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Promoting Improved Infant and Young Child Feeding:Evidence from a Field Experiment in Ethiopia∗
Seollee Park†, Yaeeun Han‡, and Hyuncheol Bryant Kim§
September 2018
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Abstract
Unhealthy diet with very low dietary diversity is prevalent among young children in the developingworld, which increases the risk of chronic undernutrition. Such suboptimal child-feeding behaviorsare partly due to mothers’ lack of nutritional knowledge, income, or both. This study implementeda clustered randomized experiment in Ethiopia to examine the effects of nutrition behavior changecommunication (BCC) and food vouchers on child-feeding behaviors and child growth. We find thatBCC increases knowledge on infant and young child feeding and improves children’s dietary qualitysignificantly, while food vouchers alone do not. The impacts on improved child-feeding behaviorsare largest for those treated by both BCC and food vouchers, and we find evidence for stuntingreduction in this group.
Keywords: infant and child nutrition, nutrition education, behavior change communication, foodvouchers, cluster randomized control trial, Ethiopia
JEL classification: I12, I26, J22, O12, O15
∗We wish to thank John Hoddinott, as well as seminar participants at Cornell University for their in-valuable feedback. We also thank Jieun Kim, Yong Hyun Nam, Minah Kim, Hyolim Kang, Jiwon Baek,Tembi Williams, Soo Sun You, and Jeong Hyun Oh at Africa Future Foundation (AFF) for their excellentfieldwork, and Rahel Getachew, Chulsoo Kim, Hongryang Moon at Myungsung Christian Medical Centerand director Hyun Jin Song for their support. This project was supported by AFF, Korea Foundation forInternational Healthcare (KOFIH), Seoul Women’s Hospital Bucheon Branch, and Dr. Taehoon Kim. Allviews expressed are ours, and all errors are our own. The study was approved by ethical review committeesat the Oromia Health Bureau (Ethiopia, BEIO/AHBHN/1-8/2670), Myungsung Medical College (Ethiopia),and Cornell University (USA, 1612006823).†Dyson School of Applied Economics and Management, Cornell University‡Division of Nutritional Sciences, Cornell University§Department of Policy Analysis and Management, Cornell University
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1. Introduction
Economists are often concerned about the causes and consequences of health and nutrition.
In developing countries, nutritional status is a critical component of health, especially for
children under 2. A particular focus is on chronic child undernutrition, as it is linked to
nearly half of all deaths in children under 5 and affects more than 150 million young children
(World Bank 2017). Chronic undernutrition during infancy and young childhood leads to
poorer health, education, and labor outcomes in adulthood (T. Ahmed et al. 2012; Avula
et al. 2013; Barrett 2010; Black et al. 2008; Hoddinott, Behrman, et al. 2013; UNFAO 2006).
The high prevalence of chronic child undernutrition in many developing countries, char-
acterized by high rates of stunted linear growth, has spawned a large body of research into
possible explanations. This includes genetic predispositions (Nube 2009), poor quality di-
ets and food systems (Headey et al. 2012), intrahousehold biases (Jayachandran and Pande
2017; Pande 2003), low status of women (Schroff et al. 2009; Menon 2012), and the inefficacy
of nutritional programs and strategies (Gupta et al. 2005, World Bank 2006).
Substantial research has sought to address these causes, mostly focusing on a single
aspect and on immediate causes of undernutrition. Interventions that address a single aspect
of undernutrition such as micronutrient deficiencies (Muller et al. 2003; Newton et al. 2007;
Merwe et al. 2013), lack of knowledge (Prina and Royer 2014), and lack of income (Manley et
al. 2013) have often shown limited success. Moreover, it is estimated that the interventions
addressing the immediate, one-dimensional causes, at nearly full coverage, would reduce
stunting by only 20% (Bhutta et al. 2013; Ruel et al. 2013). Hence, while there is a good
understanding of the causes of chronic child undernutrition, less is known about what will
accelerate its reduction. This generates the question as to whether interventions that address
multiple constraints simultaneously would be more effective in reducing undernutrition.
In this paper, we study ways to improve infant and young child nutrition by investigat-
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ing the roles of knowledge and income in changing mothers’ child-feeding practices. To do so,
we implement a community-based cluster randomized experiment in Ethiopia that provides
nutrition education in the form of behavioral change communication (BCC) and food vouch-
ers. Specifically, we explore the effects of releasing the knowledge constraint, the income
constraint, or both constraints by randomly providing four-month-long BCC only (BCC),
voucher only (V oucher), and both BCC and voucher (BCC + V oucher) interventions for
mothers with one or more children between 4 and 20 months of age. We target this age
range because stunting prevalence increases rapidly after the first 6 months because exclu-
sive breastfeeding no longer meets the energy and nutrients needed for rapid child growth,
while relatively low in the first 6 months due to breastfeeding (Cunningham et al. 1991;
Dewey et al. 1995; Beaudry et al. 1995). Adopting healthy child-feeding practices during the
transitional period from exclusive breastfeeding to complementary feeding1—i.e., 6 months
after birth—is particularly crucial for preventing undernutrition (Black et al. 2008; Jones
et al. 2003; Ruel et al. 2013).
This research contributes to several strands of literature. First, it contributes to the
empirical literature on the effects of nutrition education or BCC for caregivers on caregivers’
nutritional knowledge, child-feeding behaviors, and child growth. Secondly, this research
adds to the literature on the role of vouchers in improving child nutrition in developing
countries. Third, we contribute to the growing literature on the effectiveness of multifaceted
programs on poverty reduction. To our knowledge, this study is the first study to examine
the combined effects of providing both BCC and vouchers on child-feeding behaviors and
child nutrition.1Appropriate complementary feeding means feeding children a diverse diet that meets the nutritional
requirements. This entails feeding vitamin A-rich fruits and vegetables daily, in addition to a range of otherfruits and vegetables. Meat, poultry, fish, or eggs also need to be consumed daily to ensure the intake ofcertain micronutrients critical for growth found only in animal source foods. In this regard, healthy food inthis paper refers to these food groups (WHO 2010a).
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Our study finds that BCC significantly improves mothers’ nutritional knowledge while
voucher alone does not. This is consistent with the growing literature that examines the
causal effect of BCC on mothers’ nutritional knowledge. A recent experimental study con-
ducted in Bangladesh shows that a 2-year-long BCC resulted in answering about four more
questions correctly at endline, out of 18 infant and young child feeding (IYCF) questions,
than the control group (Hoddinott, I. Ahmed, et al. 2017). A similar 2-year-long experiment
in Burkina Faso finds that, out of three IYCF questions, BCC significantly increases the
likelihood of answering two questions correctly by more than 15 percentage points (Olney
et al. 2015). Another experiment in Malawi, which examines the effect of a nutrition BCC
that visits mothers five times over the period of just before giving birth and 5 months after,
shows a somewhat weaker effect on knowledge, with only one out of seven IYCF questions
more likely to be answered correctly by 7 percentage points (Fitzsimons et al. 2016). Our
study finds that BCC causes mothers to answer approximately two more questions correctly
for both BCC and BCC + V oucher groups out of 34 questions. In terms of proportion of
mothers answering correctly, we find that 17 questions are significantly more likely to be
answered correctly by the BCC and the BCC + V oucher groups. Hence, we show that
a similar or greater impact on mothers’ knowledge can be attained with a relatively short
treatment length.
On child-feeding behaviors, BCC has positive impacts on a number of child diet quality
measures. The impact is greatest when BCC is combined with vouchers. However, voucher
alone does not increase child diet quality on any standard child diet quality measure we
examine. As our primary measure of diet quality, we find that child dietary diversity score
(CDDS) increases by 0.3 and 0.6 food group for BCC and BCC+V oucher, respectively. The
impacts on CDDS are driven by significant increases in the proportion of children consuming
animal source foods among the BCC group, and animal source foods, vitamin A-rich fruits
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and vegetables, and nuts and legumes for the BCC+V oucher group. Furthermore, children
in the BCC + V oucher and the BCC groups were 15.2 and 7.7 percentage points more
likely to meet the World Health Organization (WHO) guidelines for minimum acceptable
diet, respectively. The BCC + V oucher group was also 17.6 percentage points more likely
to meet the minimum dietary diversity. The limited existing literature on the effects of BCC
or nutrition education on child-feeding practices find more moderate effects. For example,
Fitzsimons et al. (2016) find that only one out of eight food groups had a significant increase
in the proportion of children consuming the food group due to BCC. Olney et al. (2015) show
that BCC combined with agriculture input support and training increases the proportion of
children meeting minimum dietary diversity by 12.6 percentage points, but do not report
results on other measures. A similar study that evaluates the impact of a nutrition education
program coupled with agricultural intervention finds a comparable effect, with an increase
in CDDS by 0.52 food group (Reinbott et al. 2016). In addition, we find improvements in
diet quality at the overall household level, which is in line with the findings of Fizsimons et
al. (2016).
In terms of physical growth of children, we find evidence for chronic undernutrition
reduction by 9.5 percentage points in the BCC + V oucher group, as measured by stunting
prevalence. We do not find significant effects for other groups. This is a striking result
given a 6-month lapse between baseline and endline, on average. Other studies have yet
to find evidence on the causal effect of BCC or BCC combined with other interventions on
stunting reduction. Fitzsimons et al. (2016) find that BCC decreases wasting prevalence
by 4.2 percentage points but is significant at the 10% level, and do not show evidence for
stunting reduction. Other studies that coupled BCC with agricultural interventions do not
find evidence for stunting reduction (ibid.; Olney et al. 2015). We further show that there are
varying distributional effects by treatment arm, which confer important policy implications.
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While BCC + V oucher is effective for improving growth outcomes among undernourished
children, BCC works favorably for those who are relatively well-nourished. This suggests
that social protection programs that target undernutrition reduction may be more effective
when providing both knowledge and resources simultaneously.
Lastly, we add to the growing literature on the effectiveness of multifaceted programs
on poverty reduction, shifting the focus from poverty to undernutrition reduction. This lit-
erature suggests that a multipronged approach, often combining skills training or education
with income support and thereby releasing multiple constraints simultaneously, brings sus-
tainable and cost-effective results in reducing poverty (Banerjee et al. 2015; Bandiera et al.
2017). Consistent with this literature, our results on child-feeding behaviors and growth out-
comes collectively suggest that the impacts are greatest when both the knowledge and the
income constraints are addressed simultaneously through the BCC + V oucher treatment.
This is particularly evident in stunting reduction results, where the BCC and the V oucher
treatments, individually, show no impact but the combined BCC+V oucher treatment shows
a sizable effect.
The remainder of this paper is organized as follows: Section 2 presents the study design
and the intervention programs; Section 3 describes the data and sample characteristics;
Section 4 sets out the methods; and Section 5 presents the results. We discuss the results
and conclude in Section 6.
