Cash versus Kind:
Benchmarking a Child Nutrition Program against
Unconditional Cash Transfers in Rwanda∗
Craig McIntosh† and Andrew Zeitlin‡
Revised; November 30, 2023

Abstract
We develop a methodology to benchmark in-kind programs against cost-equivalent cashtransfers. Our application compares a multi-dimensional child nutrition intervention to unconditional cash transfers, using randomized variation in transfer amounts and regression adjustment of expenditures to estimate impacts of cash transfers at identical cost as well as toestimate the return to increasing cash transfer amounts. While neither the in-kind program nora cost-equivalent transfer costing $124 per household moves core child outcomes within a year,
cash transfers create significantly greater consumption and asset accumulation. A larger cashtransfer costing $517 substantially improves consumption and investment outcomes and drives
modest improvements in dietary diversity and child growth.
Keywords: Experimental Design, Cash Transfers, Malnutrition
JEL Codes: O12, C93, I15∗
We are grateful to DIV, Google.org, and USAID Rwanda for funding, and to USAID, CRS, GiveDirectly,
CEGA/DIL, and IPA for their close collaboration. We thank Leodimir Mfura and Marius Chabi for overseeing
the fieldwork, and Richard Appell, Sarait Cardenas-Rodriguez, Chris Gray, Ali Hamza, and Bastien Koch for research assistance. This project is covered by Rwanda National Ethics Committee IRB 143/RNEC/2017 and IPA IRB
13730, and the study is pre-registered with the AEA as trial AEARCTR-0002559. This study is made possible by
the support of the American People through the United States Agency for International Development (USAID). The
contents of this study are the sole responsibility of the authors and do not necessarily reflect the views of USAID or
the United States Government.
†
University of California, San Diegoctmcintosh@ucsd.edu‡
Georgetown University, andrew.zeitlin@georgetown.edu

1Introduction
Should governments and aid agencies provide assistance in kind, or would beneficiaries be better

off if they just received the money? Unconditional cash transfers provide a natural benchmark to
in-kind programs for several reasons. From a practical perspective, cash transfers generate welldocumented benefits for households (Aizer et al., 2016; Haushofer and Shapiro, 2016), and theircosts are falling across the developing world due to the penetration of mobile money (Suri, 2017).
Cash programs are flexible and can be rescaled in terms of targeting or expense, which allows them
to be paired at cost-equivalent levels against more complex interventions.1 By contrast, comparisons across programs in different contexts and at different costs rely on strong assumptions about
external validity (Vivalt, 2015) and linearity (both in cost-effectiveness ratios and the preferencesof policymakers across individuals). More fundamentally, decisions made over the use of unconditional transfers may reveal information about the preferences of the beneficiary households that is
important in interpreting the welfare effects of more complex programs (Finkelstein and Hendren,2020). While missing markets, externalities, or other failures can justify the provision of in-kindprograms, benchmarking the impact of such programs against cost-equivalent cash transfers forces
policymakers to be explicit about the circumstances that would merit a more multidimensional,
overhead-heavy approach. For these reasons, recent years have seen increasing calls for head-tohead studies that use cash as a benchmark for more complex forms of international development
assistance.2
In this study we develop an experimental approach to cost-equivalent benchmarking of in-kindinterventions to cash transfers, with an application to a nutritional and maternal health intervention
in Rwanda. The in-kind program, Catholic Relief Service’s Gikuriro, tackles malnutrition frommultiple dimensions, resulting in a highly bundled intervention. Since cash transfers are have beenshown in other contexts to drive child consumption outcomes (Aguero et al., 2006; Baird et al.,2019; Seidenfeld et al., 2014), this context and set of outcomes represent an ideal setting for the1

In medical research, new interventions are typically benchmarked against the best current treatment, referred
to as the ‘standard of care’. Perhaps the best-validated anti-poverty tool in the development literature is BRAC’s
Targeting the Ultra-Poor program (Banerjee et al., 2015); however given that this program is expensive ($3,700
per household), lengthy in duration (a year of intensive intervention), and targeted at very specific households (theultra-poor), it represents a somewhat inflexible benchmark for the broad range of potential development interventions.
2
In particular Blattman and Niehaus (2014) argue for the use of cash transfers as the ‘index funds’ of international
development, providing a reference rate of return that could be used to hold donors accountable.

1benchmarking comparison. Gikuriro’s multi-faceted approach has been broadly advocated in the
public health literature (Ruel et al., 2013) and has been shown to be effective in similar contexts(Leroy et al., 2016).3 Such programs attract substantial funding worldwide; for example, USAID’s
Bureau for Humanitarian Assistance invests approximately 400 million dollars annually to support
similar multi-sectoral food security programs.4 This necessarily complex and overhead-intensive
approach presents a stark contrast with the pared-down nature of a cash-transfer alternative inwhich the share of spending received by the beneficiaries is maximized, implemented here by the
U.S. non-profit GiveDirectly. Further, our study districts feature under-five stunting rates of 35–45
percent (DHS, 2016), portending dire long-term consequences for human capital formation (Currie
and Almond, 2011). This context therefore provides two contrasting approaches with distinct causal
pathways to the same, critical policy outcome.
To form this comparison, we conduct a cluster-randomized trial across 248 villages located in
Kayonza and Nyabihu districts of Rwanda.5 We enroll households into the study if they are defined
as poor by the Government of Rwanda, and contain either pregnant women or children underfive years of age. Eligible beneficiaries were assigned at the village level to be offered either the
Gikuriro intervention or a cash transfer. Given that the eventual exact costs of programs are notknown ex ante, we randomized cash transfer values around the expected cost; this variation is then
combined with an ex post linear regression adjustment of the costs across arms to form an exact,
cost-equivalent comparison. Thirteen months after baseline, we measure impacts on five primary
outcomes—household consumption, household dietary diversity, child and maternal anemia, child
growth, and household non-land net wealth—as well as a broader set of secondary measures. These
outcomes are documented as core drivers of improved long-run outcomes for children (Hoddinott etal., 2013; Maluccio et al., 2009), as well as being plausibly impacted by both intervention types.6
3

The Lancet’s Maternal and Child Nutrition Study Group specifically advocates for this type of multidimensional
approach, saying “[t]he need for investments to boost agricultural production, keep prices low, and increase incomesis indisputable; targeted agricultural programmes can complement these investments by supporting livelihoods, enhancing access to diverse diets in poor populations, and fostering women’s empowerment” (Ruel et al., 2013).
4
Most of these programs include infant and child feeding practices, maternal health and nutrition within the 1000days window, WASH, village saving and loan associations, livelihoods and agriculture, and disaster risk management
components. Currently, they have similar programs in Bangladesh, Niger, Burkina Faso, Mali, DRC, Uganda, Malawi,
Ethiopia, Kenya, Madagascar, Zimbabwe, and Haiti.
5
The village-level study design was motivated by the clustered nature of the Gikuriro intervention. In addition,
this helps to allay concerns about the potential for spillovers of cash transfers.
6
All primary and secondary outcomes were registered prior to receipt of endline data on the American Economic
Association RCT Registry, with ID AEARCTR-0002559.

2

The findings of this study design, like that of a companion study that benchmarks cash against
a youth employment program (McIntosh and Zeitlin, 2022), allow us to assess tradeoffs acrossoutcomes, across sub-populations, and across expenditure per beneficiary.7 Across outcomes, the cash
and in-kind programs vary in their relative efficacy. At a cost of $142, Gikuriro was successful atdelivering gains in savings, a domain that was the target of one core program component. It did notlead to improvements in consumption, dietary diversity, wealth, child anthropometrics, or anemia
within the thirteen-month period of the study. A cost-equivalent cash transfer has significantly larger
effects on consumption, allows households to pay down debt, and generates increased investmentin productive and consumption assets. Across sub-populations, we uncover very little evidenceof heterogeneity in impacts, suggesting that targeting these interventions more tightly would notincrease the average effect substantially. Across expenditure levels, our results show that a muchlarger cash transfer costing $567 led to across-the-board improvements in consumption-based welfare
measures, a substantial improvement in dietary diversity, a drop in child mortality, and modest
improvements of about 0.1 standard deviation in the anthropometric indicators of height-for-age,weight-for-age, and mid-upper arm circumference (all significant at 10 percent or above). The large
cash transfer delivers benefits even on outcomes specifically targeted by the other program. While
it is unsurprising that very large amounts of money show up in consumption and productive assets,
the improvements in diet and particularly child anthropometrics over such a short period of time
are impressive (although our study does not feature an arm with a high-cost implementation of thein-kind arm to correspond to the large cash transfer). For a given target population, policymakers
must therefore decide how to trade off between relatively intensive interventions that can only beprovided to a small subset, or lower-cost interventions that can reach a larger proportion of thistarget group.8
The primary contribution of this paper is to develop a methodology that allows for exact costequivalent comparisons in the face of the inevitable a priori uncertainty that exists as to program
costs. This is critical because comparison of programs operated at different costs requires an as7

The successor study uses the same benchmarking methodology (comparing an in-kind program to randomized
unconditional cash transfers) to answer an applied question from the labor literature: namely, how best to help
underemployed youth gain access to productive work.
8
A fourth dimension of tradeoff, namely across time scales, is not possible in this paper because the control group
was treated immediately after the one-year endline. In the Conclusion we discuss the implications of the relatively
short duration of this study as well as the asymmetry arising from the fact that cash transfer flows begin shortly
after baseline, while the in-kind arm was delivered in the middle of this transfer period.

3

sumption of linearity-in-expenditure that is not typically testable within each arm. Alternatively,
studies that attempt to generate cost equivalent comparisons based on anticipated costs at thetime of design will almost inevitably be wrong after the fact, undermining their core purpose. By
randomizing cash transfer amounts we can achieve two distinct goals, both allowing for cost adjustment based on the correct, ex post costs, and examination of the way that the returns to cash
vary with spending per person (that is, the question of linearity-in-expenditure). Our approachyields an ex-post cost-equivalent comparison with relatively little power loss relative to a designthat takes a single ex-ante guess about these ex-post costs, while permitting robustness checks thattest sensitivity to the nature of the interpolation.
In applying this approach, we extend the small number of studies using cash transfers as one
arm within multi-armed trials. The largest extant literature is based on the comparison of cash
aid to food aid (Ahmed et al., 2016; Hidrobo et al., 2014; Hoddinott et al., 2014; Leroy et al.,2010; Schwab et al., 2013), uncovering a fairly consistent result that food aid leads to a largerchange in total calories while cash aid leads to an improvement in the diversity of foods consumed,
while benefits for targeted individuals can be limited and there can be adverse consequences for
non-targeted individuals when market imperfections mean that cash transfers cause food priceincreases (Cunha et al., 2019; Filmer et al., 2021).9,10 Efforts to compare more complex, multidimensional programs against cash include BRAC’s Targeting the Ultra-Poor program (Chowdhury
et al., 2017), microfranchising (Brudevold-Newman et al., 2017), agricultural inputs (BrudevoldNewman et al., 2017), and sustainable livelihoods (Sedlmayr et al., 2017). These studies havetypically struggled with the question of how to anticipate costs and compliance well enough torealize an exact cost-equivalent comparison after the fact; we address this with our study design.
Further, we extend the existing literature comparing in-kind interventions to cash by illuminating
the extent of heterogeneity in the returns to cash versus kind, and by showing how the comparisonof in-kind programming with a larger cash transfer on a benefit-cost basis would lead to different
conclusion.
9
An interesting contribution to this food versus cash literature is Hirvonen and Hoddinott (2020), who find thatpoor and geographically marginalized households may prefer food transfers because of a lack of consumer markets
during times of shortfall.
10
A related literature compares food transfers with vouchers redeemable for the purchase of food. Recent work by
Banerjee et al. (2021) shows positive effects of shifting to vouchers, attributable to changes in the share of amount ofassistance reaching targeted households and in the quality of food consumed.

4

Beyond this, we add to the growing number of studies that show meaningful impacts of cashtransfers on important life outcomes in the short term, in domains with plausible channels for longterm impact. These include evidence of impacts on child nutrition (Aguero et al., 2006; Seidenfeld
et al., 2014), schooling (Skoufias et al., 2001), mental health (Baird et al., 2013; Samuels andStavropoulou, 2016), teen pregnancy and HIV (Baird et al., 2011), microenterprise outcomes (De Mel
et al., 2012), consumer durables (Haushofer and Shapiro, 2016), and productive assets (Gertler etal., 2012). The evidence on the long-term impacts of cash transfers is more mixed, but somestudies have found substantial impacts (Aizer et al., 2016; Barham et al., 2014; Fernald et al., 2009;
Hoynes et al., 2016).11 We contribute to the cash transfer literature by evaluating multiple transfer
amounts and modalities in the same context, and illuminating the scope for beneficiary choice overcash-transfer modality to drive impacts.
The rest of the paper is organized as follows. Section 2 lays out the study design. In Section
3, we present simple ITT analyses of the experimental results. presents the core empirical resultsof the benchmarking exercise, In Section 4, we formalize the policy choice problem, undertake ourcost-equivalent approach to benchmarking policy comparisons, and compare this to results of a
traditional cost-effectiveness approach to comparative evaluation. Section 5 concludes.

2

Study design

2.1

Interventions studied

The Gikuriro program was developed by USAID, Catholic Relief Services (CRS), and the Netherlands Development Organization (SNV) to “improve the nutritional status of women of reproductive
age and children under five years of age, with an emphasis on the 1,000 day window of opportunityfrom pregnancy until a child’s second birthday” (Figlio et al., 2014).12 The resulting multi-faceted
program brings together several components in order to attack this problem from multiple directions
at once, and is a central pillar of the Government of Rwanda’s approach to combatting malnutri11
For examples of studies that find dissipating long-term benefits, see Baird et al. (2016), Araujo et al. (2017), andBlattman et al. (2020). For evidence from systematic reviews of cash transfers on schooling see (Molina-Millan et al.,2016), and on child health see (Manley et al., 2013; Pega et al., 2014).
12
Gikuriro means “well-growing child” in Kinyarwanda. Its objectives are documented in USAID Cooperative
Agreement No.: AID-696-A-16-00001, 2015).

5

tion in rural Rwanda.13 Gikuriro combines an integrated nutrition program with a standard WASH
curriculum (water, sanitation, and hygiene), and seeks to build the capacity of the health infrastructure providing services to mothers and newborns, particularly Community Health Workers (CHWs).
The program also seeks to improve livelihoods by providing additional assistance to eligible households, including (a) Village Nutrition Schools (VNS); (b) Farmer field learning schools (FFLS),
which potentially includes distribution of small livestock, fortified seed, etc.; (c) Savings and Internal Lending Communities (SILCs); and (d) the Government of Rwanda’s Community-Based
Environmental Health Promotion Program (CBEHPP). In addition, Gikuriro provided a programof Behavioral Change Communication (BCC), supporting participation in all components of the
program including savings, agriculture, and nutrition, as well as hygiene. Similarly designed programs in neighboring countries have been shown to have positive impacts.14 This comprehensive
approach seeks to build supply and demand for child health services simultaneously, and is fairly
typical of the kinds of multi-sectoral child health programs implemented by USAID in many partsof the developing world.15
In terms of program expenditures, the most substantial component of Gikuriro is the Farmer
Field Schools, which consume 41% of the overall budget. The main cost driver here was the distribution of seeds, as well as small livestock and poultry. Next most important were the village
nutritional schools, with 19% of the spend. This component’s core goal was to use demonstration
plots to show how to use very small plots (or even gunny sacks for the land poor) to grow microgardens with a variety of nutrient rich greens that could be used to support child nutrition. The
other components of program expenditure were overall logistical program support (22%), monitoring and evaluation (9%), the SILCs (6%), and the BCC and WASH trainings (4%). Our estimates
13

USAID’s Global Health and Nutrition Strategy explicitly calls for multi-sectoral interventions that incorporate
agriculture, WASH, education, and outreach to mothers in the first 1,000 days through the public health system.
The agency reports reaching 27 million children worldwide under the age of 5 in 2016 alone through such programs,
which represent the prescribed USAID modality for Scaling up Nutrition (SUN) countries.
14
Leroy et al. (2016) estimate the impacts of Tubaramure, a program in Burundi undertaken by the same implementer, which provided food supplementation along with a BCC strategy akin to that of Gikuriro, finding reductionsin anemia among post-partum women and improvements in dietary diversity. Perhaps closest in design to Gikuriro
is the combined WASH and nutrition intervention studied in rural Kenya by Null et al. (2018). Like Gikuriro, theystudied the impacts of a combined WASH and nutrition intervention. They found that the nutrition intervention(whether offered together with WASH or alone) delivered improvements of between 0.11 and 0.14 Z scores of weightfor-age, with “almost all of the growth benefits in the nutrition group and combined water, sanitation, handwashing,and nutrition group were already manifest in the first year.”
15
Examples of similar integrated WASH/agriculture/child nutrition programs funded by USAID include SPRING
in Bangladesh, RING in Ghana, Yaajende in Senegal, and ENGINE in Ethiopia.

6

suggest that a household participating in every component of Gikuriro would have actually received
training and inputs worth $70.13, of which $5.06 is direct transfer of materials and inputs.
To benchmark the impact of this program against cash we worked with GiveDirectly, a US-based
501(c)3 Non-Profit organization. GiveDirectly specializes in sending mobile money transfers directly
to the mobile phones of beneficiary households to provide large-scale household grants in developing countries including Kenya, Uganda, and Rwanda. GiveDirectly’s typical model has involved
targeting households using mass-scale proxy targeting criteria such as roof quality. GiveDirectlybuilds an in-country infrastructure that allows them to enroll and make transfers to households
while simultaneously validating (via calls from a phone bank) that transfers have been received by
the correct people and in a timely manner. Their typical transfers are large and lump-sum, on theorder of $1,000, and the organization provides a programatically relevant counterfactual to standard
development aid programs because it has a scalable business model that would in fact be capable ofproviding transfers to the tens of thousands of households that are served by the Gikuriro program.
Because of the nutritional focus of the Gikuriro intervention, GiveDirectly incorporated a ‘nudge’
into the way the program was introduced (Benhassine et al., 2015), distributing a flyer at the timeof the intervention that emphasized the importance of child nutrition. An English translation of thisflyer is included in Online Appendix A.2. Given observed impacts of cash transfers on other goods,e.g., productive assets and housing value, it is evident that households felt at liberty to spend the
grants on items not directly related to child nutrition.

2.2

Study outcomes and eligibility

We pre-committed to five primary outcomes and seven secondary outcomes for the study. The
primary outcomes are consumption, dietary diversity, child growth (height-for-age and weight-forage, as well as mid-upper arm circumference), and household non-land wealth. The secondary
outcomes are borrowing and saving, pregnancy and live birth rates, health knowledge, mortality,
health-seeking behaviors, productive assets, and housing quality. A more detailed description of
all outcomes is provided in the Online Appendix. Data were collected by Innovations for Poverty
Action (IPA) teams through two survey waves; the baseline was conducted in August and Septemberof 2016, and the endline was conducted in September and October of 2017. The baseline included acomprehensive household survey module as well as anthropometric measurement of all children five7

and under in eligible households.16 The endline consisted of these same measures plus blood-based
anemia testing for the study children and for all women of childbearing age.17
Both implementers made contact with the study subjects and began enrollment immediately
after baseline. Give Directly (GD) began implementation shortly after the baseline meaning that at
endline individuals in that arm had experienced about 12 months of the household grants treatment(running up through the month before endline). Gikuriro was slower than the cash program tobegin implementation on the ground; in that arm households had typically experienced 8-9 monthsof household-level implementation at the time of the endline.18 The duration of the RCT componentof the study was limited by the fact that local governments wanted to hit targets for the broader,
national rollout of nutritional and WASH programming, and hence we were not able to maintainthe control groups for more than one year. We cannot therefore speak to the long-term impactsof the interventions. Anticipating this issue, we took two approaches to measurement. One ofthem was to try capture the stocks of intertemporal assets that would be the obvious conduits tofuture consumption benefits for the households. The second was to emphasize outcomes such as
dietary diversity and anemia that have the potential to respond quickly to changes in consumption
patterns, while also retaining the more standard metrics of child malnutrition such as height for age(HAZ), weight for age (WAZ), and mid-upper arm circumference (MUAC).19 Further, a number ofrecent RCTs have shown that programs can have meaningful impacts on biometric outcomes overtimeframes similar to that analyzed in this study, such as Desai et al. (2015), Leroy et al. (2016),
Fink et al. (2017), and Null et al. (2018).
A major practical issue with this kind of comparative study is how to define the the target group,
given that each implementer would naturally do this differently. We collectively resolved this byagreeing to a set of readily observable characteristics that both implementers deemed eligible for
their funding and desirable to target, and then tasking the survey firm with listing all households
16

We weighed all children younger than 6 years once using a Seca 385 scale. We measured length for children under
two years with Seca 417 length boards and height for older children to the nearest 0.1 cm with Seca 213 stadiometers.
These were measured twice and we took the average of the two measurements unless they differed by more than 0.7
cm in which case we took a third measurement and averaged the two closest measurements.
17
Using the guideline for anemia testing in population-based surveys, we used HemoCue Hb 301 system.
18
Since both programs had six months of notice that they would be implementing in the study sample in thesetwo districts and began national-level implementation at the same time, this differential delay likely reflects a realdifference in the relative ramp-up speeds of cash versus more complex programming.
19
Dietary diversity is an immediate indicator of improvements in consumption, and the clinical literature has shownthat anemia tests respond within 3 months of improvements in diet (Habicht and Pelletier, 1990).

