Skills and Liquidity Barriers to Youth Employment:
Medium-term Evidence from a Cash Benchmarking Experiment in
RwandaCraig McIntosh∗ and Andrew Zeitlin†
July 2023

Abstract
We present results of an experiment benchmarking a workforce training program against cashtransfers for underemployed young adults in Rwanda. 3.5 years after treatment, the training
program enhances productive time use and asset investment, while the cash transfers drive
productive assets, livestock values, savings, and subjective well-being. Both interventions havepowerful effects on entrepreneurship. But while labor, sales, and profits all go up, the implied
wage rate in these businesses is low. Our results suggest that credit is a major barrier toself-employment, but deeper reforms may be required to enable entrepreneurship to provide atransformative pathway out of poverty.

Keywords: Employment, Entrepreneurship, Cash Transfers
JEL Codes: O12, C93, J21
Study Information: This study is registered with the AEA Trial Registry as Number
AEARCTR-0004388, and is covered by Rwanda National Ethics Committee IRB 114/RNEC/2017,
IPA-IRB:14609, and UCSD IRB 161112. The research was paid for by USAID grant AID-0AA-A-13-00002(SUB 00009051). We thank the Education Development Center, GiveDirectly, USAID, and DIL/CEGA for
their close collaboration in executing the study; Leodomir Mfura and Melissa Mahoro of Innovations for
Poverty Action for outstanding management of the fieldwork; Sarait Cárdenas-Rodriguez, Grace Han,
Atsuhiro Oguri, and Aruj Shukla for excellent research assistance; USAID Rwanda, DIV, and Google.org
for funding. 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 Diego, ctmcintosh@ucsd.edu
Georgetown University, andrew.zeitlin@georgetown.edu

1Introduction

Sub-Saharan Africa combines a rapidly growing population with low formal-sector employment,
meaning that future economic growth will be largely dependent on enhancing productivity in theinformal sector (Bandiera et al., 2022). In this context, few questions have greater long-term
import than how best to help the burgeoning young population achieve a successful transition
into a productive adulthood (Bongaarts, 2016; Fox et al., 2016). The best means to achieve thisare anything but clear, however. While skills are almost certainly a constraint for a population
with the lowest average schooling levels in the world, entrepreneurship and job training programs
have an uneven record in contexts with little formal employment (Kluve et al., 2017; McKenzie,2021). Credit constraints also certainly play a role, but while a large literature has shown thatcash transfers are invested in productive assets in the short term (Blattman et al., 2013; Gertler
et al., 2012; Haushofer and Shapiro, 2016; De Mel et al., 2012), the ability of transfers to affectdurable improvements in productivity is more uncertain (Aizer et al., 2016; Baird et al., 2019;
Balboni et al., 2022; Blattman et al., 2018; Brudevold-Newman et al., 2017; Hoynes et al., 2016).
More broadly, it is possible that macro-level constraints to demand or to the scope for businessexpansion fundamentally limit the extent to which the informal sector can provide a pathway outof poverty (La Porta and Shleifer, 2014).
The effort to help youth make productive transitions into adulthood is inherently a long-term
agenda. We cannot move the needle on this long-term productivity question with interventions thathave only palliative, short-term impacts (Bouguen et al., 2019). The existing empirical evidence
evaluating labor-market interventions is largely short-term, finding that injections of skills or capitalcan drive asset ownership, entrepreneurship, and employment over a one to two year time frame.
But few studies have been able to track these outcomes experimentally over a longer period oftime (exceptions include Blattman et al., 2020, 2022). Serious questions about the durability of
the informal sector as a pathway to long-term security have been raised by the Covid-19 epidemic,
which has dealt a huge shock to self-employed individuals with income streams vulnerable to lockdowns and without access to employer-based safety nets (Egger et al., 2021; Mahmud and Riley,2021). Since the ability to affect long-term impacts requires the ability to weather shocks (Balboni
et al., 2022), the durability of impacts through the Covid-19 epidemic speak both to the dynamics
1

of wealth accumulation and also to the resiliency of different forms of shocks to wealth.
We contribute to this conversation with a study providing a multifaceted window on how best toraise the productivity of vulnerable youth, in this case under-employed 18–25 year olds in Rwanda.
Our study is a randomized controlled trial with one arm providing an intensive year-long vocational training (the Huguka Dukore Akazi Kanoze program, henceforth HD), one arm providing
unconditional cash transfers (implemented by the U.S. non-profit GiveDirectly, henceforth GD),
and an arm that receives both of these interventions at the same time. Randomization of cashtransfer amounts provides the ability to make cost-equivalent comparisons between cash and kind,
as well as to form a rich set of counterfactuals for the complementarity arm that receives bothinterventions. We follow up with subjects three years after the interventions were completed, andhave a permanently untreated control group so the study faces no internal contamination. Tracking
rates in the study were a remarkable 98.6%, and a relatively even split of male and female subjects
allows us to speak to the differential gender dimension interventions. These study features provide
an unusually rich environment in which to consider the medium-term impact of programs that
support youth productivity in the African context.
We find evidence that both interventions have sizeable impacts on primary economic outcomes
relative to control, with cash transfers out-performing training, and that these impacts persist in
the medium term, though they muted relative to the short term and no longer statistically distinguishable from one another. McIntosh and Zeitlin (2022) reported short-term impacts, finding that
HD increased hours worked, monthly income, and productive assets, and that the impacts of cashtransfers were significantly larger at cost-equivalent levels on the latter two domains; moreover, only
cash transfers significantly moved consumption outcomes. The medium-term evidence presented
here shows that HD continues to elevate productive hours per week by 3.3, productive assets arealmost twice the control group, and an index of business knowledge is higher by 0.25 standard deviations even three year later. The cash arms led to durable increases in productive assets (between
1.4–3 times the control), subjective well-being, household livestock value, and savings, along withmodest and insignificant increases in consumption per capita (10-20% above the control group).
As was the case in the one-year evaluation results from this study (McIntosh and Zeitlin, 2022), we
find no evidence of complementarity; the combined arm demonstrates the impacts seen in either
arm with no additional benefits arising from them being implemented together. Most of these out2

comes represent a ‘fade’ of about 50% relative to the impacts seen in the midline study, meaningthat roughly half of the benefit observed after one year is still present more than three years later.
Likely due to this overall diminution in the magnitude of results we find little evidence of significant
difference between programs at cost-equivalent level; HD is marginally better in producing businessknowledge and other than this we fail to reject differences across arms.
By carefully documenting study participants’ time use and entrepreneurship activities, we showthat both cash and training drive changes in occupational structure, and—to different extents—
drive profitable movements into entrepreneurship that are eroded over the medium term. Bothinterventions decrease participation in agricultural wage labor. The workforce training program
weakly pushes individuals into non-agricultural wage labor (5 pp impact), and cash transfers,
particularly large ones, drive income in micro-enterprise and particularly non-agricultural selfemployment. These sectoral shifts prove quite constant over time despite the income benefits ofthe shifts fading after three years. Both interventions lead to a burst of new business formation overthe shorter term; as of the midline the control group had created an average of 0.5 new businesses
per person, HD elevated this by 0.2, and the cash arms by 0.5–0.6 new businesses per beneficiary on
average. The rate of new business creation between midline and endline in the control slows to 0.24,
and only the GD Large treatment leads to additional new businesses during this interval. A sizeable
fraction of the businesses created at midline die by endline (0.24 in the control group) but this is
not more likely in any of the treatment arms. Many midline businesses are reported as extant but
inoperative at endline (0.14 in the control group), and here we see elevated rates for the treatment
arms (≈ 0.15 greater for the cash arms), suggesting that roughly one third of the businesses createdwith the cash transfers do not continue to operate three years later. HD training induced fewer new
business to be created, but the marginal firms created under this treatment were more durable.
Nonetheless, both programs have sizeable effects on entrepreneurship at endline, with working days,
sales, and profits being higher than the control for both training and cash, and profits for the larger
cash arms being more than double the control group on average.
Geography as well as policy responses to the Covid-19 shock provide two critical dimensions ofcontext for our study’s results. The study takes place largely in rural areas and so many microenterprises are typically engaged with agriculture in some way. Whether such interventions could
have more transformative effects in an urban context with larger demand pools remains an open3

question. Rwanda is a tightly governed, rapidly growing country. While in some ways that meansthat this study likely represents a “best-case” scenario for such interventions, it is also the casethat the three Covid-19 lock-downs imposed in the two years prior to our endline were unusuallystrongly enforced, and may have hit small businesses harder than in more loosely governed countries.
The impact of the Covid-19 era on the overall business climate can be seen in our control group:
while employment status, consumption, and consumption appear to have been protected over thecourse of the pandemic, control households have dramatically stripped productive assets, losing
approximately 63 percent of the value of the assets they held at midline. Hence the exigencies ofthis unusual time are an inextricable part of what this study has to say about long-term impacts.
This study makes several contributions to the literature.
First, we develop an empirical approach to cost-equivalent comparisons between alternative
programs and show its applicability to the study of long-term program outcomes. Ours is the firststudy to be able to conduct a rigorously cost-equivalent comparison of two programs over sucha long time frame.1 Given the variation in cash transfer amounts we can examine medium-term
impacts both allowing the program to change and holding costs constant (cost-equivalence) or
allowing program cost to change across modalities (cost-effectiveness). Finally, because the large
cash transfer arm has a cost almost identical to the combined arm that gets both interventions, we
can create multiple counterfactuals for the complementarities analysis: we ask both whether the
combination is differentially effective when compared with the sum of its components’ treatment
effects, and whether the combination is better than the cost of the combination given all in cash.
This suggests several ways of using the ready scalability of cash transfers to create transparent,
policy-relevant comparisons.
Second, by providing a clean and well-powered window on the impact of training and cash in a
relatively long-term time frame, this study makes a critical contribution to our understanding of the
durability of these interventions. The type of paired classroom and hands-on workforce trainingthat HD Provides is common in workforce programs worldwide, such as the Jóvenes en Acciónprogram in Colombia (Attanasio et al., 2011), but evidence of impacts of such programs beyond
1

The most common form of benchmarking in the literature is the comparison of food aid to cash aid (Ahmed
et al., 2016; Cunha et al., forthcoming; Hidrobo et al., 2014; Hoddinott et al., 2014; Leroy et al., 2010; Schwab etal., 2013). Efforts to benchmark more complex, multi-dimensional programs to cash include BRAC’s Targeting the
Ultra-Poor program (Chowdhury et al., 2016), microfranchising (Brudevold-Newman et al., 2017), and graduation
programs (Sedlmayr et al., 2020).

4

the short term is limited. Much long-term literature on the impact of cash programs looks at CCTs,
which have a pathway to impact either through human capital or the transfers themselves (Araujo
et al., 2017; Barham et al., 2014; Fernald et al., 2009). The long-term literature on income support
programs in developed countries illustrates potentially transformative effects on schooling, health,
income, and life expectancy (Aizer et al., 2016) and increases in economic self-sufficiency (Hoynes
et al., 2016). Fewer studies have looked at the long-term impact of unconditional transfers in thedeveloping context, but it is far from clear that these impacts are durable, with a number of RCTs
showing dissipating long-term benefits (Araujo et al., 2017; Baird et al., 2019; Brudevold-Newman
et al., 2017). A a long-term study in neighboring Uganda providing cash grants to groups to startbusinesses showed dissipation of impacts by 9 years from the intervention, with some lasting effects
on assets and skilled work (Blattman et al., 2020). For both short- and long-term studies thetraining literature has returned mixed results (Heckman et al., 1999; McKenzie, 2021), with longterm studies showing some durable impact on formal employment and earnings in the Dominican
Republic (Ibarrarán et al., 2019). Particularly in the presence of negative economic shocks, it istherefore an open question whether investments in human capital will prove more durable thaninvestments in physical capital enabled by cash transfers.
Finally, the study speaks on a structural level to the constraints that exist to the creation ofdurable income increases in the informal sector. On the one hand, our results confirm a literature
showing that skills matter (Kluve et al., 2017), and that credit constraints matter (Beaman et al.,2014). They do not suggest there is any special issue at the intersection of credit and human capital constraints that rewards a simultaneous relaxation of these two obstacles. On the other hand,neither intervention alone, nor the two together, appears capable of delivering a really meaningfulescape from poverty over a 3–4 year time frame in this population. The depressing conclusion of thisis that even high-cost interventions may struggle to achieve transformative impacts for vulnerable
youth over the longer term, at least in environments buffeted by shocks. The more expensive interventions in this study cost approximately $750 per individual, surely more than most development
agencies willing/able to spend, and still do not lead to meaningful decreases in consumption-based
poverty after 3.5 years. A possible reading of this is that we need to think more carefully about
interventions that relax constraints on the informal sector as a whole—infrastructure, titling, legal
reforms, sector-wide technological investments—rather than investing in individuals while treating5

these broader capacity constraints as fixed.
The remainder of the paper is organized as follows. Section 2 presents the design of the experiment and the approach to comparative costing. Section 3 presents the main results of the study,
including the core experimental results and a comparative cost equivalence and cost effectivenessanalysis. Section 4 4 aggregates short- and medium-term results to provide a summary comparisonof cash-flow impacts experienced since treatment. Section 5 concludes.

2

Design2.1

Interventions

Huguka Dukore: Employment and entrepreneurship readiness training
Huguka Dukore, which means “get trained and let’s work” in Kinyarwanda, was a five-year project inRwanda, run by Education Development Center, Inc. (henceforth EDC), and financed by USAID.
Over the lifetime of this project, it provided business-skill training for 40,000 vulnerable youth. All
participants are offered work readiness training; in urban areas the program promotes technical skill
and job placement, while in rural areas there is more emphasis on entrepreneurship training andenabling productive self-employment. Prior to the initiation of this program, the same organization
ran a five-year activity in Rwanda, called the Akazi Kanoze Youth Livelihoods Project, that pursued
a similar approach.
The implementation of HD studied here is built around three ten-week modules taken sequentially. The first is called Work Ready Now!, providing basic business skills such as accounting aswell as emphasizing the “soft” skills hypothesized to be both valuable and transferable across jobs
and employment sectors (see Campos et al., 2017, for related evidence).2 This module consists of
10 five-day weeks of full-day training. The second 10-week module of HD encourages students tofocus on self-employment. This Be Your Own Boss training promotes entrepreneurship and begins
to tailor content to the specific sector in which a trainee wishes to focus. Participants are asked todevelop a business idea, identify a concrete market opportunity, outline the business operation and
2Work Ready Now! consists of eight sub-modules: Personal Development, Interpersonal Communication, Work
Habits and Conduct, Leadership, Health and Safety at Work, Worker and Employer Rights and Responsibilities,
Financial Fitness, and Exploring Entrepreneurship.

6

financing, and draw up a business plan. The third 10-week module is a Technical Training coursethat provides specific skills in an employment area, such as tailoring, hairdressing, carpentry, or
beekeeping.
In terms of participation, 86% of individuals in the HD arm attend the first three weeks ofthe first module, the definition of compliance on which payment from USAID to EDC is based.
The rates at which individuals complete the different modules are 79% (Work Ready Now), 69%(Be Your Own Boss), and 48% (Technical Training). After completing these three classroom
training components, HD students would typically be placed in an apprenticeship with a local
entrepreneur working in the selected employment sector. 39% of those in the HD arm undertook
an apprenticeship during the study period, with the most common placements being in tailoring(53%) and hairdressing (22%).
McIntosh and Zeitlin (2022) provide more details of the program, participation rates in the
different components of HD, and the specific types of training received. For our experimental
analysis we do not utilize the (endogenous) choices over the specific training received and instead
focus on the Intention to Treat (ITT) effect of being offered the bundle that is Huguka Dukore.

GiveDirectly: Household grants program
The cash arm was implemented by GiveDirectly, a US nonprofit that works extensively in Kenya
and has since expanded its implementation capacity to Rwanda, as well as several other SubSaharan African countries, Yemen, and the United States. In Rwanda, they operate using mobile
money, first enrolling a household and establishing the phone number through which the transfer
can securely be made, then sending the money, and finally following up with the household toverify that transfers were received by the correct person in a timely manner. If a targeted youth
did not themselves have a telephone, they were asked to provide the name and number of a trusted
individual to whom the transfer could be sent. Cash payments for this study were made in two
tranches, with the first making up 40% of the transfer, and the second following one month laterand providing the remaining 60%. As would be expected, compliance with the cash transfers was
essentially perfect.

7

2.2

Enrollment and Assignment

The study takes place in three districts; Rwamagana, Muhanga, and Nyamagabe. Within these
districts we selected 13 “sectors” (the next geo-political unit in Rwanda below the district), andrecruited study participants at the sector level. To be included in the study youth needed to meet allof the eligibility requirements for both implementers, and to have expressed interest in participatingin HD by having come to an informational session on the program. Those youth who met HD’scriteria but not GiveDirectly’s (meaning that they were not in the poorest two government poverty
classifications, Ubudehe 1 or 2) were treated with HD but not included in the study.
The resulting sample consists of 1,848 individuals. As shown in Appendix Table A.4, study
participants are just over half female, with an average age of twenty three years, and seven and ahalf years of education. Excluding on-farm agricultural labor, exactly a third of the sample reported
being employed at baseline. Median consumption per adult equivalent is 5,879 RWF per month,which in 2018 PPP terms translates to a consumption level of USD 0.66 per day.
Study participants were randomized to treatments in a series of 13 public lotteries, held at thesector level, which were conducted jointly with local officials and representatives of both implementers. The public lottery mechanism was used given the large sums of money being transferred
and the desire to ensure that the entire process was transparent to participants and local officials.
Participants were invited to attend the lottery if they wished to do so but this was not a conditionof being included in the study. If present, participants drew their own treatments via tokens ofdifferent colors mixed up in a sack. We effectively blocked the treatment by ensuring that fixedproportions of tokens for each arm were present in each lottery. To avoid spillovers within household, for the few households that contained multiple eligible individuals we had them draw a single
status for both members, and consequently we cluster standard errors at the household level in theanalysis.
Table A.1 illustrates the core research design that emerged from the public lotteries. The four
broad arms for the study were a Control, the HD arm, the GD-administered Cash arm, and a
Combined arm that received both Cash and HD. In total, 485 individuals were assigned to HD, 672
to GD, 203 to the Combined arm, and 488 to control. Within the cash-transfer arm, individuals wererandomly assigned to the three bracketing transfer amounts (GD-Lower, receiving $317.34; GD-

8

Middle, receiving $410.19; and GD-Upper, receiving $503.04), or to the GD-Large arm, receiving$750. The Combined arm received the Middle GD cash-transfer amount, and the transfers were notconditioned on participation in HD. In order to minimize tensions over the inequalities generated
by the Cash arm, the actual amounts to be transferred were not revealed at the time of the lottery(only the arm to which they had been assigned). Delivery of the Cash intervention was delayed
relative to the start of HD both to allay implementers’ concerns that early receipt of cash in the
Combined arm might lead participants to drop out of training, and to ensure that individuals in
the Combined arm had had time to develop business ideas prior to the receipt of funds. This
meant that HD implementation typically began two months before the first of the cash transfers
was made.

2.3

Cost Measurement

All costs in this study are calculated from the perspective of USAID, meaning that they include all
downstream direct and indirect costs downstream from the funder. The costing activities consistedof an ex ante component in which we attempted to predict implementation costs during the designphase, and an ex post costing of what had actually transpired once implementation was complete.
The ex ante costing was used to set the cash transfer amounts, but understanding that this exercise
was being conducted under uncertainty, we bracketed the anticipated costs of the HD programwith multiple cash amounts so as to allow for adjustment after-the-fact based on the results ofthe (correct) ex-post exercise. Our costing followed best practices in this literature and utilized
the ‘ingredients method’ (Dhaliwal and Tulloch, 2012; Levin and McEwan, 2001; Levin et al.,2017; Walls et al., 2019). Because HD is a national-scale program serving 40,000 youth, we asked
GiveDirectly to synthetically scale up their operations in the costing exercise, providing us withthe full operating costs to run a program at that scale for each of the different transfer amounts.
Further details of the costing exercise are provided in McIntosh and Zeitlin (2022).
Table 1 shows the evolution of the costing analysis. The ex-ante exercise arrived at an anticipated HD cost of $452.47. Based on the program costs structure underlying this estimate we thenbracketed this cost by varying the number of year 1 HD beneficiaries from 8,000 to 12,000, which
produced per-capita costs of $377.05 and $565.58. The ex-post costing found HD to be less expensive than anticipated, and GD operating costs were slightly higher than anticipated. This means
9that the amount USAID spent on HD per beneficiary was only $388.32, while the spending for the
GD middle arm was $493.96. The inclusion of non-compliance further widens this gap, meaningthat USAID cost per study household in the HD arm was $332.27, while in the GD arms it was$394.93, $490.99, $590.41, and $846.71, respectively. Compliance in the cash arms was unsurprisingly very close to 100% so the cost per study household is almost exactly the cost per beneficiary.
For HD we exploit the fact that USAID paid implementers in full based on enrollment at the endof the first three weeks of training, so the cost per study household is simply the cost per beneficiary times the compliance rate at this point in time (85.6%). The combined arm, incorporatingcompliance with both components of the combined treatment, ended up costing USAID $840.20
per study individual, an amount similar to the GD Large arm. These are the numbers used in thecost equivalence analysis. Because there was no additional implementation in the study sample
between midline and endline, for the endline analysis we use the costing numbers from the midline
exercise.

