Youth Employment and Productivity:
Medium-term Experimental Evidence from Rwanda
Craig McIntosh∗ and Andrew Zeitlin†
June 2022

Abstract
We provide medium-term impacts of an experiment benchmarking a workforce training program against cash transfers for underemployed young adults in Rwanda. 3.5 years after treatment the training program enhances productive time use and asset investment, and the cashtransfers drive productive assets, livestock values, savings, and subjective well-being. Both interventions enhance the likelihood that individuals operate businesses and the sales in thosebusinesses, yet the additional profits per time spent suggest the rate of return is low. Whilethese interventions lead to modest improvements, deeper reforms may be necessary to allow
self-employment to provide a transformative 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; 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., 2019; 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 long-term productivity question with interventions thathave only palliative, short-term impacts (Bouguen et al., 2019). The existing empirical evidencebase on labor market interventions is largely short-term, making it clear that they can 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 of time (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 epidemic, which has dealt ahuge shock to self-employed individuals whose income streams are vulnerable to lock-downs and who
work 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, the durabilityof impacts through the COVID epidemic speak both to the dynamics of wealth accumulation andalso 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, one arm providing unconditional cash transfers, and an arm that receives both of these interventions at the same time. Randomization of cash transfer amounts provides the ability to makecost-equivalent comparisons between cash and kind, as well as to form a rich set of counterfactuals
for the complementarity arm that receives both interventions. We follow up with subjects three
years after the interventions were completed, and have a permanently untreated control group so2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. This environment provides an unusually rich environment in whichto consider the medium-term impact of programs that support youth productivity in the African
context.
We find evidence of durable impacts from both interventions. Huguka Dukore Akazi Kanoze(the workforce training program, henceforth HD) continues to elevate productive hours per weekby 3.3, productive assets are almost twice the control group, and an index of business knowledge
is higher by 0.25 standard deviations even three year later. The cash arms, implemented by US
non-profit GiveDirectly (henceforth GD) 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 theseoutcomes 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,3

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.
Our results should be read in light of some important contextual factors. First, the study takes
place largely in rural areas and so many micro-enterprises are typically engaged with agriculture insome way. Whether such interventions could have more transformative effects in an urban context
with larger demand pools remains an open question. Second, Rwanda is a tightly governed, rapidly
growing country. While in some ways that means that this study likely represents a ‘best-case’scenario for such interventions, it is also the case that the three COVID lock-downs imposed in the
two years prior to our endline were unusually strongly enforced, and may have hit small businesses
harder than in more loosely governed countries. The impact of the COVID era on the overallbusiness climate can be seen in our control group: while employment status, consumption, andconsumption appear to have been protected over the course of the pandemic, control households
have dramatically stripped productive assets, losing approximately 63 percent of the value of theassets they held at midline. Hence the exigencies of this unusual time are an inextricable part ofwhat this study has to say about long-term impacts.
This study makes several contributions to the literature. First, we deepen the evaluation literature by expanding the set of questions that can be asked with rigorous comparative analysis. Ours
is the first study to be able to conduct a rigorously cost-equivalent comparison of two programs oversuch a long time frame.1 Given the variation in cash transfer amounts we can examine mediumterm 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 and ask both whether
the combination is differentially effective, 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 cashtransfers 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. 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 transfersin the developing context, but it is far from clear that these impacts are durable, with a number
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

of RCTs showing dissipating long-term benefits (Araujo et al., 2017; Baird et al., 2019; BrudevoldNewman et al., 2017). A a long-term study in neighboring Uganda providing cash grants to groups
to start businesses 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 capitalconstraints 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. 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 technologicalinvestments) rather than investing in individuals while treating 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 gives the core experimental results
and conducts comparative cost equivalence and cost effectiveness analysis, Section 4 catalogs thenature of the covid shocks experienced since midline, and Section 5 accounts for the total cash flows
experienced since treatment. Section 6 concludes.

2

Design2.1

Interventions

Huguka Dukore: Employment and entrepreneurship readiness training
Huguka Dukore, which means ’get trained and get to work’ in Kinyarwanda, ran for five years in
Rwanda and overall trained 40,000 vulnerable youth. All participants are offered work readinesstraining; in urban areas the program promotes technical skill and job placement, while in rural
areas there is more emphasis on entrepreneurship training and enabling productive self-employment.