8

in study villages and allocating them to the ‘eligible’ or ‘ineligible’ stratum. Households wereconsidered eligible if they contained a child known to the government growth monitoring system
to be malnourished, or else were in the poorest two government poverty classifications (Ubudehe
1 or 2) and contained either children under the age of 5 or a pregnant/lactating woman. Allother households in study villages are classified as ineligible. The survey firm passed the namesof all eligible households to the implementing partners in an identical way, and we then randomly
sampled up to 8 eligible and 4 ineligible households per village for the study. The identity of thesampled households was not revealed to either implementer. In this paper we use survey weights toestimate impacts that are representative for all eligible households in study villages.
Both implementers concurred quite closely with our definition of eligibility on the ground, andcompliance was high: we have 80 percent of the eligibles treated by Gikuriro and 84 percent by
GiveDirectly.20

2.3

Assignment Protocol

Randomization occurred at the village level across 248 villages, using a blocked randomization
where the blocks were formed by the combination of districts and village-level poverty scores within
district, creating a total of 22 blocks with between 10 and 13 villages per block. Fixed effects forthese blocks are included in all analysis. A computer was used to conduct the randomization basedon a frame of villages agreed to by CRS and government officials.
Table 1 presents a schematic of the design of the study. 74 villages were assigned to the
Gikuriro intervention, 74 were assigned to the control group (no intervention), and 100 were assigned to GiveDirectly household grants. The GiveDirectly villages were further split into fourtransfer amounts, randomized at the village level. Three treatment amount arms, with 22 villages
in each, received transfer amounts in a range around the anticipated cost of Gikuriro. A final 34 villages were assigned to the ‘large’ GiveDirectly transfer amount which was selected by GiveDirectlyas the amount anticipated to maximize the cost effectiveness of cash. At the individual level, thosein the cash arm were assigned to receive their transfers either as a “flow” of monthly transfers over20

Because eligibility status was determined from records, in some cases the Ubudehe status of the households
proved to be incorrect or unverifiable when they were approached by GD for treatment, and hence were not offered
the program. Gikuriro implemented a consultative process with community members that formed the basis of theirtargeting.

9

twelve months (75 percent of participants); as a lump-sum transfer in the first month (17.5 percent
of participants); or as a choice between these. The transfers actually received by households in the
GD ‘main’ arms were $41.32, $83.63, and $116.91; the large GD arm actually transferred $532 tohouseholds. All transfer amounts were translated into Rwandan Francs at an exchange rate of 790
RwF per US dollar, and were rounded to the nearest hundred.21 Appendix Figure A.1 provides
a box and whisker plot of the randomly assigned mean transfer amount per village relative to theactual amount received per household observed in the GD institutional data, and shows that thetwo correspond closely.

2.4

Compliance

Compliance with assigned treatments is incomplete. This has implications for both the assignment of costs per beneficiary and the interpretation of impacts, as discussed in Section 2.5 below.
While defining compliance with cash transfers is conceptually straightforward, compliance with
Gikuriro is complex because of the multi-dimensional nature of the program. We have five formsof participation that can be ascribed directly to the program: participation in three types of training (nutritional, cooking/hygiene, and agricultural extension), whether households have themselves
harvested the nutrient-rich household vegetable plots that they were trained by the Farmer Field
Schools to grow, and whether they received livestock directly from Gikuriro.
Appendix Table A.1 examines the determinants of participation with specific sub-componentsof Gikuriro, based on self-reports within the eligible population, as well as with cash transfers asrecorded by GiveDirectly. Among the representative sample of eligible households, 63% received
nutritional training, 51% cooking training, 57% farmer training, 48% grew home vegetable gardens,
and 33% received livestock. On average eligible households engaged in 2.5 of these activities, andwell over three quarters of households engaged in at least one of them. Even among the entire
village population, 40% participated in some part of Gikuriro. The two driving determinants that
21

GiveDirectly believed that the most cost-effective use of these funds would be to attempt to equalize the amounttransferred per household member, rather than to have households of very different sizes receiving the same transfer
amount. To accomplish this, we scaled the transfer amounts within a village by household size, such that larger
households received larger transfers, but leaving the mean transfer amount at the village level unaffected. This
formula first calculated the per-capita transfer for a village using household sizes and the desired average household
transfer value. Second, it scaled household-level transfer amounts with household size, applying a minimum of 3
members and a maximum of 8 members, so as to achieve the intended mean transfer amount per household pervillage.

10

emerge from this table are that Gikuriro was successfully targeted at the poorer households even
within the (relatively poor) eligible group, and that they met more success with households headedby younger individuals. Conditional on this, however, they were not differentially successful atreaching households with children, or female-headed households. In the case of GiveDirectly’s cashtransfers, we see evidence only that GD’s strict enforcement of government eligiblity criteria makes
membership in official poverty groups a strong correlate of transfer receipt.

2.5

Cost Equivalence, Before and After the Fact

The design-based approach to cost-equivalent comparisons across programs is hampered by thefact that we can only measure costs well when the programs have been fully implemented, but thehead-to-head comparison would need to know these costs at the design phase. Anticipating thisissue, we costed both programs in detail prior to and after the intervention period following Levinand McEwan (2001), and also randomized cash transfer treatment amounts. The ex-ante costing
exercise arrived at an ex-ante cost of $119 per beneficiary household. We then randomized transfer
amounts at the village level to bracket this anticipated cost. Three smaller cash transfer costing$77, $119, and $152 (with beneficiaries actually receiving $41, $84, and $117, respectively) are used
to form the cost-adjusted comparison. The larger cash transfer cost $567 and transferred $517.
The ex-post costing was conducted using the ‘ingredients method’, valuing inputs at their opportunity costs (see Dhaliwal and Tulloch, 2012; Levin and McEwan, 2001, for more discussion).
The costing question is asked from the perspective of the donor (in this case, USAID), meaningthat we consider the total money spent per beneficiary to achieve the benefits observed. Overhead
expenditures in the implementation chain are an inherent part of these costs, and so the lower transactions costs in getting mobile money to the beneficiary play an important role in their potential
attractiveness.22
Since the Gikuriro program covers eight districts (e.g. much larger than the study populationonly) and many of the startup and administrative costs must be amortized over this whole beneficiary
22
Costs are inclusive of all direct costs, all indirect in-country management costs including transport, real estate,utilities, and the staffing required to manage the program, and all international overhead costs entailed in managing
the Gikuriro program. All administrative costs, including the appropriate share of the costs of maintaining international headquarters infrastructure, were included in the costing. Beneficiary identification costs, incurred by thesurvey firm and identical across all arms of the study, are excluded from the cost-benefit calculation. Monitoring and
Evaluation costs, similarly, were excluded so as to be costing only the implementation component.

11pool, we calculate cost per beneficiary in the full national program. Because we did not wantdifferences in scale to drive differential costs per beneficiary, we asked GiveDirectly to artificiallyscale up their operations and provide us with numbers reflecting the costs per beneficiary if they
were running a national-scale program across eight districts, including 56,127 beneficiary households
like Gikuriro. We costed each GD arm separately, asking what the overhead rate would have been
if GD had run a national program at the scale of Gikuriro giving only transfers of that amount.23
This allows us to conduct the benefit/cost comparisons ‘at scale’, rather than having the artificial,
multi-amount environment of the study contaminate the costing exercise across arms.
The ex-post costing exercise arrived at a cost per beneficiary household for Gikuriro of $141.84.
Actual GD costs per beneficiary were $66, $111, and $145, and $567. The numbers actually used
for our cost-equivalence exercise, however, need to reflect the fact that compliance with cash was
higher than compliance with Gikuriro. Since the regressions estimate the Intention to Treat (ignoring
non-compliance), the relevant cost number is not the amount spent per beneficiary, but rather theeffective spend per eligible household included in the ITT. To calculate this number, we differentiate‘averted’ costs which are not incurred by the implementer in the case of non-compliance (for example
costs of running a SILC in which the beneficiary does not participate) versus ‘non-averted’ costs(such as trainings that must be paid for no matter how many people attend). We then calculate thecost per eligible household as the sum of the non-averted costs per beneficiary and the averted costs
multiplied times the compliance rate. For GD all costs are averted in the case of non-compliance,
and for Gikuriro roughly 60% of costs (excepting WASH and BCC) are averted. Using this approach
we can recover a cost-equivalent comparison even when the compliance rates are different acrossarms.
Table 2 provides the final costing numbers arrived at by the ex-post exercise with the costs per
beneficiary in the top row, the averted share in the second row, compliance rates across arms in thethird row, and the final cost per eligible study household used in the costing exercise in the bottom
row. Gikuriro compliance rates are 80 percent among eligibles. Given an actual cost to USAID
of $141.84 per beneficiary and the role of averted costs, this gives a cost of $124.49 per eligible
household. GD compliance rates range from 81%–91% across arms. Given this, GD costs per
23

Overhead costs as a percentage of the amount transferred decline sharply with transfer amount for GD because
fixed costs represent a large share of their total overhead.

12eligible household are $54, $96, $121, and $517, so the Gikuriro costs used in the Cost Equivalence
exercise wind up being slightly higher than the largest of the three small arms that were intended
to bracket the Gikuriro cost. We use these values as described in Section 4.1 and our pre-analysis
plan to regression-adjust outcomes for the differential cost from Gikuriro, providing a way of testing
the differential impact of the programs at identical cost to the donor.

2.6

Attrition and Balance of the ExperimentAttrition from follow-up is low at the household level, at around 3.3 percent in the control
arm. Differences between arms are slight, ranging from 0.89 percentage points lower in the GD
Main arm, to 1.7 percentage points lower in the GD Large arm; only the latter is significantly
different from Control, and then only at the 10 percent level. As we show in Appendix C, patternsof attrition are not meaningfully predicted by household covariates. However, for individual-level
anthropometric outcomes among children under 6, we see significantly lower attrition rates amongchildren in treated households. Following our pre-analysis plan, we therefore also estimate models
that use inverse probability weights to balance characteristics among the sample at follow up.
Next we present the baseline comparison of primary and secondary outcomes as well as householdlevel control variables, using the non-attrited panel sample that will be the basis of the endline
evaluation. The regressions used here mimic as closely as possible the impact regressions, using
fixed effects for randomization blocks, weighting to make the sample representative of all eligibles,
and clustering standard errors at the village level to account for the design effect. All tables alsopresent p-values adjusted for False Discovery Rates within outcome families (primary or secondary)using the technique of Anderson (2008); stars in the tables are based on clustered standard errors.
Looking first at balance on study outcomes, Tables C.1, C.2 show balance for primary and secondary
outcomes, respectively. Overall balance on secondary outcomes is excellent, but we do find evidence
that we have superior outcomes on child anthropometrics in the GD Large arm. HAZ and WAZ
are about 0.2 standard deviations higher than the control in this arm, and the latter difference issignificant at the 10% level even after adjusting for multiple inference.24 Per our pre-analysis plan,
we present all core results on anthropometrics using an ANCOVA structure (controlling for baseline
24
Note that this imbalance does not arise from attrition; if we estimate these differences using the full baseline
sample the number of observations rises by 47 but the coefficients on GD Large remain very similar.

13

values) which should correct for this baseline imbalance. Table C.3 examines balance over baseline
covariates and finds all treatment arms to be comparable to the control across all covariates.

3

Intention-to-Treat impacts
Tables 3 and 4 present the core intention-to-treat results of the study on primary and secondary

outcomes for the eligible population. The analysis is conducted as an ANCOVA, including thelagged dependent variable where available, fixed effects for the randomization blocks, and for eachoutcome including a set of baseline controls selected by post-double-LASSO (Belloni et al., 2014b,
as described in Online Appendix F) for it by LASSO as described in the Online Appendix. For
parsimony, our main tables include a dummy for the Gikuriro treatment, another for the Large GD
treatment, and then a single dummy that indicates the three smaller GD transfers. Standard errors
are clustered at the village level to reflect the design effect, and we also present “sharpened” qvalues that adjust inference to control the False Discovery Rate within each table (Anderson, 2008).
Observations are weighted to make the analysis representative of all eligible study households,
incorporating both survey weights arising from the number of eligible households per village, andthe intensive tracking weights. Panel A of each table analyzes household-level outcomes, and Panel
B individual-level outcomes.
Taking the Gikuriro treatment first, we see no statistically significant impacts on primary outcomes at the household level. Estimates are sufficiently precise to allow us to rule out impacts on
consumption that would be sufficient to justify the program. For example, the upper bound of a95 percent confidence interval for Gikuriro’s impact on household consumption is 0.086, ruling out
consumption gains of more than 9 percent (equivalent to 0.064 standard deviations of consumption).
Neither are there significant impacts on household wealth (point estimate 0.01; upper bound of 95
percent CI 0.36 equivalent to 0.086 standard deviations), or, in secondary outcomes, measures ofphysical asset ownership. Dietary diversity, anthropometrics, and maternal anemia all move in theright direction but none of these changes is significant. 95 percent confidence intervals for HAZ
and WAZ rule out impacts outside of the ranges of (-0.03, 0.13) and (-0.04, 0.12), respectively.25
25

For comparison, these confidence intervals are sufficiently precise to rule out the estimated one-year HAZ impactsof 0.15 sd from a nutritional intervention from recent work in Bangladesh, but are essentially centered on the 0.05
sd one-year HAZ impacts of a combined nutrition and WASH intervention in the same study (Luby et al., 2018). A
parallel trial in Kenya found its nutrition intervention to have a one-year impact of 0.11 sd, and its nutrition and14

Examining impacts on outcomes more directly on Gikuriro’s causal path, household savings increaseby a 109 percent, consistent with the creation of SILCs, while no impacts appear for health knowledge or sanitation practices. Hence the program has been successful in moving an indicator closely
related to one of its sub-components, but at least within the time-frame of the study these changes
in savings have not translated into significant improvements in the core welfare outcomes.
We turn next to to the impact of the three smaller (“Main”) GiveDirectly arms whose average
cost per eligible is $90.28, 72 percent of the cost of Gikuriro. Transfers of this magnitude do not alter
the primary outcomes, though we do see effects on secondary outcomes, in a manner quite distinct toGikuriro’s in-kind programming. For instance, point estimates of household consumption impactsof 0.06, and corresponding 95 percent confidence interval upper bound of 0.24, rule out impacts
equivalent to 0.18 standard deviations or greater; we similarly rule out HAZ and WAZ impacts inexcess of 0.06 and 0.07 standard deviations of the reference population, respectively. Instead ofincreasing savings, small GD transfers lead to a 77 percent pay-down of debt, and an increase in thevalue of productive and consumption assets, by 26 percent and 35 percent respectively. Thus far,then, the comparison of Gikuriro to cash breaks down into two distinct dimensions of improvement,each of which has a different and entirely plausible pathway to long-term improvements: savings(Gikuriro), or debt reduction and asset investment (GiveDirectly).26
When we examine the third column, however, a more transformative impact arising from theLarge cash transfer is clearly apparent. Not only do omnibus measures of consumption and wealth go
up across the board, but metrics of consumption closely linked to child health improve. The dietary
diversity score increases by 0.55 food groups, or by 12 percent off a base of 4.77. Figure 2 displays
the fraction of each arm consuming each food group, and shows the treatment effects of the large arm
to be most pronounced in fish, fruits, oils and fats, spices and condiments, and cereals. Productive
assets increase by 80 percent, consumer assets almost double in value, and home value increases
substantially. In the individual outcomes the benefits of this surge in consumption are evident as
well; within the course of one year we see a 0.09 SD improvement in HAZ, a 0.07 SD improvement in
WAZ, and a 0.13 SD improvement in MUAC, all significant at least at the 10 percent level, prior to
WASH intervention to have a one-year impact of 0.12 standard deviations (Null et al., 2018).
26
Table A.4 shows the experimental results with each of the three treatment cells within the ‘main’ treatment
broken out individually. Because these cells are both small and feature treatment amounts that are similar, thismore granular analysis does not turn up evidence of meaningful heterogeneity across the three smaller GD transfer
amounts.

15

adjustment for multiple inference.27 The ANCOVA specification should be particularly important in
the analysis of the anthropometric indicators that showed signs of imbalance at baseline; indeed if we
examine these outcomes in post-treatment levels we see substantially stronger apparent treatment
effects. Appendix Table A.5 analyzes the binary anthropometric outcomes of stunting and wasting,and finds impacts very consonant with the continuous impacts on HAZ and WAZ. The GD largetransfer reduces stunting by 6 pp on a base of 50% and wasting by 5 pp on a base of 16%, both
effects significant at the 10% level. Anemia falls slightly (not significant) and there is a substantial
decrease in child mortality of almost 1 percentage point(or 70 percent off of the baseline value). To
contextualize these effects using unweighted numbers, the control group eligibles saw 13 cases ofchild mortality out of 2,596 children (0.5 percent) while the GD Large arm saw 2 cases out of 1,200
children (0.16 percent). Hence the GD Large arm saw meaningful improvements in consumption
and child health.
In the final columns of each table we provide p-statistics on F -tests that the ratio of the benefits
across arms differs from the ratio of their costs. These statistics ask whether we can reject thatthe impacts scale in a manner similar to the costs. For the comparison across the two GD arms
we find two significant differences: only in the case of debt reduction (where small transfers havea big effect and big transfers do not) and house quality (where small transfers have a negative andlarge transfers a positive effect) can we reject cost-proportional benefit scaling for cash transfers.
In comparing Gikuriro to the GD large arm we see more differences, with Gikuriro being morecost-effective in generating savings and GD Large superior in consumption, as well as productive
and consumption assets.
Our pre-analysis plan states that for any outcomes where we find differential attrition, we would
estimate a propensity to remain in the sample incorporating covariates, dummies for treatments,
and their interactions on the right-hand side, and then re-weight the analysis by the product of theinverse of this probability and the standard sampling weight. This procedure corrects the impacts
for the observable determinants of attrition, and uses regression weighting to attempt to make thetreatment and control samples comparable on important covariates even after attrition. Because
we primarily found significantly differential attrition for the anthropometric outcomes, in Table A.6
27

These improvements should be viewed against the backdrop of a sharp deterioration in anthropometrics subsequent to birth that typically occurs in LDCs, leaving rural African children often two full SDs below the international
norm by age 3 (Shrimpton et al., 2001; Victora et al., 2010).