2. Study Design and the Interventions
2.1. Study Context
Ethiopia is an appropriate setting for this study because it faces daunting chronic child under-
nutrition challenges which coexist with knowledge and income constraints. The prevalence of
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stunting in Ethiopia, an indicator for chronic undernutrition, was 38% among children under
five (Ethiopia DHS 2016). The prevalence of stunting rapidly increases after 6 months of age,
largely due to poor infant and young child-feeding practices in Ethiopia. As shown in Figure
1, at the age of six months, 16% of children are stunted in Ethiopia but the corresponding
number increases to a staggering 47% by the age of 24 months (ibid.). Low dietary diversity
is particularly striking among young children in Ethiopia, with only 7% of children aged
6-23 months meeting the minimum acceptable dietary standard which is a WHO standard
for adequate infant and young child nutrition.
As the second-most populous country in Africa, Ethiopia has a GDP per capita of
707USD per year in 2016, which is less than two dollars per person per day (World Bank
2017). There are also many misconceptions about child nutrition among mothers in Ethiopia.
For example, many Ethiopian mothers believe that babies under 12 months should not be
fed animal source foods. Also, it is common to give infants as old as 9 months only thin
gruel, with the misbelief that they are not able to digest solid or semi-solid food. The widely
available and inexpensive healthy food items in the area, such as mangos rich in vitamin
A, are not well recognized. This contextual evidence suggests there are considerable income
and knowledge constraints for child nutrition in Ethiopia.
2.2. Study Sample Description
Our study area is Ejere district (woreda) located in the Oromia region of central Ethiopia,
approximately 50 km west of the capital, Addis Ababa. Ejere is primarily a rural district
which is further subdivided into three urban and 27 rural wards (kebeles). Ejere has a
population of around 112,000 spread over these 30 wards, who are predominantly engaged in
mixed crop-livestock farming at a small scale. Most farmers engage in traditional practices
of rain-fed subsistence agriculture.
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The study sample is mothers with at lease one child aged between 4 and 20 months
(‘eligible group’ hereafter).2 Pregnant women and mothers with a child with less than 4
months at the time of baseline survey were additionally included in the surveys to measure
potential spillover of the knowledge (‘spillover group’ hereafter).
2.3. Experimental Design
This study is based on a cluster randomized control trial. The clusters were randomly selected
from three urban and three randomly selected rural wards in Ejere (Figure 2). A total of 79
villages (garees) from these six wards in Ejere entered a lottery, and were randomly selected
into one of four arms: BCC only (BCC), vouchers only (V oucher), BCC and vouchers
(BCC + V oucher), and the control group. Randomization was stratified by wards. There
are 101 (15), 96 (14), 154 (13), and 289 (37) mother and child pairs (villages) randomly
assigned to the BCC, V oucher, BCC + V oucher, and control study groups, respectively.
We also include pregnant women and women with children under 4 months in the same
villages to study IYCF knowledge spillovers (spillover group). The corresponding numbers
for the spillover group are 86, 54, 97, and 107 mother and child pairs, respectively. Figure 3
summarizes the study design.
2.4. Interventions
There are two interventions in this study: nutrition BCC and food vouchers. The inter-
ventions were designed by the study team through a series of focus group discussions and
pilot-testing as shown in study timeline (Figure 4), and implemented in collaboration with2 We selected the age range between 4 and 20 months as the treatment eligibility criteria in order to target
the age range that is most susceptible to undernutrition due to malpractices in child-feeding. In particular,we seek to address chronic undernutrition caused by suboptimal practices in complementary feeding, whichstarts at 6 months of age. We do not include children under 4 months because the BCC intervention doesnot address breastfeeding practices.
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Africa Future Foundation (AFF), a local non-governmental organization.
BCC.3 The BCC treatment offered weekly IYCF information sessions for 16 weeks to a
group of seven to fourteen mothers who has a child from 4 to 20 months of age at baseline.4
All eligible mothers living in the same village formed a group to receive BCC education. The
BCC treatment was a multifaceted and interactive information intervention complemented
by various participatory learning methods including weekly sharing of mothers’ experiences
applying new IYCF activities, videos and visual images, role-plays, and cooking sessions.
The focus of the BCC sessions and supporting activities was on the need to increase dietary
diversity of children aged 6-23 months, with an emphasis on animal source foods and vitamin
A-rich fruits and vegetables, appropriate feeding amounts and frequency, and feeding and
caregiving practices. Each session ended with an action plan the mothers agreed upon, and
the proceeding session reviewed and discussed past week’s action plans. In addition, the
BCC participants also received a small handbook containing a summary of IYCF contents
and weekly action plans based on contents learned each week, and a self-check diary. An
overview of the BCC curriculum is provided in Appendix B.
The BCC facilitators consisted of local female community workers who had been work-
ing in the community as AFF social workers for at least six months up to five years. The
selected BCC facilitators went through three rounds of training, passed regular knowledge3BCC is the strategic use of communication to promote positive health outcomes, based on proven
theories and models of behavior change. BCC employs a systematic process beginning with formativeresearch and behavior analysis, followed by communication planning, implementation, and monitoring andevaluation. Audiences are carefully segmented, messages and materials are pre-tested, and mass media(which include radio, television, billboards, print material, internet), interpersonal channels (such as client-provider interaction, group presentations) and community mobilisation are used to achieve defined behavioralobjectives (MEASURE Evaluation 2018).
4 The BCC program curriculum is developed based on the Alive & Thrive’s BCC program implementedin Ethiopia. Alive & Thrive is an initiative to save lives, prevent illness, and ensure healthy growth anddevelopment through the promotion and support of optimal maternal nutrition, breastfeeding, and comple-mentary feeding practices. Alive & Thrive has worked in Ethiopia since late 2009 to address widespreadand limited recognition of the long-term consequences of stunting and find ways to reach mothers (Alive &Thrive, 2018).
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tests, and implemented two pilot programs in both urban and rural areas. Each group had
two designated facilitators—one leader and one helper. The lead facilitator taught the ses-
sions and led discussions and role-plays, while the other facilitator helped by encouraging
discussion and assisting illiterate mothers. The sessions were conducted at the ward office
or health posts. Throughout the study, two supervisors randomly visited the BCC ses-
sions for quality control. The BCC facilitators, supervisors, and the study team had weekly
group meetings to discuss progress and challenges. The supervisors also made home visits
to mothers who missed more than two consecutive sessions to encourage attendance.
Food vouchers. The voucher treatment provided food vouchers of 200 ETB (approximately
10 USD) per month for four months to either mother or father of the household, which could
be used at nearby markets.5 This amount is similar to the cash or food transfers amount
of Ethiopia’s Productive Safety Net Program which was set to be about 8.5 USD at the
time of the program design (MOA 2014). Also, we provide food vouchers instead of cash or
food because food vouchers are proven to be most effective in improving dietary diversity
(Hidrobo et al. 2014).
Food vouchers were redeemable for any kind of food items sold at the market including
cereals, roots and tubers, fruits, vegetables, legumes, meat and fish, milk products, eggs,
and spices. Food vouchers were distributed every four weeks at the nearest market or at the
participant’s household if not picked up from the market. Vouchers were given in denomi-
nations of 5, 10, and 20 ETB to facilitate small transactions, and could be used over a series
of visits per month. Vouchers were required to be redeemed within expiration date noted
in the voucher (four weeks). At the first disbursement, voucher recipients were provided5 The voucher constituted approximately 10% of household food expenditure. As there is one public
market per each of the six kebeles in our study area, the vouchers could be used at any of these six publicmarkets.
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detailed instructions on how to use the vouchers. Unique household identification numbers
and names were printed on the voucher and the study participants were required to present
household photo IDs, provided by the study team, to redeem the vouchers. On all market
days of the study period, our voucher staff visited the market to facilitate transactions and
recorded voucher-based transactions.6
3. Data
3.1. Data Sources
The primary data sources are (1) census data including household demographic and socioe-
conomic information, (2) the baseline and follow-up surveys, and (3) administrative data
collected during the intervention including BCC attendance rates and voucher usage records.
The timeline of the data collection and interventions is summarized in Figure 4.
AFF conducted a census of all households in 22 wards of Ejere in May-September 2016,
covering approximately 22,000 households.7 The census collected a variety of demographic,
socioeconomic, and health variables such as age of mother and children, marital status,
education and employment, household asset, and birth history of mother. Using the census
data, we randomly selected three rural wards and selected all three urban wards, which
consisted of 108 villages in total. From these villages, we randomly selected 79 villages to be
included in this study. These villages had a total of 640 eligible mother and child pairs, all
of which were included in the study for the treatment and control groups, and 344 mother
and child pairs for the spillover group.6 The vouchers were redeemable in all major markets in the study area. The voucher staff were stationed
at the market on all market days during the study period, and followed the voucher-holders to record eachtransaction whenever they visited the market.
7 Out of 30 wards in Ejere, 8 wards in the southern part of the district were excluded from the census dueto security reasons. There were strong anti-government sentiments in this region which spread to hostilitytoward NGOs and surveyors.
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The baseline survey was conducted in April-August 2017 before the intervention pro-
gram began. The follow-up survey was conducted upon program completion in December-
March 2018, about 6 months after the baseline survey. Both the baseline and the follow-up
questionnaires include detailed information on IYCF knowledge and practices, child food
consumption, household food and non-food expenditures, health, gender, social networks,
anthropometry, demographics, and socioeconomic information. The follow-up survey also
has a section on the mothers’ experience with the program.
During the baseline and follow-up surveys, we asked study participants to list up to ten
closest friends (including relatives) within the ward (kebele). Using this social network data,
we construct BCC peer variables including whether the mother has any BCC-participating
friend and the number of BCC-participating friends by cross-referencing the networks with
BCC participants and vice versa.8 At baseline, 55% of BCC treatment mothers and 36%
of spillover group mothers had at least one BCC-participating friend, defined either by own
network or the other person’s network.
In addition, our study team collected administrative data on BCC attendance and
voucher usage during the intervention from August 2017 to February 2018. On voucher
usage, the voucher staff collected information on the type of food item, the quantity bought,
and the amount spent using the vouchers. It confirms that about most (94%) of the voucher
participants utilized the vouchers to buy food, and 77% of face value of the voucher had been
redeemed (on average 153 birr out of 200 birr). AFF staff also collected GIS information on
village and markets in the catchment area. Administrative data confirm that the mothers8 The social network was created from the network module in the baseline and follow-up surveys asking
the respondent to list the top 10 closest friends living in the same ward. Matching was done initially bymatching phone numbers, then by matching the friends’ names with survey respondent and spouse namesusing the similarity score generated by the ‘matchit’ command in Stata. Name matches with similarity scoreabove 0.6, out of a range from 0 to 1, were manually compared across name, spouse name, sex, ward, andphone number to confirm the match. Manual confirmation was necessary due to inconsistent spelling ofAmharic and Oromo names.
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attended the weekly BCC sessions regularly (74% attendance rate).
3.2. Outcome Variables
The primary outcomes for this study are IYCF knowledge scores among mothers and child
dietary diversity score (CDDS). We also constructed other measures of children’s diet quality,
household food and non-food consumption and expenditures, measures of household diet
quality, and anthropometric measures of child development.