5

Prior to the initiation of this program, there was a five-year activity in Rwanda called Akazi Kanoze
Youth Livelihoods Project that pursued a similar approach and was run by the same organization.
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. The Be Your Own Boss training is driven towards the promotion ofentrepreneurship, and begins to tailor content to the specific sector in which a trainee wishes tofocus. Participants are asked to develop a business idea, identify a concrete market opportunity,
outline the business operation and financing, and draw up a business plan. The third 10-weekmodule is a Technical Training course that provides specific skills in an employment area (example
would include tailoring, hairdressing, carpentry, or beekeeping).
In terms of participation, 86% of individuals in the HD arm attend the first weeks of the firstmodule, the definition of compliance on which payment from USAID to EDC is based. The rates atwhich 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 theselected employment sector. 39% of those in the HD arm undertook an apprenticeship during thestudy period, with the most common placements being in tailoring (53%) and hairdressing (22%).
Pairing classroom training with a hands-on workforce training is common in workforce programs
worldwide, such as the Jóvenes en Acción program in Colombia (Attanasio et al., 2011).
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 is in the process of expanding its implementation capacity in Rwanda and Uganda. They
operate using the national mobile money system, first enrolling a household and establishing thephone number through which the transfer can securely be made, then sending the money, andfinally following up with the household to verify that transfers were recived by the correct person
in a timely manner. If a targeted youth did not themselves have a telephone, they were asked toprovide the name and number of a trusted individual to whom the transfer could be sent. Cashpayments for this study were made in two tranches, with the first making up 40% of the transfer,
2
Work 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

and the second following one month later and providing the remaining 60%. As would be expected,
compliance with the cash transfers was essentially perfect.

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 tomeet all of the eligibility requirements for both implementers, and to have expressed interest in
participating in HD by having come to an informational session on the program. This intake groupof 1,848 individuals was then randomized to treatment arm through a series of 13 public lotteries
that took place at the sector level, with local officials and representatives of both implementerspresent at the lottery. Those youth who met HD’s criteria but not GiveDirectly’s (meaning thatthey were not in the poorest two government poverty classifications, Ubudehe 1 or 2) were treatedwith HD but not included in the study. The resulting sample is just over half female, with anaverage age of twenty three years, and seven and a half years of education. Excluding on-farm
agricultural labor, exactly a third of the sample reported being employed at baseline. Medianconsumption per adult equivalent is 5,879 RWF per month, which in 2018 PPP terms translates toa consumption level of USD 0.66 per day.
The public lottery mechanism was used given the large sums of money being transferred andthe 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
of different colors mixed up in a sack. We effectively blocked the treatment by ensuring that
fixed proportions of tokens for each arm were present in each lottery. To avoid spillovers withinhousehold, for the few households that contained multiple eligible individuals we had them draw asingle status for both members, and consequently we cluster standard errors at the household levelin the analysis.
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
were randomly assigned to the three bracketing transfer amounts (GD-Lower, receiving $317.34;
GD-Middle, receiving $410.19; and GD-Upper, receiving $503.04), or 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), and the Cash intervention was delayed relative tothe start of HD. This meant that HD implementation typically began two months before the firstof the cash transfers was made.
7

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 with error, we bracketed the anticipated costs of the HD program with multiple
cash amounts so as to allow for adjustment after-the-fact based on the results of the (correct) expost 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 tosynthetically scale up their operations in the costing exercise, providing us with the 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 provision costs underlying this estimate wethen bracketed 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 lessexpensive than anticipated, and GD operating costs were slightly higher than anticipated. Thismeans that the amount USAID spent 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. The combined arm, incorporating compliancewith 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 the costequivalence 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. Hence 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 We have five primary outcomes forthe study. Employment is a binary measure indicating that the individual spent more than 10 hours

8

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 thehousehold. Monthly income is the total amount earned over the prior month, including enterpriserevenue. Productive asset stocks and household consumption per adult equivalent round out the
primary outcomes.
Our secondary outcomes are divided into three groups. Additional measures of beneficiary
welfare are subjective well-being and mental health, as well as the personal consumption of thebeneficiary. Household wealth is measured using net non-land wealth, livestock wealth, and thestocks of savings and debt. Cognitive and skills dimensions are measured using Locus of Control, theBig Five index, as well as measures of the aspirations, business knowledge, and business attitudesof the beneficiary. All monetary outcomes, both primary and secondary, are winsorized at 1% and
99% and measured in inverse hyperbolic sine (so that marginal effects can be interpreted as percent
changes).