16

we present the results of this correction. We interact with each treatment dummy the same righthand side covariates we use the same controls in the anthropometric regressions: gender, a linear,quadratic, and cubic for age in months, baseline household wealth, and a dummy for membership in
Ubudehe poverty category 1. The results in this table are virtually identical to Table 3, indicatingthat the types of children who attrited from the study are similar across arms and hence differential
attrition is unlikely to be driving our impacts.28
In many ways the core surprise of these results are not those of the cash arms (which conform
quite closely to the broader literature in terms of how transfer amounts translate into impact magnitudes), but that Gikuriro proves so ineffective even on its core outcomes. Why might this be thecase? Three possibilities suggest themselves. First is the question of study timing, given that our
endline occurs only 8-9 months after the implementation of Gikuriro. The two main comparison
papers for this intervention (Leroy et al., 2016; Null et al., 2018) both focus on anthropometricmeasurement two years subsequent to treatment. Hence it is possible that given more time theGikuriro intervention would have generated stronger impacts, but Null et al. (2018) show that thebulk of anthropometric gains arise within the first year,29 and that adherence to the treatment decays substantially in the second year. Therefore, it is likely the case that we are studying the mostintensely treated period of time, and that interventions which were going to impact child anthropometrics by year 2 would have had visible effects in Year 1. A second issue is that of cost. Gikuriro,
as implemented, cost only $142 per beneficiary, while the highly effective Tubarumure program inneighboring Burundi cost between $676 and $766 dollars per beneficiary (Heckert et al., 2020). A
program that devoted more resources to direct feeding and nutrition, in concert with the WASH
components of Gikuriro, may have achieved stronger results on child anthropometrics.30 A final
28

An alternate approach to this differential attrition is the use of Lee Bounds (Lee, 2009); Tables A.7 and A.8
conduct a bounding exercise for the anthropometric outcomes and find that the impact of the Large cash arm on
Height-for-Age remains significant at the 90% level even in the lower bound. The other anthropometric effect of
this arm are not significant in the lower bound but still represent improvements of between 0.06 and 0.09 standard
deviations.
29
After one year, length-for age z scores have improved versus Control by 0.11 sd in their Nutrition arm and by
0.12 sd in their Nutrition and WASH arm, of an eventual 0.13 and 0.16 sd impact in Year 2, respectively (Null et al.,2018, Supplementary Appendix, Figure S5).
30
The fact that the smaller cash transfers also had no significant effect on child growth is consistent with Davis andHanda (2015), who suggest that transfer sizes of at least 20 percent of baseline household expenditure are required tohave broad impacts on such outcomes. In a recent meta-analysis of the cash transfers literature (Manley et al., 2022),
transfers of average value comparable to our study (mean value USD 90) have only modest effects on child growth(HAZ effect 0.024, WAZ effect 0.19, both insignificant), with each increase in transfer value by 10% of householdincome raising this impact by an additional one percentage point. When cash transfers are conditional, transfer sizes
may have an alternative pathway to health outcomes through strengthening incentives for compliance (De Groot et17concern could be implementation fidelity. Given, however, that we have independent verification ofprogram compliance from the survey firm that achieves almost as high a takeup rate as the cash
arm, this does not appear to be a concern in this case.
Finally, we investigate whether ITT effects exhibit statistically and economically meaningful heterogeneity by transfer modality or recipient characteristics. As shown in Appendix Tables A.13 andA.14 for primary and secondary outcomes, receipt via lump-sum is estimated to have meaningfully
different effects on only a few outcomes, and these do not survive multiple hypothesis corrections.
On the other hand, heterogeneity by recipient characteristics might imply that there are winners
and losers among subgroups of the targeted population that are traded off in cash-versus-kind decisions; it also creates scope for finer targeting that might improve the average effects of intervention
in the population. In Appendix E, we assess the extent of such predictable heterogeneity, testing forheterogeneous impacts by a set of pre-specified attributes that either define populations of interest
to policymakers, or that are anticipated to be important for the relative efficacy of the treatment
regimes studied. We find no statistically significant evidence of such differential effects, suggestingthat fine targeting offers limited scope for improvements in impacts in this context.

4

Comparative cost effectiveness

4.1

Conceptual Framework

Having presented the ITT results from the study, we now introduce a conceptual framework to
help structure an analysis of the tradeoffs across outcomes, across beneficiaries, and across expenditure scales that are inherent in comparisons of multiple programs. Consider a policymaker seeking
to maximize, by choice of policy P , a utilitarian social welfare function of the form

W (P ) =N X
TXδ t ωi U (Yi,t (P )),(1)

i=1 t=1

al., 2017). For older children – aged above the 1,000 day window studied here – alternative modalities such as school
feeding programs may deliver child growth benefits at lower costs (Afridi, 2010). Consequently, interventions seeking
to address child malnutrition at low cost may be better served by directly addressing the source of malnutrition throughwidely tested interventions such as multiple micro-nutrient supplementation or complementary feeding programs for
newborns (Keats et al., 2021)."18

for utilities U (·) defined over a K-dimensional vector of outcomes for individual i at time t underpolicy P , Yi,t (P ) = {y1,t (P ), . . . , yK,t (P )}. The policymaker places values on individuals in population with weights ω and discounts the stream of utility this induces exponentially with rate δ.
This representation of the policymaker’s problem embodies additive separability of outcomes acrossindividuals and time.
To operationalize this welfare function for the empirical evaluation of policies with potential
effects across multiple outcomes, we further assume additive separability of individual utility in theoutcome dimensions of Yi,t (P ).31 This allows us to write instantaneous utility as the weighted sum
Pof component-wise utilities over outcome dimensions: U (Yi,t (P )) = K
k=1 uk (yk,t ). Given additive
separability, there exists a choice of units for each outcome yk such that we can write uk (yk ) = κk yk ,where κk = {κ1 , . . . , κK } are relative weights on outcome dimensions.
Using this formulation, we can compare a policymaker’s preference over outcome distributions
arising from policies A versus B. The welfare differential can then be written as:

W (A) − W (B) =

K
X
k=1

κk

N
X

ωi (yik (A) − yik (B)) ,

(2)

i=1

where yik (P ) is the potential outcome of individual i under policy regime P ∈ {A, B}. The outcomespecific components of this welfare differential can be estimated as the (ω-weighted) difference inaverage outcomes across policies.
Within this framework, we draw three observations.
First, policies will generally move the distribution of several outcomes, so that decisions between
them require taking a stance on relative weights across outcomes, κ. Under additive separability
and given a functional form choice for y such that uk (·) is linear in y, an evaluation that estimates
impacts across multiple dimensions can place bounds on the set of outcome weights, κ, that are
required to rationalize a given policy choice.
Second, through general-equilibrium effects or other externalities (Egger et al., 2019; Haushofer
et al., 2015), policy choices will typically affect both targeted beneficiaries and non-targeted individuals in a given population. The framework of equation (2) can accommodate anonymity only
31

Since we will be interested in the changes in welfare arising from changes in policy P , this can be thought of as
a first-order approximation.

19

within classes of targeted vs non-targeted individuals by allowing a relative weight of ω to be placedon all individuals in the non-targeted beneficiary group (with weights on targeted group serving as
numeraire). A benchmarking approach that estimates average effects on these complementary targeted and non-targeted subpopulations will allow us to place bounds on the weights on non-targetedindividuals required to support a preference for a given policy, P .
Third, when the costs of alternative policies are equivalent, then the welfare differential ofequation (2) provides a sufficient statistic for the policymaker’s decision criterion. But when thecosts of policies A and B are different, it is no longer automatic that this represents a policyrelevant comparison. Policymakers with a fixed budget to allocate to these programs, faced withalternatives that differ in costs per beneficiary, can respond either on the extensive margin, bytreating fewer individuals with the less expensive program, or on the intensive margin, by increasing some aspect of the ‘dosage’ of the less expensive program. Comparison in this case requires
welfare impacts to scale linearly, either on the intensive or extensive margin, in program expenditure. Then, given welfare value of the control state, W (0), and program costs C(A), C(B),expenditure of a given amount on policy A is preferred to the same expenditure on policy B if
(W (A) − W (0)) /C(A) − (W (B) − W (0)) /C(B) ≥ 0: that is, if it has a greater cost-effectivenessratio.32 Typical experimental comparisons of alternative programs will speak to these actually feasible at-scale outcome distributions only such additional, strong, and typically untested assumptions.
Our cost-equivalence approach relaxes this linear-in-costs assumption and makes policy comparisons
more transparent.
Given the randomization of cash transfer amounts in the neighborhood of the in-kind program,we can instead make a locally-linear assumption as to how the benefits of cash scale with costs. If
the observed welfare under the in-kind program is W (A), the cost of each cash arm j relative tothe in-kind arm is τj ≡ C(Bj ) − C(A), we can linearize the welfare under the cash arms aroundthis cost as W (B(Costj )) = γW (B(τj )). By estimating the slope relationship between outcomesand expenditure in the cash arm, γ, in a neighborhood around the cost of the in-kind arm, andevaluating the cash-vs-kind difference at this point at which τ = 0 , we can then calculate thecost-equivalent welfare benefit of the in-kind program as W (A) − γ̂ ∗ W (B(τj = 0)). This is the32

If outcome weights κ are normalized to be equal to the marginal utility of an extra dollar in income, and if costs
C(P ) are considered to be net of fiscal externalities, then this is equivalent to a comparison of the marginal value of
public funds across programs, as in (Hendren and Sprung-Keyser, 2019).

20difference between the observed welfare in the in-kind arm and the simulated welfare of cash had
we observed the exact cost-equivalent transfer. If impacts are continuous in transfer values, thisestimate of the cost-equivalent comparison can be made arbitrarily close to the truth by evaluating
cash impacts sufficiently close to the in-kind costs. Moreover, comparisons based on alternative setsof cash values can be used to assess the robustness of any such estimate.

4.2

Cost-Equivalent Benchmarking

To operationalize our cost adjustment strategy, let the subscript i indicate the individual (orhousehold, depending on unit of analysis), c the cluster (village), and b the randomization block.
Outcomes are observed both at baseline (Yicb1 ) and at endline (Yicb2 ). First, begin with the total GD
donor cost per eligible within each transfer amount arm, denoted by tc . Subtract from this number
the benchmarked Gikuriro cost per eligible household C described above, and denote the differencetc − C = τc ; this is the deviation (positive or negative) of each GD arm from the benchmarked
Gikuriro cost (all cost numbers are those given in the bottom row of Table 2. Set τc to zeroin the control and Gikuriro arms. We can then run an ANCOVA impact regression as above, but
controlling for a linear term in τc , an indicator Tc for receipt of either cash or in-kind treatment, andan indicator TcGK for receiving Gikuriro. We estimate this specification, as illustrated in equation(3) below, on eligible households in our sample:

Yicb2 = αb + δ GK TcGK + δ T Tc + γ1 τc + βXicb1 + ρYicb1 + icb2 .

(3)Because τc absorbs the deviation of the GD arm from the benchmarked Gikuriro cost, thecoefficient on Gikuriro treatment, δ GK , will provide a direct benchmarking test: this estimates thedifferential impact of Gikuriro benchmarked against an exactly donor-cost-equivalent cash transfer.
In addition, subject to the assumption of linear transfer amount effects, the slope coefficient τccaptures impacts arising from deviations in GD cost from Gikuriro cost, and the coefficient δ Testimates the impact of GD at the cost of Gikuriro.
A graphical representation of our strategy is provided in the left-hand panel of Figure 1, whichplots the average outcome on the y-axis for all four GD treatment amounts (colored circles), for GK(gray diamond), and the control (gray circle). The line represents the fitted average outcome by21

GD transfer amount. By predicting the outcome on this line at the exact cost of Gikuriro (hollow
circle), the benchmarked differential is then the vertical difference between the Gikuriro impact andthe projected cost-equivalent GD impact.
The results of the cost equivalent analysis for primary and secondary outcomes are presented inTables 5 and 6, respectively.
The first column of these tables contains the heart of the comparative benchmarking exercise (δ GK ), comparing the effects of Gikuriro’s in-kind programming to cash at exact donor-costequivalent levels. There are well powered, economically important, and statistically significant differences between the interventions at cost-equivalent levels (even accounting for multiple inference)
on one primary and six secondary outcomes. Gikuriro is significantly less effective than a costequivalent cash transfer at driving consumption levels, producing approximately 19 percent lower
consumption. Standard errors of 0.08 on the Gikuriro differential in consumption imply power to detect impacts substantially smaller than the value of even the smaller cash grants. At cost-equivalentlevels, we find no statistically significant difference on other primary outcomes. These null findings
are powered to detect economically meaningful differences: for instance, the 95 percent confidence
interval for impacts on the household dietary diversity score rules out differences of a magnitude
greater than approximately 0.25 in either direction—an increase of one quarter of a food type on
average, a seemingly reasonable aspiration for an intervention seeking to change nutritional practices. Likewise, we find no statistically significant difference in child-growth outcomes between theinterventions (95 percent confidence interval: -0.058, 0.098); by contrast, the addition of a nutrition
component to a water, sanitation, and hand-washing intervention in Kenya produced a differenceof 0.17 in weight-for-age z-scores (Null et al., 2018).
We find statistically and economically significant differences in impacts at cost-equivalent levels
across a range of secondary outcomes. Gikuriro is less effective at driving the paydown of debt andthe accumulation of assets, while the in-kind program is significantly more effective than cash at
creating savings. The differential effect of the programs on savings and borrowing is interesting,and suggests that while both interventions serve to improve the net stock of liquid wealth (savings
net of borrowing), the focus on savings through the SILC groups in Gikuriro drives liquidity to bebuilt through deposits, while households making their own choices are more strongly disposed toreduce debt instead. Which of these strategies makes more sense? A simple comparison of interest
22

rates is revealing. Gikuriro SILCs were free to set their own interest rates, but typically paid about
5 percent per annum nominal. Credit interest rates, by comparison, vary from an average of 22
percent in the MFI sector to upwards of 60 percent in informal credit markets. Given that 32 percent
of eligible households reported having both borrowing and savings at baseline (and 79 percent had
either borrowing or saving) it seems that the desire to pay down debt might be warranted.
The second column of Tables 5 and 6 presents estimated coefficients on an indicator for assignment to any treatment (coefficient δ T in equation (3)). This intercept term estimates the impact
of cash transfers at a cost equivalent to Gikuriro, although this precise amount was not includedin the experiment. Given that the average transfer amount across the three smaller cash arms is
only slightly lower than the GK cost, this estimate looks generally similar to the second columnof the ITT tables (the simple average experimental effect across those three transfer amounts).
At the exact cost of Gikuriro, we estimate that cash transfers would have led to a significant 73
percent decrease in the stock of debt, and a 30 percent and 40 percent increase in productive andconsumption assets, respectively.
The third column of Tables 5 and 6 provides a direct estimate of γ1 , the marginal effect of anadditional 100 dollars in donor cost on primary and secondary outcomes, respectively. As could be
inferred from ITT estimates, this coefficient is strongly significant across a wide range of outcomes,
particularly those most related to household consumption. An extra 100 dollars leads to a 5 percent
increase in consumption, a 9 percent increase in dietary diversity, a 19 percent increase in savings,
an 13 percent and 14 percent increase in productive and consumption assets, respectively, and leads
housing value to improve by 5 percent and the index of housing quality to increase by 0.1 SD. In
terms of anthropometrics, the change in value of transfer is positive but small in absolute magnitude
for HAZ, WAZ, and MUAC, and does not survive correction for multiple inference. An extra $100
per beneficiary household—with eligible households containing an average of 2.7 children under theage of six—increases HAZ by 0.022 standard deviations. Beyond this, none of the other individual
outcomes respond to transfer amount in a manner that we can reject at 95 percent significance.33
While we pre-specified the simple linear functional form for interpolation of cash-transfer impacts
33
Per the critique of Muralidharan et al. (2019) on the use of factorial designs, we may wish to compare Gikuriro
to only the Lump Sum or Flow cash arms rather than bundling the two cash arms together. Tables A.9 and A.10
conduct the cost-equivalent analysis comparing to Lump Sum and Flow only, respectively, and find qualitatively
similar results to Table 5.

23

to preserve statistical power, a natural question is the extent to which our benchmarking results are
sensitive to the linear interpolation of cash-transfer impacts. To interrogate this, Tables A.11 and
A.12 present seven different ways of forming the cost-equivalent comparison. Column 1 in thesetables repeats the linear specification used elsewhere, column 2 uses a quadratic, and column 3 a
cubic function in project cost, and the remaining columns use the linear specification but drop one of
the cash transfer arms in each column. The table reports only the differential parameter of Gikuriro
over cost-equivalent cash reported in Column 1 of Tables 5 and 6, and the associated standard
error. Overall, the results prove highly robust to specification; all of the outcomes significant in themain specification are significant in at least five out of six of the remaining specifications, except
for the differential on the sanitation practices index. These results also confirm the power gains
arising from linear interpolation: standard errors for the differential effect of Gikuriro are generally
substantially smaller in the linear specification than in quadratic or cubic specifications. To give asense of magnitudes, to obtain a reduction in the variance of the cost-equivalent comparison for the
consumption outcome equivalent to that arising from the linear specification, a researcher using a
cubic specification would have to increase the sample size by 158 percent. Although our transfer
amounts (with three closely bunched together and one much larger) do not provide a great deal ofpower to test for non-linearity, this exercise suggests that our core results are robust to alternate
ways of forming the cost equivalence comparison.34

4.3

Cost Equivalence versus Cost EffectivenessThis study is designed to make a specific form of cost-equivalent comparison, namely the impactof a cash transfer intervention assessed at the exact cost of an in-kind intervention. This comparison
fixes the amount to be spent per beneficiary and asks which intervention is more effective. A
different but related question is that of cost effectiveness, where we compare programs that operate
at different costs and ask which generates the greatest benefit per dollar spent—potentially making
comparisons between programs that operate at radically different levels of resource intensity. Fixedcosts and indivisibility in program design mean that cost-benefit ratios do not represent alternatives
that can be delivered for a given budget to a given population, but instead represent possible gains
34

One new result that emerges from these tables is that in some specifications Gikuriro is superior to cost-equivalent
cash at improving HAZ, significant at the 10% level in three out of seven specifications.

24

to an additive welfare function for policymakers who are indifferent to the size of the beneficiary
population (for a given budget) or are willing to adjust their budget (for a given population).35
The comparison between these two approaches is represented graphically in Figure 1 for the
primary study outcomes. In the left-hand panel we plot comparative cost-equivalence; here thefocus is on the value on the X-axis that is the ex-post cost of Gikuriro, and we are interested inseeing which is greater, the observed benefit of Gikuriro (represented by a black diamond) or thepredicted benefit of cash at this cost (the hollow circle). In the right-hand panel we connect theshaded circle that represents the outcome in the control group (at zero cost) with the outcome
in each arm; because the plot represents outcomes in benefit/cost space, the intervention thatfeatures the steepest slope in this graph has the highest cost effectiveness. Interestingly, this graphic
illustrates that while in general there are not substantial differences between Gikuriro and cash atbenchmarked cost, because the smallest cash arm is so inexpensive while producing outcomes thatare generally as good as (or better than) more expensive treatments, for four out of the five outcomes
represented the smallest cash transfer has the highest cost effectiveness. The difference in linearized
cost effectiveness across arms is tested statistically for all primary and secondary outcomes in theF-statistics in the final columns of Tables 3 and 4.
Table 7 provides a statistical analysis of the benefit-cost slope terms represented in the righthand panel of Figure 1, pooling the three smaller GD arms together as done in the rest of thepaper. It presents the ITT coefficient from the prior tables divided by the cost of the arm, andso gives the household improvement generated per $100 spent through that modality, as well as
the corresponding standard error on this BCR.36 The final three columns of this Table 7 provide
statistical tests of the difference in cost-effectiveness slopes and show how difficult it is to powera study to reject these; despite the relatively large sample size of this study, it is only for the
consumption outcome that we are able to reject equality of CBRs across interventions, with the
cash interventions outperforming Gikuriro. Between the two cash arms we are unable to reject equal
BCRs for any outcome, indicating benefits that scale linearly with cash transfer amounts.
It is also worth clarifying the relationship between design choices and the relative statistical
35
Under the linear specification of equation (3), the question of whether the benefits of cash scale proportionally
to expenditure amounts to the hypothesis that δ T = τc C. We fail to reject this null across all primary outcomes.
36
Given that individual-level outcomes in this table represent average benefits for amount spent on the household,if we want the BCRs at the individual level we need to scale up the coefficients in this table by the average numberof individuals per household for each outcome.

25

power of the cost equivalence and cost effectiveness comparisons. Much of our ability to estimate
a cost slope on cash comes from the (very expensive) large cash arm. Some of the comparisonshighlighted here require a statistically well-estimated slope term on expenditure, and some do not.
The cost equivalent comparisons are primarily powered by having sufficient observations close to
the cost-equivalent level. If the two programs are similar in cost then studies can be well-powered
without having a cleanly estimated slope term on the amount of expenditure, since this slope is notimportant if little cost-adjustment is required (this is the case in our study, as demonstrated by therobustness of the cost equivalence adjustment to dropping of cash arms shown in Table A.11). As
the average cost of the two programs differs by a larger amount, then the cost adjustment becomesincreasingly important and hence a precisely estimated slope term is key to the power of costequivalence comparisons. Cost effectiveness comparisons inherently involve testing for differential
significance of slopes (now simply cost-benefit ratios that go through the origin by definition), andhence rejection is more likely when we compare expensive programs that had strongly significant
overall effects to begin with. For these reasons, caution should be exercised in attempting to perform
this type of comparative evaluation when program budgets are limited and overall impacts are likely
to be muted.

