rain project: impact evaluation report

Transcription

ealigning
griculture
mprove
utrition
to
RAIN PROJECT: IMPACT
EVALUATION REPORT
An impact evaluation report prepared by the International Food
Policy Research Institute of the Realigning Agriculture to Improve
Nutrition (RAIN) project in Zambia
May 2016
Jody Harris, Phuong Hong Nguyen, John Maluccio,
Adam Rosenberg, Lan Tran Mai, Wahid Quabili, Rahul Rawat
Acknowledgements
A large community-based study like this could not be completed without the coordinated efforts of many people.
The International Food Policy Research Institute (IFPRI) team thanks all those who facilitated every step of this process:

Concern Worldwide staff in Dublin, particularly the Strategy Advocacy and Learning (SAL) advisors and the
Zambia liaisons within the International Programmes Department, for their support and commitment to the
RAIN project and its evaluation.

Concern Worldwide staff in Zambia for their constant support for our work. In particular, Gudrun Stallkamp
who provided critical overall support to this evaluation, and who provided inputs in selecting sites for the
evaluation, commented on several pieces of this survey including the evaluation design, ethics approval
application, questionnaire revisions, and for connecting the evaluation team with other key personnel at
Concern Worldwide; this survey could not have been completed with her support

Mumbwa Child Development Agency (MCDA) for their support in implementing the project

Palm Associates for facilitation of administrative and logistical support to the survey fieldwork.

The Government of Zambia health and agriculture officials in Mumbwa District and Central Province who
provided support to this survey and to the RAIN project

The National Food and Nutrition Commission for loaning the survey team anthropometric equipment, and for
invaluable support to anthropometric training of enumerators

The entire survey team from Palm Associates from both survey rounds, including the research assistants, field
supervisors, fieldworkers, and data entry and management team
Finally, to the women and children that belong to the households who participated in the surveys, we thank you for your
role in making this a success. Your contributions of time and information, and your dreams for a more healthy future for
your children are at the very heart of this project.
The RAIN project was funded by Irish Aid and the Kerry Group with additional support from the Bank of Ireland. Funding for
the evaluation was provided by Concern Worldwide, through grants received from Irish Aid, Kerry Group, and PATH through
support provided by the UK Government’s Department for International Development.
Additional support for the evaluation was provided by the CGIAR research program on
Agriculture for Nutrition and Health, led by IFPRI.
The views expressed herein are those of the authors and in no way
can be taken to reflect the official opinions of the funding organisations.
Acronyms
BMI
Body Mass Index
CSA
Census Supervisory Area
CSO
Central Statistics Office
CWW
Concern Worldwide
DHS
Demographic and Health Survey
DPT
Diphtheria/Polio/Tetanus
ENA
Essential Nutrition Actions
FANTA
Food and Nutrition Technical Assistance
HAZ
Height-for-age z-score
HDDS
Household Dietary Diversity Score
HHS
Household Hunger Scale
IFPRI
International Food Policy Research Institute
IYCF
Infant and Young Child Feeding
NFNC
National Food and Nutrition Commission
NGO
Non-governmental Organization
OPV
Oral Polio vaccine
RAIN
Realigning Agriculture to Improve Nutrition
SEA
Supervisory Enumeration Area
WAZ
Weight-for-age z-score
WHO
World Health Organization
WHZ
Weight-for-height z-score
Table of contents
EXECUTIVE SUMMARY
IX
1 INTRODUCTION
20
1.1
1.2
1.3
1.4
20
21
22
23
OVERVIEW OF THE NUTRITION SITUATION IN ZAMBIA
DESCRIPTION OF THE RAIN PROJECT
EVALUATION OBJECTIVES
STRUCTURE OF THE REPORT
2 METHODS
24
2.1 EVALUATION DESIGN
2.1.1 RATIONALE FOR AGE RANGE TO DETECT IMPACTS ON STUNTING
2.1.2 RANDOMIZATION PROCESS
2.1.3 SAMPLE SIZE ESTIMATE
2.1.4 SAMPLING METHODOLOGY
2.2 SURVEY INSTRUMENTS
2.2.1 CONCEPTUAL BASIS FOR THE IMPACT EVALUATION QUESTIONNAIRE
2.2.2 HOUSEHOLD QUESTIONNAIRE
2.2.3 ANTHROPOMETRIC MEASUREMENT
2.3 DATA COLLECTION AND MANAGEMENT
2.3.1 TRAINING OF PERSONNEL
2.3.2 SURVEY TEAM COMPOSITION
2.3.3 DATA MANAGEMENT
2.3.4 SURVEY INFORMATION
2.4 DATA ANALYSIS
2.4.1 EXPOSURE TO THE PROGRAM
2.4.2 IMPACT ESTIMATES ON MAIN AND SECONDARY OUTCOMES
2.4.3 DECOMPOSITION ANALYSIS
2.5 ETHICAL APPROVAL
24
25
26
27
28
29
29
30
30
31
31
32
32
33
33
36
36
37
37
3 RESULTS: BASELINE CHARACTERISTICS
39
3.1 KEY BASELINE INDICATORS
39
4 RESULTS: RAIN INTERVENTION EXPOSURE
41
4.1 KEY PROGRAM EXPOSURE INDICATORS
4.2 COMMUNITY EXPOSURE TO NON-RAIN PROGRAMS
41
45
5 RESULTS: IMPACT OF RAIN INTERVENTIONS ON ANTHROPOMETRIC OUTCOMES
47
5.1 MAIN IMPACT ANALYSIS OF RAIN INTERVENTIONS ON ANTHROPOMETRIC OUTCOMES
47
5.1.1 INTENT-TO-TREAT OUTCOMES FOR ANTHROPOMETRIC OUTCOMES AMONG CHILDREN 24-59.9 MONTHS OF AGE 48
5.1.2 INTENT-TO-TREAT OUTCOMES FOR ANTHROPOMETRIC OUTCOMES AMONG CHILDREN 6-23.9 MONTHS OF AGE 50
5.2 PLAUSIBILITY ANALYSIS 1: HIGH POTENTIAL FOR EXPOSURE AGE GROUPS (24-47.9 MONTHS)
52
5.3 PLAUSIBILITY ANALYSIS 2: DOSE-RESPONSE RELATIONSHIP BETWEEN PROGRAM EXPOSURE CHILD NUTRITION
56
5.4 PLAUSIBILITY ANALYSIS 3: CHANGES IN THE UNDERLYING DETERMINANTS OF CHILD GROWTH AND NUTRITION 60
5.4.1 CHILD CHARACTERISTICS
60
5.4.2 MATERNAL CHARACTERISTICS: DEMOGRAPHIC, HEALTH SEEKING BEHAVIOR, AND NUTRITIONAL STATUS
64
5.4.3 HOUSEHOLD CHARACTERISTICS: FOOD SECURITY, DIETARY DIVERSITY, SOCIOECONOMIC STATUS, AND ACCESS TO
SERVICES
72
6 RESULTS: IMPACT OF RAIN INTERVENTIONS ON IYCF PRACTICES
79
6.1 MAIN IMPACT ANALYSIS OF RAIN INTERVENTIONS ON IYCF OUTCOMES
79
6.2 PLAUSIBILITY ANALYSIS: DOSE-RESPONSE RELATIONSHIP BETWEEN PROGRAM EXPOSURES AND IYCF PRACTICES 86
7 RESULTS: IMPACT OF RAIN INTERVENTIONS ON KNOWLEDGE AMONG CAREGIVERS
89
7.1 IMPACT RAIN PACKAGE OF INTERVENTION ON NUTRITION AND HYGIENE KNOWLEDGE
89
8 RESULTS: IMPACTS OF RAIN INTERVENTIONS ON THE WOMEN’S EMPOWERMENT
96
8.1 WOMEN’S SOCIAL EMPOWERMENT
8.2 WOMEN’S ECONOMIC EMPOWERMENT
8.3 WOMEN’S EMPOWERMENT IN AGRICULTURE AT ENDLINE
97
102
107
9 RESULTS: IMPACT OF RAIN INTERVENTIONS ON PRODUCTION OF NUTRIENT-RICH FOODS
110
9.1 FOOD PRODUCTION
110
10 RESULTS: DECOMPOSITION ANALYSIS OF THE DETERMINANTS OF CHANGES IN CHILD GROWTH
OUTCOMES OVER TIME
118
10.1 DETERMINANTS OF CHILD GROWTH
118
11 DISCUSSION
134
REFERENCES
139
12 APPENDICES
140
APPENDIX 1: DETAILED SAMPLE SIZE CALCULATIONS
APPENDIX 2: VARIABLES INCLUDED IN THE DECOMPOSITION ANALYSIS
APPENDIX 3: DETAIL ON THE RAIN PROJECT
APPENDIX 4: COMPARING RAIN AND DHS
APPENDIX 5: EXPLAINING THE IMPACT OF RAIN: PROGRAM IMPACT PATHWAYS
140
145
147
150
151
List of tables
TABLE 2.1.1. SAMPLE SIZES ................................................................................................................................................28
TABLE 2.1.1. SAMPLE SIZES USED FOR ANALYSES ....................................................................................................................34
TABLE 2.4.1: ANALYSIS METHODS- USE AND INTERPRETATION ..................................................................................................35
TABLE 3.1.1 BASELINE CORE IMPACT INDICATORS BY PROGRAM GROUP ......................................................................................39
TABLE 3.1.2. SELECTED BASELINE MATERNAL UNDERLYING FACTORS BY PROGRAM GROUP ..............................................................40
TABLE 3.1.3. SELECTED BASELINE HOUSEHOLD UNDERLYING FACTORS BY PROGRAM GROUP ............................................................40
TABLE 4.1.1 PROGRAM EXPOSURE: PARTICIPATION AND DELIVERY .............................................................................................42
TABLE 4.1.2 PROGRAM PARTICIPATION ...............................................................................................................................43
TABLE 4.1.3. PROGRAM PARTICIPATION (AMONG MOTHERS WITH CHILDREN <2 YEARS OLD)..........................................................43
TABLE 4.1.4. SMF AND CHV INTERACTION .........................................................................................................................44
TABLE 4.1.5. SMF AND CHV INTERACTION (AMONG MOTHERS WITH CHILDREN <2 YEARS OLD) ....................................................44
TABLE 4.1.6. EXPOSURE TO BROADER RAIN PROJECT EVENTS AND MATERIALS .............................................................................44
TABLE 4.1.7. EXPOSURE TO BROADER RAIN PROJECT EVENTS AND MATERIALS (AMONG MOTHERS WITH CHILDREN <2 YEARS OLD) ........44
TABLE 4.2.1 NGO PRESENCE, BY PROGRAM GROUP ................................................................................................................45
TABLE 4.2.2 SOCIAL CAPITAL CLUBS SUPPORTED BY NGOS, BY PROGRAM GROUP .........................................................................46
TABLE 4.2.3 INCOME GENERATING ACTIVITIES SUPPORTED BY NGOS, BY PROGRAM GROUP ............................................................46
TABLE 4.2.4 TRAINING ACTIVITIES SUPPORTED BY NGOS, BY PROGRAM GROUP ...........................................................................46
TABLE 5.1.1. ANTHROPOMETRIC INDICATORS AMONG CHILDREN 24-59.9 MONTHS OF AGE, BY PROGRAM GROUP AND SURVEY ROUND .49
TABLE 5.1.2 ANTHROPOMETRIC INDICATORS AMONG CHILDREN 6-23.9 MONTHS OF AGE BY PROGRAM GROUP AND SURVEY ROUND .....51
TABLE 5.2.1. ANTHROPOMETRIC INDICATORS AMONG CHILDREN 24-47.9 MONTHS OF AGE BY PROGRAM GROUP AND SURVEY ROUND ..54
TABLE 5.2.2. ANTHROPOMETRIC INDICATORS AMONG BENEFICIARIES WITH CHILDREN 24-47.9 MONTHS, BY PROGRAM GROUP AND
SURVEY ROUND........................................................................................................................................................ 55
TABLE 5.3.1. ANTHROPOMETRIC INDICATORS AMONG BENEFICERIES WITH CHILDREN 24-59.9 MONTHS OF AGE BY PROGRAM GROUP AND
SURVEY ROUND........................................................................................................................................................ 58
TABLE 5.3.2. ASSOCIATION BETWEEN PROGRAM EXPOSURE WITH CHILD STUNTING AMONG CHILDREN 24-59.9 M .............................59
TABLE 5.4.1. CHILD IMMUNIZATION AND SUPPLEMENTATION STATUS AMONG CHILDREN 0-23.9 MONTHS, BY PROGRAM GROUP AND
SURVEY ROUND........................................................................................................................................................ 61
TABLE 5.4.2. CHILD IMMUNIZATION AND SUPPLEMENTATION STATUS AMONG CHILDREN 24-59.9 MONTHS, BY PROGRAM GROUP AND
SURVEY ROUND........................................................................................................................................................ 62
TABLE 5.4.3. CHILD MORBIDITY, BY PROGRAM GROUP AND SURVEY ROUND.................................................................................63
TABLE 5.4.4. SELECTED BASELINE MATERNAL UNDERLYING FACTORS, BY PROGRAM GROUP AND SURVEY ROUND .................................65
TABLE 5.4.5 TIME ALLOCATION, BY PROGRAM GROUP .............................................................................................................66
TABLE 5.4.6 TIME ALLOCATION AMONG BENEFICIARIES, BY PROGRAM GROUP ..............................................................................67
TABLE 5.4.7 USE OF PRENATAL CARE, BY PROGRAM GROUP AND SURVEY ROUND ..........................................................................68
TABLE 5.4.8 MATERNAL DIETARY DIVERSITY, BY PROGRAM GROUP AND SURVEY ROUND (< 24 MO) .................................................69
TABLE 5.4.9 MATERNAL DIETARY DIVERSITY AMONG BENEFICIARIES, BY PROGRAM GROUP AND SURVEY ROUND .................................70
TABLE 5.4.10 WOMEN’S NUTRITIONAL STATUS, BY PROGRAM GROUP AND SURVEY ROUND ............................................................71
TABLE 5.4.11 WOMEN’S NUTRITIONAL STATUS AMONG BENEFICIARIES, BY PROGRAM GROUP AND SURVEY ROUND .............................71
TABLE 5.4.12 HOUSEHOLD PERCEPTIONS OF FOOD INSECURITY, BY PROGRAM GROUP AND SURVEY ROUND .......................................73
TABLE 5.4.13 HOUSEHOLD PERCEPTIONS OF FOOD INSECURITY AMONG BENEFICIARIES, BY PROGRAM GROUP AND SURVEY ROUND ........74
TABLE 5.4.14 HOUSEHOLD DIETARY DIVERSITY, BY PROGRAM GROUP AND SURVEY ROUND .............................................................75
TABLE 5.4.15 HOUSEHOLD DIETARY DIVERSITY AMONG BENEFICIARIES, BY PROGRAM GROUP AND SURVEY ROUND ..............................76
TABLE 5.4.16 HOUSEHOLD ASSET OWNERSHIP, BY PROGRAM GROUP AND SURVEY ROUND .............................................................77
TABLE 5.4.17 HOUSEHOLD ACCESS TO SERVICES, BY PROGRAM GROUP AND SURVEY ROUND ...........................................................78
TABLE 5.4.18 HOUSEHOLD SOCIOECONOMIC STATUS, BY PROGRAM GROUP AND SURVEY ROUND ....................................................78
TABLE 6.1.1. WHO RECOMMENDED IYCF INDICATORS, BY PROGRAM GROUP AND SURVEY ROUND .................................................81
TABLE 6.1.2. DIFFERENCE IN DIFFERENCE OF WHO RECOMMENDED IYCF INDICATORS, BY PROGRAM GROUP AND SURVEY ROUND ........82
TABLE 6.1.3. PRE-LACTEAL FEEDING AMONG CHILDREN 0-23 MONTHS OLD, BY PROGRAM GROUP AND SURVEY ROUND .......................83
TABLE 6.1.4. FOOD GROUPS CONSUMED IN THE PAST 24 HOURS AMONG CHILDREN 6-23 MONTHS OLD, BY PROGRAM GROUP AND SURVEY
ROUND .................................................................................................................................................................. 84
TABLE 6.1.5. TIMELINESS OF INTRODUCTION OF COMPLEMENTARY FOODS AMONG CHILDREN 6-23 MONTHS OLD, BY PROGRAM GROUP
AND SURVEY ROUND ................................................................................................................................................. 85
TABLE 6.1.6. REPORTED MEAL FREQUENCY AMONG CHILDREN 6-23 MONTHS OLD, BY PROGRAM GROUP AND SURVEY ROUND ..............85
TABLE 6.2.1 WHO RECOMMENDED IYCF INDICATORS AMONG BENEFICIARIES (THOSE ARE THE MEMBER OF A RAIN’S WOMEN’S GROUP)
BY PROGRAM GROUP AND SURVEY ROUND .................................................................................................................... 87
TABLE 6.2.2 DIFFERENCE IN DIFFERENCE OF WHO RECOMMENDED IYCF INDICATORS AMONG BENEFICIARIES (THOSE ARE THE MEMBER OF
A RAIN’S WOMEN’S GROUP) ..................................................................................................................................... 88
TABLE 7.1.1. KNOWLEDGE ABOUT BF AMONG MOTHERS OF CHILDREN 0-23.9 MONTHS OF AGE, BY PROGRAM AREA ..........................90
TABLE 7.1.2. KNOWLEDGE ABOUT BF AMONG MOTHERS WHO ARE MEMBERS OF RAIN GROUP WITH CHILDREN 0-23.9 MONTHS OF AGE,
BY PROGRAM AND SURVEY ROUND............................................................................................................................... 91
TABLE 7.1.3. REPORTED KNOWLEDGE ON TIMELINESS OF INTRODUCTION OF COMPLEMENTARY FOODS AMONG MOTHERS WITH CHILDREN
0-23.9 MONTHS, BY PROGRAM GROUP AND SURVEY ROUND ............................................................................................92
TABLE 7.1.4. REPORTED KNOWLEDGE ON FEEDING DURING ILLNESS AMONG MOTHERS WITH CHILDREN 0-23.9 MONTHS, BY PROGRAM
GROUP AND SURVEY ROUND ....................................................................................................................................... 93
TABLE 7.1.5 REPORTED EXPOSURE TO INFORMATION, BY PROGRAM GROUP.................................................................................94
TABLE 7.1.6. REPORTED KNOWLEDGE OF HYGIENE PRACTICES, BY PROGRAM GROUP ...................................................................95
TABLE 8.1.1. RELATIONSHIP WITH SPOUSES, BY PROGRAM GROUP AND SURVEY ROUND .................................................................99
TABLE 8.1.2. PERCEPTION OF EQUALITY, BY PROGRAM GROUP AND SURVEY ROUND ......................................................................99
TABLE 8.1.3. DECISION MAKING POWER, BY PROGRAM GROUP AND SURVEY ROUND ...................................................................100
TABLE 8.1.4. WOMEN’S SOCIAL CAPITAL, BY PROGRAM GROUP AND SURVEY ROUND ...................................................................101
TABLE 8.2.1. FINANCIAL EMPOWERMENT, BY PROGRAM GROUP AND SURVEY ROUND..................................................................102
TABLE 8.2.2. ACCESS TO ASSETS AND ABILITY TO SELL ASSETS, BY PROGRAM GROUP AND SURVEY ROUND ........................................103
TABLE 8.2.3. PURCHASING DECISIONS, BY PROGRAM GROUP AND SURVEY ROUND ......................................................................104
TABLE 8.2.4. SUMMARY KEY DOMAINS OF WOMEN EMPOWERMENT (ALL WOMEN), BY PROGRAM GROUP AND SURVEY ROUND ..........105
TABLE 8.2.5. SUMMARY KEY DOMAINS OF WOMEN EMPOWERMENT (AMONG RAIN BENEFICERIES), BY PROGRAM GROUP AND SURVEY
ROUND ................................................................................................................................................................106
TABLE 8.3.1 WOMEN'S EMPOWERMENT IN AGRICULTURE, BY PROGRAM GROUP AND SURVEY ROUND ............................................108
TABLE 8.3.2 WOMEN'S EMPOWERMENT IN AGRICULTURE AMONG RAIN BENEFICIARIES, BY PROGRAM GROUP AND SURVEY ROUND ....109
TABLE 9.1.1 NUMBER OF FIELD CROPS CULTIVATED, BY PROGRAM GROUP AND SURVEY ROUND .....................................................112
TABLE 9.1.2 NUMBER OF VEGETABLES OR FRUITS CULTIVATED, BY STUDY ARM AND SURVEY ROUND ...............................................113
TABLE 9.1.3 REARING ANIMALS AND PRODUCTION OF ANIMAL SOURCE FOODS, BY PROGRAM GROUP AND SURVEY ROUND .................114
TABLE 9.1.4 PRODUCTION OF SEVEN DIFFERENT FOOD GROUPS, BY PROGRAM GROUP AND SURVEY ROUND .....................................115
TABLE 9.1.5 DIFFERENCE IN DIFFERENCE OF FOOD PRODUCTION INDICATORS, BY PROGRAM GROUP AND SURVEY ROUND ...................116
TABLE 9.1.6 DIFFERENCE IN DIFFERENCE OF FOOD PRODUCTION INDICATORS IN HOUSEHOLDS PARTICIPATING IN RAIN, BY PROGRAM
GROUP AND SURVEY ROUND .....................................................................................................................................117
TABLE 10.1.1 CHANGES IN THE MEANS OR PERCENT OF OUTCOMES BETWEEN 2011 AND 2015 IN ZAMBIA.....................................120
TABLE 10.1.2 CHANGES IN THE MEANS OR PERCENT OF KEY VARIABLES IN ZAMBIA 2011-2015 (SAMPLES OF CHILDREN 24-59.9
MONTHS) .............................................................................................................................................................124
TABLE 10.1.3 HEIGHT-FOR-AGE Z REGRESSIONS BY ROUND WITH TEST FOR COEFFICIENT DIFFERENCES OVER TIME AMONG CHILDREN 24 –
59.9 MONTHS.......................................................................................................................................................125
TABLE 10.1.4 STUNTING REGRESSIONS BY ROUND WITH TEST FOR COEFFICIENT DIFFERENCES OVER TIME AMONG CHILDREN 24 – 59.9
MONTHS IN ZAMBIA ...............................................................................................................................................126
TABLE 10.1.5 WEIGHT-FOR-HIGHT Z REGRESSIONS BY ROUND WITH TEST FOR COEFFICIENT DIFFERENCES OVER TIME AMONG CHILDREN 24
– 59.9 MONTHS ....................................................................................................................................................127
TABLE 10.1.6 WASTING REGRESSIONS BY ROUND WITH TEST FOR COEFFICIENT DIFFERENCES OVER TIME AMONG CHILDREN 24 – 59.9
MONTHS ..............................................................................................................................................................128
TABLE 10.1.7 DECOMPOSING SOURCES OF HAZ AND STUNTING CHANGE BY SURVEY ROUND AMONG CHILDREN 24-59.9 MONTHS IN
ZAMBIA ...............................................................................................................................................................130
TABLE 10.1.8 DECOMPOSING SOURCES OF WHZ AND WASTING CHANGE BY SURVEY ROUND AMONG CHILDREN 24-59.9 MONTHS IN
ZAMBIA ...............................................................................................................................................................130
TABLE 10.1.9 DECOMPOSING SOURCES OF HAZ AND STUNTING CHANGE BY SURVEY ROUND AMONG CHILDREN 0-59.9 MONTHS IN
ZAMBIA ...............................................................................................................................................................132
TABLE 10.1.10 DECOMPOSING SOURCES OF WHZ AND WASTING CHANGE BY SURVEY ROUND AMONG CHILDREN 0-59.9 MONTHS IN
ZAMBIA ...............................................................................................................................................................133
TABLE 10.1.1 DEFINITIONS OF VARIABLES USING IN THE ANALYSES ...........................................................................................145
TABLE 1 PROCESS EVALUATION SAMPLING ..........................................................................................................................151
List of figures
FIGURE 2.1. RAIN EVALUATION DESIGN .............................................................................................................................25
FIGURE 2.2. MAP OF RANDOMIZED INTERVENTION AREAS (CONTROL NOT SHOWN).....................................................................27
FIGURE 5.1. PREVALENCE OF STUNTING, UNDERWEIGHT AND WASTING AMONG CHILDREN 24-59.9 MONTHS BY PROGRAM GROUP AND
SURVEY ROUND........................................................................................................................................................ 48
SURVEY ROUND........................................................................................................................................................ 50
SURVEY ROUND........................................................................................................................................................ 53
FIGURE 5.4. PREVALENCE OF STUNTING, UNDERWEIGHT AND WASTING AMONG BENEFICERIES WITH CHILDREN 24-59.9 MONTHS BY
PROGRAM GROUP AND SURVEY ROUND ........................................................................................................................ 57
FIGURE 10.1 HAZ BY CHILD AGE, RAIN PROGRAM AREAS 2011 AND 2015 ..............................................................................119
FIGURE 10.2 WHZ BY CHILD AGE, RAIN PROGRAM AREAS 2011 AND 2015 .............................................................................119
FIGURE 10.3 TRENDS IN STUNTING AND WASTING PREVALENCE, BY CHILD AGE AND SURVEY TIME IN ZAMBIA ...................................121
FIGURE 10.4 DISTRIBUTIONS OF CHILD HAZ SCORES AMONG CHILDREN 24-59.9 MONTHS IN ZAMBIA BY SURVEY ROUND ..................122
FIGURE 10.5 DISTRIBUTIONS OF CHILD WHZ SCORES AMONG CHILDREN 24-59.9 MONTHS IN ZAMBIA BY SURVEY ROUND .................122
FIGURE 12.1 COMPARISON OF STUNTING AND WASTING BETWEEN RAIN AND DHS ...................................................................150
FIGURE 12.2: RAIN PROGRAM IMPACT PATHWAY (PIP) ACHIEVEMENT ....................................................................................152
Executive summary
RAIN project model
Almost half of children in Zambia are stunted, and reduction of chronic malnutrition is prioritized in
nutrition policies and programs in the country. The Realigning Agriculture to Improve Nutrition (RAIN)
project was a partnership between Concern Worldwide (CWW), Mumbwa Child Development Agency
(MCDA) and the International Food Policy Research Institute (IFPRI), aiming to design, implement and
evaluate a model of multi-sectoral integration of interventions to reduce the prevalence of chronic
malnutrition in Mumbwa district, in the Central Province of Zambia. A key component of the RAIN
project was to document evidence of both impact and process for application in other contexts and at
scale, through a rigorous evaluation design. Project partners over the course of the 5-year project have
included the Mumbwa District Child Development Agency (MCDA), Women for Change (WFC), the
Zambian Ministry of Agriculture and Livestock (MAL), the Zambian Ministry of Health (MoH), and the
Zambian Ministry of Community Development Maternal and Child Health (MCDMCH).
The project targeted children during the critical period from conception through 24 months of age,
roughly equivalent to the first 1,000 days of life, through integrated agriculture, nutrition and health
community based interventions. The overall approach focused on addressing the multi-sectoral causes
of malnutrition and on learning how to effectively tackle the challenges of inter-sectoral collaboration.
The RAIN project comprised of a district-level agriculture intervention to increase year round availability
of, and access to, nutrient rich foods at the household level, in some areas accompanied by promotion
of optimal health, nutrition, and care seeking behavior through the delivery of social behavior change
communication. Most program elements were delivered through local women’s groups created by the
program, led by a female Smallholder Model Farmer (SMF) nominated by her group to receive
agricultural training and inputs and pass these on to the group during monthly meetings. In the nutrition
and health areas, groups were also linked to an existing Community Health Volunteer (CHV) who
received additional training in nutrition topics to pass on to the group. In addition, some communitywide gender sensitization and information activities were undertaken.
The RAIN project and its impact evaluation were designed to help address a critical gap in the evidence
base regarding the degree to which agricultural interventions, either alone or when combined with
nutrition and health interventions, can improve child nutrition, and ultimately reduce the prevalence of
stunting in young children. It aimed to establish ‘proof of concept’ for an intervention model that can
then be replicated and scaled up within Zambia and beyond.
RAIN evaluation
Objectives
The primary evaluation objective of this evaluation was to:
1) Assess the impact of the two different RAIN intervention packages on stunting, among children
24 -59 months of age.
In addition to the primary evaluation objective, the secondary evaluation objectives were to assess the
impact of the RAIN package of interventions on:
2) Core WHO infant and young child feeding (IYCF) indicators among children 0-23 months of age
3) Health and nutrition knowledge among caregivers of young children
4) Different domains of women’s empowerment
5) Agricultural production, and in particular, the availability of, and access to, a year-round supply
of diverse and nutritious foods
Evaluation Design
A fully randomized evaluation design to evaluate the impact of RAIN was determined to be not feasible,
primarily for practical project implementation reasons, and therefore a hybrid design was adopted that
combines a cluster randomized probability design comparing the two RAIN intervention packages, with
a plausibility design that compares the RAIN intervention arms to a non-randomized control group.
Randomization was carried out at the level of the census supervisory area (CSA), a sampling unit used by
the Central Statistics Office of Zambia. This design yielded 3 different study arms:
1. The Agriculture only (Ag-only) group, which included agricultural interventions implemented by
Concern Worldwide and its partners
2. The Agriculture-Nutrition (Ag-Nutrition) group, which included both agriculture and nutrition/
health interventions implemented by Concern Worldwide and its partners
3. The Control group, which had access to standard government agriculture and health services,
and where Concern Worldwide carried out no implementation activities
x
Sample size estimates were powered to detect a reduction in the prevalence of stunting (the primary
impact indicator) of 8 percentage points, and a 0.2 Z-score difference in the mean height-for-age Z-score
(HAZ) between any two study groups. The study was also powered to detect appreciable impacts in the
secondary impact indicators. At baseline, we sampled approximately 1000 households per arm with a
child aged 24-59 months of age, for a total sample size of 3044 households. At endline, we oversampled
the two RAIN interventions arms, by approximately 20% (for an approximately sample size of 1200
households per arm), to account for potential limited intervention exposure at the household level, for a
total sample size of 3536 households.
Repeated cross-sectional baseline (2011) and endline (2015) surveys were conducted in the same
communities over time, at the same time of the year (July-August). Data collection included a household
questionnaire and anthropometric measurements, collecting information on nutrition outcomes and
determinants of malnutrition at the child, maternal and household level.
Evaluation Analyses
Three broad sets of analyses were conducted:
1) Estimation of impact of the RAIN intervention arms (Ag-only, and Ag-Nutrition), compared to the
control, and to each other, on the primary impact indicator i.e. stunting. This included:
a. Estimation of main impact of RAIN interventions on prevalence of stunting, and mean HAZ
scores, using difference-in-difference (DID) estimates
b. A series of additional analyses to assess the plausibility of identified impacts. These
additional analyses included the following:
Plausibility analysis 1: Analysis of the change in stunting prevalence among children in
high potential-for-impact age group i.e. among children 24-47.9 months of age
Plausibility analysis 2: Dose-response analysis between program exposure and child
growth outcomes, thereby creating an internal comparison group to test program
effects with greater degree of confidence.
Plausibility analysis 3: Analysis of change in determinants of stunting over time.
2) Estimation of impact of RAIN interventions on secondary outcomes: Infant and young child feeding
practices; maternal nutrition and health knowledge; women’s empowerment; and agricultural
production.
xi
3) Decomposition analysis to examine various social, behavioral and economic factors as potential
drivers of change in linear growth and stunting over time.
All major impact analyses were conducted using: 1) intent-to-treat analysis, where all sampled
households are included in the analysis, regardless of whether they were actually exposed to the RAIN
project; as well as by 2) per-protocol analysis, where analysis is restricted to those households
confirmed to be RAIN project beneficiaries in the two intervention arms.
Results
Baseline randomization
At baseline, there were no statistically significant differences among the three study arms on
anthropometric outcomes (HAZ, WHZ, stunting and wasting). Most IYCF indicators were comparable
across the three study groups; the exceptions were for early initiation of breastfeeding and meal
frequency, which were lower in the Ag-Nutrition group. Additionally, maternal and household
characteristics, were also comparable among the three groups at baseline, with no major statistically
significant difference between groups. These results (presented in detail in the RAIN baseline report)
indicate that randomization at baseline was successful.
Household exposure to RAIN interventions
Key to interpreting the impact results is understanding intended exposure to the project interventions.
This comprises participation (whether and to what extent a household was participating in RAIN project
components) and delivery (RAIN implementation being received as planned). Overall participation was
31 percent in the Ag-only group, and 34 percent in the Ag-nutrition group. Of this third of all eligible
households participating, the intensity of program delivery varied, with approximately 50% of all
households receiving medium or high levels of program delivery. There was no difference in reported
participation when restricting the exposure analyses to households with a child < 2 years of age, when
sampled at endline. In terms of intensity of delivery, SMF attendance at RAIN groups was high
(approximately 90%), but CHV attendance was low (38-45%); group members therefore had more
opportunity to interact with trainers from the agriculture side than from the health side. The additional
home visits, aimed at providing one-to-one support for gardening and IYCF counselling and support,
were more limited still; SMF home visits were not happening as often as planned (45%, and 53% of
households were visited by an SMF in 2015, in the Ag-Nutrition and Ag-only arms, respectively), and CHV
visits even less often (13% in the Ag - Nutrition arm). In terms of spillover between study arms, leakage
xii
of CHV participation to the Ag-only group was fairly common as some clinics where RAIN-trained CHVs
were based were sited on the border between project areas, but leakage of the project to control areas
was not common, with very few women in this area reporting participation. The overall pattern and
magnitude of these results did not differ between all households sampled, and households with a child <
2 years of age, when sampled at endline.
There was no difference between intervention and control areas in presence of other non-governmental
organization (NGO)-supported projects or activities. We can therefore conclude that the control group
was functioning as a valid comparator to the intervention arms. We do not have data on the presence of
government services in these areas, although there is no reason to believe that there was any difference
in government services across study groups.
PROGRAM IMPACTS:
I.
Nutritional status
Over time, between baseline and endline, the prevalence of stunting decreased significantly in all three
study groups in the impact evaluation age range of children 24-59 months of age. However, there was a
differential decline in stunting in the three study groups, in favor of the control group, with a
significantly greater decrease, compared to the two RAIN interventions groups. DID impact estimates
suggest a negative impact of the RAIN intervention groups on stunting, ranging from +7pp for the Agonly group, to +9pp for the Ag-Nutrition group, compared to the control group, respectively. Similar
negative impacts on mean HAZ scores were seen for the Ag-only group (-0.35 HAZ, compared to the
control group).
Due to the aging of children throughout the duration of the RAIN project, there was greater potential for
certain age groups of children to be exposed to the RAIN interventions for longer than others. Caregivers
of children 24 - 48 months of age at the time of the endline survey had the opportunity to have been
enrolled in the program for the entire 1000 days period between conception and age two. Therefore,
we conducted impact analyses in this age group of children. Similar declines were observed in stunting,
over time, for all three study group, but no differential reductions in the prevalence of stunting were
observed. This suggests a null impact of the RAIN project interventions on stunting, among those with
greatest potential for exposure during the 1000 day window of opportunity.
xiii
Since fewer households than expected participated in the RAIN project, we undertook “per-protocol”
analyses, specifically restricting the impact analyses to households who reported a member having
joined a RAIN women’s group.We observe similar negative impacts on stunting.
Levels of wasting increased significantly over time, for all three study groups, in pure intent to treat
analyses among: 1) children 24-59 months of age at endline, 2) children with greater potential for
exposure (24-47 months of age, at endline), and in per-protocol analyses for: 3) children 24-59 months
of age at endline, and 4) children with greatest potential for exposure. In DID analyses, there was a
consistent positive impact on the prevalence of wasting, in the Ag-only arm, compared to the control
arm, of about -4 pp. This suggests an overall protective effect on wasting, of the Ag-only study arm.
Maternal level underlying determinants of child nutrition
We assessed caregiver demographics, health seeking behavior, time use, and nutritional status as
underlying determinants of child nutritional status. Overall, maternal characteristics were similar across
program groups, at both baseline, and endline. At endline, women in the Ag-Nutrition study group had a
higher prevalence of >4 prenatal visits and also had higher number of prenatal visits compared to
control group. However, these differences were present at baseline as well. There were significant
improvements in maternal dietary diversity (measured out of a total of 7 food groups) within study arms
over time, in both intent-to-treat, and per-protocol analysis, but there were no differential changes over
time, in favor of any study group, in impact analyses. Maternal Body Mass Index (BMI) increased over
time in both the Ag + Nutrition and the Ag-only study groups, but these increases were not differential.