2.5Enterprise 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 outcomes. For all
extant enterprises we asked whether the business was currently in operation, and if so the numberof household and non-household members employed regularly in the business, as well as the numberof days that business was operative in a typical month, and the number of customers in a typicalmonth. We then asked for the typical daily sales on a day when the business is operating, and thetotal profit the business had earned over the month prior to the survey. We transform all monetaryamounts 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
3

As in the rest of the analysis, non-binary outcomes are Winsorized at 99% and monetary outcomes are inflation

9

back into the experimental dataset (of all beneficiaries, whether they run a businesses or not),missings are replaced with zeros, and we run standard analysis of the effects of the interventions aselsewhere 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 existenceof 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’ (firm no 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 firm level; instead
we total them at the beneficiary level, replacing missings with zeros for the new businesses, and socreate 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 then examine what happens tothose 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 survey 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
to randomly sample from the remaining un-found individuals to conduct an ‘intensive tracking’
exercise, as we did in the midline, but the original tracking was so successful and the remaining
sample sufficiently small that in the end we simply intensively tracked everyone in the study. Thisintensive tracking phase involved sending an enumerator to speak with them in person if they werelocated 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 belocated 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 shock in the interim that made migration and work awayfrom home more difficult, thereby keeping the study sample less mobile than they might have beenadjusted to make them real midline US dollars.

10

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
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

Core Results

3.1

ITT Results on Primary and Secondary Outcomes

In tables 2, 3, A.5, and A.6, we estimate impacts of assignment to HD or cash transfers on primaryand secondary outcomes. These are generally attenuated since those observed at midline, whencompared 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 incomesare 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 livestock11

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. Aggregate employment effects, combining all types of work, remain
essentially unaffected. Monthly incomes, which had risen by between 70 and 114 percent among
cash and combined cash and training arms at midline, are substantially reduced, with only theLarge transfer sustaining a statistically significant, 72 percent increase in monthly income (verysimilar to that arm’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 multipleinference 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 theunadjusted p-values for this outcome, in which case most arms have impacts that are just 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 productive
assets in the control arm, these reflect even smaller absolute differences than they did at midline.5
In Table 4, we see that transitions in sectors of occupation induced by the interventions haveremained remarkably stable, in spite of the lack of movement in the overall 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 and 17 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, this appears to be mostly
associated with a commensurate movement out of agricultural wage labor, the prevalence of whichis also reduced by 8 percentage points by the HD intervention. Although no longer statisticallysignificant, estimated HD-induced movements into non-agricultural wage labor of 5 percent of the
population remain consistent with those found at midline.7 Table A.8 examines two related margins,
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.
5
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.

12looking at unpaid uses of time (education and domestic work), as well as survey-based measuresof the opportunity cost of time, and finds no treatment effects on any of these 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 andpersisted 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 productive-asset buffer in those sectors
explains the lesser persistence of HD-induced microenterprises over the course of the pandemic.
Similar fading of endline impacts relative to the midline is observed for measures of beneficiary
welfare. 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 areapproximately half of those previously observed—though still significant at 0.29 and 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-specific consumption in theUpper and Combined transfer arms.
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 tocontrol—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 Combinedarms, with the latter delivering the smallest of these impacts at approximately 126 percent overcontrol. 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 impacts of HD havelargely evaporated.
Finally, we see little evidence of sustained impacts on beneficiaries’ cognitive and non-cognitive
skills. We see no impacts on Aspirations (and survey data do not provide the Locus of Control
measure from midline). Measures of business knowledge remain statistically significant in the HD
and Combined arms, suggesting some persistent human capital effects, but these are approximately
half of their prior magnitudes.

3.2

ITT Results on Business Outcomes

For the purpose of showing the evolution of outcomes over time, we present results for both midline
and endline. We begin with the household-reported enterprises in Tables A.9 and A.10. In bothrounds, these businesses seem to have been largely untouched by the substantial interventions
being directed at youth in these households. As a starting point we see that control households
report operating only .06 businesses on average in 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 if anything seems to lead to a light decrease in the
devotion of household labor to the household enterprise. Similarly, core businesses outcomes such13

as the number of customers, daily sales, and monthly profits typically show weak negative effects,
stronger in the endline than midline. So the main picture is that there are very few businesses inthese households, they are generally unaffected by the presence of the treatments, and to the extent
that they are impacted they appear to be suffering from a drawing away of labor.
We begin our analysis of beneficiary businesses on the extensive margin, examining firm birthand death. 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 .5 new businesses in midline, HD elevates
this by .22, and all of the cash arms lead this to more than double. Control individuals created .24
new firms on average between midline and endline, and only the GD Large and Combined arms
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 asa function of treatment status. In the third row we see that in the control group .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 (.14) the rate of midline businesses becoming inoperative more than doubles in most of the
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 seethat by midline the average control individual reports operating .79 and owning .71 businesses (themaximum 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 .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 ownership14

in the control group, which falls from .79 at midline to .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 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 theirability 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 .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 .2-.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).
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 half
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.

