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American Economic Journal: Applied Economics 2016, 8(2): 35–64
http://dx.doi.org/10.1257/app.20150023
35
* Blattman: Columbia University School of International and Public Affairs (SIPA) and National Bureau of
Economic Research (NBER), 420 W 118 Street, Suite 819, New York, NY 10027 (e-mail: chrisblattman@columbia.
edu); Green: Duke Global Health Institute, Box 90519, Durham, NC 27708 (e-mail: eric.green@duke.edu); Jamison:
Global INsights Initiative, The World Bank, 1818 H Street NW, Washington, DC 20433 (e-mail: julison@gmail.
com); Lehmann: University of Brasilia, Department of Economics, Campus Universitário Darcy Ribeiro, Brasília,
DF, 70910-900, Brazil (e-mail: christianlehmann0@gmail.com); Annan: International Rescue Committee, 122
East 42nd Street, Suite 1407, New York, NY 10168 (e-mail: jeannie.annan@rescue.org). Association of Volunteers
in International Service (AVSI) implemented the program and we thank Jackie Aldrette, Fabio Beltramini, Ezio
Castelli, Filippo Ciantia, Francesco Frigerio, John Makoha, Francesca Oliva, Federico Riccio, Samuele Rizzo, and
Massimo Zucca for collaboration. For comments we thank Abhijit Banerjee, Theresa Betancourt, Gustavo Bobonis,
Nathan Fiala, Don Green, Nathan Hansen, Dean Karlan, Bentley MacLeod, David McKenzie, several anonymous
referees, and seminar participants at George Washington University (GWU), Harvard University, Massachusetts
Institute of Technology (MIT), United States Agency for National Development (USAID), the World Bank, and
Yale University. A Vanguard charitable trust and the World Bank’s Learning on Gender and Conict in Africa
(LOGiCA) trust fund funded the research. This article does not necessarily represent the views of the World Bank,
the Consumer Financial Protection Bureau, or the US government. For research assistance we thank Filder Aryemo,
Natalie Carlson, Samantha DeMartino, Mathilde Emeriau, Sara Lowes, Lucy Martin, Godfrey Okot, Richard Peck,
Alex Segura, Xing Xia, and Adam Xu through Innovations for Poverty Action.
† Go to http://dx.doi.org/10.1257/app.20150023 to visit the article page for additional materials and author
disclosure statement(s) or to comment in the online discussion forum.
The Returns to Microenterprise Support among the
Ultrapoor: A Field Experiment in Postwar Uganda†
By C B, E P. G, J J, M. C
L, J A*
We show that extremely poor, war-affected women in northern
Uganda have high returns to a package of $150 cash, ve days of
business skills training, and ongoing supervision. Sixteen months
after grants, participants doubled their microenterprise owner-
ship and incomes, mainly from petty trading. We also show these
ultrapoor have too little social capital, but that group bonds, infor-
mal insurance, and cooperative activities could be induced and had
positive returns. When the control group received cash and training
20 months later, we varied supervision, which represented half of the
program costs. A year later, supervision increased business survival
but not consumption. (JEL I38, J16, J23, J24, L26, O15, Z13)
The World Bank, the United Nations, and the United States government have
made the eradication of extreme poverty by 2030 a central development goal.
1
Since the world’s poor often live in economies with few rms, anti-poverty pro-
grams often try to foster self-employment. This includes farm enterprises such as
raising livestock for sale, and nonfarm enterprises such as trading or retail. But can
the extreme poor be expected to start and sustain such microenterprises? And what
constraints hold them back?
1
“Extreme poverty” refers to earning less than the $1.25 per day international poverty line. See Burt, Hughes,
and Milante (2014) for a discussion of the goals.
36 AMERICAN ECONOMIC JOURNAL: APPLIED ECONOMICS APRIL 2016
Two in ve of the world’s extreme poor are projected to live in fragile and
conict-affected states by 2030, yet rigorous evidence on what works in these
settings is sparse.
2
To help ll this gap, this paper studies a relatively common
approach to relieving extreme poverty—transfers of human and physical capital—
but to a postwar population: the most marginalized people living in small villages in
northern Uganda, following a 20-year war.
A humanitarian organization, the Association of Volunteers in International
Service (AVSI), identied 1,800 poor people, mostly women, in 120 war-affected
villages, and tried to help them start very small but sustainable retail and trading
enterprises. AVSI’s Women’s INncome Generating Support (WINGS) program pro-
vided people grants of $150 (about $375 in purchasing power parity, or PPP, terms),
along with ve days of business skills training and planning, plus ongoing supervi-
sion to help implement the plan. The grant was 30 times larger than the benecia-
ries’ baseline monthly earnings.
An abundance of evidence argues that the average poor person has high returns
to capital and is held back in part by poor access to credit and insurance, and that
capital transfers and insurance products help grow microenterprises and incomes.
3
Most of this evidence, however, comes from people who already have businesses
or were selected for their business aptitude.
4
It’s not clear if it applies to the most
marginalized and “ultrapoor”—the people with the lowest incomes, no capital, and
limited social networks—especially after war.
5
The WINGS program has parallels to “graduation” style programs delivered
to hundreds of thousands of ultrapoor households globally. Graduation programs
give a bundle of temporary income support, livestock, livestock training, access to
micronance, supervision, and life-skills education. On balance, these programs
have been successful: several studies show substantial shifts from casual labor to
farm self-employment, and 10 to 40 percent increases in household consumption
or earnings compared to control groups, lasting at least two to four years (Bandiera
et al. 2013; Banerjee et al. 2015). The WINGS program differed from these other
ultrapoor programs in several dimensions, however, including: the postwar setting;
fewer program components; the focus on petty business; and providing cash rather
than livestock.
6
WINGS was also focused on young women.
2
See Burt, Hughes, and Milante (2014) for population projections. For reviews of the evidence see Blattman
and Miguel (2010) and Puri et al. (2014).
3
For example, see Udry and Anagol (2006); de Mel, McKenzie, and Woodruff (2008); Banerjee and Duo
(2011); Karlan, Knight, and Udry (2015); Fafchamps et al. (2014); Blattman, Fiala, and Martinez (2014).
4
For example, Blattman, Fiala, and Martinez (2014) see high returns to a group-based cash transfer in northern
Uganda. But the program targeted young adults with much higher levels of education and existing business plans
for relatively high-skill microenterprises. That program also excluded the two most conict-affected districts, where
WINGS was implemented.
5
On the one hand, returns to capital or other inputs could be greater on the extensive margin than the intensive
one. Indeed there is growing evidence that poor households use cash to start new enterprises and earn high returns,
although little of this evidence comes from the poorest of the poor (Macours, Premand, and Vakis 2012; Gertler,
Martinez, and Rubio-Codina 2012; Blattman, Fiala, and Martinez 2014; Bianchi and Bobba 2013). Returns could
also be high in a newly stable political equilibrium, as neoclassical models of growth predict (Blattman and Miguel
2010). On the other hand, the ultrapoor could have low returns to capital, for instance because they lack crucial
inputs such as education or business experience, or because they are vulnerable to expropriation within or outside
the family.
6
Many in the aid community fear cash can be seized, wasted, or cause harm. They could be right. Besides
the lack of other important inputs, extreme poverty has also been associated with cognitive decits that impede
VOL. 8 NO. 2 37
Blattman et al.: microenterprise support For ugandan ultrapoor
We evaluated WINGS by assigning the targeted people to either receive the pro-
gram immediately or a year-and-a-half later, randomizing at the village level. Given
the extreme setting, AVSI was reluctant to have a permanent control group—a com-
mon concern in humanitarian settings, and one reason humanitarian evaluations are
rare. Thus, our design evaluates impacts a few months before the 60 control villages
entered the program.
We also tested the role of social capital in business success: could social capital
be fostered, and would it increase the returns to grants? In poor rural villages, social
networks are a main source of business advice, cooperation, and informal nance.
7
For instance, in microcredit, growing evidence suggests that group lending is help-
ful not because of joint liability, but rather because it builds social capital and pro-
motes risk-pooling.
8
To test this, in half of the treatment villages, AVSI returned a couple of months
after the grants (after individual businesses had already been started) to encour-
age the participants to form self-help groups, and offered three additional days of
training in working together. The curriculum focused on developing organizational
structures, decision-making processes, leadership, and helping them form a rotating
savings and credit association (ROSCA).
Sixteen months after grants, the standard WINGS program (without group
encouragement) led to large changes in occupation and incomes. Thirty-nine per-
cent of the control group had a nonfarm business, and this rose to 80 percent among
WINGS participants. Employment rose from 15 to 24 hours per week, and cash
earnings rose about PPP $1 a day. Since the average person in the control group
earned less than $1 a day, the program doubled earnings. As a result, a conservative
estimate of household consumption rose by almost a third, to roughly PPP $1.25
per day. Annualized, this impact corresponds to a PPP $465 increase in nondurable
consumption—about a quarter of the PPP $1,946 standard program cost.
For program participants, the gains were mainly economic. There was little evi-
dence of changes in physical health, mental health, nancial autonomy, or domes-
tic violence. Outside the household, however, the program increased self-reported
social support and community participation. Participants also reported an increase
in resentment and verbal abuse from some neighbors, however, perhaps due to jeal-
ousy, or because they posed competition for preexisting traders.
The group encouragement, meanwhile, increased the frequency and intensity of
group activities. We see no impact on consumption after 16 months, but program
participants who received group encouragement reported double the earnings of
those that did not. Interestingly, this was not because their petty trading businesses
were larger, more likely to survive, or more protable. Rather, the evidence suggests
that groups spurred informal nance as well as labor-sharing and cooperative cash
investment and raise the risk of temptation spending (e.g., Bertrand, Mullainathan, and Shar 2004). Among the
poorest women, moreover, traditional norms could pressure them to share cash, make it easy to expropriate them,
or hinder their business growth (Field, Jayachandran, and Pande 2010; Duo 2012). This is the fundamental reason
that AVSI designed WINGS to include training and supervision.
7
See Fafchamps (1992), Foster and Rosenzweig (1995), Murgai et al. (2002).
8
Feigenberg, Field, and Pande (2013) show that encouraging social interaction via group meetings reduces
default on individual loans in India. Giné and Karlan (2014) also show individual liability has little impact on
default in the Philippines.
38 AMERICAN ECONOMIC JOURNAL: APPLIED ECONOMICS APRIL 2016
cropping. Group formation also seems to have mitigated the resentment and abuse
from neighbors.
Ideally, we could have randomly varied all program components and measured
their returns. This was not possible. But, following the main evaluation, we used
the entry of the control group into the program to investigate the marginal effect of
the most expensive component: supervision. The supervisory visits provided sub-
stantive advice as well as pressure to implement the business plan, but were more
than twice as costly as the grant. When the control group received WINGS after 20
months, we randomized them individually to receive the business training and grant
plus: no supervisory visits; two visits (to provide commitment to invest); or ve
visits for both commitment and substantive business advice.