2.4

Surveys and Outcome Measurement

The baselines for the study were conducted during December of 2017 and January of 2018, HD
treatment began February 2018, and GD treatment May of 2018. Study midlines were conductedduring July and August of 2019, and this endline activity was conducted in October and Novemberof 2021. Consequently, the endline is 3 years 8 months after HD started, 3 years 6 months after
GD started, and 3 years 10 months after baseline.
The survey exercise tracked the eligible beneficiary as an individual, and administered one survey
module to this individual and one to the head of the household in which that person resided (whichmight also be the beneficiary him/herself). The study has five primary outcomes. Employment isa binary measure indicating that the individual spent more than 10 hours in the prior week in paid
work or as the primary operator of a micro-enterprise. Productive hours is the number of hours in
the prior week spent in off-farm paid work or in micro-entrepreneurship. Both measures exclude
own-farm agricultural work, namely labor put into the farm owned by the household. Monthly
income is the total amount earned over the prior month, including enterprise revenue. Productive
asset stocks and household consumption per adult equivalent round out the primary outcomes.
For the midline analysis, we specified three groups of secondary outcomes. The first group is10

comprised of components of household wealth, including net non-land wealth, livestock wealth, andthe stocks of savings and debt. The second included additional measures of beneficiary welfare:subjective well-being, mental health, and the personal consumption of the beneficiary. And a thirdgroup of outcomes brought together measures of cognitive and non-cognitive skills, measured using
Locus of Control, the Big Five index, as well as measures of the aspirations, business knowledge,
and business attitudes of the beneficiary. All monetary outcomes, both primary and secondary, arewinsorized at 1% and 99% and measured in inverse hyperbolic sine (so that marginal effects canbe interpreted as approximating percent changes).

2.5

Enterprise Data

Given the strong focus of both interventions on self-employment and the lack of formal employment
opportunities in surrounding job markets, the most likely medium for longer-term impacts on
income and welfare is enterprises run by the beneficiaries. To explore this, we examine the resultsof the two survey modules that were used to measure enterprises. One of these was based in thehousehold module, and was built to track enterprises primarily run by individuals other than thebeneficiary him or herself. There were relatively few existing household enterprises at baseline, andas we will show, the intervention had quite limited effects on these. The second module was locatedin the beneficiary survey, and was built to track businesses either run directly by the beneficiaryor to which that individual devoted substantial time or resources. Because these two instruments
were not necessarily administered to the same people or at the same time, it was impossible todesign this to completely preclude the possibility of double-counting businesses across these two
instruments. For that reason, we never add together outcomes from these two instruments, insteadcounting the “household” and “beneficiary” businesses simply as two different types of entities thatare examined separately.
For both household and beneficiary businesses, we collected a number of core enterprise outcomes. For all extant enterprises we asked whether the business was currently in operation, andif so the number of household and non-household members employed regularly in the business, aswell as the number of days that business was operative in a typical month, and the number ofcustomers in a typical month. We then asked for the typical daily sales on a day when the business
is operating, and the total profit the business had earned over the month prior to the survey. We
11transform all monetary amounts into US dollars, and then we sum all of these outcomes across all
businesses reported at the household level and at the beneficiary level for each beneficiary.3 These
totals are then merged back into the experimental dataset (of all beneficiaries, whether they run
a businesses or not), missing values are replaced with zeros, and we run standard analysis of theeffects of the interventions as elsewhere in the study. These impacts can then be interpreted as
average effects of the interventions on the total of each variable in a manner that combines extensive
margin impacts on the existence of a business with intensive margin impacts on the size of existing
businesses.
We also attempted to panel track these entities, thereby generating the ability to answer questions about the birth and death of specific enterprises over time. Because the household enterprises
are less dynamic the interest in this exercise applies mostly to the beneficiary businesses. Usingpanel tracking, we can define a number of different variables at the enterprise level that pertain to
the extensive margin. For both midline and endline we can define ‘new firms’ that had not existed
in previous waves and are born in that round. Then, for the midline, we classify the existing firmsthat we observe in that round into three categories; “will survive”, “will be inoperative” (in endline the respondent says the firm still exists but is not currently operative), and “will die” (firmno longer exists at endline). As above, because the sample of individuals with firms is strongly
endogenous to the treatments, it is unattractive to analyze these outcomes in rates at the firmlevel; instead we total them at the beneficiary level, replacing missing values with zeros for the
new businesses, and so create outcomes that can be analyzed in a standard experimental context
that are “number of new firms”. Among the endogenous sample of firms at midline, we can thenexamine what happens to those firms by endline as a function of treatment status.

2.6

Attrition and Balance

We attempted to follow up with all study beneficiaries at endline, 46 months after baseline, regardless of whether they had been successfully tracked at midline or not. We followed the beneficiary
youth as an individual, and considered the “household” to be the place in which that individual
was resident at the time of endline even when that differed from the baseline household. The sur3

As in the rest of the analysis, non-binary outcomes are Winsorized at 99% and monetary outcomes are inflation
adjusted to make them real midline US dollars.

12vey teams initiated a first phase of tracking where they attempted to find all individuals who had
moved within their home districts or had gone to Kigali, the capital. We had originally intended torandomly sample from the remaining individuals not found by this process to conduct an “intensive
tracking” exercise, as we did in the midline, but the original tracking was so successful, and theremaining sample sufficiently small, that in the end we simply intensively tracked everyone in the
study. This intensive tracking phase involved sending an enumerator to speak with them in person
if they were located anywhere in Rwanda or Uganda (where IPA has a sister office and therefore
could easily mount in-person surveys), and then conducting a phone survey with anyone who could
not be located through the above means or who had migrated to a different country.
For a potentially highly mobile sample of youth, our tracking was remarkably successful: in theend we managed to survey 98.6% of all baseline youth. This is higher than we had anticipated andmay be due to the advent of the Covid-19 shock in the interim that made migration and work awayfrom home more difficult, thereby keeping the study sample less mobile than they might have been
in business-as-usual circumstances. Of the 1,848 baseline individuals, at endline we found 8 in jail,8 passed away, 3 mentally ill, 1 in military training, 4 refused the endline survey, and 2 individuals
that we failed to find, for a total of 26 baseline individuals who were not included in the endline
survey.
Table A.2 analyzes differential attrition by arm. Overall, as is unsurprising with such a hightracking rate, we do not find differential attrition by arm. The one exception is in the GD Largearm, where we succesfully tracked all 178 individuals assigned to this arm, and the resulting tracking
rate of 100% is significantly different from the control rate of 98.6%, although clearly the absolutedifference is very small in magnitude. Table A.3 looks for signs of differential determinants of
attrition by regressing baseline covariates on a dummy for whether the individual was successfully
tracked at endline. Only for one covariate do we see any signs of differences, namely that wewere least successful in tracking those individuals who were wealthiest at baseline in terms of
consumption. Nonetheless, once adjusted for multiple inference across outcomes we find no evidenceof overall tracking differentials, meaning that the endline sample is representative of the baseline
universe.
We can then examine the balance of the experiment using the attrited endline sample thatwill be used for analysis. Table A.4 shows an exceedingly well-balanced sample, with not a single
13

covariate significant for any arm once adjusted for multiple inference. The endline sample therefore
appears to provide clean internal validity and a remarkably well-tracked sample given the durationof the effects we estimate here.

3

Results

We present the results of the study in six steps. First, we present ITT effects on primary and secondary outcomes. Second, we show that both intervention types—but particularly the cash-transfer
treatments—led to the creation of businesses in the short term, and that entrepreneurship activity
remains substantially elevated levels of beneficiary operation even 3.5 years after treatment. Third,
we test formally for complementarities, finding no evidence of technological complementarities between these interventions either at midline or endline. Fourth, we use the random assignment toalternative cash transfer amounts and a linear dose-response function to make a cost-equivalent
comparison between the two intervention modalities. Fifth, we contrast this approach to a traditional cost-effectiveness analysis, comparing arms in impacts-per-dollar terms, and we interrogate
assumptions of heterogeneity and spillovers that are potentially important to a projected scale-upof these interventions. And sixth, we examine the incidence of Covid-19-related shocks and theirpotential to explain the attenuation of impacts.

3.1

ITT Results on Primary and Secondary Outcomes

To begin, we estimate ITT impacts of assignment to HD or cash transfers on primary and secondary
outcomes. For comparison, we reprised midline results reported in McIntosh and Zeitlin (2022),
presenting these alongside new endline results. We show control means and intervention impacts
for primary outcomes in Table 2, and for secondary outcomes of wealth, beneficiary welfare, andcognitive and non-cognitive skills in Tables 3, A.5, and A.6 respectively. These are generally attenuated since those observed at midline, when compared with a control group who have maintained
their employment rate and even slightly increased both income and consumption by increasing
productive hours (or sacrificing labor) and, most strikingly, stripping productive assets.
Individuals in our control group have an endline employment rate of 50 percent, statisticallyindistinguishable from the 48 percent employment rate observed at midline. Control group incomes14

are actually 12 percent higher in real terms than they were at midline, and real consumption in
the control group is up approximately 47 percent at endline relative to midline. These successes
in sustaining income and consumption among control-group members have been accompanied byrising work hours and reductions in productive asset stocks. Work hours have increased by nearly
one hour per week, or approximately 4 percent, implying that earnings per productive hour haverisen slightly in the control group between the midline and endline. On the other hand, controlgroup participants have seen marked drops in the value of productive assets they hold: a loss ofapproximately 63 percent of the value of productive assets from midline to endline. Falls in livestock
wealth appear to be a primary contributor to these declines in productive asset values; we also see
rising debt among the control group (and, perhaps surprisingly, some increases in savings stocks).
Against this context, ITT results for primary outcomes in Table 2 show proportionally reduced
estimates on income, assets, and consumption, with a striking change in the ordering of treatment
effects on productive hours, for which HD’s impacts have actually grown to make this now theleading intervention. Aggregate employment effects, combining all types of work, remain essentially
unaffected. Monthly incomes, which had risen by between 70 and 114 percent among cash andcombined cash and training arms at midline, are substantially reduced, with only the Large transfer
sustaining a statistically significant, 72 percent increase in monthly income (very similar to thatarm’s effects at midline). Similarly, impacts on household consumption per capita are no longer
statistically significant, with point estimates falling to as little as half of their previous value.
Consumption impacts relative to control of 21 percent in GD Middle and of 17 percent in the
Combined arm, each of which is about two thirds of its midline impact, have multiple-inference
adjusted (“sharpened”) q-values just above 10 percent.4 To the extent that consumption is the
typical omnibus measure of economic welfare, it may be more appropriate to consider the unadjusted
p-values for this outcome, in which case cash arms of the Middle or larger sizes have impacts that are
significant at the 95% level. Program impacts on productive assets stocks had at midline increased
by 154 percent in the HD arm and approximately 400 percent across cash transfer and combined
arms. These differences are now smaller relative to control—with impacts ranging from 93 percent
in the HD arm to as much as 315 percent in the Combined arm—and given the reduced level of
4

The stars in our tables use the multiple inference-corrected Q-values from Anderson (2008) to account for themultiple outcomes and treatments being tested in each table.

15

productive assets in the control arm, these reflect even smaller absolute differences than they did
at midline.5
In Table 4, we see that even 3.5 years after intervention, the programs studied have induced
remarkably stable transitions in sectors of occupation, in spite of the lack of movement in theoverall employment rate.6 Cash transfers in the Upper, Large, and Combined arms have induced
statistically significant levels of entry into non-agricultural microenterprises among between 11 and17 percent of the population assigned to those arms, with a further 5-6 percent induced to enter
into non-agricultural microenterprises in the Large and Combined arm. As in the midline, thisappears to be mostly associated with a commensurate movement out of agricultural wage labor,
the prevalence of which is also reduced by 8 percentage points by the HD intervention. Although nolonger statistically significant, estimated HD-induced movements into non-agricultural wage laborof 5 percent of the population remain consistent with those found at midline.7 Table A.8 examines
two related margins, looking at unpaid uses of time (education and domestic work), as well as
survey-based measures of the opportunity cost of time, and finds no treatment effects on any ofthese margins. Taken together, these findings suggest that cash transfers—particularly those above
the Middle value—induced movements into self-employment that came at the cost of own-farm
agricultural work and persisted in spite of the pandemic. On the other hand, while HD’s impacts
on movements into wage labor did persist, it seems plausible that the more limited productiveasset buffer in those sectors explains the lesser persistence of HD-induced microenterprises over thecourse of the pandemic.
Similar fading of endline impacts relative to the midline is observed for measures of beneficiary welfare in Table A.5. HD impacts on subjective well being fall from 0.19 standard deviations
at midline to (statistically insignificant) 0.12 standard deviations at endline, while cash transfer
impacts are approximately half of those previously observed—though still significant at 0.29 and5

Because the cash arms have virtually 100% compliance, the ITT estimated here is also the Treatment on theTreated (ToT). For the HD arm where the core measure of compliance is 85.6%, if we are willing to assume thatthose not participating received no indirect effect of being included in the treatment, then we can back out the ToTby dividing by the compliance rate. The resulting ToT estimate would 17% larger than the ITT for each variable,with the same significance level.
6Note that because this whole table effectively studies a single outcome: “how are beneficiaries using their time”
we base the stars on the unadjusted p-values, although the sharpened q-values are also provided in hard brackets.
7
We note that neither treatment induces exit from rural areas altogether; Table A.7 examines whether individuals
live in a location classified as non-rural by the Rwandan government, and whether they have moved since baseline.
No significant differences are found; while both treatments weakly increase moving to new villages it is actually lesslikely that treated individuals are located in urban areas.

160.39 standard deviations, respectively, for the Middle and Upper transfer values. We see no impacts on our survey measures of mental health, and we see modest impacts on beneficiary-specificconsumption in the Upper and Combined transfer arms.
As Table 3 shows, we continue to see signs of persistent wealth effects from cash transfers,
though these are generally smaller than at midline and somewhat imprecisely estimated. Point
estimates for net non-land wealth suggest gains in the Upper and Large transfer arms of 70–80
percent relative to control—though statistically insignificant—down from impacts in excess of 110
percent at midline. There remain positive impacts on household livestock wealth from the Middle,
Large, and Combined arms, with the latter delivering the smallest of these impacts at approximately
126 percent over control. And savings impacts of cash transfers largely persist from midline, e.g.,
at a 99 percent impact over control in the Middle arm. The prior estimates of large savings impactsof HD have largely evaporated.
Finally, we see little evidence of sustained impacts on beneficiaries’ cognitive and non-cognitive
skills in Table A.6. We see no impacts on aspirations (and endline survey data do not provide
the Locus of Control measure from midline). Measures of business knowledge remain statisticallysignificant in the HD and Combined arms, suggesting some persistent human capital effects, butthese are approximately half of their prior magnitudes.

3.2

ITT Results on Business OutcomesWe begin our analysis of beneficiary businesses on the extensive margin, examining firm birth anddeath. In Table 7 we consider the number of newly born firms in midline and in endline in thefirst two rows. The average control individual created 0.5 new businesses in midline, HD elevates
this by 0.22, and all of the cash arms lead this to more than double. Control individuals created
0.24 new firms on average between midline and endline, and only the GD Large and Combinedarms continuing to elevate business creation by midline. In the remaining rows we then look at the(endogenous) sample of firms that exist at midline, and ask what happens to them at endline as afunction of treatment status. In the third row we see that in the control group 0.24 of the firms
have died, this rate is not significantly different for any treatment arm. In row four we look at therate of ‘inoperative’ businesses, however, and see that while this is relatively rare in the control
group (0.14) the rate of midline businesses becoming inoperative more than doubles in most of the17cash arms. However, because the overall number of businesses was so much larger in the cash arms,
they are have more businesses created in midline that remain operative in endline as well. So,
the takeaway from this table is that all of the interventions led to a short-term burst of businesscreation; while this effect was smaller in the HD arm those businesses proved more durable. The
cash arms created more businesses that survived to endline but also more businesses that become
inoperative by endline as well. So the interventions have powerful effects on the extensive margin.
We turn to the impacts on midline beneficiary businesses in Tables 8. Here we see transformative
effects of all the interventions. Beginning again with business ownership in the control group, we
see that by midline the average control individual reports operating 0.79 and owning 0.71 businesses(the maximum number reported by control individuals is 5 different enterprises, with 52% reporting
owning any business). The interventions all drive this number up, with HD increasing owned
businesses by 0.14, or 18% of the baseline mean, and the cash arms having at least three times thiseffect, with the largest transfers and the Combined arm almost doubling the number of businesses
owned. All interventions increase hired labor but particularly draw heavily on the use of household
workers, explaining the weak negative effects seen on household-reported enterprises. HD drives
up the number of days worked per month by 2.7 over a base of 9.1, and leads to a large increase
monthly profits (treatment effect of $4.36 per month), but does not change either customers or
sales significantly. The cash arms lead to a doubling of days worked, number of customers permonth and sales are more than doubled in the smallest arms and tripled in the Combined arm, andprofits are more than doubled everywhere. The implication of this latter result is that the receipt
of $503 was generating an enterprise profit increase of $11.32 per month 14 months after receipt of
the cash grant.
The endline beneficiary-reported business impacts are presented in Table 9. Perhaps the mostimportant thing to point out here is the sharp contraction in the overall rate of business ownershipin the control group, which falls from 0.79 at midline to 0.4 at endline. Given that we would expect
the control group to be becoming monotonically more economically active over time as they age,
this is strong evidence of the fact that Covid-19 has driven a substantial number of the self-employedout of business. Similarly, control group endline days worked in self-employment fall by 38%, sales
by 53%, and profits by 24% relative to the midline. So there seems to be no doubt that businessconditions have worsened overall and the endline impacts need to be interpreted in light of their18

ability to insulate beneficiaries against this shock.
As is the case with many of the results presented in this study, the core enterprise treatment
effects in the endline represent a fade of about 50% relative to what we saw at midline. HD
continues to elevate the number of businesses owned by about 0.1, and both days worked and sales
are significantly elevated relative to the control. Unfortunately the HD effects on profits have fallen
to about a third of what they were at midline (now $1.64 per month), and are significant only at the
10% level. The cash arms elevate the number of endline businesses by 0.2–0.4, retain substantial
impacts on days worked and sales, and continue to significantly elevate profits by amounts ranging
from $3.08 (GD Middle) to $7.11 per month (GD Large). While it is impressive to see significant
impacts on business profits across the board even 42 months after the GD intervention, these endline
impacts represent between 30%–90% of the profit impacts at midline, suggesting that all the cash
arms are seeing a fade in business profits over time.8 Given that the treatment effects on profitsand hours worked contract by similar amounts, impact on the effective wage rate appears similar
at midline and endline.
Table A.11 shows the gender interactions with treatment on endline beneficiary business outcomes. While the table contains very few significant gender interactions, the signs and the magnitudes of the differential female effects are troubling. For the key outcomes of sales and profits thefemale interactions are negative for all treatments, implying that women are benefiting less thanmen. While for the cash arms these interaction effects are smaller than the male treatment effect,
meaning that women still benefit overall, for HD this is not the case. Adding together the uninteracted male effect with the female coefficient to get the total effect on women implies that females
are getting little endline benefit from HD on customers, sales, or profits compared to women in thecontrol group. So the modest long-run effects of HD on business outcomes appear to be confined
to males (although the difference between men and women is not significant).
These impacts appear to be specific to enterprises owned and operated by the beneficiary, with
8
One admittedly heroic way of contextualizing these cash effects is as follows: take the business profit impact of
GD Upper 14 months and 42 months after treatment and linearize it from month 1 until it becomes negative (which
occurs in month 65) then the sum of the resulting profits is $463 for an arm that cost $572 and delivered $503. The
implication is that the average total improvement in business revenues is not larger than the original transfer, despite
the very substantial increase in days worked over the course of this time interval (450 total additional days, using thesame linear extrapolation method). Even taking the total profit effect as return (ignoring the initial cash received)
this method suggests a wage rate of around a dollar per day the business is open, similar to the rate observed in thecontrol group.

19

little impact on businesses of other household members. We report results for other householdreported enterprises in Tables A.9 and A.10. In both rounds, these businesses seem to have been
largely untouched by the substantial interventions being directed at youth in these households. Asa starting point we see that control households report operating only 0.06 businesses on averagein midline and endline (there is only one control household operating more than one business, so
in effect this means that only 6 percent have any business at all). Treatment of beneficiaries does
not lead to any elevation of the probability that there is a household-reported business, and ifanything seems to lead to a light decrease in the devotion of household labor to the household
enterprise. Similarly, core businesses outcomes such as the number of customers, daily sales, andmonthly profits typically show weak negative effects, stronger in the endline than midline. So themain picture is that there are very few businesses in these households, they are generally unaffectedby the presence of the treatments, and to the extent that they are impacted they appear to besuffering from a drawing away of labor.
In summary, then, even the GD Lower arm drives larger enterprise impacts than HD acrossmost outcomes at both midline and endline. Business conditions have worsened substantiallyoverall between midline and endline, and the impact of the interventions has faded by about halfacross most outcomes in the 28 months between these two surveys. These results indicate thatenterprise activity is a key conduit for the overall income and consumption impacts seen elsewherein the study, that both human and physical capital can deliver better livelihoods through selfemployment, but neither of these appears to generate a dynamic shift in business outcomes that
represents a real pathway out of poverty.

3.3

Complementarities

We can more explicitly test for complementarities by comparing impacts of the Combined arm
with the sum of impacts in the arms comprising its constituent parts—HD and the GD Middle
transfer arm. We do so by dropping active-treatment arms not involved in this comparison. We
then create indicators for whether the individual received HD or a cash transfer, defining these to
take a value of one in the combined arm as well. In Table A.12 and A.13, we estimate a modelthat includes these alongside an indicator taking a value of one for individuals assigned to the
Combined arm: the coefficient on the Combined arm indicator therefore directly estimates the20extent of complementarities.
Whereas at midline we had found some evidence of negative complementarities—in particular,
on productive hours and subjective well being—we find no such evidence here. The Combined
arm’s impacts are statistically indistinguishable from the sum of its HD and GD components. Aneven more striking demonstration of this is provided in Tables A.14 and A.15, which conduct thecomplementarities tests for the business outcomes where both treatments themselves had strong
impacts, particularly at midline. Somewhat remarkably, neither at midline nor at endline do wefind any evidence of complementarity on any outcome, even if we were to use the unadjusted (nonFDR-corrected) standard errors. The study therefore provides no evidence that extra benefits canbe unlocked when physical and human capital constraints are relaxed simultaneously.
Given the coincidental fact that the GD Large arm has almost the same cost as the Combined
arm, our study provides an alternate ability to think about complementarities. This is to ask:
given that a youth has already received a cash transfer of approximately the GD Middle amount,is it better then to spend additional resources on that individual by giving her more cash, or by
giving the HD training? This comparison is given in the p-values of the F-tests in column (c) of
Table 2, for primary outcomes, and Tables 3, A.5, and A.6 for secondary outcomes. Again, we findno significant differences between these interventions with the exception of business knowledge,
showing again that HD is uniquely productive of that outcome.