We use standardized mothers’ IYCF knowledge scores measured in the baseline and
follow-up surveys. The knowledge score is the percentage of questions answered correctly
out of 34 questions. We construct CDDS as a measure of children’s diet quality. The CDDS
sums the number of distinct food groups consumed by the child in the past 24 hours among
the following seven different food groups: cereals, roots and tubers, legumes, nuts and seeds,
dairy products, meat/poultry and fish, eggs, vitamin A-rich fruits and vegetables, other
fruits and vegetables (WHO 2010a; WHO 2010b).
We also look at other measures of child diet quality to capture the proportion of chil-
dren given appropriate diet quality and quantity. These indicators include the minimum
acceptable diet, minimum dietary diversity, and minimum meal frequency standards. The
minimum acceptable diet indicator is created assessing two different IYCF components com-
piled into one index, adjusted for child’s age: minimum dietary diversity and minimum meal
frequency. Minimum dietary diversity is proportion of children who receive food from 4 or
more food group, and minimum meal frequency is the proportion of children who consumed
minimum number of meals appropriate for the age (WHO 2010a; WHO 2010b).9 Minimum9 Minimum dietary diversity is a proxy for adequate micronutrient density of foods. The cut-off of four
food groups is associated with better-quality diets for both breastfed and non-breastfed children. The fourfood groups should come from a list of seven food groups: grains, roots, and tubers; legumes and nuts; dairyproducts (milk yogurt, cheese); flesh foods (meat, fish, poultry, and liver/organ meat); eggs; vitamin A-richfruits and vegetables; and other fruits and vegetables. Minimum meal frequency, a proxy for a child’s energyrequirements, examines the number of times children received foods other than breastmilk. The minimum
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acceptable diet differs from CDDS in that it accounts for feeding frequency in addition to
diversity.
To gauge how knowledge changes perception, we also measured mothers’ perception of
relative child growth by asking how the child fares compared to other children of the same
age in terms of diet quantity and quality on a five-tiered scale ranging from very well to very
poor. Additionally, we constructed a variable for timely introduction of complementary food
using a total standardized score aggregating indicator variables for whether the child started
eating a certain food after six months but before 12 months of age across eight different
complementary food items. This outcome measures how well mothers are doing in terms of
introducing various complementary food to their children at appropriate ages—not too early
as to incur digestive problems but not too late so that children are not undernourished. 10
In order to examine household-level expenditure responses to nutritional knowledge
and food vouchers, we calculate household food expenditure by summing the value of food
items purchased in the past seven days by food group or in total. Food items include cereals,
roots and tubers, nuts and legumes, fruits and vegetables, meat and poultry, eggs, milk and
milk products, and spices and condiments. Household non-food expenditure is calculated
from the value of durable items purchased in the last six months and non-durable items
purchased in the last month. All values are converted to weekly per capita values. Non-food
items include clothing, household items, medical costs, educational costs, energy costs, repair
costs, and wedding/funeral costs. All values are converted to weekly per capita values.
To assess how household-level diet quality changes as a result of knowledge and vouch-
ers, we also construct a food consumption score (FCS) which measures household diet quality
in terms of both energy and diversity Weismann et al. 2009.11 FCS less than or equal to 35
number is specific to the age and breastfeeding status of the child (WHO, 2010).10 The complementary food items asked are water or other non-breastmilk liquids, solid or semi-solid food,
meat, eggs, legumes, green vegetables, fruits, and snacks.11 The FCS is calculated by summing the number of days that the household consumed each of the eight
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is considered having poor to borderline consumption (WFP 2008).
Lastly, we examine the treatment effect on children’s physical growth using child an-
thropometry which was measured three times during each survey to minimize error. Child
growth outcomes include height-for-age Z scores (HAZ) and stunting, weight-for-height Z
scores (WHZ) and wasting. These were constructed using a mean of the three measurements
for height and weight. HAZ and WHZ are standardized Z scores relative to the WHO refer-
ence population. Stunting and wasting are dummy variables equal to 1 if a child’s HAZ and
WHZ, respectively, are 2 standard deviations (SD) below the WHO reference population.
3.2. Sample Characteristics and Randomization Balance
Table 1 presents the summary statistics for the whole sample (Column 1), the control
group (Column 2), and the difference between each treatment groups and the control group
(Columns 3 to 5) and between treatment groups (Columns 8-10). Panels A, B, and C present
mother, child, and household characteristics at baseline, respectively. Mothers in our sample
are, on average, 28 years old, 14 percent are household heads, 77 percent are Oromos, 84
percent are Orthodox Christians, have approximately 2 children, 77 percent are married, 56
percent have work, 50 percent are able to read, 49 percent are able to write, have about 4
years of schooling, and 45 percent are from rural areas. The mean mother IYCF knowledge
score is 21.5 out of 32 (67 percent) and the mean CDDS is 2.4. Mean age of the eligible
child is approximately 12 months and average household size is 4.5. Only 13 percent of the
sample met the minimum acceptable diet at baseline. The mean HAZ is -1.1 with a 27
percent stunting prevalence. The mean FCS is 43. Average total weekly food and non-food
expenditure per capita are approximately 132 ETB and 43 ETB, respectively.
food groups (staples, pulses, vegetables, fruit, meat and fish, milk and dairy, sugar and honey, oils andfats), multiplying the summed number of days by the food group’s weighted frequencies, and summing theseweighted scores across food groups.
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Columns 3 to 10 confirm that the randomization was successful, with the sample well
balanced across intervention and control clusters at baseline. Across 150 (25 x 6) difference-
in-means tests, only four differences are statistically significant at the 5% level, suggesting
that the baseline characteristics are balanced overall. With 150 tests being considered, the
probability of rejecting a true null hypothesis for at least one outcome is nearly 100%. As a
robustness check, we also control for these baseline covariates in the analysis.
As shown in Panel D, eligible mothers’ attrition rate at the follow-up survey is 8.4%.
Table 1 shows no significant difference in attrition rates across intervention groups. The
attrition rate of follow-up child anthropometry is 16.6 percent. It is significantly different
between the BCC and the V oucher groups and between the V oucher and the BCC +
V oucher groups at the 10 percent level (Columns 7 and 9), but these comparisons are not
the main focus of our analysis on anthropometry.
4. Methods
Our estimation strategy relies on the randomized design of the program. Our basic treatment
effects specification estimates the following equation:
yij1 = β0 + β1BCCij + β2V oucherij + β3BCC&V oucherij + β4yij0 + β5Xij + εij
where yij is the outcome of interest for household i from village j at follow-up including
mother’s nutritional knowledge score, household food and non-food expenditures, nutrition
indicators including CDDS, HDDS, and FCS, and child’s HAZ and WHZ scores. BCCij ,
V oucherij , and BCC&V oucherij are dummy variables equal to one if the participant was
randomly assigned to the BCC, V oucher, or the BCC + V oucher group, respectively, and
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zero otherwise. β1 , β2 , and β3 represent the intent-to-treat estimators. Yij0 is the outcome
of interest at baseline. Xij is a control vector of household i ’s characteristics including
demographic variables (mother’s age, eligible child’s age, marital status, household size,
number of children, ethnicity, religion) and socioeconomic status (mother’s literacy, years
of schooling, employment status, and household assets). εij is an error term and errors are
clustered at the village level. We present results using the specification that includes the
control vector, but the outcomes are almost the same when different specifications are used.
To address the issue of small number of clusters, we use the wild-cluster bootstrap method
for inference (Cameron et al. 2008; Rosenbaum 2002; Fisher 1935). In each results table, we
report clustered standard errors as well as the p-value computed using the wild-bootstrap
cluster-t procedure.
Next, we use the network data to estimate whether the treatment also influenced the
outcomes of peers of the participants. The extent of such spillover effects or information
spillovers can be estimated with the following specification:
yij1 = α0 + α1Peerij + α2yij0 + α3Xi + εij
where yij1 and yij0 are nutritional knowledge scores for household i from village j at follow-up
and baseline, respectively . Peerij is the number of BCC-participating friends the spillover
group respondent has. We use three different definitions of the Peerij variable: 1) the number
of BCC participants who listed the spillover group mother as a friend, and the spillover group
mother also listed the BCC participants as a friend; 2) the number of BCC participants who
listed the spillover group mother as a friend; and 3) the number of BCC participants the
spillover group mother listed as friends.
17
5. Results
5.1. First-stage Results: BCC Attendance and Knowledge
Table 2 presents our first-stage results on BCC attendance and mothers’ IYCF knowledge.
Using the BCC administrative data, Column 1 of Table 2 compares the overall BCC atten-
dance rates across treatment. Note that attendance rate for the V oucher group and the
control group are set to zero. On average, the BCC and the BCC + V oucher group have
73% and 76% attendance rates, respectively, and they are not statistically different from
each other.
In Column 2, we find that attendance in the BCC sessions led to significant knowledge
gains: 0.47SD and 0.42SD for the BCC and the BCC + V oucher groups, respectively. In
other words, mothers in the BCC and the BCC + V oucher groups answered about 2 more
questions correctly compared to the control group. These effects are statistically significant
at the 1% level. However, voucher itself does not have such effect. The size of the impacts are
not significantly different between the BCC and the BCC+V oucher groups, suggesting that
receiving vouchers in addition to the BCC intervention does not further increase knowledge
gains. For reference, attendance rates and knowledge scores by IYCF topic are presented in
Table 11.
5.2. First-stage Results: Voucher Redemption
Using voucher purchase record data, Table 3 presents the next set of first-stage results on
households voucher usage. Note that voucher redemption amount in the control group are
set to zero and the BCC group is not included in this analysis. Column 1 shows that the
V oucher and the BCC+V oucher groups spent, on average, 151 and 153 ETB worth of food
vouchers per month, respectively, redeeming about 76% of the disbursed voucher amount.
18
The amount redeemed per month is not statistically different between the V oucher and the
BCC + V oucher groups.
From Columns 2-11, we find that the food vouchers are spent on most food groups in
similar amounts between V oucher and BCC + V oucher. While large amounts are spent on
starchy staples and oils and fats, it is interesting that households diversity a third of their
voucher spending on non-staple food including eggs, fruits and vegetables, and nuts and
legumes. Meat and milk are not usually bought with vouchers, as they are usually not sold
in the market but obtained from their own or neighbor’s livestock. The BCC + V oucher
group spends more voucher on other fruits and vegetables and sugar, drinks, and spices
than the V oucher group. While these differences are statistically significant, they are not
economically significant, differing by less than 2 ETB per week. On other food groups, we
do not find statistically significant differences in the amount of vouchers spent on other food
groups.
