers with 95 percent Confidence Intervals, by Cash Transfer Program.

Source: Adapted from Thome et al. (2016)

Notes: SCTPP = [Ethiopia Tigray] Social Cash Transfer Pilot Program; LEAP = [Ghana] Livelihood Empowerment Against Poverty Program; CT-OVC = [Kenya] Cash Transfers for Orphans and Vulnerable Children; CGP = [Lesotho and Zambia] Child Grant Programs; SCTP = [Malawi] Social Cash Transfer Program; MCTG = [Zambia] Multiple Categorical Targeting Grant; HSCT = [Zimbabwe] Harmonized Social Cash Transfer.

Perception 6: Cash Transfers at Scale Are not Fiscally Sustainable

As cash transfers are institutionalized and scaled up as part of government programming, there have been sustainability concerns that programs are too costly to maintain over the medium- or longer-term. Critiques of high administrative costs for cash transfers have been voiced for decades, particularly for CCTs where additional components, including the monitoring of conditions, may increase costs. For example, with reference to social protection in Latin America, Grosh (1994) writes that, “Concern over high administrative costs is perhaps the reason that is most commonly given for not adopting targeted programs.” The collection of costing data is still relatively rare in large-scale evaluations. However, it will become increasingly important as governments in SSA seek to understand the fiscal sustainability and national budgeting needed to institutionalize such programs.

Cost analyses were implemented across Transfer Project evaluations in three countries (Kenya, Lesotho, and Zambia for both CGP and MCTG). The cost-transfer ratio (CTR; i.e., the ratio of administrative costs to transfer costs), has been generally used to measure the cost-efficiency of the programs.¹² Hodges, White, and Greenslade (2011, 2013) show that cash transfer programs with complex targeting approaches, such as those included in the Transfer Project, have high CTRs. However, this is largely explained by the fact that they tend to be quite recent, still have large fixed start-up costs, and have not yet achieved economies of scale. For instance, Kardan, Sindou, and Pellerano (2014) show that for the CGP in Lesotho, 100 percent of the costs in the first 15 months of the program were devoted to its start-up. However, the CTR fell from 2.28 (January 2009 to December 2011) to 0.53 (January 2012 to December 2012).¹³ CTRs across programs and countries vary enormously and are determined by the age of the program, the value of the transfer, and costs related to design and roll-out of the program. This means that a perfect comparison across countries is not always possible. However, a similar story emerges from the Kenya CT-OVC evaluation and for the two social cash transfer schemes in Zambia, for which Ward et al. (2010) and Jesse et al. (2014), respectively, found large efficiency gains after three and four years of implementation. In Kenya, the CTR for the CT-OVC in the third year of implementation declined to 0.34, while in Zambia. In the fourth year of implementation of the two SCT models, the CTR was 0.45 and 0.63 for the CGP and the MCTG, respectively. The CTRs reported from the Transfer Project evaluations are relatively larger than the most-cited example of Mexico’s Prospera (formerly Progresa and Oportunidades), but because of design differences, it is difficult to make strict comparisons (Caldés, Coady, and Maluccio 2006).

Costing studies carried out for Transfer Project evaluations also assessed the fiscal sustainability of SCTs. Under a “no expansion scenario, “ Kardan, Sindou, and Pellerano (2014) found that the cost of the CGP in Lesotho is 0.4 percent of total government expenditure (0.2 percent of GDP) in 2014/15. Further, the upper bound costs of the program are reflected under the scenario of full national expansion by 2020/21. Under this scenario, the cost of the program increases to 1.7 percent of total expenditure or 0.8 percent of GDP in 2020/21.¹⁴ Jesse et al. (2014) report that the total expenditure in Zambia for the SCT program overall was approximately only 0.06 percent of GDP in 2013, which was the last year of the study. Ward et al. (2010) estimate that if the CT-OVC in Kenya covered all households with OVCs, the total program expenditures would represent 1.29 percent of GDP.

Extending simulated scale-up costs for national programs to other countries in SSA, Plavgo, de Milliano, and Handa (2013) utilize key program parameters from Transfer Project countries for estimates. These authors assume that a hypothetical program would target the ultra-poor, scale up to 20 percent of the national population, pay an amount equivalent to 20 percent of households’ pre-intervention monthly consumption, and incur administration costs of 12 percent.¹⁵ These authors average government spending for 48 countries in SSA over the 2008–2012 period to compare transfer and administrative costs to Gross Domestic Product (GDP) and government expenditures. The results of this exercise show that the annual cost of a UCT in 2012 would range between 0.1 and 2 percent of GDP for most countries, with an overall average of 1.1 percent of GDP. As a percentage of general government expenditures, the price tag is higher: an average of 4.4 percent across countries, with a cost of below 1 percent for nine countries; between 1 and 5 percent for 21 countries; between 5 and 10 percent for 14 countries, and over 10 percent for four countries (Democratic Republic of Congo, Zimbabwe, Central African Republic, and Madagascar). In addition, since most cash transfer programs target rural areas, if expansion were restricted to rural households (as is currently the case), costs could fall by approximately 37 percent. Thus, with the exception of a few outliers—including those which have seen significant civil unrest in recent years—cash transfers at scale as a percentage of current spending and GDP are feasible and fully within the cost considerations of any national government.