3.4

Cost-equivalent benchmark

A central goal of this project is to allow the comparison of impacts between cash and in-kindprograms while holding expenditure per beneficiary constant. As discussed in McIntosh and Zeitlin(2022), we do so by estimating a regression-adjusted, cost-equivalent comparison between HD andcash transfers by estimating a model of the formHD
Yihb2 = δ T Tihb + δ HD Tibh
+ βXihb0 + ρYihb0 + γτi + µb + ϵihb2(1)

for outcome Y of individual i in household h, randomization block b, and round 2. Here, Tihb isHD an indicator for
an indicator for whether this household was assigned to any treatment, and Tibh

assignment to the HD arm in particular, such that the coefficient δ HD estimates the differential

21

effect of assignment to HD, relative to a cash grant. We include a measure, τi , defined as thedifference between a given cash arm’s cost and that of the HD arm. The cost used is the ex-post
cost per study household in each arm, as shown in the final column of Table 1, and it is set equal
to zero in both HD and Control. Because γ absorbs the (linear) effects of cost differentials with the
cash arm, the coefficient δ HD therefore estimates HD’s differential impacts relative to cash transfers
at a cost equivalent level.
At these cost-equivalent levels, we find no statistically significant differences on primary outcomes at endline between HD and cash transfers. Results are presented for primary outcomes inTable 5, which shows the result of this exercise at both midline and endline, and for secondary
outcomes at endline in Table A.16. Among secondary outcomes, these differences between arms
are different only for our measure of business knowledge. Proportional fading of program impacts
across a wide range of outcomes appears to mean not only that it is harder to detect the impacts ofindividual programs at these more modest impact levels, but, moreover, the resulting attenuationof the differences between arms makes it harder to find statistically significant contrasts between
them. If we willing to interpret differences that appear quantitatively meaningful even if statistically insignificant, HD edges cash in terms of productive hours and debt reduction, while cash has
the advantage for outcomes that pertain to asset ownership and consumption.
We can use this same approach to gauge the relative impacts of cash and training on businessoutcomes as well. In Table A.17 we look at midline outcomes. This table shows that almost acrossthe board cash has been more effective at driving business outcomes. Owning and operating businesses, days worked and customers per month, daily sales, and monthly profits are all significantly
more strongly driven by cash than HD. Every one of the eight business outcomes studied in thistable is also significantly improved by the cost-equivalent cash transfer. Over the short term then,clearly cash has proven superior to HD in driving business formation and growth. Table A.18 showsthat, as for the primary and secondary outcomes of the study, by the endline the overall effects
have faded enough that we can no longer reject equality of cost equivalent impacts for any of thebusiness outcomes. Nonetheless, the midline results provide powerful evidence that cash ‘wins’ interms of cost equivalent short-term business operations even up against a program whose specificpurpose is to promote self-employment.
Our approach to cost adjustment assumes linearity in the response to spending, an assumption
22

that can be probed by varying the functional form used to control for cost. Tables A.19 and A.20
present the estimated cost-equivalence comparison using a variety of different functional forms tocontrol for cost, for primary and secondary outcomes respectively. In each table, Column 1 present
the base linear case from the prior tables. Column 2 uses a quadratic, and column 3 a third-orderpolynomial, functional form to control for cost. Columns 4–7 then serially drop one of the GD
transfer amount arms and present the cost equivalence comparison if that arm had not been in the
study. In general the results are quite robust; the significant benefit of HD at building businessknowledge is always positive and is significant in 5 of the 7 specifications.

3.5

Cost-equivalence versus Cost effectivenessThis study provides the capacity to make comparisons both across two interventions implemented
at (nearly) the same cost, and also to compare across different costs to evaluate differential costeffectiveness per dollar spent. Tables 6 and A.21 divide the arm-specific benefits measured in ITT
regressions by the cost of each arm in hundreds of dollars, and so give the benefit per amountspent. The columns to the left of this table then provide p-values on F-tests of the differential
cost effectiveness across arms. As was the case with the cost-effectiveness comparisons, the overall
impacts are now sufficiently moderated that none of the benefit/cost ratios are different across arms,
with the exception of the business knowledge question. Figure 1 presents a graphical contrast of
the cost equivalence and cost effectiveness approaches to our study results. Cost equivalence isvisualized in the left panels by the vertical difference between the black diamond (HD) and thehollow circle (predicted cash impact at HD cost). Cost effectiveness is visualized in the right panelsby the slope of the line connecting zero with the arm-specific outcome represented in benefit/cost
space. While we have already shown that these differences are not statistically significant, thetakeaway from these different approaches emphasizes the superiority of HD at driving productive
hours (both in terms of cost equivalence and cost effectiveness), and the cost effectiveness superiorityof the middle cash transfers in producing consumption and productive assets.
For a given budget, a comparison in benefit/cost terms is particularly appealing to the extent
that results of a cost-effective, inexpensive intervention can plausibly be “scaled up” to deliver thesame benefit in aggregate welfare terms. We can empirically address two critical assumptions, theviolation of which can constrain that thought exercise. The first of these is heterogeneity: if there
23

exists a large sample of similarly eligible individuals for whom the same benefits can be achieved,
then the less expensive intervention can always be “scaled up” in aggregate welfare terms on theextensive margin by simply applying it to more people. The presence of meaningful heterogeneity
suggests the need for care in identifying a target population for scale-up. The second relates to thenature of spillover effects and the extent to which impacts estimated on the target beneficiaries
correctly reflect the full welfare benefits of the program at a population level. In the presence of
spillover effects, scale-ups of interventions that operated within a fixed geographical space mightdeliver different results than those found in less intensive interventions.
To examine heterogeneity, we use interaction analysis to examine the pre-specified dimensions
over which we anticipated the study might have differential effects. These are gender (Table A.22),
age (Table A.23), baseline consumption (Table A.24), and baseline local employment rates (Table
A.25). None of these tables surfaces any meaningful evidence of heterogeneity. One possible
explanation of this result is that our study ended up with a relatively narrow set of targeting
criteria (youth who were qualified for and interested in Huguka Dukore, while being poor enoughto qualify for the use of Give Directly transfers), thereby limiting the overall diversity within our
sample. The conclusion is that both of the interventions studied are having consistent effects andretargeting within this group would not substantially improve overall program effectiveness on
primary outcomes.
To examine spillovers, we exploit experimental variation in the shares of HD, GD Main, and GD
Huge recipients in a given village in order to test for the presence of local spillovers. We estimate two
types of models of spillovers: a levels spillovers specification that allows for neighbors’ treatments
to affect one’s own outcomes in levels, and a general interference model that allows not only forthese levels effects, but also for the share of individuals in each arm to modify the impacts of eachtreatment. Across these models, we find only very limited evidence of program spillovers at followup. As Table A.26 shows, in the levels spillover model we see some evidence of a negative spilloverfrom HD saturation levels onto peer consumption. However, there is limited additional support forthis estimate in the richer interference model for consumption outcomes presented in Table A.27,
where point estimates for spillovers from HD treatment onto control and cash-transfer arms arenegative but insignificant. If anything, the full saturation model suggests a statistically significant,negative spillover effect of the GD Main treatments onto consumption levels in Control. We see
24

no further evidence of interference on other outcomes in the analysis of spillovers to employment(A.28), income (A.29), productive hours (A.30), or asset values (A.31). Hence the within-village
variation in treatment intensity reveals very little evidence of contamination in the study.
The conclusion of these two empirical extensions is that the estimates provided here are a)broadly applicable to the entire target population and b) do not generate strong externalities.
Hence the linearization assumption underlying the standard cost effectiveness approach may bejustified in the sense that any of the less expensive interventions studied here can be scaled up interms of total welfare benefits simply by applying them to more similar people.

3.6

Contextualizing the Covid-19 shock

To understand the incidence of Covid-related shocks, we asked study participants at endline about
their experiences during three key and salient periods: first from the beginning of the nationwide
lockdown of March 2020 until the beginning of the genocide memorial period in May, 2020; second,in a period of relative normalcy, during which children were allowed to return to school, runningfrom November 2020 through January 2021; and third, during a later lockdown in July of 2021. In
Figure 3, we document the incidence of four measures of shocks during this period.9 Variation inthese outcomes across arms is of modest magnitude and statistically imprecise. Reports of direct
experience of Covid illness are relatively low in most periods, though increasing over time. We see
markedly greater measures of induced economic hardship in the first of the reference periods, withsome resurgence of these shocks in the later lockdown period. In particular, a substantial portion of
the sample reports income losses, particularly in the first lockdown period. And the consequencesof this lockdown are also visible in reported experiences of access to food: both reductions in market
access and shortages of specific products are widespread, especially in the initial lockdown period.
These shocks are borne out in stalled improvements in income-generating activities in the control group. This is documented in Figure 4. The growth trajectory in employment status, productive hours, and monthly income comes to a near-complete stop between the midline and endline,
however, as control members experience the economic consequences of the Covid-19 pandemic.
9

We focus on those that are plausibly exogenous to assigned treatments, excluding those (e.g., asset stripping)that are more likely to be driven by treatment-induced accumulation of wealth or economic opportunity. We report
the incidence of actual disease incidence, self-reported income loss, and experiences of food-market closures and foodproduct shortages.

25This pattern is particularly surprising given that our study sample is young and entrepreneurial
and would otherwise be expected to see growth across time in outcomes such as employment andbusiness ownership. Control-group members were able to protect consumption at endline, but potentially at the cost of a stripping of productive assets.10 Control-group households lose more thanhalf of the value of their assets at midline and the rate of business operation contracts markedly.
Taken together, these findings suggest that individuals in the control arm reduced or sold off thebusinesses that they had launched by midline; in doing so, they did not become unemployed, butrather switched back to a focus on agriculture, with some surplus generated by asset sales thatdrove a rise in consumption.
Could it be that Covid-driven support provided by outside actors during the epidemic led to
an attenuation of the impacts we observe here? The most problematic pattern would be one where
Covid aid was being targeted around our experiment, disproportionately benefiting the control
group. Our endline survey asked directly whether individuals had received any food, cash, or othersupport that they did not typically receive in each of the three retrospective Covid time periods.
We analyze the responses to these questions in Table A.32, and find little evidence of this pattern.
While it is the case that more than a quarter of the control sample reports getting some unusual
support during the first lockdown, the rates of receiving support are not systematically lower in thetreatment groups. None of the q-values are significant at the 5% level, and half of the coefficientsare positive and half negative. Hence while again it is difficult to separate the overall effect of Covid
from what we observe in this study, there is no reason to think that Covid aid has been targetedin a manner that directly attenuates the results we find here.
Our ability to distinguish the consequences of Covid-19 shocks hinges on the existence of measurable, cross-sectional variation in exposure to exogenous measures of shocks. Unfortunately, thelarge majority of shock incidence is time-series: the geographic sector intra-class correlation is lessthan 0.01 across all four of the “exogenous” shock measures highlighted above. A separate way ofmaking the same point is shown in Table A.33, where we follow the “endogenous stratification”
approach suggested by Abadie et al. (2018) to predict exposure to Covid-19 shocks using a setof baseline covariates. The “index” variable refers to the predicted value of that outcome in thecontrol group. Overall, we see very limited predictive power of this index on endline outcomes.
10Consumption figures are deflated to midline prices, so this does not reflect an inflationary effect.

26

Perhaps unsurprisingly given this, there is also little evidence of heterogeneity in treatment effects
across levels of the predictive index, with the exception of the GD Large treatment arm’s impacts on
employment and consumption.11 For those outcomes, results are suggestive of particularly strong
deterioration in outcomes among those GD-Large recipients who experienced negative shocks.
Finally, in Table A.34 we use a broader set of measures describing the incidence of shocks(including those more directly endogenous to behavior), and examine how these correlate withthe key outcomes of the study. This analysis shows very clearly that the reported incidence ofeconomic shocks from Covid-19 is actually higher in households that were more deeply engaged in
non-farm entrepreneurship. Strong positive coefficients are observed between the economic blow
from Covid-19 and measures of economic activity, including employment, productive hours, income,and productive assets (both in levels and changes). The implication is that those who were most
exposed to the small business sector were hit hardest by Covid-19.
In sum, then three pieces of evidence suggest that the Covid-19 shock hurt those who had“stuck their neck out” by engaging in entrepreneurship, which is of course precisely what theseinterventions intended to do. First, the absolute fall in measures of entrepreneurship during theCovid-19 era are larger for the treatment groups than the control.12 Second, Table A.33 showsthat the kinds of individuals who were most likely to be hit by Covid-19 shocks in many cases havesomewhat larger income fluctuations if treated than if in the control. Finally, table A.34 illustrates
that within the control group reported incidence of shocks to income were actually higher among
those with higher employment rates, income, and productive assets. So the overall takeaway isthat this period was particularly difficult for people who were doing the things that the treatments
intended to induce (running their own businesses rather than working on the farm). Consequently,
the presence of the Covid-19 shock is likely to have attenuated the enterprise-driven income effects
observed in this study (although the Covid lockdowns likely hit the self-employed sector harderthan agriculture in a way that would not be seen from other shocks, such as drought or food pricechanges). While the nature of our evidence on the role of Covid-19 is necessarily more speculative
than the experimental analysis, this study nonetheless illustrates the role that shocks can play in
11
If attenuation in treatment effects since midline were attributable to the observed shock measures, we would
expect coefficients on the interaction between predicted endline outcomes and treatments to be positive: this would
indicate that income losses are more severe among the treated.
12
Midline to endline changes in business ownership rates are -0.39 in the control, -0.43 for HD, and -0.63 for GD
large. IHS productive asset changes are -1.6, -2.3, and -2.6 respectively.

27

eroding the gains that transfers can enable. It might appear that human capital interventions would
prove robust to such shocks, in this context we find the proportional fade to be almost perfectly
symmetric.

4

Aggregating Impacts over Time

We conclude our empirical analysis with an exercise intended to sum up all of the financial flows
observed through both follow-up rounds of the survey to estimate the total effect of the cash
intervention on household financial flows. Aggregating impacts across financial outcomes and overtime provides one approach to summarizing the relative impact of each intervention studied.
This exercise us requires us to impute values of financial flows between study measurement
points. Given that our study asks beneficiaries about wealth, transfers, income, and consumption
with different recall periods as recommended by the survey literature, and given that we do not have
survey data explicitly asking about these flows in all months from baseline to endline, aggregating
the total flow of resources during the course of the study necessarily requires making some strong
assumptions about how these flows change over time. The extent to which our measures of inflows,
expenditures, and asset value changes balance—that is, the extent to which we can account for
all impacts on beneficiary income in either expenditure or asset accumulation—will provide one
indication of the accuracy of these assumptions.
The simplest values to account for are stocks; the survey directly asks for the current valueof things such as productive assets, livestock, savings, and debt, and so the total effect of thetreatment on these values at a moment in time is fully reflected by differences in stock value. Anintermediate case are irregular flows that asked about in the survey ‘over the past year’, which
include transfers made to and received from other households. It is standard to ask these questions
at a longer time frequency since these flows tend to be large and irregular, meaning that shortrecall windows become very noisy. Then we have variables that are measured as short-term flows,of which the most important are consumption and income. Here we ask questions either over amonth recall window (durables) or a week (non-durables) and so can aggregate consumption andincome to monthly levels over the month prior to the survey. For the annual and monthly flow
variables, to calculate total impacts over any period of time, an assumption must be made about28how these flows change during the course of the study.
At midline we conducted a cash accounting exercise covering the 12 months since treatment
by summing the stock values, the annual flows, and then multiplying the monthly flows times 12
which implicitly assumes that the impacts seen a year out had been exactly sustained during thecourse of that year. We now attempt to repeat this exercise for a survey conducted 40 months
after baseline and 28 months after midline. To aggregate values at this point we assume the flows
followed a step function, taking their midline values up through the midline and their endline
values between the midline and the endline. Total stock values are simply the endline treatment
effects on stocks. Annual flow values are then the midline treatment effect plus 2.33 times theendline annual treatment effects (reflecting the ratio of the period between midline and endline tothe duration of the one-year recall period), and the monthly flow values are 12 times the midline
treatment effect plus 28 times the endline treatment effect. The estimates arrived at through thisstep-function assumption are conservative relative to the other obvious assumption which would
involve a linear interpolation of the outcomes through the midline and endline outcomes for theduration of treatment. The ingredients for this exercise, then, can be seen in the midline andendline monthly flow income and consumption ITTs in Table 2, the midline and endline annual
flow impacts on intra-household transfers shown in Table A.35, the endline treatment effect on
stock variables in Table 3.
The results of this exercise are presented in Table 10. The top row of the table provides the cash
amount received in each arm. A starting point is to observe that, relative to impacts observed at
midline, 50–85% of the original transfer is still present in the total stock value of assets, depending
on arm. So the incremental wealth value of the transfer has not been spent down, even in a period inwhich the control group was spending down assets on the whole. If the remainder had been simply
spent without ever producing any income, we would then expect to see the total inflow equal thetransfer amount, and the total consumption equal the difference between the original transfer andthe current stock impact. Instead, we see that the outflows exceed the difference between transfers
and stock values, but an amount ranging from about $140 in the lower and large arms to $430 in
the middle arm. So what then is the source of this extra money? The answer is clear in the total
inflows; in every case the total inflows greatly exceed the value of the transfer. The ratio of thetotal inflows to the transfer is a simple measure of the ‘multiplier’ effect of the cash; this ratio varies
29

from 1.3 in the upper arm to 2.5 in the middle arm, showing that in all arms the transfers were put
to work to create additional income. In all arms the additional income generated (over and above
the transfer) is similar to or larger than the spending not accounted for by the draw-down in asset
value (original transfer minus current stock impacts).13
This approach to aggregating financial impacts across the full duration of the study also allows
an omnibus test of the statistical significance of cumulative impacts relative to control. To undertakesuch a test, we undertake a randomization inference exercise: we permute treatments to provide
alternative randomizations consistent with our block-randomized allocation. To test the significanceof individual arms, we permute (within blocks) the assignment between those arms and control; totest the significance of comparisons between arms, we permute assignment of the relevant pairwise
combinations of arms, again within blocks. Randomization inference provides a distribution of thetotal income, total expenditure, and total final stock values. We compare the realized differences
between arms to these permutation distributions to obtain a randomization inference p-value.
Consistent with ITT results, this exercise in Table 10 confirms the statistical significance of
cumulative expenditure and stock-value effects of all cash-transfer arms, relative to control. Cashtransfer effects on cumulative income are significant for the Middle and Large transfer values. The
Combined arm has significant effects on cumulative values of each of income, expenditure, and asset
values. HD alone has statistically insignificant cumulative effects on income and expenditure, andjust misses significance at the ten percent level for its impacts on final stock values (p = 0.11).
Clearly, on average, the cash transfers have been put to work to drive substantial additional
income, enabling outflows to increase by a total of 65 to 120% of the transfer amount while leaving
the majority of the transfer intact after almost four years in asset values. At the same time, thefact that arms that more than double the value of productive assets lead only to a 20% increase in
consumption at endline suggests that the return on these assets is low, and the labor devoted tooperating them is receiving an effective wage rate similar to counterfactual uses of time. So thesetransfers are used by beneficiaries in a careful, productive way with an eye to the long term, but
the opportunities for transformative enterprise-driven growth appear limited.
13
The last row ‘Survey share accounted’ gives the fraction of outflows expected as a function of changes in net
inflows that we are able to capture. If all measures are complete, this is an accounting identity, so the ratio of outflows
to inflows provides a measure of survey quality.

30

5Conclusions

This study provides a unique window on the comparative effects of efforts to help disadvantaged
youth climb the economic ladder. Given clean experimental variation, high treatment compliance
rates, excellent survey tracking for more than three years, and little apparent contamination fromspillovers, it is a very straightforward environment in which to view the relative benefits of cash versus in-kind programming in the medium term. Divergent trajectories are apparent, with workforce
training weakly increasing paid work and strongly elevating the chance of full-time employment,
and cash transfers enabling self-employment and engendering the creation of profitable businesses
that survive for years. Both interventions induce quantitatively large improvements in productive assets. The effect of HD on productive hours, and the effect of cash on productive assets,remains relatively constant across midline and endline, suggesting that these impacts may prove
durable in the long term. Meaningful changes in the lives of beneficiaries are visible years after theinterventions.
On the other hand, the general pattern is that the benefits that are seen in this 3.5-year endline
are about a half of what was observed in the midline one year after treatment. This suggests
that—while there were significant effects on economic well-being during the study duration—over
the longer term, beneficiaries are on a slow slide back towards the outcomes that they would haveachieved in the absence of the programs. Roughly half of the new enterprises started at midline
were no longer operative at endline, and the critical final outcomes of income and consumption
are no longer improved relative to the control. So the takeaway is that while HD beneficiaries areindeed working more, and cash beneficiaries are operating more businesses with larger productive
assets, ultimately the economic returns of these activities to the youth may not be higher thanthe own-farm agriculture or agricultural wage labor that they would typically have been doingotherwise. This suggests more systemic problems with the nature of the markets in which theseyouth work. Low returns to skill in local labor markets will limit what can be gained throughtraining programs, and weak demand will constrain the potential of a self-employment led exitfrom poverty. So the fact that such substantial and expensive interventions do not transform thelives of disadvantaged youth refocuses attention on the macro constraints to growth that limit theability of individuals to climb out of poverty.
31

A critical contextual factor for this study is that the period between the midline and endline
includes the Covid-19 era. While many programs that find diminishing long-term impacts use thelanguage of “catch-up by the control” (Blattman et al., 2018), in this case the control group has
seen a 30% drop in productive asset value between midline and endline, and the share of controls
operating businesses dropped from 79% to 40%. Since both interventions induced beneficiaries to
go into business and put assets at risk, the treatment groups were more exposed to the Covid19 shock at the same time as they had more wealth to protect themselves against it. While thetreatment groups lost more productive assets between midline and endline than the control during
Covid, they nonetheless retained more by endline as well. While more treatment-induced businesses
became inoperative during Covid, because so many had been created initially, still more survived
at the end than in the control. Evidence in this study suggests that it may be the limited abilityof interventions to shield beneficiaries from the consequences of such shocks—as much as the risingtide of economic outcomes among those not receiving such benefits—that drives the diminution ofprogram impacts in the long term. While the specifics of the Covid-19 shock were certainly unique,uncertainty and disruption are an unfortunate fact of life for entrepreneurs in developing economies.
Hence, while our study sample lacks the cross-sectional variation in Covid-19 intensity that would
facilitate a more direct empirical analysis, this mixture of treatment and resiliency impacts may
provide a realistic picture of the extent to which these interventions are able to deliver longer-term
benefits that persist through good times and bad.
Over the longer term we can focus our emphasis on the core outcomes of income, consumption,and subjective well-being that exemplify the ultimate impacts on economic welfare, rather than
things like business assets or business knowledge that are merely instrumental to long-term welfare.
In the endline none of these outcomes have significant cost-equivalent differences, but for household
and individual consumption, subjective well-being, and business income the point estimates all
suggest an advantage for cash. Only overall income is somewhat higher at endline for HD, whichhints at the possibility of more persistent income effects from the human-capital investment of
its training. In the midline every one of these outcomes is significantly better for cash at costequivalent levels. Integrating these two snapshots in time over the entire duration of the study,
then, it seems relatively clear that cash has done a better job of moving ultimate welfare outcomesat cost-equivalent levels than HD. These results suggest that investments seeking create economic
32

well-being over the short- to medium-term will do well to incorporate cash as at least a part oftheir programming.