5.3. Child Diet Quality
Having established that the BCC intervention resulted in knowledge gain among BCC-
participating mothers and that voucher recipients spent the food vouchers, we now look
at how this changed mothers’ child-feeding behaviors, reflected in the quality of children’s
diets. The first outcome we examine is CDDS which is our pre-specified primary outcome
(Column 1 of Table 4). Among children in the BCC + V oucher group, CDDS increased by
0.62 food group, an impact statistically significant at the 1% level. The BCC treatment by
itself had a smaller impact, 0.35, and was less precisely measured. The V oucher treatment
had no effect on CDDS. The result for the BCC group suggests that mothers are able to feed
more diverse food to their children when provided appropriate education to some extent, even
without financial support. However, we find that the increase in the BCC +V oucher group
19
is almost twice as big as that of the BCC group, although the difference is not statistically
significant.
Results on minimum acceptable diet (Column 2 of Table 4) confirm that mothers in
the BCC + V oucher group show the greatest changes in child-feeding behaviors, which is
consistent with the results on CDDS. The proportion of children who met the minimum
acceptable diet standard increases for both the BCC and the BCC + V oucher groups by 8
and 15 percentage points, respectively. The magnitude of the increase for theBCC+V oucher
group is double that of the BCC group and statistically significant at the 10% level. This
measure is different from CDDS in that it accounts for not only diet quality but also quantity,
by accounting for feeding frequency.12 Also, by using a minimum cutoff, this measure focuses
on improvements in the lower tail of the distribution.
As shown in Column 3 of Table 4, timely introduction of complementary food score
increased for children in the BCC and the BCC +V oucher groups, but not for those in the
V oucher group. Unlike the previous two measures, it is interesting that the coefficient on
BCC has nearly the same size as the coefficient on BCC+V oucher for this outcome, 0.15SD
and 0.13SD, respectively. As there is relatively little or no cost to adjusting the timing of
introducing various foods, compared to increasing food quantity or diversity, both BCC and
BCC + V oucher households similarly improved their child-feeding behavior in this regard.
The proportion of mothers who perceive that their children consume relatively better
diets in terms of quantity and quality increases for the BCC + V oucher group by 5 and 8
percentage points, respectively, but not for those who receive BCC only (Columns 4 and 5
of Table 4). Interestingly, mothers in the voucher group perceive that their children have
better diet quality when, in fact, they do not. This affirms that, without a proper education
program, mothers may have a misconception of what constitutes a good quality diet for their12Results on minimum dietary diversity and minimum feeding frequency, which are the components of
minimum acceptable diet, are presented in Table 12.
20
children.
5.4. Child Consumption by Food Group
To explain what is driving the improvements in child diet quality, we examine child food
consumption by food groups. Columns 1 to 4 present results on animal products, including
meat and fish, milk and milk products, eggs, and Column 5 on vitamin A-rich fruits and
vegetables, which were highlighted during the BCC program. Food groups in Columns 6 to
8 were not emphasized in the BCC program.
Table 5 shows that the BCC treatment increases child’s consumption on meat and milk
products. The BCC + V oucher treatment increases child’s consumption on a wider range
of food groups including meat and fish, milk and milk products, eggs, and vitamin A-rich
fruits and vegetables, all of which were highlighted as important sources of micronutrients
needed for healthy child growth in the BCC sessions. However, among children in the
V oucher group, meat is the only food group on which they increase consumption. While
more children in both the BCC and the BCC +V oucher groups ate animal source foods, it
is worth noting that the magnitude of the increase in the BCC+V oucher group is more than
twice as large as that of the BCC group—larger by more than 10 percentage points (Column
4). This difference in point estimates are statistically significant at the 5% level. This greater
impact of BCC + V oucher on children’s animal source food consumption shows that it is
the greater consumption of animal source foods that is driving the greater improvements in
diet quality in the BCC + V oucher group.
5.5. Household Expenditures
We now turn to changes in household expenditures in response to the treatments to explain
the child diet results. Table 6 presents results household food expenditure per week per
21
capita in total and by food groups. Note that household expenditure data was collected
during the follow-up survey which was conducted after the completion of the interventions.
Hence, household expenditure data do not include voucher expenditures. Column 1 presents
weekly total household expenditure which should equal the sum of all expenditures by food
group in Columns 3-12.
We first look at expenditures at the aggregate level, including food expenditures per
week per capita (Column 1) and non-food expenditures per week per capita (Column 2). We
find positive coefficients on total food expenditures, but they are not statistically significant
(Column 1). The size of the coefficients on V oucher and BCC+V oucher are similar to that
of the voucher transfer in per week per capita terms (200 ETB ÷ average household size of
4.5 ÷ 4 weeks ≈ 11 ETB). This suggests that the households at least did not drop their food
expenditures immediately after intervention completion, which is a common phenomenon
among voucher schemes in developed countries, but rather maintained comparable food
spending amounts. The BCC group spent similar amounts on food to other treatment
groups.
As shown in Column 2, we do not find any impact on non-food expenditures for the
BCC and the V oucher groups. This is particularly interesting for the V oucher group,
as it suggests that there is no evidence for crowding-out of food expenditures into non-
food expenditures due to the food vouchers. We find increases in non-food expenditures in
the BCC + V oucher group, but is imprecisely measured. While this may be a suggestive
evidence for a partial crowding-out of food expenditures among the BCC +V oucher group,
food expenditure results suggest otherwise. While an increase in non-food expenditures
should lead to a decrease in food expenditures–especially given that the expenditure data
was collected after the voucher scheme was completed—we do not find this among the BCC+
V oucher group.
22
We investigate further in to household expenditures by looking at food group expen-
ditures. We find that households in the V oucher and BCC + V oucher groups continue to
spend more on non-staple food groups. This result is comparable to existing studies that find
a positive relationship between income and non-staple nutrients (Dereje 2015; Bilal et al.
2013; Skoufias et al. 2011). There is a statistically significant difference in impacts on vitamin
A-rich fruits and vegetables expenditures between the V oucher and BCC+V oucher groups,
suggesting that BCC expanded the set of food expenditures to this food group. However,
the magnitude of this effect is 1 ETB per week per capita which is not large.
As for the BCC group, we find that they also increased non-staple expenditures to
some extent, particularly on animal source foods, although it is less precisely measured. This
suggests that nutritional knowledge gained through BCC influences households to diversify
food expenditure to some extent, even without additional income.
Taking together the results on voucher redemption from Section 5.2 and food expendi-
tures, we find that increasing income through vouchers causes non-staple expenditures to rise
in a similar way for both V oucher and BCC+V oucher groups. Comparing this finding with
the diverging results on child diet quality between these two groups, we can conclude that,
while the food items bought at the household level are similar, it is the nutritional knowledge
gained through BCC that causes mothers in the BCC + V oucher group to allocate more
non-staple food to young children.
5.6. Child Physical Growth
Given the improvements in child diet quality, we further investigate how the changes in
children’s diets affected their physical growth and stunting, in particular. We find that the
BCC + V oucher treatment improves nutritional status at least in the short run. Table 7
presents the results on HAZ and stunting, measures of chronic nutritional status among
23
infants and young children (Columns 1 and 2), as well as on WHZ and wasting which are
related to acute nutritional status (Columns 3 and 4).
We do not find impact on HAZ. However, stunting prevalence significantly decreases
by 9.5 percentage points among children in the BCC + V oucher group, but not in other
groups. In the overall sample, stunting prevalence increased from 27% at baseline to 42%
at follow-up. This pattern is similar to the increasing trend of stunting rates with age
among children over 6 months in developing countries. Amid this rapidly increasing trend,
our stunting results show that the BCC + V oucher treatment prevented stunting from
occurring for about 10% of its participants. Likewise, as Panel A of Figure 5 illustrates, the
BCC + V oucher group shows greater improvements among children in the lower tail of the
HAZ distribution, representing that chronic nutritional status improves for those who are
otherwise more prone to be chronically undernourished.
We find an increase in WHZ scores by 0.3SD and a decrease in wasting prevalence for
the BCC group but the latter is not statistically significant. Graphical evidence shows that
WHZ scores improve for the upper tail of the distribution for the BCC group, which explains
why there is no effect on wasting (Panel B of Figure 5). This implies that weight-for-height
increases among children who are relatively well-nourished for the BCC group. One possible
explanation could be that children in the upper tail of the WHZ distribution are most likely
from households that were able to adjust their spending to improve child diet quality. On
the other hand, for the BCC + V oucher group, the additional financial support may have
empowered households with children in the lower tail of the HAZ distribution to afford IYCF
improvements. To summarize, our child growth results suggest that the BCC + V oucher
treatment improves the nutritional status of chronically undernourished children, while the
BCC treatment helps improve the nutritional status of relatively well-nourished children.
24
5.7. Household Food Consumption
Although our interventions were focused on improving children’s diets, we find positive
impact on household diet quality. As shown in Column 1 of Table 8, diet quality, as measured
by FCS, also improves at the household level among the BCC and the BCC + V oucher
groups but not the V oucher group. While the BCC program focused on healthy child-
feeding practices, it is possible that some nutritional information with general application was
applied to the overall household diet—e.g., the emphasis on dietary diversity and essential
micronutrients. This is also supported by results on household food consumption by food
group (Columns 2-11 of Table 8), which shows that improvements in household diet quality
is driven by the consumption of food groups highlighted in the BCC sessions, notably animal
source foods and vitamin A-rich fruits and vegetables.
5.8. Spillover Effects
Lastly, we examine whether a one-time intervention could be sustained in the community
through peer networks (Table 9). It is plausible that mothers primarily seek IYCF advice
from their peers who gave birth just a few months ahead of them, in which case we expect
the spillover group mothers who have a BCC-participating friend to be better-informed than
those who do not. To assess this, we take advantage of the data on the spillover group which
consists of mothers with children under 4 months and pregnant women at baseline. Column
1 of Table 9 presents the effect of having BCC friends defined by the networks of both the
spillover group and BCC participants. Columns 2 and 3 show the effect of having BCC
friends defined by the spillover group respondent’s network and BCC participants’ network,
respectively.
We find suggestive evidence that knowledge on IYCF can be transferred to mother’s
peer group. Those in the spillover group who have a friend who received the BCC treatment
25
have higher IYCF knowledge score compared to those who do not. The coefficients are
all positive and economically large even though the size and significance of the coefficients
are different across the definition of the peer. For an additional BCC-participating peer,
overall knowledge scores shown in Columns 1 to 3 increase by 0.11SD, 0.18SD, and 0.07SD,
respectively, although statistically significant only in Column 2. It is also worth noting
that the magnitude of this increase is approximately one-third of the increase among BCC
participants.
6. Extensions and Robustness Checks
6.1. Heterogeneity Analysis
We conduct heterogeneity analysis to assess whether treatment impacts differ by various
household characteristics. We examine differences in the following characteristics: urban/rural,
whether mother had no formal education, whether eligible child is the first child, age of the
mother and of the eligible child, and whether single mother. We analyze this by including the
heterogeneous variable of interest as control and interacting this variable with the treatment
group dummy variables.