5Conclusion
This study uses a large-scale randomized experiment to pose a number of questions in com-parative cost-effectiveness. Most centrally, we establish an approach to ask whether it is betterto run complex multi-dimensional programs or simply to provide cash grants of equal ex-post cost,when these costs are not known with perfect certainty before the trial. We combine randomization of
cash-transfer amounts with linear interpolation of their effects to make exact ex-post cost-equivalent
comparisons, and demonstrate that, while point estimates do not generally depend on linearity, thisapproach delivers power gains equivalent to more than a doubling of sample size relative to alternative methods of interpolation. A simple theoretical framework provides structure to the assumptions
required to make welfare comparisons across outcomes, beneficiary groups, and program costs.
Rwanda may be a particularly interesting environment in which to pose the benchmarking question for several reasons. First, child malnutrition rates overall are high—the prevalence of stunting

26

among children under age five in the 2014-15 Demographic and Health Survey was 37.9 percent,underweight 9.3 percent, and wasted 2.2 percent—though this represents an improvement in recent
years (DHS, 2016) Second, Rwanda is a country notable in Africa for its bureaucratic capacity
and the public health infrastructure has been successful in delivering substantial improvements inchild and maternal health outcomes (NISR, 2015) through schemes such as Pay-for-Performance(Basinga et al., 2011). Hence, it may provide a relatively strong case in terms of interventions like
Gikuriro that are led through the public health system and lean heavily on Community HealthWorkers (CHWs). Third, the Government of Rwanda has been experimenting extensively with cashtransfer programs over the past few years, such as the inclusion of cash in the flagship Umurengepoverty reduction program (Gahamanyi and Kettlewell, 2015), the $50 million ‘Cash-to-poor’ program supported by the World Bank, as well as a number of efforts to transition the support systems
for the country’s large population of refugees to cash transfers (such as a World Food Programme(WFP) program that is now supporting 15,000 refugees in Gihembe Camp using cash rather than
traditional in-kind aid mechanisms (Taylor et al., 2016)). Hence there should be the bureaucratic
capacity to implement Gikuriro well, and there is both experience with and interest in cash transfers
as a safety net modality in the country.37
Our application of this approach to the critical battle against child malnutrition highlights aneven more basic lesson: evidently, the two intervention modalities studied here each require morethan $140 per household to deliver clinically relevant impacts on child malnutrition outcomes within
a year.38 Interventions wishing to move child health at low cost may do better to focus on more
direct approaches such as school feeding (Afridi, 2010) or micro-nutrient supplementation (Keats etal., 2021). Even at this relatively low cost, however, the programs do trigger meaningfully different
responses in the household use of intertemporal assets, with cost-equivalent cash transfers leading tohigher levels of consumption, the pay-down of debt, and growth in asset investment, while Gikuriro
households save more than those receiving cost-equivalent cash transfers.
Our cost-equivalent estimates allow us to make the trade-off across outcomes very explicit. For
example, a policy preference for Gikuriro is equivalent to asserting that the greater savings induced
37
Given the framing provided by GiveDirectly and the unusually strong degree of social control exerted by local
officials in the Rwandan context, it is certainly possible that our ‘unconditional’ transfers have been more forcibly
devoted to child consumption than they would have been in a different context.
38
Gikuriro’s costs were substantially lower than a related program in neighboring Burundi.

27

by Gikuriro is worth foregoing the higher consumption flows and productive asset stocks obtained
under a cost-equivalent cash transfer. In dollar terms, the estimates of Tables 5 and 6 mean thatmoving from the predicted mean consumption of a cost-equivalent cash transfer to Gikuriro implies
giving up $28.32 in monthly consumption and adding $8.35 in debt stocks, in exchange for an
increase of $18.70 in savings stocks. This kind of precision can help policymakers be much moreexact as to the types of trade-offs required to justify one type of intervention over another.
A fundamental idea in development economics is that poor households should have a single“shadow value” of cash which pulls down investment in all capital-hungry endeavors in a symmetricway. Our ITT impacts are generally consistent with this view of the world, as an intervention that
relaxes credit constraints leads to shifts in consumption patterns that are very broadly spread acrossdomains. This property means that small cash transfers are hard to detect because they move too
many outcomes by too small an amount to be significant, while large cash transfers result in a broadbased increase in consumption in many dimensions. It also means that benchmarking exercises thatcompare in-kind impacts against cash on a single-dimensional setting are likely to understate thewelfare advantages of cash, even if stronger assumptions about preferences—as discussed in Section
4.1—are required to address this.
Rich survey sampling enables us to test how the program impacts differ according to the subpopulations targeted. Overall this analysis provides evidence of surprisingly homogeneous returns
from these interventions, whether looking across pre-identified study subgroups, using machinelearning to identify optimal targeting rules that span multiple outcomes, or by introducing selftargeting through beneficiary choice. The general lack of heterogeneity or scale effects, the lack ofevidence that flow transfers are superior, absence of essential heterogeneity, and the uncorrelated
individual-level benefits across different dimensions of impact all suggest that it is reasonable forpolicymakers to use simple lump-sum cash transfers and to target them using preference weights
based on individual attributes or inequality.
Finally, comparisons across expenditure levels highlight a key policy tradeoff. When we compare
the cost-equivalent programs to a cash transfer of almost five times the amount, we see that larger
sums of money can not only powerfully improve overall consumption and dietary diversity, but also
lead to modest improvements in child growth. Transfer amounts in this study are not well-powered
to test the linearity assumption (three small transfers of similar size, one much larger), however28

we generally find outcomes scaling with transfer amount in a simple way. Randomized variationin transfer values lets us form a number of interesting counterfactuals; for example the smallest
transfer at which the benefit of cash would exceed Gikuriro; for savings this number is $694, andfor HAZ it is $277.39 Policymakers seeking to move child growth outcomes face difficult tradeoffs
between the depth and breadth of their interventions; the comparative ease with which the resource
intensity of cash transfers can be adapted makes this counterfactual modality particularly capableof revealing these tradeoffs.
There are some important limitations to what can be learned in this application of cash benchmarking. First, the cross-village experiment lets us measure the impact of those parts of the Gikuriro
intervention that are implemented at the village or household level only. Village-level assignment
means that some of our tests involving transfer amounts are not highly powered statistically. Most
importantly, the time frame of the study (13 months, but only 8–9 months after treatment with
Gikuriro) means that we are measuring endline outcomes more quickly than would be ideal, particularly for anthropometrics that may respond slowly to improvements in nutrition. Gikuriro madesubstantial investments in local capacity around health and sanitation and this infrastructure may
drive future benefits in a way not captured in our study. Both of the companion papers examining comparable programs have their core outcome measurement two years after implementation,a duration that we were not able to observe given the desire to implement Gikuriro universally(including to the control) as quickly as possible. Particularly given that we may expect cash effects
to fade over time, understanding the relative time trajectory of treatment effects is an important
agenda for future comparative evaluation work. However, given that the targeting criteria for these
programs will cause children to age out of eligibility within a few years, it remains important toshow that such programs can generate benefits quickly for a given set of vulnerable children, as do
the larger cash transfers we study.
Given the nuance of our findings, it is hard to square them with any simple idea of cash transfers
as a kind of uni-dimensional ‘index fund’. While business investment may have a single, cardinal
objective—financial profit—development policy is undertaken with many goals in mind, and a per39
Evidence from the sister study in Rwanda (McIntosh and Zeitlin, 2022), which featured more intermediate
treatment amounts, did in fact find evidence of a non-linear effect, with outcomes involving investment and time use
peaking at transfers a little over $400. Nonetheless, in this study for no primary outcome are we able to reject aconstant benefit/cost ratio across different cash amounts.

29

fect reconciling of these competing benefits would require a clear statement of trade-offs in thismulti-dimensional space. Perhaps a clearer way of expressing the counterfactual provided by unconditional cash is that it gives us a statement of the priorities that the beneficiaries themselves
hold when credit constraints are relaxed, and thereby motivates us to be clearer about the logic
underlying paternalistic development programs. While beneficiary decisions may not be ‘optimal’in terms of long-term social welfare (for example, due to high discount rates, to self- or othercontrol problem, or to resource and information constraints), the impact of unconditional cash is
nonetheless a powerful statement of the outcomes that the beneficiaries themselves want changed.
For us to argue that a program is justified in using resources to drive outcomes different from theones the beneficiaries would choose, we should have a clear reason why they fail to arrive at thewelfare-maximizing outcome themselves. This is a view of benchmarking that quantifies tradeoffs
rather than picking a winner.

30

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———36

Tables and Figures
Table 1: Research design
GiveDirectly

Control

Gikuriro

Lowertransfer($66)

Middletransfer($111)

Uppertransfer($145)

Large
transfer($566)

Total

22
88
165

22
87
154

22
88
167

34
137
246

248
995
1,794

147
68
31

421
210
101

Panel A. Village-level randomization
Villages
Ineligible households
Eligible households

74
298
521

74
297
541

Panel B. Household-level randomization of cash payment modality among eligibles
Flow transfers
Lump-sum transfers
Choice

83
51
31

87
50
17

104
41
22

Notes: Table gives the number of observations across the arms of the study. The first row is the number of villages,
the unit of assignment, and the remaining rows are numb er of households.

37

Table 2: Intervention costs and compliance rates
GiveDirectly
Gikuriro

Lower

Middle

Upper

Large

$141.84$66.02$111.09$145.43$566.55

Share averted if untreated

0.60

1.00

1.00

1.00

1.00

Compliance rate among eligibles

0.80

0.81

0.86

0.83

0.91

$124.49$53.58$95.86$121.24$517.44

Cost to USAID per beneficiary

Cost to USAID per eligible household

Notes: Table gives costs for each of the study arms. The first row is the cost per beneficiary that emerged fromthe ex-post costing exercise. The second row gives the share of program costs averted among non-compliers, andthe third row the compliance rate. Using these the fourth row provides the average amount spent per study eligible
household. The fifth and sixth rows provide cost numbers for the average household in the village population.

38

Table 3: ITT estimates: Primary outcomes
GiveDirectly
Gikuriro

Main

Large

Control
Mean (SD)p-values: F -tests
Obs.

R

2

GK=GD

GD=GDL

GK=GDL

Panel A. Household outcomes
Consumption†−0.11(0.10)[0.41]

0.06(0.09)[0.57]

0.30∗∗∗(0.11)[0.02]

10.69(1.34)

1750

0.14

0.03

0.02

0.00

Household dietary
diversity score

0.19(0.12)[0.38]

0.17(0.15)[0.41]

0.55∗∗∗(0.13)[0.00]

4.77(1.84)

1751

0.18

0.86

0.01

0.00

Household non-land
wealth†0.01(0.18)[0.60]

0.00(0.21)[0.60]

0.40(0.28)[0.38]

13.04(4.24)

1751

0.22

0.98

0.16

0.15

Panel B. Individual outcomes
Height-for-age

0.05(0.04)[1.00]−0.02(0.04)[1.00]

0.09∗∗(0.05)[0.78]−1.97(1.10)

2125

0.71

0.08

0.01

0.37

Weight-for-age

0.04(0.04)[1.00]

0.01(0.03)[1.00]

0.07∗(0.04)[0.78]−1.04(0.98)

2104

0.68

0.49

0.14

0.46

Mid-upper arm
circumference

0.02(0.06)[1.00]−0.01(0.07)[1.00]

0.13∗(0.08)[0.78]−0.59(0.95)

1629

0.51

0.66

0.10

0.17

Child anemia

0.00(0.02)[1.00]

0.02(0.02)[1.00]−0.01(0.04)[1.00]

0.18(0.39)

2372

0.07

0.45

0.43

0.74

Maternal anemia−0.02(0.03)[1.00]−0.00(0.03)[1.00]−0.03(0.03)[1.00]

0.12(0.33)

1581

0.11

0.33

0.34

0.90

Notes: Table presents Intention to Treat impacts on primary outcomes, with the three study arms presented in rows
and the three smaller GiveDirectly transfers pooled into the ‘Main’ treatment. Regressions include but do not report
the lagged dependent variable, fixed effects for randomization blocks, and a set of LASSO-selected baseline covariates,
and are weighted be representative of the eligible population. Standard errors (in parentheses) are clustered at thehousehold level to reflect the design effect. Asterices denote significance at the 10, 5, and 1 percent levels, andare based on clustered standard errors, in parentheses. Anderson (2008) sharpened q-values presented in brackets.
Variables marked with a † are in inverse hyperbolic sines. Reported p-values in final three columns derived fromF -tests of hypotheses that impacts are equal between Gikuriro and GD Main (GK=GD), between GD Main andLarge transfer amounts (GD=GDL), and between Gikuriro and GD Large (GK=GDL).

39

Table 4: ITT estimates: Secondary outcomes
GiveDirectly
Gikuriro

Main

Large

Control
Mean (SD)p-values: F -tests
Obs.

R2

GK=GD

GD=GDL

GK=GDL

Panel A. Household outcomes
Stock of borrowing†

0.067(0.350)[0.71]−0.765∗∗(0.316)[0.05]−0.341(0.397)[0.64]

7.39(4.82)

1751

0.12

0.00

0.21

0.27

Stock of saving†

1.115∗∗∗(0.324)[0.01]−0.128(0.345)[0.71]

0.656∗∗(0.329)[0.14]

5.88(4.87)

1751

0.16

0.00

0.03

0.17

Health knowledge
index−0.076(0.368)[0.71]

0.153(0.321)[0.71]

0.075(0.468)[0.71]

2.89(4.01)

1751

0.04

0.49

0.86

0.75

Sanitation practices
index−0.280(0.219)[0.37]

0.146(0.227)[0.71]

0.087(0.284)[0.71]−0.68(2.71)

1751

0.07

0.06

0.84

0.19

Productive assets†

0.020(0.100)[0.71]

0.257∗∗(0.100)[0.05]

0.800∗∗∗(0.116)[0.00]

11.22(1.81)

1751

0.29

0.02

0.00

0.00

Consumption assets†−0.367(0.240)[0.28]

0.354∗(0.206)[0.22]

0.932∗∗∗(0.243)[0.00]

8.70(4.08)

1751

0.35

0.01

0.02

0.00

House value†−0.023(0.056)[0.71]−0.029(0.061)[0.71]

0.196∗∗∗(0.061)[0.01]

13.81(0.87)

1654

0.34

0.92

0.00

0.00

Housing quality index−0.195(0.146)[0.36]−0.217∗(0.132)[0.24]

0.211(0.174)[0.37]−0.17(1.46)

1751

0.10

0.89

0.03

0.03

Child mortality−0.006(0.005)[1.00]−0.004(0.006)[1.00]−0.009∗∗(0.004)[1.00]

0.01(0.11)

2687

0.01

0.63

0.31

0.48

Pregnancy−0.031(0.025)[1.00]−0.035(0.031)[1.00]−0.007(0.027)[1.00]

0.20(0.40)

2552

0.08

0.90

0.34

0.32

Live birth

0.103(0.079)[1.00]

0.091(0.072)[1.00]−0.068(0.081)[1.00]

0.68(0.47)

411

0.13

0.87

0.04

0.03

Birth in facility−0.046(0.059)[1.00]

0.069(0.052)[1.00]−0.062(0.099)[1.00]

0.84(0.37)

293

0.16

0.03

0.18

0.88

Any vaccinations inpast year

0.010(0.034)[1.00]−0.010(0.032)[1.00]−0.005(0.039)[1.00]

0.72(0.45)

1291

0.26

0.54

0.90

0.71

Completed vaccination
schedule

0.011(0.039)[1.00]−0.013(0.035)[1.00]

0.006(0.038)[1.00]

0.58(0.49)

1291

0.17

0.52

0.63

0.90

Disease burden−0.020(0.032)[1.00]−0.031(0.030)[1.00]−0.018(0.033)[1.00]

0.54(0.50)

2680

0.05

0.74

0.72

0.97

Diarrheal prevalence−0.003(0.015)[1.00]−0.000(0.016)[1.00]−0.007(0.015)[1.00]

0.09(0.29)

2680

0.04

0.83

0.69

0.84

Panel B. Individual outcomes

Notes: Table presents Intention to Treat impacts on secondary outcomes, with the three study arms presented in rows
and the three smaller GiveDirectly transfers pooled into the ‘Main’ treatment. Regressions include but do not report
the lagged dependent variable, fixed effects for randomization blocks, and a set of LASSO-selected baseline covariates,
and are weighted be representative of the eligible population. Standard errors (in parentheses) are clustered at thehousehold level to reflect the design effect. Asterices denote significance at the 10, 5, and 1 percent levels, andare based on clustered standard errors, in parentheses. Anderson (2008) sharpened q-values presented in brackets.
Variables marked with a † are in inverse hyperbolic sines. Reported p-values in final three columns derived fromF -tests of hypotheses that impacts are equal between Gikuriro and GD Main (GK=GD), between GD Main andLarge transfer amounts (GD=GDL), and between Gikuriro and GD Large (GK=GDL).

40

Table 5: Cost-equivalent comparisons: Primary outcomes
Gikuriro:Differential

Cost-equivalent
GD impact

Transfer
Cost

Control
Mean

Observations

R2

Consumption†−0.19∗∗(0.08)[0.06]

0.08(0.09)[0.49]

0.05∗∗(0.02)[0.06]

10.69

1750

0.14

Household dietary
diversity score−0.01(0.13)[0.71]

0.20(0.14)[0.28]

0.09∗∗(0.03)[0.06]

4.77

1751

0.18

Household non-land
wealth†−0.03(0.20)[0.71]

0.04(0.21)[0.71]

0.09(0.07)[0.30]

13.04

1751

0.22

Height-for-age

0.06(0.04)[0.96]−0.01(0.04)[1.00]

0.02∗∗(0.01)[0.40]

−1.97

2125

0.71

Weight-for-age

0.02(0.04)[1.00]

0.02(0.03)[1.00]

0.01(0.01)[0.96]

−1.04

2104

0.68

Mid-upper arm
circumference

0.02(0.06)[1.00]

0.00(0.06)[1.00]

0.03(0.02)[0.96]

−0.59

1629

0.51

Child anemia−0.02(0.03)[1.00]

0.02(0.02)[1.00]−0.01(0.01)[1.00]

0.22

2372

0.07

Maternal anemia−0.02(0.02)[1.00]−0.00(0.03)[1.00]−0.00(0.01)[1.00]

0.12

1581

0.10

A. Household outcomes

B. Individual outcomes

Notes: First column is a dummy for Gikuriro treatment, giving the differential effect of Gikuriro over cash at
equivalent cost. Second column is a dummy for either treatment, giving the impact of cash at the cost of Gikuriro.
Third column is the cost slope, measured as the dollar-value deviation (in hundreds of dollars) of the treatment
received from the cost of Gikuriro. Asterices denote significance at the 10, 5, and 1 percent levels, and are basedon clustered standard errors, in parentheses. Regressions are weighted be representative of the eligible population.
Anderson (2008) sharpened q-values presented in brackets. Variables marked with a † are in inverse hyperbolic sines.