33

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Tables and Figures

Table 1: Results of Costing Exercise

39

Treatment Arm:
Huguka Dukore
GD lower
GD midGD upper
GD large
Combined

Ex Ante Cost
$464.25$377.03$464.25$571.74$828.47$928.5

Value received$153.47$317.16$410.65$502.96$750.3$561.11

Ex Post Cost
$388.32$394.39$493.96$590.41$846.71$885.64

Fraction operating cost
60.5%
19.6%
16.9%
14.8%
11.3%
36.3%

Compliance Rate
85.6%
100%
99.4%
100%
100%
89.6%(HD), 100%(GD)

Cost per study household$332.27$394.39$490.99$590.41$846.71$840.20

Note: The first column shows the ex-ante costing data on which study was designed; the core number is the HD cost around which the GD actual transfer
amounts in column 2 were designed. Column 3 shows the results of the ex post costing exercise. Column 4 provides the share of spending that did not reach thebeneficiaries either in cash or in direct training and materials costs. Column 5 shows the compliance rates, and since all costs are averted for non-compliers thenthe final column shows the final cost per study subject for each arm that are the basis of the cost-equivalent comparisons.

Table 2: Simple ITT, Primary Outcomes.
GiveDirectly

ControlMeanp-values
Obs.

R

2

(a)

(b)

(c)

HD

Lower

Middle

Upper

Large

Combined

Employed

0.02(0.03)[0.24]

0.03(0.05)[0.26]

0.03(0.05)[0.24]

0.01(0.05)[0.39]

0.02(0.05)[0.38]

0.01(0.04)[0.39]

0.47

1770

0.16

0.98

0.63

0.90

Productive hours

2.85∗∗(1.52)[0.05]

2.53(2.22)[0.15]

5.05∗∗(2.21)[0.02]

3.99∗(2.50)[0.08]

1.70(2.05)[0.23]

2.67(2.01)[0.13]

18.42

1770

0.18

0.72

0.43

0.69

Monthly income

0.29(0.24)[0.15]

0.79∗∗(0.34)[0.02]

1.01∗∗∗(0.33)[0.00]

1.08∗∗∗(0.34)[0.00]

0.74∗∗(0.33)[0.02]

0.94∗∗∗(0.31)[0.00]

8.11

1770

0.21

0.21

0.18

0.59

Productive assets

1.47∗∗∗(0.35)[0.00]

3.79∗∗∗(0.46)[0.00]

3.81∗∗∗(0.48)[0.00]

3.83∗∗∗(0.45)[0.00]

4.08∗∗∗(0.45)[0.00]

4.36∗∗∗(0.43)[0.00]

5.66

1770

0.26

0.00

0.00

0.54

HH consumption percapita0.07(0.06)[0.15]

0.21∗∗∗(0.08)[0.01]

0.24∗∗∗(0.08)[0.00]

0.25∗∗∗(0.07)[0.00]

0.38∗∗∗(0.07)[0.00]

0.27∗∗∗(0.06)[0.00]

9.44

1737

0.33

0.11

0.63

0.19

Employed

0.03(0.03)[0.65]

0.03(0.05)[0.73]−0.02(0.05)[0.73]−0.03(0.04)[0.73]

0.02(0.04)[0.73]

0.01(0.04)[0.98]

0.50

1822

0.15

0.91

0.71

0.79

Productive hours

3.32∗(1.53)[0.09]

0.35(2.17)[0.98]

0.63(2.21)[0.95]

0.56(2.23)[0.95]

2.32(2.13)[0.46]

2.09(2.04)[0.48]

19.43

1822

0.18

0.13

0.74

0.93

Monthly income

0.20(0.26)[0.73]

0.17(0.37)[0.73]

0.58(0.35)[0.16]−0.23(0.38)[0.73]

0.71∗(0.33)[0.09]

0.21(0.35)[0.73]

8.11

1822

0.16

0.86

0.65

0.21

Productive assets

0.92∗(0.37)[0.06]

1.39∗(0.54)[0.06]

2.43∗∗∗(0.55)[0.00]

2.63∗∗∗(0.54)[0.00]

2.97∗∗∗(0.51)[0.00]

3.13∗∗∗(0.51)[0.00]

3.90

1822

0.14

0.61

0.99

0.77

HH consumption percapita0.03(0.06)[0.73]

0.11(0.10)[0.43]

0.20(0.10)[0.11]

0.17(0.09)[0.11]

0.18(0.09)[0.11]

0.18∗(0.08)[0.07]

9.84

1810

0.19

0.48

0.76

0.94

Panel A. Midline

40
Panel B. Endline

Note: The six columns of the table provide the estimate on dummy variables for each of the treatment arms, compared to the control group. The five primary
outcomes are in rows. Regressions include but do not report the lagged dependent variable, fixed effects for randomization blocks, and a set of LASSO-selectedbaseline covariates. Standard errors (in parentheses) are clustered at the household level to reflect the design effect, and p-values corrected for False Discovery
Rates across all the outcomes in the table are presented in brackets. Stars on coefficient estimates are derived from the FDR-corrected p-values, *=10%, **=5%,and ***=1% significance. Reported p-values in final three columns derived from F -tests of hypotheses that cost-benefit ratios are equal between: (a) GD Lower
and HD; (b) GD Lower and GD Large; and (c) GD Large and Combined treatments. Employed is a dummy variable for spending more than 10 hours per week
working for a wage or as primary operator of a microenterprise. Productive hours are measured over prior 7 days in all activities other than own-farm agriculture.
Monthly income, productive assets, and household consumption are winsorized at 1% and 99% and analyzed in Inverse Hyperbolic Sine, meaning that treatment
effects can be interpreted as percent changes.

Table 3: Simple ITT, Secondary Outcomes: Wealth.
GiveDirectly

ControlMeanp-values
Obs.

R

2

(a)

(b)

(c)

HD

Lower

Middle

Upper

Large

Combined

HH net non-land wealth−0.28(0.37)[0.28]

0.16(0.53)[0.35]

1.04∗∗(0.43)[0.01]

1.26∗∗∗(0.40)[0.00]

0.98∗∗(0.40)[0.01]

0.83∗∗(0.46)[0.05]

11.41

1770

0.21

0.40

0.56

0.78

HH livestock wealth−0.04(0.36)[0.36]

1.63∗∗∗(0.49)[0.00]

1.90∗∗∗(0.50)[0.00]

2.66∗∗∗(0.43)[0.00]

2.23∗∗∗(0.45)[0.00]

2.22∗∗∗(0.45)[0.00]

7.88

1770

0.26

0.00

0.21

0.98

Savings

1.03∗∗∗(0.23)[0.00]

1.04∗∗∗(0.32)[0.00]

1.27∗∗∗(0.32)[0.00]

1.62∗∗∗(0.29)[0.00]

1.46∗∗∗(0.30)[0.00]

1.71∗∗∗(0.26)[0.00]

9.22

1770

0.20

0.55

0.25

0.40

Debt

0.37
(0.27)[0.11]−0.07(0.39)[0.35]−0.08(0.41)[0.35]−0.60(0.44)[0.11]−0.51(0.42)[0.13]−0.07(0.38)[0.35]

8.81

1770

0.20

0.23

0.69

0.38

HH net non-land wealth

0.27(0.42)[0.64]

0.68(0.52)[0.38]−0.05(0.61)[0.82]

0.65(0.52)[0.39]

0.79(0.52)[0.29]

0.53(0.54)[0.46]

11.28

1822

0.11

0.53

0.54

0.67

HH livestock wealth−0.00(0.39)[0.82]

0.61(0.55)[0.46]

1.53∗∗(0.51)[0.01]

0.84(0.54)[0.29]

1.65∗∗∗(0.50)[0.01]

1.26∗∗(0.50)[0.04]

7.65

1818

0.17

0.29

0.77

0.52

Savings

0.33(0.23)[0.29]

0.26(0.33)[0.53]

0.98∗∗∗(0.26)[0.00]

1.17∗∗∗(0.25)[0.00]

0.65∗(0.29)[0.07]

0.82∗∗∗(0.26)[0.01]

10.10

1822

0.14

0.69

0.89

0.59

Debt

−0.44
(0.28)[0.29]−0.02(0.41)[0.82]−0.03(0.42)[0.82]−0.23(0.40)[0.64]−0.24(0.40)[0.64]

0.00(0.37)[0.82]

9.75

1822

0.14

0.24

0.83

0.61

Panel A. Midline

41

Panel B. Endline

Notes: Regressions include but do not report the lagged dependent variable, fixed effects for randomization blocks, and a set of LASSO-selected baseline covariates.
Standard errors are (in soft brackets) are clustered at the household level to reflect the design effect, and p-values corrected for False Discovery Rates across all theoutcomes in the table are presented in hard brackets. Stars on coefficient estimates are derived from the FDR-corrected p-values, *=10%, **=5%, and ***=1%
significance. Reported p-values in final three columns derived from F -tests of hypotheses that cost-benefit ratios are equal between: (a) GD Lower and HD; (b)GD Lower and GD Large; and (c) GD Large and Combined treatments.

Table 4: ITT Employment Breakdown.
GiveDirectly

Control
Mean

Obs.

R2

p-value

0.14∗∗∗(0.04)[0.00]

0.16

1822

0.09

0.00

0.05∗∗(0.02)[0.08]

0.06∗∗(0.02)[0.07]

0.04

1822

0.06

0.08

0.03
(0.03)[0.30]

0.05(0.03)[0.18]

0.02(0.03)[0.33]

0.12

1822

0.06

0.61

−0.05
(0.04)[0.22]−0.10∗∗∗(0.03)[0.03]−0.05(0.04)[0.22]−0.08∗∗(0.03)[0.05]

0.26

1822

0.15

0.04

−0.01
(0.04)[0.36]−0.03(0.04)[0.32]−0.05(0.04)[0.22]−0.03(0.04)[0.30]

0.02(0.04)[0.33]

0.30

1822

0.20

0.09

0.02
(0.03)[1.00]

0.01(0.04)[1.00]

0.01(0.04)[1.00]−0.01(0.04)[1.00]

0.06(0.04)[1.00]

0.04(0.04)[1.00]

0.68

1822

0.13

0.73

Employed (10 hr)

0.03(0.03)[1.00]

0.03(0.05)[1.00]−0.02(0.05)[1.00]−0.03(0.04)[1.00]

0.02(0.04)[1.00]

0.01(0.04)[1.00]

0.50

1822

0.15

0.85

Employed (20 hr)

0.07∗∗(0.03)[0.27]

0.03(0.04)[1.00]

0.01(0.04)[1.00]−0.01(0.04)[1.00]

0.01(0.04)[1.00]

0.03(0.04)[1.00]

0.29

1822

0.15

0.27

Employed (30 hr)

0.08∗∗∗(0.03)[0.13]

0.04(0.04)[1.00]

0.01(0.04)[1.00]

0.03(0.04)[1.00]

0.02(0.03)[1.00]

0.06(0.03)[1.00]

0.20

1822

0.15

0.14

Employed (40 hr)

0.06∗∗(0.02)[0.28]

0.03(0.03)[1.00]

0.02(0.03)[1.00]

0.01(0.03)[1.00]

0.01(0.03)[1.00]

0.05∗(0.03)[1.00]

0.14

1822

0.14

0.33

HD

Lower

Middle

Upper

Large

Combined

Non-agricultural
microenterprise

0.05∗(0.03)[0.16]

0.07∗(0.04)[0.16]

0.07∗(0.04)[0.17]

0.11∗∗∗(0.04)[0.03]

0.17∗∗∗(0.04)[0.00]

Other microenterprise or
self-employment

0.02(0.01)[0.20]

0.04∗(0.03)[0.18]

0.04(0.02)[0.20]

0.01(0.02)[0.34]

Agricultural processing or
trading

0.04∗(0.02)[0.18]

0.01(0.03)[0.35]

0.02(0.03)[0.33]

Agricultural wage labor−0.08∗∗∗(0.03)[0.02]−0.05(0.04)[0.22]

Non-agricultural wage labor

0.05∗(0.03)[0.16]

Employed (0 hr)

Panel A. Employment composition

42

Panel B. Alternative hours thresholds

Notes: Panel A presents impacts on indicators for employment of any hours in the corresponding activity type in the preceding week. Panel B presents impacts on
an indicator for overall employment, using the reported threshold for minimum hours. Regressions include but do not report an indicator for lagged employment
status, fixed effects for randomization blocks, and a set of LASSO-selected baseline covariates. Standard errors are (in soft brackets) are clustered at the household
level to reflect the design effect, and p-values corrected for False Discovery Rates across outcomes in each panel are presented in hard brackets. Stars on coefficientestimates are derived from unadjusted standard errors, *=10%, **=5%, and ***=1% significance.

Table 5: Cost Equivalent Analysis, Primary Outcomes.
Differential impact
of HD

Cost-equivalent
GD impact

Transfer
Value

Control
Mean

Obs.

R2

Employed−0.00(0.04)[0.75]

0.03(0.04)[0.49]−0.00(0.01)[0.69]

0.47

1578

0.17

Productive hours−1.26(2.08)[0.49]

4.16∗(2.02)[0.07]−0.37(0.54)[0.49]

18.42

1578

0.19

Monthly income−0.70∗(0.31)[0.05]

0.97∗∗∗(0.31)[0.01]−0.02(0.09)[0.69]

8.11

1578

0.22

Productive assets−2.22∗∗∗(0.43)[0.00]

3.73∗∗∗(0.43)[0.00]

0.07(0.12)[0.49]

5.66

1578

0.27

HH consumption percapita−0.11(0.07)[0.14]

0.18∗∗(0.07)[0.04]

0.04∗(0.02)[0.09]

9.44

1548

0.32

Employed

0.03(0.04)[0.88]−0.01(0.04)[1.00]

0.00(0.01)[1.00]

0.50

1622

0.15

Productive hours

3.41(2.07)[0.48]−0.07(1.99)[1.00]

0.44(0.56)[0.88]

19.43

1622

0.18

Monthly income

0.11(0.33)[1.00]

0.06(0.33)[1.00]

0.11(0.09)[0.59]

8.11

1622

0.16

Productive assets−0.72(0.50)[0.59]

1.62∗∗(0.49)[0.02]

0.30(0.14)[0.22]

3.90

1622

0.15

HH consumption percapita−0.11(0.09)[0.59]

0.15(0.09)[0.48]

0.01(0.02)[1.00]

9.84

1612

0.20

Panel A. Midline

Panel B. Endline

Note: This table uses a linear adjustment of primary outcomes for program cost to compare HD and GD at exactly
equivalent costs. The Transfer value column estimates the marginal effect of spending an extra $100 through cashtransfers. The Cost-equivalent GD impact column is estimated as a dummy for either HD or GD treatment, andestimates the impact of cash at the exact cost of HD. The Differential impact of HD column then estimates thedifferential effect of HD above cash at this benchmarked cost. Regressions include but do not report the lagged
dependent variable, fixed effects for randomization blocks, and a set of LASSO-selected baseline covariates. Standard
errors are (in soft brackets) are clustered at the household level to reflect the design effect, and p-values corrected
for False Discovery Rates across all the outcomes in the table are presented in hard brackets. Stars on coefficientestimates are derived from the FDR-corrected p-values, *=10%, **=5%, and ***=1% significance. Employed isa dummy variable for spending more than 10 hours per week working for a wage or as primary operator of a
microenterprise. Productive hours are measured over prior 7 days in all activities other than own-farm agriculture.
Monthly income, productive assets, and household consumption are winsorized at 1% and 99% and analyzed in
Inverse Hyperbolic Sine, meaning that treatment effects can be interpreted as percent changes.

43

Table 6: Benefit-Cost Ratios, Primary Outcomes.
GiveDirectly

p-values

HD

Lower

Middle

Upper

Large

Combined

(a)

(b)

(c)

(d)

Employed

0.008(0.010)0.007(0.012)−0.004(0.010)−0.005(0.007)0.002(0.005)0.001(0.005)

0.61

0.91

0.71

0.79

Productive hours

0.999(0.460)0.088(0.550)0.129(0.450)0.095(0.378)0.274(0.251)0.249(0.243)

0.36

0.13

0.74

0.93

Monthly income

0.060(0.080)0.043(0.094)0.118(0.072)−0.039(0.065)0.084(0.039)0.025(0.041)

0.35

0.86

0.65

0.21

Productive assets

0.278(0.112)0.352(0.137)0.494(0.112)0.445(0.091)

0.351(0.060)0.373(0.061)

0.43

0.61

0.99

0.77

HH consumption
per capita

0.010(0.019)0.028(0.024)0.041(0.021)0.028(0.015)0.021(0.010)0.022(0.009)

0.70

0.48

0.76

0.94

Note: Table gives the impact per $100 spent, which is calculated by dividing the estimated ITT impacts by the cost
per arm in hundreds of dollars. The standard errors in the table are similarly the ITT standard errors divided bycosts. Reported p-values in final three columns derived from F -tests of hypotheses that cost-benefit ratios are equal
between: (a) joint test across all arms, (b) GD Lower and HD; (c) GD Lower and GD Large; and (d) GD Large andCombined arms.

44

Table 7: Beneficiary Business Birth and Death.

45

HD

Lower

GiveDirectly
Middle
Upper

Large

Combined

New business at midline

0.22∗∗∗(0.05)[0.00]

0.55∗∗∗(0.08)[0.00]

0.55∗∗∗(0.08)[0.00]

0.61∗∗∗(0.08)[0.00]

0.64∗∗∗(0.08)[0.00]

New business at endline

0.05(0.03)[0.11]

0.01(0.04)[0.53]

0.05(0.04)[0.22]

0.05(0.04)[0.19]

Dead at endline-0.00(0.04)[0.54]

0.00(0.06)[0.54]

0.02(0.05)[0.50]

Inoperative at endline-0.00(0.04)[0.54]

0.07(0.06)[0.19]

Operative at endline

0.04(0.05)[0.28]

0.12∗(0.07)[0.09]

Control
Mean

Obs.

R2

0.65∗∗∗(0.07)[0.00]

0.50

1770

0.12

0.16∗∗∗(0.05)[0.00]

0.10∗∗(0.05)[0.05]

0.24

1822

0.02

-0.01
(0.05)[0.50]-0.03(0.05)[0.40]

0.03(0.05)[0.36]

0.24

1240

0.02

0.14∗
(0.07)[0.05]

0.16∗∗(0.06)[0.02]

0.20∗∗∗(0.06)[0.00]

0.17∗∗∗(0.05)[0.00]

0.14

1240

0.04

0.16∗∗(0.07)[0.03]

0.16∗∗(0.07)[0.03]

0.26∗∗∗(0.08)[0.00]

0.16∗∗(0.06)[0.02]

0.36

1240

0.03

Notes: Table uses the panel of firms reported on in the beneficiary survey, totalling variables within each round. The first two rows count the number of new
firms in each round that had not existed in the prior round, by beneficiary, and then impute zeros for individuals who reported no firms in the survey. Rows 3-5
then take the universe of individuals who reported on any firm in the midline, and count the outcomes for those firms at endline; whether they were no longer
operational (died), existed but were inoperative, or were operative. Standard errors are (in soft brackets) are clustered at the household level to reflect the design
effect, and p-values corrected for False Discovery Rates across all the outcomes in the table are presented in hard brackets. Stars on coefficient estimates arederived from the FDR-corrected p-values, *=10%, **=5%, and ***=1% significance.

Table 8: Midline Beneficiary enterprise analysis.

46

HD

Lower

GiveDirectly
Middle
Upper

Large

Combined

Operating Businesses

0.14∗∗∗(0.06)[0.01]

0.44∗∗∗(0.09)[0.00]

0.55∗∗∗(0.10)[0.00]

0.62∗∗∗(0.09)[0.00]

0.65∗∗∗(0.10)[0.00]

Owned Businesses

0.14∗∗∗(0.06)[0.01]

0.48∗∗∗(0.08)[0.00]

0.57∗∗∗(0.09)[0.00]

0.60∗∗∗(0.09)[0.00]

Household Employees

0.17∗∗∗(0.06)[0.00]

0.22∗∗∗(0.09)[0.01]

0.22∗∗∗(0.08)[0.00]Non Household
Employees

0.09∗∗(0.05)[0.01]

0.14∗∗(0.07)[0.01]

Days Worked permonth

2.71∗∗∗(1.07)[0.00]

Customers per month

Control
Mean

Obs.