A month after grants, but before any potential visits, expecting a visit had an
ambiguous effect on business investment: those assigned to supervision increased
investment by some measures and decreased by others. A year later, the effect of
supervision on incomes is also ambiguous: nondurable consumption is margin-
ally lower among those assigned to visits, earnings are marginally higher, but nei-
ther effect is statistically signicant. Supervision, however, did increase business
survival.
Altogether, these results come with caveats. First, the control group knew they
were on the waitlist, and so anticipation of treatment could affect their behavior.
Second, all measures are self-reported. Experimenter demand is a risk, but given the
size of impacts (and the absence of noneconomic impacts, where we might expect
experimenter demand) the bias seems unlikely to drive our results. Third, there is
mild randomization imbalance and attrition. Treatment effects, however, are robust
to corrections and to missing data scenarios. Finally, these are 16-month impacts
and given the fact that the control group entered the program, we cannot say whether
they persist.
Nonetheless, WINGS illustrates that the poorest may be able to start and sus-
tain small enterprises, even in very small, fairly poor communities. Moreover, the
16-month consumption impacts of WINGS are almost identical to the one-year or
two-year impacts of livestock-based ultrapoor programs, although WINGS was
about half as costly. So far the livestock programs have longer term evidence in their
favor, and the sustainability of cash-centric programs to the very poorest is an open
question.
9
Even so, studies of cash transfers to the non-extreme poor show sustained
or growing impacts after four to six years (Blattman, Fiala, and Martinez 2014; de
Mel, McKenzie, and Woodruff 2012b).
Cash should be much cheaper and easier to deliver than livestock or capital goods,
so if it stimulates employment as well as the accumulation of income-generating
assets it could affect how ultrapoor programs are designed and scaled. This war-
rants more investigation, especially in humanitarian settings where cash is becom-
ing more common as it can be difcult to provide in-kind capital. Also important to
investigate are cost-effective forms of supervision and training.
9
Livestock programs sustain gains after two to four years, while ultrapoor cash transfer studies have 1 to 2 years
of evidence so far (e.g., Haushofer and Shapiro 2013; Macours, Premand, and Vakis 2012).
VOL. 8 NO. 2 39
Blattman et al.: microenterprise support For ugandan ultrapoor
Finally, the results support the view that social interactions encourage coopera-
tion, and that such social capital delivers economic returns. Most social capital is
endogenously formed, and it’s unusual to have experimental variation in local bonds.
Echoing Feigenberg, Field, and Pande (2013) on microcredit, we see that a program
that simply encourages group and ROSCA formation can increase social interactions,
enhance social capital, increase risk-pooling and cooperation, and perhaps even raise
incomes. What’s striking is that these protable social bonds did not form in the
absence of encouragement, and yet were provoked by a relatively short training. It
implies the poor may be social capital constrained as well as credit constrained, and
external intervention seems to help overcome barriers to collective action.
I. Setting and Study Participants
Uganda as a whole is a poor but stable and growing country. National income grew
roughly 6.5 percent per year for the two decades prior to this study (Government of
Uganda 2007). A long-running, low-level insurgency in northern Uganda, however,
meant that most of the north was left out.
From 1987 to 2006, small bands of rebels conscripted, abused, and stole from
civilians in northern Uganda, especially the Kitgum and Gulu districts. Equally dev-
astating was the Ugandan government’s decision to ght the insurgency by forcibly
moving nearly the entire rural population of Kitgum and Gulu—about two million
people—into dozens of displacement camps. The camps were often no more than
a few miles from people’s rural homes, but people generally could not access their
farmland during the war. Most households lost everything—livestock, homes, sav-
ings, and household durables—as a result.
By 2006 the rebels were mostly defeated or pushed out of the country, and by
2007 the government permitted displaced people to return home and rebuild. The
north’s economy began growing quickly, aided by an increase in demand from
a newly peaceful Sudan. The government started a number of large-scale devel-
opment programs to help the north catch up to the rest of the country. Even so,
northern Uganda had some of the lowest standards of living in the world. By 2007,
two-thirds of households were unable to meet basic needs and lived mainly on food
aid (Government of Uganda 2007).
By 2009, when this study began, most people had rebuilt their homes and had
begun farming again. Food distribution and other emergency relief had ended. Most
rural villagers, however, were still desperately impoverished.
A. Study Sites and Participants
AVSI identied 120 villages in the two most war-affected districts, Kitgum
and Gulu. Most villages ranged in size from 350 to 1,000 people, with an average
population of 699 (about 100 households). The study villages represented about a
quarter of the population of the six rural subcounties where AVSI worked.
10
10
AVSI actively worked in six subcounties—Odek, Lakwana, and Lalogi in Gulu and Omiya Anyima,
Namokora, and Orom in Kitgum. These have 252 total villages: 84 in Gulu; 168 in Kitgum. AVSI excluded from
40 AMERICAN ECONOMIC JOURNAL: APPLIED ECONOMICS APRIL 2016
AVSI held community meetings to describe the program and asked communities
to nominate 20 marginalized villagers, asking that three-quarters be women aged
14 to 30. AVSI staff interviewed all nominees and selected 10 to 17 participants per
village, excluding relatives of leaders and the least poor.
Table 1 describes the 120 villages and all 1,800 study participants, based on a
baseline survey of participants and each community leader. Twenty-six percent of
villages had a weekly market, and while on average there were three shops or kiosks
selling goods, the median village had none. Most goods were imported from the
district capital and retailed by a handful of shop owners.
Outside of traditional occupations (e.g., subsistence agriculture and some casual
labor), main work opportunities came from petty trade and retail, cottage production
(e.g., bricks, charcoal), livestock rearing, and cash crops. These farm and nonfarm
microenterprises required few new skills or education, but they were capital-intesive
and had xed costs of entry.
The average participant in the program was female, 27 years old, and had 2.8 years
of education. Half were married or partnered. They reported an average of 15 hours
of work a week in the past month, mainly in their own agriculture. Just 3 percent did
any petty trade or business.
In general they were poor with no access to nance. Average cash earnings in the
month before the survey were 8,940 Ugandan shillings (UGX) ($4.47 at 2009 mar-
ket exchange rates). Formal insurance was unknown, and almost no formal lenders
were present in the north at the outset of this study in 2008. Only 9 percent of the
sample were members of a village savings and loans group. On average they had
UGX 4,860 ($2.42) in cash savings and a nearly equal amount in debts, usually
from family and friends. Just 4 percent said they could get a loan of $50, which
is unsurprising because of high transaction costs and the near absence of informal
or formal lending institutions. Formal loan terms seldom extended beyond three
months, moreover, with annual interest rates of 100 to 200 percent. Because of high
fees, real interest rates on savings were typically negative. Given the startup costs
of microenterprise, this absence of credit and insurance was a major barrier to entry.
Effects of the War.—The war affected and displaced everyone in the study sam-
ple, impoverishing all. Until about a year before the program, everyone in the vil-
lage had lived in a nearby displacement camp for at least three years, with no access
to farmland, during which their lands became overgrown and their houses destroyed.
At the time of the WINGS program, households were reestablishing agriculture for
the rst time since at least 2003.
One in ve people in our sample were abducted into the armed group at some
point, usually only for a few days to carry looted goods. Long stays with the armed
group were less common, and only 5 percent of the sample became ghters or were
forced to marry a rebel commander. Abduction and conscription, however, were not
the sample villages with fewer than 80 households. AVSI then chose program villages proportional to parish popula-
tion, whereby more populous parishes would have a higher number of program villages (A parish is an administra-
tive unit within the subcountry with ve to ten villages). Ofcial population gures did not exist and estimates were
based on 2008 data from AVSI and the United Nations. We estimate participant households in treatment villages
were less than 2 percent of households in the subcounty.
VOL. 8 NO. 2 41
Blattman et al.: microenterprise support For ugandan ultrapoor
necessarily a source of relative poverty. Annan et al. (2011) used exogenous vari-
ation in conscription to identify the long-term effects. Social acceptance of former
conscripts was high, and most people were psychologically resilient.
11
These nd-
ings held even for the longest-serving females and those who were forcibly married
11
Psychological distress is commonplace, but serious symptoms are concentrated in the minority exposed to the
most violence and with the least social support.
T 1—D S R B S C
Means, full sample Balance test
Variable Treatment Control
(Observations = 896) (Observations = 904)Difference p-value
(1) (2) (3) (4)
Demographics
Age 27.02 27.63 −0.62 0.17
Female 0.86 0.86 −0.01 0.72
Married or living with partner 0.46 0.50 −0.05 0.26
Single-headed household 0.51 0.47 0.04 0.17
Highest grade reached at school 2.82 2.75 0.07 0.70
Forcibly recruited into rebel group 0.20 0.25 −0.05 0.03
Carried gun within rebel group 0.03 0.04 −0.01 0.45
Forcibly married within rebel group 0.03 0.03 −0.00 0.63
Lagged outcomes
Any nonfarm self-employment 0.03 0.04 −0.01 0.17
Average work hours per week 14.57 16.19 −1.62 0.12
Agricultural 11.27 13.36 −2.09 0.02
Nonagricultural 3.29 2.83 0.46 0.25
Average hours of chores per week 34.88 34.25 0.63 0.68
No employment hours in past month 0.23 0.18 0.05 0.07
Monthly cash earnings, 000s UGX 8.54 9.32 −0.78 0.26
Durable assets (consumption), z-score −0.67 −0.59 −0.07 0.05
Durable assets (production), z-score −0.53 −0.50 −0.02 0.48
Number of community groups 0.48 0.58 −0.10 0.04
Member of a savings group 0.08 0.11 −0.03 0.07
Total savings, 000s UGX 4.24 5.47 −1.23 0.20
Total debts, 000s UGX 4.24 4.08 0.15 0.82
Activities of daily life, z-score −0.06 −0.04 −0.02 0.75
Symptoms of distress, z-score 0.09 −0.09 0.18 0.02
Quality of family relationships, z-score −0.09 0.09 −0.19 0.00
Any community maltreatment, past year 0.19 0.16 0.03 0.11
Village-level covariates (Observations = 120)
Village population 749.62 649.05 100.58 0.34
Inverse distance to all villages 0.51 0.58 −0.07 0.34
Inverse distance to treatment villages 0.56 0.47 0.09 0.43
Distance to capital (km)46.21 44.72 1.48 0.58
Accessible by bus 0.98 0.91 0.08 0.05
Village has a market 0.18 0.34 −0.16 0.05
Number of shops in village 1.23 1.75 −0.52 0.30
Total NGOs in village 7.13 7.42 −0.29 0.68
p-value from joint signicance of 76 covariates < 0.01
Notes: All variables denominated in UGX and hours were top-censored at the nintey-ninth percentile to contain out-
liers. The durable asset indexes (z-scores) are calculated so that they have mean zero and unit standard deviation for
the full sample over all survey waves, and hence the sign is negative at baseline. The differences in columns 3 and
4 come from OLS regressions of baseline covariates on an indicator for treatment plus a district xed effect, with
robust standard errors clustered by village.