15

across 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 theextent 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: giventhat 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 Tables 2
and ??. Again, we find no significant differences between these interventions with the exception ofbusiness knowledge, showing again that HD is uniquely productive of that outcome.

3.4

Cost-equivalent benchmark

As discussed in McIntosh and Zeitlin (2022), we estimate a regression-adjusted, cost-equivalent
comparison between HD and cash transfers by estimating a model of the formHD
Yihb2 = δ T Tihb + δ HD Tibh
+ βXihb0 + ρYihb0 + γ1 τc + µb + +ϵihb2

16

(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
effect of assignment to HD, relative to a cash grant. We include a measure, τc , defined as thedifference between a given arm’s expenditure per beneficiary and that of the HD arm (set equal tozero in both HD and Control); doing so ensures that the coefficient δ HD estimates HD’s differential
impacts relative to cash transfers at costs equivalent to HD costs to donors per beneficiary.
Results presented in tables 5 and A.16 show that at these cost-equivalent levels, we find nostatistically significant differences on primary outcomes at endline between HD and cash transfers.
Among secondary outcomes, these differences between arms are different only for our measureof 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 of individual programs at thesemore modest impact levels, but, moreover, the resulting attenuation of 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 thatpertain 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 assumptionthat 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.

17

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. Tables 6 and A.21 divide the arm-specific benefits measured in ITT regressions by thecost of each arm in hundreds of dollars, and so give the benefit per amount spent. 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 is visualized in the left
panels by the vertical difference between the black diamond (HD) and the hollow circle (predictedcash impact at HD cost). Cost effectiveness is visualized in the right panels by the slope of theline connecting zero with the arm-specific outcome represented in benefit/cost space. While wehave already shown that these differences are not statistically significant, the takeaway from thesedifferent approaches emphasizes the superiority of HD at driving productive hours (both in termsof cost equivalence and cost effectiveness), and the cost effectiveness superiority of the middle cashtransfers in producing consumption and productive assets.
In assessing the linear benefit/cost assumption that underlies the standard approach to costeffectiveness, we can empirically address two critical questions. The first of these is heterogeneity.
If there exists a large sample of similarly eligible individuals for whom the same benefits can beachieved, then the less expensive intervention can always be ‘scaled up’ in aggregate welfare termson the extensive margin by simply applying it to more people. The second relates to the natureof spillover effects and the extent to which impacts estimated on the target beneficiaries correctlyreflect the full welfare benefits of the program at a population level.
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 each18

treatment. 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
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 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.
Control-group members were able to protect consumption at endline, but potentially at the cost9
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.

19

of a stripping of productive assets.10 Control-group households lose more than half of the value of
their assets at midline and the rate of business operation contracts markedly. Taken together, thesefindings suggest that individuals in the control arm reduced or sold off the businesses that they hadlaunched by midline; in doing so, they did not become unemployed, but rather switched back to afocus on agriculture, with some surplus generated by asset sales that drove a rise in consumption.
Our ability to distinguish the consequences of Covid 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.32, where we follow the “endogenous stratification”
approach suggested by Abadie et al. (2018) to predict exposure to Covid shocks using a set of baseline covariates. The ‘index’ variable refers to the predicted value of that outcome in the control
group. Overall, we see very limited predictive power of this index on endline outcomes. Perhaps
unsurprisingly given this, there is also little evidence of heterogeneity in treatment effects acrosslevels 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.33 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 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 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.
In sum, then three pieces of evidence suggest that the covid shock hurt those who had ‘stuck
their neck out’ by engaging in entrepreneurship, which is of course precisely what these interventions
intended to do. First, the absolute fall in measures of entrepreneurship during the covid era arelarger for the treatment groups than the control.12 Second, Table A.32 shows that the kinds ofindividuals who were most likely to be hit by covid shocks in many cases have somewhat larger
income fluctuations if treated than if in the control. Finally, table A.33 illustrates that within thecontrol group reported incidence of shocks to income were actually higher among those with higher
employment rates, income, and productive assets. So the overall takeaway is that this period wasparticularly difficult for people who were doing the things that the treatments intended to induce(running their own businesses rather than working on the farm), and hence the presence of the10