41

Table 6: Cost-equivalent comparisons: Secondary outcomes
Gikuriro:Differential

Cost-equivalent
GD impact

Transfer
Cost

Control
Mean

Observations

R2

A. Household outcomes
Stock of borrowing†

0.793∗∗∗(0.277)[0.01]−0.726∗∗(0.309)[0.04]

0.094(0.077)[0.19]

7.39

1751

0.12

Stock of saving†

1.184∗∗∗(0.333)[0.01]−0.070(0.327)[0.54]

0.188∗∗(0.080)[0.04]

5.88

1751

0.16

Health knowledge
index−0.227(0.329)[0.44]

0.151(0.316)[0.54]−0.023(0.103)[0.54]

2.89

1751

0.04

Sanitation practices
index−0.421∗∗(0.213)[0.06]

0.141(0.220)[0.44]−0.014(0.069)[0.54]

−0.68

1751

0.07

Productive assets†−0.283∗∗∗(0.097)[0.01]

0.302∗∗∗(0.096)[0.01]

0.125∗∗∗(0.026)[0.00]

11.22

1751

0.29

Consumption assets†−0.764∗∗∗(0.256)[0.01]

0.397∗∗(0.199)[0.06]

0.139∗∗(0.058)[0.04]

8.70

1751

0.35

House value†−0.017(0.051)[0.54]−0.007(0.059)[0.55]

0.049∗∗∗(0.014)[0.01]

13.81

1654

0.34

Housing quality index−0.008(0.151)[0.56]−0.187(0.126)[0.12]

0.104∗∗(0.048)[0.05]

−0.17

1751

0.10

Child mortality−0.002(0.005)[1.00]−0.004(0.006)[1.00]−0.001(0.001)[1.00]

0.01

2687

0.01

Pregnancy

0.002(0.027)[1.00]−0.033(0.029)[1.00]

0.007(0.007)[1.00]

0.20

2552

0.08

Live birth

0.024(0.067)[1.00]

0.079(0.069)[1.00]−0.037∗∗(0.017)[1.00]

0.68

411

0.13

Birth in facility−0.104∗∗(0.052)[1.00]

0.058(0.051)[1.00]−0.029(0.023)[1.00]

0.84

293

0.16

Any vaccinations inpast year

0.018(0.032)[1.00]−0.008(0.031)[1.00]−0.000(0.009)[1.00]

0.72

1291

0.26

Completed vaccination
schedule

0.021(0.036)[1.00]−0.010(0.033)[1.00]

0.003(0.009)[1.00]

0.58

1291

0.17

Disease burden

0.012(0.033)[1.00]−0.031(0.029)[1.00]

0.004(0.008)[1.00]

0.54

2680

0.05

Diarrheal prevalence−0.003(0.014)[1.00]−0.000(0.015)[1.00]−0.002(0.004)[1.00]

0.09

2680

0.04

B. Individual outcomes

Notes: First column is a dummy for Gikuriro treatment, giving the differential effect of Gikuriro over cash at
equivalent cost. Second column is a dummy for either treatment, giving the impact of cash at the cost of Gikuriro.
Third column is the cost slope, measured as the dollar-value deviation (in hundreds of dollars) of the treatment
received from the cost of Gikuriro. Asterices denote significance at the 10, 5, and 1 percent levels, and are basedon clustered standard errors, in parentheses. Regressions are weighted be representative of the eligible population.
Anderson (2008) sharpened q-values presented in brackets. Variables marked with a † are in inverse hyperbolic sines.

42

Table 7: Benefit-cost ratios for primary outcomes
p-values

GiveDirectly
Gikuriro

Main

Large

(a)

(b)

(c)

Consumption†−0.090(0.077)0.066(0.100)0.058(0.021)

0.05

0.93

0.04

Household dietary diversity
score

0.155
(0.098)0.186(0.162)0.106(0.024)

0.83

0.60

0.59

Household non-land wealth†

0.007(0.148)0.005(0.235)0.077(0.054)

0.99

0.74

0.60

Height-for-age

0.042(0.036)−0.021(0.044)0.018(0.009)

0.11

0.33

0.47

Weight-for-age

0.031(0.032)0.012(0.037)0.013(0.007)

0.61

0.97

0.54

Mid-upper arm
circumference

0.018(0.045)−0.007(0.072)0.026(0.015)

0.71

0.63

0.85

Child anemia

0.003(0.018)0.026(0.028)−0.002(0.007)

0.38

0.30

0.77

Maternal anemia−0.019(0.022)−0.002(0.032)−0.005(0.006)

0.46

0.91

0.48

Panel A. Household outcomes

Panel B. Individual outcomes

Notes: Benefit-cost ratios derived form ITT estimates and estimated costs per eligible household. p-values reporttests of equal BCR between (a) Gikuriro and GD-Main; (b) GD-Main and GD-Large; and (c) Gikuriro and GD-large.
Household-level BCRs; the average eligible household contains 5.2 members, 1.5 anthro-eligible children, 1.7 children
eligible for anemia testing, and 1.2 adult women eligible for anemia testing. Per-person BCRs should be scaled upby these numbers.

43

Figure 1: Cost Equivalence versus Cost Effectiveness

100

200
300
Treatment Cost

400

500

Dietary Diversity

0

100

200
300
Treatment Cost

400

500

Household Wealth

0

100

200
300
Treatment Cost

400

Consumption (Asinh)

13.6
13.5
13.4
13.3
13.2

0

Cost Effectiveness
11.1
11
10.9
10.8
10.7

Dietary Diversity

Dietary Diversity

5.4
5.2
5
4.8
4.6
4.4

Consumption

5.4
5.2
5
4.8
4.6
4.4

Wealth (Asinh)

Consumption (Asinh)

11.1
11
10.9
10.8
10.7

Wealth (Asinh)

Cost Equivalence

13.6
13.5
13.4
13.3
13.2

Consumption

0

100

0

100

0

400

100

0

100

HAZ

200
300
Treatment Cost

500

200
300
Treatment Cost

400

500

200
300
Treatment Cost

400

500

400

500

Weight for Age
WAZ

WAZ

Weight for Age

100

400

-1.85
-1.9
-1.95
-2
-2.05

500

-.85
-.9
-.95
-1
-1.05
0

200
300
Treatment Cost

Height for Age
HAZ

200
300
Treatment Cost

500

Household Wealth

Height for Age

100

400

Dietary Diversity

500

-1.85
-1.9
-1.95
-2
-2.05
0

200
300
Treatment Cost

400

500

-.85
-.9
-.95
-1
-1.05
0

100

200
300
Treatment Cost

Control

Gikuriro

GD Lower

GD Middle

GD UpperGD Large

Estimated GD impact
at Gikuriro cost

Fitted in GD
treatment arms

Notes: Figures in left column visualize a cost-equivalent comparison (with no covariate adjustment). Dots represent
mean outcomes in each treatment arm. Purple line represents a (population-weighted) regression of outcomes ontreatment cost in the cash-transfer arms only. Hollow circle represents the point on that regression line for which
expenditure per beneficiary is equivalent to the ex-post cost of Gikuriro; cost-equivalent comparison compares thisto the diamond, which is the mean outcome in the Gikuriro arm. By contrast, figures in the right column illustrate acost-benefit approach to comparing treatments (with no covariate adjustment). Here, the slope of the ray extending
from the control group to the relevant treatment-specific mean represents the benefit-cost ratio; policymakers following
this approach would favor the arm with the steepest slope.

44

Figure 2: Impacts on Dietary Diversity

Notes: Figure presents estimates of shares of households consuming goods that comprise the dietary diversity scorein the past 7 days, by treatment arm. Control group represents unadjusted mean consumption rate of each food type.
Other treatment arms represent the value in the control group, with the estimated impact of that treatment added,
using the regression specification used to estimate intent-to-treat program impacts in Section ??. Food types sortedby the share of control-group households who consume each item.

45

Online AppendicesAppendix A. Supplementary Tables and FiguresA.1-0.00436∗∗(0.00190)
0.190∗∗(0.0766)
0.147∗∗(0.0589)-0.00543∗∗∗(0.00204)0.150∗∗(0.0746)
0.165∗∗(0.0632)
0.0212(0.0812)0.0159(0.0584)-0.0460(0.0688)

Age of HH head

Poorest poverty
group

Next poorest group

HH in Agriculture

HH in Wage Work

HH in
Microenterprise
0.511
524

0.0294(0.0802)

0.0539(0.0532)

0.568
524

-0.0501(0.0604)

0.0795(0.0483)-0.0462(0.0815)0.151∗∗(0.0612)0.222∗∗∗(0.0740)-0.00153(0.00182)-0.102(0.0762)0.0679(0.0409)-0.0344(0.0448)

Farmer Training

0.480
529

0.335
529

-0.00755(0.0662)0.139∗∗∗(0.0487)

0.0994∗(0.0554)0.0194(0.0645)-0.0458(0.0711)0.211∗∗∗(0.0509)

0.238∗∗∗(0.0745)-0.00377∗∗(0.00143)-0.0472(0.0650)-0.0271(0.0301)

0.0351(0.0341)

Received Livestock-0.0895(0.0808)0.0869(0.0687)

0.162∗∗(0.0756)-0.00178(0.00182)-0.0392(0.0693)0.0806(0.0518)-0.0496(0.0551)

Farmer Harvest

0.863
718

0.0369(0.0382)

0.0416(0.0455)

0.0285(0.0448)0.256∗∗∗(0.0839)0.234∗∗(0.0973)-0.00109(0.000969)-0.0539(0.0454)-0.00469(0.0273)-0.00711(0.0341)

GD transfer

Notes: Outcome is a dummy variable indicating that household received different components of Gikuriro (columns 1–5), or a cash transfer from GiveDirectly(column 6). First three columns are indicators for receiving training in proper nutrition (1), hygenic cooking habits (2), agricultural extension (3). Column 4
is an indicator that the household has successfully harvested a farm garden as instructed by FFLS, and Column 5 for the receipt of livestock from Gikuriro.
Regressions are run among all households defined as eligible at baseline based on intended targeting criteria for the relevant treatment arm.

0.628
524

-0.00581(0.0628)0.0190(0.0691)

Female headed HH

Mean DVN

0.0894∗∗(0.0367)

0.0815∗(0.0423)

Number of Members-0.0215(0.0762)-0.0594(0.0390)-0.0639(0.0436)

Cooking Training

Number of Children

Nutrition Training

Gikuriro

Table A.1: Correlates of compliance with intervention components

A.2
0.033
1793
0.0019

0.033
1793
0.023

(2)
Household-0.011(0.0085)-0.0097(0.010)-0.015(0.0093)-0.015(0.012)0.0076(0.0093)-0.0083(0.0075)0.00040(0.0085)0.021(0.053)-0.00034(0.00022)
0.11(0.14)
0.035(0.030)-0.0036(0.0023)0.0097(0.011)0.0061(0.0085)

0.030
9189
0.0014(3)
Roster
-0.010(0.0083)-0.0077(0.0093)-0.015*(0.0086)(4)
Roster
-0.0086(0.0079)-0.0076(0.0097)-0.013(0.0088)-0.020(0.013)0.016(0.0099)-0.0085(0.0063)-0.0051(0.0071)0.015(0.048)-0.00025(0.00019)
0.21(0.21)
0.027(0.032)-0.0031(0.0023)-0.0055(0.0098)-0.0048(0.0094)0.0011(0.0026)
0.030
9189
0.028
0.071
2265
0.0049(5)
Anthro-0.037***(0.013)-0.024*(0.013)-0.022(0.016)(6)
Anthro-0.038***(0.012)-0.025**(0.013)-0.020(0.015)-0.037*(0.020)0.019(0.015)-0.00060(0.017)-0.015(0.012)0.032(0.050)0.00013(0.00034)
0.053(0.11)
0.058(0.056)-0.0060(0.0040)0.0065(0.018)0.0059(0.014)0.0012(0.0088)
0.071
2265
0.025
0.80
5192
0.00068(7)
Anemia-0.0044(0.021)-0.0069(0.019)0.027(0.026)(8)
Anemia-0.0046(0.016)-0.0013(0.016)0.0056(0.023)0.023(0.023)0.0038(0.016)-0.039*(0.022)-0.0020(0.017)-0.0030(0.060)-0.0017***(0.00052)
0.13***(0.025)0.029(0.063)-0.040***(0.0058)-0.017(0.021)0.0064(0.017)-0.045***(0.014)
0.80
5192
0.068
0.057
9509
0.000054(9)
New Member-0.00050(0.0081)-0.0027(0.0088)-0.0051(0.0096)

Notes: First two columns analyze household-level attrition from the baseline sample of households. (3) and (4) examine individual-level attrition from the roster ofhousehold members. (5) and (6) examine attrition from the baseline sample of child anthropometrics. (7) and (8) analyze attrition among individuals who shouldhave received anemia testing, and (9) analyzes the probability of new household members appearing in rosters. Regressions are weighted to be representative of
the eligible population.

Control group mean
Observations
R2

femaleNext Poorest Category

Poorest Category

Household Size

Dependency Ratio

Schooling of HeadAge of Head

Village Eligibility Ratio

Savings Group

Microenterprise

Wage Worker

Agricultural

GD Large

GD Main

Gikuriro(1)
Household-0.013(0.0092)-0.0089(0.010)-0.017*(0.0097)

Table A.2: Attrition

Table A.3: Regressions with Granular GD Treatment Cells
GiveDirectly
Gikuriro

Small

Mid

Upper

Large

Control
Mean

Obs.

R2

Panel A. Household outcomes
Consumption†−0.11(0.10)[0.48]

0.10(0.11)[0.66]−0.02(0.12)[0.96]

0.10(0.12)[0.66]

0.30∗∗∗(0.11)[ 0.04]

10.69

1750

0.14

Household dietary
diversity score

0.20(0.12)[0.29]

0.40∗∗(0.19)[0.12]−0.25(0.22)[0.48]

0.36∗∗(0.15)[0.10]

0.56∗∗∗(0.13)[ 0.00]

4.77

1751

0.19

Household non-land
wealth†0.01(0.19)[0.96]

0.06(0.40)[0.96]

0.04(0.26)[0.96]−0.09(0.26)[0.96]

0.40(0.28)[ 0.41]

13.04

1751

0.22

Panel B. Individual outcomes
Height-for-age

0.05(0.04)[1.00]

0.04(0.05)[1.00]−0.04(0.06)[1.00]−0.05(0.05)[1.00]

0.09∗∗(0.05)[ 1.00]

−1.97

2125

0.71

Weight-for-age

0.04(0.04)[1.00]

0.01(0.05)[1.00]−0.01(0.05)[1.00]

0.03(0.04)[1.00]

0.07∗(0.04)[ 1.00]

−1.04

2104

0.68

Mid-upper arm
circumference

0.02(0.06)[1.00]−0.04(0.08)[1.00]

0.07(0.12)[1.00]−0.06(0.07)[1.00]

0.13∗(0.08)[ 1.00]

−0.59

1629

0.51

Child anemia

0.00(0.02)[1.00]

0.05(0.06)[1.00]

0.01(0.03)[1.00]

0.02(0.02)[1.00]-0.01(0.04)[ 1.00]

0.18

2372

0.07

Maternal anemia−0.02(0.03)[1.00]−0.03(0.03)[1.00]−0.00(0.03)[1.00]

0.03(0.05)[1.00]-0.03(0.03)[ 1.00]

0.12

1581

0.11

Notes: Analysis includes dummies for each of the four GD transfer amounts separately. Asterices denote significanceat the 10, 5, and 1 percent levels, and are based on clustered standard errors, in parentheses. Anderson (2008)
sharpened q-values presented in brackets. Variables marked with a † are in inverse hyperbolic sines. Regressions areweighted to be representative of the eligible population.

A.3

Table A.4: Secondary Regressions with Granular GD Treatment Cells
GiveDirectly
Gikuriro

Small

Mid

Upper

Large

Control
Mean

Obs.

R2

Panel A. Household outcomes
Stock of borrowing†

0.068(0.349)[0.79]−0.670∗∗(0.334)[0.19]−0.780∗(0.466)[0.35]−0.846∗∗(0.383)[0.16]-0.339( 0.397)[ 0.77]

7.39

1751

0.12

Stock of saving†

1.123∗∗∗(0.326)[0.01]−0.144(0.422)[0.79]−0.584(0.487)[0.49]

0.353(0.507)[0.77]

0.661∗∗( 0.329)[ 0.19]

5.88

1751

0.16

Health knowledge
index−0.074(0.369)[0.79]

0.295(0.480)[0.79]

0.091(0.410)[0.79]

0.072(0.378)[0.79]

0.077( 0.468)[ 0.79]

2.89

1751

0.04

Sanitation practices
index−0.273(0.219)[0.49]

0.299(0.369)[0.77]−0.217(0.276)[0.77]

0.356(0.274)[0.49]

0.091( 0.284)[ 0.79]

−0.68

1751

0.07

Productive assets†

0.021(0.100)[0.79]

0.270∗∗(0.122)[0.16]

0.212(0.151)[0.49]

0.289∗∗(0.138)[0.19]

0.801∗∗∗( 0.116)[ 0.00]

11.22

1751

0.29

Consumption assets†−0.363(0.241)[0.45]

0.284(0.337)[0.77]

0.127(0.267)[0.79]

0.659∗∗(0.266)[0.11]

0.933∗∗∗( 0.244)[ 0.00]

8.70

1751

0.35

House value†−0.023(0.056)[0.79]

0.042(0.079)[0.79]

0.003(0.087)[0.87]−0.130(0.086)[0.45]

0.196∗∗∗( 0.061)[ 0.01]

13.81

1654

0.34

Housing quality index−0.195(0.145)[0.49]−0.331(0.237)[0.49]−0.283(0.217)[0.49]−0.033(0.121)[0.79]

0.210( 0.174)[ 0.49]

−0.17

1751

0.10

Notes: Analysis includes dummies for each of the four GD transfer amounts separately. Asterices denote significanceat the 10, 5, and 1 percent levels, and are based on clustered standard errors, in parentheses. Anderson (2008)
sharpened q-values presented in brackets. Variables marked with a † are in inverse hyperbolic sines. Regressions areweighted to be representative of the eligible population.

A.4

Table A.5: ITT Impacts on Stunting and Wasting

Gikuriro

GiveDirectly
Main
Large

Control
Mean

Obs.

R2

p-values: B/C ratios
GD=GDL GK=GDL

Stunted0.01(0.04)[1.00]

0.02(0.04)[1.00]−0.06∗(0.04)[0.35]

0.50

2360

0.04

0.37

0.41

Wasted0.00(0.02)[1.00]−0.01(0.02)[1.00]−0.05∗(0.03)[0.35]

0.16

2347

0.03

0.96

0.50

Notes: Table reports the Intention to Treat Impacts of the study arms on the binary outcomes of stunting andwasting (HAZ and WAZ respectively <-2). Asterices denote significance at the 10, 5, and 1 percent levels, andare based on clustered standard errors, in parentheses. Variables marked with a † are in inverse hyperbolic sines.
Regressions are weighted to be representative of the eligible population.

A.5

Table A.6: Anthropometric Impacts using Attrition IPW
Gikuriro

Main

Large

Mean

Obs.

R2

Height-for-age

0.051(0.045)[0.62]−0.021(0.039)[1.00]

0.091∗∗(0.046)[0.35]

−2.06

2125

0.71

Weight-for-age

0.038(0.040)[0.69]

0.010(0.034)[1.00]

0.067∗(0.036)[0.35]

−1.06

2104

0.68

Mid-upper arm
circumference

0.022(0.056)[1.00]−0.007(0.065)[1.00]

0.135∗(0.078)[0.35]

−0.58

1629

0.50

Notes: Regressions weighted using the product of standard survey weights and inverse propensity weights calculated
from the probability that a child with baseline anthropometrics attrites from the endline.

A.6

Table A.7: Lower Lee Bounds for Anthropometric Effects.
Gikuriro

Main

Large

Mean

Obs.

R2

Height-for-age

0.010(0.042)[1.00]−0.036(0.039)[1.00]

0.076∗(0.045)[0.92]

−1.97

2080

0.70

Weight-for-age

0.004(0.037)[1.00]−0.006(0.034)[1.00]

0.058(0.036)[0.92]

−1.04

2059

0.67

Mid-upper arm
circumference−0.037(0.055)[1.00]−0.045(0.059)[1.00]

0.090(0.075)[1.00]

−0.59

1593

0.48

Notes: Table provides the Lower Lee Bounds for the anthro effects with the three treatment groups trimmed on theright tail of the outcome to match the (higher) degree of attrition observed in the control group. Regressions areweighted to be representative of the eligible population.

Table A.8: Upper Lee Bounds for Anthropomentric Effects.
Gikuriro

Main

Large

Mean

Obs.

R2

Height-for-age

0.071(0.045)[0.19]

0.001(0.039)[0.54]

0.107∗∗(0.044)[0.05]

−1.97

2083

0.69

Weight-for-age

0.061(0.041)[0.19]

0.033(0.034)[0.32]

0.092∗∗(0.036)[0.05]

−1.04

2062

0.67

Mid-upper arm
circumference

0.065(0.054)[0.30]

0.029(0.062)[0.47]

0.181∗∗(0.073)[0.05]

−0.59

1596

0.49

Notes: Table provides the Upper Lee Bounds for the anthro effects with the three treatment groups trimmed on theleft tail of the outcome variable to match the (higher) degree of attrition observed in the control group. Regressions
are weighted to be representative of the eligible population.