Overall, in DID impact analyses, there was an increase of approximately 10pp in the proportion of
overweight women in the Ag-only arm, compared to the control arm. There was a commensurate
decrease in the proportion of women classified as of normal weight, in the Ag-only arm. In per-protocol
analysis, there was a marginally significant positive program impact on maternal BMI of 0.57 kg/m2 in
the Ag-only arm, compared to the control arm.
Household level underlying determinants of child nutrition
We assessed household food security, dietary diversity, socio-economic status, and access to services as
underlying determinants of child nutrition. Overall, respondents’ perception of their household food
security (using the household hunger scale) decreased significantly over time for all groups, in both the
full sample, and among confirmed RAIN beneficiaries. In impact analyses, there was a significant
decrease in the prevalence of “little to no hunger” in the Ag-Nutrition group, compared to the control
group, and a significant increase in the level of “moderate hunger” in this group. This was the case in
xiv
both intent-to-treat analysis, and among confirmed RAIN beneficiaries. There was a significant positive
program impact on the level of “severe hunger”; DID impact estimates indicate reduction of between 47 pp in the Ag-only group, compared to the control, in both intent-to-treat, and per-protocol analyses.
At the same time, the Ag-Nutrition group had significant positive impacts on household dietary diversity,
with an increase of about 1 food group, based on a 12 food group scale, in both intent-to-treat, and perprotocol analyses. Overall, there were no significant differences in socio-economic status or access to
services between groups over time.
II.
Infant and young child feeding
The second evaluation objective was to assess the impact of the RAIN package of interventions on core
WHO infant and young child feeding (IYCF) indicators among children 0-23 months of age. At endline, all
breastfeeding-related IYCF indicators were high across all three study groups, but complementary
feeding practices were sub-optimal ranging from approximately 25-30% for the minimum acceptable
diet, to 60% for the minimum meal frequency. Several of these IYCF indicators improved over time,
within groups, such as early initiation of breastfeeding (ranging from a 24-31 pp increase over time), or
complementary feeding indicators (with increases ranging from 6 to 12 pp for different indicators, in the
RAIN intervention groups), but these increases were not differential in favor of any group. Of note,
consumption of iron rich foods decreased over time (ranging from 13-15pp), in all groups. Overall, there
was no attributable program impact on improving IYCF practices in both intent-to-treat and per protocol
analyses. The only impact we found was the consumption of legumes/nuts which was higher in both
intervention arms compared to control.
III.
Caregiver Health and Nutrition Knowledge
The third evaluation objective was to assess the impact of the RAIN package of intervention on health
and nutrition knowledge among caregivers. Overall, IYCF knowledge increased over time, except for
breastfeeding based on child’s demand and continue breastfeeing if mother is ill. The overall
improvements in breastfeeding knowledge over time was lower in the Ag-only group, when compared
to the control group. As with complementary feeding, the knowledge of timely introduction of
complemetary food was significant higher in the Ag-Nutrition arm, compared to the control arm; this
xv
was most notable for animal source foods (flesh foods and eggs, where there was an approximate 20pp
greater knowledge regarding the appropriateness of feeding these foods to children 6-8 months of age).
Hygiene knowledge was significantly different among study arms at endline for many aspects relating to
hand washing; protecting children from worms; and making drinking water safer, but the direction of
difference was not consistently favorable to a single study arm, or to the RAIN intervention arms.
IV.
Women’s Empowerment
The fourth evaluation objective was to assess the impact of the RAIN package of intervention on
women’s empowerment. We assessed impacts on eight social and economic domains of women’s
empowerment, plus empowerment in agriculture. There were clear impacts of the RAIN interventions
on different domains of women’s empowerment. In DID impact analyses, we observe significant
program impacts in the Ag-only group, compared to the control group, on social capital, asset access,
financial empowerment, perception of equality. The Ag—Nutrition group had significant program
impacts, when compared to the control group on social capital only. Overall, the direction of impacts
was similar in intent-to-treat, and per-protocol analyses, with slightly higher impacts in per-protocol
analyses.
There was a clear shift over time in women’s involvement in decision making in agriculture. This shift
occurred across all study groups for different aspects of decision making. The change was greater in the
RAIN intervention groups compared to the control group, suggesting a clear impact of the RAIN
interventions on improving women’s empowerment in agriculture in both intent-to-treat, and perprotocol analysis.
V.
Agriculture Production
The fifth evaluation objective was to assess the impact of the RAIN agriculture package of interventions
on the availability of and access to a year-round supply of diverse and micronutrient-rich plant and
animal source foods at household level. Overall the RAIN interventions had a consistent significant
attributable impact on several different dimensions of agricultural production and consequent
availability during the year of nutritious foods. Both the Ag-Nutrition and the Ag-only arms, had greater
increases over time, compared to the control group, on the total number of foods produced , the total
number of agricultural activities engaged in by the households, and the number of months producing
Vitamin A rich foods, and dairy. Consistently, program effect sizes were approximately two-fold larger
in per-protocol analyses.
xvi
Drivers of nutrition change
Finally, we conducted decomposition analysis to explain factors that may have contributed to the large
reductions in stunting over time, and increase in wasting. Our analysis however explain only a small
proportion of these changes. The model explains 8.4% and 6.3% of the actual change in HAZ scores and
stunting prevalence, respectively, in our key age group (24-59 months) during this period. Among the
sources of predicted change, receipt of nutrition counseling and reductions in child morbidity stand out
as the largest factors, explaining the largest proportion of the predicted change in HAZ scores.
Unexpectedly, household hunger and agriculture production variables did not predict reductions in
stunting prevalence or increases in HAZ scores. The model only explains 1% actual change for WHZ and
13% actual change for wasting. For children aged 0-59 months, the models explain 4-6% of actual
change for HAZ/stunting and around 16% for WHZ and wasting. Similar analyses with several rounds of
the Demographic and Health Survey (DHS) data from Zambia showed similar results.
Discussion
This report presents findings from a large, complex, multi-year, inter-sectoral project that combined
agriculture and nutrition interventions to impact child nutrition. The RAIN project is one of a handful of
projects that includes a rigorous randomized design to examine impact, and responds directly to recent
calls for stronger evaluation designs of agriculture and nutrition interventions to strengthen the
evidence base on these links.
Overall, the RAIN project had mixed impacts. The project had: 1) consistently positive impacts on
agricultural production, 2) impacts on different domains of women’s social and economic
empowerment, as well as women’s empowerment in agriculture, 3) impacts on household food security
as measured by household dietary diversity, and 4) a potential protective effect on child wasting. In
general, where there were significant program impacts, the magnitude of these impacts was larger in
per-protocol analyses, among confirmed RAIN beneficiaries. There were however no discernable
impacts on reducing the prevalence of stunting, on improving IYCF practices among young children, or
on improving caregiver health and nutrition knowledge. There appears to be little to no additional
benefit of the Ag-Nutrition arm, compared to the Ag-only intervention arm for the impacts achieved.
This is further evidenced by greater exposure to the agricultural intervention components of the RAIN
project, compared to the nutrition intervention components.
xvii
The low coverage achieved by the program is one possible factor contributing to the lack of consistent
impacts across project objectives. The larger effect sizes in per-protocol analyses support this
hypothesis. As noted previously, only a third of eligible households surveyed had participated in the
project through joining a women’s group—the main point of entry into the RAIN project. In addition,
home visits by SMFs and CHVs, a critical component of the intervention delivery by the program, did not
materialize as envisaged, losing a key one-to-one element that would bolster the project. Across both
the women’s groups and home visits, it was clear that the agriculture frontline workers (SMFs) were
more active than the health-side workers (CHVs), which plays out in the improved agriculture outcomes
compared to nutrition and health outcomes in our evaluation. Only 12-16 percent of households had
both high participation and high levels of program delivery; around half of households were
categoriezed as receiving medium or high level of delivery. The process evaluation (conducted in 2013 &
2014) suggested that the SMF, a position created specifically for the RAIN project, were initially more
incentivized to act for RAIN through RAIN’s provision of incentives (agricultural inputs) than the CHV
positions that already existed in the community and which did not receive similar incentives until after
2014, though both groups received additional training. In addition, CHVs serviced the entire community,
whereas SMFs were working specifically with RAIN groups; also SMFs are a newly created positions, and
the information they provide was new, whilst the CHVs were existing positions so women should have
been exposed to many of the messages before. However, the project strengthened the knowledge of
the CHVs and increased the number of CHVs in the area. It is clear from the evaluation that by and large,
the Ag-only group fared better than the Ag-Nutrition group, suggesting no additional value of nutrition
interventions in this project, implemented primarily by CHVs. As noted in the results section, perprotocol analysis (whereby analysis was restricted to those individuals that were confirmed RAIN
beneficiaries) did not alter the main findings, though almost two-thirds of the sample was lost in this
process. As such it is hard to make definitive claims about lack of impact being solely driven by low
participation.
The impact evaluation design attempts to attribute changes in key impact indicators of interest to the
RAIN interventions. As such, it is important to note a limitation of the design is its inability to accurately
account for elements beyond the RAIN project interventions. For several indicators of interest, we
observe changes over time in both the RAIN intervention groups, as well as the control group. While we
are able to document the lack of leakage of formal program delivery of RAIN interventions to the control
group, we are unable to document informal leakage of secondary intervention components (knowledge,
agricultural inputs etc. provided by non-project staff) among peers or relatives across program study
xviii
arms. Additionally, we are unable to adequately account for general improvements in government
health services across the district, regardless of RAIN study group. There are clear improvements over
time, between baseline and endline, in access and use of government-run health services across all
study groups, as documented in this report. This is evident in increased access and use of prenatal care,
as well as receipt of health and nutrition counseling services at government clinics. As such, it is
plausible that this increase in access and use directly impacts health and nutrition knowledge, and IYCF
practices, components of prenatal counseling and under 5 clinics at government health centers. This
increase may be sufficiently large to prevent detection and attribution of RAIN interventions, over and
beyond general increases and secular trends.
There is limited evidence from other evaluations of similar programs. An evaluation of Helen Keller
International’s (HKI) Enhanced Homestead Food Production (EHFP) intervention on Burkina Faso (one of
only a very few rigorous impact evaluations of a similar agriculture-nutrition programs designed to
improve child nutrition) demonstrated results that were similar for many, but not all, of the outcomes
examined for the RAIN project (Olney, Pedehombga et al. 2015). The evaluation of the EHFP program in
Burkina Faso found 1) positive impacts on wasting among children, 2) small positive impacts on anemia,
3) small positive impacts on maternal dietary diversity, 4) positive impacts on maternal underweight,
and 5) positive impacts on several dimensions of women’s empowerment. Of note, the EHFP program
had no impact on reducing the level of stunting, and no impact on food security.
Overall, the results from the RAIN evaluation contribute to alleviating some of the dearth of evidence
from rigorous impact evaluations of integrated agriculture and nutrition programs. There are clear and
important maternal and household level benefits of this program, which may be achieved in other,
similar programs, and lessons learnt can be used to to scale-up the existing project. The clear and
consistent impact of the RAIN intervention on agriculture production, and on women’s empowerment,
two core objectives of the program, are noteworthy and consistent with the limited evidence to date
from similar interventions.
xix
1
Introduction
1.1 Overview of the nutrition situation in Zambia
Zambia ranks low on human development metrics, national income per capita, Gini coefficient, and
other indices and measures of poverty and inequality. With a young, rural and very sparsely spread
population, with low life expectancy and poor gender equity (Central Statistical Office, Ministry of
Health et al. 2009; UNDP 2010), and fairly low agricultural productivity largely due to poverty and poor
infrastructure, the Government sees little financial revenue. Production of maize is heavily promoted in
Government policy and programs, and is the predominant cash and subsistence crop, with food security
in Zambia generally equated to ‘maize security’ (Smale and Jayne 2009). The Global Hunger Index (K. von
Grebmer, J. Bernstein et al. 2015) ranks Zambia as having an ‘extremely alarming’ hunger situation,
highlighting major deficits in nutrition and child survival.
Zambia suffers from a high rate of under-five mortality at 75 per 1000 live births , though this is a
substantial improvement since the last Demographic and Health Survey (DHS) and more than halved
over the past 15 years (Central Statistical Office, Ministry of Health et al. 2014). An estimated 13 percent
of adults 15-49 years old are HIV-positive (Central Statistical Office, Ministry of Health et al. 2014). Fiftyeight percent of children are fully vaccinated at 12 months, and only around half of all children who
suffer from diarrhoea, fever or pneumonia received appropriate treatment (Central Statistical Office,
Ministry of Health et al. 2014). Use of improved drinking-water sources is low (85% urban, 49% rural), as
is access to improved sanitation (56% urban; 34% rural) (UNICEF 2015). Vitamin A supplementation is
relatively high nationally (80 – 90 percent for children under one year), but pockets of poor coverage
persist. Despite endemic malaria, only 40 percent of under-fives sleep under insecticide-treated nets.
Primary health care is free in Zambia for pregnant women and children under five, although barriers to
access still exist; fertility rate (6.2 per woman) is very high (Central Statistical Office, Ministry of Health
et al. 2014).
Prevalence of undernutrition (stunting) in Zambia had increased since 1990, remaining stable at around
45 percent from 2000 to 2011 (WFP and FAO 2010); the most recent DHS survey shows a reduction in
stunting to 40% nationally (Central Statistical Office, Ministry of Health et al. 2014). Six percent of
children in Zambia are wasted and 15 percent are underweight; about 10 percent of women have a low
body mass index indicating maternal underweight, and 23 percent are overweight (Central Statistical
20
Office, Ministry of Health et al. 2014). Infant and young child feeding practices are variable, with 73
percent of children under six months exclusively breastfed, but only 11 percent fed appropriately for
their age (Central Statistical Office, Ministry of Health et al. 2014). In Central Province, in which
Mumbwa district is located, 42.5 percent of children below five years of age are stunted, 15.3 percent
are underweight, and 4.6 percent are wasted (Central Statistical Office, Ministry of Health et al. 2014).
Chronic malnutrition is prioritized in the National Food and Nutrition Strategic Plan (2011), and the Sixth
National Development Plan explicitly mentions nutrition as an essential cross-cutting issue for achieving
the country’s socio-economic development. The Government of Zambia is an ‘early riser’ country within
the global Scaling Up Nutrition (SUN) movement and is a Feed the Future focal country.
Mumbwa district, where the RAIN project is being implemented, is a rural district in Central Province in
the middle of Zambia, around a two hour drive from the capital Lusaka, with a good trunk road
connection but little in the way of local roads, transport, or energy infrastructure. Mumbwa is classed by
the Famine Early Warning System as the Central Maize-Cotton Zone, with maize and cotton growing
widespread; this area is not prone to drought as rainfall is normally adequate and the area has
moderate access to the market, though this varies between Wards (ZVAC 2004). Stunting in Central
Province was slightly higher than the national average at the last survey, at 42.5%, and wasting slightly
lower, at 4.6% (Central Statistical Office, Ministry of Health et al. 2014).
1.2 Description of the RAIN Project
The Realigning Agriculture to Improve Nutrition (RAIN) project was a partnership between Concern
Worldwide (CWW) and the International Food Policy Research Institute (IFPRI), aiming to design,
implement and evaluate a model of multi-sectoral integration to improve stunting rates in Mumbwa
district, Zambia, and to document evidence of both impact and process for application in other contexts
and at scale. The project targeted children during the critical period from conception through 23 months
of age, roughly equivalent to the first 1,000 days of life, through integrated agriculture, nutrition and
health interventions. The overall approach focused on addressing the multi-sectoral causes of
malnutrition and on learning how to effectively address the challenges of multi-sectoral collaboration;
the RAIN project was rooted in literature suggesting that integrated actions by several sectors can
provide more effective and sustainable processes through which improved nutrition outcomes can be
achieved.
21
The RAIN project comprised of a district-level agriculture intervention to increase year round availability
of, and access to, nutrient rich foods at the household level, in some areas accompanied by promotion
of optimal health, nutrition, and care seeking behavior through the delivery of social behavior change
communication. Most program elements were delivered through local women’s groups created by the
program, led by a female Smallholder Model Farmer (SMF) nominated by her group to receive
agricultural training and inputs and pass these on to the group during monthly meetings. In the nutrition
and health areas groups were also linked to an existing Community Health Volunteer (CHV) who
received additional training in nutrition topics to pass on to the group. In addition, some communitywide social marketing and information activities were undertaken. A critical characteristic of the RAIN
model was hoped to be its ability to be brought to scale and contribute to the achievement of
development goals on hunger and poverty. The RAIN project enrolled 4,437 beneficiaries over the four
years of implementation, in staged enrolment, with no phase-out strategy for women once they were
recruited. Project partners over the lifetime of the project included the Mumbwa District Child
Development Agency (MCDA); Women for Change (WFC); the Zambian Ministry of Agriculture and
Livestock (MAL), and the Zambian Ministry of Health (MoH). For more information see Appendix 3:
Detail on the RAIN project.
The RAIN project was designed to help address a critical gap in the evidence base regarding the degree
to which agricultural interventions, either alone or when combined with nutrition and health activities,
can reduce the prevalence of stunting in young children. It aimed to establish ‘proof of concept’ for an
intervention model that could then be replicated and scaled up within Zambia and beyond. The project
was designed to detect and attribute impact to this RAIN intervention model, and was implemented,
monitored and evaluated using a design involving two different intervention groups vs. a comparison
group: Intervention Group One was supported to participate both in agriculture and nutrition/ health
activities, while Intervention Group Two was supported to participate in agricultural activities only, and
the results compared to a control group receiving standard Government services. In line with Concern’s
core commitment to eradicate extreme poverty, the RAIN project ensured that extremely poor and
vulnerable households were included in the project.
1.3 Evaluation Objectives
The objectives of this impact evaluation are to assess the impact of two different packages of RAIN
interventions delivered through community-based agriculture and health platforms.
22
The primary objective of this evaluation is:
1. To assess the impact of the two different RAIN intervention packages on nutrition outcomes
among children 24 months and older
In addition to the primary evaluation objective, there are several secondary objectives. These include:
2. To assess the impact of the RAIN package of interventions on core WHO infant and young child
feeding (IYCF) indicators among children 0-23 months of age
3. To assess the impact of the RAIN package of intervention on health and nutrition knowledge
among caregivers
4. To assess the impact of the RAIN package of intervention on women’s empowerment
5. To assess the impact of the RAIN agriculture package of interventions on the availability of and
access to a year-round supply of diverse and micronutrient-rich plant and animal source foods at
household level
1.4 Structure of the Report
This report is structured as follows. Chapter 2 presents the evaluation design, sampling methodology,
the main components of the survey questionnaire, and the logistics of fieldwork. Chapter 3 describes
sample characteristics at baseline. Chapter 4 presents data on exposure to the different components of
the RAIN intervention. Chapter 5 presents findings on the core anthropometric impact indicators by
program group and survey round, including descriptive statistics and difference-in-difference analysis to
look at changes between groups over time. This chapter also presents three different plausibility
analyses to dig deeper into the findings, looking at age groups most likely to have benefited from the
program; at confirmed beneficiaries of the program; and at changes in the underlying determinants of
nutrition that might explain the results. Chapter 6 presents findings on the impact of RAIN on infant and
young child feeding (IYCF) practices by program group and survey round, as well as plausibility analysis
looking at confirmed beneficiaries of the program. Chapter 7 presents findings on the impact of RAIN on
nutrition-related knowledge among caregivers. Chapter 8 presents findings on the impact of RAIN on
women’s empowerment, and chapter 9 on the impact of RAIN on access to diverse foods. Chapter 10
then presents a decomposition analysis to examine various social, behavioral and economic factors as
potential drivers of child growth over time, before chapters 11 and 12 discuss the findings in light of
23
process evaluation data and existing literature, and provide some conclusions. The Appendix provides
more detailed results for some chapters, where necessary.
2
Methods
2.1 Evaluation Design
To evaluate the impact of RAIN activities, two different evaluation designs were considered, both
employing 2 different intervention groups (community based agriculture interventions alone; and
agriculture + health/nutrition interventions) and a control group, but differing in how these groups are
assigned. A fully randomized cluster evaluation design to evaluate the impact of RAIN was determined
to be infeasible primarily for practical project implementation reasons, and therefore a hybrid design
was adopted that combines a cluster randomized probability design of the RAIN interventions, with a
plausibility design that compares RAIN interventions to a control group (Figure 2.1.1).
This cluster randomized design yields 3 different study arms:
1. Agriculture interventions only
2. Agriculture AND nutrition/health interventions
3. Comparison group with no RAIN project interventions
Repeated cross-sectional surveys were administered in the same communities and at the same time of
the year in project years 1 and 5 (baseline 2011 and endline 2015, respectively). This design will allow us
to 1) document changes in key impact and process indicators over 4 years of program implementation in
the two RAIN project intervention areas and the control area; and 2) determine the impact of
agriculture and health and nutrition interventions relative to agricultural interventions alone and
relative to the control group.
24
Figure 2.1. RAIN Evaluation Design
2.1.1 Rationale for Age Range to Detect Impacts on Stunting
The assessment of the impact of nutritional interventions on child anthropometry (as well as other
outcomes) should consider the age at which assessments should be made to detect the greatest
difference between intervention and comparison areas. Evidence suggests that (1) the longer children
are exposed to early nutrition inputs like improved IYCF practices and nutrition supplementation in the
under-two age period, the greater the impact will be; and (2) the earlier children are exposed, the
greater the impact will be (Martorell 1995).
The logic of the RAIN project targeting this vulnerable age period is that the investments in the first two
years of life are progressive and cumulative. Therefore, our ability to detect a significant impact on
anthropometric status, particularly, will be greatest among those children who were exposed to RAIN
interventions in the entire period preceding the age of 18-24 months, which signifies the peak age of
growth faltering in Zambia. At that point in time, the differences between RAIN intervention and control
children will be much larger than in the preceding period because RAIN interventions, if received early
enough and long enough, should protect children and prevent the deterioration of their height-for-age
25
measurements. Measuring the anthropometric impacts of early nutrition interventions in a younger age
group (i.e., <18 months of age) will likely underestimate the total impact of the inputs.
For the RAIN project, this means that the age at which impact on anthropometry should be assessed is
dependent on (1) the child’s age at the onset of exposure to RAIN interventions, (2) the total duration of
exposure to the RAIN interventions within the 0-23 month target age-focus for the RAIN project, and (3)
the age of peak growth faltering. We apply these principles with the impact evaluation design to define
the exact age group on whom to assess impact on anthropometry outcomes, below.
2.1.2 Randomization Process
During an early exercise with district government officials, two control wards were randomly chosen
from a set of three ward pairs; the two other ward pairs were selected to receive RAIN project
interventions. Within the 4 intervention wards there were a total of 29 survey areas previously defined
for the Zambian census (areas known as CSAs). From an evaluation perspective, it was desirable to
randomly assign these 29 CSAs to the two different RAIN intervention groups. From an operational point
of view, however, it was desirable to keep as few as possible smaller units. This would reduce the risk of
spill-over effects and would support staff working on the project to select and maintain properly the
specific areas that are assigned to them over a period of several years. Considering both statistical and
operational aspects, six CSAs were merged into three. This resulted in a total of 26 CSAs that would be
available for randomisation. A map was prepared that showed the location of all 26 areas and their
boundaries within the four intervention wards, and a process of ‘drawing lots without replacement’ was
used during a community meeting in Mumbwa with representation from the District MOH, MACO,
MOLFD, MCDA and Concern Worldwide. The resulting randomization is shown in Figure 2.2.
26
Figure 2.2. Map of randomized intervention areas (control not shown)
Agriculture and nutrition
Agriculture only
2.1.3 Sample Size Estimate
For both Baseline and Endline, all sample size calculations were carried out using STATA 11 software.
Two different sample size estimates were obtained. The first was to detect changes in stunting, and the
second was to detect changes in height-for-age Z scores.
At baseline, sample size of 1000 children aged 24-59 months per study arm, for a total of 3000 children,
was determined to be sufficient to allow us to detect, at endline, a minimum difference in any two
groups of a:
I.
8 percentage point difference in the prevalence of stunting
II.
0.2 Z-score difference in the mean height-for-age Z-score (HAZ), assuming a standard
deviation of 1.3.
This estimate was based on an estimated baseline prevalence of stunting of 53 percent, with a mean
HAZ of -1.8 as reported by the 2009 Demographic and Health Survey (Central Statistical Office, Ministry
of Health et al. 2009), a power of 80, a one-sided test, a minimum of 13 sampling clusters per study arm,
and considers a relatively high level of clustering (ρ=0.01) of undernutrition within each of the 3 study
groups (see Appendices
27
Appendix 1: Detailed sample size calculations). At baseline, we sampled approximately 1000 households
with a child aged 24-59 months of age, for a total sample size of 3044 households. At endline, we
oversampled the two RAIN interventions arms, by approximately 20% (for an approximately sample size
of 1200 households per arm), to account for potential limited intervention exposure at the household
level, for a total sample size of 3536 households. 1.
Table 2.1.1. Sample sizes
Baseline
Endline
Agriculture + Nutrition
Agriculture only
Control
All
978
1212
1025
1244
1041
1080
3044
3536
2.1.4 Sampling Methodology
Sampling of children in these surveys was specific to households that had at least one child aged 24-59
months of age, the age range for detecting impacts on stunting i.e. the primary RAIN project impact
indicator. However, to capture impacts of the RAIN project on key IYCF indicators, children between 023 months of age were also sampled.
Logistical considerations prevented us from sampling children 0-23 months and 24-59 months of age
from unique households; sampling unique household would substantially increase our sample size which
was not feasible. Therefore, preference was given to households that had both a child 24-59 months of
age as well as a child 0-23 months of age. Where multiple children within these age ranges were present
in a household, the youngest child within the household was selected.
A listing exercise was undertaken in the 6 wards to determine eligible households i.e. households with
the presence of at least one child aged 24-59 months of age. A child aged 24-59 months old was
identified as the INDEX child. A child aged 0-23 months old was identified as the NON-INDEX child. All
households were categorized into 3 possible categories:
1. Households with an INDEX child only
2. Households with both an INDEX and a NON-INDEX child
3. Households with a NON-INDEX child only
1
Based on an assumption of 80% coverage of eligible women, as estimated in Concern’s 2013 coverage survey
28
Because the CSA is the unit of randomization, it is therefore the unit of most interest from an evaluation
perspective as it is the unit by which treatments are applied. However for sampling purposes the CSA
covers a very large geographic area and a large number of households and is therefore not practical as a
unit for sampling. For sampling purposes the unit that is most practical is the Supervisory Enumeration
Area (SEA), which is a clearly identified geographic area as per the Government Central Statistics Office
(CSO). The majority of CSAs have 3 SEAs within them, though within the 6 wards from which the
baseline survey sample was drawn, there was a range of 2-5 SEAs per CSA, as per the 2010 CSO sampling
frame.
The sample size calculations required that a total of 69 households be sampled in the endline survey per
unit of randomization-the CSA. The majority of CSAs had 3 SEAs, and within these SEAs 23 households
were sampled. In CSAs that had 4 or 5 SEAs, 3 SEAs were randomly sampled and 23 households were
sampled from each SEA. In CSAs that had only 2 SEAs, 35 households were sampled from the first SEA,
and 34 households from the second. Therefore, based on this sampling procedure, a total of 69
households were sampled per CSA as required by the sample size calculations.
Within each SEA, households were sampled based on the household listing exercise conducted at the
start of field work, using a systematic random sampling procedure that used a random number table. A
total of 3044 households were sampled for this baseline survey. Only 34 households that were selected
as part of the systematic random sampling procedure refused to be interviewed.
2.2 Survey Instruments
Several instruments were created for the collection of field data in this survey, based on a conceptual
framework designed to capture the various factors known to affect child nutrition as described in the
baseline report (Harris, Quabili et al. 2011). The conceptual framework and survey instruments are
described below. Data was collected through a multi-module household questionnaire, and
anthropometric measurements of children and caregivers.
2.2.1 Conceptual basis for the impact evaluation questionnaire
The UNICEF conceptual framework (UNICEF 1990) formed the basis for the questionnaires, which were
designed to capture information on all levels of influence that contribute to child undernutrition. The
framework identifies the causes as immediate, underlying and basic, with each level of factors having
influence on the other. The framework identifies inadequate dietary intake and diseases as the
immediate causes of undernutrition, which are in turn influenced by a range of underlying factors at the
29
household and family level. These are mainly insufficient or lack of access to food, clean water and
sanitation, health services, inadequate maternal and child care, and maternal undernutrition. The
immediate causes are embedded within the larger societal factors or basic causes which range from
human and social capital, women status in the society, political and social atmosphere, etc.
In addition to the above key impact indicators, because the household is the unit of analysis, and the
focus of our data collection, we captured information from sampled households on the impact of the
RAIN project on key agriculture, health, and nutrition indicators as outlined in the log frame (e.g.
household food security, dietary diversity, and IYCF practices). In addition, information was collected on
other factors that influence the uptake, adoption and impact of RAIN interventions, such as
socioeconomic status; local food production; household market access and food purchasing behaviour;
household parental characteristics; maternal knowledge of essential nutrition actions (ENA); exposure to
other agriculture, health, and nutrition interventions; exposure to media; and household gender
relationships. At endline, we also included questions on women's time use, and exposure to the RAIN
intervention.
2.2.2 Household questionnaire
The household questionnaire was written in English and translated simultaneously by data collectors
into one of the several local languages; responses were noted in English. It was administered to the
mother of the index child chosen for the survey (or the child’s primary caregiver, if this was not the
child’s biological mother; the word ‘caregiver’ is used for the rest of this report, apart from those
instances where questions relate to the biological mother of a child only, such as with pre- and postnatal care).
The household questionnaire was based on previous nutrition evaluation questionnaires used by IFPRI,
which were grounded in the questionnaire model used for Demographic and Health Surveys and
incorporated several validated instruments to measure different determinants of nutrition. The
questionnaire was adapted substantially for the purposes of the RAIN evaluation, particularly in terms of
local foods, agricultural techniques, and indicators of socio-economic status. Feedback was sought from
the implementing NGO, and the questionnaire was piloted at baseline and revised into the final version.
2.2.3 Anthropometric measurement
Anthropometric measurements (height and weight) were taken for eligible children in each household
(less than 5 years of age) and their caregivers. Anthropometric data was collected using a standardized
30
method (WHO Multicentre Growth Reference Study Group 2006) and assessments made by trained and
standardized field staff. Weight of the children was measured using electronic weighing scales precise to
100 g. Locally manufactured collapsible length/height boards, which were precise to 1 mm, were used to
measure recumbent length of the children < 24 months of age and standing height of children ≥24
months. Quality control in the field was overseen by supervisors checking measurements daily against
tables listing outlier values, and feedback given to enumerators.
2.3 Data collection and management
2.3.1 Training of personnel
IFPRI hired an experienced and well-qualified survey firm, Palm Associates in Lusaka, Zambia, to conduct
the RAIN baseline and endline surveys. The firm has experience with numerous large-scale surveys in
rural Zambia for clients such as the World Bank, and works closely with the Zambia Central Statistics
Office (CSO). The senior administration of the firm worked with the IFPRI team in all planning and
training activities, and was also closely engaged in field supervision of the survey along with his
management staff.
The training of enumerators took place in Lusaka for 10 days in June 2015. The training was led by the
IFPRI team along with senior researchers and trainers from Palm Associates. Several specialists were
invited to provide training in specific sections, including 1) training on the use of census maps and
household listing techniques by a specialist from the Central Statistical Office (CSO); 2) training on
comportment and field logistics by senior Palm Associates administration; and 3) training on
anthropometric measurement by a specialist consultant from the Zambia National Food and Nutrition
Commission (NFNC).
Both enumerators and data entry staff, and team supervisors, attended the training, in order to
standardize the teams and ensure familiarity with the questionnaire and procedures; supervisors also
underwent further training related to their specific duties in the field, including administration of a
community questionnaire, quality control of questionnaires, and sampling methods. The training for the
household survey component was undertaken using lecture, role-play, and practice methods. The
training course consisted of instructions regarding interviewing techniques and field procedures, a
detailed review of modules and questions included on the questionnaires, mock interviews between
participants in the classroom, and practice interviews.
31
The team members were re-trained and fully standardized in anthropometric assessment for the RAIN
endline survey, whether or not individuals had previously undertaken anthropometric work. Team
members were trained by an anthropometry specialist from the National Food and Nutrition
Commission using Food and Nutrition Technical Assistance ( FANTA) guidelines, including explanation of
the theory of anthropometric measurement and its application in the RAIN project; use of the
equipment, including both demonstration and practice; and recording of anthropometric data
accurately.. Standardization for anthropometry was done on young children within the age range for the
RAIN survey (recruited from the local community, and attending the training with their caregivers),
following the methods recommended in FANTA guidelines.
2.3.2 Survey Team Composition
The endline survey was conducted by twelve field teams, double that at baseline, to reduce field time.
The team members for the baseline survey were recruited and trained in Lusaka, and the teams then
traveled to Mumbwa District to carry out the fieldwork. Selection criteria included 1) education and
training background as well as experience in survey work; 2) understanding and fluency in the local
language(s); and 3) time commitment. Each of the teams comprised two enumerators, whose primary
purpose was to undertake household interviews; two anthropometrists, who were specially trained to
undertake anthropometric measurements for women and children; one driver, whose job was to ensure
that the teams got to the right places, in time, and with the correct equipment; and one supervisor,
whose responsibility was to coordinate the team members and to oversee the logistical arrangements
and survey quality. The two anthropometrists also conducted the field interviews whenever they were
not taking anthropometric measurements. Each team was also assigned a 4WD vehicle to facilitate
mobility.
2.3.3 Data Management
The data management process involved designing data entry programs for the household questionnaire,
training data entry personnel, supervision of data entry, and developing error identification and data
cleaning syntaxes. Data entry screens were developed in CS Pro 4.1, and all data cleaning was done in
STATA 11. In both screen development and data cleaning tasks, the Palm Associates IT specialist
collaborated very closely with IFPRI. Data was double-entered by two data entry operatives, and the
data entry screens were specially designed to prompt the verifier whenever discrepancies arose
between the first and second entries. As the data entry was taking place, the DES also performed
32
frequent consistency checks. Cleaned raw data files were submitted to IFPRI where further data cleaning
and variable generation was carried out. Among the more critical steps involved at this stage of data
cleaning was ensuring the validity and utility of the household roster which is a key module in the
survey, against which all other modules are matched. Additionally, standard data cleaning procedures
were followed during the process of variable generation and tabulation of survey results which included
range and extreme value checks, skip pattern checks, and basic verification of question responses that
should be consistent across survey modules.
2.3.4 Survey information
Endline data collection was carried out over a period of 4 weeks between July and August 2015. On
average each interview took between 2 to 2.5 hours, with longer interview times occurring at the start
of the data collection period. There was little resistance from the community or individual households
to the data collection or the burden it placed on respondents. This was evident by the high response
rate among households that were approached to be interviewed. Sample sizes at baseline and endline
are shown in Error! Reference source not found.; an over-sample of 20% at endline allowed greater
power for sub-analyses of households containing program beneficiaries.
2.4 Data analysis
Data analysis was carried out according to the impact evaluation statistical analysis plan that was
developed in 2010, and updated in 2015, and shared with Concern. The main impact analyses were
conducted to estimate the impact of the RAIN interventions on anthropometric outcomes and
determining factors. Further analyses were carried out to strengthen evidence of impact attributable to
the program by using a dose-response analysis that assessed outcomes based on exposure and intensity
of exposure to RAIN interventions, adjusting for characteristics that may have an effect on outcomes,
and by tracing impacts through the theory of change. Finally, analyses were also conducted to identify
factors that may have contributed to changes in child growth over the evaluation period.
Three broad sets of analyses were conducted:
1) Estimation of impact of the RAIN intervention arms (Ag-only, and Ag-Nutrition), compared to the
control, and to each other, on the primary impact indicator i.e. stunting. This included:
33
a. Estimation of main impact of RAIN interventions on stunting, and HAZ scores, using
difference-in-difference estimates
b. A series of additional analyses to assess the plausibility of identified impacts. These
additional analyses included the following:
Plausibility analysis 1: Analysis of the change in stunting prevalence among children in
high potential-for-impact age group i.e. among children 24-27 months of age
Plausibility analysis 2: Dose-response analysis between program exposure and child
growth outcomes, thereby creating an internal comparison groups to test program
effects with greater degree of confidence.