R2

0.63∗∗∗(0.08)[0.00]

0.79

1770

0.10

0.66∗∗∗(0.09)[0.00]

0.66∗∗∗(0.07)[0.00]

0.71

1770

0.11

0.42∗∗∗(0.11)[0.00]

0.43∗∗∗(0.10)[0.00]

0.24∗∗∗(0.09)[0.00]

0.26

1770

0.03

0.17∗∗∗(0.08)[0.01]

0.32∗∗∗(0.09)[0.00]

0.29∗∗∗(0.09)[0.00]

0.24∗∗∗(0.08)[0.00]

0.14

1770

0.03

8.01∗∗∗(1.51)[0.00]

6.65∗∗∗(1.77)[0.00]

11.59∗∗∗(1.70)[0.00]

11.37∗∗∗(1.76)[0.00]

8.63∗∗∗(1.50)[0.00]

9.11

1770

0.07

9.95∗∗(6.69)[0.02]

42.22∗∗∗( 14.72)[0.00]

46.65∗∗∗( 18.21)[0.00]

38.47∗∗∗( 12.72)[0.00]

42.46∗∗∗( 11.93)[0.00]

38.26∗∗∗( 11.53)[0.00]

26.28

1770

0.03

Daily Sales

1.67∗∗(1.42)[0.03]

7.76∗∗∗(2.36)[0.00]

13.48∗∗∗(3.11)[0.00]

12.25∗∗∗(3.13)[0.00]

10.23∗∗∗(2.85)[0.00]

13.73∗∗∗(3.44)[0.00]

6.64

1770

0.05

Monthly Profits

4.36∗∗∗(1.38)[0.00]

9.00∗∗∗(2.25)[0.00]

8.63∗∗∗(2.02)[0.00]

11.34∗∗∗(2.42)[0.00]

10.85∗∗∗(2.35)[0.00]

8.88∗∗∗(1.70)[0.00]

6.04

1770

0.05

Notes: Table analyzes the results of the midline beneficiary enterprise survey, totalling variables across all businesses operated by a given beneficiary and thenimputing zeros to survey respondents with no businesses. Analysis is weighted using attrition weights. Standard errors are (in soft brackets) are clustered at thehousehold level to reflect the design effect, and p-values corrected for False Discovery Rates across all the outcomes in the table are presented in hard brackets.
Stars on coefficient estimates are derived from the FDR-corrected p-values, *=10%, **=5%, and ***=1% significance.

Table 9: Endline Beneficiary enterprise analysis.

47

HD

Lower

GiveDirectly
Middle
Upper

Large

Combined

Operating Businesses

0.10∗∗(0.04)[0.03]

0.18∗∗(0.07)[0.01]

0.27∗∗∗(0.07)[0.00]

0.29∗∗∗(0.07)[0.00]

0.42∗∗∗(0.08)[0.00]

Owned Businesses

0.11∗∗(0.05)[0.02]

0.17∗∗(0.07)[0.02]

0.27∗∗∗(0.07)[0.00]

0.30∗∗∗(0.07)[0.00]

Household Employees

0.06∗(0.04)[0.07]

0.10∗(0.06)[0.05]

0.10∗(0.06)[0.05]Non Household
Employees

0.03(0.04)[0.14]

0.04(0.05)[0.14]

Days Worked per
month

1.34∗
(0.80)[0.05]

Customers per month

Control
Mean

Obs.

R2

0.33∗∗∗(0.06)[0.00]

0.40

1822

0.05

0.44∗∗∗(0.08)[0.00]

0.34∗∗∗(0.07)[0.00]

0.43

1822

0.05

0.20∗∗∗(0.07)[0.00]

0.20∗∗∗(0.06)[0.00]

0.13∗∗(0.06)[0.03]

0.17

1822

0.02

0.06∗
(0.05)[0.10]

0.03(0.05)[0.16]

0.23∗∗∗(0.08)[0.01]

0.13∗∗(0.06)[0.03]

0.09

1822

0.03

3.03∗∗(1.27)[0.02]

3.33∗∗∗(1.18)[0.01]

4.69∗∗∗(1.28)[0.00]

5.59∗∗∗(1.31)[0.00]

6.27∗∗∗(1.29)[0.00]

5.66

1822

0.03

-0.55
(8.10)[0.22]

24.18∗( 16.35)[0.06]

8.90( 13.73)[0.14]

13.50( 15.25)[0.14]

24.78∗∗( 13.41)[0.05]

11.40( 11.87)[0.14]

33.29

1822

0.01

Daily Sales

2.12∗∗(0.99)[0.03]

6.54∗∗∗(2.18)[0.01]

1.93∗(1.24)[0.06]

5.12∗∗(1.95)[0.01]

5.50∗∗∗(1.72)[0.00]

3.25∗∗(1.47)[0.03]

3.11

1822

0.02

Monthly Profits

1.64∗(0.96)[0.05]

3.30∗∗(1.61)[0.03]

3.08∗∗(1.46)[0.03]

5.13∗∗∗(1.69)[0.00]

7.11∗∗∗(1.97)[0.00]

6.46∗∗∗(1.57)[0.00]

4.57

1822

0.04

Notes: Table analyzes the results of the endline beneficiary enterprise survey, totalling variables across all businesses operated by a given beneficiary and thenimputing zeros to survey respondents with no businesses. Standard errors are (in soft brackets) are clustered at the household level to reflect the design effect,
and p-values corrected for False Discovery Rates across all the outcomes in the table are presented in hard brackets. Stars on coefficient estimates are derived
from the FDR-corrected p-values, *=10%, **=5%, and ***=1% significance.

Table 10: Aggregating cash flows over the study period.
Cash transfers
Control mean

HD

Lower

Middle

Upper

Large

Combined

0.00

317.16410.65502.96750.30410.65

Panel A. Flow impacts through midline
Cash received

0.00

Beneficiary income

209.3665.73

158.39226.36239.48152.48218.09Transfers received

23.38

−7.78

40.91

62.42

61.20

72.71

57.55

Household consumption

625.8534.19

124.50166.52146.09223.07169.13Loans made

3.83

0.43

3.46

1.74

5.41

2.61

4.74

Transfers made

4.53

1.78

3.08

8.48

2.56

1.47

3.66

−140.25437.05127.87

Panel B. Flow impacts between midline and endline
Beneficiary income

612.46121.86103.35354.79

27.83

−8.00

−25.64−17.791.36−37.89−6.88

1606.4554.99

179.17325.16269.98282.53292.69Loans made

20.44

−2.32

−11.62−16.18−10.76−20.47−7.43

Transfers made

7.54

1.26

1.15

0.36

1.99

−1.41

1.41

Livestock

118.64

−0.35

72.79

181.61

99.88

196.00149.56

Productive assets

49.89

46.14

69.29

120.98131.07148.07156.20

Savings

51.99

17.22

13.53

50.98

60.93

33.96

42.42

Debt

61.93

−27.46

−1.41

−1.77

−14.01−14.950.31

Total income.

171.81[0.29]

594.16[0.24]

1036.44[0.01]

664.75[0.51]

1374.65[0.01]

807.29[0.06]

Total expenditure

.

90.33
[0.47]

299.74[0.09]

486.08[0.01]

415.26[0.01]

487.81[0.01]

464.20[0.00]

Total stock values

.

90.48
[0.11]

157.02[0.09]

355.34[0.00]

305.88[0.00]

392.98[0.00]

347.87[0.00]

Share accounted

.

1.05

0.77

0.81

1.08

0.64

1.01

Transfers received
Household consumption

Panel C. Final stock values

Panel D. Totals

Notes. Table presents control means and estimated impacts on financial values, in dollars. Flow consumption ismeasured in the survey monthly, and here we use the midline treatment effect for the first 12 months and the endline
effect for the subsequent 28 months. Inter-household flows are measured with an annual recall, and we take a similar
approach, using midline estimates for the midline period and endline estimates for the period between midline andendline. All other variables are stocks measured at follow-up. Total income is the sum of cash received, beneficiaryincome, and transfers received. Total expenditure is the sum of household consumption, loans made, and transfers
made. Total stock values are the sum of livestock values, other productive asset values, savings values, and thenegative of debt values. Randomization inference p-values, in brackets, from test of null of no cumulative effect ofeach arm on income. Share accounted is the ratio of the sum of total outflows plus stock values to total inflows.

48

Figure 1: Cost Equivalence versus Cost Effectiveness, Endline

Cost Effectiveness

0

200

400
600
Treatment Cost

Employment Rate
.46.48 .5 .52.54

Employment

800

0

200

400
600
Treatment Cost

Productive hours/week
19 20 21 22 23

Productive Hours

800

0

200

400
600
Treatment Cost

Monthly Income (IHS)
7.8 8 8.28.48.68.8

Monthly Income

800

0

200

400
600
Treatment Cost

Productive Assets(IHS)
4567

Productive Assets

800

HH Consumption

0

200

400
600
Treatment Cost

HH Consumption (IHS)
9.859.99.95 1010.05

HH Consumption per capita (IHS) Productive Asset Value (IHS)
9.859.99.95 1010.05
4
5
6
7Monthly Income (IHS)
7.8 8 8.28.48.68.8

productive hours/week
19 20 21 22 23

Employment Rate
.46.48 .5 .52.54

Cost Equivalence

800

Employment

0

200

400
600
Treatment Cost

Productive Hours

0

200

400
600
Treatment Cost

0

200

400
600
Treatment Cost

0

200

400
600
Treatment Cost

800

HH Consumption

0

200

400
600
Treatment Cost

GD Lower

GD Middle

GD UpperGD Large

Estimated GD impact at HD cost

800

Productive Assets

HD Actual49

800

Monthly Income

Control

Fitted in GD treatment

800

Extrapolation

800

.6

.8

1

0

.2

.4

.6

.8

1

0

.2

.4

.6

.8

1

0

.2

.4

.6

.8

1

0

.2
.4
.6
.8
Saturation rate: HD

1

0

.2

.4

.6

.8

1

0

.2

.4

.6

.8

1

0

.2

.4

.6

.8

1

0

.2

.4

.6

.8

1

0

.2
.4
.6
.8
Saturation rate: GD main

1

0

.2

.4

.6

.8

1

0

.2

.4

.6

.8

1

0

.2

.4

.6

.8

1

0

.2

.4

.6

.8

1

4 6 8 1012
0 2.5 5 7.510
8 9 10 11

0 2.5 5 7.510

4 6 8 1012

-10010203040.40 .25.5.75 1

0 .25.5.75 1.2-10010203040

0

8 9 10 11

Household consumption Productive assets
8 9 10 11
0 2.5 5 7.510

Monthly income
4 6 8 1012

Productive hours-10010203040

Employed
0 .25.5.75 1

Figure 2: Graphical Representation of Spillovers

0
.2
.4
.6
.8
1
Saturation rate: GD Huge treatment

Treatment statuscontrolHD

GD_main

GD_huge

Notes: Each panel presents predicted outcomes under each of the four main treatment arms (Control, HD, GD Main,
and GD Large), as the saturation level of a specific active treatment arm changes. Rows correspond to the outcomes of
employment, productive hours, and the inverse hyperbolic sine of monthly incomes, productive assets, and household
consumption per adult-equivalent, respectively. Horizontal shaded bands highlight one standard deviation above andbelow the control mean. Columns illustrate effects of variation in saturation rates in HD, GD-main, and GD-large,
respectively. All predicted outcomes evaluated at means of covariates used in the estimating equation.

50

Figure 3: Incidence of Covid-19 shocks, by Treatment Arm

Figure 4: Evolution of control-group primary outcomes across survey rounds

Notes: Endline outcomes deflated to nominal midline Rwanda francs.

51

Appendix A

Appendix Tables

Table A.1: Study Design
GiveDirectly

Combined

Control

HugukaDukore317.16410.65502.96750.30HD + 410.65

KaduhaKibumbwe
Kigabiro
Kiyumba
MuganoMuhaziMunyagaMunyiginya
MusangeMushishiro
Nyakariro
Nyarusange
Shyogwe

63
32
14
17
51
39
34
25
30
24
49
57
53

60
37
12
17
51
40
34
25
29
23
50
54
53

21
10
4
6
18
13
10
8
10
6
16
21
18

21
10
5
6
18
19
10
8
10
6
17
20
18

22
12
4
6
18
13
10
8
10
6
19
19
18

22
13
5
6
18
18
12
10
9
9
17
19
20

26
13
5
8
22
17
14
10
12
8
22
24
22

Total

488

485

161

168

165

178

203

Sector52

Note : This table gives the number of study individuals assigned to each treatment arm in each of the 13 sectors within which lotteries were conducted. The
lotteries were blocked so that fixed fractions of individuals are assigned to each arm.

Table A.2: Attrition: Tracking at Endline by Treatment.
Any
Treatment

Basic
Treatment

Granular
Treatment

HD

0.00607(0.00790)

0.00607(0.00790)

Treatment arm: Gd

0.00654(0.00738)

Combined

0.00366(0.0104)

Any Treatment

0.00594(0.00678)

0.00366(0.0104)

GD Small

0.00602(0.0107)

GD Middle

0.000585(0.0119)

GD Upper0.000261(0.0120)

GD Huge0.0184∗∗∗(0.00608)

Observations
Mean DV
R2

1848
0.986
0.000495

1848
0.986
0.000545

1848
0.986
0.00202

Notes: Table examines overall attrition rates from the endline survey by treatment arm. Sample is the entire
baseline survey, outcome variable is a dummy for being successfully tracked at endline. Covariates are the treatment
arm for each individual. Standard errors clustered at the household level, *=10%, **=5%, and ***=1% significance.

53

Table A.3: Attrition: Tracking at Endline on Covariates.
R3 tracked

Control mean

Observations

R2

Ubudehe category I−0.10(0.10)[1.00]

0.33

1797

0.00

Beneficiary female

0.18(0.10)[0.89]

0.60

1848

0.00

Beneficiary age

0.17(0.66)[1.00]

23.36

1848

0.00

Beneficiary years ofeducation

0.04(0.43)[1.00]

7.58

1848

0.00

Household members−0.32(0.49)[1.00]

4.84

1844

0.00

Employed−0.08(0.10)[1.00]

0.34

1848

0.00

Productive hours−5.86(4.47)[1.00]

11.19

1848

0.00

Monthly income−1.05(1.01)[1.00]

4.45

1848

0.00

Productive assets−0.30(0.91)[1.00]

2.20

1848

0.00

HH consumption percapita−0.17(0.19)[1.00]

9.39

1844

0.00

Beneficiary-specificconsumption−0.67(0.24)[0.10]

7.51

1848

0.00

HH net non-land
wealth−0.22(1.17)[1.00]

10.59

1844

0.00

Savings

0.41(0.94)[1.00]

7.87

1848

0.00

Debt

−1.01
(0.83)[1.00]

7.97

1848

0.00

HH livestock wealth−0.01(1.17)[1.00]

7.32

1844

0.00

Business Knowledge

0.15(0.18)[1.00]

−0.00

1848

0.00

Notes: Table correlates attrition from the endline survey with baseline covariates. Sample is the entire baseline
survey, outcome variable is a dummy for being successfully tracked at endline. Standard errors are (in soft brackets)
are clustered at the household level to reflect the design
54effect, and p-values corrected for False Discovery Rates
across all the outcomes in the table are presented in hard brackets. Stars on coefficient estimates are derived fromthe FDR-corrected p-values, *=10%, **=5%, and ***=1% significance.

Table A.4: Balance using Endline Sample.
GiveDirectly

Control
Mean

Obs.

R2

p-value−0.03(0.04)[1.00]

0.32

1771

0.07

0.90

0.01
(0.04)[1.00]−0.03(0.04)[1.00]

0.60

1822

0.04

0.66

−0.72
(0.31)[1.00]

0.28(0.31)[1.00]−0.38(0.30)[1.00]

23.53

1822

0.04

0.11

−0.15
(0.19)[1.00]

0.10(0.19)[1.00]

0.15(0.20)[1.00]−0.25(0.17)[1.00]

7.55

1822

0.07

0.47

−0.34
(0.23)[1.00]

0.02(0.33)[1.00]

0.06(0.20)[1.00]−0.04(0.21)[1.00]−0.26(0.18)[1.00]

4.99

1818

0.03

0.31

0.04
(0.03)[1.00]

0.02(0.04)[1.00]−0.03(0.04)[1.00]

0.00(0.04)[1.00]

0.01(0.04)[1.00]

0.02(0.04)[1.00]

0.33

1822

0.02

0.74

Productive hours

0.62(1.20)[1.00]

0.24(1.78)[1.00]

0.32(1.79)[1.00]

1.76(1.93)[1.00]−0.24(1.52)[1.00]−0.59(1.37)[1.00]

10.89

1822

0.02

0.94

Monthly income

0.20(0.32)[1.00]

0.09(0.46)[1.00]−0.18(0.45)[1.00]−0.04(0.46)[1.00]−0.11(0.44)[1.00]

0.09(0.41)[1.00]

4.42

1822

0.01

0.98

Productive assets−0.54(0.27)[1.00]−0.36(0.38)[1.00]−0.09(0.38)[1.00]−0.27(0.39)[1.00]−0.44(0.37)[1.00]−0.26(0.36)[1.00]

2.48

1822

0.03

0.57

HH consumption percapita−0.12(0.07)[1.00]−0.09(0.09)[1.00]−0.12(0.09)[1.00]−0.10(0.10)[1.00]−0.16(0.09)[1.00]−0.03(0.09)[1.00]

9.46

1818

0.05

0.49

Beneficiary-specificconsumption−0.09(0.14)[1.00]

0.05(0.19)[1.00]−0.11(0.21)[1.00]−0.24(0.22)[1.00]

0.15(0.17)[1.00]−0.00(0.19)[1.00]

7.55

1822

0.03

0.74

HH net non-land
wealth

−0.09(0.45)[1.00]

0.06(0.56)[1.00]−0.02(0.64)[1.00]−0.03(0.68)[1.00]

1.11(0.45)[1.00]−0.17(0.57)[1.00]

10.57

1818

0.03

0.17

Savings−0.27(0.29)[1.00]−0.47(0.40)[1.00]−0.46(0.43)[1.00]

0.05(0.39)[1.00]

0.10(0.37)[1.00]

0.21(0.36)[1.00]

8.00

1822

0.04

0.61

Debt

0.04
(0.31)[1.00]−0.24(0.44)[1.00]−0.51(0.46)[1.00]−0.17(0.44)[1.00]

0.04(0.43)[1.00]

0.57(0.38)[1.00]

7.97

1822

0.02

0.48

HH livestock wealth

0.18(0.40)[1.00]−0.14(0.56)[1.00]

0.28(0.57)[1.00]

0.26(0.54)[1.00]−0.16(0.53)[1.00]−0.25(0.51)[1.00]

7.31

1818

0.02

0.96

Business Knowledge−0.03(0.07)[1.00]

0.06(0.09)[1.00]−0.03(0.09)[1.00]−0.03(0.09)[1.00]−0.02(0.09)[1.00]

0.06(0.09)[1.00]

0.01

1822

0.02

0.91

HD

Lower

Middle

Upper

Large

Combined

Ubudehe category I

0.01(0.03)[1.00]−0.00(0.05)[1.00]

0.04(0.05)[1.00]

0.00(0.04)[1.00]

0.00(0.04)[1.00]

Beneficiary female

0.02(0.03)[1.00]−0.02(0.04)[1.00]

0.06(0.04)[1.00]−0.01(0.04)[1.00]

Beneficiary age−0.12(0.22)[1.00]−0.48(0.30)[1.00]−0.20(0.34)[1.00]

Beneficiary years ofeducation

0.10(0.14)[1.00]

0.00(0.20)[1.00]

Household members−0.29(0.15)[1.00]

Employed

Notes: Table examines balance of the experiment across treatment arms using baseline covariates and the attrited
sample that is used for endline analysis. Standard errors55are (in soft brackets) are clustered at the household level
to reflect the design effect, and p-values corrected for False Discovery Rates across all the outcomes in the table
are presented in hard brackets. Stars on coefficient estimates are derived from the FDR-corrected p-values, *=10%,**=5%, and ***=1% significance.

Table A.5: Simple ITT, Secondary Outcomes: Beneficiary welfare.
GiveDirectly

p-values

Control
Mean

Obs.

R2

(a)

(b)

(c)

HD

Lower

Middle

Upper

Large

Combined

Subjective well-being

0.18∗∗∗(0.07)[0.00]

0.41∗∗∗(0.09)[0.00]

0.53∗∗∗(0.10)[0.00]

0.49∗∗∗(0.09)[0.00]

0.55∗∗∗(0.09)[0.00]

0.41∗∗∗(0.09)[0.00]

−0.01

1770

0.13

0.04

0.08

0.19

Mental health−0.04(0.07)[0.28]−0.05(0.09)[0.27]

0.07(0.10)[0.23]

0.02(0.09)[0.33]

0.11(0.10)[0.16]

0.12(0.09)[0.11]

0.01

1770

0.06

0.92

0.27

0.87

Beneficiary-specificconsumption

0.17∗(0.11)[0.08]

0.53∗∗∗(0.11)[0.00]

0.59∗∗∗(0.13)[0.00]

0.65∗∗∗(0.12)[0.00]

0.53∗∗∗(0.12)[0.00]

0.71∗∗∗(0.11)[0.00]

8.25

1770

0.24

0.01

0.01

0.14

Subjective well-being

0.12(0.07)[0.13]

0.17(0.09)[0.12]

0.28∗∗(0.10)[0.04]

0.39∗∗∗(0.08)[0.00]

0.16(0.08)[0.12]

0.28∗∗∗(0.08)[0.01]

0.00

1822

0.10

0.80

0.29

0.21

Mental health

0.06(0.07)[0.23]

0.10(0.09)[0.20]

0.11(0.09)[0.19]

0.03(0.09)[0.41]

0.14(0.09)[0.16]−0.01(0.09)[0.48]

−0.00

1822

0.06

0.78

0.67

0.14

Beneficiary-specificconsumption

0.11(0.10)[0.20]

0.23(0.13)[0.12]

0.20(0.14)[0.17]

0.33∗(0.15)[0.08]

0.11(0.14)[0.28]

0.28∗(0.11)[0.06]

8.31

1822

0.23

0.46

0.14

0.25

Panel A. Midline

56

Panel B. Endline

Notes: Regressions include but do not report the lagged dependent variable, fixed effects for randomization blocks, and a set of LASSO-selected baseline covariates.
Standard errors are (in soft brackets) are clustered at the household level to reflect the design effect, and p-values corrected for False Discovery Rates across all theoutcomes in the table are presented in hard brackets. Stars on coefficient estimates are derived from the FDR-corrected p-values, *=10%, **=5%, and ***=1%
significance. Reported p-values in final three columns derived from F -tests of hypotheses that cost-benefit ratios are equal between: (a) GD Lower and HD; (b)GD Lower and GD Large; and (c) GD Large and Combined treatments.