Selected results are reported in Table 10. We find heterogeneous impacts by area,
mother’s schooling, eligible child age, whether single, and wealth. For households in rural
areas (Panel A), the effect of BCC + V oucher on CDDS is smaller. Mothers in rural
areas are more likely to increase self-employed farming labor supply in response to BCC.
Stunting prevalence is higher for rural population that received BCC + V oucher, although
not significant using bootstrap -t p-value. For mothers with no formal schooling (Panel B),
the impact of BCC and BCC + V oucher on IYCF knowledge is greater by 0.4SD, farm
labor supply is lower for both fathers and mothers, and wasting prevalence is lower by 10
26
percentage points. For households with 12 months or younger child at baseline (Panel C),
the impact of BCC + V oucher on CDDS is greater and mothers among the BCC group
increase farm labor supply, but the impact of BCC + V oucher on stunting prevalence is
higher for this group. The impact of BCC and BCC + V oucher on is smaller for single
mothers and Vouchers increase stunting prevalence in this group (Panel D). As for other
household characteristics we examined, we do not find significant differences in impact.
6.2. Robustness Checks
We perform several robustness checks. First, to address the issue of small number of clusters,
we use the wild-cluster bootstrap method for inference (Cameron et al. 2008; Rosenbaum
2002; Fisher 1935). Our results show that the degrees of statistical significance do not differ
for the most part when using the wild-cluster bootstrap p-values. As a second robustness
check, we estimated all of the regressions in Tables 3 to 10 without control variables. We
find that the results are robust to the exclusion of control variables, and the point estimates
and their degree of statistical significance remain similar.
7. Discussion and Conclusion
High rates of stunting in many developing countries pose important health threats to young
children. Many interventions that target a single dimension of causes of child undernutrition
have often found limited effect. Interventions that address multidimensional and interrelated
causes of undernutrition, such as lack of awareness and affordability, may be more effective in
bringing about healthy child development. We test this by implementing a community-based
cluster randomized experiment in Ethiopia that randomly provide IYCF education through
a nutrition BCC and increase affordability through food vouchers to mothers of children
27
aged between 4 and 20 months.
We find that receiving child nutrition education through the BCC program improves
mothers’ child-feeding knowledge, which in turn translates into better child-feeding practices,
while food vouchers alone do not. When only provided with knowledge and not financial
support, mothers purchase more diverse food to some extent and allocates them to their chil-
dren, evidenced by household food group expenditures and child food consumption results.
Also, to procure additional food or income to support improved child-feeding practices, these
mothers may increase their self-employed farming labor supply. With these new purchases
or production, children are fed more diverse food items that they would not have eaten
otherwise. Moreover, BCC improves the diet quality of the household at large regardless
of whether with or without vouchers.Informed mothers also start introducing these healthy
food items at an earlier age so that complementary feeding is not delayed, either with or
without financial support.
Receiving food vouchers in addition to the BCC program considerably augments the
positive impacts. While the magnitude of the knowledge gain is similar between the BCC
and the BCC + V oucher groups, the increase in child dietary diversity doubles for the
BCC + V oucher group. This suggests that the lack of resources could be a barrier to
improving child-feeding practices for those with proper child-feeding knowledge. The increase
in dietary diversity is driven by eating more diverse animal source foods and vitamin A-rich
fruits and vegetables. Diet quantity also increases doubly, evidenced by increased feeding
frequency reflected in the minimal acceptable diet standard. Mothers also become more
confident in how their children fare in terms of diet quantity and quality compared to other
children of the same age. Consequently, improved diet quality leads to stunting reduction. In
terms of introducing complementary foods at appropriate ages, BCC and BCC + V oucher
improve similarly. From this, we can infer that when there is little or no cost associated
28
with a behavior change—i.e., there is no income constraint to behavior change—providing
knowledge alone brings about similar improvements to providing both knowledge and income.
This has important implications for improving other suboptimal health behaviors caused by
misinformation but not cost, such as breastfeeding practices.
Comparing the BCC + V oucher group with the V oucher group, we find that income
through vouchers increases the overall non-staple food stock, whereas nutritional knowledge
through BCC alters mothers’ intrahousehold food allocation decisions. When provided both
education and income, mothers allocate the healthy non-staple food purchased with the
vouchers to infant and young children. Voucher recipients who did not receive BCC still
purchase more non-staple food, but they simply do not allocate it to their children. Food
vouchers alone play a very limited role in changing mothers’ feeding behaviors, and does not
increase child nor household dietary diversity.
This study has some limitations. First, our study looks at relatively short-term results
measured during the period between completion of the intervention and three months there-
after. Thus, we are not able to examine whether improved knowledge and IYCF practices are
sustained in the long-term, and whether or not chronic nutritional status improves further
with time. Secondly, there are only 79 villages (clusters) in our study sample, which calls
for small-sample correction of standard errors.
Our results show that both awareness and affordability are challenges for improved
IYCF which is critical for preventing stunted growth. We also demonstrate that an intensive
nutrition education program (BCC) successfully improves feeding practices, while financial
support (voucher) itself does not effectively improve IYCF. The impacts on IYCF are greatest
when education and financial programs are combined, leading to stunting reduction.
29
Figures and Tables
Figure 1: Predicted Height-for-age Z Scores by Child Age in Months
Source: Local polynomial smoothing predictions with 95% confidence intervals estimated
using the DHS data (Ethiopia DHS, 2000, 2011).
30
Figure 2: Map of Ejere District
31
Figure 3: Study Design
32
Figure4:
Stud
yTim
eline
33
Figure5:
Heigh
t-for-ag
e(H
AZ)
andweigh
t-for-height
(WHZ)
ZScoreKernelD
ensity
Graph
34
Table1:
BaselineMeanCha
racteristics
byIntervention
Group
s
Mean
Difference
betw
een
treatm
entan
dcontrol
P-
value:
NDifference
betw
een
treatm
ents
All
Con
trol
B-C
B-V
BV-C
B=V=BV
B-V
B-B
VV-B
V(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
Pan
elA
.M
othe
rch
arac
teri
stic
sMotherage(years)
28.29
28.21
-0.681
-0.063
0.696
0.318
632
0.618
1.377
0.759
Motheris
Oromo
0.766
0.765
-0.016
0.000
0.001
0.988
634
0.016
0.017
0.001
Motheris
Ortho
doxChristian
0.844
0.851
0.018
0.010
-0.038
0.776
634
-0.008
-0.057
-0.048
Motheris
married
0.768
0.778
-0.049
-0.055
0.028
0.403
633
-0.007
0.077
0.083
Motherha
swork
0.564
0.543
0.045
0.040
0.042
0.998
634
-0.005
-0.003
0.002
Motherab
leto
read
0.495
0.471
0.081
0.052
0.008
0.797
633
-0.029
-0.074
-0.045
Motherab
leto
write
0.485
0.457
0.085
0.056
0.021
0.846
633
-0.029
-0.064
-0.034
Motheryearsof
scho
oling
4.258
3.958
0.595
-0.221
0.835
0.611
634
-0.817
0.240
1.057
MotherIY
CFkn
owledg
escore
21.49
21.46
0.005
-0.168
0.195
0.808
634
-0.173
0.190
0.363
Pan
elB.C
hild
char
acte
rist
ics
Elig
ible
child
age(m
onths)
12.48
12.29
1.100∗∗
-0.183
0.016
0.057
634
-1.282∗∗
-1.083∗
0.199
Child
dietarydiversityscore
2.359
2.433
0.022
-0.236
-0.185
0.456
634
-0.258
-0.207
0.051
Minim
umacceptab
lediet
0.128
0.116
0.074
0.004
0.004
0.361
627
-0.070
-0.070
0.000
Heigh
t-for-ageZscore
-1.060
-1.038
-0.043
-0.111
-0.128
0.930
613
-0.068
-0.084
-0.016
Stun
ting
0.272
0.266
0.005
-0.015
0.045
0.549
613
-0.020
0.040
0.060
Pan
elC
.H
ouse
hold
char
acte
rist
ics
Femaleho
useholdhead
0.139
0.135
0.026
0.004
-0.008
0.772
634
-0.022
-0.033
-0.012
Hou
seho
ldsize
4.539
4.495
-0.100
0.160
0.140
0.500
634
0.260
0.240
-0.020
Num
berof
child
ren
2.348
2.315
-0.101
0.126
0.146
0.572
634
0.227
0.247
0.020
Asset
index
-0.015
-0.057
0.162
-0.086
0.113
0.807
634
-0.249
-0.050
0.199
Rural
0.445
0.505
-0.017
-0.060
-0.194
0.673
634
-0.043
-0.177
-0.133
Total
weeklyfood
expe
nditure,
percapita
131.7
129.9
11.26
-11.92
2.275
0.818
634
-23.18
-8.984
14.20
Total
weeklyno
n-food
expe
nditure,
percapita
17.05
16.82
3.522
-5.266∗∗
1.302
0.076
634
-8.788∗
-2.220
6.568
Hou
seho
ldfood
consum
ptionscore
43.19
43.28
-1.003
-0.016
0.333
0.830
634
0.987
1.337
0.349
Distanceto
thenearestmarket(km)
3.593
4.978
-1.924
-2.327
-3.013
0.521
626
-0.403
-1.089
-0.686
Pan
elD
.A
ttri
tion
Follo
w-upSu
rvey
Attrition
Rates
0.084
0.093
0.007
-0.040
-0.013
0.478
634
-0.047
-0.020
0.027
Anthrop
ometry
Attrition
Rates
0.166
0.156
0.045
-0.049
0.038
0.049
634
-0.094∗
-0.007
0.087∗∗
Tab
le1repo
rtsmeanof
selected
baselin
evariab
les.
Brepresents
thosewho
wereoff
ered
theBCC
program
only.V
represents
thosewho
wereoff
ered
thevouche
rprogram
only.BV
represents
thosewho
wereoff
ered
both
theBCC
andthevouche
rprograms.
Colum
ns1-2show
asummaryof
thewho
lesamplean
dthecontrol
grou
p.Colum
ns3-5repo
rtmeandiffe
rences
andsign
ificancelevels
from
test
ofmeandiffe
renc
esbe
tweeneach
treatm
entgrou
pan
dcontrol.
Colum
n6show
sp-values
from
thejointtest
ofequa
lityof
parametersrepo
rted
incolumns
3-5,
column7thenu
mbe
rof
observations,an
dcolumns
8-10
test
ofmeandiffe
rences
betw
eentreatm
entgrou
ps.
∗,∗
∗,a
nd∗∗
∗de
note
sign
ificanceat
10%
,5%
,and
1%,r
espe
ctively.