Plausibility analysis 3: Analysis of change in determinants of stunting over time.
2) Estimation of impact of RAIN interventions on secondary outcomes: Infant and young child feeding
practices; maternal nutrition and health knowledge; women’s empowerment; and food production.
3) Decomposition analysis to examine various social, behavioral and economic factors as potential
drivers of change in linear growth and stunting over time.
All major analyses were conducted using intent-to-treat analyses, as well as among those households
confirmed to be RAIN project beneficiaries (per protocol analyses)
Table 2.4.1. Sample sizes used for analyses
Intention to treat analyses
Baseline
Endline
Per-protocol analyses
Baseline
Endline
Agriculture + Nutrition
Agriculture only
Control
978
1212
1025
1244
1041
1080
978
415
1025
378
1041
1080
Below we provide an overview of the statistical analysis methods used in these analyses, the aim of each
method, and some basics of interpretation of the outputs produced (Table 2.4.2).
34
Table 2.4.2: Analysis methods- use and interpretation
Statistical analysis method
Aim
Interpretation
Descriptive statistics: Percentages and
means
To describe the sample at baseline and at endline.
Percentages or means (with standard deviations) are
shown for each study arm at different time points,
sometimes disaggregated by age group.
Joint ANOVA F test, adjusted for clustering
effects
To compare the balance among three groups at
baseline or endline.
Statistical difference between study arms at one point in
time is shown using asterisks. If the study arms are
significantly different to each other, asterisks will be
shown in the first column of the baseline or endline
results.
Student T test
To compare the difference between baseline and
endline within each study aim
Statistical difference within each study arm over time is
shown using asterisks. The numbers refer to the
percentage point difference between baseline and
endline within each study arm.
Difference in difference (DIFF commands)
controlled for clustering effect, child age
and gender (for anthropometry and IYCF
outcomes)
To test for statistical differences between study
arms over the two rounds of surveys.
Percentage point differences between study arms are
shown (either positive increases or negative decreases
over time), along with asterisks denoting whether
differences between arms are significant over time.
Multivarate regression analyses and linear
decomposition analyses
To assess whether particular factors are
determinants of change over time.
Percentages are shown that denote the share of
predicted change explained by each variable, as an
indication of the importance of each variable as a
determinant of the outcome.
35
2.4.1
Exposure to the program
Key to interpreting the impact results is exposure to the intervention. We constructed an ‘exposure to
the program’ scale that comprises participation (whether and how much a household was participating
in RAIN project components) and delivery (RAIN implementation being received as planned). Using data
from the exposure module of the questionnaire, exposure scores were calculated in the following way:
A Participation score was created based on 1) being a member of a RAIN women’s group (possible
scores of 0 or 1); the number of group meetings in the first six months of 2015 (a score of 1 is assigned
for 1-3 group meetings, a score of 2 for 4-6 meetings, and a score of 3 for ≥7 meetings); and other RAIN
events attended (possible scores of 0 or 1). The total scores were summed, and divided by tertiles to
create low, medium and high participation categories.
An Ag Delivery score was created by summing performance across two major areas: whether SMFs
attended group meetings (possible scores of 0 and 1); and the number of home visits conducted by
SMFs (a score of 1 for 1 visit, a score of 2 for 2 visits, and a score 3 for ≥3 visits). The total delivery score
was then used to create three categories: no delivery, medium delivery (score 1) or high delivery (score
≥2).
An Ag- Nutrition Delivery Score was measured by CHV meeting attendance (a possible score of 0 or 1);
and CHV home visits (a score of 1 for 1 visit, a score of 2 for 2 visits, and a score 3 for ≥3 visits). These
measures were in addition to all of the measures described above for the Ag only delivery score, and
then divided into low, medium and high intensity categories as with the Ag-only group.
2.4.2
Impact estimates on main and secondary outcomes
The 2011 and 2015 household datasets were merged into a single dataset with a time period identifier.
The difference-in-differences (DID) (or double-difference) method was applied to assess the difference
between the change in the outcomes for intervention (RAIN Ag-only or Ag-nutrition interventions) and
control groups in the baseline and endline, accounting for geographic clustering effect for all outcomes,
and additional control for child age and gender for anthropometry and IYCF outcomes. The impact
estimates yielded (1) percentage point changes binary outcomes; and (2) change in mean scores if they
are continuous outcomes.
We first conducted analyses for all sample as intent to treat analyses, then restricted analyses in the
subsample who exposed to RAIN program, or among those with high potential of impact (in case of child
anthropometry)
36
For the analyses noted above, regression models were run using the diff and xtreg commands in Stata
13. Both approaches to the regression modeling provided estimates of impact that capture changes over
time and between groups. Results are reported at P<0.05, P<0.01, and P<0.001 significance levels.
2.4.3
Decomposition analysis
As with analyses that examine household exposure to RAIN interventions, a key component of
strengthening the plausibility of RAIN impact results is to examine changes in underlying determinants
over time. Decomposition analysis was conducted to examine various social, behavioral and economic
factors as potential drivers in child growth over time, particularly HAZ, stunting, WHZ and wasting that
changed over time. These analyses draw upon previous work on explaining the drivers of change in
anthropometric outcomes over time in other countries (Headey 2014; Headey, Hoddinott et al. 2014).
Using the two rounds of impact evaluation surveys (baseline and endline) and focusing on variables that
were consistently measured in both rounds, we begun by looking at the determinants for child growth
among children 24-59 months of age, as it is the main impact evaluation group. Further, we looked at
the determinants for child growth among all children less than 5 years of age.
Two sets of independent variables were included in the analysis. First, the time-variant intermediate
determinants which can help both in understanding which factors explain nutritional differences across
children as well as in decomposing changes across time. These include household factors, maternal
factors, health service access, sanitation, child morbidity. The second set was control variables that are
essentially fixed over time such as maternal height, child and maternal age, child gender, location fixed
effects, and RAIN intervention (see Appendices
Appendix 1: Detailed sample size calculations).
Basic regression analysis was used to test a model of nutrition outcomes against underlying
determinants. Then, the Oaxaca-Blinder type decomposition analysis (if several independent variables
changed over time) or simple linear decomposition (if parameters of interest are stable over time) was
used to determine which factors contributed to change in nutrition outcomes and by how much.
2.5 Ethical approval
Ethical permission for the RAIN project, including baseline and endline surveys, was sought and
approved in 2010 from the Humanities and Social Sciences Research Ethics Committee of the University
of Zambia for the project titled “Impact Evaluation of the Realigning Agriculture to Improve Nutrition
37
Project in Mumbwa District, Central Province”. Written authorization was also received from the Office
of the Permanent Secretary, Ministry of Health, Government Republic of Zambia.
Written informed consent was obtained from all survey respondents. A contact sheet was left with each
household as well as a copy of informed consent sheet which was also stored by the survey team.
38
3
Results: Baseline characteristics
KEY RESULTS:
At baseline, there were no statistically significant differences among the three study arms on
anthropometric outcomes (HAZ, WHZ, stunting and wasting). Most IYCF indicators were comparable
across the three study groups; the exceptions were for early initiation of breastfeeding and meal
frequency, which were lower in the Ag-Nutrition group. Additionally, maternal and household
characteristics, were also comparable among the three groups at baseline, with no major statistically
significant difference between groups. These results (presented in detail in the RAIN baseline report)
indicate that randomization at baseline was successful.
3.1 Key baseline indicators
Overall, the three study arms at baseline were comparable. There were no significant differences
between study arms on HAZ, WHZ, stunting or wasting (Table 3.1.1). There were few significant
differences on IYCF indicators, eg. Early initiation of breastfeeding and minimum meal frequency were
significantly different within the ag-nutrition group over time, but no other results were (Table 3.1.1).
Maternal (Table 3.1.2) and household (Table 3.1.3) characteristics were also similar among three groups.
Table 3.1.1 Baseline core impact indicators by program group
Age group
(months)
N
24-59.9
24-59.9
24-59.9
24-59.9
3003
3003
3003
3003
-1.78
42.66
0.43
3.11
-1.76
44.25
0.29
2.26
-1.86
47.05
0.39
2.41
0-23.9
0-5.9
12-15.9
6-8.9
6-23.9
6-23.9
6-23.9
6-23.9
2134
598
374
279
1536
1536
1536
1536
54.70**
74.06
95.12
89.53
29.55
48.27*
20.34
53.53
62.62
74.18
97.58
93.33
33.26
58.19
22.61
59.57
60.95
72.28
91.20
91.30
31.58
51.92
22.92
53.91
Nutritional status of children
HAZ
Prevalence of stunting
WHZ
Prevalence of wasting
Core IYCF indicators
Early initiation of breastfeeding
Exclusive breastfeeding among children< 6 months
Continued breastfeeding at 1 year (12-15.9 months)
Introduction of solid, semi-solid food, or soft food
Minimum diet diversity ( 4 food groups)
1
Minimum meal frequency
2
Minimum acceptable diet
3
Consumption of iron-rich food
AgNutrition
Agriculture
only
Control
* P<0.05, ** P<0.01, *** P<0.001
1
Minimum is defined as 2 times for breastfed infants 6-8 months; 3 times for breastfed children 9-23.9 months; 4 times for
non-breastfed children 6-23.9 months. “Meals” include both meals and snacks and frequency is based on mother’s report.
2
Acceptable diet is defined as who had at least the minimum dietary diversity and the minimum meal frequency during the
previous day.
3
Iron-rich or iron-fortified foods include flesh foods, commercially fortified foods especially designed for infants and young
children that contain iron, or foods fortified in the home with a micronutrient powder containing iron.
39
Table 3.1.2. Selected baseline maternal underlying factors by program group
Agriculture +
Nutrition
N= 978
Age (years) (mean± SD)
Year of schooling (mean± SD)
Education
Never attend school
Primary school
Middle school
High school or higher
Occupation
Agriculture
Others
Body Mass Index (kg/m2)
Underweight (< 18.5)
Normal (18.5-24.9)
Overweight (>=25)
Agriculture only
Control
N= 1,025
N= 1,041
30.46 (8.35)
6.48 (2.49)
Percent
30.49 (8.48)
6.34 (2.60)
Percent
30.69 (9.14)
6.47 (2.40)
Percent
4.62
22.15
66.15
7.08
5.77
24.56
63.80
5.87
4.09
21.60
68.77
5.54
75.72
24.28
63.21
36.79
76.83
23.17
4.70
76.80
18.05
6.34
75.86
17.81
6.35
73.57
20.08
Table 3.1.3. Selected baseline household underlying factors by program group
Agriculture +
Nutrition
N= 978
Household hunger category (HHS)
Little to no hunger (score 0-1)
Moderate hunger (score 2-3)
No hunger (score 4-6)
Agriculture only
Control
N= 1,025
N= 1,041
91.40
7.78
0.82
92.18
5.57
2.25
91.01
7.83
1.16
10.64***
42.37
25.38
21.60
5.77
30.21
28.15
35.87
12.08
29.66
27.05
31.21
95.08***
8.39 (6.25)
8.58 (32.97)
92.17
8.06 (6.47)
8.49 (34.83)
96.43
8.49 (6.80)
7.40 (4.70)
18.04
18.35
21.55
20.52
21.55
23.49
17.08
22.70
16.98
19.74
17.89
19.26
22.19
20.43
20.23
Household dietary diversity category (HDDS)
0-4 food groups
5-6 food groups
7-8 food groups
9-12 food groups
Household assets
Ownership of house (%)
Total number of durable goods (mean)
Total number of productive assets (mean)
Household socioeconomic status
First quintile
Second quintile
Third quintile
Fourth quintile
Fifth quintile
40
4
Results: RAIN intervention exposure
KEY RESULTS:
Overall participation was 31 percent in the Ag-only group, and 34 percent in the Ag-nutrition group.
Of this third of all eligible households participating, 50% received medium or high levels of program
delivery. There is no difference in reported exposure when restricting the exposure analyses to
households with a child < 2 years of age, when sampled at endline. In terms of intensity of delivery,
SMF participation in RAIN groups was high (approximately 90%), but CHV participation was low (3845%); group members therefore had more opportunity to interact with trainers from the agriculture
side than from the health side. The additional home visits, aimed at providing one-to-one support for
gardening and IYCF counselling and support, were more limited still; SMF home visits were not
happening as often as planned (45, and 53% of households were visited by an SMF in 2015, in the AgNutrition and Ag only arms, respectively), and CHV visits even less often (13% in the Ag-Nutrition
arm). In terms of spillover between study arms, leakage of CHV participation to the ag-only group was
fairly common as some clinics were sited on the border between project areas, but leakage of the
project to control areas was not common, with very few women in these areas reporting
participation. The overall pattern and magnitude of these results did not differ between all
households sampled, and households with a child < 2 years of age, when sampled at endline.
4.1 Key program exposure indicators
Key to interpreting the impact results is understanding exposure to the intervention; this comprises
participation (whether and how much a household was participating in RAIN project components) and
delivery (RAIN implementation being received as planned). Table 4.1.1 shows participation (column)
and delivery (row) for both intervention groups. Overall participation was 31 percent in the Ag-only
group, and 34 percent in the Ag-nutrition group. Of this third of all eligible households participating,
around 50% received medium or high levels of program delivery.Table 4.1.2 shows the main exposure
results in terms of the participation of eligible women in the RAIN project2, and Table 4.1.3 shows this
data for caregivers of children under 2 years. Most respondents in both groups had heard of the RAIN
project in the two intervention areas, and around a third of households in these areas had a member
who was identified as currently belonging to a RAIN group. Around a quarter had ever attended a RAIN
group meeting, and a very small proportion had attended in the past six months3. These low
participation rates are borne out in other data (not shown) which finds that of 39 percent of households
reporting any agricultural training in the past year, 62 percent was provided by RAIN. Date of joining was
2
This survey sampled only households where there was a child under age 5, therefore respondents to the survey
would have been eligible to join RAIN. We ask about current participation of any woman in the household in RAIN.
3
The survey was undertaken between July-Aug 2015, and respondents were asked about meetings since January
2015.
41
split evenly over the four rounds of recruitment between 2011 and 2014 in the ag-nutrition areas, but
tailed off slightly in the final years in the ag-only area. Very few women in the control area reported
participating in RAIN, suggesting low program leakage between the intervention and control areas, but
16 percent had heard of the project.
Another aspect of exposure is contact with the SMFs (agriculture) and CHVs (health) who are the
frontline workers on the RAIN project. Table 4.1.4 and Table 4.1.5 show that SMFs were present at the
last RAIN group in a majority of cases, but CHVs were present at fewer than half of those meetings in the
Ag-Nutrition areas, and were present at almost 40 percent of group meetings even in the agricultureonly areas, suggesting large program leakage between the two intervention arms. Home/garden visits
by SMFs were reported by around half of households in both intervention groups (fewer in Ag only), for
around 25 minutes per visit. CHV visits were much less common: only around 10% of households
reported having been visited in 2015, again for 20-30 minutes. As well as group meetings and home
visits, RAIN also organized other means to get messages to the broader population in the intervention
areas.
Table 4.1.6 and Table 4.1.7 show that around 60 percent of respondents reported having heard RAIN
radio broadcasts; around 30 percent had seen RAIN posters; and around 20 percent had participated in
other events such as gender days.
Table 4.1.1 Program exposure: Participation and delivery
Agriculture group
(n= 1,244)
Agriculture and Nutrition group
(n=1212)
Non participation
N=859 (69.1%)
Participation in Ag (%)
N=385 (30.9%)
Low
Medium
High
Non participation
N=797 (65.8%)
Delivery
in Ag (%)
No (score
0)
Medium
31.2
1.3
1.6
12.7
14.0
10.1
Delivery
in AgNutrition
High
6.0
11.2
12.0
42
Participation in Ag-Nutrition
N= 415 (34.2%)
Low
Medium
High
No
(score 0)
Medium
18.3
1.7
1.2
9.2
10.8
9.6
High
16.1
16.4
16.6
Table 4.1.2 Program participation
Have you heard of the RAIN project, run by Concern
Worldwide?
Is any member of this household a member of a RAIN
women’s group?
Are you a member of a RAIN women’s group?
Are you an SMF (Model farmer) for the RAIN project?
Have you ever attended a RAIN group meeting?
Number of RAIN group meetings attended since
1
January 1, 2015
2
When did you join this group
2010
2011
2012
2013
2014
2015
Ag-Nutrition
(n=1212)
Agriculture
(n=1244)
Control
(n=1080)
95.63***
83.84
15.57
35.89***
32.72
0.74
34.24***
6.68***
26.57***
30.97
7.07
21.95
0.74
0.65
0.09
4.20
3.96
2.00
2.71
27.41
28.61
23.19
13.86
4.22
3.03
32.66
31.99
17.51
10.77
4.04
0
0
-0
0
0
Ag-Nutrition
(n= 635)
Agriculture
(n= 568)
Control
(n= 727)
95.12***
85.04
16.39
33.23***
33.45
0.83
31.34***
7.09***
22.83***
31.41
6.87
23.06
0.83
0.69
0.14
3.94
3.66
2.00
1.96
27.45
28.76
23.53
15.03
3.27
3.57
27.86
29.29
20.71
13.57
5.00
0.00
0.00
0.00
0.00
0.00
0.00
Responses are among the entire sample, unless indicated otherwise:
1
2
Among people who attended a RAIN group meeting
Among people who is a not a SMF for the RAIN project
Table 4.1.3. Program participation (among mothers with children <2 years old)
Have you heard of the RAIN project, run by Concern
Worldwide?
Is any member of this household a member of a RAIN
women’s group?
Are you a member of a RAIN women’s group?
Are you an SMF (Model farmer) for the RAIN project?
Have you ever attended a RAIN group meeting?
Number of RAIN group meetings attended since
1
January 1, 2015
2
When did you join this group
2010
2011
2012
2013
2014
2015
Responses are among the entire sample, unless indicated otherwise:
1
2
Among people who attended a RAIN group meeting;
Among people who is not a SMF for the RAIN project
43
Table 4.1.4. SMF and CHV interaction
Ag-Nutrition
(n= 322)
Percent
90.37
45.61
53.11
13.61
Mean (SD)
24.05 (14.38)
20.61 (17.28)
Percent of groups with RAIN SMF present last meeting
Percent of groups with RAIN CHV present last meeting
Percent of households visited by SMF in 2015
Percent of households visited by CHV in 2015
Duration of SMF visits (n=295)
Duration of CHV visits (n=270)
Agriculture
(n= 273)
Percent
90.48
38.21
45.59
8.44
Mean (SD)
25.10 (16.72)
28.85 (32.28)
Note: figures are calculated among people who attended a RAIN group meeting
Table 4.1.5. SMF and CHV interaction (among mothers with children <2 years old)
Ag-Nutrition
(n= 145 )
Percent
89.66
44.00
51.72
11.97
Mean (SD)
25.69 (16.87)
23.43 (22.10)
Percent of groups with RAIN SMF present last meeting
Percent of groups with RAIN CHV present last meeting
Percent of households visited by SMF in 2015
Percent of households visited by CHV in 2015
Duration of SMF visits (n=295)
Duration of CHV visits (n=270)
Agriculture
(n= 131)
Percent
92.37
35.59
45.38
7.39
Mean (SD)
27.53 (18.22)
33.67 (41.15)
Note: figures are calculated among people who attended a RAIN group meeting
Table 4.1.6. Exposure to broader RAIN project events and materials
Percent of households participating in other RAIN events
Percent of households who have seen RAIN posters
Percent of households who have heard RAIN radio messages
Percent of households with access to solar drier
Ag-Nutrition
(n= 1212)
Agriculture
(n= 1244)
22.19
27.56
60.03
8.29
17.60
29.04
57.32
8.78
Table 4.1.7. Exposure to broader RAIN project events and materials (among mothers with children <2 years old)
Percent of households participating in other RAIN events
Percent of households who have seen RAIN posters
Percent of households who have heard RAIN radio messages
Percent of households with access to solar drier
44
Ag-Nutrition
(n= 635 )
Agriculture
(n= 568 )
22.68
29.61
61.36
7.92
19.01
29.75
56.16
7.95
4.2 Community exposure to non-RAIN programs
KEY RESULTS:
There was no difference between intervention and control areas in presence of other NGO-supported
projects or activities.
Using the community-level data, we can see whether NGO programs other than RAIN were working in
the intervention and control areas. Overall, there were no significant differences across study areas in
NGO presence (Table 4.2.1), social capital clubs (Table 4.2.2) or income generating activities (Table 4.2.3)
supported by NGOs, or training activities supported by NGOs (Table 4.2.4), other than the presence or
absence of RAIN. We do not have data on the presence of government services in these areas, though
there was reported spillover of nutrition/health information between intervention arms from RAINtrained CHVs working in the health facilities which are visited by people from the Ag-only areas as well,
as some health facilities were on the border between Ag-nutrition and Ag-only areas; this is unlikely to
have affected the control areas, which are geographically distant from the intervention areas. We can
therefore conclude that the control group appears to function as a valid comparator to the intervention
arms.
Table 4.2.1 NGO presence, by program group
Agriculture +
Nutrition
N= 41
AGDC
Alliance
Cargile
CEF
Conservation farming unit
Child fund
Climate for change
Concern Worldwide/RAIN
Dunavant
Farmer group/ AGRO club
Women for change
Women’s club
0
4.88
4.88
0
19.5
4.88
2.44
60.98
2.44
2.44
0
2.44
45
Agriculture only
Control
N= 40
N= 41
0
0
0
2.5
5.0
5.0
0
72.5
0
0
2.5
0
2.44
2.44
2.44
0
0
0
0
0
2.44
0
0
0
Table 4.2.2 Social capital clubs supported by NGOs, by program group
Agriculture +
Nutrition
N= 41
Farmers’/Agricultural Club
Fishery Club
Bee Keeper’s Club
Women’s Club
Credit Club
Peer Educators
Agriculture only
Control
N= 40
N= 41
25.0
0
0
62.5
0
0
0
0
0
0
0
0
Agriculture only
Control
N= 40
N= 41
67.5
0
0
5.0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Agriculture only
Control
N= 1,025
N= 1,041
2.5
2.5
2.5
0
0
52.5
17.5
0
0
0
0
0
2.4
0
29.3
0
0
51.2
0
0
Table 4.2.3 Income generating activities supported by NGOs, by program group
Agriculture +
Nutrition
N= 41
Agricultural production
Agricultural marketing
Agricultural processing
Business development (general)
Tailoring
Carpentry
Black smithing
Kniting/weaving
Crafts
63.4
0
0
0
0
0
0
0
0
Table 4.2.4 Training activities supported by NGOs, by program group
Agriculture +
Nutrition
N= 978
Financial Management Training
Business / Enterprise Training
Natural Resource Management Training
Tourism Training
Health training
Agricultural training
Nutrition training
4.9
0
4.9
0
2.4
56.1
9.8
46
5
Results: Impact of RAIN interventions on anthropometric outcomes
Children’s weight and height measurements were used to derive statistical scores by comparing each
child’s anthropometric measurements to the 2006 WHO child growth standards reference for his/her
age and sex (WHO 2010). The three indicators created were height-for-age Z-score (HAZ), weight-for-age
Z-score (WAZ) and weight-for-height Z-score (WHZ). Stunting was defined as HAZ < -2 Z-scores;
underweight was defined as WAZ < -2 Z-scores; and wasting was defined as WHZ < -2 Z-scores. Results
on mean z-scores and proportions malnourished by the different indicators were generated separately
for RAIN intervention and control areas.
5.1 Main impact analysis of RAIN interventions on anthropometric outcomes
KEY RESULTS:
Over time, between baseline and endline, the prevalence of stunting decreased significantly in all
three study groups in the impact evaluation age range of children 24-59 months of age. However,
there was a differential decline in stunting in the three study groups, in favor of the control group,
with a significantly greater decrease, compared to the two RAIN interventions groups. There was no
difference in stunting declines between the two intervention groups. DID impact estimates in this age
group, suggest a negative impact of the RAIN intervention groups on stunting, ranging from +7pp for
the Ag-only group, to +9pp for the Ag-Nutrition group, compared to the control group, respectively.
Similar negative impacts on mean HAZ scores were seen for the Ag-only group (-0.35 HAZ, compared
to the control group).
Levels of wasting increased significantly over time, for all three study groups, in pure intent to treat
analyses among: 1) children 24-59 months of age at endline, 2) children with greater potential for
exposure (24-47 months of age, at endline), 3) children 24-59 months of age at endline, in perprotocol analysis, and 4) children with greatest potential for exposure, in per protocol analysis. In DID
analyses, there was a consistent positive impact on the prevalence of wasting, in the Ag-only arm,
compared to the control arm, of about -4 pp. This suggests an overall protective effect on wasting, of
the Ag-only study arm.
47
5.1.1 Intent-to-treat outcomes for anthropometric outcomes among children 24-59.9 months of age
Overall, levels of stunting reduced significantly between 2011 and 2015 in all three study arms, in the
main impact evaluation group (24-59 months) (Figure 5.1). However, the magnitude of stunting
reduction was smaller in intervention groups (reductions of 12.3 pp for Ag-Nutrition and 13.7 for Agonly) compared to the control group (22 pp) (Table 5.1.1). The difference-in-difference (DID) impact
estimates for stunting show a significant differential change over time, with lower impact estimates of
Ag-Nutrition group and the Ag-only groups compared to control group (Impact estimate of +9.0 and +7.4
pp, compared to the control group, respectvely). No differential reduction in stunting was observed
between the two intervention group (Ag-Nutrition vs Ag only). There was also a statistically significant
increase in mean HAZ for all three study groups over time, with a greater increase in the control group
compared to Ag only group (DID -0.35 HAZ).
In contrast to stunting, levels of wasting increased significantly over time for all three groups (Table
5.1.1). The DID estimations for wasting among children 24-59 months of age show a significant
differential change of approximately -4 pp in favor of the two intervention groups, compared to the
control group.
Figure 5.1. Prevalence of stunting, underweight and wasting among children 24-59.9 months by program group
and survey round
60
Baseline
Endline
50
%
40
30
20
10
0
Ag-Nutrition Agriculture
Stunting
Control
Underweight
48
Control
Wasting
Control
Table 5.1.1. Anthropometric indicators among children 24-59.9 months of age, by program group and survey round
Baseline
Impact indicators
Ag-Nutrition
Agriculture
Control
(n= 963)
(n= 1,014)
-1.78
(1.37)
-0.74
(1.03)
0.43
(1.34)
42.66
Underweight, %
Wasting, %
HAZ, mean (SD)
WAZ, mean (SD)
WHZ, mean (SD)
Stunting, %
Difference in difference
Endline
AgNutrition
T2-T1
Agriculture
0.65***
0.51***
AgAgAgriculture
Nutrition
nutrition
T2-T1
vs
vs
vs
control
control
Agriculture
0.87***
-0.22
-0.35*
0.14
0.25***
0.30***
0.19***
0.03
0.08
-0.04
-0.18***
0.00
-0.43***
0.23
0.41**
-0.18
-12.28*** -13.65*** -21.98***
9.00*
7.40*
1.20
-4.07**
2.10
1.30
0.80
7.42***
-4.20*
-4.00*
-0.10
Ag-Nutrition
Agriculture
Control
(n= 1,026)
(n= 1212)
(n=1,244)
(n=1,080 )
-1.76
(1.39)
-0.81
(1.00)
0.29
(1.16)
44.25
-1.86
(1.52)
-0.82
(1.07)
0.39
(1.22)
47.05
-1.13**
(1.82)
-0.49**
(1.11)
0.25 ***
(1.54)
30.38**
-1.25
(1.71)
-0.52
(1.08)
0.30
(1.48)
30.60
-0.99
(1.65)
-0.63
(1.00)
-0.05
(1.56)
25.07
8.56
10.32
10.76
6.64
7.60
6.69
-1.92
-2.72*
3.11
2.26
2.41
6.42***
5.69
9.83
3.31**
3.43***
†
T2-T1
Control
Significant differences: *** p<0.001, ** p<0.01, * p<0.05; Double difference impact estimates with clustered standard errors comparing 2010 and 2014. Accounts for child age,
gender and geographic clustering.
49
5.1.2 Intent-to-treat outcomes for anthropometric outcomes among children 6-23.9 months of age
There were similar trends and differences in stunting among the younger age group (6-23.9 months),
but the magnitudes of difference were larger (Table 5.1.2): The DID estimations for stunting among
children 6-23 months of age show a significant differential impact of an increase in the prevalence of
stunting between +11 pp and +21 pp for the Ag only and Ag-Nutrition study groups, compared to the
control group.
The impact on wasting was larger among children 6-23.9 months with DID estimates between -11 to -14
pp in favor of the Ag-Nutrition and Ag only study groups respectively, compared to the control group.
and survey round
60
Baseline
Endline
50
%
40
30
20
10
0
Stunting
Control
Underweight
50
Control
Wasting
Control
Table 5.1.2 Anthropometric indicators among children 6-23.9 months of age by program group and survey round
Baseline
Impact indicators
Ag-Nutrition
Agriculture
Control
(n= 478)
(n=463)
(n= 595)
Stunting, %
-1.11
(2.08)
-0.23
(1.46)
0.54
(1.61)
39.39
-1.48
(1.96)
-0.37
(1.45)
0.55
(1.61)
43.05
Underweight, %
12.03
Wasting, %
5.58
HAZ, mean (SD)
WAZ, mean (SD)
WHZ, mean (SD)
Endline
Ag-Nutrition
AgAgriculture
Nutrition
T2-T1
T2-T1
Control
Agriculture
Control
(n=433)
(n= 401)
(n= 498)
-1.66
(1.98)
-0.61
(1.45)
0.29
(1.59)
46.31
-0.59 ***
(2.42)
0.08
(1.41)
0.26 ***
(2.08)
29.82***
-0.40
(2.08)
0.22
(1.25)
0.39
(1.74)
21.41
0.50
(2.59)
0.15
(1.27)
-0.52
(2.03)
16.47
12.33
16.08
5.37
3.28
5.11
-6.65*** -9.05*** -10.97***
4.28
5.52
11.96***
8.18
22.80
6.38***
†
T2-T1
-1.25**
Agnutrition
vs
Agriculture
-0.46
-0.46*
-0.22
-0.24
0.53*
0.70**
-0.16
11.20**
10.10
2.30
2.00
-13.70**
2.30
Agriculture
Agvs
Nutrition
vs control control
0.52***
1.08***
2.16*** -1.69***
0.31**
0.59***
0.78***
-0.28*
-0.16
-0.81***
-9.56** -21.64*** -29.83*** 21.10***
3.90**
4.50
17.29*** -11.00*
Significant differences: *** p<0.001, ** p<0.01, * p<0.05; Double difference impact estimates with clustered standard errors comparing 2010 and 2014. Accounts for
geographic clustering only.
51
5.2 Plausibility analysis 1: High potential for exposure age groups (24-47.9 months)
KEY RESULTS:
Due to the aging of children throughout the duration of the RAIN project, there was greater potential
for certain age groups of children to be exposed to the RAIN interventions for longer than others.
Caregivers of children 24 - 48 months of age at the time of the endline survey had the opportunity to
have been enrolled in the program for the entire 1000 days period between conception and age two.
Therefore, we conducted impact analyses in this age group of children. Similar declines were observed
in stunting, over time, for all three study groups, but no differential reductions in the prevalence of
stunting were observed. This suggests a null impact of the RAIN project interventions on stunting,
among those with greatest potential for exposure during the 1000 day window of opportunity.
Due to the aging of children throughout the duration of the RAIN project, there was more potential for
certain age groups to be exposed to the interventions than others. Caregivers of children aged over 24
months and under 48 months at the time of the endline survey had the opportunity to have been
enrolled in the program for the entire 1000 days period between conception and age two, and so we
conducted analyses in this child age group (24-47.9 months) to see whether impact was greater for this
cohort.
Levels of stunting significantly reduced (and HAZ scores significantly increased) in all three study arms
since baseline in this age group (Figure 5.3 and Table 5.2.1). The DID estimates show no significant
difference in stunting or HAZ between study arms over time in the intent to treat group. Differences
were significant in the per-protocol anlaysis however, with stunting in the Ag-nutrition group reducing
less than in the other two study arms among confirmed beneficiaries (Table 5.2.2).
Levels of wasting significantly increased in all three study arms since baseline in this age group (Figure
5.3 and Table 5.2.1), while WHZ scores only significantly decreased in the control arm. DID estimates
show no significant differences in wasting between study arms over time in the intent to treat anlaysis in
this age group, though WHZ scores were significantly increased over time in the Ag-only group
compared to control. Changes approached significance in the per-protocol anlaysis, with wasting
increasing less in the Ag-nutrition arm than in the other control arm among confirmed beneficiaries
(Table 5.2.2).
52
and survey round
60
Baseline
Endline
50
%
40
30
20
10
0
Stunting
Control
Underweight
53
Control
Wasting
Control
Table 5.2.1. Anthropometric indicators among children 24-47.9 months of age by program group and survey round
Baseline
Impact indicators
Endline
Control
Ag-Nutrition
Agriculture
Control
AgAgriculture
Nutrition
T2-T1
T2-T1
Control
0.72***
0.57***
0.89***
-0.18
-0.33
Agnutrition
vs
Agriculture
0.15
0.34***
0.36***
0.31***
0.02
0.05
-0.02
-0.09
0.07
-0.32***
0.22
0.38*
-0.16
2.70
1.90
T2-T1
Agriculture
Agvs
Nutrition
vs control control
(n= 698)
(n= 759)
(n= 786)
(n= 809)
(n=850)
(n= 687)
-1.79
(1.45)
-0.66
(1.08)
0.50
(1.36)
45.21
-1.83
(1.43)
-0.76
(1.03)
0.39
(1.14)
49.59
-1.88
(1.56)
-0.77
(1.08)
0.46 (1.25)
48.08
-1.08*
(1.97)
-0.32+
(1.17)
0.41***
(1.61)
31.99
-1.26
(1.84)
-0.40
(1.16)
0.45
(1.48)
34.25
-0.99
(1.82)
-0.46
(1.03)
0.14
(1.62)
30.00
Underweight, %
8.70
10.57
10.29
6.35
7.61
6.05
-2.34
-2.96*
-4.25**
1.90
1.40
0.60
Wasting, %
3.39
1.79
2.19
6.88*
5.29
9.12
3.50**
3.50***
6.93***
-3.50
-3.40
-0.00
HAZ, mean (SD)
WAZ, mean (SD)
WHZ, mean (SD)
Stunting, %
-13.22*** -15.34*** -18.08***
Significant differences: *** p<0.001, ** p<0.01, * p<0.05, +p<0.10
†
Double difference impact estimates with clustered standard errors comparing 2011 and 2015. Accounts for geographic clustering only.
54
4.80
Table 5.2.2. Anthropometric indicators among beneficiaries with children 24-47.9 months, by program group and survey round
Baseline
Impact indicators
Endline
AgAgriculture
Nutrition
T2-T1
T2-T1
Control
(n= 698)
(n= 759)
(n= 786)
(n= 278)
(n=263)
(n= 687)
-1.79
(1.45)
-0.66
(1.08)
0.50
(1.36)
45.21
-1.83
(1.43)
-0.76
(1.03)
0.39
(1.14)
49.59
-1.88
(1.56)
-0.77
(1.08)
0.46
(1.25)
48.08
-1.53***
(1.76)
-0.52
(1.20)
0.47**
(1.61)
39.42*
-1.15
(1.79)
-0.43
(1.18)
0.38
(1.48)
32.68
-0.99
(1.82)
-0.46
(1.03)
0.14
(1.62)
30.00
0.26*
0.68***
0.89***
-0.65**
-0.22
Agnutrition
vs
Agriculture
-0.42*
0.14+
0.33***
0.31***
-0.19+
0.02
-0.20
-0.03
-0.01
-0.32***
0.28
0.31+
-0.03
Underweight, %
8.70
10.57
10.29
9.09
7.25
6.05
0.39
-3.32
-4.25**
4.8+
1.0
3.8
Wasting, %
3.39
1.79
2.19
6.23
5.41
9.12
2.84*
3.62**
6.93***
-4.1+
-3.3
-0.8
HAZ, mean (SD)
WAZ, mean (SD)
WHZ, mean (SD)
Stunting, %
Control
Ag-Nutrition
Agriculture
Control
-5.80
-16.90*** -18.08*** 12.4**
Significant differences: *** p<0.001, ** p<0.01, * p<0.05; +p<0.10
†
Double difference impact estimates with clustered standard errors comparing 2011 and 2015. Accounts for geographic clustering.