Table A.6: Simple ITT, Secondary Outcomes: Skills.
GiveDirectly

p-values

Control
Mean

Obs.

R2

(a)

(b)

(c)

HD

Lower

Middle

Upper

Large

Combined

Aspirations−0.00(0.07)[1.00]

0.10(0.09)[0.43]−0.04(0.09)[0.75]

0.14(0.09)[0.27]

0.03(0.09)[0.88]

0.16(0.08)[0.23]

−0.01

1770

0.08

0.35

0.36

0.17

Business knowledge

0.65∗∗∗(0.07)[0.00]

0.08(0.10)[0.56]

0.06(0.09)[0.71]

0.06(0.09)[0.71]−0.00(0.09)[1.00]

0.61∗∗∗(0.09)[0.00]

0.00

1770

0.22

0.00

0.38

0.00

Business attitudes

0.12(0.07)[0.23]

0.19(0.10)[0.23]

0.17(0.09)[0.23]

0.10(0.09)[0.43]

0.04(0.09)[0.75]

0.14(0.09)[0.27]

−0.00

1770

0.08

0.65

0.08

0.35

Aspirations

0.07(0.07)[1.00]

0.15(0.08)[0.47]

0.08(0.08)[1.00]−0.02(0.09)[1.00]

0.08(0.09)[1.00]

0.01(0.08)[1.00]

0.00

1822

0.06

0.43

0.14

0.50

Business knowledge

0.26∗∗∗(0.07)[0.00]−0.03(0.09)[1.00]

0.03(0.10)[1.00]

0.04(0.10)[1.00]−0.03(0.09)[1.00]

0.37∗∗∗(0.09)[0.00]

−0.00

1822

0.11

0.00

0.85

0.00

Business attitudes−0.01(0.06)[1.00]

0.14(0.09)[0.56]

0.05(0.09)[1.00]

0.10(0.08)[0.91]

0.00(0.09)[1.00]

0.13(0.08)[0.56]

−0.00

1822

0.08

0.12

0.12

0.22

Panel A. Midline

57

Panel B. Endline

Notes: Regressions include but do not report the lagged dependent variable, fixed effects for randomization blocks, and a set of LASSO-selected baseline covariates.
Standard errors are (in soft brackets) are clustered at the household level to reflect the design effect, and p-values corrected for False Discovery Rates across all theoutcomes in the table are presented in hard brackets. Stars on coefficient estimates are derived from the FDR-corrected p-values, *=10%, **=5%, and ***=1%
significance. Reported p-values in final three columns derived from F -tests of hypotheses that cost-benefit ratios are equal between: (a) GD Lower and HD; (b)GD Lower and GD Large; and (c) GD Large and Combined treatments.

Table A.7: Moving across villages.

58

HD

Lower

GiveDirectly
Middle Upper

Large

Combined

Urban R2

-0.00
(0.02)[1.00]

0.04(0.04)[0.85]-0.03(0.03)[0.88]-0.04(0.03)[0.80]-0.04(0.03)[0.80]New Village R2

0.05(0.03)[0.74]

0.06(0.04)[0.80]

0.07(0.04)[0.80]

0.02(0.04)[0.88]

Urban R3

-0.01
(0.03)[0.98]

0.03(0.04)[0.88]-0.02(0.04)[0.88]New Village R3

0.01(0.03)[0.88]

0.04(0.05)[0.88]

0.06(0.04)[0.80]

Control
Mean

Obs.

R2

-0.03
(0.03)[0.88]

0.18

1770

0.24

0.07
(0.04)[0.74]

0.05(0.04)[0.80]

0.16

1770

0.02

-0.06
(0.03)[0.74]-0.07(0.03)[0.62]-0.05(0.03)[0.80]

0.22

1822

0.16

0.04
(0.04)[0.88]

0.02(0.04)[0.88]-0.01(0.04)[1.00]

0.37

1822

0.02

Notes: Table analyzes the extent to which the beneficiary had moved across villages at midline (R2) or endline (R3). ‘Urban’ is a dummy variable indicatingthat the village in which the beneficiary resides in that round is classified as semi-urban, peri-urban, or urban (rather than rural). ‘New Village’ is a dummy forthe village being a different one than the baseline village. Standard errors are (in soft brackets) are clustered at the household level to reflect the design effect,
and p-values corrected for False Discovery Rates across all the outcomes in the table are presented in hard brackets. Stars on coefficient estimates are derived
from the FDR-corrected p-values, *=10%, **=5%, and ***=1% significance.

Table A.8: Education and Time Use.

59

HD

Lower

GiveDirectly
Middle Upper

Large

Combined

Highest Grade

0.28(0.25)[1.00]-0.14(0.35)[1.00]-0.14(0.33)[1.00]

0.51(0.33)[1.00]

0.36(0.34)[1.00]

Hours in School-0.68(0.45)[1.00]

0.25(0.66)[1.00]-0.88(0.51)[1.00]-0.42(0.65)[1.00]

Hours Domestic Work

-0.23
(1.18)[1.00]-3.16(1.51)[1.00]-0.40(1.55)[1.00]

Res wage in Village-0.09(0.09)[1.00]-0.00(0.13)[1.00]

Res wage in Town

-0.25
(0.17)[1.00]

0.07(0.29)[1.00]

Control
Mean

Obs.

R2

0.00
(0.33)[1.00]

12.02

1822

0.05

-0.52
(0.58)[1.00]-0.47(0.60)[1.00]

3.05

1822

0.01

0.18
(1.53)[1.00]

0.95(1.76)[1.00]

0.09(1.59)[1.00]

24.49

1822

0.02

1.43
(1.33)[1.00]

0.05(0.13)[1.00]

0.03(0.13)[1.00]

0.16(0.12)[1.00]

1.68

1810

0.01

1.80
(1.68)[1.00]

0.02(0.23)[1.00]-0.20(0.20)[1.00]

0.28(0.23)[1.00]

2.85

1804

0.01

Notes: Table analyzes endline education and time use variables. Highest grade is an ordinal variable measuring completed schooling with the control mean
representing one year of post-primary education. ‘Hours in School’ and ‘Hours Domestic Work’ give the number of hours over the seven days prior to the endlinethat the respondent reports spending in each activity. ‘Reservation wages’ give the survey response to the daily wage the respendent said they would need tobe paid to take a job in their village and in the nearest town, respectively (USD). Standard errors are (in soft brackets) are clustered at the household level toreflect the design effect, and p-values corrected for False Discovery Rates across all the outcomes in the table are presented in hard brackets. Stars on coefficientestimates are derived from the FDR-corrected p-values, *=10%, **=5%, and ***=1% significance.

Table A.9: Midline Household Enterprise Analysis.

60

HD

Lower

GiveDirectly
Middle Upper

Large

Combined

Owned Businesses-0.02(0.02)[0.92]-0.01(0.03)[1.00]

0.04(0.04)[1.00]

0.00(0.02)[1.00]-0.02(0.02)[1.00]

Household Employees-0.05(0.05)[1.00]-0.08(0.04)[0.92]-0.03(0.06)[1.00]-0.03(0.05)[1.00]Non Household
Employees-0.02(0.05)[1.00]-0.05(0.04)[1.00]-0.04(0.05)[1.00]

Days Worked per
month

-1.04
(0.74)[0.92]

0.13(1.00)[1.00]

Customers per month-2.47(1.61)[0.92]

Daily Sales

Monthly Profits

Control
Mean

Obs.

R2

-0.01
(0.02)[1.00]

0.06

1770

0.02

-0.08
(0.04)[0.92]-0.08(0.04)[0.92]

0.12

1770

0.02

-0.03
(0.05)[1.00]

0.04(0.07)[1.00]

0.06(0.07)[1.00]

0.10

1770

0.01

0.31
(1.23)[1.00]-1.51(0.86)[0.92]

1.46(1.11)[0.92]

0.31(1.01)[1.00]

4.57

1770

0.02

-2.97
(1.54)[0.92]-0.98(1.93)[1.00]-3.29(1.55)[0.92]-1.90(1.84)[1.00]-2.45(1.65)[0.92]

3.92

1770

0.01

-0.15
(0.86)[1.00]-1.36(0.78)[0.92]

0.54(1.30)[1.00]-0.88(0.97)[1.00]

2.65(1.71)[0.92]

0.33(1.05)[1.00]

3.42

1770

0.01

-1.53
(1.26)[1.00]-1.54(1.33)[1.00]-0.78(1.55)[1.00]-1.93(1.41)[0.92]

0.58(1.74)[1.00]

0.21(1.77)[1.00]

5.22

1770

0.02

Notes: Table analyzes the results of the midline household enterprise survey, totalling variables across all businesses operated within the household (other thanby the beneficiary) and then imputing zeros to households with no businesses. Analysis is weighted using attrition weights. Standard errors are (in soft brackets)
are clustered at the household level to reflect the design effect, and p-values corrected for False Discovery Rates across all the outcomes in the table are presented
in hard brackets. Stars on coefficient estimates are derived from the FDR-corrected p-values, *=10%, **=5%, and ***=1% significance.

Table A.10: Endline Household Enterprise Analysis.

61

HD

Lower

GiveDirectly
Middle Upper

Large

Combined

Owned Businesses

0.01(0.02)[1.00]

0.01(0.02)[1.00]

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

0.03(0.02)[0.90]

Household Employees-0.02(0.03)[1.00]-0.08(0.03)[0.26]-0.02(0.04)[1.00]-0.05(0.03)[0.56]Non Household
Employees

0.00(0.03)[1.00]-0.04(0.03)[0.62]-0.05(0.02)[0.36]

Days Worked per
month

-0.23
(0.59)[1.00]

1.40(0.97)[0.62]

Customers per month

5.70(4.57)[0.88]

Daily Sales

Monthly Profits

Control
Mean

Obs.

R2

-0.00
(0.02)[1.00]

0.06

1822

0.01

-0.02
(0.05)[1.00]-0.07(0.03)[0.36]

0.10

1822

0.02

-0.05
(0.02)[0.38]-0.01(0.03)[1.00]-0.05(0.02)[0.26]

0.06

1822

0.01

0.06
(0.84)[1.00]-1.17(0.76)[0.62]

0.20(0.86)[1.00]

0.14(0.90)[1.00]

3.59

1822

0.03

-1.00
(3.04)[1.00]-3.18(2.28)[0.65]-2.24(3.46)[1.00]

1.00(4.08)[1.00]

0.17(3.53)[1.00]

5.19

1822

0.02

0.74
(1.80)[1.00]

4.12(4.95)[1.00]-1.02(1.36)[1.00]-2.53(1.31)[0.40]

0.18(1.83)[1.00]-2.72(1.24)[0.36]

3.66

1822

0.01

-0.25
(1.37)[1.00]

0.49(1.72)[1.00]-0.61(1.93)[1.00]-2.14(1.40)[0.62]-0.71(1.48)[1.00]-2.27(1.25)[0.47]

5.24

1822

0.02

Notes: Table analyzes the results of the endline household enterprise survey, totalling variables across all businesses operated within the household (other than by
the beneficiary) and then imputing zeros to households with no businesses. Standard errors are (in soft brackets) are clustered at the household level to reflect thedesign effect, and p-values corrected for False Discovery Rates across all the outcomes in the table are presented in hard brackets. Stars on coefficient estimates
are derived from the FDR-corrected p-values, *=10%, **=5%, and ***=1% significance.

Table A.11: Endline Beneficiary Enterprise with Gender Interactions.
OperatingBusiness

Own
Business

Household
Employees

NonHH
Employees

Days
WorkedMonthly
Customers

Daily
Sales

Monthly
ProfitsHD

0.16(0.08)[0.18]

0.18(0.08)[0.14]

0.08(0.07)[0.58]

0.05(0.08)[0.84]

2.40(1.45)[0.36]

0.59(15.51)[1.00]

5.26(2.15)[0.10]

3.54(1.94)[0.29]GD Lower

0.21(0.11)[0.29]

0.18(0.12)[0.39]

0.09(0.10)[0.63]

0.02(0.09)[1.00]

3.17(2.14)[0.40]

19.50(25.50)[0.74]

6.82(3.65)[0.29]

3.39(2.66)[0.48]GD Mid

0.28∗(0.11)[0.10]

0.27(0.11)[0.10]

0.02(0.08)[1.00]

0.17(0.11)[0.39]

3.35(1.99)[0.35]−2.00(20.97)[1.00]

3.17(1.86)[0.35]

4.71(2.77)[0.35]GD Upper0.34∗∗(0.11)[0.03]

0.36∗∗(0.12)[0.03]

0.13(0.09)[0.46]

0.00(0.10)[1.00]

4.27(2.02)[0.18]−1.07(18.48)[1.00]

7.39(3.96)[0.29]

7.60∗(2.93)[0.09]GD Large

0.45∗∗(0.14)[0.02]

0.46∗∗(0.14)[0.02]

0.19(0.10)[0.29]

0.44(0.18)[0.10]

7.48∗∗(2.33)[0.02]

51.11(29.15)[0.33]

8.52(3.58)[0.11]

11.11∗∗(3.83)[0.04]

Combined

0.39∗∗(0.11)[0.02]

0.36∗∗(0.11)[0.02]

0.04(0.08)[0.84]

0.17(0.11)[0.40]

8.46∗∗(2.37)[0.02]

17.76(22.08)[0.74]

4.61(2.92)[0.39]

7.48∗(2.71)[0.06]

HD × Female−0.11(0.09)[0.58]−0.11(0.10)[0.58]−0.04(0.09)[0.94]−0.03(0.09)[1.00]−1.66(1.73)[0.63]−1.41(17.76)[1.00]−5.03(2.34)[0.18]−3.01(2.17)[0.46]GD Lower × Female−0.05(0.14)[1.00]−0.02(0.15)[1.00]

0.02(0.12)[1.00]

0.03(0.10)[1.00]−0.28(2.65)[1.00]

7.78(33.26)[1.00]−0.52(4.46)[1.00]−0.20(3.32)[1.00]GD Mid × Female−0.02(0.14)[1.00]−0.00(0.14)[1.00]

0.13(0.12)[0.58]−0.16(0.12)[0.48]

0.06(2.50)[1.00]

17.60(28.31)[0.84]−1.82(2.43)[0.74]−2.36(3.24)[0.74]GD Upper × Female−0.10(0.14)[0.82]−0.09(0.15)[0.84]

0.13(0.13)[0.63]

0.04(0.11)[1.00]

0.70(2.70)[1.00]

24.49(29.22)[0.74]−3.84(4.27)[0.69]−4.19(3.55)[0.56]GD Large × Female−0.06(0.17)[1.00]−0.04(0.17)[1.00]

0.03(0.13)[1.00]−0.34(0.19)[0.29]−3.10(2.82)[0.59]−43.23(31.35)[0.46]−4.97(3.95)[0.49]−6.57(4.35)[0.40]

Combined × Female−0.10(0.14)[0.74]−0.02(0.14)[1.00]

0.15(0.12)[0.48]−0.08(0.13)[0.84]−3.90(2.70)[0.43]−11.67(25.15)[0.89]−2.45(3.27)[0.74]−1.87(3.33)[0.84]

Female−0.04(0.06)[0.84]−0.03(0.07)[0.84]−0.08(0.05)[0.39]−0.08(0.06)[0.48]−1.07(1.11)[0.63]−10.44(14.23)[0.74]−0.96(1.14)[0.74]−1.36(1.43)[0.63]

Control mean
Observations
R2

0.40
1822
0.05

0.43
1822
0.05

0.17
1822
0.02

0.09
1822
0.04

5.66
1822
0.04

33.29
1822
0.01

3.11
1822
0.03

4.57
1822
0.05

Notes: Table presents tests for heterogeneity of beneficiary enterprise effects by Gender. Interacted coefficients givethe differential effect of each arm for women, ‘Female’ gives the difference between women and men in the control
group, and the uninteracted treatment terms give the impact of each arm for men. Standard errors are (in softbrackets) are clustered at the household level to reflect the design effect, and p-values corrected for False Discovery
Rates across all the outcomes in the table are presented in hard brackets. Stars on coefficient estimates are derived
from the FDR-corrected p-values, *=10%, **=5%, and ***=1% significance.

62

Table A.12: Standard Complementarities Test, Primary Outcomes.
Control
Mean

Obs.

R2

−0.02
(0.06)[1.00]

0.50

1323

0.17

0.22
(2.21)[1.00]−1.80(3.05)[1.00]

19.43

1323

0.20

0.27
(0.27)[0.67]

0.54(0.36)[0.42]−0.58(0.52)[0.67]

8.11

1323

0.16

Productive assets

0.95∗(0.38)[0.06]

2.35∗∗∗(0.56)[0.00]−0.26(0.77)[1.00]

3.90

1323

0.16

HH consumption percapita0.04(0.06)[1.00]

0.21(0.10)[0.14]−0.08(0.13)[1.00]

9.84

1313

0.24

HD

GD

Complementarity

Employed

0.04(0.03)[0.67]−0.01(0.05)[1.00]

Productive hours

3.78∗(1.51)[0.06]

Monthly income

Notes: Table tests for complementarities of cash and training on Primary outcomes. ‘HD’ is a modified dummy
for receiving HD, and ‘GD’ cash, including those in the Combined arm as having received both interventions. The
Complementarity dummy, for the Combined arm only, therefore tests whether the Combined arm produces an outcome
different from what we would expect based on the sum of the HD and GD impacts alone. Standard errors are (in softbrackets) are clustered at the household level to reflect the design effect, and p-values corrected for False Discovery
Rates across all the outcomes in the table are presented in hard brackets. Stars on coefficient estimates are derived
from the FDR-corrected p-values, *=10%, **=5%, and ***=1% significance.

63

Table A.13: Standard Complementarities Test, Secondary Outcomes.
Control
Mean

Obs.

R2

−0.12
(0.14)[0.48]

0.00

1323

0.11

0.11
(0.09)[0.46]−0.20(0.13)[0.45]

−0.00

1323

0.08

0.11
(0.10)[0.47]

0.19(0.14)[0.46]−0.03(0.19)[0.61]

8.31

1323

0.24

HH net non-land
wealth

0.24(0.43)[1.00]

0.04(0.62)[1.00]

0.25(0.82)[1.00]

11.30

1321

0.13

HH livestock wealth

0.03(0.39)[1.00]

1.60∗∗(0.52)[0.01]−0.24(0.74)[1.00]

7.65

1321

0.19

Savings

0.38(0.23)[0.51]

0.94∗∗∗(0.27)[0.01]−0.45(0.38)[0.76]

10.10

1323

0.16

Debt

−0.36
(0.28)[0.76]−0.03(0.43)[1.00]

0.54(0.59)[1.00]

9.75

1323

0.17

HD

GD

Complementarity

Subjective well-being

0.13(0.07)[0.38]

0.29∗(0.11)[0.06]

Mental health

0.06(0.07)[0.48]

Beneficiary-specificconsumption

Panel A. Beneficiary welfare

Panel B. Household wealthPanel C. Beneficiary cognitive and non-cognitive skills
Aspirations

0.07(0.07)[1.00]

0.06(0.08)[1.00]−0.11(0.12)[1.00]

0.00

1323

0.08

Business knowledge

0.26∗∗∗(0.07)[0.00]

0.03(0.10)[1.00]

0.09(0.14)[1.00]

−0.00

1323

0.14

Business attitudes

0.01(0.06)[1.00]

0.06(0.09)[1.00]

0.04(0.13)[1.00]

−0.00

1323

0.13

Notes: Table tests for complementarities of cash and training on Secondary outcomes. ‘HD’ is a modified dummy
for receiving HD, and ‘GD’ cash, including those in the Combined arm as having received both interventions. The
Complementarity dummy, for the Combined arm only, therefore tests whether the Combined arm produces an outcome
different from what we would expect based on the sum of the HD and GD impacts alone. Standard errors are (in softbrackets) are clustered at the household level to reflect the design effect, and p-values corrected for False Discovery
Rates across all the outcomes in the table are presented in hard brackets. Stars on coefficient estimates are derived
from the FDR-corrected p-values, *=10%, **=5%, and ***=1% significance.

64

Table A.14: Complementarities Test, Midline Business Outcomes.
Control
Mean

Obs.

R2

−0.08
(0.13)[0.29]

0.80

1289

0.09

0.59∗∗∗(0.09)[0.00]−0.08(0.12)[0.29]

0.72

1289

0.10

0.16∗∗(0.06)[0.01]

0.24∗∗(0.09)[0.01]−0.16(0.13)[0.15]

0.27

1289

0.03

Non Household
Employees

0.08∗(0.05)[0.07]

0.17∗∗(0.07)[0.03]−0.02(0.11)[0.42]

0.14

1289

0.03

Days Worked permonth

2.64∗∗(1.06)[0.02]

6.68∗∗∗(1.72)[0.00]−0.94(2.34)[0.34]

9.23

1289

0.05

Customers per month

9.58(6.32)[0.11]

43.59∗∗∗(14.75)[0.01]−13.77(19.07)[0.29]

26.35

1289

0.03

Daily Sales

1.66(1.46)[0.17]

12.94∗∗∗(2.99)[0.00]−1.45(4.45)[0.35]

6.76

1289

0.05

Monthly Profits

4.28∗∗∗(1.38)[0.01]

8.35∗∗∗(1.85)[0.00]−3.78(2.75)[0.13]

6.09

1289

0.04

HD

GD

Complementarity

Operating Businesses

0.12∗(0.06)[0.05]

0.57∗∗∗(0.10)[0.00]

Owned Businesses

0.12∗∗(0.06)[0.03]

Household Employees

Notes: Table tests for complementarities of cash and training on midline business outcomes. ‘HD’ is a modified
dummy for receiving HD, and ‘GD’ cash, including those in the Combined arm as having received both interventions.
The Complementarity dummy, for the Combined arm only, therefore tests whether the Combined arm produces an
outcome different from what we would expect based on the sum of the HD and GD impacts alone. Standard errors
are (in soft brackets) are clustered at the household level to reflect the design effect, and p-values corrected for False
Discovery Rates across all the outcomes in the table are presented in hard brackets. Stars on coefficient estimates
are derived from the FDR-corrected p-values, *=10%, **=5%, and ***=1% significance.