42 AMERICAN ECONOMIC JOURNAL: APPLIED ECONOMICS APRIL 2016
or bore children. Conscription also had little effect on women’s schooling and labor
market outcomes. Women’s options outside the armed group were not much better
than inside the armed group, since most would not have been schooled or accumu-
lated capital. Conscripted men, however, were well behind their peers after the war,
because they missed out on opportunities to accumulate physical and human capital.
In short, the war was so destructive that few young people emerged with any
assets or schooling. At the time of the WINGS program, they were rebuilding their
livelihoods from almost nothing.
B. Comparison to Nonparticipating Villagers
In general, the program successfully targeted the villages’ poorest, but it’s worth
noting that almost all villagers were very poor by any measure. We do not have data
on nonparticipants at baseline. Twenty months after the start of the program, how-
ever, we surveyed 2,836 nonparticipant households in treatment and control villages
(about 25 from each village), and sought to interview two working age adults per
household, in order to measure spillovers.
12
Table 2 reports summary statistics for
participants and nonparticipants in the control villages only, in order to compare
people in the absence of direct treatment effects. We distinguish between house-
holds that were and were not traders at baseline.
If we look at similar-aged adults in “nonparticipant” households, participants
have similar cash earnings, but 24 percent lower consumption, 0.63 standard devi-
ations fewer durable consumption assets (e.g., house quality, furniture, and house-
hold items), and 0.22 standard deviations fewer production assets (e.g., livestock
or farm tools). Participants also have about half the education and 63 percent of
nonparticipants’ work hours. About a third of nonparticipant households have at
least one adult engaged in trading at baseline, and these tend to be wealthier than
average.
II. Intervention, Experimental Design, and Data
The WINGS program had four components:
Basic Skills Training.— Participants received ve days of business skills training,
ending with the preparation of a simple business plan. Training was designed for the
illiterate and focused on business planning, sales, marketing, record-keeping, and
budgeting (see online Appendix A for the curriculum). Trainers reviewed plans with
the participants and returned unsatisfactory plans for revision. They encouraged
participants to consider high cash ow activities that would diversify their income
sources, especially petty trading and retailing.
12
Shortly before Phase 2 disbursement, we created village household lists, randomly sampled 25 nonparticipant
households from each village (excluding the roughly 15 participant households), and sought to interview two work-
ing age adults per household on their economic activities, plus household data on assets and expenditures. We also
collected village prices on a variety of goods. Nonresponse to the survey was only 5.5 percent.
VOL. 8 NO. 2 43
Blattman et al.: microenterprise support For ugandan ultrapoor
Cash.—Once a plan was approved, the participant received a grant of 300,000
UGX or $150 at 2009 market exchange rates. The grant was framed as funds to
implement the business plan. AVSI delivered cash in two equal installments about
two and six weeks after training.
Supervision.—AVSI trainers traveled four to ve times to the villages in the six
months after the grant to provide one-on-one advising and supervision.
Group Formation.—About two months after grants were disbursed, AVSI also
offered a three-day group dynamics training that encouraged participants in the vil-
lage to form self-help groups that would exchange ideas for improving their petty
business and agriculture, organize savings and credit, and (to a lesser extent) col-
laborate or cooperate in activities such as marketing their produce or buying inputs.
The vast majority of the three days focused on providing information and skills
related to working effectively as a group, including: leader selection, group decision
making, communication and listening skills, and conict resolution methods. It
applied these skills to the same topics that were the focus of the ve-day business
skills training: business planning, saving, and record-keeping. The difference is
that the group dynamics training mostly focused on how they could adapt these
skills when working as a group. The nal day focused on how to organize a sav-
ings group, including best practices and record-keeping. Other forms of business
cooperation, such as joint purchasing and collaborative marketing, were mentioned
in passing as advantages of working in groups, but these production economies of
scale received very little attention. Indeed, AVSI deliberately did not encourage
T 2—P N (control villages at Phase 1 endline)
Nonparticipants ages 17–40, control villages
Covariate Participants Traders Non-traders All
(1) (2) (3) (4)
Age 28.10 29.35 28.55 28.71
Years of education 2.81 5.58 4.48 4.70
Average weekly work hours 15.02 31.08 21.93 23.78
Agricultural weekly hours 9.68 21.11 16.80 17.67
Nonagricultural weekly hours 5.47 9.98 5.13 6.11
Reports any hours in petty business 0.16 0.26 0.07 0.11
Monthly cash earnings, 000s of UGX 15.76 23.45 10.14 12.82
Monthly household consumption, 000s of UGX 108.38 175.05 134.04 142.30
Durable assets (consumption), z-score −0.45 0.64 0.06 0.18
Metal roof 0.00 0.03 0.00 0.01
Number of goats 0.97 1.62 1.22 1.30
Number of bicycles 0.39 0.77 0.60 0.63
Number of mobile phones 0.14 0.58 0.35 0.39
Durable assets (production), z-score −0.21 0.30 −0.07 0.01
Observations 917 360 1,427 1,787
Notes: For work hours and income, we report household averages in nonparticipant households, restricting data to
adults aged 17–40. A household is coded as a trading household if at least one household respondent says he or she
regularly sold an item a year ago, and did not obtain that item from his or her own production, for any items in a list
of 35. Individual-level covariates come from a self-reported survey of all respondents. All variables denominated in
UGX and hours were top-censored at the ninety-nineth percentile to contain outliers.
44 AMERICAN ECONOMIC JOURNAL: APPLIED ECONOMICS APRIL 2016
participants to form rms or cooperatives. This is one reason AVSI offered the
group training some weeks after the individual business plans, grants, and initial
investment decisions.
Groups decided on their own aims and organization, however, and at the end of
the training AVSI helped groups write a constitution that formalized the aims, lead-
ership, and decision-making structure of the group. Online Appendix A describes
the curriculum.
On average, WINGS cost $860 per person at market exchange rates: $150 for
grants; $125 for targeting and disbursement; $124 for business training; $82 for
group dynamics training; $348 for ve supervisory visits; and $31 in other costs.
This is equivalent to PPP $2,150.
A. Phase 1 Research Design
In Phase 1, we randomized 60 of the 120 villages to receive the WINGS program.
The other 60 were randomized to a waitlist group (Phase 2) to be treated at least 18
months later. The participants in the waitlist villages were aware of this treatment
status.
Within these 60 treatment villages in Phase 1, we randomized 30 to receive the
group dynamics training and 30 to no group encouragement. Participants in the
60 control villages were told they would receive the intervention in about 18 to 24
months, called Phase 2. Figure 1 illustrates the sample, design, and timing.
We randomized by public draw, to ensure village buy-in and transparency.
13
The
draw resulted in a slight imbalance in baseline covariates, reported in Table 1.
14
Treatment participants were slightly worse off, with lower durable assets, employ-
ment, literacy, savings group memberships, participation in armed groups, and fam-
ily and community support. The villages they lived in were also less likely to have
a market. A test of joint signicance of all covariates has a p-value of < 0.001. If
anything, this may lead us to understate treatment effects. To account for possible
bias, we will control for these covariates in all treatment effects estimates and show
robustness to difference-in-differences measures.
To evaluate Phase 1, we attempted to survey all 1,800 participants 20 months
after baseline, 16 months after the rst grant (at the median). Attrition was minimal;
we tracked migrants to their current location and found 96.3 percent (not including
three who died). Attrition is generally not signicantly correlated with treatment or
baseline covariates (see online Appendix B).
15
13
We gathered village leaders in each district. They drew village names without replacement to be assigned to
Phase 1 or 2. The authors were present for the draw to ensure its validity. We randomized group dynamics training
via computer.
14
See online Appendix B for all 76 covariates, as well as balance tests for the group dynamics randomization. In
total, 24 percent of the (nonindependent) covariates have p < 0.10. In general, the group dynamics randomization
was balanced.
15
In addition to these survey data, we collected formal qualitative data to better understand program experi-
ences, constraints, and mechanisms. Two Ugandan research assistants interviewed 32 randomly selected partici-
pants in eight villages three times during and after the program. They followed semi-scripted questionnaires and
recorded, transcribed, and translated all interviews and notes.
VOL. 8 NO. 2 45
Blattman et al.: microenterprise support For ugandan ultrapoor
B. Phase 2 Research Design
In Phase 2, participants in the 60 control villages received the WINGS program.
16
We used this as an opportunity to evaluate the marginal impact of the highest-cost
component, supervision.
17
The rst supervisory visit or two was mainly to hold
grantees accountable for implementing their business plan. The later visits were
primarily to provide advice. Of the 904 people in Phase 2, we randomly assigned
16
Program changes were minor. AVSI increased grants to 360,000 UGX to account for ination, and disbursed
grants in a single tranche for efciency.
17
Of the 60 villages, 30 were also randomized to have spouses included in the training, and present at the grant
disbursement, described in a companion paper (Green et al. 2015).
02/09: Selected 120 villages (60 per district) and
communities nominated ~2,300 persons
04/09–06/09: Baseline survey of 1,800 clients (100%)
Persons deemed not
“vulnerable”
excluded from study
06/09: 60 villages (896 clients) randomized to
receive training (06/09–08/09), cash (08/09–10/09),
and follow-up (10/09–10/10).
Performed by implementer (AVSI)
Performed by researchers
06/09: 60 villages (904 clients) randomized to
waitlist treatments
30 villages receive core
program only
02/09–03/09: 30
villages also receive
group dynamics
training
318 clients:
No follow-up
300 clients:
1 to 2 follow-
ups
286 clients:
3 to 5 follow-
ups
01/11: 57 clients no
longer in village replaced
03/09–04/09: Registered 1,800 clients in 120 villages
11/10–02/11: Surveyed 861 (96%) of clients (0
deaths, and 0 villages, and 35 people unfound)
11/10–02/11: Surveyed 870 (96%) of clients (3
deaths, and 0 villages, and 31 people unfound)
09/11–10/11: Surveyed 858 (95%) of clients (3
deaths, and 0 villages, and 35 people unfound)
09/11 to 06/12: Follow-ups performed (94%
adherence rate)
904 clients (847 original Phase 2 clients and 57
replacements) receive training (03/11–05/11) and
cash grants (08/11–09/11)
06/12–08/12: Surveyed 868 (96%) of clients (1
death, and 0 villages, and 35 people unfound)
F 1. D S S E D
46 AMERICAN ECONOMIC JOURNAL: APPLIED ECONOMICS APRIL 2016
individuals to receive the cash and basic training with one of three treatments: no
supervisory visits; one to two supervisory visits, focused principally on commitment
to invest; or all ve supervisory visits, to provide commitment but also substantive
advice on business management and household bargaining.