55
T2-T1
Agriculture
Agvs
Nutrition
vs control control
1.3
11.0*
5.3 Plausibility analysis 2: Dose-response relationship between program exposure
child nutrition
KEY RESULTS:
Since fewer households than expected participated in the RAIN project, we undertook “per-protocol”
analyses, specifically restricting the impact analyses to households who reported a member having
joined a RAIN women’s group. Among households with children 24-59 months of age, stunting rates
decline over time in all three study groups. Additionally, we observe a significant negative impact in
the Ag-Nutrition group, compared to the control group (+14pp, compared to the control group). There
was no significant impact on stunting for any other comparisons. Among households with children
who had the greatest potential to benefit (households with children 24-48 months of age, at endline),
we observe similar negative impacts on stunting.
Fewer households than expected participated in the RAIN program (around 35% of eligible households;
Table 4.1.2). Stunting decreased significantly over time in all three study arms among these confirmed
beneficiaries (Figure 5.4). At endline, there was significantly higher stunting and lower HAZ scores in the
ag-nutrition arm than the control arm; DID impact estimates for stunting show a significant differential
change over time of 13.9pp in favor of the control group (Table 5.3.1). There was significantly more
stunting among children whose caregivers were members of a RAIN women’s group in the ag-nutrition
study arm than the ag only arm (Table 5.3.2).
Wasting increased significantly over time in all three study arms among confirmed beneficiaries (Figure
5.4). At endline, there was significantly less wasting and higher WHZ scores in the ag-nutrition and Ag
arm than the control arm; DID impact estimates show a significant –impact of 4.6pp, in favor of ag only
over control arms (Table 5.3.1).
56
Figure 5.4. Prevalence of stunting, underweight and wasting among beneficeries with children 24-59.9 months
by program group and survey round
60
Baseline
Endline
50
%
40
30
20
10
0
Stunting
Control
Underweight
57
Control
Wasting
Control
Table 5.3.1. Anthropometric indicators among beneficeries with children 24-59.9 months of age by program group and survey round
Baseline
Impact indicators
Endline
AgAgriculture
Nutrition
T2-T1
T2-T1
Control
-1.78
(1.38)
-0.74
(1.03)
0.43
(1.34)
42.66
-1.76
(1.39)
-0.81
(1.00)
0.29
(1.16)
44.25
-1.86
(1.52)
-0.82
(1.07)
0.39
(1.22)
47.05
-1.47***
(1.69)
-0.61
(1.09)
0.33***
(1.56)
35.40***
-1.17
(1.69)
-0.51
(1.09)
0.28
(1.42)
29.44
-0.99
(1.65)
-0.63
(0.10)
-0.05
(1.56)
25.07
0.31***
0.59***
AgAgAgriculture
Nutrition
nutrition
T2-T1
vs
vs
vs
control
control
Agriculture
0.87*** -0.56**
-0.27
-0.29
0.13*
0.30***
0.19***
-0.10
0.09
-0.18
-0.10
-0.01
0.43***
0.30*
0.39**
-0.09
6.30
7.40
Underweight, %
8.56
10.32
10.76
7.16
7.43
7.11
-1.14
-3.22
4.07**
2.90
0.70
2.10
Wasting, %
3.11
2.26
2.41
6.97**
5.05
9.83
3.86**
2.79**
7.42***
-3.50
-4.60*
1.10
AgNutrition
(n= 963)
HAZ, mean (SD)
WAZ, mean (SD)
WHZ, mean (SD)
Stunting, %
Agriculture
Control
(n= 1014) (n=11026)
AgNutrition
(n= 415)
Agriculture Control
(n= 385) (n= 1080)
†
-7.26**
-14.81*** -21.98*** 13.90***
Significant differences: *** p<0.001, ** p<0.01, * p<0.05; Double difference impact estimates with clustered standard errors comparing 2010 and 2014. Accounts for child age,
gender and geographic clustering.
58
Table 5.3.2. Association between program exposure with child stunting among children 24-59.9 m
Program exposure
A member of RAIN women’s group
Yes
No
Time joining the group
<2 year
2-2.9 years
3-3.9 years
≥ 4 years
Number of RAIN group meeting
attended (since January 1, 2015)
0-1
2
3
4
>=5
Ag-Nutrition
Agriculture
n
Stunting
prevalence
n
Stunting
prevalence
415
797
35.40**
27.78
385
859
29.44
31.12
60
77
95
100
47.37
38.67
35.48
38.54
44
52
95
106
41.46
17.31
25.81
29.81
41
70
39
60
112
34.21
38.57
34.21
45.61
41.44
42
53
42
53
83
29.27
24.53
21.95
35.85
26.92
59
5.4 Plausibility analysis 3: Changes in the underlying determinants of child growth
and nutrition
In this section, we examine changes in various underlying determinants at the child, maternal and
household levels over time, as potential factors influencing child growth and nutrition.
5.4.1
Child characteristics
KEY RESULTS:
We assessed health service coverage and child morbidity as immediate determinants of child
nutritional status. Vitamin A supplementation and deworming coverage were fairly high, but full
vaccination was low. Overall child morbidity improved in all study arms over time, but was worse in
the ag only arm compared to the other arms.
We assessed health service coverage and child morbidity as determinants of nutritional status. Vitamin
A supplementation coverage was fairly high, at 75-90% coverage in both 0-23.9 month olds and 24-59.9
month olds. Deworming coverage was slightly lower at 60-90% coverage in both age groups.
Immunizations varied widely, with up to 80% coverage of some key vaccines, but no more than 4% of
children were fully vaccinated according to Zambian vaccination schedules (Table 5.4.1and Table 5.4.2).
Child morbidity (fever, cough, and diarrhea) improved in almost all study arms between baseline and
endline. Outcomes were worse at endline in the ag only study arm than the ag-nutrition or control arms
across the board; outcomes were significantly better in the ag-nutrition arm than the ag only arm for
fever and cough at 0-5.9 months; for fever and diarrhea at 6-23.9 months; and for all three outcomes at
24-59.9 months, but there were no difference from control (Table 5.4.3). DID impact estimates for
health outcomes show a significant differential change over time for fever at 0-5.9 months of 9.7pp for
the Ag only study group compared to the control group and -12.6pp for the ag-nutrition group
compared to the ag only group; for cough at 6-23.9 months of 9.8pp for the ag only study arm
compared to control; and for cough at 24-59.9 months of 8.9pp for ag only compared to control, and 10.7pp for ag-nutrition compared to ag only (Table 5.4.3).
60
Table 5.4.1. Child immunization and supplementation status among children 0-23.9 months, by program group
and survey round
Impact indicators
Vitamin A
De-worming
Immunizations
- BCG
- OPV 0
- OPV 1
- OPV 2
- OPV 3
- OPV 4
- DPT 1
- DPT 2
- DPT 3
- DPT-Hep B-Hib 1
- DPT-Hep B-Hib 2
- DPT-Hep B-Hib 3
- Measles (MMR) at 9
months
Measles (MMR) at 18
months
PCV 1
PCV 2
PCV 3
ROTA 1
ROTA 2
Ag-Nutrition
(n= 690)
Baseline
Agriculture
(n=645)
Control
(n= 799)
Ag-Nutrition
(n= 635)
Endline
Agriculture
(n=568 )
Control
(n= 727)
76.81**
60.29*
77.67
64.34
84.11
68.21
77.80
60.31
83.27
69.19
77.72
59.83
69.42
15.65
72.32*
65.94**
56.09***
18.26
6.09
5.65
6.23
63.77
58.55**
50.87**
70.54
17.67
67.60
59.53
52.40
17.83
6.82
5.58
6.36
60.93
55.04
51.01
75.09
21.15
76.97
72.47
64.96
24.66
8.76
7.38
8.39
66.96
65.58
58.95
61.89**
29.61**
58.90***
50.55*
42.52*
12.13
11.65*
11.34
12.44
51.02***
46.93***
41.57***
65.49
30.11
62.50
52.29
42.61
8.63
5.46
5.11
6.69
58.27
53.17
45.95
49.52
21.05
44.84
41.40
34.39
8.80
10.87
9.63
8.80
33.84
32.32
29.30
24.72
25.00
19.67
8.19
37.17
32.13
26.77
26.14
19.84
5.99
37.32
30.63
25.00
31.69
24.12
6.05
30.26
26.41
19.81
25.58
19.94
46.52**
------
47.13
------
58.70
------
61
Table 5.4.2. Child immunization and supplementation status among children 24-59.9 months, by program group
and survey round
Impact indicators
Vitamin A
De-worming
Immunizations
- BCG
- OPV 0
- OPV 1
- OPV 2
- OPV 3
- OPV 4
- DPT 1
- DPT 2
- DPT 3
- DPT-Hep B-Hib 1
- DPT-Hep B-Hib 2
- DPT-Hep B-Hib 3
- Measles (MMR) at 9
months
Measles (MMR) at 18
months
PCV 1
PCV 2
PCV 3
ROTA 1
ROTA 2
Ag-Nutrition
(n= 963)
Baseline
Agriculture
(n= 1,014)
Control
(n= 1,026)
90.86*
87.54*
85.80
82.94
88.21
81.97
78.22
76.32***
82.80
80.55
78.52
66.76
72.38
15.37
77.67
77.78
76.43*
39.46*
3.84
3.32
5.19
73.94*
74.45
73.42
68.05
17.16
71.20
71.30
69.13
25.64
3.16
3.06
5.52
67.06
68.84
67.75
69.10
19.10
74.66
75.44
73.00
35.77
6.63
6.14
8.58
68.81
69.69
69.20
46.70
15.18**
46.37
45.05
39.60
12.13
7.10
6.60
6.44
44.14*
43.48*
41.58*
41.16
15.43
40.51
38.91
33.84
10.37
4.34
4.26
5.55
38.83
38.50
36.41
39.17
10.74
39.63
37.78
33.80
10.19
6.48
6.85
7.41
33.33
33.06
31.30
36.88
31.27
30.46
7.84
8.17
7.51*
6.27**
4.70*
3.30
8.92
6.03
5.06
4.58
4.42
3.38
6.39
4.72
3.52
2.59
2.50
1.94
76.95
------
70.22
------
74.27
------
62
Endline
(n= 1,212)
(n= 1244)
Control
(n= 1080)
Table 5.4.3. Child morbidity, by program group and survey round
Baseline
Indicators
0 – 5.9 mo
Fever
Cough/ cold
Diarrhea
6 – 23.9 mo
Fever
Cough/ cold
Diarrhea
24 – 59.9 mo
Fever
Cough/ cold
Diarrhea
Ag-Nutrition
(n= 212)
12.32
15.17
8.96
(n= 478)
23.40
25.90
24.04
(n= 963)
17.57
25.70
13.01
Agriculture
(n= 182)
6.67
14.36
8.79
(n=463)
19.17
22.66
26.36
(n= 1014)
18.07
24.31
12.04
Endline
Control
(n= 204)
10.95
20.79
8.42
(n= 595)
22.18
28.69
26.18
(n= 1026)
17.09
26.69
10.87
Ag-Nutrition
(n= 201)
0.50***
7.96*
3.48
(n= 433)
8.08*
13.86
9.01***
(n= 1212)
5.45***
12.22***
3.71***
Agriculture
Control
(n= 159) (n= 226)
8.81
2.22
14.47
7.56
6.33
2.65
(n= 401 ) (n= 498)
11.72
7.23
17.96
14.06
17.96
8.84
(n= 1244) (n= 1080)
11.25
6.49
21.48
15.00
8.77
3.89
†
AgAgriculture
Nutrition
T2-T1
T2-T1
Control
T2-T1
Agriculture
Agvs
Nutrition
vs control control
-11.82***
-7.21*
-5.48*
2.14
0.10
-2.46
-8.72***
-13.24***
-5.76**
-3.00
6.30
0.30
9.70*
10.50*
2.30
-12.60***
-5.20
-1.90
-15.32***
-12.05***
-15.04***
-7.45**
-4.70
-8.41**
-14.96***
-14.64***
-17.35***
-0.20
2.60
2.40
7.80
9.80*
9.00
-8.10
-7.30
-6.50
-12.12*** -6.81*** -10.60***
-13.48***
-2.83
-11.69***
-9.29***
-3.27*
-6.98***
-1.60
-1.70
-2.50
3.90
8.90*
3.70
-5.40
-10.70**
-6.10*
geographic clustering.
63
Agnutrition
vs
Agriculture
5.4.2
Maternal characteristics: Demographic, health seeking behavior, and nutritional status
KEY RESULTS:
We assessed caregiver demographics, health seeking behavior, time use, and nutritional status as
underlying determinants of child nutritional status. Overall, maternal characteristics were similar
across program groups, at both baseline, and endline. At endline, women in the Ag-Nutrition study
group had a higher prevalence of >4 prenatal visits and also had higher number of prenatal visits
compared to control group. However, these differences were present at baseline as well. There were
significant improvements in maternal dietary diversity (measured out of a total of 7 food groups)
within study arms over time, in both intent-to-treat, and per-protocol analysis, but there were no
differential changes over time, in favor of any study group, in impact analyses. Maternal BMI
increased over time in both the Ag-Nutrition and the Ag-only study groups, but these increases were
not differential. Overall, in DID impact analyses, there was an increase of approximately 10pp in the
proportion of overweight women in the Ag-only arm, compared to the control arm. There was a
commensurate decrease in the proportion of women classified as of normal weight, in the Ag-only
arm. In per-protocol analysis, there was a marginally significant positive program impact on maternal
BMI of 0.57 kg/m2.
Baseline and endline maternal characteristics were similar across program groups, with most women
married, with low levels of education, and working in agriculture; average age of women with children
eligible for the RAIN project was around 30 years, but this varied between 20 to 40 years (Table 5.4.4).
No difference was found for time allocation to childcare, rest, agricultural work, or any other category of
time use across three groups in either per-protocol or intent to treat analysis (Table 5.4.5 and Table
5.4.6).
Significantly fewer women in the ag-nutrition study arm reported receiving nutrition or breastfeeding
counselling during pregnancy, and significantly more reported night-blindness when pregnant.
Additionally (not related to program activities) significantly more women in the ag-nutrition arm had 4
or more antenatal visits, and visits earlier in pregnancy, and took iron supplements for 7 or more
months of pregnancy (Table 5.4.7).
There were no significant differences in maternal dietary diversity between study arms over time in
caregivers of younger children, either among the full sample (Table 5.4.8) or among confirmed
beneficiaries (Table 5.4.9). Within all study arms, consumption increased significantly of legumes, eggs,
and vitamin A rich fruits and vegetables, and decreased significantly of other fruits and vegetables and
animal source foods.
64
Average body mass index (BMI) was around 23kg/m2 for women at baseline and endline; at endline,
normal weight and overweight were significantly different among study arms, but there were no
differences in underweight between study arms in the intent to treat anlaysis (Table 5.4.10). Levels of
underweight reduced significantly over time in the ag only and control arms, while levels of overweight
increased significantly in the ag only arm. DID estimates show a reduction over time in levels of normal
weight of -8.1 to -11.4pp in the ag-nutrition and ag only arms compared to control, respectively, and an
increase over time in overweight by 10.3pp in the ag-only group compared to control. Findings were
similar in the per-protocol anlaysis among RAIN beneficiaries, though magnitudes of difference were
larger (
Table 5.4.11).
Table 5.4.4. Selected baseline maternal underlying factors, by program group and survey round
Baseline 2011
Ag-Nutrition
(n=978)
Endline 2015
Mean (SD)
Agriculture
(n=1025)
Mean (SD)
Control
(n=1041)
Mean (SD)
Ag-Nutrition
(n=1212)
Mean (SD)
Agriculture
(n=1244)
Mean (SD)
Control
(n=1080)
Mean (SD)
Age (years)
30.46 (8.35)
30.49 (8.48)
30.69 (9.14)
29.88 (8.35)
30.02 (8.55)
30.32 (9.03)
Year of schooling
6.48 (2.49)
percent
6.34 (2.60)
percent
6.47 (2.40)
percent
6.43 (2.38)
percent
6.47 (2.44)
percent
6.70 (2.40)
percent
4.62
5.77
4.09
3.55
3.06
2.50
Primary school
22.15
24.56
21.60
23.84
25.66
21.89
Middle school
66.15
63.80
68.77
66.58
64.60
68.92
High school or higher
7.08
5.87
5.54
6.02
6.68
6.88
86.30
82.99
84.25
84.74
83.60
85.09
13.70
17.01
15.75
15.26
16.40
14.91
Agriculture
75.72
63.21
76.83
85.31
71.12
84.51
Others
24.28
36.79
23.17
14.69
28.88
15.49
Education
Never attend school
Civil status
Married (union)
Living alone (unmarried,
widowed, divorced, separated)
Occupation
* P<0,05, ** P<0,01, *** P<0,001
65
Table 5.4.5 Time allocation, by program group
Endline
Ag-Nutrition
(n= 1212)
Sleeping and resting
Eating and drinking
Personal care
School (also homework)
Work as employed
Own business work
Tending a garden
Tending livestock
Other farming/ agriculture
Shopping/ getting service (include health
services)
Weaving, sewing
Cooking
Domestic work
Care for children/ adult/ elderly
Travelling and commuting
Watching TV/ listening to radio/ reading
Exercising
Social activities and hobbies
Religious activities
Others
Mean (SD)
10.94 (2.18)
1.56 (0.76)
0.75 (0.58)
0.02 (0.29)
0.03 (0.37)
0.33 (1.52)
0.38 (1.05)
0.02 (0.22)
2.8 (2.77)
0.17 (0.83)
0.07 (0.4)
1.83 (0.85)
1.6 (1.19)
1.18 (1.22)
0.54 (1.23)
0.2 (0.65)
0.03 (0.21)
0.7 (1.34)
0.26 (0.98)
0.05 (0.58)
66
Agriculture
(n= 1244)
Mean (SD)
Control
(n=1080)
Mean (SD)
11.03 (1.81)
1.54 (0.64)
0.73 (0.49)
0.02 (0.3)
0.07 (0.64)
0.79 (1.97)
0.27 (0.92)
0.02 (0.29)
2.38 (2.66)
11.58 (2.05)
1.56 (0.67)
0.76 (0.61)
0.02 (0.25)
0.06 (0.63)
0.29 (1.33)
0.21 (0.76)
0.01 (0.14)
2.35 (2.42)
0.19 (0.83)
0.1 (0.49)
1.86 (0.82)
1.7 (1.1)
1.04 (0.97)
0.64 (1.22)
0.21 (0.61)
0.02 (0.26)
0.63 (1.18)
0.33 (1.09)
0.06 (0.55)
0.11 (0.55)
0.09 (0.41)
1.8 (0.9)
1.74 (1.22)
1.37 (1.75)
0.42 (0.94)
0.31 (0.84)
0.01 (0.12)
0.37 (0.86)
0.28 (1.01)
0.15 (0.94)
Table 5.4.6 Time allocation among beneficiaries, by program group
Endline
Ag-Nutrition
(n= 1212)
Sleeping and resting
Eating and drinking
Personal care
School (also homework)
Work as employed
Own business work
Tending a garden
Tending livestock
Other farming/ agriculture
Shopping/ getting service (include health
services)
Weaving, sewing
Cooking
Domestic work
Care for children/ adult/ elderly
Travelling and commuting
Watching TV/ listening to radio/ reading
Exercising
Social activities and hobbies
Religious activities
Others
Agriculture
(n= 1244)
Control
(n=1080)
Mean (SD)
11.05 (2.38)
1.49 (0.74)
0.69 (0.5)
0.01 (0.08)
0.01 (0.09)
0.29 (1.37)
0.72 (1.29)
0.03 (0.18)
2.76 (2.69)
Mean (SD)
Mean (SD)
10.87 (1.75)
1.5 (0.63)
0.72 (0.5)
0.02 (0.25)
0.04 (0.51)
0.38 (1.35)
0.5 (1.15)
0.03 (0.21)
2.78 (2.64)
11.58 (2.05)
1.56 (0.67)
0.76 (0.61)
0.02 (0.25)
0.06 (0.63)
0.29 (1.33)
0.21 (0.76)
0.01 (0.14)
2.35 (2.42)
0.16 (0.8)
0.08 (0.45)
1.82 (0.88)
1.66 (1.25)
1.24 (1.29)
0.52 (1.43)
0.15 (0.6)
0.04 (0.24)
0.67 (1.26)
0.22 (0.83)
0.03 (0.45)
0.15 (0.72)
0.09 (0.45)
1.99 (0.85)
1.86 (1.14)
1.12 (1.06)
0.62 (1.17)
0.2 (0.58)
0.02 (0.36)
0.62 (1.18)
0.27 (0.94)
0.04 (0.5)
0.11 (0.55)
0.09 (0.41)
1.8 (0.9)
1.74 (1.22)
1.37 (1.75)
0.42 (0.94)
0.31 (0.84)
0.01 (0.12)
0.37 (0.86)
0.28 (1.01)
0.15 (0.94)
67
Table 5.4.7 Use of prenatal care, by program group and survey round
Baseline 2011
Endline 2015
Ag-Nutrition
(n=978)
Agriculture
(n=1025)
Control
(n=1041)
Ag-Nutrition
(n=1212)
Agriculture
(n=1244)
Control
(n=1080)
percent
percent
percent
percent
percent
percent
98.02
96.88
96.35
93.74**
96.56
95.34
64.60***
58.20
48.05
73.31***
66.87
59.02
25.88*
21.37
21.66
42.69***
37.83
31.87
Second trimester
69.97
74.17
72.28
55.76
60.87
65.99
Third trimester
4.15
4.46
6.06
1.55
1.30
2.14
97.35*
94.82
95.99
97.34
98.07
98.16
5.37*
5.12
7.24
25.33***
28.24
21.24
43.20
39.49
37.15
43.42
46.70
51.50
51.43
55.39
55.61
31.24
25.06
27.25
10.41
8.15
10.14
15.78***
14.21
10.09
65.68
68.19
67.47
85.59***
87.23
91.46
58.58*
62.47
65.06
85.51***
85.46
90.40
Mean (SD)
4.00***
(1.23)
4.33*
(1.18)
Mean (SD)
Mean (SD)
Mean (SD)
Mean (SD)
Mean (SD)
3.84
(1.24)
4.46
(1.16)
3.63
(1.19)
4.48
(1.24)
4.21***
(1.63)
3.83***
(1.00)
4.01
(1.17)
3.89
(1.04)
3.92
(1.15)
4.05
(1.15)
Had prenatal consultation during
pregnancy
Had 4 prenatal visits or more
Stage of pregnancy at first
prenatal visit
First trimester
Took iron pills when pregnant
Average months of iron
supplement intake
7 months or more
4 to 6 months
Less than 4 months
Experienced night blindness
when pregnant
Received nutrition counselling
during pregnancy
Received breastfeeding
counselling
Number of prenatal visits to a
health professional
Months of pregnancy at first
prenatal consultation
68
Table 5.4.8 Maternal dietary diversity, by program group and survey round (< 24 mo)
Grains, roots and tubers
98.84
99.38
99.12
98.11
97.71
98.21
AgAgriculture Control
AgAgriculture
AgNutrition
nutrition
vs
Nutrition
T2-T1
T2-T1
T2-T1
vs
vs control control
Agriculture
-0.73
-1.67*
-0.91
0.2
-0.8
0.9
Legumes and nuts
19.74
14.35
19.17
31.02
30.46
29.44
11.28*** 16.11*** 10.26***
1.0
5.9
-4.8
Dairy (milk , yogurt, cheese)
18.78
20.22
17.79
27.09
31.87
27.65
8.31***
-1.5
1.8
-3.3
42.52
a
51.23
b
42.92
-19.60*** -16.56*** -15.98***
-3.5
-0.5
-3.0
4.3
5.7
-1.5
Baseline
Indicators
Animal foods (meat, fish,
AgAgriculture
Nutrition
(n= 690)
(n= 645)
a
b
Endline
(n= 799)
AgNutrition
(n= 635)
Control
a
Control
(n= 568)
(n= 727)
a
11.65***
9.85***
62.12
67.80
12.65
10.19
14.09
23.15
22.18
20.36
10.50*** 12.00***
73.77
73.96
73.68
92.28
92.78
92.30
18.52*** 18.82*** 18.61***
-0.1
0.2
-0.3
Other fruits and vegetables
21.19
28.86
26.19
11.50
8.10
13.20
-9.69*** -20.76*** -12.99***
3.3
-7.7
11.1*
Number of food groups (0/7)
3.07
3.15
3.09
3.26
3.34
3.24
(0.98)
(0.92)
(1.04)
(1.08)
(1.10)
(1.05)
0.19***
0.04
0.05
-0.01
poultry and liver/organ meats)
Eggs
Vitamin A rich fruits and
vegetables
58.90
Agriculture
69
0.20***
6.27**
0.15**
Table 5.4.9 Maternal dietary diversity among beneficiaries, by program group and survey round
Baseline
Indicators
AgAgriculture
Nutrition
(n= 690)
(n= 645)
Endline
(n= 799)
AgNutrition
(n= 199)
Control
Agriculture Control
(n= 179)
(n= 727)
AgAgriculture
Nutrition
T2-T1
T2-T1
Control
T2-T1
Agriculture Ag-nutrition
Agvs
Nutrition
vs
vs control control Agriculture
1.1
1.0
1.0
98.84
99.38
99.12
98.99
99.44
98.21
0.15
0.06
-0.91
Legumes and nuts
19.74
14.35
19.17
31.66
26.26
29.44
11.92**
11.91***
10.26***
1.6
1.6
0.0
18.78
20.22
17.79
23.12
27.37
27.65
4.34
7.16*
9.85***
-5.5
-2.7
-2.8
44.72
43.02
42.92
-15.98***
-1.4
-8.8
7.4
6.27**
3.2
2.5
0.7
18.61***
0.6
1.3
-0.7
-17.12*** -12.99***
4.9
-4.1
9.0
0.05
-0.09
0.14
a
b
58.90
a
62.12
67.80
-17.40*** 24.78***
Eggs
vegetables
12.65
10.19
14.09
22.11
18.99
20.36
9.47***
73.77
73.96
73.68
92.96
93.85
92.30
19.20*** 19.89***
21.19
28.86
26.19
13.07
11.73
13.20
Number of food groups (0/7)
3.07
3.15
3.09
3.27
3.21
3.24
(0.98)
(0.92)
(1.04)
(1.01)
(1.00)
(1.05)
-8.12*
8.81**
0.20*
Significant differences: *** p<0.001, ** p<0.01, * p<0.05
Note: This analysis is restricted to women with children aged less than 24 months, as this was asked alongside IYCF questions.
70
0.06
0.15**
Table 5.4.10 Women’s nutritional status, by program group and survey round
Baseline 2011
Ag-Nutrition
(n=978)
Agriculture
(n=1025)
Endline 2015
Control
(n=1041)
Ag-Nutrition
(n=1212)
Agriculture
(n=1244)
Control
(n=1080)
Ag- Agriculture Control AgAgriculture
Nutrition
Nutrition
vs
T2-T1
T2-T1
T2-T1
vs
control
Indicators
Underweight
(< 18.5)
Normal (18.524.9)
Overweight
(≥25)
4.70
6.34
6.35
3.31
3.71
2.70
-1.39
-2.63**
76.80
75.86
73.57
75.25***
70.99
79.93
-1.55
18.50
17.81
20.08
21.44***
25.30
17.38
2.95
2.30
3.66***
control
Agnutrition
vs
Agriculture
1.00
1.20
-4.86** 6.36*** -8.10*
-11.40**
3.30
7.50***
10.30**
-4.50
-2.70
5.80
Mean Body
22.86 (3.10) 22.62 (3.29) 22.79 (3.72) 23.14 (3.10) 23.27 (3.15) 23.03 (2.86) 0.28* 0.65*** 0.24
0.05
0.42
-0.37
mass index
2
(kg/m )
†
Table 5.4.11 Women’s nutritional status among beneficiaries, by program group and survey round
Baseline 2011
Ag-Nutrition
(n=978)
Agriculture
(n=1025)
Indicators
Underweight
(< 18.5)
Normal
(18.5-24.9)
Overweight
(≥25)
Endline 2015
Control
(n=1041)
Ag-Nutrition
(n=1212)
Agriculture
(n=1244)
Ag- Agriculture Control
AgAgriculture
Nutrition
Ag-nutrition
Control Nutrition
vs
T2-T1
T2-T1
T2-T1
(n=1080)
vs
vs Agriculture
control
3.66***
4.70
6.34
6.35
3.40
2.60
2.70
-1.30
-3.73**
76.80
75.86
73.57
74.51***
68.49
79.93
-2.29
-7.37** 6.36*** -8.3*
18.50
17.81
20.08
22.09***
28.91
17.38
3.59
11.10*** -2.70
2.4
5.9+
control
0.00
2.4
-13.2**
4.8
13.2**
-7.3+
Mean Body
22.86
22.79
22.62 (3.29)
23.21* (3.35) 23.49 (3.17) 23.03 (2.86) 0.35+ 0.87*** 0.24+
0.07
0.57+
-0.50+
mass index
2
(3.10)
(3.72)
(kg/m )
†
Significant differences: *** p<0.001, ** p<0.01, * p<0.05; +p<0.10 Double difference impact estimates with clustered standard errors comparing 2011 and 2015. Accounts for
geographic clustering only.
71
5.4.3
Household characteristics: Food security, dietary diversity, socioeconomic status, and access
to services
KEY RESULTS:
We assessed household food security, dietary diversity, socio-economic status, and access to services
as underlying determinants of child nutrition. Overall, respondent’s perception of their household
food security decreased significantly over time for all groups, in both the full sample, and among
confirmed RAIN beneficiaries. In impact analyses, there was a significant decrease in the prevalence of
“little to no hunger” in the Ag-Nutrition group, compared to the control group, and a significant
increase in the level of “moderate hunger” in this group. This was the case in both intent-to-treat
analysis, and among confirmed RAIN beneficiaries. There was a significant positive program impact on
the level of “severe hunger”; DID impact estimates indicate reduction of between 4-7 pp in the Agonly group, compared to the control, in both intent-to-treat, and per-protocol analyses. At the same
time, the Ag-Nutrition group had significant positive impacts on household dietary diversity, with an
increase of about 1 food group, based on a 12 food group scale, in both intent-to-treat, and perprotocol analyses. Overall, there were no significant differences in socio-economic status or access to
services between groups over time.
Household perception of food security as measured by the Household Hunger Score is significantly
worse in the ag-nutrition group in terms of responses to individual questions, hunger scores, and
proportion in the category ‘little or no hunger’ (Table 5.4.12). The expanded Household Food Insecurity
Access Scale was only undertaken at endline, but this also showed worse perception of hunger among
the ag-nutrition group on less severe questions about hunger. In the beneficiary-only sub-sample, the
trends and magnitudes were no different to the full group.
Conversely, the Household Dietary Diversity Score shows household food security in terms of access to
foods was significantly better by one food group over time in the ag-nutrition group compared to both
other groups (Table 5.4.14); HDDS decreased in all study arms, but decreased less in the ag-nutrition
group. Household consumption of cereal, fish, oil, sweets and condiments decreased over time in all
study arms; consumption of fruit increased in all study arms; and consumption of roots and legumes
increased more in the intervention arms. In the final analysis, consumption of roots, legumes,
vegetables, fruits, meat, eggs and milk were all better over time in the ag-nutrition group. Similar
directions and magnitudes of effect were seen in the beneficiary-only sub-group analysis (Table 5.4.15).
There was no difference in socio-economic status (Table 5.4.18) or in household assets (Table 5.4.16)
between groups at baseline or endline, though there was a significantly higher productive asset count in
the intervention groups at endline. There were no differences between groups at endline in terms of
access to services (Table 5.4.17) except for lower toilet access in the ag-only group.