35
Table 2: Effects on BCC Attendance and Mother IYCF Knowledge
BCCAttendance rate
Mother IYCFknowledge score(standardized)
(1) (2)
BCC (B) 0.730∗∗∗ 0.468∗∗∗
(0.020) (0.100)[0.000]
Voucher (V) -0.004 0.060(0.007) (0.137)
[0.669]
BCC & Voucher (BV) 0.762∗∗∗ 0.415∗∗∗
(0.011) (0.103)[0.001]
Observations 630 577R-squared 0.898 0.127Control group mean 0.000 -0.166P-value: B=V 0.007P-value: B=BV 0.182 0.657P-value: V=BV 0.028P-value: B+V=BV 0.553This table reports results on overall BCC attendance rate and mothers’ IYCFknowledge score (standardized). Column 1 uses administrative data and com-pares BCC attendance rates with the control group where the control andthe voucher group’s attendance rates are set to zero. Column 2 uses surveydata on mothers’ IYCF knowledge. All estimations include area dummies,mother’s age, whether mother is married, working, able to read, and able towrite, mother’s years of schooling, eligible child age, household size, number ofchildren, whether female-headed household, household asset index, ethnicity,and religion. Column 2 additionally controls for the baseline outcome. Ro-bust standard errors clustered at the unit of randomization, the village level,in parentheses. Wild-cluster bootstrap p-values in square brackets. The p-value for difference in coefficients is a F-test for whether the coefficient differsbetween treatment groups. The p-value for difference between B+V and BVtests whether there is any complementarity between BCC and vouchers. ∗∗∗
p<0.01, ∗∗ p<0.05, ∗ p<0.1.
36
Table3:
Effe
ctson
Vou
cher
Redem
ption(D
uringIntervention
)
Average
total
vouche
rexp./m
onthAverage
voucherredemptionpe
rweekby
food
grou
p
Meat
andfish
Milk
and
milk
prod
-ucts
Eggs
Animal
prod
-ucts
total
Vitam
inA-rich
fruits
&veg.
Other
fruits
and
veg.
Nuts
and
legu
mes
Starchy
stap
les
Oilan
dfats
Sugar,
drinks,
and
spices
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
Vou
cher
(V)
151.7∗∗∗
0.145
0.045
1.394∗∗∗
1.584∗∗∗
1.232∗∗∗
5.463∗∗∗
1.870∗∗∗
14.90∗∗∗
10.10∗∗∗
2.742∗∗∗
(4.073)
(0.093)
(0.040)
(0.282)
(0.332)
(0.144)
(0.396)
(0.435)
(0.900)
(0.646)
(0.281)
[0.000]
[0.011]
[0.276]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
BCC
&Vou
cher
(BV)
153.2∗∗∗
0.031
0.100∗
1.119∗∗∗
1.250∗∗∗
1.566∗∗∗
7.375∗∗∗
1.527∗∗∗
13.50∗∗∗
9.672∗∗∗
3.316∗∗∗
(3.176)
(0.028)
(0.053)
(0.167)
(0.166)
(0.158)
(0.394)
(0.455)
(0.755)
(0.505)
(0.195)
[0.000]
[0.301]
[0.172]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
Observation
s516
516
516
516
516
516
516
516
516
516
516
R-squ
ared
0.902
0.071
0.030
0.261
0.248
0.398
0.623
0.317
0.622
0.611
0.403
P-value:V=BV
0.785
0.217
0.350
0.348
0.324
0.129
0.002
0.547
0.239
0.589
0.081
Thistablerepo
rtsresultson
averagevo
uche
rrede
mptionin
totalpe
rmon
than
dby
food
grou
pspe
rweekusingthevouche
rpu
rcha
serecord
data.
The
’Animal
prod
ucts’food
grou
pis
anaggregationof
meatan
dfish,
milk
andmilk
prod
ucts,an
deggs.Using
thevo
uche
rusageda
ta,theresults
compa
retheBCC+Vou
cher
grou
pan
dtheVou
cher
grou
pwiththecontrolgrou
pwhich
haszero
vouche
rspen
ding
.Allestimations
controlforarea
dummies,
mothe
r’sage,
whe
ther
mothe
ris
married
,working
,ab
leto
read
,an
dab
leto
write,mothe
r’syearsof
scho
oling,
eligible
child
age,
househ
old
size,n
umbe
rof
child
ren,
whe
ther
female-he
aded
househ
old,
househ
oldassetinde
x,ethn
icity,
andrelig
ion.
Rob
uststan
dard
errors
clusteredat
theun
itof
rand
omization,
thevilla
gelevel,in
parenthe
ses.
Boo
tstrap
pedclusteredstan
dard
errors
insqua
rebrackets.W
ild-cluster
bootstrapp-values
insqua
rebrackets.The
p-valuefordiffe
rencein
coeffi
cients
isaF-testforwhe
ther
thecoeffi
cientdiffe
rsbe
tweentreatm
entgrou
ps.
∗∗∗p<
0.01,
∗∗p<
0.05,
∗
p<0.1.
37
Table 4: Effects on Child Diet Quality
CDDSMinimumacceptable
diet
Timely intro.of comple-mentaryfood score
(standardized)
Perceivedrelative
child dietaryquantity
Perceivedrelative
child dietaryquality
(1) (2) (3) (4) (5)
BCC (B) 0.331∗ 0.078∗∗ 0.146∗∗ 0.004 0.037(0.174) (0.034) (0.065) (0.041) (0.027)[0.086] [0.045] [0.049] [0.916] [0.197]
Voucher (V) 0.032 0.006 -0.046 0.0474 0.0534∗∗
(0.184) (0.030) (0.085) (0.032) (0.025)[0.883] [0.824] [0.646] [0.144] [0.044]
BCC & Voucher 0.604∗∗∗ 0.152∗∗∗ 0.130∗∗ 0.047∗ 0.076∗∗∗
(BV) (0.172) (0.031) (0.063) (0.026) (0.024)[0.014] [0.002] [0.081] [0.102] [0.004]
Observations 576 531 565 577 577R-squared 0.125 0.126 0.045 0.067 0.053Control group mean 3.073 0.124 0.153 0.893 0.905P-value: B=V 0.152 0.059 0.050 0.361 0.528P-value: B=BV 0.177 0.070 0.826 0.332 0.140P-value: V=BV 0.007 0.0001 0.073 1.000 0.317P-value: B+V=BV 0.381 0.193 0.796 0.941 0.687This table reports results on child dietary diversity score (CDDS), minimum acceptable diet standard, stan-dardized score on timely introduction of complementary foods, and mothers’ perception of their child’s relativedietary quantity and quality. All estimations include the baseline outcome, area dummies, mother’s age, whethermother is married, working, able to read, and able to write, mother’s years of schooling, eligible child age, house-hold size, number of children, whether female-headed household, household asset index, ethnicity, and religion.Robust standard errors clustered at the unit of randomization, the village level, in parentheses. Wild-clusterbootstrap p-values in square brackets. The p-value for difference in coefficients is a F-test for whether thecoefficient differs between treatment groups. The p-value for difference between B+V and BV tests whetherthere is any complementarity between BCC and vouchers. ∗∗∗ p<0.01, ∗∗ p<0.05, ∗ p<0.1.
38
Table5:
Effe
ctson
Child
Food
Con
sumption
Whether
child
atein
thelast
24ho
urs:
Meat
Milk
Eggs
Aminal
prod
ucts
total
Vitam
inA-rich
fruits
&veg.
Other
fruits
&veg.
Nuts&
legu
mes
Starchy
stap
les
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
BCC
(B)
0.131∗∗
0.091∗
0.083
0.094∗
0.003
-0.022
0.067
-0.024
(0.050)
(0.050)
(0.066)
(0.050)
(0.055)
(0.062)
(0.057)
(0.017)
[0.029]
[0.098]
[0.299]
[0.087]
[0.957]
[0.779]
[0.219]
[0.224]
Vou
cher
(V)
0.136∗∗∗
-0.023
-0.010
0.139∗∗
-0.055
-0.043
0.031
-0.004
(0.037)
(0.045)
(0.070)
(0.056)
(0.041)
(0.053)
(0.071)
(0.013)
[0.003]
[0.628]
[0.886]
[0.030]
[0.197]
[0.478]
[0.696]
[0.860]
BCC
&Vou
cher
(BV)
0.119∗∗∗
0.102∗∗
0.181∗∗∗
0.200∗∗∗
0.098∗
0.004
0.087∗
0.005
(0.024)
(0.044)
(0.051)
(0.038)
(0.050)
(0.046)
(0.047)
(0.010)
[0.001]
[0.039]
[0.008]
[0.000]
[0.113]
[0.933]
[0.107]
[0.682]
Observation
s576
576
576
576
576
576
576
576
R-squ
ared
0.099
0.099
0.100
0.173
0.060
0.042
0.047
0.038
Con
trol
grou
pmean
0.119
0.275
0.286
0.437
0.226
0.805
0.368
0.992
P-value:B=V
0.932
0.036
0.273
0.490
0.323
0.753
0.624
0.267
P-value:B=BV
0.817
0.832
0.186
0.049
0.149
0.709
0.722
0.125
P-value:V=BV
0.677
0.015
0.017
0.297
0.005
0.413
0.418
0.529
P-value:B+V=BV
0.023
0.631
0.307
0.677
0.055
0.442
0.912
0.176
Thistablerepo
rtsresultson
child
food
consum
ptionby
food
grou
p.Eachou
tcom
eindicateswhe
ther
thechild
atean
yfood
item
from
the
food
grou
pin
thelast
24ho
urs.
The
’Animal
prod
ucts’foo
dgrou
pisan
aggregationof
meatan
dfish,
milk
andmilk
prod
ucts,a
ndeggs.
Allestimations
controlfor
theba
selin
eou
tcom
e,area
dummies,mothe
r’sage,
whe
ther
mothe
rismarried
,working
,ableto
read
,and
able
towrite,m
othe
r’syearsof
scho
oling,
eligible
child
age,
househ
oldsize,n
umbe
rof
child
ren,
whe
ther
female-he
aded
househ
old,
househ
old
assetinde
x,ethn
icity,
andrelig
ion.
Rob
uststan
dard
errors
clusteredat
theun
itof
rand
omization,
thevilla
gelevel,in
parenthe
ses.
Wild
-cluster
bootstrapp-values
insqua
rebrackets.The
p-valuefordiffe
rencein
coeffi
cients
isaF-testforwhe
ther
thecoeffi
cientdiffe
rsbe
tweentreatm
entgrou
ps.The
p-valuefordiffe
rencebe
tweenB+V
andBV
testswhe
ther
thereis
anycomplem
entarity
betw
eenBCC
andvouche
rs.
∗∗∗p<
0.01,∗
∗p<
0.05,∗
p<0.1.
39
Table6:
Effe
ctson
Hou
seho
ldExp
enditures(A
fter
Intervention
)
Total
food
expe
n-diture
Total
non-
food
expe
n-diture
Amou
ntspentpe
rcapita
inthelast
week:
Meat
and
fish
Milk
and
milk
prod
-ucts
Eggs
Animal
prod
-ucts
total
Vitam
inA-rich
fruits
&veg.
Other
fruits
&veg.