18
To evaluate impacts, we rst surveyed Phase 2 participants about a month after the
grant, shortly before the rst follow-up. We intended this short-run survey to assess
how participants’ actions and investments varied with expectations of any supervi-
sion. We surveyed them again about a year after the grants to study the impacts of
actual supervision. Again, attrition was low; we found 95 percent at the one-month
survey and 96 percent at the one-year survey.
III. Empirical Strategy
We estimate intent-to-treat (ITT) effects via the ordinary least squares (OLS)
regression:
(1) Y
ij = θ T
j + δ
T D j T + δ
T
–
D j A + X
ij β + ε
ij ,
where Y is an outcome for participant i in village j, T is an indicator for treatment
(e.g., assignment to Phase 1 versus Phase 2), and X is a vector of controls that
includes a district xed effect and all baseline covariates, including lagged out-
comes.
19
Robust standard errors are clustered by village.
The terms D j T and D j A are weighted measures of distance from the village to other
treatment villages and all other evaluation villages. We include them to account for
and estimate potential spillovers from participants in treatment villages to those in
wait-list villages, which could otherwise bias treatment effects. The average wait-
list village has at least ve treatment villages within ten kilometers (km), and many
villages share markets. We can identify and control for cross-village externalities
using exogenous variation in the local density of treatment villages generated by the
randomization, conditional on the density of all evaluation villages in the sample
(Miguel and Kremer 2004).
20
There are two other threats to identication. One is anticipation of treatment.
If participants delayed investments by one to two years in expectation of a grant,
this would lead us to overstate treatment effects. Alternatively, the expectation of
a future grant could act as a form of insurance and increase investment, leading us
to understate impacts. For instance, Bianchi and Bobba (2013) show that expecta-
tion of conditional cash transfers in Mexico increase microenterprise investment by
18
We randomized via computer at the individual level, blocking by village. The groups are relatively balanced,
with balance tests reported in online Appendix B. Before Phase 2 began, 57 of the 904 participants assigned to
Phase 2 had died or left the village and were no longer eligible. In February 2011, AVSI replaced these participants
with others from the same village following the same nomination and screening procedures described above.
19
See online Appendix B for all baseline covariates. Also, note that baseline and endline variables denominated
in UGX or hours can have a long upper tail. Extreme values will be inuential in any treatment effect, and we there-
fore top-code them at the nintey-ninth percentile.
20
That is, D j T is a random variable conditional on D j A . Previous papers estimate distance measures as the number
of villages within a xed radius. We use a slightly less dichotomous measure, described in Appendix D, using road
network distances. In practice these spillovers appear to be negligible and so they do not materially change our
conclusions (online Appendix D).
VOL. 8 NO. 2 47
Blattman et al.: microenterprise support For ugandan ultrapoor
relaxing an insurance rather than a liquidity constraint. Our design does not allow us
to say what these anticipation effects might be.
A second threat is that, since outcomes are self-reported, we will overestimate
the impact if experimenter demand leads treated subjects to overreport well-being,
or if the controls underreport outcomes in the (mistaken) belief that they could be
dropped from eligibility. We feel these biases are unlikely to drive our results for a
few reasons. Most of all, as we will see below, we observe no impact on variables
such as empowerment or health, meaning that incentives to misreport would have
to be conned to economic outcomes alone. Related to this, misreporting would
have to be highly systematic within economic outcomes: income and employment
was collected through more than 100 questions across 25 activities, and assets and
expenditures were calculated from 150 questions.
IV. Results
A. Impacts of the Full Intervention
Ninety-ve percent of the treated made initial business plans for the buying and
selling of goods, and the remainder made plans for butchery, livestock, or other busi-
ness.
21
As we will see, however, over time their investments diversied into more
nonfarm businesses and also livestock.
While the capital was seen as crucial to starting their enterprises, most partici-
pants also reported that the training, business planning, and supervision were valu-
able. Sixteen months after the grants, 98 percent said that AVSI staff were important
in planning their business, 77 percent of people said the supervisory visits made
them somewhat anxious, and 94 percent would recommend the supervisory visits.
Yet this advising was not too constricting: 95 percent said they felt free to spend the
grant as they saw t.
Table 3 reports impacts 16 months after grants.
22
We start by focusing on the
program without group dynamics training.
At baseline, only 3 percent of the sample reported any nonfarm business, a cat-
egory that includes mainly petty trading and cottage production, but excludes agri-
cultural enterprise and livestock-related work (Table 1). These nonfarm businesses
grew substantially in the 20 months after the baseline survey. In control villages,
half of the participants eligible for the program say they attempted to start some
nonfarm business since baseline, and 39 percent have some form of nonfarm busi-
ness at the endline survey—an increase of 36 percentage points. This investment
could be an anticipation effect of the future grant (since they will enter Phase 2 a few
months after the endline, reducing the risk of investments) in which case treatment
effects understate the effect of the WINGS program. Alternatively, the investment
and enterprise start-up could reect the rise in business we would expect as people
21
Ninety-six percent of people assigned to treatment received the training and grant in Phase 1. The main rea-
sons for not receiving a grant were death or migration, in which case someone outside the study sample received
the program instead.
22
We focus on occupational choice, employment levels, and incomes. This focus is in line with the model in
online Appendix C.
48 AMERICAN ECONOMIC JOURNAL: APPLIED ECONOMICS APRIL 2016
recover from an adverse shock. In principle, the control group could delay some
investment in anticipation of the grant, though this would mean delaying for nearly
two years. If so, we overestimate the effect of the WINGS program.
Nonetheless, there are even greater changes in treatment villages. Nearly every
participant tried to start a nonfarm enterprise, and 79 percent have one at end-
line—a doubling compared to the control group. We see a large shift in occupation
choice towards nonfarm enterprise, mainly wholesale and retail trade, kiosks, and
shops, but also including some services.
As a result, nonfarm hours of work in the treatment group double compared
to controls, rising from about 5 to 11 per week. But there is no crowding out
of other activities, as the participants were generally underemployed beforehand.
T 3—E I WINGS P G F
ITT estimates, 16 months after grants
(Observations = 1,734)
Outcome Control mean
No group
training
Group
training Difference
(1) (2) (3) (4)
Occupational choice
Any nonfarm self-employment 0.39 0.401 0.409 0.008
[0.030]*** [0.033]*** [0.034]
Started enterprise since baseline 0.50 0.487 0.485 −0.002
[0.025]*** [0.025]*** [0.018]
Average work hours per week 14.98 9.391 9.877 0.486
[1.608]*** [1.794]*** [2.029]
Agricultural 9.52 3.496 4.002 0.506
[1.389]** [1.300]*** [1.524]
Nonagricultural 5.342 5.895 5.875 −0.020
[0.893]*** [0.916]*** [1.217]
Average hours of chores per week 39.75 0.305 1.416 1.111
[1.013] [1.203] [1.301]
Income and food security
Index of income measures, z-score −0.22 0.464 0.616 0.151
[0.068]*** [0.080]*** [0.087]*
Monthly cash earnings, 000s UGX 15.53 10.372 23.390 13.018
[3.443]*** [4.607]*** [5.512]**
Durable assets (consumption), z-score 0.12 0.327 0.384 0.056
[0.067]*** [0.068]*** [0.073]
Monthly nondurable consumption, 000s UGX 107.74 31.031 33.439 2.408
[5.010]*** [5.227]*** [6.049]
Durable assets (production), z-score −0.02 0.402 0.397 −0.005
[0.064]*** [0.058]*** [0.064]
Times went hungry, past week 0.19 −0.098 −0.084 0.013
[0.039]** [0.036]** [0.043]
Usual number of meals per day 1.76 0.057 0.078 0.021
[0.028]** [0.031]** [0.031]
Notes: All variables denominated in UGX and hours were top-censored at the nintey-ninth percentile to contain
outliers. Columns 2 and 3 report the coefcients and standard errors on indicator for assignment to Phase 1 without
and with the group dynamics component in an OLS regression of each outcome on treatment indicators, a Gulu dis-
trict (strata) xed effect, and baseline covariates. Column 4 reports the difference between the two treatment groups.
Standard errors are robust and clustered at the village level.
*** Signicant at the 1 percent level.
** Signicant at the 5 percent level.
* Signicant at the 10 percent level.
VOL. 8 NO. 2 49
Blattman et al.: microenterprise support For ugandan ultrapoor
Farm hours actually rise compared to controls, from about 9.5 hours per week in
the control group to about 13 with treatment. Most of this increase comes from
increased hours caring for livestock, as ownership of cattle, sheep, goats, and pigs
more than doubles.
We have three measures of income. The rst is cash earnings in the past month,
in 2009 UGX. Cash earnings are noisy, subject to seasonality, and will understate
income by omitting home production. Thus, we also emphasize two measures of
permanent income. One is an index of 33 durable assets for consumption pur-
poses (including housing quality, furniture, and household items), standardized to
have mean zero across all survey rounds, and hence measures asset changes over
time. This is commonly used as a proxy for consumption (Filmer and Scott 2012).
Unfortunately, we do not have asset values.
The other measure is estimated total household consumption of 57 goods in the
past month, including food, small household items, communications, transport, and
so forth.
23
This is a partial list of goods, and may understate true consumption, espe-
cially because we do not have durable asset prices and cannot impute consumption
of durables. Of course it is possible that the consumption measure overstates the
value of home-produced items because of quality differences. We also consider a
family index of all three measures.
The baseline was run at a time of intensive planting and weeding, shortly before
the lean season began. The Phase 1 endline was run at a time of planting and har-
vesting at the outset of a dry season, during which dry season crops are produced
and nonfarm activities such as brick-making are common. Major activities include
the sale of crops and animals for festivals and payment of school tuition fees. We
see incomes rise from baseline to endline partly because of this seasonality, but also
because the 20 months from baseline to endline were a time of increasing incomes
and productivity.
Durable assets in control villages rose 0.75 standard deviations since baseline.
Their cash earnings rose by two thirds, from UGX 9,320 per month (about $4.70 at
2009 market exchange rates) to UGX 15,530 ($7.72), while hours of work stayed
steady. We do not have nondurable consumption data at baseline.
The WINGS program leads to large increases in all three income measures, by
0.46 standard deviations overall. This includes a 66 percent increase in monthly cash
earnings relative to the control group, though this is just UGX 10,372 ($5.19) in
absolute terms. Durable consumption assets rise by 0.33 standard deviations. Non-
durable consumption rises 29 percent relative to the control group, UGX 31,031 per
month, or about $15.50 at market exchange rates.
24
Note this is a total household
level measure of consumption, and there are 6.9 members in the average household.
This consumption treatment effect is three times the earnings treatment effect,
which could reect a number of factors besides measurement error: low seasonal
earnings; earnings that are reinvested in other household members’ enterprises or
23
This abbreviated approach follows Beegle et al. (2012). Some items (such as food) are asked with a three-day
recall period, some items (such as communications and transport) are asked with a one or two week period, and
larger nondurable spending (such as festivals, etc.) are asked with a recall of several months. All items are then
adjusted to monthly totals and added.