Consumption figures are deflated to midline prices, so this does not reflect an inflationary effect.
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 -.39 in the control, -.43 for HD, and -.63 for GD large.
IHS productive asset changes are -1.6, -2.3, and -2.6 respectively.
1120

covid 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 harder than agriculture in a waythat would not be seen from other shocks, such as drought or food price changes). This illustrates
the role that shocks can play in eroding the gains that transfers can enable. While 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 values of financial resources between study measurement points.
Given that our study asks beneficiaries about wealth, transfers, income, and consumption withdifferent 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 about
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

21

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 monthly
flow income and consumption ITTs (replicated for clarity in Appendix Table ??), the midline andendline annual flow impacts on intra-household transfers shown in Table A.34, the endline treatment
effect on stock variables and consumption flows in Tables 2 and ??.
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
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 provides anomnibus 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.
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.

22

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.

5

Conclusions

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. So 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 than what
they would have been doing otherwise (typically own-farm agriculture or agricultural wage labor).

23

This suggests more systemic problems with the nature of the markets in which these youth work.
Low returns to skill in local labor markets will limit what can be gained through training programs,
and weak demand will constrain the potential of a self-employment led exit from poverty. So thefact that such substantial and expensive interventions do not transform the lives of disadvantaged
youth refocuses attention on the macro constraints to growth that limit the ability of individuals
to climb out of poverty.
A critical contextual factor for this study is that the period between the midline and endline
includes the COVID 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 COVID shock at
the same time as they had more wealth to protect themselves against it. While the treatment 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 theend than in the control. Evidence in this study suggests that it may be the limited ability ofinterventions 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 shock were certainly unique,uncertainty and disruption are an unfortunate fact of life for entrepreneurs in developing economies.
Hence, 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 edge for cash. Only overall income is somewhat higher at endline for HD, which hints
at the possibility of more persistent income effects from the human-capital investment of HD. In
the midline every one of these outcomes is significantly better for cash at cost-equivalent 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 outcomes at cost-equivalent levels
than HD. The policy takeaway from this is that investments seeking create economic well-being overthe short- to medium-term will do well to incorporate cash as at least a part of their programming.

24

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28

Tables and Figures

Table 1: Results of Costing Exercise

29

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

30

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.

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

31

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 composition32

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 the FDR-corrected p-values, *=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.

33

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 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.

34

Table 7: Beneficiary Business Birth and Death.

35

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 nolonger operational (died), existed but were inoperative, or were operative. Standard errors are clustered at the household level, *=10%, **=5%, and ***=1%
significance.

Table 8: Midline Beneficiary enterprise analysis.

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

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

36

Control
Mean

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 clustered at the household level,*=10%, **=5%, and ***=1% significance.

Table 9: Endline Beneficiary enterprise analysis.

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

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

37

Control
Mean

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 clustered at the household level, *=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.

38

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 Actual39800

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.

40

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.

41

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

Sector42

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.

43

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 clustered at thehousehold level, *=10%, **=5%, and ***=1% significance.
44

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
45 clustered at the household level, *=10%, **=5%, andsample that is used for endline analysis. Standard errors***=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

46

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

47

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.

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]48

HD

p-values

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 clustered at the household level, *=10%, **=5%, and ***=1% significance.

Table A.8: Education and Time Use.

49

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 to be
paid to take a job in their village and in the nearest town, respectively (USD). Standard errors are clustered at the household level, *=10%, **=5%, and ***=1%
significance.

Table A.9: Midline Household enterprise analysis.

50

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 clustered at thehousehold level, *=10%, **=5%, and ***=1% significance.

Table A.10: Endline Household enterprise analysis.

51

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 thanby the beneficiary) and then imputing zeros to households with no businesses. Standard errors are clustered at the household level, *=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 clustered at
the household level, *=10%, **=5%, and ***=1% significance.

52

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.

53

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.

54

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.

55

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.

56

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.

57

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

58

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

59

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.

60

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 wealth61

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.
GiveDirectlyHD

p-values

Lower

Middle

Upper

Large

Combined

(a)

(b)

(c)

(d)

Panel A. Beneficiary welfare
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

0.034
Beneficiary(0.030)
specific
consumption
Panel B. Household wealth

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
wealth0.082(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 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

Businessknowledge

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.

62

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.

63

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.

64

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.

65

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.

66

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.

67

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.

68

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.

69

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.

70

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.

71

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.

72

Table A.32: 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.

73

Table A.33: Association between Covid-19 shock measures and endline outcomes in control 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.

74

Table A.34: ITT Effects on Transfers.
GiveDirectly

Panel A. Midline
HH loans madeHH gifts received

HH gifts made75

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.