Nuts
and
legu
mes
Starchy
stap
les
Oils
and
fats
Sugars,
drinks,
spices
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
BCC
(B)
12.80
-3.927
9.858∗∗
-1.048
-0.136
8.742∗
0.209
1.915∗
3.613∗∗∗
0.558
-0.326
-1.726
(10.47)
(3.161)
(4.276)
(1.248)
(0.250)
(4.775)
(0.257)
(1.043)
(1.315)
(6.000)
(1.140)
(2.628)
[0.293]
[0.236]
[0.042]
[0.460]
[0.609]
[0.108]
[0.479]
[0.111]
[0.026]
[0.946]
[0.825]
[0.521]
Vou
cher
(V)
13.23
4.602
11.70∗∗
-0.381
0.058
11.51∗∗
-0.185
0.322
0.273
-2.112
1.015
2.078
(12.02)
(3.664)
(5.152)
(1.513)
(0.293)
(5.326)
(0.222)
(0.658)
(1.119)
(6.813)
(1.330)
(2.306)
[0.311]
[0.217]
[0.049]
[0.805]
[0.861]
[0.0591]
[0.433]
[0.636]
[0.832]
[0.784]
[0.468]
[0.365]
BCC
&Vou
cher
(BV)
14.40
9.752∗
5.732
1.662
0.650∗
8.058∗
1.004∗∗∗
0.973
0.429
-1.541
1.127
3.747∗∗
(10.27)
(5.252)
(3.716)
(1.338)
(0.331)
(4.179)
(0.278)
(0.610)
(1.162)
(6.710)
(0.946)
(1.679)
[0.206]
[0.219]
[0.172]
[0.248]
[0.073]
[0.086]
[0.005]
[0.121]
[0.728]
[0.844]
[0.311]
[0.024]
Observation
s576
576
576
576
576
576
576
576
576
576
576
576
R-squ
ared
0.231
0.167
0.097
0.112
0.104
0.125
0.154
0.256
0.104
0.173
0.062
0.124
Con
trol
grou
pmean
81.312
31.451
13.053
4.320
0.964
18.336
0.772
6.611
3.012
30.942
5.819
15.820
P-value:B=V
0.974
0.035
0.763
0.695
0.544
0.670
0.159
0.132
0.036
0.693
0.402
0.188
P-value:B=BV
0.894
0.012
0.392
0.074
0.027
0.897
0.012
0.408
0.047
0.751
0.289
0.040
P-value:V=BV
0.928
0.266
0.286
0.252
0.099
0.563
0.0001
0.423
0.914
0.931
0.940
0.480
P-value:B+V=BV
0.493
0.098
0.025
0.151
0.094
0.104
0.012
0.373
0.078
0.999
0.821
0.360
Thistablerepo
rtsresultson
weeklyho
useh
oldfood
andno
n-food
expe
nditurein
totalan
dby
food
grou
p.Eachou
tcom
eindicatestheam
ount
spentby
househ
oldin
thelast
weekpe
rcapita
inEthiopian
Birr.
The
’Animal
prod
ucts’food
grou
pis
anaggregationof
meatan
dfish,
milk
andmilk
prod
ucts,an
deggs.Allestimations
controlfortheba
selin
eou
tcom
e,area
dummies,
mothe
r’sage,
whe
ther
mothe
ris
married
,working
,ab
leto
read
,an
dab
leto
write,
mothe
r’syearsof
scho
oling,
eligible
child
age,
househ
oldsize,nu
mbe
rof
child
ren,
whe
ther
female-he
aded
househ
old,
househ
oldassetinde
x,ethn
icity,
and
relig
ion.
Rob
uststan
dard
errors
clusteredat
theun
itof
rand
omization,
thevilla
gelevel,in
parenthe
ses.
Wild
-cluster
bootstrapp-values
insqua
rebrackets.
The
p-valuefordiffe
rencein
coeffi
cients
isaF-testforwhe
ther
thecoeffi
cientdiffe
rsbe
tweentreatm
entgrou
ps.The
p-valuefordiffe
rencebe
tweenB+V
and
BV
testswhe
ther
thereis
anycomplem
entarity
betw
eenBCC
andvouche
rs.
∗∗∗p<
0.01,∗
∗p<
0.05,∗
p<0.1.
40
Table 7: Effects on Child Physical Growth
HAZ Stunted WHZ Wasted(1) (2) (3) (4)
BCC (B) -0.048 0.080 0.328∗∗ -0.043(0.173) (0.073) (0.160) (0.034)[0.789] [0.312] [0.041] [0.254]
Voucher (V) -0.178 0.073 -0.024 -0.002(0.155) (0.059) (0.156) (0.035)[0.304] [0.280] [0.863] [0.964]
BCC & Voucher (BV) 0.181 -0.095∗∗ -0.0003 0.009(0.159) (0.046) (0.190) (0.034)[0.386] [0.094] [0.999] [0.783]
Observations 486 486 494 494R-squared 0.325 0.232 0.126 0.070Control group mean -1.543 0.416 0.026 0.082P-value: B=V 0.529 0.940 0.056 0.364P-value: B=BV 0.221 0.021 0.125 0.227P-value: V=BV 0.071 0.016 0.914 0.806P-value: B+V=BV 0.099 0.011 0.268 0.337This table reports results on height-for-age Z scores (HAZ), stunting prevalence,weight-for-height Z scores (WHZ), and wasting prevalence. All estimations includebaseline outcome, area dummies, mother’s age, whether mother is married, work-ing, able to read, and able to write, mother’s years of schooling, eligible child age,household size, number of children, whether female-headed household, householdasset index, ethnicity, and religion. Robust standard errors clustered at the unit ofrandomization, the village level, in parentheses. Wild-cluster bootstrap p-valuesin square brackets. The p-value for difference in coefficients is a F-test for whetherthe coefficient differs between treatment groups. The p-value for difference be-tween B+V andBV tests whether there is any complementarity between BCC andvouchers. ∗∗∗ p<0.01, ∗∗ p<0.05, ∗ p<0.1.
41
Table8:
Effe
ctson
Hou
seho
ldFo
odCon
sumption
FCS
Whether
househ
oldatein
thelast
week:
Meat&
poultry
Milk
&milk
prod
.Eggs
Animal
prod
-ucts
total
Vitam
inA-rich
fruits
&veg.
Nuts&
legu
mes
Other
fruits
&veg.
Stap
les
Oils
&fats
Sugar&
spices
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
BCC
(B)
5.804∗∗∗
0.153∗∗
-0.042
0.0133
0.149∗∗
-0.042
-0.035∗
-0.014
0.014∗
0.016∗
-0.004
(1.707)
(0.070)
(0.050)
(0.065)
(0.061)
(0.050)
(0.019)
(0.016)
(0.008)
(0.009)
(0.012)
[0.003]
[0.061]
[0.458]
[0.866]
[0.035]
[0.458]
[0.084]
[0.450]
[0.088]
[0.082]
[0.731]
Vou
cher
(V)
1.566
0.069
0.023
0.002
0.086∗
0.023
-0.002
-0.019
-0.006
0.003
-0.007
(2.172)
(0.065)
(0.057)
(0.066)
(0.048)
(0.057)
(0.028)
(0.012)
(0.016)
(0.010)
(0.011)
[0.535]
[0.340]
[0.684]
[0.981]
[0.100]
[0.684]
[0.961]
[0.174]
[0.763]
[0.835]
[0.726]
BCC
&Vou
cher
(BV)
5.748∗∗∗
0.137∗∗
0.171∗∗∗
0.224∗∗∗
0.213∗∗∗
0.171∗∗∗
0.014
-0.014
0.005
0.003
0.004
(1.658)
(0.060)
(0.058)
(0.052)
(0.042)
(0.058)
(0.027)
(0.011)
(0.011)
(0.014)
(0.004)
[0.008]
[0.047]
[0.009]
[0.002]
[0.000]
[0.009]
[0.635]
[0.227]
[0.711]
[0.825]
[0.355]
Observation
s576
576
576
576
576
576
576
576
576
576
576
R-squ
ared
0.226
0.203
0.246
0.207
0.289
0.246
0.063
0.065
0.055
0.047
0.052
Con
trol
grou
pmean
53.425
0.360
0.234
0.345
0.536
0.402
0.061
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0.256
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0.390
0.271
0.426
P-value:V=BV
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0.370
0.017
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0.007
0.017
0.663
0.728
0.549
0.957
0.346
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0.588
0.393
0.0185
0.025
0.769
0.0185
0.226
0.408
0.863
0.280
0.372
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42
Table 9: Spillover Effects
(1) (2) (3)
Peer definitions:Mutually listed
as friends
BCC partic-ipant listed
spillover groupmother as friend
Spillover groupmother listedBCC partici-pant as friend
Dependent variable: Mother IYCF knowledge score (standardized)
# of BCC peers 0.111 0.182∗ 0.071(0.305) (0.103) (0.104)
Observations 275 275 275R-squared 0.112 0.120 0.113This table reports results on the number of BCC-participating peers spillover group mother has defined by both the
spillover group and BCC-participating mother networks (Column 1), the BCC-participating mothers’ networks
(Column 2), and the spillover group mothers’ networks (Column 3). All estimations control for the baseline
outcome, area dummies, mother’s age, whether mother is married, pregnant, working, able to read, and able to
write, mother’s years of schooling, total number of friends listed as network, eligible child age, household size,
number of children, whether female-headed household, household asset index, ethnicity, and religion. Robust
standard errors clustered at the unit of randomization, the village level, in parentheses. ∗∗∗ p<0.01, ∗∗ p<0.05, ∗
p<0.1.