24
We cannot reject equality of treatment effects by gender (not shown).
50 AMERICAN ECONOMIC JOURNAL: APPLIED ECONOMICS APRIL 2016
home-produced forms of consumption (e.g., products from livestock); or the con-
sumption of some of the saved grant.
Because no income measure is perfect, this introduces uncertainty into the true
effect on poverty. But if we use $5.16 per month as a lower bound and $15.50 as an
upper one (about $13 and $39 in PPP terms) this is indisputably a large impact on
income in absolute and relative terms.
We also have an index of 19 production-related durables, mainly livestock, farm
equipment, and vocational tools (e.g., sewing machines). These are not included in
the consumption durables. These investments rise 0.4 standard deviations, primarily
from livestock purchases—0.27 more cattle, two more fowl, and two more other
animals such as goats, sheep, or pigs (online Appendix D). Hence, prots are being
reinvested in productive assets.
Food security improves slightly as a result of this increase in incomes and enter-
prise. Times going hungry in the past week fall from 0.2 to 0.1, and the number of
meals per day rises from 1.76 to 1.82. Since the endline was not run in the lean sea-
son, the effects may be muted. The bulk of earnings seems to go into assets, savings,
and non-food consumption. For instance, savings more than tripled, increasing to
UGX 107,344 ($54).
Two concerns are bias arising from baseline imbalance and systematic attri-
tion. The results, however, are robust to exclusions of the baseline covariates, a
difference-in-differences ITT estimate controlling for other baseline covariates, and
also relatively extreme attrition bounds (see online Appendix D).
Comparison to Nonparticipants.—We can also see how WINGS affects the posi-
tion of program participants in the community by comparing them to nonparticipants
in control villages. Figure 2 depicts the distribution of the standardized income index
for participants in treatment villages, participants in control villages (i.e., those
nominated for the program), and adults from nonparticipant households in the con-
trol villages, divided into households that did and did not engage in petty trading at
baseline. Comparing distributions, we see that treatment brings the ultrapoor nearly
past the income levels of nontrading households, but not past the income levels
of traders. In control villages, means are −0.36 among participants, −0.06 among
nontrading nonparticipants, and 0.59 among trading nonparticipants. Participants in
treatment villages have a mean of 0.02.
The income index is scaled to have zero mean and unit standard deviation across
all participants and nonparticipants. We restrict the sample to 17 to 40 year olds.
Within-Village Spillovers.—About 10 percent of village households received
WINGS, meaning WINGS provided a large cash inux into the village. Most par-
ticipants entered petty trading, and so we might wonder about the effect of new
entrants on prices or preexisting traders in treatment villages (especially given that
the median village has no shops).
25
Comparing treatment and control villages,
25
If the local retail market is not perfectly competitive (e.g., due to liquidity constraints serving as entry bar-
rier), a program that promotes petty trading should drive down the price of imported goods by increasing compe-
tition. Cunha, De Giorgi, and Jayachandran (2011) nd some evidence that imperfect competition partly explains
VOL. 8 NO. 2 51
Blattman et al.: microenterprise support For ugandan ultrapoor
prices of imported and produced/exported goods both fell a slight amount (0.05 and
0.09 standard deviations, not statistically signicant), potentially because increased
trade decreased the market power of existing traders and brought prices closer to the
competitive equilibrium. We present these results in detail in online Appendix D5.
We can also compare nonparticipants in treatment villages to nonparticipants in
control villages (with the caveat that within villages participants were not randomly
selected). Overall, despite the size of the program, there are limited spillovers to
markets or nonparticipant households. We see no effect on the incomes or occupa-
tional choice of nonparticipant households, although if we look only at preexisting
traders we see a slight shift from petty trading to casual and agricultural work, with
incomes holding more or less steady.
the price effects of transfers in rural Mexico. This would benet consumers, but potentially decrease the prots of
existing retail sellers, leading them to shift occupations and potentially lose income. Also, as in Buera, Kaboski, and
Shin (2012), new entrepreneurs could supply less labor to the market, more labor to their own business, and may
even demand labor from the market instead. This will push up market wages. Given that most people in northern
Ugandan villages are not fully employed, most enterprises are owner-operated, and there is a limited market for
casual labor, we did not expect to see a signicant change in wages. At the same time, to the extent that higher
earnings increases participants’ consumption, it could create a countervailing effect on prices, diminishing any
decline. Studies of Mexican conditional cash transfer programs nd that the transfers increase consumption of
both cash recipients and program-ineligible households (Hoddinott and Skouas 2004, Angelucci and De Giorgi
2009). Higher demand can increase prices as a result, especially in more remote locales (Cunha, De Giorgi, and
Jayachandran 2011).
0
0.2
0.4
0.6
0.8
−2 0 2 4 6
Control nonparticipants (traders)Control nonparticipants (non-traders)
Treated participants Control participants
Density
z-score
F 2. I I D, W-A A P N
Notes: The income index is scaled to have zero mean and unit standard deviation across all participants and nonpar-
ticipants. We restrict the sample to 17 to 40 years olds.
52 AMERICAN ECONOMIC JOURNAL: APPLIED ECONOMICS APRIL 2016
B. Impacts of Group Formation
Columns 3 and 4 of Table 3 report the impacts of receiving the group encourage-
ment. There is no evidence of an impact on business start-up, occupational choice,
and levels of work. By one measure, though, group formation increased incomes.
With group formation and training, the earnings treatment effect is more than double
the size of the WINGS program without the group intervention. Durable assets and
nondurable consumption, however, are not much higher. Overall, because of the
earnings impact, the income index is 0.15 standard deviations greater but signicant
only at the 10 percent level.
We thus treat the earnings effect with caution. The magnitude is large enough,
however, that it’s worth exploring potential reasons. The evidence suggests that
informal insurance and cooperative activities (especially farming) play a role.
Table 4 reports ITT estimates for various group interactions. Two-thirds of the
control group are members in a community group of some kind, from water and
school committees to savings and farming groups. Being in a treatment village with-
out group encouragement increases group membership by 12.9 percentage points,
and the group formation treatment nearly doubles this effect. The people in group
formation villages go from being in 1.7 groups to 2.9.
Treatment without group encouragement increases the frequency of meeting with
a group. People in control villages meet with their “most important group” (not
necessarily the WINGS-identied group) 1.4 times a month. This rises to about 1.8
times a month with the standard WINGS program, and to 2.4 times with the group
dynamics training. Meetings are principally for savings-related activities or commu-
nal farming, and seldom for petty business-related activities.
The group is more likely to save together and lend to one another—in effect
providing a form of informal insurance. The group dynamics training leads to an
increase in loans to and from other households, but no signicant change in transfers
(which do not have to be repaid). Total debts rise as a result of group dynamics to
almost double the control level of debts.
Communal farming is one of the most commonplace forms of economic coop-
eration—people pool their labor and either assist each other on one another’s plots,
or farm a new plot collectively for cash or own consumption. Control villagers meet
1.3 times a month for farming, and this rises by almost two-thirds with WINGS and
group dynamics training. We do not have a measure of total cooperative farming hours,
but note that with WINGS alone, earnings from the last harvest falls by UGX 33,211
(about $18) compared to the control group, perhaps because petty trading crowds
out these activities. But there is no decrease in the group dynamics trained villages.
C. Health, Social Relations, and Empowerment
Health.—Table 5 reports non-economic program impacts. There is little change
in an index of physical health measures.
26
But of the three people who died between
26
It is a standardized index of days ill, a subjective “overall” health question, and three activities of daily living (walk-
ing a distance, carrying a heavy load, and working on a farm). See online Appendix D for component treatment effects.
VOL. 8 NO. 2 53
Blattman et al.: microenterprise support For ugandan ultrapoor
T 4—P F I G F
ITT estimates, 16 months after grants
(Observations = 1,734)
Outcome
Control mean
No group
training
Group
training
Difference
(1) (2) (3) (4)
Group engagement
Member of any community group 0.668 0.129 0.245 0.116
[0.028]*** [0.026]*** [0.025]***
Number of community groups 1.721 0.448 1.169 0.720
[0.138]*** [0.155]*** [0.173]***
Of your “most important” group:
Times you meet per month 1.409 0.362 0.954 0.592
[0.156]** [0.147]*** [0.175]***
For communal farming 1.297 0.415 0.843 0.428
[0.327] [0.294]*** [0.375]
For savings 1.037 0.767 1.555 0.788
[0.196]*** [0.148]*** [0.217]***
For social support 0.117 0.051 0.201 0.150
[0.032] [0.040]*** [0.043]***
For business 0.142 0.089 0.142 0.053
[0.104] [0.062]** [0.116]
Financial access and inter-HH transfers
Transfers to other HH, 000s UGX 8.495 7.269 9.126 1.857
[2.225]*** [1.961]*** [2.802]
Transfers from other HH, 000s UGX 19.809 44.663 40.858 −3.805
[6.900]*** [7.078]*** [9.641]
Loans to other HH, 000s UGX 6.317 4.487 10.457 5.970
[1.876]** [1.997]*** [2.450]**
Loans from other HH, 000s UGX 9.215 1.408 6.578 5.170
[1.634] [2.336]*** [2.511]**
Member of a savings group 0.272 0.271 0.517 0.246
[0.048]*** [0.038]*** [0.052]***
Total savings, 000s UGX 37.374 107.607 115.467 7.860
[12.312]*** [12.544]*** [16.934]
Total debts, 000s UGX 5.232 0.974 4.141 3.167
[1.007] [1.270]*** [1.514]**
Other outcomes
Access to business advice in village, z-score −0.081 0.145 0.213 0.068
[0.062]** [0.064]*** [0.063]
Earnings in last harvest, 000s UGX 152.768 −33.211 2.204 35.415
[14.844]** [14.022] [16.514]**
Note: See notes to Table 3.
*** Signicant at the 1 percent level.
** Signicant at the 5 percent level.
* Signicant at the 10 percent level.
54 AMERICAN ECONOMIC JOURNAL: APPLIED ECONOMICS APRIL 2016
baseline and endline, all were control group members. This translates to a 0.5 percent
decrease in mortality, signicant at the 10 percent level.
27
Social and Community Participation and Status.—Increased income and employ-
ment is associated with greater social support, community participation, but also
community hostility, according to several index measures.
28
We see little change
in an index of three questions on the quality of family relationships, but we see
a 0.2 standard deviation increase in seven forms of social support received in the
past month (such as someone comforting you when you are feeling sad); and a
27
Also, as shown by Green et al. (forthcoming), there is also no change in mental health, as measured by an
index of 35 symptoms of depression and distress.
28
Means and treatment effects for individual components are available in online Appendix D.