A.7

Table A.9: Cost-equivalent impacts of Gikuriro versus cash using only lump-sum cash transfers:
Primary Outcomes.
Gikuriro:Differential

Cost-equivalent
GD impact

Transfer
Cost

Control
Mean

Observations

R2

Consumption†−0.22∗∗(0.09)[0.21]

0.12(0.10)[0.60]

0.05(0.03)[0.50]

10.69

1379

0.15

Household dietary
diversity score

0.05(0.16)[0.80]

0.16(0.17)[0.60]

0.08(0.05)[0.50]

4.77

1380

0.19

Household non-land
wealth†−0.21(0.25)[0.60]

0.22(0.24)[0.60]

0.07(0.07)[0.60]

13.04

1380

0.23

Height-for-age

0.09∗∗(0.04)[0.33]−0.04(0.04)[1.00]

0.04∗∗∗(0.01)[0.04]

−1.97

1663

0.72

Weight-for-age

0.03(0.05)[1.00]

0.01(0.04)[1.00]

0.02(0.01)[1.00]

−1.04

1641

0.70

Mid-upper arm
circumference−0.02(0.08)[1.00]

0.05(0.08)[1.00]

0.01(0.03)[1.00]

−0.59

1273

0.51

Child anemia−0.02(0.03)[1.00]

0.03(0.03)[1.00]−0.01(0.01)[1.00]

0.22

1852

0.08

Maternal anemia−0.00(0.03)[1.00]−0.02(0.03)[1.00]−0.00(0.01)[1.00]

0.12

1238

0.10

A. Household outcomes

B. Individual outcomes

Notes: Table estimated as in the main cost-effectiveness comparisons but uses only the Lump Sum cash treatments
rather than pooling the Lump Sum and Flow cash arms together. Regressions are weighted to be representative of
the eligible population.

A.8

Table A.10: Cost-equivalent impacts of Gikuriro versus cash using only “flow” cash transfers: Primary Outcomes.
Gikuriro:Differential

Cost-equivalent
GD impact

Transfer
Cost

Control
Mean

Observations

R2

Consumption†−0.19∗∗(0.09)[0.16]

0.07(0.10)[0.79]

0.05∗∗(0.03)[0.16]

10.69

1559

0.13

Household dietary
diversity score−0.03(0.14)[0.86]

0.22(0.15)[0.29]

0.09∗∗(0.04)[0.16]

4.77

1560

0.18

Household non-land
wealth†−0.12(0.21)[0.79]

0.11(0.22)[0.79]

0.03(0.09)[0.79]

13.04

1560

0.20

Height-for-age

0.03(0.04)[0.85]

0.02(0.04)[0.85]

0.01(0.01)[0.58]

−1.97

1891

0.70

Weight-for-age

0.05(0.04)[0.58]−0.02(0.04)[0.85]

0.01(0.01)[0.58]

−1.04

1874

0.68

Mid-upper arm
circumference

0.11∗(0.06)[0.50]−0.11∗(0.06)[0.50]

0.05∗∗(0.02)[0.50]

−0.59

1448

0.50

Child anemia−0.03(0.03)[0.58]

0.04(0.03)[0.58]−0.01(0.01)[0.58]

0.22

2114

0.08

Maternal anemia−0.02(0.02)[0.58]

0.00(0.03)[0.85]−0.01(0.01)[0.74]

0.12

1408

0.11

A. Household outcomes

B. Individual outcomes

Notes: Table estimated as in the main cost-effectiveness comparisons but uses only the Flow cash treatments rather
than pooling the Lump Sum and Flow cash arms together. Regressions are weighted to be representative of theeligible population.

A.9

Table A.11: Robustness of Linearity in Primary Cost Equivalence Adjustment
Base
LinearQuadratic

Cubic

Drop
lower

Drop
mid

Drop
upper

Drop
huge

Consumption†−0.193∗∗(0.079)−0.164(0.102)−0.240∗(0.127)−0.158∗(0.086)−0.226∗∗∗(0.086)−0.175∗∗(0.084)−0.185∗(0.103)

Household dietary
diversity score−0.010(0.132)0.107(0.179)−0.278(0.173)0.139(0.155)−0.208∗(0.125)0.047(0.155)0.109(0.183)

Household non-land
wealth†−0.032(0.199)0.066(0.246)0.117(0.294)0.010(0.192)−0.028(0.245)−0.095(0.228)0.104(0.255)Height-for-age

0.060(0.040)0.098∗(0.053)0.062(0.062)0.081∗(0.047)0.030(0.043)0.047(0.046)

0.095∗(0.053)Weight-for-age

0.024(0.039)0.009(0.048)−0.032(0.055)0.023(0.043)0.005(0.043)0.038(0.043)0.002(0.049)Mid-upper arm
circumference

0.017(0.062)0.037(0.084)0.108(0.087)0.018(0.079)0.055(0.061)−0.004(0.076)0.035(0.079)Anemia−0.018(0.026)−0.003(0.025)−0.016(0.029)−0.005(0.021)−0.027(0.032)−0.019(0.031)−0.002(0.026)

Anemia−0.018(0.021)−0.052(0.036)−0.059(0.049)−0.035(0.026)−0.017(0.024)−0.003(0.017)−0.052(0.036)

A. Household outcomes

B. Individual outcomes

Notes: Table reports the coefficient on the differential effect of Gikuriro over cost-equivalent cash using seven different
specifications. Column 1 is the linear adjustment reported elsewhere. Column 2 includes a quadratic, and column 3 aquadratic and cubic term in the cost deviations from Gikuriro. Columns 4-7 leave out one of the cash treatment arms
and repeat the linear cost adjustment. Asterices denote significance at the 10, 5, and 1 percent levels, and are basedon clustered standard errors, in parentheses. Regressions are weighted to be representative of the eligible population.
Variables marked with a † are in inverse hyperbolic sines.

A.10

Table A.12: Robustness of Linearity in Secondary Cost Equivalence Adjustment
Base
LinearQuadratic

Cubic

Drop
lower

Drop
mid

Drop
upper

Drop
huge

Stock of borrowing†

0.793∗∗∗(0.277)0.917∗∗(0.360)0.923∗∗(0.411)0.847∗∗∗(0.322)0.758∗∗∗(0.278)0.767∗∗∗(0.293)0.964∗∗∗(0.371)

Stock of saving†

1.184∗∗∗(0.333)

1.071∗∗(0.488)0.597(0.595)

1.253∗∗∗(0.392)0.958∗∗∗(0.350)1.326∗∗∗(0.337)1.037∗∗(0.500)

Health knowledge
index−0.227(0.329)−0.112(0.380)−0.148(0.442)−0.140(0.343)−0.243(0.357)−0.285(0.366)−0.162(0.379)

Sanitation practices
index−0.421∗∗(0.213)−0.397(0.264)−0.742∗∗(0.304)−0.365(0.224)−0.569∗∗(0.247)−0.352(0.240)−0.397(0.261)

Productive assets†−0.283∗∗∗(0.097)−0.241∗(0.129)−0.283∗(0.157)−0.246∗∗(0.112)−0.309∗∗∗(0.103)−0.287∗∗∗(0.104)−0.218∗(0.132)

Consumption assets†−0.764∗∗∗(0.256)−0.868∗∗∗(0.304)−1.117∗∗∗(0.337)−0.776∗∗∗(0.275)−0.872∗∗∗(0.276)−0.669∗∗(0.279)−0.854∗∗∗(0.304)

House value†−0.017(0.051)

0.082(0.073)0.128(0.092)0.029(0.059)−0.008(0.057)−0.069(0.054)0.086(0.074)

Housing quality index−0.008(0.151)−0.112(0.168)−0.204(0.162)−0.043(0.160)−0.028(0.161)

0.056(0.175)−0.189(0.163)

Child mortality−0.002(0.005)0.002(0.006)0.010∗∗(0.004)−0.001(0.006)0.001(0.004)−0.006(0.006)0.003(0.006)

Pregnancy

0.002(0.027)−0.017(0.041)−0.052(0.047)0.000(0.035)−0.013(0.027)0.017(0.029)−0.023(0.041)

Live birth

0.024(0.067)0.011(0.089)−0.098(0.110)0.048(0.078)−0.021(0.069)0.080(0.075)−0.005(0.088)Birth in facility−0.104∗∗(0.052)−0.141∗∗(0.061)−0.177∗∗∗(0.068)−0.122∗∗(0.058)−0.134∗∗(0.055)−0.060(0.063)−0.156∗∗∗(0.060)Any vaccinations inpast year

0.018(0.032)0.060(0.042)0.089(0.054)0.039(0.034)0.025(0.037)−0.003(0.035)0.060(0.041)

Completed vaccination
schedule

0.021(0.036)0.055(0.045)0.081(0.054)0.037(0.038)0.027(0.039)0.003(0.040)0.056(0.045)

Disease burden

0.012(0.033)−0.024(0.040)−0.034(0.045)−0.006(0.035)0.010(0.036)0.027(0.036)0.120∗∗∗(0.039)Diarrheal prevalence−0.003(0.014)0.010(0.023)0.001(0.032)0.007(0.017)−0.010(0.017)−0.006(0.014)0.122∗∗∗(0.038)

A. Household outcomes

B. Individual outcomes

Notes: Table reports the coefficient on the differential effect of Gikuriro over cost-equivalent cash using seven different
specifications. Column 1 is the linear adjustment reported elsewhere. Column 2 includes a quadratic, and column 3 aquadratic and cubic term in the cost deviations from Gikuriro. Columns 4-7 leave out one of the cash treatment arms
and repeat the linear cost adjustment. Asterices denote significance at the 10, 5, and 1 percent levels, and are basedon clustered standard errors, in parentheses. Regressions are weighted to be representative of the eligible population.
Variables marked with a † are in inverse hyperbolic sines.

A.11

A.12

0.012
(0.262)[1.00]

Household non-land wealth†

0.028(0.041)[0.56]
0.121(0.082)[0.52]−0.007(0.039)[0.92]−0.024(0.037)[0.56]

0.125∗(0.071)[0.52]
0.320∗∗∗(0.112)[0.11]−0.054(0.038)[0.52]−0.033(0.035)[0.56]−0.036(0.040)[0.56]−0.097(0.063)[0.52]
0.044(0.032)[0.52]
0.013(0.031)[0.72]

Weight-for-age

Mid-upper arm circumferenceChild anemia

Maternal anemia

0.009(0.053)[0.92]−0.005(0.052)[0.93]

0.150(0.107)[0.52]

0.148∗∗(0.073)[0.52]

0.047(0.062)[0.56]

0.12

0.22

−0.59

−1.04

−1.97

13.04

4.77

10.69

Control
Mean

1025

1544

1057

1369

1380

1131

1131

1131

Observations

0.12

0.08

0.53

0.70

0.74

0.25

0.19

0.15

R2

Notes: Analysis uses only the Control and GD arms; the first and third columns are dummies for GD transfer amount cells and the second and fourth are
dummies measuring the additional effect of lump sum transfers within each cell. Asterices denote significance at the 10, 5, and 1 percent levels, and are based onclustered standard errors, in parentheses. Regressions are weighted to be representative of the eligible population. Anderson (2008) sharpened q-values presented
in brackets. Variables marked with a † are in inverse hyperbolic sines.

0.074(0.050)[0.52]−0.095∗(0.057)[0.52]−0.002(0.045)[0.93]

0.631(0.441)[1.00]−0.268(0.237)[1.00]

0.614∗∗∗(0.137)[0.00]
0.209(0.388)[1.00]

0.027(0.118)[1.00]

Large GD
Lump Sum

0.286∗∗∗(0.104)[0.04]

Large GD
Treatment

Height-for-age

B. Individual outcomes

0.014(0.176)[1.00]

0.068(0.159)[1.00]

Household dietary diversity
score−0.273(0.502)[1.00]

0.028(0.127)[1.00]

0.044(0.097)[1.00]

Main GD
Lump Sum

Consumption†

A. Household outcomes

Main GD
Treatment

Table A.13: Comparison of Lump Sum and Flow Transfers: Primary

Table A.14: Comparison of Lump Sum and Flow Transfers: Secondary
Main GD
Treatment

Main GD
Lump Sum

Large GD
Treatment

Large GD
Lump Sum

Control
Mean

Observations

R2

A. Household outcomes
Stock of borrowing†−1.080∗∗∗(0.323)[0.02]

1.071∗∗(0.469)[0.16]

0.024(0.411)[1.00]−0.356(0.892)[1.00]

7.39

1131

0.16

Stock of saving†−0.584(0.375)[0.51]

0.888∗∗(0.429)[0.22]

0.573(0.410)[0.59]

0.009(0.723)[1.00]

5.88

1131

0.17

Health knowledge
index

0.229
(0.360)[1.00]

0.115(0.499)[1.00]

0.186(0.554)[1.00]

0.176(0.591)[1.00]

2.89

1131

0.05

Sanitation practices
index

0.080
(0.302)[1.00]

0.406(0.511)[1.00]

0.252(0.290)[0.91]

0.040(0.544)[1.00]

−0.68

1131

0.08

Productive assets†

0.199∗(0.121)[0.47]

0.069(0.295)[1.00]

0.792∗∗∗(0.135)[0.00]

0.148(0.225)[1.00]

11.22

1131

0.29

Consumption assets†

0.336(0.239)[0.59]

0.442(0.352)[0.72]

0.831∗∗∗(0.300)[0.07]

0.465(0.475)[0.90]

8.70

1131

0.38

House value†

0.023(0.065)[1.00]−0.162∗∗(0.068)[0.16]

0.169∗(0.088)[0.27]

0.012(0.111)[1.00]

13.81

1071

0.35

Housing quality index−0.260(0.214)[0.72]

0.177(0.433)[1.00]

0.206(0.206)[0.90]

0.253(0.235)[0.88]

−0.17

1131

0.11

Child mortality−0.001(0.010)[1.00]−0.002(0.014)[1.00]−0.010∗∗(0.004)[0.60]

0.010(0.009)[1.00]

0.01

1751

0.02

Pregnancy−0.028(0.032)[1.00]−0.031(0.034)[1.00]−0.033(0.029)[1.00]

0.045(0.052)[1.00]

0.20

1646

0.09

Live birth

0.057(0.088)[1.00]

0.099(0.112)[1.00]−0.087(0.093)[1.00]−0.123(0.148)[1.00]

0.68

273

0.22

Birth in facility

0.077(0.058)[1.00]−0.013(0.073)[1.00]−0.095(0.100)[1.00]−0.033(0.117)[1.00]

0.84

188

0.25

Any vaccinations inpast year−0.013(0.039)[1.00]

0.038(0.059)[1.00]−0.022(0.051)[1.00]

0.048(0.101)[1.00]

0.72

838

0.27

Completed vaccination
schedule−0.006(0.039)[1.00]

0.013(0.070)[1.00]−0.008(0.050)[1.00]

0.063(0.103)[1.00]

0.58

838

0.21

Disease burden−0.016(0.032)[1.00]−0.055(0.058)[1.00]−0.004(0.044)[1.00]

0.014(0.084)[1.00]

0.54

1746

0.06

Diarrheal prevalence−0.005(0.018)[1.00]

0.021(0.026)[1.00]

0.002(0.019)[1.00]−0.021(0.024)[1.00]

0.09

1746

0.05

B. Individual outcomes

Notes: Analysis uses only the Control and GD arms; the first and third columns are dummies for GD transfer
amount cells and the second and fourth are dummies measuring the additional effect of lump sum transfers withineach cell. Asterices denote significance at the 10, 5, and 1 percent levels, and are based on clustered standard errors,in parentheses. Regressions are weighted to be representative of the eligible population. Anderson (2008) sharpened
q-values presented in brackets. Variables marked with a † are in inverse hyperbolic sines.

A.13

Table A.15: Effect of Transfer Modality Choice: Primary
Got Ones
ChoiceChose
Lump Sum

TreatedLump Sum

Control
Mean

Observations

R2

Consumption†

0.089(0.115)[1.00]−0.018(0.126)[1.00]−0.032(0.117)[1.00]

10.69

534

0.18

Household dietary
diversity score

0.067(0.210)[1.00]

0.062(0.197)[1.00]−0.204(0.178)[1.00]

4.77

534

0.23

Household non-land
wealth†−0.650(0.468)[1.00]

0.033(0.471)[1.00]

0.172(0.460)[1.00]

13.04

534

0.29

Height-for-age−0.160∗∗(0.062)[0.21]

0.034(0.058)[1.00]−0.026(0.063)[1.00]

−1.97

671

0.75

Weight-for-age−0.042(0.052)[1.00]

0.013(0.045)[1.00]

0.093∗(0.049)[0.36]

−1.04

668

0.67

Mid-upper arm
circumference

0.070(0.071)[1.00]

0.025(0.065)[1.00]

0.153∗∗(0.067)[0.21]

−0.59

520

0.57

Child anemia

0.015(0.024)[1.00]−0.017(0.030)[1.00]−0.034(0.030)[1.00]

0.22

750

0.08

Maternal anemia

0.007(0.034)[1.00]

0.008(0.043)[1.00]−0.004(0.037)[1.00]

0.12

496

0.14

A. Household outcomes

B. Individual outcomes

Notes: Analysis uses only the GD arm. First column is a dummy for getting the chosen transfer modality, the second
column is an (endogenous) indicator for choosing lump sum, and the third column is a dummy for actually receiving
the lump sum treatment. Asterices denote significance at the 10, 5, and 1 percent levels, and are based on clustered
standard errors, in parentheses. Regressions are weighted to be representative of the eligible population. Anderson(2008) sharpened q-values presented in brackets. Variables marked with a † are in inverse hyperbolic sines.

A.14

Table A.16: Effect of Transfer Modality Choice: Secondary
Got Ones
ChoiceChose
Lump Sum

TreatedLump Sum

Control
Mean

Observations

R2

Stock of borrowing†−0.558(0.504)[1.00]−0.197(0.425)[1.00]

0.956∗(0.494)[0.67]

7.39

534

0.16

Stock of saving†−0.222(0.463)[1.00]

0.074(0.491)[1.00]

0.132(0.450)[1.00]

5.88

534

0.21

Health knowledge
index

0.056
(0.534)[1.00]

0.559(0.479)[1.00]

0.524(0.454)[1.00]

2.89

534

0.09

Sanitation practices
index

0.068
(0.286)[1.00]

0.632∗(0.349)[0.67]

0.221(0.383)[1.00]

−0.68

534

0.15

Productive assets†−0.082(0.212)[1.00]−0.166(0.207)[1.00]

0.001(0.203)[1.00]

11.22

534

0.29

Consumption assets†−0.189(0.362)[1.00]−0.917∗∗∗(0.319)[0.06]

0.952∗∗∗(0.301)[0.05]

8.70

534

0.37

House value†−0.065(0.085)[1.00]−0.029(0.090)[1.00]−0.094(0.070)[1.00]

13.81

508

0.40

Housing quality index−0.048(0.175)[1.00]−0.017(0.119)[1.00]−0.114(0.123)[1.00]

−0.17

534

0.21

Child mortality

0.001(0.010)[1.00]−0.011(0.014)[1.00]

0.000(0.015)[1.00]

0.01

838

0.03

Pregnancy

0.008(0.035)[1.00]

0.027(0.032)[1.00]−0.025(0.030)[1.00]

0.20

757

0.11

Live birth−0.064(0.151)[1.00]

0.286∗(0.162)[1.00]−0.007(0.120)[1.00]

0.68

129

0.32

Birth in facility

0.137(0.127)[1.00]

0.077(0.121)[1.00]−0.153(0.115)[1.00]

0.84

83

0.57

Any vaccinations inpast year

0.012(0.050)[1.00]

0.102∗(0.062)[1.00]

0.016(0.058)[1.00]

0.72

434

0.25

Completed vaccination
schedule−0.004(0.054)[1.00]

0.060(0.065)[1.00]

0.002(0.064)[1.00]

0.58

434

0.19

Disease burden

0.017(0.051)[1.00]

0.050(0.050)[1.00]−0.026(0.049)[1.00]

0.54

835

0.07

Diarrheal prevalence−0.001(0.024)[1.00]−0.030(0.026)[1.00]

0.017(0.025)[1.00]

0.09

835

0.05

A. Household outcomes

B. Individual outcomes

Notes: Analysis uses only the GD arm. First column is a dummy for getting the chosen transfer modality, the second
column is an (endogenous) indicator for choosing lump sum, and the third column is a dummy for actually receiving
the lump sum treatment. Asterices denote significance at the 10, 5, and 1 percent levels, and are based on clustered
standard errors, in parentheses. Regressions are weighted to be representative of the eligible population. Anderson(2008) sharpened q-values presented in brackets. Variables marked with a † are in inverse hyperbolic sines.