72
Table 5.4.12 Household perceptions of food insecurity, by program group and survey round
Baseline 2011
Impact indicators
AgNutrition
(n= 978)
Worried about not having
enough food
Unable to eat preferred foods
Ate just a few kinds of food
Ate food that one did not
want to eat
Ate smaller meal
Ate fewer meals
No food of any kind in the
house
Went to sleep at night hungry
Spent a whole day without
eating anything
Mean (SD) values on
household hunger score
Household hunger categories
Little to no hunger
Moderate hunger
Severe hunger
Agriculture
Endline 2015
Control
Ag-Nutrition Agriculture Control
(n= 1025) (n= 1041) (n= 1212)
(n= 1244) (n= 1080)
AgAgriculture
Nutrition
T2-T1
T2-T1
Control
T2-T1
Agriculture
Agvs
Nutrition
vs control control
Agnutrition
vs
Agriculture
69.22**
67.36
62.09
68.89**
65.92***
62.13***
67.44
64.79
63.10
61.72
56.53
55.14
55.39
49.68
23.15
47.27
40.13
19.37
25.21*** 16.90***
11.05***
14.2**
5.9
8.3+
6.14
6.26
8.32
54.79***
49.34***
31.35***
22.82**
5.23
17.69
6.16
18.67
5.71
26.90***
22.94***
20.58
14.87
16.87
15.11
4.07*
17.71***
2.89+
8.71***
1.80
9.40***
5.9
8.3+
4.7
-0.7
1.2
9.0*
0.38
(0.79)
0.36
(0.93)
0.36
(0.86)
0.90***
(1.42)
0.64
(1.21)
0.61
(1.30)
0.52***
0.28***
0.25***
0.27
0.04
0.24
91.40
7.78
0.82
92.18
5.57
2.25
91.01
7.83
1.16
73.43***
22.69
3.88
81.91
14.79
3.30
83.41
9.82
6.77
-10.4*
12.9**
-2.5
-2.7
7.2*
-4.6*
-7.7
5.7
2.0
†
-17.97*** -10.27*** -7.60***
14.91*** 9.22***
1.99
3.06***
1.05
5.60***
Significant differences*** p<0.001, ** p<0.01, * p<0.05, +p<0.10; Double difference impact estimates with clustered standard errors comparing 2010 and 2014. Accounts for
73
Table 5.4.13 Household perceptions of food insecurity among beneficiaries, by program group and survey round
Baseline 2011
Impact indicators
Worried about not having
enough food
Unable to eat preferred foods
Ate just a few kinds of food
Ate food that one did not
want to eat
Ate smaller meal
Ate fewer meals
No food of any kind in the
house
Went to sleep at night hungry
Spent a whole day without eating
anything
Mean (SD) values on
household hunger score
AgNutrition
(n=978)
Agriculture
(n=1025)
Endline 2015
Control
(n=1041)
AgNutrition
(n=415)
Agriculture Control
(n=385)
(n=1080)
AgAgriculture
Nutrition
T2-T1
T2-T1
Control
T2-T1
AgAgAgriculture
Nutrition
nutrition
vs
vs
vs
control
control
Agriculture
74.22***
63.38
62.09
73.73***
70.36***
65.06**
65.71
61.56
56.62
61.72
56.53
55.14
49.35
44.42
20.78
47.27
40.13
19.37
23.98*** 14.52***
11.05***
12.9*
3.5
9.5
6.14
6.26
8.32
56.63**
51.57***
30.12***
22.82**
5.23
17.69
6.16
18.67
5.71
26.27***
22.41***
16.10
11.95
16.87
15.11
3.44
17.18***
-1.59
5.79**
-1.80
9.40***
5.3
7.8
0.2
-3.6
5.0
11.4+
0.38
(0.79)
0.36
(0.93)
0.36
(0.86)
0.87***
(1.41)
0.53
(1.09)
0.61
(1.30)
0.49***
0.17**
0.25***
0.24
-0.08
0.32
Little to no hunger
91.40
92.18
91.01
74.70***
84.68
83.41
-16.70***
-7.5***
-7.6***
-9.1
0.1
-9.2
Moderate hunger
Severe hunger
7.78
0.82
5.57
2.25
7.83
1.16
22.41
2.89
14.03
1.30
9.82
6.77
14.63***
2.07**
8.45***
0.95
1.99
5.60***
12.6*
-3.5+
6.5
-6.6***
6.2
3.0*
†
Significant differences: *** p<0.001, ** p<0.01, * p<0.05, +p<0.10; Double difference impact estimates with clustered standard errors comparing 2010 and 2014. Accounts for
74
Table 5.4.14 Household dietary diversity, by program group and survey round
Baseline 2011
Food dietary diversity
1
experience
Cereals
Roots and tubers
Vegetables
Fruits
Meat
Eggs
Fish/ sea foods
Legumes, nuts and seeds
Milk/ dairy products
Oils and fats
Sweets
Spices, condiments and
beverages
Mean values on household
dietary diversity (SD)
(range 0-12)
AgNutrition
(n= 978)
Agriculture
(n= 1025)
Endline 2015
AgNutrition
(n= 1041) (n= 1212)
Control
AgAgriculture
Agriculture Control Nutrition
T2-T1
T2-T1
(n= 1244) (n= 1080)
Control
T2-T1
AgAgAgriculture
Nutrition
nutrition
vs
vs
vs
control
control
Agriculture
-1.10
-0.50
-0.60
23.2***
8.6
14.6**
6.6*
0.5
6.1*
9.7*
10.1*
-0.3
11.0*
-3.1
14.1**
16.7**
-1.8
18.6**
-1.6
-7.8
6.2
18.9***
6.5
12.4*
9.8
-3.9
13.8**
-8.9**
-0.8
-8.1**
13.8*
-13.0*
26.8***
99.69
21.90***
85.26
9.62***
36.95
23.64*
65.81***
44.11
30.50*
92.43***
66.63*
99.51
27.17
87.88
13.91
44.18
37.63
81.23
54.55
42.52
92.57
80.74
99.71
44.93
85.80
21.55
41.93
33.72
64.93
52.08
33.72
85.78
72.51
97.77
32.59*
88.78**
48.76
21.70**
25.08*
34.74**
51.98**
36.72
73.51**
50.74*
98.15
23.23
85.29
53.38
14.87
20.50
43.97
50.00
34.97
81.75
38.10
98.89
32.34
82.67
50.97
15.76
18.44
35.40
41.06
30.12
75.72
42.91
-1.92***
10.69***
3.52*
39.14***
-15.25***
1.44
-31.08***
7.87***
6.21**
-18.91***
-15.89***
-1.36**
-3.94*
-2.59
39.45***
-29.31***
-17.13***
-37.26***
4.55*
-7.55***
-10.82***
-42.64***
-0.82*
-12.58***
-3.13*
29.43***
-26.18***
-15.28***
-29.52***
-11.02***
-3.60
-10.07***
-29.60***
97.24***
98.24
84.49
86.96***
93.33
80.15
-10.27***
-4.91***
-4.34**
-5.9
-0.6
-5.4*
6.74***
(1.97)
7.60
(2.17)
7.20
(2.33)
6.49***
(1.98)
6.38
(1.69)
6.04
(1.71)
-0.24**
-1.23***
-1.16***
0.91**
-0.07
0.98**
†
1
geographic clustering. Dietary diversity was based on reported consumption of foods in the previous 24 hours
75
Table 5.4.15 Household dietary diversity among beneficiaries, by program group and survey round
Baseline 2011
Food dietary diversity experience
Endline 2015
1
(n=978)
(n=1025)
Cereals
Roots and tubers
Vegetables
Fruits
Meat
Eggs
Fish/ sea foods
Legumes/nuts/pulses
Milk/ dairy products
Oils and fats
Sweets
Spices, condiments and beverages
Mean values on household dietary
diversity (SD) (range 0-12)
99.69
21.90***
85.26
9.62***
36.95
23.64*
65.81***
44.11
30.50*
92.43***
66.63*
97.24***
6.74***
(1.97)
99.51
27.17
87.88
13.91
44.18
37.63
81.23
54.55
42.52
92.57
80.74
98.24
7.60
(2.17)
Control
(n=1041)
Ag-Nutrition
(n=415)
99.71
44.93
85.80
21.55
41.93
33.72
64.93
52.08
33.72
85.78
72.51
84.49
7.20
(2.33)
97.83
34.46
87.71*
48.19
21.45
21.20
32.05
51.81*
37.35
79.04
54.22*
85.78***
6.51***
(1.89)
†
Agriculture Control
(n=385)
(n=1080)
97.92
27.53
88.83
50.91
15.84
20.52
40.78
48.57
35.84
82.86
40.26
93.77
6.44
(1.71)
98.89
32.34
82.67
50.97
15.76
18.44
35.40
41.06
30.12
75.72
42.91
80.15
6.04
(1.71)
AgAgriculture
Nutrition
T2-T1
T2-T1
-1.86***
12.55***
2.45
38.57***
-15.50***
-2.44
-33.77***
7.69**
6.85*
-13.39***
-12.42***
-1.59**
0.35
0.95
37.00***
-28.33***
-17.11***
-40.45***
-5.97*
-6.68*
-9.71***
-40.48***
-11.45*** -4.47***
-0.23**
-1.16***
Control
T2-T1
-0.82*
-12.58***
-3.13*
29.43***
-26.18***
-15.28***
-29.52***
-11.02***
-3.60
-10.07***
-29.60***
AgAgAgriculture
Nutrition
nutrition
vs
vs
vs
control
control
Agriculture
-1.00
-0.80
-0.30
25.1***
12.9*
12.2*
5.6
4.1
1.5
9.2
7.6
1.6
10.7*
-2.1
12.8*
12.9*
-1.8
14.7*
-4.3
-11.0
6.7
18.8**
5.10
13.7*
10.5
-3.0
13.5*
-3.3
0.3
-3.7
17.3*
-10.8
28.1**
-4.34**
-7.1+
-0.2
-7.0+
-1.16***
0.93**
-0.01
0.94**
Significant differences: *** p<0.001, ** p<0.01, * p<0.05, +p<0.10; Double difference impact estimates with clustered standard errors comparing 2010 and 2014. Accounts for
1
geographic clustering. Dietary diversity was based on reported consumption of foods in the previous 24 hours
76
Table 5.4.16 Household asset ownership, by program group and survey round
Baseline 2011
Endline 2015
Ag-Nutrition
(n=978)
Agriculture
(n=1025)
Control
(n=1041)
Ag-Nutrition
(n=1212)
Agriculture
(n=1244)
Control
(n=1080)
percent
95.08***
percent
percent
percent
percent
percent
92.17
96.43
93.40
94.29
95.64
0–2
14.53***
21.11
16.89
18.98*
17.28
16.77
3-4
23.44
26.03
23.75
30.28
29.82
31.88
5-6
31.83
21.90
26.93
26.98
29.26
31.79
7-17
30.19
30.69
32.43
23.76
23.63
19.56
0-1
45.45
48.97
50.10
49.17
52.17
73.49
2
28.97
25.59
29.91
21.11
26.45
23.46
27.06
23.76
27.41
20.42
19.00
7.51
Mean (SD)
Mean (SD)
Mean (SD)
Mean (SD)
Mean (SD)
Mean (SD)
5.39** (2.70)
5.02 (2.96)
5.34 (2.84)
4.77 (2.52)
4.84 (2.41)
4.71 (2.34)
8.39 (6.25)
8.06 (6.47)
8.49 (6.80)
7.02** (4.85)
7.35 (4.94)
7.72 (5.37)
1.84 (1.03)
1.74 (1.02)
1.78 (1.06)
1.76*** (0.94)
1.70 (0.96)
1.32 (0.76)
8.58 (32.97)
8.49 (34.83)
7.40 (4.70)
6.34* (4.23)
8.79 (48.62)
5.40 (3.18)
Ownership of house
Durable goods count
Productive asset count
3-7
Durable goods count
1
Total number of durable goods
Productive assets count
Total number of productive
assets
2
* P<0.05, **P<0.01, ***P<0.001
1
Durable goods count is the index that consists of a simple count of whether or not the households owned the assets listed (using yes = 1
and no = 0). The productive assets count is constructed the same way.Examples of durable goods were car, motorcycle, bicycle, radio,
television, refrigator, bed, matress, table, sofa, ect
2
Total number of durable goods is, as its name implies, the total number of each asset owned by the households. The total number of
productive assets is constructed the same way. Examples of productive assets were plough/harrow, grain grinder/mill, Insecticide /
herbicide crop spraye, water pump, hand threshing machin, motorized hammermill / thresher, tractor.
77
Table 5.4.17 Household access to services, by program group and survey round
Baseline 2010
Ag-Nutrition
(n=978)
Agricul-ture
(n=1025)
percent
percent
Fire wood
94.06**
Charcoal
Had electricity
Endline 2015
Control
Control
Ag-Nutrition
(n=1212)
Agricul-ture
(n=1244)
percent
percent
percent
percent
90.31
93.05
93.15
94.77
93.14
5.94
9.69
6.95
6.85
5.23
6.86
0.51*
0.29
1.16
10.64
15.68
10.94
Unprotected well or river/ lake
24.05
25.44
25.77
13.20
11.50
24.19
Borehole or tap
66.02
61.55
63.32
81.85
85.21
70.90
Protected well
9.93
13.01
10.91
4.95
3.30
4.91
7.57
7.93
8.90
10.81
12.06
11.49
19.04***
14.07
83.37
5.94***
92.57
12.22
85.77
2.22
94.72
1.93
1.49
2.01
3.06
(n=1041)
(n=1080)
Types of fuel used for cooking
Source of drinking water
Shared toilet
Toilet facility
Have no toilet
Pit or VIP latrine
78.30
34.64
63.01
Sanitary with flush
2.66
2.35
* P<0.05, **P<0.01, ***P<0.001
Table 5.4.18 Household socioeconomic status, by program group and survey round
Baseline 2010
Endline 2015
Ag-Nutrition
(n= 978)
Agriculture
(n= 1,025)
Control
(n= 1,041)
Ag-Nutrition
(n= 1,212)
Agriculture
(n= 1,244)
Control
(n= 1,080)
percent
percent
percent
percent
percent
percent
First
18.04
23.49
17.89
17.93
19.90
22.91
Second
18.35
17.08
19.26
20.00
20.06
24.86
Third
21.55
22.70
22.19
20.17
19.98
14.01
Fourth
20.52
16.98
20.43
21.98
21.19
18.18
Fifth
21.55
Mean (SD)
19.74
Mean (SD)
20.23
Mean (SD)
19.92
Mean (SD)
18.86
Mean (SD)
20.04
Mean (SD)
0.06 (0.97)
-0.03 (1.02)
0.04 (0.97)
0.01 (0.90)
-0.01 (0.93)
-0.06 (0.93)
SES quintile
SES index
* P<0.05, **P<0.01, ***P<0.001
78
6
Results: Impact of RAIN interventions on IYCF practices
6.1 Main impact analysis of RAIN interventions on IYCF outcomes
KEY RESULTS:
The second objective was to assess the impact of the RAIN package of interventions on core WHO
infant and young child feeding (IYCF) indicators among children 0-23 months of age. In general, all
breastfeeding-related IYCF indicators were high at endline, across all three study groups. With the
exception of appropriate introduction of complementary foods at 6-8 months of age (which was
approximately 95% across all three study groups), complementary feeding practices were sub-optimal
ranging from approximately 25-30% for the minimum acceptable diet indicator, to 60% for the
minimum meal frequency indicator. There were no differences between groups, at endline, in any of
the core WHO IYCF indicators. Several of these IYCF indicators improved over time, within groups.
Early initiation of breastfeeding improved across all three study arms, ranging from a 24-31 pp
increase over time, but this increase was not differential in favor of any study group. There were
significant increases in several complementary feeding indicators (with increases ranging from 6 to 12
pp for different indicators, in the RAIN intervention groups), but these increases were not differential
in favor of any group, either compared to each other, or the control group. Of note, consumption of
iron rich foods decreased over time (ranging from 13-15pp), in all groups, but there was no differential
change in favor of any study group. This pattern of no attributable program impact, compared to the
control group, was consistent in both intent-to-treat and per protocol analyses. In terms of specific
food groups, there were increases across arms and over time in consumption of legumes/nuts in both
intervention arms compared to control.
There were no discernable patterns in impact on breastfeeding indicators (Table 6.1.1 and Table 6.1.2).
At endline, age-appropriate breastfeeding, bottle feeding, and milk feeding for non-breastfed children
were significantly different across study arms, and were all higher in the intervention arms. Early
initiation of breastfeeding increased significantly and by large margins (25-30pp) in all three study arms
(p<0.001). DID estimates showed predominant breastfeeding to be lower (-5.8pp) and bottle feeding to
be higher (8.7pp) between the ag-only and control arms (p<0.05). No other breastfeeding results were
significant.
There were no significant changes between the intervention and control arms over time in any
complementary feeding indicators (Table 6.1.1 and Table 6.1.2). There were significant increases within
the ag-nutrition arm over time in minimum dietary diversity (12.5pp), minimum meal frequency
(10.6pp), and minimum acceptable diet (9.9pp) (p<0.001), but these were not significantly different to
79
the control group at endline. Consumption of iron-rich foods decreased by around 14pp in each study
arm over time (p<0.001).
In terms of food groups consumed (Table 6.1.4) we saw significant increases in intervention arms of
dairy and eggs; we saw significant increases in all study arms of legumes/nuts, vitamin A rich fruits and
vegetables; and we saw significant decreases in all study arms of animal foods, other fruits and
vegetables. However, the only significant DID result across arms over time was increases of around 10pp
in legumes/nuts in both intervention arms compared to control (p<0.05).
80
Table 6.1.1. WHO recommended IYCF indicators, by program group and survey round
Impact indicators
Core indicators
Exclusive breastfeeding
Continued breastfeeding at 1 year
Introduction of solid, semi-solid or soft
food
Minimum diet diversity
3
4
5
Optional indicators
Continued breastfeeding at 2 years
Age-appropriate breastfeeding
Predominant breastfeeding among
children <6 mo
Bottle feeding
Milk feeding frequency for non-BF (≥2 milk
feedings/day)
Age
group
(months)
Baseline 2011
n
Endline 2015
Ag-Nutrition
Agriculture
Control
2134
598
374
279
54.70**
74.06
95.12
89.53
62.62
74.18
97.58
93.33
60.95
72.28
91.20
91.30
6-23
6-23
6-23
6-23
1536
1536
1536
1536
29.55
48.27*
20.34
53.53
33.26
58.19
22.61
59.57
20-23
0-23
265
2134
598
37.84
76.49
4.72*
2134
1536
2.36*
7.41
0-23
0-5
12-15
6-8
0-5
0-23
6-23
n
Ag-Nutrition
Agriculture
Control
1930
586
264
282
85.83
81.59
96.30
94.57
86.24
82.39
95.06
93.41
86.09
78.32
95.10
97.98
31.58
51.92
22.92
53.91
1332
1332
1332
1332
42.03
58.89
30.25
40.51
39.90
59.85
26.93
46.63
35.54
58.23
26.91
39.36
49.37
80.43
10.44
40.63
75.72
3.96
240
1930
586
56.00
82.99***
1.99
57.38
85.04
3.77
30.77
76.48
1.77
4.27
11.43
5.23
13.16
1930
1332
6.03***
16.67*
11.70
30.43
3.86
10.92
Significant differences: *** p<0.001, ** p<0.01, * p<0.05;
3
Minimum is defined as 2 times for breastfed infants 6-8 months; 3 times for breastfed children 9-23.9 months; 4 times for non-breastfed children 6-23.9 months. “Meals”
include both meals and snacks and frequency is based on mother’s report.
4
Acceptable diet is defined as who had at least the minimum dietary diversity and the minimum meal frequency during the previous day.
5
Iron-rich or iron-fortified foods include flesh foods, commercially fortified foods especially designed for infants and young children that contain iron, or foods fortified in the
home with a micronutrient powder containing iron.
6
The asterisks associated with the A&T-I areas (2010 and 2014) are for the comparison between A&T-I and NI areas in the same round of survey
81
Table 6.1.2. Difference in difference of WHO recommended IYCF indicators, by program group and survey round
Age range
Agriculture
Control
T2-T1
T2-T1
T2-T1
Ag-Nutrition
vs control
Agriculture
vs control
Ag-nutrition vs
Agriculture
0-23.9
0-5.9
12-15
6-8
6-23.9
6-23.9
6-23.9
6-23.9
31.12***
7.54
1.17
5.03
12.48***
10.62**
9.91***
-13.02***
23.63***
8.21
-2.52
0.07
6.64*
1.66
4.32
-12.93***
25.14***
6.04
3.90
6.68*
3.96
6.32*
3.99
-14.55***
5.70
1.30
-2.60
-2.00
8.30
3.90
5.90
1.40
-1.80
8.10
-6.70
6.60
3.50
-8.10
0.40
3.00
7.50
-7.00
3.80
4.40
4.70
11.60*
5.50
-1.90
20-23.9
0-23.9
0-5.9
20-23.9
18.16*
6.50**
-2.72
3.68**
9.26
8.01
4.60*
-6.67*
7.44***
19.01*
-9.86
0.76
-2.19
1.37
-2.23
30.00*
6.30
-0.50
4.80
11.30
18.90
5.00
-5.80*
8.70*
21.108
12.50
1.10
5.40
-4.00
-10.10
Impact indicators
Core indicators
Introduction of solid, semi-solid or soft food
Minimum diet diversity (≥4 food groups)
3
4
5
Optional indicators
Predominant breastfeeding
Bottle feeding
feedings/day)
Ag-Nutrition
6-23.9
* P<0.05, ** P<0.01, *** P<0.001
1
DID between Baseline and Endline adjusted for clustering effect at commune and district level only.
3
4
5
82
Table 6.1.3. Pre-lacteal feeding among children 0-23 months old, by program group and survey round
Baseline
Impact indicators
Ag-Nutrition
(n= 690)
Fed colostrum
Fed pre-lacteals during the first 3 days after
birth
Things given to infants 3 days after birth
- Honey
- Fruit juice
- Plain water
- Sugar
- Drink made from herbs or plants or gripe
water
- Milk (other than breastmilk)
- Vaccine or other medicines
- Cooking or anointing oil
Endline
Agriculture
(n= 645)
Control
(n= 799)
93.26
91.24
91.12
93.39
95.77
97.11
10.01
8.62
6.52
9.45*
8.63
4.42
1.52
4.55
12.12
4.55
5.66
1.89
7.55
3.77
5.77
7.69
21.15
15.38
1.67
3.33
5.00
4.08
2.04
10.20
3.13
3.13
21.88
8.33*
10.20
34.38
24.24*
7.58
12.12
42.42***
52.83
1.89
7.55
50.94
34.62
5.77
11.54
15.38
0.00
63.33***
23.33
2.04
65.31
8.16
3.13
21.88
28.13
83
Ag-Nutrition
(n= 635)
Agriculture
(n= 568)
Control
(n= 727)
Table 6.1.4. Food groups consumed in the past 24 hours among children 6-23 months old, by program group and survey round
Baseline
Indicators
Legumes and nuts
Eggs
vegetables
Number of food groups
Endline
AgNutrition
Agriculture
Control
(n= 478)
(n= 463)
(n= 595)
(n= 433)
(n= 401)
96.39
16.77
24.20
53.72
96.96
15.22
25.22
59.57
96.94
20.37
23.60
53.06
98.15
41.80*
34.41
39.35
97.76
38.15
34.91
46.38
14.65
67.30
11.96
71.52
16.04
69.22
22.86
90.07
20.75
85.29
20.08
86.35
8.21**
8.79***
4.04
22.77*** 13.77*** 17.13***
4.00
5.50
5.20
-2.70
-1.00
7.80
19.53*
2.93
(1.20)
25.65
3.06
(1.17)
20.10
2.99
(1.22)
9.03
3.36*
(1.21)
5.75
3.29
(1.20)
9.44
3.15
(1.05)
-10.51*** -19.90*** -10.66***
0.43***
0.23**
0.16*
-0.00
0.26+
-9.00*
0.11
9.00*
0.15
Significant differences: *** p<0.001, ** p<0.01, * p<0.05, +p<0.10;
84
AgAgriculture
Nutrition
T2-T1
T2-T1
Control
AgAgriculture
Ag-nutrition
Nutrition
vs
T2-T1
(n= 498)
vs
vs
control
control
Agriculture
98.80
1.76
0.80
1.85*
-0.10
-0.60
0.50
33.33
25.03*** 22.94*** 12.96*** 11.9*
10.00*
1.80
28.71
10.21*** 9.70**
5.12
5.10
5.80
-0.70
38.55
-14.36*** -13.18*** -14.51***
0.20
2.70
-2.60
Control
Table 6.1.5. Timeliness of introduction of complementary foods among children 6-23 months old, by program
group and survey round
Baseline
Indicators
Water
Nshima or Other starchy foods
Legumes
Green leafy vegetables
Orange fleshed vegetables
Orange fleshed fruits
Other fruits
Meat
Poultry
Fish (Big)
Fish (Small)
Eggs
Nuts
Milk
Endline
Ag-Nutrition
Agriculture
Control
Ag-Nutrition
Agriculture
Control
(n= 478)
(n= 463)
(n= 595)
(n= 433)
(n= 401)
(n= 498)
62.97
80.13
74.06**
65.27**
64.85
61.09*
67.15*
21.13
21.13
32.64
43.93
74.69
69.04
69.67
65.01
79.70
77.11
73.43
66.74
65.66
62.42
24.62
22.68
29.59
46.22
70.19
66.74
63.28
68.40
75.80
68.07
60.50
58.99
54.38
57.65
28.07
28.57
35.46
40.67
70.08
63.03
61.51
90.30***
91.22**
90.30***
89.38***
87.53***
82.45**
77.83***
69.05***
69.75***
71.82***
75.29**
87.53***
90.30***
84.76***
82.04
84.29
80.80
79.80
76.31
70.57
67.58
57.86
58.60
60.85
63.59
79.05
83.29
79.05
71.89
82.13
78.11
74.50
75.10
69.08
60.44
43.57
43.17
48.80
58.03
73.29
70.48
65.86
Table 6.1.6. Reported meal frequency among children 6-23 months old, by program group and survey round
Baseline
Indicators
Number of meals and snacks for
ab
breastfed children
6-8.9 months old child
n
279
295
901
AgAgriculture
Nutrition
65.06
53.93
42.31*
84.06
60.81
52.13
a
Endline
Control
74.55
52.94
44.57
n
AgNutrition
Agriculture
Control
82.61
60.76
50.00
78.02
57.83
53.30
84.85
64.58
47.52
282
258
736
Adequate frequency of meal and snack for breastfed children :
- 6-8.9 months: 2-3 meals, 1-2 snacks per day
b
Adequate frequency of meal and snack for non-breastfed children : give 1-2 additional meals per day
85
6.2 Plausibility analysis: Dose-response relationship between program exposures
and IYCF practices
KEY RESULTS:
Findings for IYCF outcomes were similar among confirmed beneficiaries to the intent to treat analysis,
though the magnitudes were sometimes greater. Continued breastfeeding at 2 years was significantly
higher in ag-nutrition group, improved in this group over time, and the DID estimate was higher
compared to control. Bottle feeding was higher over time in both intervention arms compared to
control. There were larger significant increases within the ag-nutrition arm over time in minimum
dietary diversity, and differences were significantly larger over time in the ag-nutrition arm compared
to ag-only, but not compared to control. There were increases in minimum meal frequency and in
minimum acceptable diet in intervention groups, but these were not significantly different to the
control group at endline. Consumption of iron-rich foods decreased in all three arms.
Findings for breastfeeding outcomes were similar among confirmed beneficiaries to the intent to treat
analysis (Table 6.2.1 and Table 6.2.2). Continued breastfeeding at 2 years was significantly higher in agnutrition group; improved 35pp in this group over time; and the DID estimate was 46pp higher
compared to control (p<0.01). Age appropriate breastfeeding and bottle feeding were significantly
higher in the ag-only group; bottle feeding was higher over time in both the ag-nutrition arm (7.7pp;
p<0.05) and the ag-only arm (14.9pp; p<0.01) compared to control.
Directions of change were similar for complementary feeding indicators among confirmed beneficiaries,
but magnitudes were different (Table 6.2.1 and Table 6.2.2). There were larger significant increases
within the ag-nutrition arm over time in minimum dietary diversity (14.4pp); the DID estimates show
that differences were significantly larger over time in the ag-nutrition arm compared to ag-only, but not
compared to control. There were also increases in minimum meal frequency (12.5pp) (p<0.01), and in
minimum acceptable diet (9.4pp) (p<0.05), but these were not significantly different to the control
group at endline. Consumption of iron-rich foods decreased by around 15pp in the ag-nutrition and
control arms, and by 22p in the ag-only arm over time (p<0.001).
86
Table 6.2.1 WHO recommended IYCF indicators among beneficiaries (those are the member of a RAIN’s women’s group) by program group and survey
round
Impact indicators
Core indicators
Introduction of solid, semi-solid or soft
food
Minimum diet diversity
3
4
5
Optional indicators
Predominant breastfeeding among
children <6 mo
Bottle feeding
Milk feeding frequency for non-BF (≥2
milk feedings/day)
Age
group
(months)
Baseline 2011
n
Endline 2015
Ag-Nutrition
Agriculture
Control
2134
598
374
279
54.70**
74.06
95.12
89.53
62.62
74.18
97.58
93.33
60.95
72.28
91.20
91.30
6-23
6-23
6-23
6-23
1536
1536
1536
1536
29.55
48.27*
20.34
53.53
33.26
58.19
22.61
59.57
20-23
0-23
265
2134
598
37.84
76.49
4.72*
2134
1536
2.36*
7.41
0-23
0-5
12-15
6-8
0-5
0-23
6-23
n
Ag-Nutrition
Agriculture
Control
1104
326
144
166
84.42
76.47
100.00
96.97
90.45
85.71
90.48
94.12
86.07
78.32
95.10
97.98
31.58
51.92
22.92
53.91
775
775
775
775
43.92
60.81
29.73
38.78
31.01
66.67
23.26
37.98
35.54
58.23
26.91
39.36
49.37
80.43
10.44
40.63
75.72
3.96
149
1104
326
72.41***
85.43**
1.96
62.50
87.15
4.08
30.77
76.58
1.77
4.27
11.43
5.23
13.16
1104
775
9.05***
6.25
17.88
21.43
3.87
10.92
3
4
5
6
The asterisks associated with the A&T-I areas (2010 and 2014) are for the comparison between A&T-I and NI areas in the same round of survey
87
Table 6.2.2 Difference in difference of WHO recommended IYCF indicators among beneficiaries (those are the member of a RAIN’s women’s group)
Age range
Agriculture
Control
T2-T1
T2-T1
T2-T1
Ag-Nutrition
vs control
Agriculture
vs control
Ag-nutrition vs
Agriculture
0-23.9
0-5.9
12-15
6-8
6-23.9
6-23.9
6-23.9
6-23.9
29.72***
2.41
4.88
7.43
14.37**
12.54**
9.39*
-14.76*
27.83***
11.54
-7.10
0.78
-2.25
8.48
0.65
-21.58***
25.12***
6.04
3.90
6.68*
3.96
6.32*
3.99
-14.55***
4.20
-3.50
0.90
0.20
10.50
5.50
5.50
-0.20
2.60
11.30
-11.60
-5.70
-4.90
-2.40
-3.00
-5.20
1.70
-14.70
12.10
5.20
15.40*
7.10
8.70
5.00
20-23.9
0-23.9
0-5.9
20-23.9
34.58**
8.93**
-2.76
6.69***
-1.16
13.13
6.72*
-6.36
13.61***
10.00
-9.86
0.86
-2.19
-1.36
-2.23
46.00**
9.50
-0.50
7.70*
0.20
24.90
6.20
-5.40
14.90**
10.90
22.60
2.70
5.00
-7.40
-11.60
Impact indicators
Core indicators
Introduction of solid, semi-solid or soft food
Minimum diet diversity (≥4 food groups)
3
4
5
Optional indicators
Predominant breastfeeding
Bottle feeding
feedings/day)
Ag-Nutrition
6-23.9
* P<0.05, ** P<0.01, *** P<0.001
1
DID between Baseline and Endline adjusted for clustering effect at commune and district level only.
3
4
5
88
7
Results: Impact of RAIN interventions on knowledge among caregivers
7.1 Impact RAIN package of intervention on nutrition and hygiene knowledge
KEY RESULTS:
The third objective was to assess the impact of the RAIN package of intervention on health and
nutrition knowledge among caregivers. As with IYCF practices, patterns in changes of IYCF knowledge
were inconsistent, in both intent to treat and per-protocol analyses. Overall, IYCF knowledge had
increased over time in the ag-nutrition group compared to control on some information and
decreased on other information. In general, in DID impact analyses, improvements in breastfeeding
knowledge over time, was lower in the Ag-only group, when compared to the control group. There
were no other differential changes in breastfeeding knowledge over time. As with IYCF practices,
overall there was a significant difference in knowledge regarding timely introduction of different
foods at 6-8 months, in the Ag-Nutrition arm, compared to the control arm; this was most notable for
animal source foods (flesh foods and eggs), where there was an approximate 20pp greater knowledge
regarding the appropriateness of feeding these foods to children 6-8 months of age.
Hygiene knowledge was significantly different among study arms at endline for many aspects relating
to hand washing; protecting children from worms; and making drinking water safer, but the direction
of difference was not consistently favorable to a single study arm, or to the RAIN intervention arms..
There were some changes in breastfeeding knowledge, but patterns are difficult to define (Table 7.1.1).
Overall breastfeeding knowledge scores were significantly worse over time in the ag-only group
compared to control (-0.41; p<0.05), and differences over time were not significant between other study
arms. DID estimates show that knowledge had increased over time in the ag-nutrition group compared
to control on expressing breastmilk (p<0.01) and breastfeeding to 24 months (p<0.05); but had
decreased over time on the giving of colostrum; giving other liquids or foods during the first six months;
and continuing to breastfeed when the mother is ill (p<0.05). There is a scattering of other significant
results in the intent to treat analysis, but no discernable patterns in findings. Among project
beneficiaries, results were similar, in the same directions, but magnitudes a few percentage points
larger (Table 7.1.2).
Overall scores on knowledge of when to introduce different complementary foods increased slightly in
all study arms over time, and were significantly higher at endline in the ag-nutrition group (p<0.001)
(Table 7.1.3). There were no significant differences in knowledge on feeding during illness (Table 7.1.4)
or on exposure to IYCF information (Table 7.1.5).