T 5—I WINGS H, S R, E
ITT estimates, 16 months after grants
Outcome
Control mean
No group
training
Group
training
Difference
(1) (2) (3) (4)
Health
Physical health index, z-score 0.002 0.020 −0.013 −0.033
[0.070] [0.069] [0.085]
Died since baseline 0.003 −0.005 −0.005 0.000
[0.002]*[0.003]*[0.002]
Social relationships
Quality of family relationships, z-score 0.018 0.034 0.011 −0.023
[0.057] [0.052] [0.067]
Social support received, z-score −0.084 0.195 0.159 −0.037
[0.069]*** [0.063]** [0.081]
Community participation, z-score −0.086 0.159 0.345 0.187
[0.055]*** [0.062]*** [0.070]***
Community hostility index, z-score −0.070 0.164 −0.018 −0.182
[0.073]** [0.050] [0.076]**
Empowerment
Autonomy in purchases, z-scorea−0.026 0.082 0.089 0.007
[0.059] [0.062] [0.075]
Physical and emotional abuse, z-scorea−0.030 0.066 −0.046 −0.113
[0.079] [0.078] [0.088]
Degree of partner control, z-scorea−0.110 0.170 0.129 −0.041
[0.082]** [0.079] [0.086]
Partner relationship quality, z-scorea−0.086 0.180 0.201 0.021
[0.085]** [0.111]*[0.119]
Female and lives with partner at endline 0.536 0.046 0.072 0.026
[0.020]** [0.022]*** [0.024]
Notes: See notes to Table 3. Means and ITTs for index components are in online Appendix D.
*** Signicant at the 1 percent level.
** Signicant at the 5 percent level.
* Signicant at the 10 percent level.
a
Women with partners at endline only (n = 961).
VOL. 8 NO. 2 55
Blattman et al.: microenterprise support For ugandan ultrapoor
0.16 standard deviation increase in ve forms of community participation (such as
speaking out at community meetings or being a community leader).
But program participants (at least those who did not receive the group dynamics
training) were more likely to report disputes with neighbors or verbal abuse from
others. Our qualitative interviews, however, suggest they were targets of jealousy
or resentment from a small number of other households in the village, rather than
the village at large. Group formation and training may have insulated marginalized
women from such abuse.
Empowerment.—Finally, we see little evidence that enhanced business activity
and incomes increased reports of empowerment. We ask all subjects about their
autonomy in household nancial decisions, such as whether they can decide how to
spend their pocket money, use their earnings to buy clothes without permission, or
have a say in the purchase of large assets in the household.
29
This autonomy mea-
sure increases by 0.08 standard deviations (not statistically signicant).
30
We also
asked women with a partner at endline about aspects of their relationship.
31
There
is almost no change in self-reported physical and emotional abuse by the partner.
32
And, if anything, women actually report an increase in the degree of control their
spouse asserts over their nances and freedoms of movement and association. The
husband increases tendencies to control contact outside the home and also demands
or seizes some of the women’s newfound earnings (see online Appendix D). At the
same time, women report a 0.18 standard deviation increase in the quality of the
relationship, feeling more free to express their opinions and reporting a healthier
relationship. Overall, these results paint a picture of husbands who encourage but
then control their wife’s business earnings, in return for weak increases in purchas-
ing autonomy.
D. Impacts of Supervision
In Phase 2, when control villages received the program, we surveyed participants
about grant use and future expectations a month after they received it (a few weeks
before the rst follow-up visits), and again a year later. Table 6 reports one-month
treatment effects of expecting any follow-up, and Table 7 reports 12-month treat-
ment effects of two visits (supervision without advice) and ve visits (supervision
with extended advice).
29
We adapted empowerment questions from the Uganda Demographic and Health Survey.
30
For women alone the result is signicant at the 10 percent level.
31
Treated women were more likely to answer these questions because they were slightly more likely to be mar-
ried at endline, principally because of new marriages rather than any change in divorce rates. This could introduce
positive or negative selection from “marginal marriages.” We are interested in spousal abuse and relations as an
outcome, and so the current results including selection are relevant. Alternatively, we could conne our analysis to
the subset of women reporting partners at baseline. These results are not shown, but in general abuse and marital
control are lower (though not signicantly so) implying the marginal marriages are slightly better quality on aver-
age than baseline ones.
32
See Green et al. (2015) for a more detailed study of intimate partner violence in this setting. Note, however,
that abuse is reported by fewer than a quarter of women, and so is probably underreported. Even so, the effect of
treatment is close to zero and so even signicant underreporting is unlikely to affect the basic conclusion, so long
as it is not signicantly correlated with treatment.
56 AMERICAN ECONOMIC JOURNAL: APPLIED ECONOMICS APRIL 2016
One-Month Impacts.—People appear to have believed their treatment assignment:
only 10 percent of the “no follow-up” group said they expected a visit, whereas 98
percent of those assigned to any follow-ups did.
33
As in Phase 1, participants also
expressed independence and control over the funds. For instance, 94 percent of the
no supervision group said they felt free to spend the grant how they wished, and
80 percent said they could deviate from the business plan. An index of seven such
measures of autonomy fell by 0.17 standard deviations with supervision. With or
without supervision, however, there is little evidence of diversion of the grant or
social pressure to share it.
34
Expecting supervision has a modest impact on investment, at least by some mea-
sures. First, we asked participants to categorize how they spent the grant. Second,
we sum all investment-related expenses in the last month from an expenditure mod-
ule. Using the grant allocation measure, people have saved or not spent 54 percent
of the grant and reported almost no spending on celebrations and gifts, whether they
expected supervision or not. But those expecting a visit increased their share of the
grant spent on business investment by 5 percentage points, reducing the share spent
on durables (e.g., homes or livestock).
By the expenditure survey, however, follow-up has little effect on the amount
reported spent on business items. One reason could be that money is fungible, and
33
AVSI followed through on this assignment: none of the “no follow-up” people were visited, and 91 percent
of those assigned to two or ve follow-ups reported receiving the correct amount.
34
We asked participants how much of the grant they had to give to household members and other community
members. In total this was typically less than 1 percent of the grant, and expecting follow-up had little impact on
the amount.
T 6—I E S, 1 M G
ITT estimates, any supervision
(Observations = 858)
Mean, no
supervision
Outcome Coefcient Standard error
(1) (2) (3)
Expects AVSI staff to visit them in future 0.097 0.878 [0.021]***
Autonomy in grant planning and spending, z-score 0.082 −0.170 [0.072]**
Grant money diverted to others, 000s UGX 1.290 0.281 [1.293]
Proportion of grant spent on
Business investments and expenditures 0.269 0.050 [0.022]**
Large assets or home improvements 0.119 −0.038 [0.018]**
Food, clothing, or personal items 0.018 −0.007 [0.002]***
Gifts, contributions, or celebrations 0.002 0.000 [0.001]
Health or education 0.035 −0.007 [0.005]
Saved or unspent 0.537 0.002 [0.022]
Expenditure data (000s UGX)
Nondurable consumption 47.821 −0.509 [2.211]
Total business investments since grant 27.481 −1.841 [3.539]
Notes: Columns 2 and 3 report the coefcients and standard errors on assignment to either two or ve visits from an
OLS regression of each outcome on this treatment indicator, a stratum xed effect, and baseline covariates. Standard
errors are robust and clustered by village.
*** Signicant at the 1 percent level.
** Signicant at the 5 percent level.
* Signicant at the 10 percent level.
VOL. 8 NO. 2 57
Blattman et al.: microenterprise support For ugandan ultrapoor
while expenditures in the days following the grant might have fallen more heavily
on durables, the participants may have made up the investment shortfall by spending
other earned income on the relevant materials for their business.
One-Year Impacts.—Table 7 reports the impacts of supervision a year after grants.
We conducted the survey during a time of wet season planting and the beginning of
the main harvest, at the closing of a lean season.
Two supervisory visits increase the likelihood someone is operating a business
by 11 percentage points (19 percent), and hours of nonfarm work per week by 2.3
hours (44 percent). The three further visits lead to slight, not statistically signicant
increases.
Incomes are 0.021 standard deviations higher as a result of the two visits, and
0.036 standard deviations higher from ve visits. Neither result is statistically
T 7—E I S, 12 M G
Mean, no ITT estimates (Observations = 868)
Outcome supervision 2 visits 5 visits Difference
(1) (2) (3) (4)
Occupational choice
Any nonfarm self-employment 0.58 0.110 0.152 0.042
[0.039]*** [0.044]*** [0.037]
Started enterprise since baseline 0.92 0.045 0.061 0.016
[0.019]** [0.018]*** [0.014]
Average work hours per week 31.49 1.105 4.764 3.659
[2.229] [2.187]** [2.453]
Agricultural 26.28 −1.168 1.588 2.757
[2.213] [2.168] [2.230]
Nonagricultural 5.206 2.274 3.176 0.902
[0.907]** [0.892]*** [1.137]
Average hours of chores per week 33.42 1.698 1.797 0.099
[1.718] [1.481] [1.570]
Income and food security
Index of income measures, z-score −0.06 0.021 0.036 0.015
[0.082] [0.068] [0.073]
Monthly cash earnings, 000s UGX 13.06 3.308 2.170 −1.138
[2.523] [2.010] [2.637]
Durable assets, z-score 0.81 −0.065 0.052 0.117
[0.089] [0.088] [0.082]
Nondurable consumption, 000s UGX 132.57 −1.008 −3.022 −2.014
[4.968] [5.464] [4.781]
Times went hungry, past week 0.16 −0.019 −0.077 −0.058
[0.043] [0.030]** [0.036]
Usual number of meals per day 1.73 0.029 0.043 0.014
[0.041] [0.038] [0.037]
Notes: All variables denominated in UGX and hours were top-coded at the ninety-ninth percentile to contain outli-
ers. Standard errors are robust and clustered at the village level.
*** Signicant at the 1 percent level.
** Signicant at the 5 percent level.
* Signicant at the 10 percent level.
58 AMERICAN ECONOMIC JOURNAL: APPLIED ECONOMICS APRIL 2016
signicant.
35
Compared to those with no visits, those with two visits have nearly a
quarter higher cash earnings. Overall, cash earnings are low because of the season.
This is one reason we focus on measures of permanent income, including dura-
ble assets and nondurable consumption, where we see no signicant increase with
supervision.
We also tested whether supervision has greater impact on the most present-biased
or least autonomous individuals. We measured future orientation using incentiv-
ized games and self-reported survey questions. We also use a composite measure
of three self-reported nancial autonomy questions. We interact these baseline
measures with treatment in online Appendix D. The future orientation level and
interaction have the expected sign (i.e., more investment and earnings) though the
autonomy measure does not. None are statistically signicant, but the coefcients
on treatment—which represents the effect of treatment on the present-biased and
less autonomous—are larger and more statistically signicant than in Table 7.
E. Rate of Return on the Program
We lack the long term data to do a full cost-effectiveness analysis, but in Table 8
we imagine the simple case where the increase in income is permanent, and calcu-
late a simple internal rate of return (IRR) given program costs. Our preferred mea-
sure is our estimate of nondurable consumption. We also report IRR calculations for
the lower earnings treatment effect, although earnings probably understate returns
because it ignores other household members, non-cash earnings, and the proportion
of the grant that went into savings or durable assets.