65

Table A.15: Complementarities Test, Endline Business Outcomes.
Control
Mean

Obs.

R2

−0.03
(0.09)[0.73]

0.40

1323

0.04

0.27∗∗∗(0.07)[0.00]−0.03(0.10)[0.73]

0.43

1323

0.04

0.06
(0.04)[0.32]

0.10(0.06)[0.21]−0.02(0.09)[0.75]

0.17

1323

0.01

Non Household
Employees

0.03(0.04)[0.52]

0.06(0.05)[0.36]

0.03(0.08)[0.73]

0.09

1323

0.02

Days Worked per
month

1.35
(0.80)[0.21]

3.35∗∗(1.18)[0.04]

1.58(1.76)[0.52]

5.66

1323

0.03

Customers per month−0.60(8.10)[0.83]

9.26(13.75)[0.55]

2.99(17.56)[0.82]

33.29

1323

0.00

Daily Sales

2.11(0.98)[0.12]

1.92(1.25)[0.24]−0.77(2.03)[0.73]

3.11

1323

0.01

Monthly Profits

1.62(0.96)[0.21]

3.08(1.47)[0.12]

1.76(2.18)[0.52]

4.57

1323

0.04

HD

GD

Complementarity

Operating Businesses

0.10(0.04)[0.12]

0.27∗∗∗(0.07)[0.00]

Owned Businesses

0.11∗(0.05)[0.08]

Household Employees

Notes: Table tests for complementarities of cash and training on endline business outcomes. ‘HD’ is a modified
dummy for receiving HD, and ‘GD’ cash, including those in the Combined arm as having received both interventions.
The Complementarity dummy, for the Combined arm only, therefore tests whether the Combined arm produces an
outcome different from what we would expect based on the sum of the HD and GD impacts alone. Standard errors
are (in soft brackets) are clustered at the household level to reflect the design effect, and p-values corrected for False
Discovery Rates across all the outcomes in the table are presented in hard brackets. Stars on coefficient estimates
are derived from the FDR-corrected p-values, *=10%, **=5%, and ***=1% significance.

66

Table A.16: Cost Equivalent Analysis, Secondary Outcomes.
Differential impact
of HD

Cost-equivalent
GD impact

Transfer
Value

Control
Mean

Obs.

R2

Panel A. Beneficiary welfare
Subjective well-being−0.15(0.09)[0.22]

0.29∗∗∗(0.08)[0.01]−0.01(0.02)[0.76]

0.00

1622

0.11

Mental health−0.02(0.08)[0.82]

0.08(0.08)[0.52]

0.01(0.02)[0.82]

−0.00

1622

0.07

Beneficiary-specificconsumption−0.18(0.13)[0.30]

0.29(0.12)[0.10]−0.03(0.03)[0.58]

8.31

1622

0.23

HH net non-land
wealth−0.05(0.51)[0.96]

0.32(0.50)[0.85]

0.09(0.13)[0.85]

11.28

1622

0.11

HH livestock wealth−0.75(0.49)[0.73]

0.77(0.49)[0.73]

0.16(0.13)[0.73]

7.65

1618

0.18

Savings−0.32(0.27)[0.73]

0.66(0.28)[0.29]

0.04(0.08)[0.85]

10.10

1622

0.15

Debt

−0.50
(0.37)[0.73]−0.01(0.37)[0.96]−0.06(0.11)[0.85]

9.75

1622

0.14

Panel B. Household wealthPanel C. Beneficiary cognitive and non-cognitive skills
Aspirations−0.04(0.08)[0.80]

0.12(0.08)[0.32]−0.01(0.02)[0.75]

0.00

1622

0.07

Business knowledge

0.24∗(0.09)[0.05]

0.01(0.09)[0.84]−0.01(0.02)[0.84]

−0.00

1622

0.11

Business attitudes−0.15(0.08)[0.31]

0.14(0.08)[0.31]−0.03(0.02)[0.37]

−0.00

1622

0.09

Note: This table uses a linear adjustment of secondary outcomes for program cost to compare HD and GD at
exactly equivalent costs. The Transfer value column estimates the marginal effect of spending an extra $100 throughcash transfers. The Cost-equivalent GD impact column is estimated as a dummy for either HD or GD treatment,
and estimates the impact of cash at the exact cost of HD. The Differential impact of HD column then estimates
the differential effect of HD above cash at this benchmarked cost. Regressions include but do not report the lagged
dependent variable, fixed effects for randomization blocks, and a set of LASSO-selected baseline covariates. Standard
errors are (in soft brackets) are clustered at the household level to reflect the design effect, and p-values corrected
for False Discovery Rates across all outcomes within each family are presented in hard brackets. Stars on coefficientestimates are derived from the FDR-corrected p-values, *=10%, **=5%, and ***=1% significance.

67

Table A.17: Midline Business Cost Equivalence.
Cost-equivalent
GD impact

Transfer
Value

Control
Mean

Obs.

R2

Operating Businesses−0.34∗∗∗(0.09)[0.00]

0.46∗∗∗(0.09)[0.00]

0.04∗(0.03)[0.06]

0.80

1578

0.09

Owned Businesses−0.36∗∗∗(0.08)[0.00]

0.49∗∗∗(0.08)[0.00]

0.04∗(0.02)[0.06]

0.72

1578

0.10

Household Employees−0.05(0.09)[0.18]

0.21∗∗(0.08)[0.02]

0.05∗(0.03)[0.06]

0.27

1578

0.04

Non Household Employees−0.06(0.07)[0.14]

0.15∗∗(0.07)[0.04]

0.03∗(0.02)[0.09]

0.14

1578

0.03

Days Worked per month−4.19∗∗∗(1.52)[0.01]

6.84∗∗∗(1.47)[0.00]

1.01∗∗(0.45)[0.03]

9.23

1578

0.07

Customers per month−29.61∗∗(12.77)[0.03]

39.06∗∗∗(12.56)[0.00]

0.82(3.68)[0.25]

26.35

1578

0.04

Daily Sales−8.56∗∗∗(2.59)[0.00]

10.28∗∗∗(2.44)[0.00]

0.27(0.79)[0.24]

6.76

1578

0.05

Monthly Profits−4.33∗∗(2.24)[0.05]

8.63∗∗∗(1.99)[0.00]

0.51(0.66)[0.14]

6.09

1578

0.05

68

Differential impactof HD

Notes: This table uses a linear adjustment of midline business outcomes for program cost to compare HD and GD at exactly equivalent costs. The Transfer
value column estimates the marginal effect of spending an extra $100 through cash transfers. The Cost-equivalent GD impact column is estimated as a dummy
for either HD or GD treatment, and estimates the impact of cash at the exact cost of HD. The Differential impact of HD column then estimates the differential
effect of HD above cash at this benchmarked cost. Regressions include but do not report the lagged dependent variable, fixed effects for randomization blocks,
and a set of LASSO-selected baseline covariates. Standard errors are (in soft brackets) are clustered at the household level to reflect the design effect, and p-values
corrected for False Discovery Rates across all outcomes within each family are presented in hard brackets. Stars on coefficient estimates are derived from the
FDR-corrected p-values, *=10%, **=5%, and ***=1% significance.

Table A.18: Endline Business Cost Equivalence.
Cost-equivalent
GD impact

Transfer
Value

Control
Mean

Obs.

R2

Operating Businesses−0.07(0.06)[0.32]

0.17∗(0.06)[0.08]

0.05∗(0.02)[0.08]

0.40

1622

0.05

Owned Businesses−0.04(0.07)[0.46]

0.15∗(0.06)[0.08]

0.06∗(0.02)[0.08]

0.43

1622

0.05

Household Employees−0.04(0.06)[0.46]

0.09(0.06)[0.20]

0.02(0.02)[0.26]

0.17

1622

0.02

Non Household Employees

0.05(0.05)[0.34]−0.02(0.05)[0.57]

0.04∗(0.02)[0.08]

0.09

1622

0.02

Days Worked per month−1.37(1.17)[0.32]

2.70∗(1.14)[0.08]

0.59(0.35)[0.20]

5.66

1622

0.03

Customers per month−14.64(13.22)[0.32]

14.12(13.85)[0.32]

1.45(3.81)[0.57]

33.29

1622

0.01

Daily Sales−2.35(1.82)[0.32]

4.47∗(1.70)[0.08]

0.12(0.52)[0.60]

3.11

1622

0.02

Monthly Profits−0.73(1.51)[0.56]

2.36(1.49)[0.21]

0.93(0.51)[0.17]

4.57

1622

0.03

69

Differential impactof HD

Notes: This table uses a linear adjustment of endline business outcomes for program cost to compare HD and GD at exactly equivalent costs. The Transfer
value column estimates the marginal effect of spending an extra $100 through cash transfers. The Cost-equivalent GD impact column is estimated as a dummy
for either HD or GD treatment, and estimates the impact of cash at the exact cost of HD. The Differential impact of HD column then estimates the differential
effect of HD above cash at this benchmarked cost. Regressions include but do not report the lagged dependent variable, fixed effects for randomization blocks,
and a set of LASSO-selected baseline covariates. Standard errors are (in soft brackets) are clustered at the household level to reflect the design effect, and p-values
corrected for False Discovery Rates across all outcomes within each family are presented in hard brackets. Stars on coefficient estimates are derived from the
FDR-corrected p-values, *=10%, **=5%, and ***=1% significance.

Table A.19: Robustness of Cost Equivalence Adjustment, Primary Outcomes.

70

Base
LinearQuadratic

Cubic

Drop
lower

Drop
mid

Drop
upper

Drop
huge

Employed

0.030(0.042)−0.039(0.068)−0.063(0.146)0.076(0.057)0.020(0.047)0.018(0.044)−0.012(0.056)

Productive hours

3.412∗(2.069)2.756(3.401)

3.661(7.041)

3.889(2.867)3.466(2.293)3.248(2.160)2.982(2.764)Monthly income

0.111(0.332)−0.382(0.553)1.397(1.146)0.230(0.447)0.357(0.384)−0.089(0.343)−0.328(0.452)

Productive assets−0.721(0.498)0.048(0.810)0.857(1.742)−1.287∗(0.686)−0.511(0.562)−0.644(0.519)−0.277(0.666)

HH consumption per capita−0.110(0.086)−0.058(0.144)0.077(0.315)−0.171(0.120)−0.081(0.096)−0.117(0.091)−0.070(0.118)Notes: Table reports the coefficient on the differential effect of HD over cost-equivalent cash using seven different specifications. Column 1 is the linear adjustment
reported elsewhere. Column 2 includes a quadratic, and column 3 a quadratic 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 based on household-clustered
standard errors, in parentheses.

Table A.20: Robustness of Cost Equivalence Adjustment, Secondary Outcomes.
BaseLinearQuadratic

Cubic

Drop
lower

Drop
mid

Drop
upper

Drop
huge

Subjective well-being−0.155∗(0.087)0.098(0.136)−0.016(0.310)−0.293∗∗(0.123)−0.160∗(0.096)−0.105(0.092)0.013(0.114)

Mental health−0.019(0.080)−0.085(0.132)0.027(0.269)0.021(0.106)−0.023(0.091)−0.039(0.084)−0.059(0.109)Beneficiary-specificconsumption−0.179(0.125)−0.055(0.192)−0.305(0.417)−0.229(0.181)−0.219(0.138)−0.152(0.128)−0.099(0.160)

HH net non-land wealth−0.053(0.509)−0.416(0.774)−2.292(1.764)0.510(0.730)−0.435(0.549)0.002(0.530)−0.265(0.645)

HH livestock wealth−0.747(0.488)−0.773(0.818)1.351(1.671)−0.928(0.644)−0.426(0.569)−0.844∗(0.507)−0.903(0.667)

Savings−0.325(0.271)0.539(0.461)0.811(0.898)−0.895∗∗∗(0.319)−0.211(0.321)−0.183(0.283)0.231(0.376)Debt−0.500(0.374)−0.565(0.614)−0.036(1.373)−0.542(0.516)−0.301(0.429)−0.579(0.387)−0.561(0.505)

Panel A. Beneficiary welfare

Panel B. Household wealth71

Panel C. Beneficiary cognitive and non-cognitive skills
Aspirations−0.037(0.075)−0.194(0.120)−0.069(0.248)0.047(0.105)−0.036(0.084)−0.070(0.077)−0.160(0.099)Business knowledge

0.243∗∗∗(0.087)0.330∗∗(0.141)0.367(0.305)0.185(0.122)0.253∗∗∗(0.098)0.245∗∗∗(0.091)0.299∗∗∗(0.116)

Business attitudes−0.151∗(0.081)−0.150(0.128)−0.365(0.277)−0.103(0.111)−0.190∗∗(0.088)−0.130(0.084)−0.147(0.106)

Notes: Table reports the coefficient on the differential effect of HD over cost-equivalent cash using seven different specifications. Column 1 is the linear adjustment
reported elsewhere. Column 2 includes a quadratic, and column 3 a quadratic 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 based on household-clustered
standard errors, in parentheses.

Table A.21: Benefit-Cost Ratios, Secondary Outcomes.
GiveDirectly

p-values

HD

Lower

Middle

Upper

Large

Combined

(a)

(b)

(c)

(d)

Subjective well-being

0.036(0.021)

0.043(0.023)0.058(0.021)

0.067(0.014)0.019(0.010)0.033(0.010)

0.01

0.80

0.29

0.21

Mental health

0.019(0.020)0.026(0.022)0.022(0.018)0.006(0.015)0.017(0.010)−0.001(0.010)

0.89

0.78

0.67

0.14

Beneficiary-specific consumption

0.034(0.030)0.059(0.032)0.042(0.029)0.055(0.025)0.013(0.016)0.033(0.013)

0.43

0.46

0.14

0.25

HH net non-land wealth

0.082(0.126)0.172(0.133)−0.010(0.124)0.110(0.088)0.094(0.062)0.063(0.064)

0.84

0.53

0.54

0.67

HH livestock wealth−0.001(0.117)0.156(0.139)0.312(0.104)0.143(0.092)

0.195(0.059)0.150(0.059)

0.15

0.29

0.77

0.52

Savings

0.100(0.068)0.066(0.083)0.200(0.053)0.198(0.042)0.077(0.035)0.097(0.031)

0.02

0.69

0.89

0.59

Debt

−0.133(0.084)−0.006(0.103)−0.006(0.086)−0.038(0.068)−0.029(0.048)0.001(0.044)

0.64

0.24

0.83

0.61

Panel A. Beneficiary welfare

Panel B. Household wealth72

Panel C. Beneficiary cognitive and non-cognitive skills
Aspirations

0.022(0.020)0.038(0.020)0.017(0.017)−0.003(0.016)0.009(0.011)0.001(0.010)

0.40

0.43

0.14

0.50

Business knowledge

0.079(0.021)−0.008(0.024)0.006(0.020)0.007(0.016)−0.004(0.011)0.044(0.011)

0.00

0.00

0.85

0.00

Business attitudes−0.002(0.019)0.035(0.022)0.010(0.019)0.018(0.014)0.001(0.010)0.016(0.010)

0.42

0.12

0.12

0.22

Note: Table gives the impact per $100 spent, which is calculated by dividing the estimated ITT impacts by the cost per arm in hundreds of dollars. The standard
errors in the table are similarly the ITT SEs divided by costs. Reported p-values in final three columns derived from F -tests of hypotheses that cost-benefit ratios
are equal between: (a) joint test across all arms, (b) GD Lower and HD; (c) GD Lower and GD Large; and (d) GD Large and Combined arms.

Table A.22: Heterogeneity Analysis by Gender.

Employed

Productive
Hours

Monthly
IncomeProductive
Assets∗Consumption

HD

0.03
(0.05)[1.00]

4.78(2.73)[0.36]

0.37(0.33)[1.00]

1.47(0.61)[0.07]

0.07(0.10)[1.00]

GD main−0.03(0.05)[1.00]

2.19(2.78)[1.00]

0.10(0.35)[1.00]

2.76∗∗∗(0.61)[0.00]

0.33∗∗(0.11)[0.01]

GD large−0.02(0.07)[1.00]

3.25(4.02)[1.00]

0.37(0.45)[1.00]

2.86∗∗∗(0.83)[0.00]

0.14(0.13)[1.00]

Combined

0.01(0.06)[1.00]

3.83(3.55)[1.00]

0.20(0.47)[1.00]

3.58∗∗∗(0.80)[0.00]

0.30∗(0.12)[0.05]

HD × Female−0.00(0.06)[1.00]−1.51(3.28)[1.00]−0.25(0.49)[1.00]−1.06(0.76)[0.66]−0.04(0.13)[1.00]

GD main × Female

0.05(0.06)[1.00]−0.95(3.26)[1.00]

0.02(0.51)[1.00]−1.07(0.77)[0.66]−0.25(0.14)[0.34]

GD large × Female

0.06(0.09)[1.00]−1.62(4.63)[1.00]

0.48(0.65)[1.00]−0.14(1.06)[1.00]

0.01(0.17)[1.00]

Combined × Female

0.01(0.08)[1.00]−1.52(4.38)[1.00]

0.03(0.68)[1.00]−0.92(1.02)[1.00]−0.23(0.16)[0.66]

Female−0.29∗∗∗(0.04)[0.00]−13.80∗∗∗(2.20)[0.00]−2.15∗∗∗(0.35)[0.00]

0.03(0.51)[1.00]

0.00(0.09)[1.00]

Control mean
Observations
R2
p-value

0.50
1822
0.08
0.86

19.43
1822
0.10
0.99

8.11
1822
0.07
0.86

3.90
1822
0.06
0.54

9.84
1810
0.04
0.25

Notes: Table presents tests for heterogeneity of treatment effects by Gender. Uninteracted coefficients in the first
four rows give the treatment effect of the program on men, and the next four rows test for the differential effectbetween women and men. Standard errors are (in soft brackets) are clustered at the household level to reflect thedesign effect, and p-values corrected for False Discovery Rates across all the outcomes in the table are presented
in hard brackets. Stars on coefficient estimates are derived from the FDR-corrected p-values, *=10%, **=5%, and***=1% significance. p-value in the last row from an F-test on whether treatments have a jointly differential effectby gender.

73

Table A.23: Heterogeneity Analysis by Age.

Employed

Productive
Hours

Monthly
IncomeProductive
AssetsConsumption

HD

0.04
(0.05)[1.00]

4.42(2.48)[0.71]

0.65(0.42)[0.75]

0.68(0.55)[1.00]−0.02(0.10)[1.00]

GD main

0.00(0.05)[1.00]

1.63(2.40)[1.00]

0.02(0.43)[1.00]

2.77∗∗∗(0.55)[0.00]

0.24(0.10)[0.16]

GD large

0.05(0.07)[1.00]

1.70(3.55)[1.00]

1.29(0.54)[0.16]

2.56∗∗(0.78)[0.02]

0.04(0.13)[1.00]

Combined−0.02(0.06)[1.00]

2.82(3.14)[1.00]

0.54(0.56)[1.00]

2.36∗∗(0.76)[0.03]

0.11(0.12)[1.00]

HD × Older than 22−0.03(0.06)[1.00]−1.60(3.26)[1.00]−0.86(0.54)[0.75]

0.22(0.73)[1.00]

0.10(0.13)[1.00]

GD main × Older
than 220.00(0.06)[1.00]−0.16(3.18)[1.00]

0.24(0.54)[1.00]−1.23(0.74)[0.75]−0.10(0.13)[1.00]

GD large × Older than
22

−0.06
(0.09)[1.00]

0.65(4.56)[1.00]−1.12(0.69)[0.75]

0.34(1.02)[1.00]

0.18(0.17)[1.00]

Combined × Older
than 220.07(0.08)[1.00]

1.14(4.25)[1.00]−0.41(0.72)[1.00]

1.34(1.00)[0.98]

0.11(0.16)[1.00]

Older than 22

0.06(0.05)[0.97]

1.33(2.19)[1.00]

1.09∗(0.38)[0.05]

0.25(0.50)[1.00]

0.04(0.09)[1.00]

Control mean
Observations
R2
p-value

0.50
1822
0.02
0.76

19.43
1822
0.02
0.97

8.11
1822
0.02
0.16

3.90
1822
0.06
0.10

9.84
1810
0.04
0.41

Notes: Table presents tests for heterogeneity of treatment effects by age. First four rows give effect of treatment
among young, and next four rows test for differential treatment effect for those 23 and over. Standard errors are(in soft brackets) are clustered at the household level to reflect the design effect, and p-values corrected for False
Discovery Rates across all the outcomes in the table are presented in hard brackets. Stars on coefficient estimates arederived from the FDR-corrected p-values, *=10%, **=5%, and ***=1% significance. p-value in the last row from an
F-test on whether treatments have a jointly differential effect by gender.

74

Table A.24: Heterogeneity Analysis by Baseline Consumption.