A.15

A.16

(2)
Diet Diversity
b/se
0.37
(0.25)
0.31(0.27)-0.49(0.32)-0.037(0.32)-0.33(0.26)-0.27(0.28)-0.22(0.20)
0.34(0.27)
0.050(0.19)
0.16(0.21)
0.011(0.23)
4.16
1509
0.18
0.80
0.85

(3)
Wealth†b/se-0.34(0.39)
0.00021(0.50)
0.025(0.52)-0.19(0.52)-0.67*(0.40)-0.63(0.45)-0.041(0.25)-0.10(0.33)
0.25(0.21)
0.43(0.33)
0.23(0.49)
12.9
1509
0.22
0.52
0.95

(4)
Borrowing†
b/se
0.85
(0.77)
0.25(0.77)-0.86(0.85)-0.29(1.02)
0.89(0.64)-0.34(0.68)-0.41(0.61)
0.088(0.72)
0.20(0.47)-0.54(0.63)-0.81(0.60)
5.96
1509
0.12
0.38
0.066

(5)
Saving†b/se
1.04(0.82)
0.69(0.87)-0.57(0.87)-0.072(0.83)-0.54(0.54)-0.44(0.65)-0.98(0.70)
0.62(0.62)
1.01**(0.41)
0.69(0.60)-0.47(0.63)
5.18
1509
0.17
0.61
0.88

(6)
Healthb/se-0.34(0.68)
0.40(0.70)
0.78(0.78)-0.020(0.90)-1.51***(0.57)-0.60(0.73)-0.19(0.42)-0.27(0.60)
0.77*(0.39)
0.43(0.68)
0.14(0.69)
0.19
1509
0.049
0.35
0.22

(7)
Sanitation
b/se
0.54
(0.42)
0.68(0.47)-0.90*(0.53)
0.21(0.47)
0.28(0.39)-0.11(0.43)-0.67**(0.27)
0.094(0.38)-0.045(0.27)-0.51(0.32)-0.39(0.39)
-0.23
1509
0.079
0.77
0.38

(8)
Prod assets†b/se
0.56**(0.27)
0.56*(0.30)-0.59**(0.26)
0.11(0.33)-0.32(0.24)-0.34(0.29)-0.61***(0.17)
0.14(0.20)
0.20(0.19)-0.12(0.19)-0.080(0.22)
11.4
1509
0.32
0.98
0.93

(9)
Cons assets†
b/se
0.23
(0.42)
0.66(0.52)-1.06*(0.57)-0.86*(0.48)-0.060(0.48)-0.23(0.52)-0.67**(0.29)
1.07***(0.36)-0.16(0.27)-0.31(0.32)
0.10(0.42)
8.71
1509
0.38
0.42
0.77

Notes: Analysis excludes the GD Large arm, and uses interaction with three behavioral parameters to ask whether the effect of GD Main or Gikuriro areheterogeneous by Impatience, Time Inconsistency, or Other Control Problems. SEs clustered at the village level are in parentheses.

Control Mean
Observations
R2
Self_control_GD=GK
Other_control_GD=GK

GiveDirectly

Gikuriro

Lack Other Control

ImpatientTime Inconsistent

Lack Other Cont x GD

Lack Other Cont x GK

Impatient x GD

Impatient x GK

Inconsistent x GD

Inconsistent x GK

(1)
Consumption†
b/se
0.22
(0.20)
0.42*(0.23)-0.044(0.20)
0.30(0.22)
0.16(0.22)
0.16(0.22)-0.17(0.17)-0.18(0.16)-0.16(0.16)-0.29*(0.16)-0.33**(0.16)
10.4
1508
0.15
0.29
0.97

Table A.17: Cash versus Kind Heterogeneity by Behavioral Attributes

A.17

(2)
Diet Diversity
b/se
0.34
(0.30)
0.13(0.30)-0.27(0.32)-0.40(0.34)
0.11(0.33)-0.25(0.30)-0.19(0.18)
0.40*(0.23)-0.067(0.18)-0.0054(0.29)
0.47*(0.25)
4.16
1131
0.19
0.60
0.31

(3)
Wealth†b/se
0.74(0.70)
0.72(0.61)
0.27(0.76)-1.17*(0.68)-0.75(0.88)-0.028(0.55)-0.25(0.23)-0.029(0.28)
0.082(0.21)-0.35(0.69)-0.28(0.54)
12.9
1131
0.25
0.97
0.49

(4)
Borrowing†
b/se
0.097
(1.05)
0.17(0.89)-1.61(1.10)
1.23(1.06)
0.087(1.08)
0.076(0.81)-0.32(0.55)-0.064(0.61)
0.054(0.43)
0.35(0.82)-1.08*(0.64)
5.96
1131
0.16
0.95
0.99

(5)
Saving†b/se
0.48(0.91)
0.51(0.80)-0.58(1.12)-0.059(0.83)-0.27(0.71)
0.15(0.70)-0.72(0.64)
0.53(0.58)
0.86**(0.43)
0.54(0.72)
0.050(0.62)
5.18
1131
0.18
0.97
0.58

(6)
Healthb/se
0.061(0.97)-0.48(0.86)
0.047(0.80)
0.21(0.90)-0.78(0.75)
0.33(0.84)
0.22(0.42)-0.48(0.59)
0.48(0.36)
0.34(0.76)-0.029(0.65)
0.19
1131
0.060
0.61
0.26

(7)
Sanitationb/se
1.10**(0.48)
0.58(0.38)
0.47(0.48)
0.086(0.48)-0.68(0.71)-0.54(0.49)-0.73***(0.23)
0.10(0.31)
0.019(0.27)-0.26(0.51)-0.14(0.31)
-0.23
1131
0.091
0.31
0.85

(8)
Prod assets†b/se
0.77**(0.33)
1.13***(0.27)
0.49(0.56)-0.41(0.36)
0.093(0.34)-0.21(0.29)-0.81***(0.18)
0.090(0.19)
0.14(0.18)-0.20(0.29)-0.094(0.22)
11.4
1131
0.31
0.27
0.40

(9)
Cons assets†
b/se
0.96
(0.69)
1.09*(0.61)-0.42(0.77)-0.95(0.73)
0.41(0.80)-0.64(0.48)-0.61*(0.36)
0.56(0.43)-0.13(0.26)
0.15(0.61)-0.0084(0.44)
8.71
1131
0.38
0.85
0.26

Notes: Analysis uses only the control and the GD Main arm assigned to Lump Sum or Flow transfers to ask whether the impact of Lump Sum or FLow transfers
is heterogeneous by Impatience, Time Inconsistency, or Other Control Problems. SEs clustered at the village level are in parentheses. .

Control Mean
Observations
R2
Self_control_LS=Flow
Other_control_LS=Flow

GD Flow

GD Lump Sum

Lack Other Control

ImpatientTime Inconsistent

Lack Other Cont x Flow

Lack Other Cont x Lump Sum

Impatient x Flow

Impatient x Lump Sum

Inconsistent x Flow

Inconsistent x Lump Sum(1)
Consumption†
b/se
0.37
(0.25)
0.35*(0.19)
0.31(0.26)
0.18(0.23)
0.28(0.21)
0.19(0.23)-0.13(0.15)-0.15(0.17)-0.12(0.16)-0.27(0.20)-0.21(0.16)
10.4
1131
0.15
0.93
0.65

Table A.18: Lump Sum vs Flow Heterogeneity by Behavioral Attributes

A.18

(2)
Diet Diversity
b/se
0.075
(0.84)-0.79(0.71)
0.29(0.74)
0.18(0.79)
0.30(0.62)-0.86(0.59)-0.0070(0.86)
4.16
200
0.36

(3)
Wealth†b/se
1.93(1.41)-3.60*(1.85)
1.54(2.13)-0.24(0.71)
2.78**(1.37)-2.81(2.03)-1.50(1.15)
12.9
200
0.33

(4)
Borrowing†
b/se
-0.28
(1.86)
3.60(2.74)-0.86(2.01)
0.49(1.33)-2.92(2.21)-0.38(1.66)-1.37(1.49)
5.96
200
0.21

(5)
Saving†b/se
0.52(1.45)
0.78(2.58)
0.53(1.77)-0.039(1.30)-0.34(2.40)-0.95(1.63)-1.40(1.29)
5.18
200
0.31

(6)
Healthb/se-2.14(1.73)
1.94(1.80)-1.57(1.76)
1.76(1.30)-2.75*(1.50)
2.40*(1.42)
0.40(1.31)
0.19
200
0.25

(7)
Sanitation
b/se
-1.07
(1.06)-0.056(1.35)-1.46(0.99)
0.80(0.91)
0.59(1.14)
0.34(0.87)
1.18(0.81)
-0.23
200
0.27

(8)
Prod assets†
b/se
0.77
(0.69)-1.03(0.81)
0.12(0.78)-0.40(0.61)
0.71(0.71)-0.53(0.72)-0.75(0.66)
11.4
200
0.33

(9)
Cons assets†
b/se
1.46
(1.20)-2.85**(1.28)-0.095(1.24)-0.43(0.91)
1.84*(1.02)-1.97**(0.97)-1.39(0.96)
8.71
200
0.37

Notes: Analysis uses only the GD Main households that chose Flow transfers, and asks whether the impact of actually getting this choice (rather than being
assigned to Lump Sum) is heterogeneous by Impatience, Time Inconsistency, or Other Control Problems. SEs clustered at the village level are in parentheses.

Control Mean
Observations
R2

Got Choice in Choice Experiment

Lack Other Control

ImpatientTime Inconsistent

Lack Other Cont x Got It

Impatient x Got It

Inconsistent x Got It(1)Consumption†
b/se
0.61
(0.41)-0.32(0.46)
0.084(0.48)-0.38(0.36)
0.42(0.41)-0.22(0.37)-0.41(0.40)
10.4
200
0.27

Table A.19: Getting Flow when one Chose It, Heterogeneity by Behavioral Attributes

Figure A.1: Actual and Assigned Treatment Amounts

0

Actual Transfer Amount from GD Data
200
400
600
800
1,000

Comparing Assigned and Actual GD Transfer Amounts

41.32

83.63

116.91

Horizontal lines represent intended transfer amounts

A.19

532

Figure A.2: GiveDirectly Nutritional Handout

GDID: _________________________PAYMENT METHOD: _______________________RECIPIENT INFORMATION REGARDING NUTRITION AND HYGIENE
GiveDirectly’s program is supported by made possible by the generous support of the American
people through USAID. The information below is approved by the Rwanda Ministry of Health.
●●●Infant Nutrition○ Infants less than 6 months old should be fed by breast only. During this period an infant
receives only breast milk and no other liquids or solids, not even water, unless medicallyindicated. A non-breastfed baby is 14 times more likely to die than an exclusively
breastfed baby in the first 6 months.
○ Infants 6 to 24 months old should continue to be fed by breast, but should also receive
complementary feeding that includes animal-source foods (meats, fish, milk products,eggs) and fruits and vegetables that are rich in vitamin A (such as mango, papaya,oranges, yellow sweet potato and carrots). Guidelines are for kids 6-24 months to eat atleast 4 food groups: fruits, vegetables and legumes, grains, meats, dairy.
■ Infants 6 to 8 months old should be fed complementary foods 2-3 times daily;■ Infants 9 to 24 months old should be fed complementary foods 3-4 times daily,plus 1-2 snacks.
Reducing Illness○ If you or your children get diarrhoea, use Oral Rehydration Salts (ORS) to replace thenutrients being lost. Typical symptoms of diarrhoea include frequent, loose, waterystools, abdominal cramps, and/or abdominal pain. If ORS is not available, a simple
solution can be prepared for drinking by mixing one liter of clean drinking water and mix
it with ½ teaspoon of salt and 6 teaspoons of sugar.
○ The government has a 6-monthly deworming program and Vitamin A supplementation
program. Ask your Community Health Worker for more information.
Dietary Diversity○ Anemia■ Anemia is a health condition, commonly caused by nutritional deficiency of ironand other nutrients (folate or vitamin B12). Around 72% of 6-8 months-olds in
Rwanda have Anemia. Anemia can be an underlying cause for maternal deathand prenatal and perinatal infant loss. Anemia among children is associatedwith low mental performance and physical development.
■ Examples of iron-rich food: fish, meat, milk products, oranges, lemons,
grapefruits, guavas, papayas, and green leafy vegetables. Breast milk for yourchild is an important source of iron, too.
Here are some other examples of food you can produce/buy/eat to cheaply increasenutrition:
○

1

●

●

■ Breeding small, inexpensive animals such as hens, rabbits and guinea pigs canprovide you and your children with important body building protein and otherimportant nutrients.
■ Grow kitchen gardens if you have time. You can grow different vegetables foryour family throughout the year, like amaranths, carrots, and dark-green leaves
such as spinach and dodo, all of which are important sources of body protecting
nutrients.
■ Consume soya beans, yogurt, avocados and dodo (which you could grow)■ Eat orange-flesh rather than white-flesh sweet potatoes
Hygiene○ Handwashing with soap or wood ash can kill bacteria/viruses and prevents the spreadof disease. Handwashing with soap at critical times is estimated to reduce diarrhoea by
47%. The most important times that hands should be washed with soap and water are:■ After defecating■ After cleaning a child who has defecated■ Before eating or handling food○ Recommended practices for personal hygiene further include:■ Washing hair every week with shampoo■ Washing the face every day after sleeping■ Brushing teeth twice every day, in the morning and the night after eating○ Safe disposal of waste means defecating into a latrine, disposing into a latrine, or burial.
Inappropriate disposal of human feces, such as open defecation, facilitates thetransmission of pathogens and disease.
Birth preparedness for delivery○ Early initiation of antenatal care (ANC) can reduce common maternal complications andmaternal and perinatal mortality. Visit your nearest health facility early during pregnancy
for medical tests and more information. The World Health Organization promotes fourantenatal clinic visits, one in each trimester, during each pregnancy.

2

A.20

Appendix B. Eligibility for the Study
The study aims to compare nutrition and health gains among poor households with young
children across the two programs and a control. We therefore used a definition of eligibility tailoredto Gikuriro’s stated target population: namely, households that contained malnourished children, orpregnant and lactating mothers. A core challenge of the benchmarking endeavor is the need to use
a measure of eligibility in a manner that can be defined identically across arms.40 As a result, weestablished a set of ‘hard’ eligibility criteria on the basis of which beneficiaries would be selected andthe survey would be stratified. Households meeting these criteria would be identified by the survey
firm, Innovations for Poverty Action (IPA), prior to sampling for the baseline study, to establish acomparable population of eligible households in all arms—including control—of the study.
CRS and USAID agreed that the following criteria represent the target population for Gikuriro:• Criteria 1. All households in a village with a malnourished child (defined by a threshold valueof weight/age) were enrolled.
– Weight/age is used because it is believed that this data is more consistently available
than data on middle-upper arm circumference (MUAC) and height/age, and because it
is used by CHWs as a basis for referring children to their local Health Centers.
– The threshold weight/age value for inclusion was determined using the Rwandan Ministryof Health standards for malnutrition. The data used to identify eligibles was based on
the Community Health Worker data from Growth Monitoring and Promotion visits.
• Criteria 2. All households in Ubudehe 1 or 2 with children under the age of 5 (Ubudehe is
the Rwandan government household-level poverty classification, with 1 being the poorest, 3
being non-poor, and rural areas containing very few of the wealthiest Ubudehe 4 households).
• Criteria 3. All households in Ubudehe 1 or 2 with a pregnant or lactating mother.
Both implementers agreed to attempt to treat all eligible households that were identified as meetingany of these criteria. CRS anticipated an average of 30 eligible households per village, and in40

We did not intend the scope of the benchmarking exercise to include the implementers’ (potentially different)
ability to cost-effectively identify this target population, so as to maintain the interpretation of impacts as being
differential impacts on a consistently defined beneficiary group.

B.1

principle had established a rationing rule in case that number was exceeded. As will be described
below, the number of households per village that could be identified by the survey firm as meetingthese targets turned out to be substantially lower. We did not try to impose restrictions on how
Gikuriro would target outside of the households identified by the survey firm to be eligible.
We asked IPA to identify the universe of households that they could locate who met these criteria,using three sources. First, CHW records from the national ‘Growth Monitoring and Promotion’
exercise, which is intended to provide monthly height and weight measurements for all children
under two and annual measurements for all children under five; second, government (census) recordsof household Ubudehe classifications; and finally local health facility information, which provides analternative data point on children’s nutritional status.41 Children were defined as malnourished ifthey had at least one measurement that met government thresholds for malnourishment definitionsin the past year, and households were defined as eligible if they had any individual meeting thecriteria above. In each village we recorded the number of households in each stratum and sampled
up to eight eligibles and four ineligibles for inclusion in the study. Throughout this document we
use the words ‘eligible’ and ‘ingeligible’ to refer to the classification made by the survey firm atbaseline.

41
In practice, most children attending local clinics are referred by a CHW and so are also recorded as malnourishedin the Growth Monitoring process.

B.2

Appendix C. Attrition
Endline outcome measurement is subject to a number of distinct forms of attrition; we start
our empirical analysis by considering each in turn. The most straightforward of these is standard
household-level attrition, meaning that a household sampled into the baseline survey attrited fromthe endline survey. In Table A.2, we see that overall rates of attrition at the household level were
low, around 3.3 percent in the control. We see the pattern typical in RCT studies where attrition
is somewhat lower in the treatment groups (where both ongoing contact and a sense of reciprocity
may keep individuals in the endline), but these differentials are small, from 0.89 percentage points
in the GD ‘small’ arm to 1.7 percentage points in the GD ‘large’ arm; only the latter is significant,and only at the 10 percent level. Looking at the other covariates of attrition in column 2 we see that
attriters and non-attriting households are similar. Hence we conclude that household-level attritionis unlikely to be a source of bias in the study.
When we turn to the analysis of individual-level outcomes in Columns (3)–(9) the picture ismore complex because many of the primary and secondary outcomes are only measured for certain
types of individuals (anthropometrics for children, birth outcomes only for those pregnant). We
analyze four types of individual missingness that may occur. First, we compare the attrition of allhousehold members from the roster in the household survey; both the rates and the differentials here
are very similar to the household attrition problem suggesting that there has been little additional
differential attrition of individuals. Next we examine the anthropometric panel, whereby all children
under 6 at baseline who were given anthropometrics at the baseline should have been followed up
with at endline. Here the absolute rates of attrition are a little more than double what they are forindividuals overall, presumably because of the greater difficulty of finding and measuring children
for this exercise. More concerningly, the decline in attrition in the treatment groups now becomesstrongly significant, particularly for Gikuriro villages (which perhaps is evidence of the superior
monitoring of malnourished children taking place in those villages). Given this significance, we
follow our pre-analysis plan in also presenting results for the anthropometric impacts that are
corrected by inverse propensity weights to correct for the observable determinants of selection, and
we also present Lee Bounds for this attrition. Third, we examine whether individuals who shouldhave been anemia tested in the followup were; here we see no evidence of differential attrition across

C.1

Table C.1: Balance on Primary Outcomes
Gikuriro
Village

GD Main
Village

GD Large
Village

Control
Mean

Observations

R2

Consumption†

0.053(0.116)[1.00]

0.047(0.122)[1.00]−0.103(0.130)[1.00]

10.39

1751

0.05

Household dietary
diversity score−0.064(0.137)[1.00]−0.071(0.139)[1.00]−0.058(0.172)[1.00]

4.16

1751

0.10

Household non-land
wealth†0.027(0.227)[1.00]−0.042(0.214)[1.00]−0.288(0.254)[1.00]

12.94

1751

0.06

Height-for-Age

0.046(0.086)[1.00]

0.080(0.097)[1.00]

0.215∗∗(0.098)[0.13]

−1.93

2187

0.02

Weight-for-Age

0.023(0.069)[1.00]

0.041(0.070)[1.00]

0.187∗∗∗(0.069)[0.07]

−1.06

2180

0.02

Mid-Upper Arm Circ

0.015
(0.068)[1.00]

0.025(0.068)[1.00]

0.070(0.081)[1.00]

−0.72

1987

0.04

A. Household outcomes

B. Individual outcomes

Notes: Columns present coefficients and standard errors from a regression of each baseline outcome on treatment
indicators, with fixed effects for blocks. Asterices denote significance at the 10, 5, and 1 percent levels, and are basedon clustered standard errors, in parentheses. Anderson (2008) sharpened q-values presented in brackets. Variables
marked with a † are in inverse hyperbolic sines.

arms. Finally, we examine the likelihood that a new household member appears (typically due tobirths subsequent to baseline), and find no significant differences. Overall, then, differential selection
across treatment arms is not a major problem for study outcomes other than anthropometrics. We
return to the issue of unequal attrition in anthropometrics in the following section.