89
Table 7.1.1. Knowledge about BF among mothers of children 0-23.9 months of age, by program area
Baseline
Impact indicators
Baby should be breastfed
immediately after birth
Mother should give colostrum
to her baby
whenever he/she wants
Mother should breastfeed
more often/ more frequently
Mother should not give other
liquids/ foods even when she
thinks the baby is not getting
enough breastmilk
Continuation of BF if the
mother is ill
Mother should express
breastmilk to feed her baby in
certain circumstances
Baby should be breastfed at
24 months or above
Breastfeeding score
(range 0-8)
Endline
AgAgriculture Control
Nutrition
T2-T1
T2-T1
T2-T1
AgNutrition
Agriculture
Control
(n= 690)
(n= 645)
(n= 799)
(n= 635)
(n= 690)
(n= 645)
81.57
85.58
80.98
97.01
97.71
97.25
Agvs
Nutrition
vs
vs control control
Agriculture
15.44*** 12.13*** 16.27***
-0.8
-4.1
3.3
90.43**
90.67
86.11
95.28
96.65
97.38
4.84***
61.39**
70.34
67.04
54.96***
47.18
71.35
-6.43*
-23.16***
36.92***
39.29
28.27
38.58
38.03
37.88
1.66
60.47**
62.73
53.64
66.46**
69.72
73.00
6.00*
94.91***
94.70
86.97
82.18
6.69***
4.82
13.44
25.35*
22.89
19.42
38.56**
29.92
34.11
42.36***
35.21
23.11
3.80
4.71***
(1.17)
4.80
(1.09)
4.51
(1.20)
5.02
(1.12)
4.92
(1.05)
5.04
(1.02)
0.31***
84.15
1
*** p<0.001, ** p<0.01, * p<0.05, +p<0.10; DID between Baseline and Endline
90
Control
84.16
5.99*** 11.28***
-6.40*
-5.30*
-1.10
4.31
-10.7
-27.5***
16.7**
-1.26
9.61***
-7.9
-10.9
2.9
6.99*
19.36***
-13.4*
-12.4*
-1.0
-12.73*** -10.55***
-2.81
-9.9*
-7.7+
-2.2
18.67*** 18.07***
5.98**
12.70**
12.1**
0.6
5.29+
11.00***
14.8*
16.3**
-1.5
0.12
0.53***
-0.22
-0.41**
0.20
Table 7.1.2. Knowledge about BF among mothers who are members of RAIN group with children 0-23.9 months of age, by program and survey round
Baseline
Impact indicators
immediately after birth
Mother should give
colostrum to her baby
whenever he/she wants
Mother should breastfeed
more often/ more frequently
Mother should not give
other liquids/ foods even
when she thinks the baby is
not getting enough
breastmilk
Continuation of BF if the
mother is ill
Mother should express
breastmilk to feed her baby
in certain circumstances
Baby should be breastfed at
24 months or above
Breastfeeding score
AgNutrition
Endline
Control
Agvs
Nutrition
vs
T2-T1
vs control control
Agriculture
15.92*** 11.63*** 16.27***
-0.30
-4.60
4.30
Agriculture
Control
(n= )
(n= )
(n= )
(n= )
(n= )
(n= )
81.57
85.58
80.98
97.49
97.21
97.25
90.43**
90.67
86.11
96.98
97.77
97.38
6.55**
7.10**
11.28***
-4.70
-4.20
-0.50
61.39**
70.34
67.04
53.27***
45.81
71.35
-8.13*
-24.53***
4.31
-12.40*
-28.80***
16.40*
36.92***
39.29
28.27
35.18
35.75
37.88
-1.74
-3.53
9.61***
-11.40
-13.10
1.80
60.47**
62.73
53.64
66.83
69.83
73.00
6.37
7.10
19.36***
-13.00
-12.30
-0.70
94.91***
94.70
86.97
77.89*
77.65
84.16
-17.02*** -17.05***
-2.81
-14.20**
-14.20**
0.00
6.69***
4.82
13.44
28.14**
22.35
19.42
21.45*** 17.53***
5.98**
15.50**
11.50*
3.90
38.56**
29.92
34.11
46.23***
35.20
23.14
7.67
5.27
-10.97*** 18.60**
16.20*
2.40
4.71***
(1.17)
4.80
(1.09)
4.51
(1.20)
5.02*
(1.13)
4.82
(1.02)
5.04
(1.02)
0.31***
0.02
-0.51**
0.29
*P<0.05, **P<0.01, ***P<0.001
1
DID between Baseline and Endline
91
Control
AgAgriculture
Nutrition
T2-T1
T2-T1
0.53***
-0.22
Table 7.1.3. Reported knowledge on timeliness of introduction of complementary foods among mothers with
children 0-23.9 months, by program group and survey round
Baseline
Indicators
Water
Nshima or other starchy foods
Legumes
Green leafy vegetables
Orange fleshed vegetables
Orange fleshed fruits
Other fruits
Meat
Poultry
Fish (Big)
Fish (Small)
Eggs
Nuts
Milk
1
Knowledge score (range 0-14)
Endline
Ag-Nutrition
Agriculture
Control
Ag-Nutrition
Agriculture
Control
(n= 690)
(n= 645)
(n= 799)
(n= 635)
(n= 690)
(n= 645)
87.68
90.43**
85.07**
76.96**
76.38**
74.49*
77.97**
29.71
30.00
40.87
53.33
82.75
74.35
81.30*
9.61**
(3.34)
85.43
85.12
84.65
78.91
75.35
72.56
72.25
29.61
29.30
37.05
56.43
81.86
75.19
77.52
9.41
(3.48)
82.85
81.73
77.22
68.96
67.96
65.83
68.84
33.79
34.54
43.80
50.19
78.97
70.09
75.22
9.00
(3.67)
96.85
97.80*
96.85**
94.65***
94.02**
92.28**
88.66**
74.80***
74.96***
75.59**
80.00**
95.43***
95.12***
95.43***
12.52***
(2.59)
97.71
96.83
94.37
91.37
86.97
84.15
80.46
68.84
69.01
71.13
73.77
89.96
93.31
95.25
11.93
(3.31)
94.90
93.66
87.88
81.68
83.47
81.54
76.58
50.41
51.93
54.96
62.81
79.61
82.23
79.20
10.61
(3.45)
*P<0.05, **P<0.01, ***P<0.001
1
Each food for which respondents correctly identified the time for introduction scores 1- so 14 foods get 14 score
92
Table 7.1.4. Reported knowledge on feeding during illness among mothers with children 0-23.9 months, by
program group and survey round
Baseline
Indicators
Endline
Ag-Nutrition
Agriculture
Control
Ag-Nutrition
Agriculture
Control
(n= 690)
(n= 645)
(n= 799)
(n= 635)
(n= 690)
(n= 645)
85.51
3.19
4.93
3.48
1.16
5.80**
10.87
3.19
1.88
1.30*
10.43
2.46
1.45
68.70**
90.70
4.34
4.81
4.50
1.86
4.19
11.01
3.10
3.10
3.88
12.25
1.86
1.86
75.19
89.10
3.38
5.64
3.88
2.76
11.03
13.66
4.51
3.51
3.38
12.41
3.76
2.51
62.28
92.13***
2.99**
2.68*
5.67
0.94
5.83
10.08***
4.72
0.63**
3.78*
12.44
1.89**
0.00*
41.10***
86.80
1.06
2.11
3.87
0.53
5.46
7.22
2.82
2.64
2.82
11.09
3.70
0.35
51.41
95.73
3.17
5.51
8.95
1.10
6.20
19.28
4.55
3.03
0.83
9.23
8.26
1.24
32.51
What should be done when a child
has diarrhea
Give ORS
Feed less than usual
Feed as much food as usual
Feed more than usual
Give less liquids than usual
Give as much liquids as usual
Give more liquids than usual
Continue breastfeeding
Breastfeed more often
Give syrups
Give traditional medicine
Give treated water
Give Zinc
See health professional
*P<0.05, **P<0.01, ***P<0.001
93
Table 7.1.5 Reported exposure to information, by program group
Baseline
Ever heard these information
Endline
(n= 978)
(n= 1025)
Putting baby to breast immediately after birth
Giving only colostrum in the first day or two until
breastmilk comes in
Not putting anything into the child mouth before
breastmilk or colostrum (no pre-lacteal, including
anointing oil)
Feed only breastmilk up to six months
Not giving the child any water or other liquids up to
six months
How to hold the baby at the breast so he/ she can
breastfed easily
How to put the baby’s mouth at the breast so that
he/she can breastfeed easily
Emptying one breast before giving to the other side
Feeding mashed family food after 6 months
Feeding eggs, meat, fish, and other animal source
foods to children older than 6 months
Cooking children’s food with oil (or adding oil to
children’s food)
Adding vitamin and mineral powder to the child’s
food
Washing hand with water and soap before food
preparation or feeding the child
Washing hand with water and soap after using the
toilet, defecating or urinating, or after cleaning a
child who has defecated
How to help your child eat better
How to feed your child when he/she is sick
Feeding the child an extra meal, or extra food after
illness
Control Ag-Nutrition Agriculture
(n= 1041) (n= 1212)
(n= 1244)
Control
(n= 1080)
81.70
80.25
75.77
95.21
95.66
94.62
85.69
83.97
81.26
96.29
93.33
95.37
74.74*
93.35
81.12
94.23
74.20
93.72
94.88***
97.61
86.74
96.95
81.65
96.39
92.13
92.38
93.43
94.88
94.05
92.59
71.88*
66.57
74.49
84.65
78.94
81.65
65.54**
41.82*
86.91**
61.97
31.09
78.01
70.79
34.30
79.13
81.35*
67.57
84.57
73.55
69.13
86.50
77.94
71.18
85.91
90.18
86.02
85.41
93.56
91.16
89.90
87.63
87.00
87.44
90.68***
81.59
80.82
49.28**
35.68
48.65
82.59***
68.73
72.29
95.71
93.16
93.33
94.22
95.02
95.55
78.22***
69.40***
68.43
63.34
79.71
76.04
94.22
91.58**
90.68**
95.02
84.24
81.67
94.16
83.50
83.13
69.22
65.10
70.99
89.85***
81.50
78.31
*P<0.05, **P<0.01, ***P<0.001
94
Table 7.1.6. Reported knowledge of hygiene practices, by program group
Baseline
Indicators
Endline
Ag-Nutrition
Agriculture
Control
(n= 978)
(n= 1025)
(n= 1041)
(n= 1212)
Control
75.33*
80.02
78.26
70.21***
80.12
72.56
62.13***
55.53
66.18
55.78
49.67
55.94*
32.26***
7.92*
35.07**
53.70
49.20
59.08
32.48
5.39
36.09
56.26
54.77
61.72
39.76
6.12
41.06
Wash hands of child
Wash hands before preparing food and feeding
child
29.04**
21.47***
32.97
32.09
26.43
28.17
34.49***
35.56**
37.30
34.57
46.62
41.52
Cut nails
13.70
5.62***
10.63**
5.62
12.27**
62.27***
11.74
4.70
11.94
4.11
9.78
47.65
12.97
10.45
15.39
6.00
15.39
51.31
18.23***
8.09***
12.05*
9.74
19.97**
40.68***
16.56
8.92
13.26
9.81
24.36
41.96
29.29
15.29
15.66
8.62
18.44
30.49
90.29
90.49***
5.11
0.61*
0.51
13.39***
90.13
92.86
5.57
1.17
0.88
25.22
88.49
87.43
4.16
1.84
1.06
18.09
81.02
87.62**
2.48***
1.73***
0.74
9.24***
77.81
88.91
1.45
0.80
0.80
10.05
(n= 1244) (n= 1080)
When should a person wash their hands
Before eating
Before cooking or food preparation
After using the toilet /latrine
After cleaning a child who has defecated
Before and after taking care of a sick person
Before feeding a child
Ways to protect a child from worms
Children should wear pants
Wash fruits and vegetables
Children should wear sandals
Give them treated water
Tablets/medicines
Ways to make drinking water safer
Boil water
Treat with chlorine, Clorin, bleach
Strain through cloth
Use ceramic, sand, or other filter
Solar disinfection
Let it stand and settle
*P<0.05, **P<0.01, ***P<0.001
95
83.13
91.66
12.70
5.10
0.46
5.00
8
Results: Impacts of RAIN interventions on the women’s empowerment
KEY RESULTS:
The fourth objective was to assess the impact of the RAIN package of intervention on women’s
empowerment. We assessed impacts on eight social and economic domains of women’s
empowerment, plus empowerment in agriculture. There were clear impacts of the RAIN interventions
on different domains of women’s empowerment, but there was no consistent impact across all
domains, in favor of a single group. Over time, there were significant increases in several domains of
women’s empowerment, including scores for “buying power”, “social capital”, and “decision making”.
However, there was no consistent pattern of change over time, across groups. In DID impact analyses,
we observe significant program impacts of the Ag-only group, compared to the control group on asset
access, financial empowerment, social capital, and a composite score of women’s empowerment. The
Ag—Nutrition group had significant program impacts, when compared to the control group on
financial empowerment, and social capital. The Ag-only group had greater impacts on different
domains of empowerment, compared to the Ag-Nutrition group; these impacts were fairly consistent
across 5 of the 8 different domains examined. Overall, the direction of impacts was similar in intentto-treat, and per-protocol analyses, with slightly higher impacts in per-protocol analyses.
There was a clear shift over time in women’s involvement in decision making in agriculture, away
from having men only involved towards having women involved (either solely, or jointly with her
spouse). This shift occurred across all study groups, but for different aspects of decision making (e.g.
for crop production, use of money from sale of crops, as well as in a composite score of women’s
empowerment in agriculture), the change was greater in the RAIN intervention groups compared to
the control group, suggesting a clear impact of the RAIN interventions on improving women’s
empowerment in agriculture. These impacts were greater still in per-protocol analysis.
96
8.1 Women’s social empowerment
There were changes in some aspects of social empowerment for women, but there was no consistent
pattern of change.
There are some significant differences between groups at baseline and endline on some individual
variables describing communication between husband and wife (Table 8.1.1), however, no differential
impact among intervention arms was found for overall “relationship score’, either in the whole sample
(Table 8.2.4) or among RAIN benefceries (Table 8.2.5).
Improved communication does not seem to add up to a feeling of greater equality among women (Table
8.1.2) or greater decision making power for women in the household (
97
Table 8.1.3.), though intervention groups do agree more than control with statements supporting
equality4 and reporting decision-making power over certain nutrition-related decisions5.
Social capital scores across domains of social support and social networking are significantly higher in
the intervention groups (Table 8.1.4); upwards of 70% of women have someone outside of their
immediate household who could help them out if a significant problem occurred (no significant
differences across study arms), and women meet to discuss key personal and social issues significantly
more in intervention areas, though still fewer than 45% of women regularly meet with other women in
the community to discuss issues or share information. DID shows higher impacts of the intervention on
social capital scores for the whole sample (DID 0.13-0.21) (Table 8.2.4) and even higher among RAIN
benefceries (DID 0.15-0.26) (Table 8.2.5).
4
If the woman works outside home, her husband or partner should help her with the daily housework and
A husband should not let his wife work outside home, even if she would like to do it.
5
Whether or not you breastfeed the child and when to wean the child and
What and how to feed the infant in his/her first year of life
98
Table 8.1.1. Relationship with spouses, by program group and survey round
Percent often communicating
with spouse on the following
subjects:
Work activities/agricultural
activities
What happens at home
Expenditures
What happens in community
or area
Your child’s health
Your own health
Child feeding
Family planning
Spouse relationship score
Baseline
Endline
Ag-Nutrition
Agriculture
Control
Ag-Nutrition
(n= 978)
(n= 1025)
(n= 1041)
(n= 1212)
(n= 1244)
(n= 1080)
65.24*
66.05**
59.30**
62.34
67.51
64.39
56.77
57.73
53.12
66.67*
63.37
65.43*
61.98
59.57
59.16
56.11
55.37
52.22
51.64
63.80**
60.84**
62.78**
50.10
0.60***
(0.43)
50.24
68.20
66.73
64.39
50.24
0.62
(0.41)
47.93
58.02
56.29
54.08
47.65
0.54
(0.44)
54.54
66.58
65.35
60.97
62.13
0.63***
(0.40)
52.49
62.70
61.98
59.89
57.07
0.59
(0.41)
44.63
61.67
57.22
55.74
52.59
0.54
(0.41)
1
Agriculture
Control
*P<0.05, **P<0.01, ***P<0.001
1
This score is an average over the eight indicator variables presented here. The higher score means higher
empowerment
Table 8.1.2. Perception of equality, by program group and survey round
Percent who agree
with the following
statements:
In a household, the man should make
1
the important decisions .
If the woman works outside home,
her husband or partner should help
her with the daily housework
A husband should not let his wife
work outside home, even if she
1
would like to do it ..
A woman has the right to express her
opinion if she does not agree with
what the husband or partner says
A woman must accept that her
husband or partner beats her in
1
order to keep the family together .
It is better to send a son to school
1
than a daughter .
Perception of equality score
2
Ag-Nutrition
(n= 978)
Baseline
Agriculture
(n= 1025)
Endline
(n= 1212)
(n= 1244)
Control
(n= 1041)
70.18*
68.82
61.68
84.97
81.27
84.80
66.09
57.05
59.56
67.66***
72.35
56.90
21.00*
19.77
25.68
37.62***
26.13
41.15
86.27**
82.11
77.61
79.13
81.91
82.67
10.86
7.92
10.33
16.75
12.14
11.12
8.92**
6.84
11.79
15.43
13.10
16.68
0.73*
(0.17)
0.72 (0.17)
0.71
(0.19)
0.65***
(0.18)
0.70
(0.19)
0.64
(0.17)
*P<0.05, **P<0.01, ***P<0.001
1
These items were reverse coded before creating the overall score
This score is an average over the six indicator variables presented here. The higher score means higher
empowerment
2
99
Control
(n= 1080)
Table 8.1.3. Decision making power, by program group and survey round
Baseline
Percent who can decide on her own
or with the spouse the following:
Buying important things for the
family
What food is prepared every day
If you have to work to earn money
Visiting other family members,
friends or relatives
Seeing a doctor or visiting a
dispensary when you are pregnant
Use of family planning methods
Sending your child/children to
school
What to do when a child is ill
How to make children listen or obey
Having another child or not
Whether or not you breastfeed the
child and when to wean the child
What and how to feed the infant in
his/her first year of life
1
Decision making scores (SD)
Ag-Nutrition
(n= 978)
Endline
Agriculture
(n= 1025)
Control
(n= 1041)
Ag-Nutrition
(n= 1212)
Agriculture
(n= 1244)
Control
(n= 1080)
43.95***
90.88***
75.72
30.50
84.16
67.94
46.72
83.01
67.37
68.56
89.69
82.10
67.77
91.08
85.77
63.02
88.51
86.84
78.59*
66.47
68.82
79.54
82.96
85.54
86.48
80.84
81.43
79.96
79.63
74.98
87.29
80.10
89.07
82.96
87.49
85.17
71.11
87.09*
81.95
71.21
64.81
80.74
75.76
64.32
70.75
80.41
75.97
69.98
80.86
86.88
80.94
79.54
82.80
89.55
83.36
84.81
83.04
87.12
83.41
86.19
90.37
84.26
85.14
90.18**
93.49
88.42
88.17
0.72 (0.27)
87.55
0.74
(0.31)
90.92**
0.83*
(0.26)
95.26
0.86
(0.21)
89.34
0.85
(0.25)
92.32
0.79*** (0.25)
*P<0.05, **P<0.01, ***P<0.001
1
This score is an average over the twelve indicator variables presented here. The higher score means higher
empowerment
100
Table 8.1.4. Women’s social capital, by program group and survey round
Baseline
Percent have somebody who
could help when needed or
when having problems
To accommodate you for
several nights if you need it
To help you out with money or
lend you some money
To help when you don’t have
enough food at home
To talk to when you have a
problem
Percent usually meet with
other women to discuss any
of the following
Education problems
Health problems
Problems of women
Problems of the community
To receive information on
health and nutrition
Social capital scores
1
Endline
Ag-Nutrition
(n= 978)
Agriculture
(n= 1025)
Control
(n= 1041)
Ag-Nutrition
(n= 1212)
Agriculture
(n= 1244)
Control
(n= 1080)
71.18
67.42
67.67
82.51
81.91
82.54
60.60
54.76
56.67
76.88
78.86
79.93
67.73
61.76
63.95
86.37
84.81
85.13
78.03
75.34
75.90
87.70
91.00
90.71
19.90***
9.97
28.41
23.51*
25.58
16.68
21.64***
12.02
34.01
35.23***
40.06
23.73
24.31***
13.59
32.66
39.93***
43.44
22.52
23.69***
16.13
33.82
47.11***
44.97
24.65
21.74***
0.43***
(0.30)
9.38
0.36 (0.26)
30.46
0.47
(0.32)
43.68***
0.58***
(0.25)
40.10
0.59
(0.24)
18.16
0.49
(0.24)
*P<0.05, **P<0.01, ***P<0.001
1
This score is an average over the nine indicator variables presented here. The higher score means higher
empowerment
101
8.2 Women’s economic empowerment
As with social empowerment, impacts on women’s economic empowerment were not consistent.
Women’s overall financial empowerment has changed little since baseline, and is slightly worse in
intervention areas (Table 8.2.1). Access to assets for women is slightly better in intervention areas
however (Table 8.2.2) and in specific domains more closely related to the project’s aims (ownership of
animals) and long-term female-specific savings (ownership of jewellery) the intervention areas are doing
significantly better. However, ability to make the decision to dispose of these assets is limited, with less
than 45% of women reporting ability to sell jewelery, less than 25% able to sell animals, and less than
10% able to sell land. Access to assets has not therefore translated into empowerment to make
purchases (Table 8.2.3): At endline, women’s empowerment to make purchasing decisions and buy
household items appears to have improved in many domains, but has declined relative to control. There
are few significant differences among study arms on economic empowerment, and where differences
are significant they are in favor of the control group.
Overall socres for women’s economic empowerment domains changed very little over time and
between study arms (Table 8.2.4), mostly at less than 0.1 units of scores in the difference-in-difference
analysis; where there was change, it was generally better in the agriculture-only arm than the agnutrition arm and the control. The story was similar when restricting the analysis to beneficiaries only
(Table 8.2.5).
Table 8.2.1. Financial empowerment, by program group and survey round
Percent of women who:
Have own money that can be
used when want it
Know a project that can lend
money for women to start or
extend business
Benefit from loan to start or
extend business
Finance empowerment score
1
Ag-Nutrition
(n= 978)
56.32
Baseline
Agriculture
(n= 1025)
51.17
Ag-Nutrition
(n= 1212)
56.93
Endline
Agriculture
(n= 1244)
63.99
Control
(n= 1041)
53.24
Control
(n= 1080)
62.65
13.04***
17.73
26.72
9.17
7.15
9.30
2.45***
2.54
7.69
1.73
1.45
2.13
0.24***
(0.23)
0.24 (0.23)
0.29
(0.27)
0.23*
(0.21)
0.24
(0.19)
0.25
(0.21)
*P<0.05, **P<0.01, ***P<0.001
1
This score is an average over the three indicator variables presented here. The higher score means higher
empowerment
102
Table 8.2.2. Access to assets and ability to sell assets, by program group and survey round
Baseline
(n= 978)
(n= 1025)
Percent possess alone or
together with somebody else
the following things:
Land
This house or the house you
usually live
Another house
Animals like cow, goat, pig
Small animals or poultry, like
rabbits, ducks, chickens
Jewelry
Motorbike/bicycle
Asset access score
1
Percent able to sell the
following things without the
authorization of someone else
Land
This house or the house you
usually live
Another house
Animals like cow, goat, pig
Small animals or poultry, like
rabbits, ducks, chickens
Jewelry
Motorbike/bicycle
Asset selling score
1
Endline
Control
(n= 1041)
(n= 1212)
(n= 1244)
Control
(n= 1080)
60.86
58.26
66.60
77.97*
80.31
86.28
77.97
6.56
46.00*
74.88
8.41
36.95
78.57
8.88
49.03
82.92**
11.06
52.89**
88.75
10.77
52.89
91.94
11.12
44.58
73.46*
16.09
50.05
0.47***
(0.26)
61.09
10.85
43.05
0.42
(0.28)
73.33
11.98
44.06
0.48
(0.26)
66.67***
29.30***
55.53**
0.53***
(0.27)
63.99
15.51
61.09
0.53
(0.24)
51.99
10.47
50.23
0.50
(0.22)
5.73
6.39
7.54
8.80*
8.20
14.50
6.71
15.87
10.49
6.27
5.81
9.79
6.40
15.96
13.56
6.68**
13.43
12.66*
7.16
5.93
16.72
12.90
10.00
19.33
26.82
56.49
10.22*
0.20**
(0.35)
19.49
57.66
5.23
0.15
(0.31)
28.99
48.41
9.43
0.19
(0.33)
19.95
39.92
9.39
0.14**
(0.29)
22.11
43.52
10.13
0.14
(0.29)
24.60
28.32
12.73
0.18
(0.33)
*P<0.05, **P<0.01, ***P<0.001
1
This score is an average over the seven indicator variables presented here. The higher score means higher
empowerment
103
Table 8.2.3. Purchasing decisions, by program group and survey round
Baseline
(n= 978)
(n= 1025)
Women usually decide on buying:
Maize meal, Rice, legumes, etc
Meat, fish, poultry, etc.
Fruits and vegetables
Packaged products (breads, snacks,
etc)
Cooking Oil
Special foods for child
Other food
Milk/yogurt
Medicines
A woman can decide on her own to
buy:
Small amounts of food like rice,
vegetables, and beans
Bigger amounts of food like a bag of
rice?
Clothes for yourself?
Medicine for yourself?
Toilet articles for yourself like soap,
toothpaste
Clothes for the children
Medicine for the children
Special foods for your children
Purchase decision scores
1
Control
(n= 1041)
Endline
(n= 1212)
(n= 1244)
Control
(n= 1080)
43.72**
55.29**
75.85**
33.99
43.54
68.40
36.73
50.73
64.81
70.61
69.82
81.14
68.73
71.83
80.21
66.34
74.62
82.57
65.16*
68.66*
72.10**
67.88**
70.64*
65.67*
52.85
61.14
61.99
55.24
58.92
53.39
55.28
59.42
58.95
58.28
61.64
58.79
67.87
71.61
72.64*
73.14
72.96
73.46
70.91
71.86
72.97
75.54
76.46
72.80
78.49
79.52
83.02
79.22
80.23
78.03
82.99
80.45
80.97
90.10
91.64
92.40
44.16
68.75
80.12
39.00
67.84
81.72
46.33
66.41
73.07
58.50***
75.50***
88.86
53.46
83.60
88.83
70.81
88.32
90.55
76.64
67.93
75.20
64.24
0.67***
(0.28)
79.47
69.60
75.46
61.09
0.61
(0.29)
71.91
66.22
69.79
65.54
0.61
(0.29)
83.42*
76.24**
86.80
74.17*
0.76***
(0.25)
86.09
79.26
83.52
75.56
0.77
(0.27)
89.06
85.91
84.24
81.00
0.81
(0.24)
*P<0.05, **P<0.01, ***P<0.001
1
This score is an average over the seventeen indicator variables presented here. The higher score means higher
empowerment
104
Table 8.2.4. Summary key domains of women empowerment (all women), by program group and survey round
Baseline
Ag-Nutrition
(n= 978)
Agriculture
Endline
Control
(n= 1025) (n= 1041)
Women’s empowerment domains:
Social capital score
Decision making power
Asset access score
Assess selling score
Buying power score
Financial empowerment score
0.43***
(0.30)
0.79***
(0.25)
0.60***
(0.43)
0.73*
(0.17)
0.47***
(0.26)
0.20**
(0.35)
0.67***
(0.28)
0.24***
(0.23)
0.36
(0.26)
0.72
(0.27)
0.62
(0.41)
0.72
(0.17)
0.42
(0.28)
0.15
(0.31)
0.61
(0.29)
0.24
(0.23)
0.47
(0.32)
0.74
(0.31)
0.54
(0.44)
0.71
(0.19)
0.48
(0.26)
0.19
(0.33)
0.61
(0.29)
0.29
(0.27)
AgNutrition
Agriculture
Control
(n=
1212)
(n=
1244)
(n=1080)
0.58***
(0.25)
0.83*
(0.26)
0.63***
(0.40)
0.65***
(0.18)
0.53***
(0.27)
0.14**
(0.29)
0.76***
(0.25)
0.23*
(0.21)
0.59
(0.24)
0.86
(0.21)
0.59
(0.41)
0.70
(0.19)
0.53
(0.24)
0.14
(0.29)
0.77
(0.27)
0.24
(0.19)
0.49
(0.24)
0.85
(0.25)
0.54
(0.41)
0.64
(0.17)
0.50
(0.22)
0.18
(0.33)
0.81
(0.24)
0.25
(0.21)
*P<0.05, **P<0.01, ***P<0.001
105
AgAgriculture
Nutrition
T2-T1
T2-T1
Control
T2-T1
Agriculture
Agvs
Nutrition
vs control control
Agnutrition
vs
Agriculture
0.15***
0.23***
0.02
0.13***
0.21***
-0.09**
0.04***
0.13***
0.10***
-0.07*
0.03
-0.10**
0.03
-0.02
0.00
0.03
-0.03
0.06
-0.08***
-0.02**
-0.07***
-0.01
0.05*
-0.06**
0.05***
0.11***
0.02
0.03
0.09**
-0.06
-0.06***
-0.01
-0.01
-0.05
0.00
-0.05
0.09***
0.16***
0.20***
-0.11**
-0.04
-0.07*
-0.01
0.01
-0.04***
0.03
0.05*
-0.02
Table 8.2.5. Summary key domains of women empowerment (among RAIN beneficeries), by program group and survey round
Baseline
(n= 978)
Endline
Control
(n= 1025) (n= 1041)
AgNutrition
Agriculture
Control
(n=)
(n= )
(n=1080)
0.60***
(0.24)
0.83
(0.25)
0.64***
(0.41)
0.64***
(0.18)
0.51***
(0.25)
0.15
(0.31)
0.75***
(0.25)
0.25
(0.22)
0.63
(0.24)
0.86
(0.20)
0.62
(0.40)
0.69
(0.19)
0.55
(0.24)
0.14
(0.30)
0.78
(0.26)
0.25
(0.20)
0.49
(0.24)
0.85
(0.25)
0.54
(0.41)
0.64
(0.17)
0.50
(0.22)
0.18
(0.33)
0.81
(0.24)
0.25
(0.21)
0.17***
0.28***
0.02
0.15***
0.26***
Agnutrition
vs
Agriculture
-0.11***
0.04**
0.14***
0.10***
-0.06*
0.04
-0.10**
0.05
0.00
0.00
0.04
-0.01
0.05
-0.10***
-0.03**
-0.07***
-0.03
0.04
-0.06**
0.04*
0.13***
0.02
0.02
0.12**
-0.10**
-0.05*
-0.01
-0.01
-0.04
0.00
-0.04
0.08***
0.16***
0.20***
-0.12**
-0.04
-0.08*
0.02
0.01
-0.04***
0.06**
0.05*
0.00
AgAgriculture
Nutrition
T2-T1
T2-T1
Control
T2-T1
Women’s empowerment domains:
Social capital score
Decision making power
Asset access score
Assess selling score
Buying power score
Financial empowerment score
0.43***
(0.30)
0.79***
(0.25)
0.60***
(0.43)
0.73*
(0.17)
0.47***
(0.26)
0.20**
(0.35)
0.67***
(0.28)
0.24***
(0.23)
0.36
(0.26)
0.72
(0.27)
0.62
(0.41)
0.72
(0.17)
0.42
(0.28)
0.15
(0.31)
0.61
(0.29)
0.24
(0.23)
0.47
(0.32)
0.74
(0.31)
0.54
(0.44)
0.71
(0.19)
0.48
(0.26)
0.19
(0.33)
0.61
(0.29)
0.29
(0.27)
*P<0.05, **P<0.01, ***P<0.001
106
Agriculture
Agvs
Nutrition
vs control control
8.3 Women’s empowerment in agriculture at endline
Women’s empowerment in agriculture was measured across seven domains, looking at whether a
woman had input (either alone or jointly with her partner) on agriculture-related decisions. In a
composite score across all seven domains, women were significantly more empowered in the two
intervention arms than the control arm in the intent to treat analysis (Table 8.3.1), and both the
magnitides of improvement and the statistical significance were greater in the per-protocol analysis,
among beneficiaries (Table 8.3.2). Spcifically, women in the intervention arms were significantly more
empowered in the domains of deciding what to grow on land, and spending money from the sale of field
crops; women in these arms were significantly less empowered than control however in spending
money from the sale of animals (Table 8.3.1), and again the magnitudes for each of these findings were
greater among beneficiaries (Table 8.3.2).
107
Table 8.3.1 Women's empowerment in agriculture, by program group and survey round
Baseline
Proportion reporting
participation (self or joint) in
decisions on:
Ag-Nutrition
Agriculture
Endline
Control
Ag-Nutrition
Agriculture
Control
AgNutrition
T2-T1
Agriculture
Control
T2-T1
T2-T1
Agriculture
Agvs
Nutrition
vs control
control
(n= 978)
(n= 1025)
(n= 1041)
(n= 1212)
(n= 1244)
(n= 1080)
What to grow on land
30.16***
21.17
31.89
73.27***
66.08
63.98
43.10***
44.91***
32.09***
11.3*
12.6*
Ag-nutriti
vs
Agriculture
-1.5
What to do with field crops
43.05***
31.71
37.75
74.54**
70.78
67.67
31.49***
39.07***
29.91***
2.2
9.3
-7.4
47.62
39.33
41.75
91.00**
86.48
82.83
43.38***
41.15***
41.08***
1.5
5.2
-4.3
25.26***
18.63
25.46
62.90***
55.26
52.95
37.65***
36.63***
27.50***
10.3*
9.0*
1.3
22.34
22.47
24.50
60.40
53.85
61.80
38.06***
31.17***
37.30***
0.9
-6.4
7.0
46.03**
42.04
35.38
47.98***
46.41
61.36
1.95
4.37
25.99***
-23.2***
-21.3***
-2.1
41.30
35.58
42.72
8.80
16.22
18.71
1.45***
1.10
1.41
2.84***
2.46
2.38
What to do with fruits and
vegetables
Spending money from sale of
field crops
fruits and vegetables
animals
animal products
Total score of Women
empowerment in Agriculture
(0-7)
-32.50*** -19.36***
1.39***
Significant differences: *** p<0.001, ** p<0.01, * p<0.05, +p<0.10;
†
Double difference impact estimates with clustered standard errors comparing 2011 and 2015. Accounts for geographic clustering.
108
1.36***
-24.01***
-9.6
3.9
-13.8
0.97***
0.43*
0.39+
0.04
Table 8.3.2 Women's empowerment in agriculture among RAIN beneficiaries, by program group and survey round
Baseline
Proportion reporting
participation (self or joint) in
decisions on:
Ag-Nutrition
Agriculture
Endline
Control
Ag-Nutrition
Agriculture
Control
AgNutrition
T2-T1
Agriculture
Control
T2-T1
T2-T1
Agriculture
Agvs
Nutrition
vs control
control
(n= 978)
(n= 1025)
(n= 1041)
(n= 415)
(n=385)
(n= 1080)
What to grow on land
30.16***
21.17
31.89
81.93***
78.18
63.98
51.76***
57.01***
32.09***
20.0***
24.9***
Ag-nutriti
vs
Agriculture
-5.0
What to do with field crops
43.05***
31.71
37.75
78.57***
72.11
67.67
35.52***
40.40***
29.91***
6.2
10.7
-4.6
47.62
39.33
41.75
93.33***
90.59
82.83
45.71***
51.26***
41.08***
3.8
9.5
-6.1
25.26***
18.63
25.46
66.26***
62.63
52.95
41.00***
44.00***
27.50***
13.7**
16.4**
-2.9
22.34
22.47
24.50
64.33
57.65
61.80
41.99***
35.18***
37.30***
4.8
-2.5
7.1
46.03**
42.04
35.38
46.31
44.51
61.36
0.28
2.48
25.99***
-24.8***
-23.2**
-1.8
41.30
35.58
42.72
6.67
11.94
18.71
-24.01***
-11.4
-0.4
-11.5
1.45***
1.10
1.41
3.55***
3.30
2.38
0.97***
1.15***
1.23***
-0.09
What to do with fruits and
vegetables
field crops
fruits and vegetables
animals
animal products
Total score of Women
empowerment in Agriculture
(0-7)
†
-34.64*** -23.64***
2.10***
2.21***
Significant differences: *** p<0.001, ** p<0.01, * p<0.05, +p<0.10; Double difference impact estimates with clustered standard errors comparing 2011 and 2015. Accounts for geographic
clustering only.
109
9
Results: Impact of RAIN interventions on production of nutrient-rich foods
9.1 Food production
KEY RESULTS:
The fifth objective was to assess the impact of the RAIN agriculture package of interventions on the
availability of and access to a year-round supply of diverse and micronutrient-rich plant and animal
source foods at household level. Overall the RAIN interventions had a consistent significant
attributable impact on several different dimensions of agricultural production and consequent
availability during the year of nutritious foods. Both the Ag-Nutrition and the Ag-only arms, had
greater increases over time, compared to the control group, on the total number of foods produced
(constructed using different indices), the total number of agricultural activities engaged in by the
households, and the number of months producing Vitamin A rich foods, and dairy. There were no
significant program impact differences on these outcomes, between the two RAIN intervention
groups. Consistently, program effect sizes were approximately two-fold larger in per-protocol
analyses.
The dominant field crops in the study area are maize, groundnuts, and seed cotton with less than 5% of
households planting other specific field crops (Table 9.1.1). At endline, all households surveyed are
engaged in cultivating a field crop and the average number of crops is modestly higher in the treatment
areas but lower in control areas. Production of garden crops, too, is concentrated on a relatively small
number of vegetables and fruits. For example, at baseline only two crops, rape and tomato, are
produced by more than 10% of households (Table 9.1.2). Both of these, as well as in particular other
fruits, are somewhat more common by endline. Nearly all households also own animals or poultry (Table
9.1.3). Here again, there is a concentration in similar activities with most (90%) keeping chickens, about
half with cattle, and a little less than half with goats or sheep.
The variety of different agricultural activities carried out by RAIN households makes assessment of the
program using specific individual activities difficult; instead, we begin an assessment of program impacts
by categorizing agricultural outputs in line with the program design. The first way of doing this is shown
in Table 9.1.4, organizing production into seven different food groups. At baseline, most households are
producing grains, about a third producing pulses, and one-half producing eggs, with smaller percentages
producing in the other categories. At endline, production in treatment areas is maintained or increased
across all of these food groups; in control groups, in contrast, it declines in all but grains.
110
With this context, we next examine the impact of RAIN on agricultural production using the research
design and difference-in-differences analyses. Overall, there is little difference in whether households
participated in agriculture at all, though RAIN households do have more plots in 2015 and are cultivating
greater overall plot area (not shown). Table 9.1.5 presents the intent to treat estimates of the full
sample and Table 9.1.6 the estimates for households who reported participating in RAIN. In the first five
rows we present measures of diversity in agricultural production; in the bottom four rows, access to
different food types during the year.
Baseline values make clear the relatively low level of diversity in agricultural production in field and,
especially, garden crops. The double difference estimators indicate large and statistically significant
increases in that diversity due to RAIN, increasing the number of total crops by nearly 1.5, with much of
this increase concentrated in garden crops. Even with this increase, however, diversity remains low. At
least part, and on average approximately half, of the double-difference improvement stems from
unexplained declines in the control group over time. In analyses among beneficiary households (Table
9.1.6), the impact estimates are nearly double, and the share explained by declines in the control group
much smaller. Together, the analyses suggest that the RAIN interventions increase diversity in
agricultural production. The comparatively high change in per protocol compared to intent to treat
suggests that the interventions, and not merely changes in the control group, drive increases in diversity
of agricultural production.
Consistent with the increase in crops, there was a concurrent increase in the number of food groups
(from Table 9.1.4) and the number of agricultural activities carried out. With this evidence of increased
diversity of production and potential availability in hand, we turn to an examination of the availability of
nutritious foods throughout the year, as targeted by the program.
Table 9.1.5 indicates that the number of months home-produced fruits and vegetables rich in vitamin A
are available increases by more than one and in beneficiary households this increase is doubled. Smaller,
and significant, increases are also seen in the availability of dairy though there is no evidence of
increased availability of meat or eggs despite a small increase in the probability of keeping chickens due
to the program (not shown).
The difference in estimated program effects by program arm (Agriculture versus Agriculture and
Nutrition) are generally small and both positive and negative across the different measures. In only one
case in Table 9.1.5 (and two in Table 9.1.6) are they significantly different, leading us to conclude that
overall the program arms had largely similar impacts on the agricultural outcomes measured.