43
Table 10: Heterogeneity Analysis
(1) (2) (3) (4) (5)Mother
knowledgescore (stan-dardized)
CDDS (0-7)Mother self-employedfarming
Stunting Wasting
Panel A. Rural
BCC x Rural (B) 0.388∗ 0.204 0.128∗∗ -0.169 -0.0703(0.198) (0.341) (0.062) (0.122) (0.056)[0.090] [0.556] [0.064] [0.231] [0.254]
Voucher x Rural (V) -0.174 0.0259 -0.117 0.0847 0.005(0.282) (0.369) (0.079) (0.103) (0.068)[0.604] [0.964] [0.197] [0.459] [0.961]
BCC & Voucher 0.083 -0.599∗∗ 0.001 0.156∗ -0.035x Rural (BV) (0.226) (0.290) (0.096) (0.084) (0.057)
[0.754] [0.082] [0.994] [0.123] [0.549]
Observations 580 579 580 513 526R-squared 0.086 0.113 0.360 0.177 0.015P-value: B=V 0.0575 0.670 0.003 0.066 0.321P-value: B=BV 0.222 0.024 0.212 0.012 0.597P-value: V=BV 0.449 0.105 0.296 0.507 0.584Panel B. Mothers with no formal schooling
BCC x No schooling 0.423∗ 0.221 -0.080 -0.012 -0.102∗
(B) (0.239) (0.428) (0.107) (0.120) (0.056)[0.114] [0.651] [0.472] [0.901] [0.118]
Voucher -0.157 -0.170 -0.201∗∗ -0.009 -0.001x No schooling (V) (0.274) (0.368) (0.089) (0.155) (0.068)
[0.570] [0.650] [0.045] [0.954] [0.988]
BCC & Voucher 0.337∗ -0.224 -0.092 0.004 -0.105∗
x No schooling (BV) (0.171) (0.300) (0.085) (0.091) (0.054)[0.092] [0.488] [0.317] [0.97] [0.106]
Observations 580 579 580 513 526R-squared 0.100 0.110 0.247 0.168 0.021P-value: B=V 0.081 0.448 0.320 0.985 0.183P-value: B=BV 0.726 0.332 0.918 0.894 0.968P-value: V=BV 0.087 0.896 0.288 0.932 0.166
continued on next page
44
continued from previous page
(1) (2) (3) (4) (5)Mother
knowledgescore (stan-dardized)
CDDS (0-7)Mother self-employedfarming
Stunting Wasting
Panel C. Eligible child 12 months or younger at baseline
BCC x Under 12m -0.254 0.0875 0.108∗ 0.057 -0.031(B) (0.229) (0.296) (0.057) (0.129) (0.064)
[0.310] [0.800] [0.060] [0.674] [0.658]
Voucher 0.089 -0.025 0.004 -0.051 -0.018x Under 12m (V) (0.247) (0.255) (0.125) (0.097) (0.065)
[0.723] [0.920] [0.983] [0.603] [0.798]
BCC & Voucher 0.254 0.438∗∗ 0.0477 0.141∗ -0.0447x Under 12m (BV) (0.177) (0.212) (0.065) (0.075) (0.056)
[0.209] [0.049] [0.481] [0.065] [0.426]
Observations 580 579 580 513 526R-squared 0.094 0.109 0.364 0.172 0.014P-value: B=V 0.237 0.755 0.398 0.438 0.876P-value: B=BV 0.029 0.276 0.321 0.498 0.858P-value: V=BV 0.504 0.112 0.728 0.041 0.728Panel D. Single mothersBCC x Single mother 0.370 -0.755∗ -0.199 0.210 0.133(B) (0.256) (0.387) (0.143) (0.199) (0.132)
[0.170] [0.082] [0.199] [0.348] [0.365]
Voucher -0.122 -0.401 -0.196 0.328∗∗ 0.176x Single mother (V) (0.357) (0.467) (0.122) (0.153) (0.110)
[0.763] [0.439] [0.145] [0.0480] [0.157]
BCC & Voucher 0.177 -0.939∗ -0.206 0.102 0.0324x Single mother (BV) (0.510) (0.487) (0.142) (0.127) (0.088)
[0.901] [0.190] [0.158] [0.449] [0.716]
Observations 580 579 580 513 526R-squared 0.088 0.113 0.360 0.174 0.024P-value: B=V 0.137 0.518 0.983 0.550 0.782P-value: B=BV 0.695 0.744 0.956 0.547 0.473P-value: V=BV 0.586 0.385 0.931 0.067 0.219This table reports heterogeneous impacts of intervention by various household characteristics (whether reside in ruralarea, whether mother has no formal schooling, whether eligible child is 12 months or younger at baseline, and whethersingle mother). The point estimates are from interaction terms between treatment and the household characteristic ofinterest. All estimations include baseline outcome and area dummies. Robust standard errors clustered at the unit ofrandomization, the village level, in parentheses. Wild-cluster bootstrap p-values in square brackets. The p-value fordifference in coefficients is a F-test for whether the coefficient differs between treatment groups. ∗∗∗ p<0.01, ∗∗ p<0.05,∗ p<0.1.
45
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48
Appendix A. Figures and Tables
Table A1: Effects on BCC Attendance and Mother IYCF Knowledge by Topic
IYCF Topics:
Animalsourcefoods
VitaminA-richfruits& veg.
Malnutrition& care
Feedingquantity,frequency,thickness
Age ofintro-duction
Hygiene
(1) (2) (3) (4) (5) (6)Panel A. Attendance rate by topic
BCC (B) 0.690∗∗∗ 0.607∗∗∗ 0.732∗∗∗ 0.764∗∗∗ 0.655∗∗∗ 0.960∗∗∗
(0.028) (0.040) (0.052) (0.027) (0.024) (0.022)Voucher (V) -0.009 -0.013 -0.006 -0.0001 -0.0004 -0.001
(0.011) (0.021) (0.017) (0.010) (0.005) (0.012)BCC & Voucher (BV) 0.721∗∗∗ 0.664∗∗∗ 0.718∗∗∗ 0.782∗∗∗ 0.789∗∗∗ 0.808∗∗∗
(0.024) (0.043) (0.025) (0.016) (0.018) (0.033)
Observations 630 630 630 630 630 630R-squared 0.837 0.741 0.757 0.878 0.832 0.842P-value: B=BV 0.389 0.294 0.819 0.572 0.000 0.000Panel B. Knowledge score by topic
BCC (B) 0.352∗∗ 0.392∗∗∗ 0.334∗∗ 0.203∗∗ 0.308∗∗∗ 0.035(0.134) (0.085) (0.127) (0.092) (0.097) (0.160)[0.034] [0.000] [0.024] [0.035] [0.004] [0.832]
Voucher (V) 0.008 0.098 0.096 0.002 0.015 0.005(0.122) (0.105) (0.167) (0.114) (0.102) (0.118)[0.944] [0.394] [0.608] [0.992] [0.887] [0.971]
BCC & Voucher (BV) 0.282∗∗ 0.302∗∗∗ 0.346∗∗∗ 0.242∗∗∗ 0.209∗∗ 0.043(0.113) (0.094) (0.086) (0.088) (0.093) (0.103)[0.026] [0.004] [0.000] [0.006] [0.044] [0.695]
Observations 577 577 577 577 577 577R-squared 0.079 0.075 0.081 0.072 0.105 0.073P-value: B=V 0.025 0.010 0.226 0.088 0.004 0.866P-value: B=BV 0.638 0.371 0.931 0.708 0.297 0.966P-value: V=BV 0.056 0.067 0.158 0.050 0.065 0.791P-value: B+V=BV 0.695 0.177 0.706 0.814 0.437 0.992This table reports results on BCC attendance rate and mothers’ IYCF knowledge score (standardized) by IYCFtopic. Panel A uses administrative data and compares BCC attendance rates with the control group wherethe control and the voucher group’s attendance rates are set to zero. Panel B uses survey data on mothers’IYCF knowledge. All estimations include area dummies, mother’s age, whether mother is married, working, ableto read, and able to write, mother’s years of schooling, eligible child age, household size, number of children,whether female-headed household, household asset index, ethnicity, and religion. Panel B additionally controlsfor the baseline outcome. Robust standard errors clustered at the unit of randomization, the village level, inparentheses. Wild-cluster bootstrap p-values in square brackets. The p-value for difference in coefficients is aF-test for whether the coefficient differs between treatment groups. The p-value for difference between B+VandBV tests whether there is any complementarity between BCC and vouchers. ∗∗∗ p<0.01, ∗∗ p<0.05, ∗ p<0.1.
49
Table A2: Effects on Other Child Diet Quality Measures
Minimumdietarydiversity
Minimummeal
frequency
Numberof timesbreastfedyesterday
Number oftimes ate solidor semi-solidfood yesterday
(1) (2) (3) (4)
BCC (B) 0.044 0.070 0.347∗∗ 0.105(0.052) (0.076) (0.171) (0.228)[0.443] [0.414] [0.067] 0.637]
Voucher (V) -0.021 0.038 0.259 0.281∗
(0.050) (0.061) (0.179) (0.145)[0.648] [0.548] [0.185] 0.062]
BCC & Voucher (BV) 0.176∗∗∗ 0.135∗ -0.024 0.457∗∗
(0.050) (0.070) (0.178) (0.195)[0.021] [0.085] [0.913] 0.038]
Observations 577 434 484 573R-squared 0.129 0.064 0.147 0.057Control group mean 0.328 0.565 5.272 2.678P-value: B=V 0.227 0.684 0.672 0.401P-value: B=BV 0.030 0.476 0.085 0.188P-value: V=BV 0.001 0.186 0.147 0.360P-value: B+V=BV 0.060 0.802 0.019 0.822This table reports results on minimum dietary diversity, minimum meal frequency, number of timesbreastfed yesterday, and number of times ate solid or semi-solid food yesterday. All estimations includethe baseline outcome, area dummies, mother’s age, whether mother is married, working, able to read,and able to write, mother’s years of schooling, eligible child age, household size, number of children,whether female-headed household, household asset index, ethnicity, and religion. Robust standard errorsclustered at the unit of randomization, the village level, in parentheses. Wild-cluster bootstrap p-valuesin square brackets. The p-value for difference in coefficients is a F-test for whether the coefficient differsbetween treatment groups. The p-value for difference between B+V and BV tests whether there is anycomplementarity between BCC and vouchers. ∗∗∗ p<0.01, ∗∗ p<0.05, ∗ p<0.1.
50
Table A3: Effects on Hygiene Behavior
Hygiene score (standardized)(1)
BCC (B) 0.055(0.051)[0.337]
Voucher (V) -0.111∗∗
(0.051)[0.051]
BCC & Voucher (BV) 0.038(0.040)[0.363]
Observations 518R-squared 0.131Control group mean -0.071P-value: B=V 0.013P-value: B=BV 0.765P-value: V=BV 0.008P-value: B+V=BV 0.220This table reports results on hygiene score which takes a mean of howoften the mother washes hands before cooking, washes hands before feed-ing, washes food before preparing child meal, washes dishes before usingto feed child, washes dish with clean water, washes bottle before feeding,and sterilizes bottle (on a scale of 1=Never to 5=Always). All esti-mations control for the baseline outcome, area dummies, mother’s age,whether mother is married, working, able to read, and able to write,mother’s years of schooling, eligible child age, household size, numberof children, whether female-headed household, household asset index,ethnicity, and religion. Robust standard errors clustered at the unit ofrandomization, the village level, in parentheses. Wild-cluster bootstrapp-values in square brackets. The p-value for difference in coefficients is aF-test for whether the coefficient differs between treatment groups. Thep-value for difference between B+V and BV tests whether there is anycomplementarity between BCC and vouchers. ∗∗∗ p<0.01, ∗∗ p<0.05, ∗
p<0.1.
51
Appendix B. Mother IYCF BCC Curriculum
Week Contents Week Contents
1 Introduction 9A: Frequency & amount of complementary food
B: Eating schedule & discussion
2 Dietary diversity and weekly diet schedule 10 Recipe and cooking demonstration
3 When to start complementary feeding 11 Responsive feeding
4 Thickness & consistency of complementary food 12 Feeding during illness
5 Role play & discussion 13 Role play & discussion
6 Food variety-iron, proteins from meat 14 Hygienic preparation & storage of food
7A: Enrichment of complementary food
15 Group discussion & reviewB: Household food processing strategy
8 Role play & discussion 16 Testimonials & ceremony
52