The full WINGS program has an internal rate of return of roughly 24 percent
using total household nondurable consumption. Since we used an abbreviated con-
sumption measure, this is probably an underestimate of the true effect. This con-
sumption-based return is similar with or without group encouragement, since the
impact and cost rise more or less proportionally.
36
If we use the social discount rate
of 5 percent commonly used by the World Bank and International Monetary Fund
(IMF), the present value of the consumption treatment effect is nearly ve times the
cost of the program.
By way of comparison, the six livestock-based graduation programs evaluated by
Banerjee et al. (2015) had IRRs ranging from 2 percent to 24 percent two years after
the program, with an average IRR of 8 percent. Total consumption benets were
about 2.3 times as great as total costs of that program. The graduation programs
have an additional year of data, however, and so we can be more condent in the
sustainability of its gains.
Finally, despite the fact that supervisory visits represent about half of program
costs, we cannot reject a negative or zero return. The sign of the IRR for super-
visory visits is ambiguous, however, because the treatment effects of supervision
35
Given the sample size, we estimated we would require income increases of 0.15 or greater to have 80 percent
statistical power.
36
If we use the earnings treatment effects, the IRRs are lower but still large and positive: 8 percent without
group encouragement and 16 percent with it.
VOL. 8 NO. 2 59
Blattman et al.: microenterprise support For ugandan ultrapoor
on earnings and consumption run in different directions, and were in neither case
statistically signicant. The IRR is negative for the preferred consumption measure.
It’s possible, however, that supervision pays off in the longer term since business
survival rates are higher.
V. Discussion and Conclusions
These results show that a package of $150 cash ($375 in PPP terms), ve days of
business training, and ongoing supervision led to a doubling of new nonfarm enter-
prises and a signicant rise in incomes among extremely poor and conict-affected
villagers, most of whom were women who had never operated such an enterprise
before.
Second, the results show that simply encouraging people to form self-help
groups—centered around communal savings, lending, and work—was enough to
boost incomes. By some measures (such as consumption) this benet was propor-
tional to the cost. By other measures (monthly earnings) the benet was more than
proportional. Either way, it suggests that the poorest were not simply constrained
by a lack of capital, credit, and insurance. Social interactions among the margin-
alized were simple to encourage and this stimulated valuable labor-sharing and
risk-sharing.
Finally, by some measures, supervising how participants spent the grant strength-
ened their incentives and commitment to invest. We see no evidence that supervi-
sion increased long-run performance, however, since the impact on income by some
measures (such as consumption) was negative, and by other measures (earnings)
was positive, but in neither case was it statistically signicant after one year. We
were not able to test the effects of a grant with or without supervision against a
T 8—P C S I R R (IRR) C, 2009 PPP USD
Hypothetical perpetuity
Consumption ITT Program
cost
Present value at
5% discount rate
Proportion of
program cost
Phase Program Monthly Annualized IRR
Panel A. Using aggregate consumption treatment effect
1 Without group training 38.79 465 1,946 24% 9,309 478%
With group training 41.80 502 2,150 23% 10,302 467%
2 Two supervisory visits −1.26 −15 348 −4% −302 −87%
Five supervisory visits −3.78 −45 871 −5% −907 −104%
Hypothetical perpetuity
Earnings ITT Program
cost
Present value at
5% discount rate
Proportion of
program cost
Phase Program Monthly Annualized IRR
Panel B. Using monthly earnings treatment effect
1 Without group training 12.97 156 1,946 8% 3,112 80%
With group training 29.24 351 2,150 16% 7,017 163%
2 Two supervisory visits 4.14 50 348 14% 992 142%
Five supervisory visits 2.71 33 871 4% 651 37%
Notes: The internal rate of return is calculated as the discount rate r at which the net present value (NPV) of
the program returns are equal to the program cost. Since NPV =
∑
t=1
∞
(I T T × 12) × (1 − r) t = I T T × 12
______
r
,
IRR = IT T × 12
________
Program Cost
.
60 AMERICAN ECONOMIC JOURNAL: APPLIED ECONOMICS APRIL 2016
pure control group in Phase 1 of the study, which is an unfortunate limitation. We
also only have one-year data on the impacts of supervision. Nonetheless, given that
supervision costs two to three times as much as the grant itself, the impacts are sur-
prisingly ambiguous.
The returns to streamlining programs such as WINGS are potentially large, possi-
bly allowing more marginalized people to benet for the same level of aid spending.
For instance, targeting and disbursement were nearly as costly as the grant itself,
yet participants were not much different than nonparticipants in the village who
were not already traders. Moreover, mobile banking technologies are cheaper and
becoming available in the region. Finally, the last three to ve visits cost more than
the grant itself, but the returns to additional supervision appear to be diminishing at
best.
Reducing these costs could have high returns. For instance, a hypothetical pro-
gram that delivered similar impacts for half to two-thirds the cost could have a
35–55 percent internal rate of return.
37
It’s impossible to say what would happen to
program impacts from such cost-cutting, but given the estimates in Table 8 it is dif-
cult to imagine that impacts would fall proportionally to costs. Given the growing
amount of cash-based programming in humanitarian and development settings, this
proposition demands testing.
A. Comparisons to Other Ultrapoor Programs
It’s difcult to compare this program to the alternatives without randomized,
head-to-head comparisons, but the impacts of WINGS compare favorably to evi-
dence on transfers of livestock and unconditional cash, at least within the timeframe
we can evaluate with our design.
“Graduated” Ultrapoor Programs.—These provide in-kind capital, such as live-
stock, along with training and other services, such as temporary income support.
The fact that the WINGS participants invested much of their microenterprise prots
in livestock suggests that animals are important investments and stores of value.
Yet the fact that the poorest chose to make these investments on their own, plus the
relatively high returns to petty business, also suggest that the ultrapoor can make
forward-looking, protable investment choices with some basic assistance.
Graduated programs have more and longer-term evidence in their favor than
cash-based programs, however, with studies showing 10 to 40 percent increases in
consumption or earnings over two to four years (Banerjee et al. 2011, Bandiera et
al. 2013, Banerjee et al. 2015). The 16-month impacts of WINGS are comparable to
the upper end of this range using an abbreviated consumption measure. We cannot
say if the WINGS impacts persist, but several cash transfer studies with non-extreme
37
For instance, if targeting and disbursement costs could be limited to 10 percent of the cost of grants (a cost
reached in the Kenya GiveDirectly experiment, as discussed by Haushofer and Shapiro 2013) and if there were
merely one to two supervisory visits, the average program cost would be half of the current amount.
VOL. 8 NO. 2 61
Blattman et al.: microenterprise support For ugandan ultrapoor
poor demonstrate steady or increasing four to six year impacts (Blattman, Fiala, and
Martinez 2014; de Mel, McKenzie, and Woodruff 2012b).
38
Unconditional Cash Transfers.—These lie at the other extreme. This is the
approach taken by GiveDirectly in Kenya. Results from a randomized control trial
of grants of one-time transfers of $400 and $1,500 (in PPP terms) show high returns
to investment in household durables after an average of about seven months, and
large short-term increases in consumption (Haushofer and Shapiro 2013). It remains
to be seen if these returns persisted, as there is some indication they decline over the
seven months.
B. Mechanism
Why might WINGS have had such large impacts on income? To generate invest-
ment and high returns, programs such as WINGS must help overcome some con-
straint. Otherwise the ultrapoor in our sample would already be operating at their
efcient scale, and a cash inux would not be invested in such a way as to generate
high returns.
39
Four of the most common constraints in the literature are: lack of
credit, imperfect insurance, low business knowledge (or skill) levels, and present
bias. Most anti-poverty interventions implicitly or explicitly target one or more of
these.
While all these constraints undoubtedly play a role, the circumstantial evidence
points to the importance of cash and group encouragement relieving a credit con-
straint. First, credit constraints in northern Uganda were extreme. As we noted in
Section I, participants’ ability to borrow before the program was almost nonexistent.
Just 4 percent of our sample said they could get a loan of $50, and those loans were
short term and typically carried interest rates in excess of the business returns we
observe. The cash relieved this constraint directly, obviously, but group formation
also had signicant effects on the formation of ROSCAs and the incidence of bor-
rowing. Group formation may also have increased informal insurance (though since
the main effect of group formation was on inter-household loans rather than trans-
fers, insurance is difcult to separate from increased access to credit).
40
38
Note that, like WINGS, none of these cases are truly unconditional cash transfers, as most cash transfers are
labelled or involve some selection, such as preparation of a business plan.
39
See online Appendix C for a formal model of occupational choice under various constraints, and a proof of
the efcient scale argument.
40
It’s unlikely that a single cash transfer relieved the insurance constraint. Bianchi and Bobba (2013) show that
knowledge of future cash transfers can increase current entrepreneurship because future transfers insure against
risky enterprise income. In the program they study, however, households knew they would receive regular transfers
over several years, whereas in our case people receive a grant only once and in the very near future. Furthermore,
relieving an insurance constraint is only consistent with high returns to capital if the petty business is signicantly
more risky than traditional occupations. We asked respondents about expected variance in incomes, and found that
even though treated people expect more volatile incomes in the future, their lowest expected income after the pro-
gram is twice as high as in the control group. We asked people to estimate their highest, median, and lowest incomes
in the coming year. The difference between highest and lowest expectation increases by two thirds but their lower
bound doubles (online Appendix Table D2). Qualitatively, our interviews also suggested that subsistence farming
is perceived to be as or more risky than petty trade, if only because petty trade has higher cash ow and plentiful
local markets.
62 AMERICAN ECONOMIC JOURNAL: APPLIED ECONOMICS APRIL 2016
The success of the group encouragement program component is intriguing. The
impacts on income are ambiguous, but the effects on behavior are not. It illustrates
that social capital of various forms was important and may not always form with-
out third-party encouragement. Our design probably understates the importance of
group encouragement, moreover, since some of the success of the core intervention,
without formal group encouragement, was surely due in part to the group encour-
agement implicit in the group-based training and other elements of the program
design. There are close parallels to ndings by Feigenberg, Field, and Pande (2013),
who show that encouraging social interaction via group meetings reduced default on
individual loans in India. It seems that encouraging group and ROSCA formation
can increase social interactions, enhance social capital, increase risk-pooling and
cooperation, and raise incomes.
Finally, the training and follow-up provided business advice and information.
Most of the people in our sample had little experience in microenterprises in general
or petty trading in particular. Moreover, the positive effect of two initial supervisory
visits (by some measures, at least) is consistent with the hypothesis that at least a
subset of people are time-inconsistent. Thus, skills training and supervision cannot
be separated from the effects of credit constraints and low social capital. Even so,
the training and supervision cost four times as much to deliver as the grant, and six
times as much as the group dynamics training. Also, a review of more than a dozen
evaluations of business skills training fails to nd that it passes a cost benet test,
at least on its own (McKenzie and Woodruff 2014). Given the prevalence of train-
ing and supervision components in other programs, and their obvious cost, a clear
experimental test of their efcacy with cash (or other capital transfers) is important.