111
Table 9.1.1 Number of field crops cultivated, by program group and survey round
Field crops:
Maize
Sorghum
Finger millet
Pearl millet/ bulrush
Rice
Groundnuts
Soyabeans
Seed cotton
Irish potato
Virgina tobacco
Burley tobacco
Mixed beans
Bambara nuts
Cowpeas
Velvet beans
Coffee
Sweet potatoes
Cassava
Kenaf
Cashew nut
Paprika
Wheat
Sugar cane
Sunflower
Other
Growing any field
crops
Ag-Nutrition
(n= 978)
Baseline
Agriculture
(n= 1025)
Control
(n= 1041)
Ag-Nutrition
(n= 1212)
Endline
Agriculture
(n=1244)
Control
(n= 1080)
89.98
0.82
0.00
0.00
0.10
33.84
0.72
62.68
0.10
0.00
0.00
2.66
0.20
2.97
0.10
0.00
3.48
0.51
0.00
0.00
0.00
0.00
0.20
1.23
0.20
74.44
0.00
0.10
0.00
0.10
23.41
0.88
42.24
0.20
0.00
0.20
1.66
1.07
1.27
0.10
0.00
2.15
0.00
0.00
0.00
0.00
0.00
0.00
1.76
0.00
90.87
1.06
0.19
0.10
0.10
35.83
1.83
50.43
0.48
0.00
0.10
2.50
0.19
2.11
0.38
0.00
3.55
0.29
0.00
0.00
0.19
0.00
0.38
5.67
0.86
92.74
0.58
0.41
0.17
0.17
43.89
3.96
58.33
0.17
0.08
0.00
1.40
0.25
1.32
0.17
0.08
2.81
0.00
0.00
0.00
0.00
0.00
0.08
0.41
0.25
87.06
0.56
0.40
0.08
0.16
38.59
4.02
51.69
0.00
0.00
0.00
1.85
0.40
1.53
0.24
0.00
3.94
0.00
0.00
0.00
0.00
0.00
0.08
0.64
0.48
94.26
1.20
0.28
0.19
0.19
20.19
1.57
35.00
0.00
0.00
0.00
0.09
0.09
0.37
0.09
0.00
2.22
0.00
0.00
0.00
0.00
0.00
0.00
0.56
0.28
92.54***
75.71
92.60
100.00
100.00
100.00
* P<0.05, ** P<0.01, *** P<0.001
112
Table 9.1.2 Number of vegetables or fruits cultivated, by study arm and survey round
Baseline
Vegetables
Cabbage
Carrot
Chilli
Fresh maize
Cucumber
Egg plant
Impwa
Green leaves
Okra
Onion
Sweet pepper
Cleome/ Luyuni
Rape
Tomato
Pumpkin
Other vegetables
Fruits
Avocado
Bananas
Guava
Lemon
Mangoes
Masuku
Oranges
Papaya
Water melon
Other fruits
Growing any vegetables and
fruits
Endline
Ag-Nutrition
(n= 978)
Agriculture
(n= 1025)
Control
(n= 1041)
Ag-Nutrition
(n= 1212)
Agriculture
(n=1244)
Control
(n= 1080)
1.53
0.00
0.10
0.00
0.00
0.00
0.20
4.40
3.58
2.45
0.00
0.00
24.44
9.92
3.68
0.10
2.44
0.00
0.00
0.00
0.20
0.59
1.76
5.56
6.54
2.44
0.00
0.00
18.93
7.90
4.10
1.17
2.50
0.10
0.00
0.00
0.10
0.29
0.96
7.40
7.01
2.98
0.00
0.00
31.51
12.78
5.76
0.48
2.12
2.71
0.51
0.28
0.03
1.78
2.43
4.3
4.98
1.92
0.11
0.06
29.92
13.46
0.68
0.03
2.06
4.37
0.83
0.50
0.00
3.14
3.55
6.52
7.59
2.23
0.00
0.00
38.61
18.98
0.50
0.08
2.49
3.46
0.64
0.32
0.08
2.01
3.22
4.98
5.79
2.57
0.32
0.08
30.79
15.59
1.21
0.00
0.00
0.10
0.00
0.00
0.00
0.00
0.00
0.00
0.72
0.82
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.68
1.56
0.00
0.96
0.00
0.10
0.00
0.00
0.00
0.00
2.21
1.25
0.03
0.03
0.00
0.03
0.03
0.00
0.03
0.00
0.57
32.83
0.00
0.08
0.00
0.00
0.00
0.00
0.08
0.00
0.66
41.25
0.08
0.00
0.00
0.08
0.08
0.00
0.00
0.00
0.96
34.41
27.91***
26.05
38.42
41.25***
34.49
21.57
* P<0.05, ** P<0.01, *** P<0.001
113
Table 9.1.3 Rearing animals and production of animal source foods, by program group and survey round
Own animals or poultry
Beehives
Cattle
Chicken
Turkey
Guinea fowl / pigeon
Duck
Goat / Sheep
Donkey/ Mule /Horse
Pig
Rabbit
Fish (aquaculture)
Others
Own any animals or poultry
Animal products
Animal meat / offal
Poultry meat / offal
Milk and products
Eggs
Honey
Hide/ leather/ wool
Others
Produced any products
Ag-Nutrition
(n= 978)
percent
Baseline
Agriculture
(n= 1025)
percent
Control
(n= 1041)
percent
Ag-Nutrition
(n= 1212)
percent
Endline
Agriculture
(n=1244)
percent
Control
(n= 1080)
percent
0.35
54.46
93.63
1.39
14.27
5.45
43.27
1.16
7.08
0.00
0.12
0.00
99.07
0.00
54.63
94.68
0.76
18.88
5.20
35.87
1.01
5.83
0.38
0.00
0.00
99.11
1.08
50.43
92.94
1.41
15.29
6.29
46.20
0.33
6.40
0.11
0.00
0.33
98.81
0.10
48.65
88.21
1.00
5.99
3.10
49.75
0.80
6.19
0.20
0.00
0.10
99.90
0.00
48.04
86.88
0.62
5.17
4.03
51.14
0.72
4.65
0.21
0.00
0.00
99.90
0.00
44.47
81.71
0.92
3.03
4.21
30.92
0.26
2.50
0.00
0.00
0.13
99.61
5.68
13.67
20.63
61.99
0.12
2.90
0.12
66.74
7.10
24.71
21.80
71.19
0.38
3.82
0.00
75.16
2.60
11.85
19.24
59.37
0.44
0.11
0.00
63.77
3.20
19.20
23.70
65.50
0.30
0.00
0.00
70.23
5.37
31.71
24.69
65.70
0.21
0.00
0.00
72.62
8.30
20.92
15.26
63.95
0.13
0.53
0.00
70.79
* P<0.05, ** P<0.01, *** P<0.001
114
Table 9.1.4 Production of seven different food groups, by program group and survey round
Baseline
Endline
Ag-Nutrition
(n= 978)
percent
Agriculture
(n= 1025)
percent
Control
(n= 1041)
percent
Ag-Nutrition
(n= 1212)
percent
Agriculture
(n=1244)
percent
Control
(n= 1080)
percent
Grains
89.78
74.44
90.96
92.82
87.30
94.44
Pulses
34.66
25.46
38.46
45.79
41.16
21.94
Meats
15.03
20.68
11.25
17.66
25.48
16.94
Milk and products
18.20
16.78
17.02
19.55
19.21
10.74
Eggs
54.70
54.73
52.40
54.04
51.13
45.00
Vitamin A rich fruits and vegetables
13.60
13.46
18.85
23.35
20.58
5.93
27.20
23.71
35.96
40.59
32.72
20.28
* P<0.05, ** P<0.01, *** P<0.001
115
Table 9.1.5 Difference in difference of food production indicators, by program group and survey round
Baseline
Agricultural
Indicators:
Number of
crops
Number of field
crops
Number of
garden crops
Food groups
(of 7)
Agricultural
activities (of 3)
Mths producing
vit-A rich plants
Mths producing
meat
Mths producing
dairy
Mths producing
eggs
AgNutrition
(n=978)
Agriculture
(n=1,025)
2.52
Endline
Control
(n=1,041)
AgNutrition
(n=1,212)
Control
(n=1,080)
AgNutrition
T2-T1
Agriculture
(n=1,244)
Agriculture
T2-T1
Control
T2-T1
2.05
2.75
2.99
2.69
1.86
0.47***
0.64***
-0.89***
(1.78)
(1.89)
(1.95)
(1.89)
(1.97)
(1.10)
2.00
1.51
1.99
2.08
1.93
1.57
(1.16)
(1.19)
(1.16)
(0.95)
(1.09)
(0.76)
0.52
0.54
0.77
0.91
0.76
0.29
(1.01)
(1.11)
(1.22)
(1.43)
(1.34)
(0.65)
2.51
2.24
2.62
2.91
2.74
2.14
(1.53)
(1.73)
(1.53)
(1.54)
(1.66)
(1.25)
2.66
2.34
2.74
2.76
2.57
2.36
(1.02)
(1.38)
(1.07)
(1.04)
(1.17)
(1.06)
0.73
0.55
0.98
1.32
1.07
0.32
(2.20)
(1.67)
(2.49)
(2.77)
(2.52)
(1.55)
1.25
1.69
0.78
1.35
1.83
1.14
(3.35)
(3.70)
(2.62)
(3.28)
(3.57)
(2.84)
1.34
1.19
1.26
1.35
1.41
0.72
(3.25)
(3.00)
(3.27)
(3.18)
(3.28)
(2.34)
3.54
3.66
3.12
3.03
2.87
2.31
(4.53)
(4.58)
(4.39)
(3.87)
(3.71)
(3.40)
* P<0.05, ** P<0.01, *** P<0.001
116
0.08
0.39***
0.40***
0.10***
0.60***
0.10
0.01
-0.51
0.42***
0.22***
0.50***
0.23***
0.53***
0.14
0.22*
-0.79
-0.42***
-0.47***
-0.48***
-0.38***
-0.66***
0.35***
-0.54***
-0.80***
AgAgNutrition Agriculture
Nutrition
vs
vs
vs
Control
Control
Agriculture
1.36***
1.53***
-0.17
(0.19)
(0.28)
(0.28)
0.50***
0.84***
-0.34**
(0.10)
(0.15)
(0.16)
0.86***
0.70***
0.16
(0.12)
(0.15)
(0.16)
0.87***
0.98***
-0.11
(0.19)
(0.23)
(0.24)
0.48***
0.60***
-0.12
(0.12)
(0.19)
(0.19)
1.25***
1.18***
0.07
(0.24)
(0.25)
(0.24)
-0.26
-0.22
-0.04
(0.36)
(0.37)
(0.41)
0.55**
0.76***
-0.21
(0.24)
(0.23)
(0.24)
0.30
0.01
0.28
(0.51)
(0.47)
(0.45)
Table 9.1.6 Difference in difference of food production indicators in households participating in RAIN, by program group and survey round
Baseline
Agricultural
Indicators:
Number of
crops
Number of field
crops
Number of
garden crops
Food groups
(of 7)
Agricultural
activities (of 3)
Mths producing
vit-A rich plants
Mths producing
meat
Mths producing
dairy
Mths producing
eggs
AgNutrition
(n=978)
Agriculture
(n=1,025)
2.52
Endline
Control
(n=1,041)
AgNutrition
(n=415)
Control
(n=1,080)
AgNutrition
T2-T1
Agriculture
(n=385)
Agriculture
T2-T1
Control
T2-T1
2.05
2.75
4.28
4.21
1.86
1.76***
2.16***
-0.89***
(1.78)
(1.89)
(1.95)
(2.15)
(2.13)
(1.10)
2.00
1.51
1.99
2.36
2.52
1.57
(1.16)
(1.19)
(1.16)
(0.88)
(0.96)
(0.76)
0.52
0.54
0.77
1.92
1.69
0.29
(1.01)
(1.11)
(1.22)
(1.82)
(1.74)
(0.65)
2.51
2.24
2.62
3.65
3.67
2.14
(1.53)
(1.73)
(1.53)
(1.51)
(1.57)
(1.25)
2.66
2.34
2.74
3.19
3.12
2.36
(1.02)
(1.38)
(1.07)
(0.96)
(0.93)
(1.06)
0.73
0.55
0.98
2.94
2.36
0.32
(2.20)
(1.67)
(2.49)
(3.56)
(3.22)
(1.55)
1.25
1.69
0.78
1.13
2.06
1.14
(3.35)
(3.70)
(2.62)
(2.82)
(3.70)
(2.84)
1.34
1.19
1.26
1.21
1.70
0.72
(3.25)
(3.00)
(3.27)
(2.84)
(3.41)
(2.34)
3.54
3.66
3.12
2.83
2.97
2.31
(4.53)
(4.58)
(4.39)
(3.29)
(3.62)
(3.40)
* P<0.05, ** P<0.01, *** P<0.001
117
0.36***
1.4***
1.14***
0.53***
2.22***
-0.13
-0.14***
-0.71
1.01***
1.15***
1.43***
0.78***
1.81***
0.37***
0.52***
-0.69*
-0.42***
-0.47***
-0.48***
-0.38***
-0.66***
0.35***
-0.54***
-0.8***
AgAgNutrition Agriculture
Nutrition
vs
vs
vs
Control
Control
Agriculture
2.65***
3.06***
-0.41
(0.23)
(0.31)
(0.34)
0.79***
1.43***
-0.65***
(0.11)
(0.16)
(0.17)
1.87***
1.63***
0.24
(0.16)
(0.18)
(0.21)
1.62***
1.91***
-0.3
(0.21)
(0.26)
(0.28)
0.91***
1.16***
-0.25
(0.13)
(0.20)
(0.20)
2.87***
2.47***
0.4
(0.32)
(0.29)
(0.36)
-0.48
0.02
-0.50
(0.40)
(0.41)
(0.48)
0.40
1.06***
-0.65***
(0.27)
(0.27)
(0.29)
0.10
0.12
-0.02
(0.52)
(0.52)
(0.52)
10 Results: Decomposition analysis of the determinants of changes in child
growth outcomes over time
KEY RESULTS
Finally, we conducted decomposition analysis to explain factors that may have contributed to the
large reductions in stunting over time, and increase in wasting. Our analysis however explain only
a small proportion of these changes. The model explains 8.4% and 6.3% of the actual change in
HAZ scores and stunting, respectively, in our key age group (24-59 months) during this period.
Among the sources of predicted change, receipt of nutrition counseling and reductions in child
morbidity stand out as the largest factors, explaining the largest proportion of the predicted
change in HAZ scores. Unexpectedly, household hunger and agriculture production variables did
not predict reductions in stunting or increases in HAZ. The model only explains 1% actual change
for WHZ and 13% actual change for wasting. For children aged 0-59 months, the models explain 46% of actual change for HAZ/stunting and around 16% for WHZ and wasting. Similar analyses with
several rounds of the DHS data from Zambia showed similar results.
10.1 Determinants of child growth
Figure 10.1and Figure 10.2Figure 10.1 and Figure 10.2 show HAZ and WHZ scores by child age,
respectively. HAZ scores significantly improved among all ages of children over time, but
improved more among children <24 months of age. In contrast, WHZ scores were lower in
2015 compared to 2011, and the reduction is more visible among children <20 months or
>50 months of age.
118
-2
-1
0
1
2
Figure 10.1 HAZ by child age, RAIN program areas 2011 and 2015
0
10
20
30
40
Age of child (months)
50
60
50
60
2015
2011
-1
-.5
0
.5
1
Figure 10.2 WHZ by child age, RAIN program areas 2011 and 2015
0
10
20
30
40
Age of child (months)
95% CI
95% CI
2015
2011
Table 10.1.1 presents the changes in the means or percent of key outcomes between
baseline and endline surveys, by different age groups. There were significant improvements
119
between 2011-2015 for HAZ increases and reduced stunting prevalence for all age groups. In
contrast, there were deteriorations as WHZ reduced and wasting increased over time, with
steeper WHZ reductions in children 0- 24-23.9 months.
Table 10.1.1 Changes in the means or percent of outcomes between 2011 and 2015 in Zambia
24 – 59.9 mo
HAZ
WHZ
Stunting
Wasting
0 – 23.9 mo
HAZ
WHZ
Stunting
Wasting
0 – 59.9 mo
HAZ
WHZ
Stunting
Wasting
Changes
2015-2011
% change
2011-2015
Percent
-1.13
0.18
28.84
7.20
0.67
-0.19
- 15.82
4.60
37.22
-51.35
-35.42
177.61
<0.001
<0.001
<0.001
<0.001
-1.30
0.45
40.28
6.18
0.11
-0.07
19.79
16.55
1.41
-0.52
- 20.48
10.36
108.46
-115.56
-50.84
167.64
<0.001
<0.001
<0.001
<0.001
-1.61
0.40
43.02
3.94
-0.76
0.10
26.14
10.14
0.85
-0.30
-16.88
6.20
-52.85
-74.94
-39.30
158.97
<0.001
<0.001
<0.001
<0.001
2011
2015
Percent or
mean
Percent or
mean
-1.80
0.37
44.66
2.59
120
P value
Figure 10.3 present trends in undernutrition in children 24-59.9 months and 6-23.9 months
by survey round. Stunting reduced from 44.7% to 28.8% between 2011 and 2015 among
children 24-59.9 months. A similar trend was observed for stunting among children 6-23.9
months. In contrast, wasting increased in both two age groups.
Figure 10.3 Trends in stunting and wasting prevalence, by child age and survey time in Zambia
50
2011
44.7
2015
40.3
40
28.8
%
30
19.8
20
16.6
10
7.2
6.2
2.6
0
Stunting
Wasting
Stunting
24-59.9 months
Wasting
6-23.9 months
Figure 10.4 and Figure 10.5 show kernel density estimations of the distribution of child HAZ
and WHZ for both 2011 and 2015. These figures suggest an improvement in HAZ where the
entire distributions shift to the right. For WHZ, the distribution is wider with some move to
the left and the peak of the bell curve is lower.
121
0
.1
Kdensity
.2
.3
Figure 10.4 Distributions of child HAZ scores among children 24-59.9 months in Zambia by survey round
-5
0
HAZ
2011
5
2015
.2
.1
0
Kdensity
.3
.4
Figure 10.5 Distributions of child WHZ scores among children 24-59.9 months in Zambia by survey round
-6
-4
-2
0
WHZ
2011
2
2015
122
4
6
Table 10.1.2 looks at trends in key explanatory variables over time in the entire sample of children
aged 24-59 months (all three study arms). We did not see economic and social changes in the last 4
years in terms of socio-economic status scores. The household hunger score was higher at endline,
and more households were categorized as perceiving themselves to experience moderate or severe
hunger. Household dietary diversity reduced significantly, though the number of food crops,
vegetables and fruits cultivated as well as the number of food groups produced increased. The
number of children under 5 years in the household reduced.
Among this full group, women’s education did not change over time, but more women classified
themselves primarily as farmers at endline. Maternal health changes were seen in increased BMI
scores, though these were still in the normal range on average. There was improvement in maternal
IYCF knowledge and women’s empowerment. The mean number of prenatal visits increased over
time, with more mothers attending at least 4 prenatal visits, and more mothers receiving counseling
on nutrition and breastfeeding. Child morbidity decreased over the last 4 years, however child
deworming and immunization also decreased.
Next, we conducted pooled regression for both rounds, then regressions by survey round. We tested
for stability of coefficients between rounds (Table 10.1.3 for HAZ, Table 10.1.4 for stunting, Table
10.1.5 for WHZ and Table 10.1.6 for wasting) using a barrage of Chow tests. Our findings showed few
differences in coefficients between baseline and endline for HAZ, but we saw evidence of stability
for most parameters for stunting, WHZ and wasting. Based on these tests, we conclude that there
are few signs of any secular changes in the main parameters of interest. We therefore conducted
simple linear decompositions, in which we use the coefficients from these four tables.
123
Table 10.1.2 Changes in the means or percent of key variables in Zambia 2011-2015 (samples of children 2459.9 months)
Household factors
SES index (0/10)
Household hunger score (0/6)
Moderate or severe hunger (HHS)
Household dietary diversity
Household economic shock (0/2)
Number of children <5y
Agriculture production
Number of food crops, vegetables and
fruits cultivated
Number of agricultural activities
Number of food groups produced
Maternal factors
Maternal education (years)
Maternal occupation as farmer
Maternal BF knowledge (0/10)
Maternal CF knowledge (0/10)
Maternal BMI
Women’s empowerment (0/10)
Health care service access
Number of prenatal visits
≥4 prenatal visits
during pregnancy
Received BF counselling
during pregnancy
Child factors
Child gender
Child age
ARI (any fever, cold or fast breathing)
Diarrhea
Deworming
Child immunization score (1/13)
Changes
2015-2011
% change
2011-2015
Percent
5.45
0.72
20.53
6.31
0.25
1.64
-0.09
0.35
12.06
-0.87
-0.02
-0.33
-1.62
94.59
142.38
-12.10
-7.41
-16.75
0.194
<0.001
<0.001
<0.001
0.131
<0.001
2.47
2.87
0.41
16.60
<0.001
2.59
2.49
2.58
2.64
-0.02
0.15
-0.77
6.02
0.558
<0.001
6.43
71.89
5.79
5.79
22.75
4.97
6.53
80.07
6.19
7.24
23.15
5.92
0.10
8.19
0.40
1.45
0.40
0.94
1.56
11.39
6.91
25.04
1.76
18.91
0.101
<0.001
<0.001
<0.001
<0.001
0.0001
3.82
56.84
67.13
4.05
66.67
87.95
0.23
9.83
20.82
6.02
17.29
0.0001
0.0001
0.0001
62.10
86.98
24.88
50.78
39.88
33.49
11.96
84.11
6.43
50.62
41.27
21.13
5.53
74.96
3.26
-0.16
1.39
-12.36
-6.43
-9.15
-3.17
2011
2015
Percent or
mean
Percent or
mean
5.55
0.37
8.47
7.19
0.27
1.97
124
31.01
40.06
-0.32
3.49
-36.91
-53.76
-10.88
-49.30
P value
0.0001
0.898
0.0001
0.0001
0.0001
0.0001
0.0001
Table 10.1.3 Height-for-age z regressions by round with test for coefficient differences over time among
children 24 – 59.9 months
Pooled
Baseline 2011
Endline 2015
Coefficient
Coefficient
(b)
-
Significant
differences
between a and b
0.877***
Coefficient
(a)
-
Program intervention (Ag- Nutrition)
0.163*
0.124+
-0.140+
-
Program intervention (Ag)
Year (2015)
-
0.112
0.174+
-0.289**
-
Year * Ag- Nutrition
-0.315**
-
-
-
-0.410***
-
-
-
0.012
0.018
-0.000
No
0.303***
-0.047
0.391**
<0.01
0.032**
-0.023
0.076**
<0.001
-0.053
-0.048
-0.096
No
-0.018
-0.154**
0.161**
<0.001
-0.098***
0.015
-0.151**
<0.001
0.110**
0.102+
0.104+
No
0.061*
-0.051
0.134**
<0.01
0.000
0.030+
-0.026+
<0.01
-0.085
0.123+
-0.220**
<0.01
0.021
0.023
0.026
No
0.006
0.006
0.005
No
Maternal BMI
0.010
-0.002
0.027+
<0.05
0.006
0.008
0.005
No
0.083+
0.059
0.102
No
0.140*
0.030
0.247+
No
0.059
0.015
0.201+
No
-0.305***
-0.296**
-0.316**
No
-0.002
0.004
-0.009**
<0.01
-0.170**
-0.196**
-0.172+
No
Diarrhea
-0.337***
-0.354**
-0.334+
No
0.041
0.070
0.019
No
SES index (0/10)
fruits cultivated
during pregnancy
during pregnancy
Child gender
Child age
Deworming
*** p<0.001, ** p<0.01, * p<0.05, +p<0.10
125
Table 10.1.4 Stunting regressions by round with test for coefficient differences over time among children 24
– 59.9 months in Zambia
Year (2015)
Pooled
Baseline 2010
Endline 2015
Coefficient
Coefficient
(a)
Coefficient
(b)
-0.216***
-0.059*
-0.047+
0.025
-0.031
-0.036
0.046+
0.095**
0.084*
SES index (0/10)
-0.008**
-0.012**
-0.004
No
-0.036+
0.001
-0.044*
No
-0.007*
0.002
-0.013**
<0.05
-0.005
0.006
-0.003
No
0.000
0.034+
-0.042**
<0.001
0.016**
-0.001
0.023**
<0.05
-0.032**
-0.016
-0.042**
No
0.000
0.008
-0.004
No
0.002
-0.002
0.005
No
0.022
-0.035
0.059**
<0.01
-0.005
-0.009
-0.002
No
0.003+
0.002
0.004
No
Maternal BMI
-0.004*
-0.002
-0.008**
No
-0.005*
-0.005
-0.004
No
-0.016
-0.026
-0.008
No
-0.026
0.008
-0.065+
<0.1
-0.011
-0.008
-0.031
No
0.085***
0.088**
0.082**
No
-0.005***
-0.005**
-0.004**
No
0.049**
0.064**
0.039+
No
Diarrhea
0.074**
0.080+
0.073+
No
-0.007
0.004
-0.008
No
fruits cultivated
during pregnancy
during pregnancy
Child gender
Child age
Deworming
+
Significant
differences
between a and b
P<0.1, * P<0.05, ** P<0.01, *** P<0.001
126
Table 10.1.5 Weight-for-Hight z regressions by round with test for coefficient differences over time among
children 24 – 59.9 months
Year (2015)
Pooled
Baseline 2011
Endline 2015
Coefficient
Coefficient
(a)
Coefficient
(b)
Significant
differences
between a and b
-0.400***
0.071
0.084
0.256**
-0.033
-0.038
0.322***
0.184+
0.353***
-0.009
0.009
-0.019+
<0.05
-0.413***
-0.340***
-0.417***
-0.036**
-0.019
-0.044**
No
No
-0.005
-0.002
-0.000
No
-0.004
0.025
-0.036
No
0.057***
0.045+
0.061**
No
-0.076*
-0.085+
-0.060
No
-0.012
-0.010
-0.024
No
-0.014+
-0.020+
-0.008
No
-0.009
-0.023
0.009
No
0.001
0.006
-0.009
No
-0.003
0.012
-0.014
0.030***
0.038***
0.023*
<0.05
No
0.008
0.010
0.009
No
0.073+
0.004
0.132*
No
0.014
-0.001
0.003
No
0.019
0.106
-0.105
No
0.044
0.046
0.046
No
-0.020***
-0.016***
-0.022***
No
0.063
0.014
0.087
No
Diarrhea
0.035
0.013
0.022
No
Deworming
-0.070
-0.069
-0.074
No
SES index (0/10)
fruits cultivated
Maternal BMI
during pregnancy
during pregnancy
Child gender
Child age
+
P<0.1, * P<0.05, ** P<0.01, *** P<0.001
127
Table 10.1.6 Wasting regressions by round with test for coefficient differences over time among children 24
– 59.9 months
Year (2015)
Pooled
Baseline 2011
Endline 2015
Coefficient
Coefficient
(a)
Coefficient
(b)
Significant
differences
between a and b
0.060***
0.004
0.004
-0.041***
-0.014
-0.007
-0.043**
0.001
0.000
0.001
No
0.073***
0.040**
0.079***
No
0.004*
0.000
0.008**
<0.05
-0.012*
-0.001
-0.020*
No
0.002
0.001
0.003
No
-0.007**
-0.000
-0.010**
<0.05
0.005
0.008
0.003
No
0.003
-0.004
0.009
No
0.002
0.003*
0.001
No
0.009
0.011
0.008
No
0.007*
0.004
0.010*
No
0.002+
-0.001
0.004**
<0.01
Maternal BMI
-0.001
-0.000
-0.002
No
0.001
-0.000
0.002
No
-0.019**
-0.011
-0.027**
No
0.010
0.007
0.018
No
-0.008
-0.011
0.001
No
0.011+
0.011
0.011
No
Child age
0.000
0.000
-0.000
No
-0.009
-0.012
-0.005
No
Diarrhea
-0.010
-0.011
-0.002
No
Deworming
-0.012
-0.006
-0.014
No
-0.041**
-0.025
SES index (0/10)
fruits cultivated
during pregnancy
during pregnancy
Child gender
+
P<0.1, * P<0.05, ** P<0.01, *** P<0.001
128
Table 10.1.7 reports detailed decomposition results for HAZ and stunting for children 24-59.9
months using only those variables that are statistically significant at the 10 percent level or
higher in the first column of Table 10.1.3 and Table 10.1.4. The first column reports the
estimated coefficient from that regression. The next three columns, respectively, report the
2011 and 2015 sample means and the change in means across time. The predicted change
in HAZ scores is the product of this change in means and the estimated coefficient. The last
column reports the share of predicted change accounted for by each variable.
There are two important findings in Table 10.1.7. First, the model explains 8.4% (or 0.056
standard deviations-SD) of the actual change in HAZ scores observed during this period
(0.670 SD). The model also explains only 6.3% of the actual change in stunting. Second,
among the sources of predicted change, received nutrition counseling and reduction in child
morbidity stand out as the largest factors, explaining 3-4 percent of the predicted change in
HAZ scores. Unexpectedly, the household hunger and agriculture production variables
predicted the changes in HAZ or stunting in the opposite direction.
Table 10.1.8 also shows that the model only explains 1% actual change for WHZ and 13%
actual change for wasting.
For children aged 0-59 months, the models explain 4-6% of actual change for HAZ/stunting
(Table 10.1.9) and around 16% for WHZ and wasting (Table 10.1.10).
129
Table 10.1.7 Decomposing sources of HAZ and stunting change by survey round among children 24-59.9
months in Zambia
Variables
Estimated
coefficient
(a)
HAZ
Moderate or severe HH hunger
0.303
HH dietary diversity
0.032
Number of crops, vegetables
-0.098
and fruits cultivated
Number of agricultural
0.110
activities
Number of food group
0.061
produced
>4 prenatal visits
0.083
0.140
during pregnancy
Child gender
-0.305
ARI
-0.170
Diarrhea
-0.337
Ratio of predicted HAZ change to actual (%)
Unexplained (%)
Stunting
Sample
mean
2011
(b)
-1.800
0.085
7.187
Sample
mean 2015
(e= a*d)
0.056
0.036
-0.028
Share of
predicted
change
(%)
100.00
5.43
-4.16
2.467
2.873
0.406
-0.040
-5.94
2.592
2.576
-0.016
-0.002
-0.26
2.491
2.641
0.150
0.009
1.37
0.568
0.667
0.099
0.008
1.23
0.671
0.880
0.209
0.029
4.37
0.507
0.330
0.120
0.506
0.211
0.055
-0.001
-0.119
-0.065
0.000
0.020
0.022
0.05
3.02
3.27
8.35
91.65
0.447
0.288
-0.158
-0.010
100.00
(c)
-1.130
0.205
6.315
Change in
means
(d=c-b)
0.670
0.120
-0.872
Predicted
change
SES index (0/10)
Number of food crops,
vegetables and fruits cultivated
activities
-0.008
-0.036
-0.007
5.549
0.085
7.187
5.453
0.205
6.315
-0.096
0.120
-0.872
0.001
-0.004
0.006
-0.49
2.73
-3.86
0.016
2.467
2.873
0.406
0.006
-4.11
-0.032
2.592
2.576
-0.016
0.001
-0.32
0.003
5.792
7.239
1.447
0.004
-2.74
Maternal BMI
-0.004
22.754
23.153
0.399
-0.002
1.01
Women empowerment (0/10)
-0.005
4.974
5.917
0.943
-0.005
2.98
Child gender
0.085
0.507
0.506
-0.001
0.000
0.05
Child age
ARI (any fever, cold or fast
breathing)
Diarrhea
-0.005
39.895
41.275
1.380
-0.007
4.36
0.049
0.330
0.211
-0.119
-0.006
3.69
0.074
0.120
0.055
-0.065
-0.005
3.04
Ratio of predicted stunting change to actual (%)
6.34
Unexplained (%)
93.66
Table 10.1.8 Decomposing sources of WHZ and wasting change by survey round among children 24-59.9
months in Zambia
Variables
Estimated
coefficient
Sample
mean
130
Sample
mean 2015
Change in
means
Predicted
change
Share of
predicted
(a)
WHZ
(moderate or severe hunger)
Household food diversity
activities
Maternal education
Maternal BMI
>4 prental visits
Child age
Ratio of predicted WHZ
change to actual (%)
Unexplained (%)
-0.413
-0.036
0.057
-0.076
-0.014
0.03
0.073
-0.02
2011
(b)
0.367
(c)
0.177
(d=c-b)
-0.190
(e= a*d)
-0.004
change
(%)
100.00
0.085
7.187
0.205
6.315
0.120
-0.872
-0.050
0.031
26.08
-16.52
2.467
2.873
0.406
0.023
-12.18
2.592
6.426
22.754
0.57
39.895
2.576
6.526
23.153
0.67
41.275
-0.016
0.100
0.399
0.10
1.380
0.001
-0.001
0.012
0.01
-0.028
-0.64
0.74
-6.30
-3.80
14.53
1.90
98.10
Wasting
0.026
0.072
0.046
0.006
100.00
0.073
0.004
Economic shock
-0.012
-0.007
0.007
0.002
>4 prental visits
-0.019
Child gender
0.011
Ratio of predicted wasting change to actual (%)
Unexplained (%)
0.085
7.187
0.273
0.205
6.315
0.253
0.120
-0.872
-0.020
0.009
-0.003
0.000
19.04
-7.58
0.52
2.467
2.873
0.406
-0.003
-6.18
4.632
5.792
0.57
0.507
4.955
7.239
0.67
0.506
0.323
1.447
0.10
-0.001
0.002
0.003
0.00
0.000
4.92
6.29
-4.09
-0.02
12.90
87.10
131
Table 10.1.9 Decomposing sources of HAZ and stunting change by survey round among children 0-59.9
months in Zambia
Variables
Estimated
coefficient
(a)
HAZ
0.279
0.022
No of children <5
0.178
-0.144
0.073
activities
0.096
produced
-0.094
Maternal BF knowledge
0.034
BMI
0.013
0.029
>4 prental visits
0.084
0.210
during pregnancy
Child gender
-0.145
Diarrhea
-0.286
Deworming
0.150
Sample
mean 2011
Sample
mean 2015
Change in
means
Predicted
change
(b)
-1.614
0.084
7.163
2.099
2.505
(c)
-0.761
0.215
6.312
1.790
2.829
(d=c-b)
0.853
0.131
-0.851
-0.309
0.324
(e= a*d)
0.032
0.037
-0.019
-0.055
-0.047
Share of
predicted
change
(%)
100.00
4.28
-2.19
-6.45
-5.47
2.614
2.572
-0.042
-0.003
-0.36
2.524
2.624
0.100
0.010
1.13
0.723
4.644
22.706
4.950
0.560
0.670
0.801
4.968
23.087
5.916
0.657
0.882
0.078
0.324
0.381
0.966
0.097
0.212
-0.007
0.011
0.005
0.028
0.008
0.045
-0.86
1.29
0.58
3.28
0.96
5.22
0.510
0.114
0.841
0.502
0.049
0.742
-0.008
-0.065
-0.099
0.001
0.019
-0.015
0.14
2.18
-1.74
3.72
Unexplained (%)
96.28
-0.006
-0.006
-0.015
0.430
5.559
7.163
2.099
0.261
5.452
6.312
1.790
-0.169
-0.107
-0.851
-0.309
-0.009
0.001
0.005
0.005
100.00
-0.38
-3.02
-2.74
0.017
2.505
2.829
0.324
0.006
-3.26
-0.022
2.614
2.572
-0.042
0.001
-0.55
0.723
0.801
0.078
0.002
-1.06
22.706
4.950
23.087
5.916
0.381
0.966
-0.002
-0.006
0.90
3.43
0.670
0.882
0.212
-0.008
4.64
0.510
0.502
-0.008
0.000
0.23
40.546
0.110
42.284
0.037
1.738
-0.073
-0.005
-0.003
3.09
1.86
0.114
0.049
-0.065
-0.004
2.46
Stunting
SES
No of children <5
0.023
BMI
-0.004
-0.006
-0.037
during pregnancy
0.049
Child gender
-0.003
Child age
0.043
ARI
Diarrhea
0.064
5.60
Unexplained (%)
94.40
132
Table 10.1.10 Decomposing sources of WHZ and wasting change by survey round among children 0-59.9
months in Zambia
Variables
Estimated
coefficient
(a)
WHZ
-0.316
0.069
-0.089
activities
Maternal education
-0.013
Maternal CF knowledge
-0.009
BMI
0.035
>4 prental visits
0.077
Child age
-0.012
Diarrhea
0.140
Deworming
-0.076
Sample
mean
2011
(b)
0.399
0.084
Sample
mean 2015
2.505
2.829
0.324
0.022
-7.48
2.614
2.572
-0.042
0.004
-1.25
6.424
9.283
22.706
0.560
40.546
0.114
0.841
6.548
11.598
23.087
0.657
42.284
0.049
0.742
0.124
2.315
0.381
0.097
1.738
-0.065
-0.099
-0.002
-0.021
0.013
0.007
-0.021
-0.009
0.008
0.54
6.97
-4.46
-2.50
6.98
3.04
-2.52
15.69
(c)
0.100
0.215
Change in
means
(d=c-b)
-0.299
0.131
Predicted
change
(e= a*d)
-0.047
-0.041
Unexplained (%)
Wasting
SES
0.004
0.059
Economic shock
-0.023
No of children <5
0.016
-0.010
0.002
BMI
-0.002
0.004
>4 prental visits
-0.020
0.018
during pregnancy
Diarrhea
-0.019
Unexplained (%)
Share of
predicted
change
(%)
100.00
13.84
84.31
0.039
5.559
0.084
0.280
2.099
0.101
5.452
0.215
0.254
1.790
0.062
-0.107
0.131
-0.026
-0.309
0.011
0.000
0.008
0.001
-0.005
100.00
-0.69
12.47
0.96
-7.97
2.505
2.829
0.324
-0.003
-5.23
9.283
22.706
4.950
0.560
11.598
23.087
5.916
0.657
2.315
0.381
0.966
0.097
0.005
-0.001
0.004
-0.002
7.47
-1.23
6.23
-3.13
0.670
0.882
0.212
0.004
6.15
0.114
0.049
-0.065
0.001
1.99
17.03
82.97
133
11 Discussion
This report presents findings from a large, complex, multi-year, inter-sectoral project that combined
agriculture and nutrition interventions to impact child nutrition. The RAIN project is one of a handful
of projects that includes a rigorous randomized design to examine impact, and responds directly to
recent calls for stronger evaluation designs of agriculture and nutrition interventions to strengthen
the evidence base on these links.