To contextualize these costs in terms of national spending, it is useful to draw some comparisons to other government programs. Jayne and Rashid (2013) synthesized recent literature on input subsidy programs (ISPs) in SSA, which have both economic productivity and poverty alleviation objectives and tend to be important “competitors” to cash transfer programs in SSA. In 2011, the latest year for which data were available, 10 African countries spent roughly US$1.05 billion on ISPs, amounting to 28.6 percent of their public expenditures on agriculture. Despite this large investment, evidence indicates that the costs of these programs generally out-weigh their benefits. Another example of an input subsidy program comes from the Malawi Farm Input Subsidy Program (FISP), which distributes fertilizer and seed coupons to farmers that are redeemable for two 50 kg bags of subsidized fertilizer and 5–10 kg bags of seeds. The program covers approximately two-fifths of the population and accounts for approximately 9 percent of the national budget and between 3 and 6 percent of GDP (Arndt, Pauw, and Thurlow 2016). Evidence shows that while the FISP in Malawi contributed to raise national food production (Lunduka, Ricker-Gilbert, and Fisher 2013), its effects were highly asymmetric across the distributions of farm size and wealth, tending to be concentrated on better-off farm households. Finally, overall spending on cash transfers or vouchers is low compared to other types of social safety nets in SSA. In a review of spending by program type in 11 countries, only in two countries (Lesotho and Mauritius) did transfers make up a substantial percentage of total social safety net spending, at 47 and 87 percent, respectively (Monchuk 2014). In all other countries, cash makes up a small sliver of spending (ranging from 0 to 5 percent), dwarfed by spending on school feeding, public works, and other nutrition or in-kind spending. Overall, although there is competition for budget allocations in any public program, cash transfers at scale appear to be fully within the fiscal envelope of national governments in SSA (Plavgo, de Milliano, and Handa 2013; Monchuk 2014).

Discussion and Conclusion

Using rigorous evaluations conducted on large-scale government UCTs in SSA, we summarize evidence on six common perceptions that dominate the policy discourse around cash transfer programs. Specifically, we investigate if transfers: 1) induce higher spending on alcohol or tobacco; 2) are fully consumed (rather than invested); 3) create dependency (reduce participation in productive work); 4) increase fertility; 5) lead to negative community-level economic impacts (including price distortion and inflation); and 6) are fiscally unsustainable. We find ample evidence to refute each claim, which leads us to the conclusion that these perceptions are actually “myths”, and insofar as they continue to be cited in policy debates, limit the range of feasible tools that governments can consider to reduce poverty and support inclusive growth. It is also worth highlighting that all the evidence reviewed here derives from large-scale national programs owned and operated by African governments and is therefore directly applicable to national policy dialogues and debates.

There are also a number of outstanding perceptions or “possible myths” that are debated in the literature, with implications for program design, which the Transfer Project evaluations are not well positioned to answer. We mention only a small sub-set of the outstanding questions related to cash transfers. First, the role of conditionalities in delivering impacts is a topic of extensive debate (Pellerano and Barca 2014), and one which we are not able to address using Transfer Project evaluations. There are long-standing perceptions around the sex of the transfer recipient, and related comparative impacts, particularly on children’s and household wellbeing outcomes, which remain largely untested across program designs and settings (Yoong, Rabinovich, and Diepeveen 2012). Relatedly, there is a long-standing debate as to whether and how cash transfers (both UCTs and CCTs) empower women, which remains unresolved largely due to the multitude of program designs, which may have differentiated impacts as well as a lack of consensus in how to best measure empowerment (Peterman et al. 2015; Bonilla et al. 2017). These debates have led to perceptions that transfers may in fact reinforce traditional gender norms, including investment of women in the care economy, as well as fears that cash transfers may increase conflict and exposure to IPV. The former is generally cited as a result of conditions in programing related to child health and nutrition (Molyneux 2006; Chant 2008), which is not applicable within the UCTs examined as part of the Transfer Project. In addition, there is increasingly accumulating evidence that cash transfers can actually decrease women’s exposure to IPV, largely through decreases in the structural determinants of violence, including reductions in poverty-related stress and overall increases in household wellbeing (Buller et al. 2016, 2018; Hidrobo, Peterman, and Heise 2016). The Transfer Project is increasingly interested in this potential, and has case studies in ongoing evaluations of national programs in Ghana and Tanzania to explore these dynamics (baselines conducted in 2015).

A number of limitations to the current summary are worth mentioning. First, although the Transfer Project analyzes data from an impressive array of impact evaluations, the majority are from Eastern and Southern Africa, and only one is from West Africa (Ghana). In addition, the program duration for these impact evaluations can be taken as medium-term, as the majority cover periods from 12 to 36 months. As we largely summarize existing results from peer-reviewed publications and country technical reports, we are often limited in the ability to make full comparisons across indicators for lack of comparable indicators across countries. Despite the subtle differences across indicators or evaluations, we believe with full disclosure of these differences, that this does not detract from the conclusions drawn. Further, we do not explicitly address power limitations and multiple hypothesis testing in this review unless the original estimates did so. Finally, the distributional impacts across baseline consumption levels of the beneficiary sample undoubtedly would result in interesting findings, particularly given ongoing targeting debates; however, to date none of the studies have conducted such an analysis.

Our collaborative work with national governments over the past decade through the Transfer Project has demonstrated the importance of the political environment in facilitating the financing, scale-up, and public acceptance of programs. A recently published Transfer Project book shows that government-run UCTs have been responsible for improvements across social and economic domains, and that differences across countries tend to be a factor of the size and regularity of transfers, the demographic profile of beneficiaries, effectiveness of targeting, and availability of supply-side services (Davis et al. 2016b). The evolution of the Transfer Project and the inclusion of rigorous impact evaluations as part of government programing highlight the role that research can play in the design, scale up and political acceptability of UCTs as part of social protections in SSA. To this end, we have drawn on cross-country evaluation data to summarize evidence on six common perceptions that we believe hold back political acceptance of such programs. While the political context is such that these perceptions will need to be tested in each specific program in order to be fully internalized, we hope that the growing body of evidence, including that presented in this paper, will permit more evidence-based rather than ideologically-based debates around cash transfers in LMICs.