Employed

Productive
Hours

Monthly
IncomeProductive
AssetsConsumption

HD

0.03
(0.03)[0.90]

3.66(1.60)[0.13]

0.16(0.27)[1.00]

0.84(0.37)[0.13]

0.07(0.06)[0.87]

GD main

0.00(0.03)[1.00]

1.62(1.59)[0.87]

0.09(0.27)[1.00]

2.15∗∗∗(0.37)[0.00]

0.20∗∗(0.07)[0.03]

GD large

0.02(0.04)[1.00]

2.28(2.23)[0.87]

0.65(0.34)[0.22]

2.83∗∗∗(0.51)[0.00]

0.17(0.08)[0.20]

Combined

0.02(0.04)[1.00]

3.54(2.12)[0.33]

0.28(0.35)[1.00]

3.06∗∗∗(0.50)[0.00]

0.18(0.08)[0.13]

HD × Baseline HH
consumption per AE−0.00(0.03)[1.00]−1.46(1.60)[0.90]−0.01(0.28)[1.00]−0.48(0.37)[0.57]

0.04(0.07)[1.00]

GD main × Baseline
HH consumption per
AE

−0.03
(0.03)[0.87]−1.55(1.63)[0.90]

0.02(0.27)[1.00]

0.08(0.36)[1.00]

0.04(0.07)[1.00]

GD large × Baseline
HH consumption per
AE

−0.03
(0.05)[1.00]−2.08(2.32)[0.90]−0.06(0.37)[1.00]−0.24(0.54)[1.00]−0.11(0.08)[0.57]

Combined × Baseline
HH consumption per
AE

−0.03
(0.04)[1.00]−2.94(2.02)[0.50]−0.03(0.34)[1.00]

0.08(0.47)[1.00]−0.08(0.09)[0.90]

Baseline HH
consumption per AE

0.03(0.02)[0.57]

2.07(1.13)[0.25]

0.06(0.20)[1.00]

0.49(0.25)[0.20]

0.18∗∗∗(0.05)[0.00]

Control mean
Observations
R2
p-value

0.50
1822
0.01
0.80

19.43
1822
0.02
0.64

8.11
1822
0.01
1.00

3.90
1822
0.06
0.58

9.84
1810
0.07
0.30

Notes: Table presents tests for heterogeneity of treatment effects by baseline Household Consumption. Consumption
demeaned before interaction so first four rows give effect of treatment at average value, and next four rows test fordifferential treatment effect by consumption. Standard errors are (in soft brackets) are clustered at the household
level to reflect the design effect, and p-values corrected for False Discovery Rates across all the outcomes in thetable are presented in hard brackets. Stars on coefficient estimates are derived from the FDR-corrected p-values,*=10%, **=5%, and ***=1% significance. p-value in the last row from an F-test on whether treatments have ajointly differential effect by gender.

75

Table A.25: Heterogeneity Analysis by Sector-level Employment.

Employed

Productive
Hours

Monthly
IncomeProductive
AssetsConsumption

HD

0.02
(0.03)[1.00]

3.41(1.61)[0.25]

0.14(0.27)[1.00]

0.79(0.37)[0.25]

0.04(0.06)[1.00]

GD main

0.00(0.03)[1.00]

1.54(1.60)[1.00]

0.08(0.27)[1.00]

2.10∗∗∗(0.38)[0.00]

0.18(0.07)[0.10]

GD large

0.02(0.04)[1.00]

2.15(2.25)[1.00]

0.63(0.33)[0.40]

2.78∗∗∗(0.52)[0.00]

0.14(0.08)[0.53]

Combined

0.02(0.04)[1.00]

3.47(2.11)[0.53]

0.28(0.35)[1.00]

3.06∗∗∗(0.50)[0.00]

0.17(0.08)[0.25]

HD × Baseline cellshare employed

0.03(0.31)[1.00]

19.50(15.88)[1.00]−0.61(2.53)[1.00]

0.62(3.51)[1.00]−0.30(0.62)[1.00]

GD main × Baselinecell share employed−0.01(0.32)[1.00]−7.40(16.04)[1.00]−1.53(2.66)[1.00]

0.00(3.64)[1.00]

0.01(0.75)[1.00]

GD large × Baselinecell share employed

0.35(0.41)[1.00]

4.67(21.44)[1.00]−1.56(3.24)[1.00]−0.29(5.10)[1.00]−1.06(0.77)[0.77]

Combined × Baselinecell share employed

0.56(0.39)[0.75]

32.59(20.00)[0.53]

1.79(3.41)[1.00]

1.14(4.74)[1.00]−0.57(0.74)[1.00]

Baseline cell share
employed

0.17(0.24)[1.00]

0.26(11.06)[1.00]

1.68(1.88)[1.00]

1.16(2.61)[1.00]

0.16(0.49)[1.00]

Control mean
Observations
R2
p-value

0.50
1822
0.01
0.52

19.43
1822
0.02
0.27

8.11
1822
0.01
0.89

3.90
1822
0.06
1.00

9.84
1810
0.04
0.65

Notes: Table presents tests for heterogeneity of treatment effects by baseline Employment Rates. Employment
demeaned before interaction so first four rows give effect of treatment at average value, and next four rows testfor differential treatment effect by employment rates. Standard errors are (in soft brackets) are clustered at thehousehold level to reflect the design effect, and p-values corrected for False Discovery Rates across all the outcomes inthe table are presented in hard brackets. Stars on coefficient estimates are derived from the FDR-corrected p-values,*=10%, **=5%, and ***=1% significance. p-value in the last row from an F-test on whether treatments have ajointly differential effect by gender.

76

Table A.26: Simple Spillover Analysis.

Employed

Productive
Hours

Monthly
IncomeProductive
Assets∗∗Consumption

HD

0.02
(0.03)[0.69]

2.70(1.24)[0.12]

0.14(0.20)[0.69]

0.93(0.31)[0.02]

0.03(0.05)[0.69]

GD main−0.01(0.03)[0.69]

0.07(1.27)[0.91]

0.11(0.20)[0.69]

2.18∗∗∗(0.33)[0.00]

0.16∗∗(0.05)[0.01]GD Huge treatment

0.02(0.04)[0.69]

2.08(2.22)[0.69]

0.69(0.31)[0.12]

2.97∗∗∗(0.51)[0.00]

0.17(0.09)[0.17]HD Saturation−0.03(0.05)[0.69]−4.10(2.88)[0.40]−0.37(0.45)[0.69]−0.56(0.63)[0.69]−0.33∗∗(0.12)[0.03]GD Main Saturation−0.05(0.05)[0.69]−3.41(2.57)[0.40]−0.56(0.43)[0.40]−0.96(0.63)[0.37]−0.13(0.11)[0.40]GD Large Saturation

0.01(0.09)[0.85]−2.78(4.35)[0.69]

0.37(0.74)[0.69]−1.46(1.08)[0.40]−0.36(0.19)[0.18]

Control mean
Observations
R2
p-value

0.50
1822
0.15
0.75

19.43
1822
0.18
0.34

8.11
1822
0.15
0.34

3.90
1822
0.14
0.34

9.84
1810
0.20
0.02

Notes: Table analyzes spillover effects of the three main treatments (HD, GD Main, and GD Large) on the five
primary outcomes. The first three rows are dummy variables for own treatment status, and the next three are thesaturation rates for the three treatments among others in the village, so measure the marginal effect of going fromno one else treated to everyone else treated. 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 to reflect
intensive tracking. Standard errors are (in soft brackets) are clustered at the household level to reflect the design
effect, and p-values corrected for False Discovery Rates across all the outcomes in the table are presented in hardbrackets. Stars on coefficient estimates are derived from the FDR-corrected p-values, *=10%, **=5%, and ***=1%
significance. Bottom row is the p-value on an F-test of the joint significance of the three saturation terms.

77

Table A.27: Spillovers on Household Consumption.
TreatmentHD

GD Main

GD Huge

Direct effects of treatment at saturation level of zero
Direct effect−0.14(0.13)[0.30]−0.04(0.14)[0.60]

0.45(0.26)[0.16]

Spillover effects of treatment onto control individuals
Spillover to control−0.26(0.19)[0.23]−0.52∗(0.18)[0.05]−0.52(0.28)[0.15]

Additional effect of treatment onto individuals assigned to. . .
HD

0.15(0.21)[0.42]

0.40(0.21)[0.15]−0.28(0.37)[0.42]

GD main−0.16(0.23)[0.42]

0.49(0.22)[0.14]

0.78(0.34)[0.14]

GD large−0.79(0.41)[0.15]

0.20(0.36)[0.49]−0.80(0.75)[0.30]

0.36
0.23
0.01

0.36
0.23
0.03

0.09
0.13
0.02

Saturation mean
Saturation SD
p-value

Notes: Each column describes the direct and spillover effects of a specific treatment on Household Consumption(IHS); all results in the table are from a single estimation. Saturation mean and standard deviation correspond to the
distribution of saturation rates for the treatment in question. Standard errors are (in soft brackets) are clustered at thehousehold level to reflect the design effect, and p-values corrected for False Discovery Rates across all the outcomes inthe table are presented in hard brackets. Stars on coefficient estimates are derived from the FDR-corrected p-values,*=10%, **=5%, and ***=1% significance. p-value in the last row corresponds to a test for whether the treatment inquestion has interference effects on any arm, including control.

78

Table A.28: Spillovers on Employment.
TreatmentHD

GD Main

GD Huge

Direct effects of treatment at saturation level of zero
Direct effect−0.05(0.08)[1.00]−0.07(0.07)[0.98]−0.11(0.12)[0.98]

Spillover effects of treatment onto control individuals
Spillover to control−0.13(0.09)[0.98]−0.16(0.09)[0.98]

0.14(0.15)[0.98]

Additional effect of treatment onto individuals assigned to. . .
HD

0.13(0.12)[0.98]

0.10(0.11)[0.98]−0.12(0.19)[1.00]

GD main

0.03(0.12)[1.00]

0.19(0.12)[0.98]−0.22(0.19)[0.98]

GD large

0.35(0.18)[0.98]

0.06(0.18)[1.00]−0.13(0.39)[1.00]

Saturation mean
Saturation SD
p-value

0.36
0.23
0.33

0.36
0.23
0.40

0.09
0.13
0.84

Notes: Each column describes the direct and spillover effects of a specific treatment on Employment; all results inthe table are from a single estimation. Saturation mean and standard deviation correspond to the distribution ofsaturation rates for the treatment in question. 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 to reflect
intensive tracking. Standard errors are (in soft brackets) are clustered at the household level to reflect the design
effect, and p-values corrected for False Discovery Rates across all the outcomes in the table are presented in hardbrackets. Stars on coefficient estimates are derived from the FDR-corrected p-values, *=10%, **=5%, and ***=1%
significance. p-value in the last row corresponds to a test for whether the treatment in question has interference
effects on any arm, including control.

79

Table A.29: Spillovers on Monthly Income.
TreatmentHD

GD Main

GD Huge

Direct effects of treatment at saturation level of zero
Direct effect−0.76(0.57)[0.74]−0.00(0.57)[1.00]−0.69(0.96)[0.74]

Spillover effects of treatment onto control individuals
Spillover to control−1.00(0.72)[0.74]−0.90(0.70)[0.74]−1.16(1.21)[0.74]

Additional effect of treatment onto individuals assigned to. . .
HD

1.39(0.91)[0.74]

0.47(0.85)[0.74]

2.36(1.48)[0.74]

GD main−0.36(0.90)[0.74]

0.48(0.90)[0.74]

0.70(1.54)[0.74]

GD large

1.99(1.54)[0.74]

0.54(1.24)[0.74]

5.08(2.74)[0.74]

Saturation mean
Saturation SD
p-value

0.36
0.23
0.35

0.36
0.23
0.74

0.09
0.13
0.24

Notes: Each column describes the direct and spillover effects of a specific treatment on Monthly Income (IHS);
all results in the table are from a single estimation. Saturation mean and standard deviation correspond to the
distribution of saturation rates for the treatment in question. Regressions include but do not report the lagged
dependent variable, fixed effects for randomization blocks, and a set of LASSO-selected baseline covariates, and areweighted to reflect intensive tracking. Standard errors are (in soft brackets) are clustered at the household level
to reflect the design effect, and p-values corrected for False Discovery Rates across all the outcomes in the table
are presented in hard brackets. Stars on coefficient estimates are derived from the FDR-corrected p-values, *=10%,**=5%, and ***=1% significance. p-value in the last row corresponds to a test for whether the treatment in question
has interference effects on any arm, including control.

80

Table A.30: Spillovers on Productive Hours.
TreatmentHD

GD Main

GD Huge

Direct effects of treatment at saturation level of zero
Direct effect

0.92(3.71)[1.00]−1.25(3.75)[1.00]

0.64(6.44)[1.00]

Spillover effects of treatment onto control individuals
Spillover to control−6.37(4.49)[1.00]−3.49(4.15)[1.00]−7.10(6.69)[1.00]

Additional effect of treatment onto individuals assigned to. . .
HD

3.28(5.84)[1.00]−0.50(5.35)[1.00]

7.88(9.50)[1.00]

GD main−0.08(6.08)[1.00]

3.01(5.91)[1.00]

2.24(9.20)[1.00]

GD large

8.65(9.63)[1.00]−5.42(7.97)[1.00]

4.22(17.10)[1.00]

Saturation mean
Saturation SD
p-value

0.36
0.23
0.47

0.36
0.23
0.60

0.09
0.13
0.85

Notes: Each column describes the direct and spillover effects of a specific treatment on Productive Hours; all resultsin the table are from a single estimation. Saturation mean and standard deviation correspond to the distribution ofsaturation rates for the treatment in question. 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 to reflect
intensive tracking. Standard errors are (in soft brackets) are clustered at the household level to reflect the design
effect, and p-values corrected for False Discovery Rates across all the outcomes in the table are presented in hardbrackets. Stars on coefficient estimates are derived from the FDR-corrected p-values, *=10%, **=5%, and ***=1%
significance. p-value in the last corresponds to a test for whether the treatment in question has interference effects
on any arm, including control.

81

Table A.31: Spillovers on Productive Asset Values.
TreatmentHD

GD Main

GD Huge

Direct effects of treatment at saturation level of zero
Direct effect1.17(0.87)[0.58]

2.61∗∗(0.85)[0.03]

4.30∗∗(1.47)[0.03]

Spillover effects of treatment onto control individuals
Spillover to control

0.21(1.04)[1.00]−0.95(0.97)[0.80]−0.99(1.72)[0.86]

Additional effect of treatment onto individuals assigned to. . .
HD

0.57(1.47)[1.00]−0.29(1.35)[1.00]−3.45(2.22)[0.58]

GD main−2.73(1.44)[0.35]

0.83(1.40)[0.86]

2.49(2.46)[0.80]

GD large−0.02(2.27)[1.00]−2.10(2.16)[0.80]−5.57(4.17)[0.58]

0.36
0.23
0.30

0.36
0.23
0.34

0.09
0.13
0.04

Saturation mean
Saturation SD
p-value

Notes: Each column describes the direct and spillover effects of a specific treatment on Productive Assets (IHS);
all results in the table are from a single estimation. Saturation mean and standard deviation correspond to the
distribution of saturation rates for the treatment in question. Regressions include but do not report the lagged
dependent variable, fixed effects for randomization blocks, and a set of LASSO-selected baseline covariates, and areweighted to reflect intensive tracking. Standard errors are (in soft brackets) are clustered at the household level
to reflect the design effect, and p-values corrected for False Discovery Rates across all the outcomes in the table
are presented in hard brackets. Stars on coefficient estimates are derived from the FDR-corrected p-values, *=10%,**=5%, and ***=1% significance. p-value in the last row corresponds to a test for whether the treatment in question
has interference effects on any arm, including control.

82

Table A.32: Incidence of Other Support during COVID

HD

Lower

GiveDirectly
Middle Upper

Large

Combined

First
Lockdown

0.01(0.03)[1.00]-0.01(0.04)[1.00]

0.04(0.04)[1.00]-0.10∗(0.04)[0.07]-0.05(0.04)[0.93]

Middle Period

0.03(0.02)[0.93]

0.06(0.04)[0.48]

0.01(0.03)[1.00]-0.03(0.03)[0.97]

SecondLockdown-0.01(0.02)[1.00]

0.03(0.04)[0.97]

0.07(0.04)[0.48]

0.01(0.03)[1.00]

Control
Mean

Obs.

R2

-0.06
(0.04)[0.48]

0.29

1822

0.04

0.04
(0.03)[0.93]-0.00(0.03)[1.00]

0.13

1822

0.02

-0.03
(0.03)[0.97]-0.01(0.03)[1.00]

0.18

1822

0.01

Note: Table analyzes response to question ”During this period, did you receive any food, cash or other support
that you do NOT usually receive?” Row 1 refers to the first national lockdown (March 21, 2020 till May 4th 2020),
the second row refers to the interim period (From November 2nd, 2020, till January 18th 2021), and the third row
to the second national lockdown (July 17th till July 28th 2021).

83

Table A.33: Are treatments protective against measured Covid shocks?
Employed

Productive hours

IncomeProductive assets

Consumption

0.02(0.03)

3.52∗∗(1.63)

0.16(0.26)

0.82∗∗(0.37)

0.05(0.06)

× index0.15(0.32)-0.17(0.31)

0.29(0.24)

0.12(0.26)

0.46(0.28)

GD main

0.00(0.03)

1.48(1.64)

0.10(0.26)

2.14∗∗∗(0.37)

0.17∗∗∗(0.06)

× index0.17(0.33)

0.38(0.32)-0.12(0.24)

0.33(0.27)

0.19(0.28)

GD large

0.02(0.04)

2.17(2.22)

0.64∗(0.36)

2.80∗∗∗(0.50)

0.16∗(0.09)

× index0.75∗(0.43)

0.58(0.43)

0.27(0.31)

0.00(0.35)

0.77∗∗(0.38)

Combined

0.02(0.04)

3.36(2.13)

0.28(0.34)

3.09∗∗∗(0.48)

0.15∗(0.08)

× index0.09(0.41)-0.03(0.41)-0.24(0.30)

0.30(0.35)

0.18(0.37)

0.09(0.24)

0.06(0.24)

0.59∗∗∗(0.18)

0.51∗∗(0.20)-2.73∗∗∗(0.41)

1822

1822

1822

1822

1810

HD

Predictive index

Notes: This table proceeds in the following steps. First, we use second-order polynomials in the cumulative shock
indices to predict endline outcomes in the control group; following Abadie et al. (2018), we use a leave-one-out
approach to omit each control-group observation from the regression on which its prediction is based. We then use
predicted endline outcomes in the control group to predict counterfactual endline outcomes in other treatment arms,
and interact the centered predictions with treatment indicators. All specifications include controls for the baseline
value of the outcome, as well as lasso-selected controls and block fixed effects, as in the ITT specification. Predictive
index is the predicted value of the endline outcome, based on plausibly exogenous covid shock measures, with model
estimated by cross-validated lasso in the control group only.

84

Table A.34: Association between Covid-19 shock measures and endline outcomes incontrol group

Employed

Productive
hours

IncomeProductive
assetsConsumption

Model A. Conditional on baseline value of outcome
Cumulative incidence ofCovid 19 in household

0.01(0.07)

4.71(3.21)

0.22(0.55)

0.35(0.73)

0.05(0.13)

Cumulative experienceof lower income

0.06∗∗∗(0.02)

2.01∗∗(0.91)

0.69∗∗∗(0.16)

0.71∗∗∗(0.21)-0.00(0.04)

Cumulative experienceof food market closures-0.01(0.03)-3.01∗∗(1.38)-0.40∗(0.24)-0.01(0.32)-0.01(0.06)

Cumulative experienceof food shortage

0.00(0.02)

1.32(1.11)

0.16(0.19)-0.33(0.25)-0.04(0.05)

Model B. Conditional on baseline and midline value of outcome
Cumulative incidence ofCovid 19 in household

0.01(0.07)

4.28(3.17)

0.17(0.54)

0.41(0.68)

0.05(0.13)

Cumulative experienceof lower income

0.06∗∗∗(0.02)

2.13∗∗(0.90)

0.65∗∗∗(0.15)

0.69∗∗∗(0.19)-0.01(0.04)

Cumulative experienceof food market closures-0.01(0.03)-2.87∗∗(1.37)-0.45∗(0.24)-0.13(0.29)-0.02(0.06)

Cumulative experienceof food shortage

0.00(0.02)

1.21(1.09)

0.20(0.19)-0.35(0.24)-0.03(0.04)

Midline outcome

0.01(0.05)

0.18∗∗∗(0.05)

0.18∗∗∗(0.05)

0.37∗∗∗(0.04)

0.28∗∗∗(0.05)

Observations

479

479

479

479

473

Note: Each column within each panel represents a separate regression. All regressions control for baseline values of
the corresponding outcome and for block fixed effects.

85

Table A.35: ITT Effects on Transfers.
GiveDirectly

Panel A. Midline
HH loans madeHH gifts received

HH gifts made86

Panel B. Endline
HH loans madeHH gifts received

HH gifts made

HD

Lower

Middle

Upper

Large

Combined

0.11(0.30)[0.31]−0.33(0.36)[0.19]
0.39(0.32)[0.15]

0.90∗∗(0.44)[0.05]
1.75∗∗∗(0.57)[0.00]
0.68∗(0.42)[0.09]

0.45(0.44)[0.19]
2.67∗∗∗(0.55)[0.00]
1.87∗∗∗(0.48)[0.00]

1.41∗∗∗(0.45)[0.00]
2.62∗∗∗(0.59)[0.00]
0.56(0.47)[0.15]

0.68∗(0.42)[0.09]
3.11∗∗∗(0.53)[0.00]
0.32(0.44)[0.20]

1.24∗∗∗(0.43)[0.01]
2.46∗∗∗(0.50)[0.00]
0.81∗(0.45)[0.08]−0.11(0.30)[1.00]−0.29(0.33)[1.00]
0.17(0.28)[1.00]−0.57(0.41)[0.67]−0.92(0.43)[0.20]
0.15(0.40)[1.00]−0.79(0.44)[0.36]−0.64(0.47)[0.67]
0.05(0.41)[1.00]−0.53(0.43)[0.82]
0.05(0.48)[1.00]
0.26(0.41)[1.00]−1.00∗(0.37)[0.07]−1.36∗∗(0.40)[0.01]−0.19(0.38)[1.00]−0.36(0.40)[1.00]−0.25(0.42)[1.00]
0.19(0.37)[1.00]

p-values

Control
Mean

Obs.

R2

(a)

(b)

(c)

2.24

1705

0.15

0.10

0.18

0.27

4.90

1704

0.17

0.00

0.60

0.33

3.41

1675

0.15

0.63

0.20

0.36

3.08

1794

0.13

0.31

0.79

0.16

3.34

1784

0.10

0.20

0.48

0.02

2.42

1770

0.11

0.91

0.54

0.40

Note: 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 to reflect intensive tracking. Standard errors are (in soft brackets) are clustered at the household level to reflect the design effect, and p-values
corrected for False Discovery Rates across all the outcomes in the table are presented in hard brackets. Stars on coefficient estimates are derived from the
FDR-corrected p-values, *=10%, **=5%, and ***=1% significance. Reported p-values in final three columns derived from F -tests of hypotheses that cost-benefit
ratios are equal between: (a) GD Lower and HD; (b) GD Lower and GD Large; and (c) GD Large and Combined treatments.