C. Generalizability
How generalizable are these results? The post-conict context in which WINGS
was implemented could mean that returns to capital are higher than elsewhere: low
levels of initial competition, scarce capital, and low nancial development, all within
a national economy that is growing as it adjusts to a politically stable equilibrium.
41
On the other hand, we observe similar impacts in nonconicted regions, whether
from the graduation program evaluations mentioned above, or microenterprise pro-
grams elsewhere in Uganda.
42
In the event that post-conict arenas do offer higher returns, however, there is
unfortunately no shortage of refugees, displaced persons, disaster victims, and con-
ict-affected populations. Our results suggest that cash-centric ultrapoor programs
could reduce extreme poverty in such settings, in addition to the stable contexts
where they are more familiar. This is useful to know, since with logistical and fund-
ing challenges, the UN and other donors are turning to cash as a main form of
41
A study in post-tsunami Sri Lanka nds a slow return of rms to pre-disaster capital, that rms receiving
grants recovered sooner, that returns to capital doubled in damaged areas, and that capital had the largest impacts
on retail rms (de Mel, McKenzie, and Woodruff 2012a).
42
See Bandiera et al. (2012); Blattman, Fiala, and Martinez (2014).
VOL. 8 NO. 2 63
Blattman et al.: microenterprise support For ugandan ultrapoor
support for complex emergencies and humanitarian settings, such as cash support to
the millions of Syrian refugees across the Middle East.
The matter is unsettled. Given the potential scalability and cost-effectiveness of
cash, and the huge number of extreme poor, what is needed most is a multi-country
trial, ideally one that evaluates the returns to additional components (such as train-
ing) and pits cash transfers head-to-head with other asset transfer strategies.
REFERENCES
Angelucci, Manuela, and Giacomo De Giorgi. 2009. “Indirect Effects of an Aid Program: How Do
Cash Transfers Affect Ineligibles’ Consumption?” American Economic Review 99 (1): 486–508.
Annan, Jeannie, Christopher Blattman, Dyan Mazurana, and Khristopher Carlson. 2011. “Civil War,
Reintegration, and Gender in Northern Uganda.” Journal of Conict Resolution 55 (6): 877–908.
Bandiera, Oriana, Niklas Buehren, Robin Burgess, Markus Goldstein, Selim Gulesci, Imran Rasul,
and Munshi Sulaiman. 2012. “Empowering Adolescent Girls: Evidence from a Randomized Con-
trol Trial in Uganda.” http://econ.lse.ac.uk/staff/rburgess/wp/ELA.pdf.
Bandiera, Oriana, Robin Burgess, Narayan Das, Selim Gulesci, Imran Rasul, and Munshi Sulaiman.
2013. “Can Entrepreneurship Programs Transform the Economic Lives of the Poor?” http://
eprints.lse.ac.uk/58032/1/__lse.ac.uk_storage_LIBRARY_Secondary_libfile_shared_repository_
Content_STICERD_EOPP_eopp43.pdf.
Banerjee, Abhijit V., and Esther Duo. 2011. Poor Economics: A Radical Rethinking of the Way to
Fight Global Poverty. New York: Public Affairs.
Banerjee, Abhijit V., Esther Duo, Raghabendra Chattopadhyay, and Jeremy Shapiro. 2011. “Target-
ing the Hard-Core Poor: An Impact Assessment.” Unpublished.
Banerjee, Abhijit V., Esther Duo, Nathanael Goldberg, Dean Karlan, Robert Osei, William Pariente,
Jeremy Shapiro, et al. 2015. “A multifaceted program causes lasting progress for the very poor:
Evidence from six countries.” Science 348 (6236).
Beegle, Kathleen, Joachim De Weerdt, Jed Friedman, and John Gibson. 2012. “Methods of house-
hold consumption measurement through surveys: Experimental results from Tanzania.” Journal of
Development Economics 98 (1): 3–18.
Bertrand, Marianne, Sendhil Mullainathan, and Eldar Shar. 2004. “A Behavioral-Economics View
of Poverty.” American Economic Review 94 (2): 419–23.
Bianchi, Milo, and Matteo Bobba. 2013. “Liquidity, Risk, and Occupational Choices.” Review of Eco-
nomic Studies 80 (2): 491–511.
Blattman, Christopher, Nathan Fiala, and Sebastian Martinez. 2014. “Generating Skilled Self-Em-
ployment in Developing Countries: Experimental Evidence from Uganda.” Quarterly Journal of
Economics 129 (2): 697–752.
Blattman, Christopher, Eric P. Green, Julian Jamison, M. Christian Lehmann, and Jeannie Annan.
2016. “The Returns to Microenterprise Support among the Ultrapoor: A Field Experiment in
Postwar Uganda: Dataset.” American Economic Journal: Applied Economics. http://dx.doi.
org/10.1257/app.20150023.
Blattman, Christopher, and Edward Miguel. 2010. “Civil War.” Journal of Economic Literature 48
(1): 3–57.
Buera, Francisco J., Joseph P. Kaboski, and Yongseok Shin. 2012. “The Macroeconomics of Micro-
nance.” National Bureau of Economic Research (NBER) Working Paper 17905.
Burt, Alison, Barry Hughes, and Gary Milante. 2014. “Eradicating Poverty in Fragile States: Prospects
of Reaching the ‘High-Hanging’ Fruit by 2030.” World Bank Policy Research Working Paper 7002.
Cunha, Jesse M., Giacomo De Giorgi, and Seema Jayachandran. 2011. “The Price Effects of Cash
Versus In-Kind Transfers.” National Bureau of Economic Research (NBER) Working Paper 17456.
de Mel, Suresh, David J. McKenzie, and Christopher Woodruff. 2008. “Returns to Capital in Microen-
terprises: Evidence from a Field Experiment.” Quarterly Journal of Economics 123 (4): 1329–72.
de Mel, Suresh, David McKenzie, and Christopher Woodruff. 2012a. “Enterprise Recovery Following
Natural Disasters.” Economic Journal 122 (559): 64–91.
de Mel, Suresh, David J. McKenzie, and Christopher Woodruff. 2012b. “One-Time Transfers of Cash or
Capital Have Long-Lasting Effects on Microenterprises in Sri Lanka.” Science 335 (6071): 962–66.
Duo, Esther. 2012. “Women Empowerment and Economic Development.” Journal of Economic Lit-
erature 50 (4): 1051–79.
64 AMERICAN ECONOMIC JOURNAL: APPLIED ECONOMICS APRIL 2016
Fafchamps, Marcel. 1992. “Solidarity Networks in Preindustrial Societies: Rational Peasants with a
Moral Economy.” Economic Development and Cultural Change 41 (1): 147–74.
Fafchamps, Marcel, David J. McKenzie, Simon Quinn, and Christopher Woodruff. 2014. “Microen-
terprise growth and the ypaper effect: Evidence from a randomized experiment in Ghana.” Journal
of Development Economics 106 (1): 211–26.
Feigenberg, Benjamin, Erica Field, and Rohini Pande. 2013. “The Economic Returns to Social Inter-
action: Experimental Evidence from Micronance.” Review of Economic Studies 80 (4): 1459–83.
Field, Erica, Seema Jayachandran, and Rohini Pande. 2010. “Do Traditional Institutions Constrain
Female Entrepreneurship? A Field Experiment on Business Training in India.” American Economic
Review 100 (2): 125–29.
Filmer, Deon, and Kinnon Scott. 2012. “Assessing asset indices.” Demography 49 (1): 359–92.
Foster, Andrew D., and Mark R. Rosenzweig. 1995. “Learning by Doing and Learning from Others:
Human Capital and Technical Change in Agriculture.” Journal of Political Economy 103 (6): 1176–
1209.
Gertler, Paul, Sebastian Martinez, and Marta Rubio-Codina. 2012. “Investing Cash Transfers to Raise
Long-Term Living Standards.” American Economic Journal: Applied Economics 4 (1): 164–92.
Giné, Xavier, and Dean S. Karlan. 2014. “Group versus individual liability: Short and long term evi-
dence from Philippine microcredit lending groups.” Journal of Development Economics 107:
65–83.
Government of Uganda. 2007. “National Peace, Recovery and Development Plan for Northern Uganda:
2006–2009.” Unpublished.
Green, Eric P., Christopher Blattman, Julian Jamison, and Jeannie Annan. 2015. “Women’s entrepre-
neurship and intimate partner violence: A cluster randomized trial of microenterprise assistance and
partner participation in post-conict Uganda.” Social Science and Medicine 133: 177–88.
Green, Eric P., Christopher Blattman, Julian Jamison, and Jeannie Annan. Forthcoming. “Does pov-
erty alleviation decrease depression symptoms in post-conict settings? A cluster-randomized trial
of microenterprise assistance in northern Uganda.” Global Mental Health.
Haushofer, Johannes, and Jeremy Shapiro. 2013. “Welfare Effects of Unconditional Cash Transfers:
Evidence from a Randomized Controlled Trial in Kenya.” Unpublished.
Hoddinott, John, and Emmanuel Skouas. 2004. “The Impact of PROGRESA on Food Consumption.”
Economic Development and Cultural Change 53 (1): 37–61.
Karlan, Dean, Ryan Knight, and Christopher Udry. 2015. “Consulting and Capital Experiments with
Microenterprise Tailors in Ghana.” Journal of Economic Behavior and Organization 118: 281–302.
Macours, Karen, Patrick Premand, and Renos Vakis. 2012. “Transfers, Diversication and Household
Risk Strategies: Experimental Evidence with Lessons for Climate Change Adaptation.” World Bank
Policy Research Working Paper 6053.
McKenzie, David, and Christopher Woodruff. 2014. “What Are We Learning from Business Training
and Entrepreneurship Evaluations around the Developing World?” World Bank Observer 29 (1):
48–82.
Miguel, Edward, and Michael Kremer. 2004. “Worms: Identifying Impacts on Education and Health
in the Presence of Treatment Externalities.” Econometrica 72 (1): 159–217.
Murgai, Rinku, Paul Winters, Elisabeth Sadoulet, and Alain de Janvry. 2002. “Localized and incom-
plete mutual insurance.” Journal of Development Economics 67 (2): 245–74.
Puri, Jyotsna, Anastasia Aladysheva, Vegard Iversen, Yashodhan Ghorpade, and Tilman Brück. 2014.
“What methods may be used in impact evaluations of humanitarian assistance?” International Ini-
tiative Impact Evaluation (3ie) Working Paper 22.
Udry, Christopher, and Santosh Anagol. 2006. “The Return to Capital in Ghana.” American Economic
Review 96 (2): 388–93.