C.2

Table C.2: Balance on Secondary Outcomes
Gikuriro
Village

GD Main
Village

GD Large
Village

Control
Mean

Observations

R2

Stock of borrowing†−0.459(0.363)[0.89]−0.007(0.409)[1.00]−0.262(0.408)[1.00]

5.96

1751

0.04

Stock of saving†−0.157(0.378)[1.00]−0.665∗(0.364)[0.89]−0.269(0.421)[1.00]

5.18

1751

0.02

Health knowledge
index−0.590(0.366)[0.89]−0.119(0.412)[1.00]−0.225(0.520)[1.00]

0.19

1751

0.03

Sanitation practices
index

0.285∗(0.169)[0.89]−0.105(0.190)[1.00]−0.069(0.210)[1.00]

−0.23

1751

0.04

Productive assets†

0.281∗∗(0.125)[0.89]

0.195(0.132)[0.89]

0.231∗(0.122)[0.89]

11.41

1751

0.12

Consumption assets†

0.158(0.290)[1.00]−0.034(0.316)[1.00]

0.426(0.300)[0.89]

8.71

1751

0.08

House value†−0.042(0.059)[1.00]−0.012(0.074)[1.00]−0.067(0.066)[1.00]

13.59

1751

0.09

Housing quality index

0.018(0.112)[1.00]−0.195(0.132)[0.89]−0.014(0.198)[1.00]

0.02

1751

0.04

Pregnancy−0.018(0.025)[1.00]−0.031(0.022)[1.00]−0.021(0.024)[1.00]

0.28

2358

0.03

Live Birth−0.017(0.050)[1.00]−0.007(0.049)[1.00]

0.085(0.061)[1.00]

0.81

645

0.10

Birth in Facility

0.011(0.038)[1.00]−0.056(0.043)[1.00]−0.024(0.044)[1.00]

0.93

544

0.11

Any Vaccinations inpast year

0.009(0.019)[1.00]−0.006(0.021)[1.00]

0.001(0.030)[1.00]

0.93

1349

0.01

Completed
Vaccinations−0.015(0.037)[1.00]−0.015(0.045)[1.00]

0.017(0.042)[1.00]

0.72

1347

0.02

Disease Burden

0.030(0.040)[1.00]

0.004(0.032)[1.00]

0.007(0.043)[1.00]

0.42

1146

0.02

A. Household outcomes

B. Individual outcomes

Notes: See prior table. Indexes are unweighted sums of z-scores of their underlying components. Individual secondary
outcomes all measured as rates within respective populations.

C.3

Table C.3: Balance on Household Covariates
Gikuriro
Village

GD Main
Village

GD Large
Village

Control
Mean

Observations

R2

Female Headed

0.036(0.025)[1.00]

0.043∗(0.026)[0.84]−0.018(0.029)[1.00]

0.16

1751

0.06

Agricultural

0.017(0.028)[1.00]−0.027(0.029)[1.00]

0.002(0.035)[1.00]

0.85

1751

0.04

Wage Worker−0.002(0.029)[1.00]−0.063∗∗(0.031)[0.63]−0.084∗∗(0.035)[0.46]

0.25

1751

0.04

Microenterprise−0.015(0.025)[1.00]

0.008(0.024)[1.00]−0.024(0.023)[1.00]

0.13

1751

0.02

Savings Group−0.013(0.038)[1.00]−0.022(0.039)[1.00]

0.026(0.044)[1.00]

0.25

1751

0.02

Village Eligibility
Ratio−0.015(0.025)[1.00]

0.037(0.029)[1.00]

0.017(0.033)[1.00]

0.16

1751

0.50

Age of Head

2.186∗∗(1.047)[0.63]

2.868∗∗(1.200)[0.46]

1.415(1.487)[1.00]

34.16

1751

0.07

Schooling of Head

−0.006(0.005)[1.00]−0.002(0.006)[1.00]−0.005(0.004)[1.00]

0.00

1751

0.02

Dependency Ratio

0.008(0.012)[1.00]−0.007(0.012)[1.00]

0.003(0.016)[1.00]

0.59

1751

0.04

Household Size

−0.082(0.134)[1.00]−0.054(0.151)[1.00]−0.183(0.163)[1.00]

5.18

1751

0.02

Poorest Category−0.040(0.033)[1.00]−0.002(0.045)[1.00]−0.068∗(0.039)[0.84]

0.22

1751

0.05

Next Poorest Category

0.067∗(0.040)[0.84]

0.056(0.046)[1.00]

0.061(0.051)[1.00]

0.50

1751

0.12

Notes: Columns present coefficients and standard errors from a regression of baseline covariates on treatment
indicators, with fixed effects for blocks. Asterices denote significance at the 10, 5, and 1 percent levels, and are basedon clustered standard errors, in parentheses. Anderson (2008) sharpened q-values presented in brackets.

C.4

Appendix D. Study OutcomesPrimary Outcomes. The study focuses on five dimensions. Here we briefly summarize each;details of the construction of these outcomes are included in Appendix A.
1. Household monthly consumption per capita (inverse hyperbolic sine—henceforth IHS—to deal
with skewness).
2. Household Dietary Diversity, measured using hte WHO standard Household Dietary Diversity
Score.
3. Anemia: measured with a biomarker test following DHS protocols at endline only.
4. Child growth and development: measured using in height-for-age, weight-for-age and Mid
Upper Arm Circumference at baseline and endline for children under the age of 6 in eligible
households.
5. Value of household non-land net wealth. This outcome is the sum of productive and consumption assets; the value of the household’s dwelling, if owned; and the value of the stock of net
savings, less the stock of debt (IHS).
Secondary Outcomes. Three types of outcomes are selected to be secondary: proximate
outcomes of one or both interventions that do not have an intrinsic welfare interpretation (suchas borrowing and saving stocks); outcomes that have welfare weight but are not within the causal
chain of both programs (such as investments in health-seeking behavior, which Gikuriro seeks to
impact, or housing quality, which has been identified as a dimension of benefit in prior evaluationsof GiveDirectly (Haushofer and Shapiro, 2016)); or outcomes of common interest on which power islimited (such as disease burden and mortality).
1. Stock of borrowing and stock of savings (IHS).
2. Birth outcomes: the likelihood of pregnancy and likelihood of live birth within 12 months
prior to endline.
3. Health knowledge and sanitation practices.

D.1

4. Disease burden and mortality. Mortality is measured as the likelihood that an individual
member of the household from baseline has died prior to endline. Disease burden is measured
as the prevalence of fever, fever with diarrhea or vomiting, or coughing with blood at endline,
5. Health-seeking behavior/preventative care. We focus on the share of pregnancies resulting
in births in medical facilities, the share of children under two years of age with at least one
vaccination in the prior year, and the share of children under two years of age with a complete
dose of vaccines.
6. Household productive assets (IHS).
7. Housing quality. Two measures are used: the self-reported replacement cost of the current
dwelling (irrespective of ownership status, IHS), and an index of housing construction quality,
constructed from measures of wall and roof materials and from the number of rooms in thedwelling.
The inverse hyperbolic sine is commonly used in analysis of outcomes such as consumption, savings,
and asset values that tend to be highly right-skewed and also to contain zeros. The IHS transformation preserves the interpretation of a log (meaning that impacts can be interpreted as percent
changes) but does not drop zeros. Only outcomes that we expected to be skewed were pre-registered
to be analyzed using IHS. All non-binary outcomes are also Winsorized at the 1 percent and 99
percent level (values above the 99th percentile are overwritten with the value at the 99th percentile
to reduce skewness and increase statistical power). Because we restrict the analysis in this paper tothe pre-specified primary and secondary outcomes only, we do not correct the results for multiple
inference (Anderson, 2008).

D.2

Appendix E. Heterogeneous impacts of cash and kindIn this Appendix, we assess the extent of heterogeneity in ITT effects across observable subpopulations. Our pre-analysis plan highlighted two forms of heterogeneity that we anticipated would
be important at the design phase; namely how baseline malnutrition and child age may moderate
the impact of nutritional interventions. Given that we have children who start the study outsideof the first 1,000 days (those 2–5 years old at baseline), we might expect that the impact of the
program on these more fully developed children would be smaller. Similarly, we might expect thatboth of these interventions would be most effective for children who began the intervention mostmalnourished.
Our analysis generally reveals a lack of heterogeneity, in that impacts are not larger for children
most malnourished at baseline (Table E.1), or for children exposed to the treatments at younger
ages (Table E.2). Figures E.1a and E.1b provide fan regressions of impacts by child age and there isa suggestion that children exposed to large cash transfers in utero realize the largest benefits.42 In
general, however, our results are not suggestive of strong age- or malnutrition-driven heterogeneityof these interventions.43

42
This pattern is similar to the medium-term results in Baird et al. (2016), who find unconditional transfers in
Malawi to have the largest effect on children exposed in utero.
43
The longer-term literature has typically found impacts of large cash transfer programs on HAZ in the range of
0.2–0.45 standard deviations (Aguero et al., 2006; Barham et al., 2014); comparison to the broader literature suggeststhat these impacts may grow over time.

E.1

Table E.1: Heterogeneity by Baseline Malnutrition(1)
Height-for-Age-0.0416(0.0444)(2)
Weight-for-Age-0.0349(0.0619)(3)
Mid-Upper Arm Circ
0.0852(0.0564)

Baseline outcome x GD Main

-0.0247(0.0457)-0.0654(0.0445)0.0776(0.0653)

Baseline outcome x GD Large

0.0220(0.0433)

0.00599(0.0461)0.0804(0.0603)

Gikuriro

0.0434(0.0428)0.0323(0.0362)0.0253(0.0557)

GD Main-0.0252(0.0398)

0.00182(0.0357)-0.00498(0.0647)

GD Large

0.0940∗(0.0517)0.0641(0.0392)

0.135∗(0.0795)

Baseline Outcome

0.768∗∗∗(0.0336)2125-2.0310.696

0.748∗∗∗(0.0355)2104-1.0430.673

0.600∗∗∗(0.0425)1629-0.572
0.507

Baseline outcome x Gikuriro

Observations
Mean of DV
R squaredStandard errors in parentheses∗
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Notes: Regressions with both baseline and endline outcome measurement are ANCOVA with lagged dependent
variables as controls, run on the panel sample. Regressions include fixed effects for the randomization blocks, andare weighted to be representative of all households in study villages. Anthropometric outcomes are demeaned prior
to interaction so that the uninteracted treatment terms provide impact at average level of baseline anthro measure..

E.2

E.3

-0.115(0.139)0.0105(0.106)

Indicator x GD Large

Gikuriro

0.141(0.148)2360-2.0310.0722

Indicator-0.0101(0.142)2020-0.5720.07260.0773(0.111)-0.108(0.0905)-0.0813(0.0822)0.160(0.144)0.159(0.104)(3)
Mid-Upper Arm Circ
0.115
(0.109)-0.0102(0.254)2360-2.031
0.06990.196∗∗(0.0848)-0.0171(0.0988)0.00489(0.0833)

0.407(0.396)0.382(0.522)

Newborn(4)
Height-for-Age
0.599(0.645)0.0247(0.295)2347-1.0430.03580.185∗∗∗(0.0668)

0.00905(0.0678)0.00325(0.0629)0.729(0.469)0.594(0.495)(5)Weight-for-Age
0.251(0.505)0.177(0.270)2020-0.5720.07400.159∗∗(0.0783)-0.0346(0.0723)-0.0263(0.0690)0.304(0.281)

0.666(0.506)(6)
Mid-Upper Arm Circ
0.282
(0.491)

Notes: First three columns present an interaction with an indictor for a child in the ‘First Thousand Days’ (<33 months at endline) and the last three columns
present interactions with an indicator for ‘Newborn’ (< 13 months at endline). Regressions are endline cross-sections, run on the panel sample, and do not include
the lagged outcome variable so as to be able to consider children who are newborns in R2. Regressions include fixed effects for the randomization blocks, and areweighted to be representative of eligible households in study villages.

Standard errors in parentheses∗
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Observations
Mean of DV
R squared

0.191∗∗(0.0817)0.246∗∗(0.105)GD Large

0.123(0.117)2347-1.0430.03560.0779(0.0829)0.115(0.119)GD Main

0.0114(0.0801)-0.0159(0.122)-0.152(0.115)-0.300∗∗(0.138)

Indicator x GD Main

Indicator x Gikuriro(2)
Weight-for-Age-0.0206(0.113)First Thousand Days
(1)
Height-for-Age-0.00731(0.138)

Table E.2: Heterogeneity by Baseline AgeFigure E.1: Fan Regression Impacts by Age

The GD Large treatment effect on Height for Age-.1

0

Height for Age
.1
.2

.3

.4

With 95 % Confidence Intervals

0

20
40
Age in Months at Endline

60

Fan Regression for impact of the GD Large treatment, smoothed over age at endline(a) Impacts on Height-for-Age

The GD Large treatment effect on Child Anemia-.2

Impact on Anemia Prevalence
-.15
-.1
-.05
0

.05

With 95 % Confidence Intervals

0

20
40
Age in Months at Endline

Fan Regression for impact of the GD Large treatment, smoothed over age at endline(b) Impacts on Child Anemia

E.4

60

Appendix F. Selection of Control Variables.
In our pre-analysis plan, we state that control variables for the primary specification “will beselected on the basis of their ability to predict the primary outcomes”. In doing so, we seek to build
on recent developments that balance the challenge of using baseline data to select variables thatwill reduce residual variance with the danger that researcher freedom in the selection of control
variables can lead to p-hacking, in which right-hand-side variables are selected specifically on thebasis of the statistical significance of the coefficient of interest (Card and Krueger, 1995; Casey etal., 2012), thereby invalidating inference.
To balance these concerns, we follow the post-double-selection approach set forth in Belloni et al.
(2014b). Those authors advocate a two-step procedure in which, first, Lasso is used to automate theselection of control variables, and second, the post-Lasso estimator (Belloni et al., 2012) is used toestimate the coefficients of primary interest in in the ITT, effectively using Lasso as a model selection
device but not imposing the shrunken coefficients that results from the Lasso estimates directly.
Belloni et al. (2014b) demonstrate that this approach not only reduces bias in estimated treatment
effects better than alternative approaches—less a concern given the successful randomization in our
experiment—but that it may improve power while retaining uniformly valid inference.
In the first stage, model selection is undertaken by retaining control variables from the unionof those chosen either as predictive of the treatment assignment or of the outcome. This model
selection stage can be undertaken after residualizing to account for a set of control variables thatthe authors have a priori determined belonw in the model, as in Belloni et al. (2014a); in ourcase, we retain block fixed effects, lagged values of the outcome, and lagged values of (the inverse
hyperbolic sine of) household wealth in all specifications, per our pre-analysis plan. We modify theheteroskedasticity-robust Lasso estimator of Belloni et al. (2012) to incorporate sampling weights
consistent with our design, using the Lasso penalty is chosen as a function of the sample size andthe number of potential covariates, as in Belloni et al. (2014a).
Resulting covariates selected for each of the primary and secondary outcomes, at household andindividual level, are presented in Tables F.1 and F.2, respectively.

F.1

Table F.1: Covariates selected in Belloni et al. (2014) post-double-lasso selection procedure forhousehold outcomes
Outcomeconsumption_asinhHousehold dietary diversity
score

Control set
Baseline value of consumption_asinh, present in both rounds
L.Lhh_wealth_asinh
L.Fraction of village defined eligible by IPA
Baseline value of dietarydiversity, present in both rounds

L.Lhh_wealth_asinh
L.Fraction of village defined eligible by IPA
Lsavingsstock_asinh3
Lconsumpti_x_Ldietarydi
Lconsumpti_x_Lproductiv
Ldietarydi_x_Lassetscon
wealth_asinh
Baseline value of wealth_asinh, present in both rounds
L.Lhh_wealth_asinh
L.Fraction of village defined eligible by IPA
L.Own dwellingborrowingstock_asinh
Baseline value of borrowingstock_asinh, present in both rounds
L.Lhh_wealth_asinh
L.Fraction of village defined eligible by IPA
savingsstock_asinh
Baseline value of savingsstock_asinh, present in both rounds
L.Lhh_wealth_asinh
L.Fraction of village defined eligible by IPA
Lconsumpti_x_Lproductiv
Lconsumpti_x_Lassetscon
Health Knowledge Index
Baseline value of health_knowledge, present in both rounds
L.Lhh_wealth_asinh
L.Fraction of village defined eligible by IPA
Sanitation Practices Index
Baseline value of sanitation_practices, present in both rounds
L.Lhh_wealth_asinh
L.Fraction of village defined eligible by IPA
Lproductiv_x_Lassetscon
productiveassets_asinh
Baseline value of productiveassets_asinh, present in both rounds
L.Lhh_wealth_asinh
L.Fraction of village defined eligible by IPA
Lconsumpti_x_Lassetscon
assetsconsumption_asinh
Baseline value of assetsconsumption_asinh, present in both rounds
L.Lhh_wealth_asinh
L.Fraction of village defined eligible by IPA
L.Number of rooms
L.Durables expenditure (12-month recall)
Ldietarydi_x_Lassetscon
Lproductiv_x_Lassetscon
selfcostdwell_asinh
Baseline value of selfcostdwell_asinh, present in both rounds
L.Lhh_wealth_asinh
L.Fraction of village defined eligible by IPA
L.Number of rooms
L.Durables expenditure (12-month recall)
Housing Quality Index
Baseline value of housing_quality, present in both rounds
L.Lhh_wealth_asinh
L.Fraction of village defined eligible by IPA
L.Number of rooms
Note: block fixed effects and lag of the relevant outcome included in all specifications. Specifications that include
both eligible and ineligible households include an indicator for eligibility status.

F.2

Table F.2: Covariates selected in Belloni et al. (2014) post-double-lasso selection procedure forindividual outcomes
Outcomehaz06, Winsorized fraction.005, high only

waz06, Winsorized fraction.005, high only

muacz, Winsorized fraction.01

SampleUnder 5s

Control set
L.haz06, Winsorized fraction .005, high only

Under 5s

femaleagemonths
agemonths_sq
agemonths_cu
L.Lhh_wealth_asinh
L.Food expenditure (weekly recall)
L.Food consumption-value own production (weekly recall)
L.waz06, Winsorized fraction .005, high only
Lconsumpti_x_Lselfcostd
L.waz06, Winsorized fraction .005, high only

Under 5s

femaleagemonths
agemonths_sq
agemonths_cu
L.Lhh_wealth_asinh
L.Food expenditure (weekly recall)
L.Food consumption-value own production (weekly recall)
Lconsumpti_x_Lproductiv
L.muacz, Winsorized fraction .01

anemia_dummy

Under 5sanemia_dummyPregnant/lactating
women

mortality

All

Was this women pregnant at
any point in the past 12
monthsPregnant/lactating
women

femaleagemonths
agemonths_sq
agemonths_cu
L.Lhh_wealth_asinh
L.waz06, Winsorized fraction .005, high only
Lconsumpti_x_Lproductiv
femaleagemonths
agemonths_sq
agemonths_cu
L.Lhh_wealth_asinh
agemonths
agemonths_sq
agemonths_cu
L.Lhh_wealth_asinh
femaleagemonths
agemonths_sq
agemonths_cu
L.Lhh_wealth_asinh
agemonths

agemonths_sq
agemonths_cu
Continued on next pageF.3

Table F.2 (continued)
OutcomeSampleDid pregnancy conclude inlive birth

Pregnant/lactating
women

facility_birth

Pregnant/lactating
women

Control set
L.Lhh_wealth_asinh
L.Lwealth_asinh
agemonths
agemonths_sq
agemonths_cu
L.Lhh_wealth_asinh
L.Food expenditure (weekly recall)
L.Food consumption-value own production (weekly recall)
Lconsumpti_x_Lwealth_asagemonths

anthro_vacc_year

Under 3santhro_vacc_complete

Under 3s

Any fever, diarrhea, or
coughing blood at individual/round level

Under 5s

agemonths_sq
agemonths_cu
L.Lhh_wealth_asinh
femaleagemonths
agemonths_sq
agemonths_cu
L.Lhh_wealth_asinh
Lconsumpti_x_Lproductiv
femaleagemonths
agemonths_sq
agemonths_cu
L.Lhh_wealth_asinh
femaleUnder 5s

agemonths
agemonths_sq
agemonths_cu
L.Lhh_wealth_asinh
L.Food consumption-value own production (weekly recall)femaleIndividual reported with diarrhea/vomiting/fever now

agemonths
agemonths_sq
agemonths_cu
L.Lhh_wealth_asinh
Note: block fixed effects and lag of the relevant outcome included in all specifications. Specifications that include
both eligible and ineligible households include an indicator for eligibility status.

F.4