Overall, the RAIN project had mixed impacts. The project had: 1) large and consistently positive
impacts on agricultural production, 2) impacts on different domains of women’s social and economic
empowerment, as well as women’s empowerment in agriculture, 3) impacts on household food
security as measured by household dietary diversity, and 4) a potential protective effect on child
wasting. In general, where there were significant program impacts, the magnitude of these impacts
was larger in per-protocol analyses, among confirmed RAIN beneficiaries. There were however no
discernable impacts on reducing the prevalence of stunting, on improving IYCF practices among
young children, or on improving caregiver health and nutrition knowledge. There appears to be little
to no additional benefit of the Ag-Nutrition arm, compared to the Ag-only intervention arm for the
impacts achieved. This is further evidenced by greater exposure to the agricultural intervention
components of the RAIN project, compared to the nutrition intervention components.
The agricultural production element of the RAIN intervention achieved its aim of increasing both
diversity and stability of plant foods available to households. Though the animal interventions were
largely unsuccessful in increasing the consumption of animal sourced foods, there were clear
improvements in production of diverse foods across the households surveyed, and improvements
were around double in per-protocol analyses (confirmed beneficiary households). Given increases in
production of different food groups, it follows that food security as measured by access of
households to diverse food groups also improved in this study. Previous reviews of the effects of
intersectoral agriculture-based projects for nutrition outcomes have found that these projects do
tend to achieve increases in both production and consumption of the food groups they promote
(Masset, Haddad et al. 2011). In this study, as well as the objective Household Dietary Diversity
measure of food security (HDDS), we also measured subjective household perception of hunger
using the Household Hunger Scale (HHS). Interestingly, while household access to diverse food
groups improved, household perception of food security is worse in intervention households.
The main domains of empowerment most affected by the program were ‘social capital’ involving
contact with other women to discuss issues and share information; ownership of animals; and input
into decisionmaking on what foods to grow and spending money from the sale of field crops. These
134
outcomes make sense in the context of the specific RAIN interventions involving creating women’s
groups and providing agricultural inputs. However there is still a long way to go in improving
women’s perceptions of equality, decision making power, and power to hold and use economic
assets for the nutrition and health of themselves and their children.
Improvements in IYCF practices require both food security and caregivers who are empowered to
make decisions for their children. Several elements of the RAIN agricultural interventions aimed to
improve determinants of IYCF, including making diverse foods available and accessible, and bringing
women together into groups. The nutrition interventions in the Ag-Nutrition group built on these to
provide information to improve knowledge on breastfeeding, complementary feeding, and
knowledge. No differences were seen in IYCF knowledge or practices attributable to the RAIN
project, other than an increase in children’s consumption of legumes (likely groundnuts, which were
promoted by RAIN).
The low exposure within the study area is one possible factor contributing to the lack of consistent
impacts across project objectives. The larger effect sizes in per-protocol analyses support this
hypothesis. We have explored program implementation further in Appendix 4: Comparing RAIN and
DHS
This study was not designed to assess the role of external factors in impact findings; the inclusion of
a control group and the use of difference-in-difference and decomposition analyses allows us to
assess the differential effect of the RAIN project without this kind of anlaysis. However, given the
apparently steep changes in stunting and wasting seen in the endline analysis, we show below how
the RAIN data fits with similar data from two rounds of DHS in Central Province, in 2007 and 2014.
Figure 12.1 shows stunting rates broadly in line with ongoing reductions seen in the DHS, though
with a major reduction in 2015. Wasting rates have increased in the RAIN data but decreased in the
DHS.
135
Figure 12.1 Comparison of stunting and wasting between RAIN and DHS
60
52.7
50
44.7
42.5
40
28.7
30
20
10
5.9
4.6
2.6
7.3
0
DHS 2007_central,
all <59m
RAIN 2011,
all 24-59m
Stunting
DHS 2014_central,
all <59m
Wasting
136
RAIN 2015,
all 24-59m
Appendix 5: Explaining the impact of RAIN: Program impact pathways. As noted previously, only a third
of eligible households surveyed had participated in the project through joining a women’s group—the
main point of entry into the RAIN project. In addition, home visits by SMFs and CHVs, a critical
component of the intervention delivery by the program, did not materialize as envisaged, losing a key
one-to-one element that would bolster the project. Across both the women’s groups and home visits, it
was clear that the agriculture frontline workers (SMFs) were more active than the health-side workers
(CHVs), which plays out in the improved agriculture outcomes compared to nutrition and health
outcomes in our evaluation. Only 12-16 percent of households had both high participation and high
levels of program delivery; around half of households scored either medium or high. The process
evaluation (conducted in 2013 & 2014) suggests that the SMF, a position created specifically for the
RAIN project, were initially more incentivized to act for RAIN through RAIN’s provision of incentives
(agricultural inputs) than the CHVs, positions that already existed in the community and which did not
receive similar incentives until after 2014, though both groups received additional training. In addition,
CHVs serviced the entire community, whereas SMFs were working specifically with RAIN groups; also
SMFs are newly created positions, and the information they provide was new, whilst the CHVs were
existing positions so women should have been exposed to many of the messages before. However, the
project strengthened the knowledge of the CHVs and increased the number of CHVs in the area. It is
clear from the evaluation that by and large, the Ag-only group fared better than the Ag-Nutrition group,
suggesting no additional value of nutrition interventions in this project, implemented primarily by CHVs.
As noted in the results section, per-protocol analysis (whereby analysis was restricted to those
individuals that were confirmed RAIN beneficiaries) did not alter the main findings, though almost twothirds of the sample was lost in this process. As such it is hard to make definitive claims about lack of
impact being solely driven by low participation.
The impact evaluation design attempts to attribute changes in key impact indicators of interest to the
RAIN interventions. As such, it is important to note a limitation of the design is its inability to accurately
account for elements beyond the RAIN project interventions. For several indicators of interest, we
observe changes over time in both the RAIN intervention groups, as well as the control group. In
Appendix 4: Comparing RAIN and DHS we can see that reductions in stunting are broadly in line with
other surveys in Central province, but increases in wasting are not. While we are able to document the
lack of leakage of formal program delivery of RAIN interventions to the control group, we are unable to
document informal leakage of secondary intervention components (knowledge, agricultural inputs etc.
provided by non-project staff) among peers or relatives across program study arms. Additionally, we are
unable to adequately account for general improvements in government health services across the
137
district, regardless of RAIN study group. There are clear improvements over time, between baseline and
endline, in access and use of government-run health services across all study groups, as documented in
this report. This is evident in increased access and use of prenatal care, as well as receipt of health and
nutrition counseling services at government clinics. As such, it is plausible that this increase in access
and use directly impacts health and nutrition knowledge, and IYCF practices, components of prenatal
counseling and under 5 clinics at government health centers. This increase may be sufficiently large to
prevent detection and attribution of RAIN interventions, over and beyond general increases and secular
trends. While the decomposition analyses found large changes in nutrition outcomes across study arms,
analyses left a lot of this change unexplained, meaning that the food, health, and care variables available
in the RAIN dataset could not explain the change.
There is limited evidence from other evaluations of similar programs. An evaluation of HKI’s
International’s Enhanced Homestead Food Production (EHFP) intervention in Burkina Faso (one of only a
very few rigorous impact evaluations of similar agriculture-nutrition programs designed to improve child
nutrition) demonstrated results that were similar for many, but not all, of the outcomes examined for
the RAIN project (Olney, Pedehombga et al. 2015). The evaluation of the EHFP program in Burkina Faso
found 1) positive impacts on wasting among children, 2) small positive impacts on anemia, 3) small
positive impacts on maternal dietary diversity, 4) positive impacts on maternal underweight, and 5)
positive impacts on several dimensions of women’s empowerment. Of note, the EHFP program had no
impact on reducing the level of stunting, and no impact on food security.
Overall, the results from the RAIN evaluation contribute to remedying the dearth of evidence from
rigorous impact evaluations of integrated agriculture and nutrition programs. There are clear and
important maternal and household level benefits of this program, which may be achieved in other,
similar programs, and lessons used to to scale-up the existing project. The clear and consistent impact of
the RAIN intervention on agriculture production, and on women’s empowerment, two core objectives of
the program, are noteworthy and consistent with the limited evidence to date from similar
interventions.
138
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139
12 Appendices
Appendix 1: Detailed sample size calculations
For both Baseline and Endline, all sample size calculations were carried out using STATA 11 software. A
two stage process was followed:
1. Sample sizes were estimated to detect changes in stunting and height-for-age Z scores
assuming simple random sampling from a single cluster. This was carried out using the
sampsi command
2. The sample size estimates obtained in (1) were then adjusted to account for clustering
using the sampclus command
Two different sample size estimates were obtained. The first was to detect changes in stunting, and the
second was to detect changes in height-for-age Z scores. The highest was used.
Baseline
Stunting
The following assumptions were used:
1. A baseline prevalence of stunting of 53 percent (Based on the prevalence in Central Province
from the 2007 Zambia DHS), and a reduction of 8 percentage points
2. A β of 0.8, and an α of 0.05
3. A one sided test
4. The availability of 13 clusters per study arm
5. An intra-cluster coefficient of 0.01
Height-for-Age Z scores
The following assumptions were used:
1.
2.
3.
4.
5.
6.
A change in height-for-age Z score of 0.2
A standard deviation of 1.3
A β of 0.8, and an α of 0.05
A one sided test
The availability of 13 clusters per study arm
An intra-cluster coefficient of 0.01
869 for HAZ; 829 for stunting: children aged 2-5 per arm
Rounded up to 1000 per arm for the survey, as we didn’t know exact stunting rates in Mumbwa
+10% = 3300 total
140
STATA Output- Stunting
. sampsi .53 .45, p(0.8) onesided
Estimated sample size for two-sample comparison of proportions
Test Ho: p1 = p2, where p1 is the proportion in population 1
and p2 is the proportion in population 2
Assumptions:
alpha =
0.0500 (one-sided)
power =
0.8000
p1 =
0.5300
p2 =
0.4500
n2/n1 =
1.00
Estimated required sample sizes:
n1 =
507
n2 =
507
. sampclus, numclus(26) rho(0.01)
Sample Size Adjusted for Cluster Design
n1 (uncorrected) = 507
Intraclass correlation
= .01
Average obs. per cluster
= 64
Minimum number of clusters = 26
Estimated sample size per group:
n1 (corrected) = 827
STATA Output- HAZ
. sampsi 0 0.2, sd(1.3) p(0.8) onesided
Estimated sample size for two-sample comparison of means
Test Ho: m1 = m2, where m1 is the mean in population 1
and m2 is the mean in population 2
Assumptions:
alpha =
0.0500 (one-sided)
power =
0.8000
m1 =
0
m2 =
.2
sd1 =
1.3
sd2 =
1.3
n2/n1 =
1.00
n1 =
523
n2 =
523
= .01
Average obs. per cluster
= 67
141
Endline
Sample size estimate AMENDED based on actual stunting rate found in Baseline survey (44%):
959 children aged 2-5 per arm
x 3 arms = 2877
+10% un-useable data = 3021
+ over-sampling of beneficiaries 5% in each intervention arm = 3453 total
STATA output- amended
. sampsi .44 .365, p(0.8) onesided
Estimated sample size for two-sample comparison of proportions
Test Ho: p1 = p2, where p1 is the proportion in population 1
and p2 is the proportion in population 2
Assumptions:
alpha = 0.0500 (one-sided)
power = 0.8000
p1 = 0.4400
p2 = 0.3650
n2/n1 = 1.00
n1 =
554
n2 =
554
= .01
Average obs. per cluster = 74
n2 (corrected) = 959 per arm
142
Sampling Methodology
Sampling of children in baseline surveys was specific to households that had at least one child aged 2459 months of age, the age range for detecting impacts on stunting i.e. the primary RAIN project impact
indicator. However, to capture impacts of the RAIN project on key IYCF indicators, children between 023 months of age were also sampled. The endline survey will follow a similar protocol.
Logistical considerations prevented us from sampling children 0-23 months and 24-59 months of age
from unique households; sampling unique household would substantially increase our sample size which
was not feasible. Therefore, preference was given to households that had both a child 24-59 months of
age as well as a child 0-23 months of age. Where multiple children within these age ranges were present
in a household, the youngest child within the household was selected.
A listing exercise was undertaken in the 6 wards to determine eligible households i.e. households with
the presence of at least one child aged 24-59 months of age. A child aged 24-59 months old was
identified as the INDEX child. A child aged 0-23 months old was identified as the NON-INDEX child. All
households were categorized into 3 possible categories:
4. Households with an INDEX child only
5. Households with both an INDEX and a NON-INDEX child
6. Households with a NON-INDEX child only
The following procedures and guidance were taken in sampling households for the RAIN project baseline
survey:
1. A listing exercise was conducted to identify all households with a child between the ages 0-23
months of age, and 24-59 months of age. This was done using maps provided by the
Government central statistics office (CSO) and experienced “mappers” who were involved in the
Zambia 2010 household census.
2. The total number of households listed was 9541. This included the total number of households
for which no information was obtainable (identified as “non-contact” households) or who
refused to participate in this listing exercise (identified as “refusal” households) which totaled
682 households.
3. Households were then categorized into one of the three categories listed above. Details of this
listing exercise can be found in Annex 3- Listing book.
4. Of the identified 9541 households listed, 6435 households were identified as having a child
between 0-59 months of age. A systematic sampling process (outlined in steps 4-6, below) was
then followed to yield the total number of households within each unit of randomization (the
CSA) as required by the sample size calculations.
5. Because the CSA is the unit of randomization, it is therefore the unit of most interest from an
evaluation perspective as it is the unit by which treatments are applied. However for sampling
143
purposes the CSA covers a very large geographic area and a large number of households and is
therefore not practical as a unit for sampling. For sampling purposes the unit that is most
practical is the SEA, which is a clearly identified geographic area as per the Government CSO.
The majority of CSAs have 3 SEAs within them, though within the 6 wards from which the
baseline survey sample was drawn, there was a range of 2-5 SEAs per CSA, as per the 2010 CSO
sampling frame.
6. The sample size calculations require that a total of 69 households be sampled in the baseline
survey per unit of randomization-the CSA. The majority of CSAs had 3 SEAs, and within these
SEAs 23 households were sampled. In CSAs that had 4 or 5 SEAs, 3 SEAs were randomly sampled
and 23 households were sampled from each SEA. In CSAs that had only 2 SEAs, 35 households
were sampled from the first SEA, and 34 households from the second. Therefore, based on this
sampling procedure, a total of 69 households were sampled per CSA as required by the sample
size calculations.
7. Within each SEA, households were sampled based on the household listing exercise conducted
at the start of field work, using a systematic random sampling procedure that used a random
number table. This sampling procedure is outlined in Annex 4- Stratification and selection of
sample households.
8. A total of 3044 households were sampled for this baseline survey. Only 34 households that were
selected as part of the systematic random sampling procedure refused to be interviewed.
144
Appendix 2: Variables included in the decomposition analysis
The dependent variables in this analysis include child nutrition indicators. We primarily focus on
HAZ and WHZ as measured against World Health Organization growth standards, which are
described in de Onis et al. (2007) and WHO (2006). We also use outcomes as stunting (HAZ<-2)
and wasting (WHZ<-2) as they are commonly used to indicate child nutritional status and
stunting is the main interest of the intervention. As study design, we looked at the
determinants for child growth among children 24-59 months of age, as we expected seeing the
most change in this group. Additionally, we also looked at the determinants for child growth
among children 0-59 months of age.
The independent variables included two sets. The first set was time-varying intermediate
determinants which can help both for understanding which factors explain nutritional
differences across children, and for decomposing changes across time. These include household
factors (household economic status, HH hunger score, HH food dietary diversity and economic
shock), maternal factors (education, occupation, knowledge on IYCF), health service access
(prenatal and postnatal care), sanitation, and child morbidity (acute respiratory infection ARI
and diarrhea). The second set was control variables that are essentially fixed over time such as
maternal BMI, child and maternal age, child gender, location fixed effects, and RAIN
intervention. Definition of variables used in the analysis was presented in Table 1.1.1 below.
Table 0.1 Definitions of variables using in the analyses
Variable name
Definition
Outcomes
HAZ
Height-for-age z score (HAZ) measured against WHO (2006) norms
Stunting
HAZ < –2
WHZ
Weight-for-height z score (WHZ) measured against WHO (2006) norms
Wasting
WHZ < –2
Determinants
Household factors
SES index
Index based on principal component analysis and standardized score 1-10
Household hunger score (0/6)
Continuous variable
Household food diversity
Dummy = 1 if household experienced moderate or severe food hunger in
the last 30 days
12 food groups, used as continuous variables
Household economic shock (0–3)
Disaster events that household experienced in the last 12 months
145
Agriculture production
fruits cultivated
including field crops and fruits and vegetables
The production of 7 different food groups that correspond to those
groups used in the child dietary diversity index
Including production of field crops, fruits and fruits/vegetables, rearing
animals and production of animal source foods.
Maternal factors
Mother’s years of education
Dummy = 1 if mother was a farmer
Maternal BF knowledge
Score of knowledge on breastfeeding
Score of knowledge on timeliness of complimentary food
Maternal BMI
Kg/m2
Women empowerment
Use composite score
Health care service access
during pregnancy
during pregnancy
Child factors
Dummy = 1 if mother received 4 or more prenatal care visits
Dummy = 1 if mother received nutrition counseling
Dummy = 1 if mother received BF counseling
Child gender
Dummy = 1 if child is female
Child age
Child’s age as continuous variable
ARI
Dummy = 1 if the child had any symptoms of fever of cough
Diarrhea
Dummy = 1 if the child had symptom of diarrhea
Deworming
Dummy = 1 if the child got deworming
Child immunization score (0/13)
Each immunization get one score. Use variables that are common
between baseline and endline
Others
Ward
Code for commune, used to control for cluster effect
146
Appendix 3: Detail on the RAIN project
Frontline workers
The women groups were the target for recurring agriculture and nutrition trainings delivered by
Smallholder Model Farmers (SMFs) and Community Health Volunteers (CHVs). All SMFs were female and
they provided training to vegetable/fruit production and rearing small livestock. The initial project set up
was that each of the 112 SMFs would be responsible for the training and support of one group in the first
year, with an additional group in the second year of the project as part of a phased-in approach.
However, due to the low population density and the scattered housing set up in Mumbwa District, it was
not possible for all SMFs to include a second group and therefore, an additional group of 69 SMFs was
trained in the second year. The total number of SMFs is 181, reaching out to 4499 beneficiaries.
A total of 96 CHVs were engaged in the project and responsible for the delivery of nutrition and health
education and communication. Each CHV is linked to one or two women groups.
In each of the four wards where the project is being implemented, there were two Community
Development Facilitators (CDF -MCDA partner staff) who provided training and mentorship to the SMFs
and CHVs, with assistance from government workers (Camp Extension Officers, Rural Health Centre
Staff), other MCDA and Concern staff. Each CDF was responsible for 30 women groups.
Training
Cascading trainings were used to build capacity for specific agricultural and nutrition skills and gender
awareness.
The SMFs received an intensive training course on agricultural practices including homestead gardens,
organic manure, integrated pest management and rearing small livestock. Bi-monthly refresher trainings
were organised at community level by the CDFs with assistance from government workers. The CHVs
received an intensive training course in Infant and Young Child Feeding (IYCF). In addition, Health Facility
Staff trained as Trainer of Trainers (ToT) provided monthly nutrition refresher trainings to the CHVs on
various subjects including maternal health, how to conduct cooking demonstrations, WASH and
micronutrient deficiencies. CHVs and SMFs received a bicycle to facilitate their movements.
Agriculture
The women groups aimed to meet for 2 hours each week, either at one of the member’s home garden
for a hands-on demonstration or at any central gathering point in the community. The SMFs were
providing the agricultural trainings to the members of her group. Agricultural inputs were provided to all
group members during the dry and wet season. The crops were chosen based on their nutritional value
and included legumes (cowpeas, groundnuts, Fe/Zn bio fortified beans), vegetables (e.g. amaranthus,
rape, tomatoes, carrots, spinach, pumpkin (leaves), paprika, green beans), fruits (banana suckers,
granadillas, watermelons) and orange fleshed sweet potato vines.
In order to facilitate the watering of the dry season gardens, each group was provided with treadle
pumps. Each group also received a food solar dryer in order to facilitate preserving fruits and vegetables
to increase access to micronutrient rich foods throughout the year. Seeds, vines and fruit tree saplings
are provided to all group members with the goal of diversifying their homestead food production. SMFs
received more seeds than other group members with the rationale that they will establish small-scale
nurseries for seed production and distribution in the future.
Lack of adequate water supply especially during the dry season was affecting all year round nutritional
gardens. To address this, the project identified and rehabilitated boreholes and conducted training to the
village –WASH committees on borehole management.
147
To facilitate livestock production, a pass-on scheme was designed whereby all SMFs were provided with a
male and a female goat, whilst some group members received a female goat, and passed on their first off
spring to the other group members. Half of the group members received a chicken.
Nutrition and Health
The nutrition and health activities focussed on behaviour change communication for improved child and
maternal nutrition, especially infant and young child feeding, and linkages to the existing health system,
including the prevention of mother to child transmission services. A Behavioural Change Communication
(BCC) strategy was developed based on a barrier analysis conducted for several behaviours related to
infant and young child feeding. The BCC materials used are based on the standard IYCF training package
(counselling cards, participant’s manual and brochures) developed by the government, but translated in
the local language.
In order to promote the integration of nutrition and agriculture at field level, the SMFs have received
some basic nutrition knowledge in addition to their agricultural training. A small booklet with the main
nutrition message around the first 1000 days was provided to them as a reference book.
Gender
At the initial stage of the project, a gender need assessment was conducted to better understand the
gender constraints related to food and nutrition security. Community sensitizations were conducted for
all relevant community members including men and village elderly. Special gender trainings were
conducted for community leaders. Women’s group members and their husbands were trained in the
importance of gender in improving nutrition (e.g. husbands providing land for homestead gardens,
assisting with chores like fetching water and supporting health facility visits). Continuous gender
awareness is created by integrating gender messages in the nutrition and agricultural messages provided
to the beneficiaries, drama group performances and a series of IEC materials with gender messages
distributed within the project area. CDFs of MCDA and government extension workers have received a
ToT in gender, and are now capable of facilitating the trainings. Activities aimed at reducing the workload
of women include the promotion of a fuel efficient cooking stoves and the provision of glyricidea seed
(fast growing firewood tree).
Coordination and alignment
A multi sectoral committee, the Mumbwa District Nutrition Coordinating Committee (DNCC), was
established as a coordination mechanism to align activities of the key stakeholders to effectively address
malnutrition. This DNCC includes district representatives from the Ministry of Agriculture and Livestock
(MAL), Community Development, Mother and Child Health (MCDMCH), Ministry of Education (MoE) and
from civil society organisations. The District Commissioner for Mumbwa has endorsed the committee as a
subcommittee of the District Development Coordinating Committee (DDCC) and reports through that
mechanism, to provincial and national bodies.
Through reflective and learning processes designed to bring sectors together, new and interesting ways
are being explored to promote coordination between officials in the agriculture, health and community
development sectors, starting at district level in Mumbwa, and then cascading down to extension
workers at community level. The coordinating structures at ward level have received regular orientation
in order to build their capacity in nutrition.
The DNCC has developed a multi-sectoral district nutrition plan which will be implemented as part of the
Most Critical 1000 days programme/ Scaling Up Nutrition (SUN).
148
HIV and AIDS
The main objective of the HIV and AIDS mainstreaming is to ensure that the project does not pose an
increased risk of HIV transmission and that people living with HIV and AIDS can benefit from the project
interventions. The CDFs have received intensive HIV and AIDS training with a special focus on the
prevention of mother to child transmission (PMTCT). Both SMFs and CHVs received HIV and AIDS training,
and the CHVs have received additional training on PMTCT as part of the IYCF training, and a number of
additional refresher trainings. The SMFs and CHVs sensitize the communities.
A total of 16 local drama groups have been trained on how to convey messages in the area of nutrition,
health, gender and agriculture diversification. Field days are organised during both dry and wet season in
order to demonstrate best practices, share experience and lessons learnt among beneficiaries as well as
other community members.
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Appendix 4: Comparing RAIN and DHS
This study was not designed to assess the role of external factors in impact findings; the inclusion of
a control group and the use of difference-in-difference and decomposition analyses allows us to
assess the differential effect of the RAIN project without this kind of anlaysis. However, given the
apparently steep changes in stunting and wasting seen in the endline analysis, we show below how
the RAIN data fits with similar data from two rounds of DHS in Central Province, in 2007 and 2014.
Figure 12.1 shows stunting rates broadly in line with ongoing reductions seen in the DHS, though
with a major reduction in 2015. Wasting rates have increased in the RAIN data but decreased in the
DHS.
Figure 12.1 Comparison of stunting and wasting between RAIN and DHS
60
52.7
50
44.7
42.5
40
28.7
30
20
10
5.9
4.6
2.6
7.3
0
DHS 2007_central,
all <59m
RAIN 2011,
all 24-59m
Stunting
DHS 2014_central,
all <59m
Wasting
150
RAIN 2015,
all 24-59m
Appendix 5: Explaining the impact of RAIN: Program impact pathways
Figure 1 below shows the Program Impact Pathways (PIP) for the RAIN project. Generated through a collaborative
process involving Concern and IFPRI during the initial year of the RAIN project, the PIP draws on program
implementation documents, RAIN objectives, and international best practices frameworks to map out the entire
project from inputs and implementation processes to the expected outputs, outcomes and impacts of the project.
This document uses data from the baseline and endline surveys, and two rounds of process evaluation (PE), to assess
each step in the PIP. A ‘traffic light’ system6 is used to indicate to what extent a particular step in the PIP was
achieved, and notes are provided on each step in order to explain these achievements.
The first round of PE was undertaken in 2013 and considered program delivery. For this round, key informant
interviews were undertaken with managers; a small non-representative survey of qualitative and quantitative
questions was undertaken with frontline workers and supervisors; and focus group interviews were undertaken with
a small number of beneficiaries (Table 1). The aim was to understand the process of implementation, both to enable
understanding of the final evaluation results, and to enable changes to be made to the program in real time.
The second round of PE was undertaken in 2014 and considered program utilization. For this round, focus groups
and observations were undertaken with known beneficiaries, and in addition a small survey was undertaken to
provide interim reporting to donors on key outcomes (though this was undertaken in a different season and so
cannot be compared with the main evaluation survey). Sampling for the survey was of adopters and non-adopters in
intervention areas (note that samples were purposive, so we can’t say anything about enrollment rates from this
data).
The main evaluation survey was undertaken at baseline and endline, sampling around 1000 households eligible for
the RAIN project in each study arm, and using a detailed household survey to assess differences between study arms
and over time in key outcome and impact indicators.
Table 2 Process evaluation sampling
Round 1: Assessing program delivery
Managers
Supervisors and trainers
Frontline workers
Beneficiaries
5-10
-
-
-
Survey
-
-
20 CHVs per arm (40 for health arm)
=80
25 SMFs per intervention arm =50
See note 4 on gender workers = 4
-
-
Focus groups
8 CDFs
7 TOTs
At least 2 from each cadre
in ag and health= 10
-
Observation
-
-
5 group meetings in each
intervention arm =10
Interviews
Same 5 groups as
observation =10
Plus 2 non-beneficiary=2
-
Round 2: Assessing program use
Control
(potentially
eligible)
100
Ag only
(participating)
Beneficiaries
Ag only
(not participating)
Ag+Health
(participating)
Ag+Health
(not participating)
100
50
100
50
Focus groups
-
10
-
10
-
Observation
-
5
-
5
-
Survey
6
Green indicates that the step was broadly fulfilled; yellow indicates that there were some problems with the step; and red
indicates that the step was broadly not fulfilled as planned.
151
Agriculture inputs
procured
2
3
Agriculture master
trainers identified
4
8
Women’s groups
established
3b
SMFs recruited and
trained (+ refreshers)
8a
Women attend
groups
SMFs receive
agricultural inputs
SMFs establish model
farms
9
2c
OUTPUTS
9a
2b
Women acquire
resources
10
OUTCOMES
CHVs recruited and
trained (+ refreshers)
6a
6b
CHVs retain skills and
knowledge
Women establish
small animal
production
Change in resources
(controlled by
women)
13a
16
Change in women’s
influence/ decisionmaking power
CHVs deliver group
BCC and home visits
9b
6d
7a
Gender
trainers deliver
messaging to
groups
Borehole
rehabilitation
undertaken
Improved access to
water
Change in
gender
attitudes
7b
Reduced burden on
women
13
6e
Improved post- 14
harvest management
Change in women’s
time use
17
Increased
consumption of
nutrient-rich foods
21
Gender master
trainers trained
Introduction of
labor saving
6c
technology
(stoves)
9c
Women acquire
health knowledge
and skills
Increased animal
production
Boreholes assessed
5c
CDFs
facilitate
frontline
work
152
IMPACT
Health master
trainers trained
Gender master
trainers identified
11
Increased fruit and
vegetable production
15
SMFs deliver group
ag messaging and
home visits
5b
7
6
5a
Women acquire ag
knowledge and skills
Women establish
12
gardens
12a
5
Health master
trainers identified
Agriculture master
trainers trained
Women’s groups
receive agricultural
inputs
18
RAIN partnerships
Training manuals and
tools created
SMFs retain skills and
knowledge
3c
2a
1
3a
PROCESS
INPUTS
Figure 12.2: RAIN program impact pathway (PIP) achievement
Change in stunting rates
19
Improved health
knowledge and
practices
20
Improved IYFC
knowledge and
practices
1. RAIN management and partnerships- A major success of the RAIN project was the process of
formation of a District Nutrition Coordination Committee (DNCC) in Mumbwa district. The process catalyzed by the Concern, facilitated by an international consultant, and now increasingly led by
members of the DNCC itself - has involved strengthening both technical and strategic capacity within the
DNCC, with emphasis on its leadership. This is not a prescribed process, but it has been carefully
designed to both bring in key nutrition and management information at the right times, and to respond
to the needs and processes of the DNCC itself. This is an innovative design: normally these processes are
started at national level, often with little attempt to understand or engage with structures at the local
level, where implementation actually occurs. In this case, the focus on intersectoral action started in
international discourse; jumped directly to local level implementation through the facilitation of
Concern; and then contextually-relevant learning from this pilot was fed back up to national level where
it altered policies and program designs to incorporate a focus on coordination between sectors, before
being rolled out more broadly in 7 of the 14 ‘1000 Most Critical Days Programme’ pilot districts. In
essence, this is good development NGO practice in action: taking a key international idea; supporting its
piloting it in a local context; rigorously documenting the experience7, with explicit attention to informing
national policy and working through existing systems; and supporting government in trying to take
successful elements of the project to scale. The innovations of NGOS like Concern who are willing to
support these emergent processes are showing that change is possible.
More centrally to the RAIN project, bilateral partnerships were formed through MOUs with the
ministries of agriculture, health and community development; with Women for Change (WfC), a
prominent national gender NGO that would plan and implement the gender component of the project;
and with Mumbwa Child Development Agency (MCDA), a local CBO who would undertake
implementation:



The partnership with WfC dissolved early in implementation, due in part to a lack of management
and administrative capacity in WfC to deal with Concern’s financial accountability systems.
Relations with the ministries remained strong and grew stronger through joint work on the DNCC
over the course of the project. However it is unclear to what extent the relationships through the
DNCC were helpful to RAIN engagement with CHVs and health centers on the ground; in the 2013 PE
it was found that “Government staff in the field do not appear to be undertaking RAIN duties other
than training; it is unclear exactly what is expected of them in terms of supervision or monitoring, if
anything”. In particular, the 2013 PE found issues with incentives for CHVs (an existing government
volunteer position) in the field.
The key partnership, with MCDA, remained throughout the duration of the project and throughout
numerous changes in personnel on both sides, and implementation remained through this CBO. The
2013 PE found that this CBO partnership was amicable, but there were concerns around the systems
imposed by Concern and lack of an inception period to cement relationships. Capacity was not built
to deal with the large administrative and monitoring load required of MCDA by the project; capacity
for financial and other administrative reporting was low, causing significant delays to funding
throughout the duration of the project, and therefore delays to implementation. Several respondents
in 2013 felt that direct implementation by Concern should have been pursued from the start in such
an important ‘proof of concept’ project.
7
In addition to notes, briefs and papers generated throughout the lifetime of the RAIN and DNCC projects, this
work has been written up partly under the Stories of Change project, and will be available for dissemination in May
2016. Further work on this coordination aspect of RAIN forms Jody’s PhD, and will be ready in 2017.
153
Finally, a challenge to overall management of RAIN was lack of an effective monitoring system for the
majority of the project. At the time of the 2013 PE, roll-out of a monitoring systems was delayed, with
only basic monitoring data available at the end of year one; this did not allow for appropriate action to
be taken to correct any problems, and a functioning monitoring system was not established until mid2014, a year before the end of implementation.
2. Ag inputs procured and transferred- The 2013 PE found that most inputs planned for 2012 were
procured and distributed. However, for many items significantly more was required than planned
(particularly seeds, but also some larger items such as drums, treadle pumps and solar dryers), and for
some items it appeared that planned items could not be procured in sufficient numbers (goats, chickens,
granadilla). In particular, animal distribution was limited, reaching 35-30% of first round beneficiaries
(likely mostly SMFs), which equaled 14-16% of all beneficiaries. The 2014 PE then found that regularity
of input provision was variable, and in particular that the animal pass-on scheme was slow to function,
causing tension among beneficiaries and limiting potential nutrition impact. At endline, significantly
more women in intervention areas owned animals than in control (around 65% of women in
intervention areas), though far fewer reported themselves able to dispose of these assets without
authorization (around 12%).
3. Agriculture training cascade- CDFs hired by MCDA, along with ministry of agriculture employees,
were used to train SMFs. In the 2013 PE, the process of training was found to be effective, with
adequate use of materials and use of teaching aids and engaging training sessions and high satisfaction
from participants. There were some challenges with refresher training participation however, mostly
due to seasonal weather issues. SMF training was found to be a little chaotic, with women bringing their
babies to the classes, which could undermine detailed learning. There was some concern over the
volume of topics covered over the residential training, and therefore the quality of learning. In the
knowledge assessment in 2013, agriculture supervisors were found to focus on quantity of food as a
cause of malnutrition, and at that point SMFs did not appear to have good nutrition knowledge.
4. RAIN materials- The 2013 PE found “The nutrition training uses the government IYCF manual, which is
comprehensive in scope and locally-relevant, and the IYCF counseling cards are in a locally-appropriate
language. The agriculture manual is also comprehensive and clear. It is likely that some CHVs have
already been trained using the IYCF manual, so there is a question over value added by the RAIN
nutrition training for them. SMF training is likely to be new to them, given their previous training and
experience. In the nutrition manual the focus is on IYCF only, and not on the broader determinants of
nutrition, and the agriculture manual does not contain nutrition considerations; requiring frontline
workers to consider agriculture, nutrition and health holistically or connection with the aims of RAIN
therefore are not happening at training level. There is some concern over literacy levels of CHVs and
SMFs, and ability to read and assimilate the content, particularly of such a long manual, but the
embedded illustrations and quality of training sessions themselves could overcome this. It is unclear that
more frontline workers can read Tonga than English, but having manuals in both languages is likely to be
an advantage. Links with RAIN are not clear in the manuals, either through content or branding.”
The 2014 PE found that many respondents had not seen some RAIN materials, and that was borne out in
the endline survey, where less than 30% of those surveyed had seen RAIN posters, and around 60% had
heard RAIN radio messages.
5. Health and nutrition training cascade- Ministry of Health staff were hired to train CHVs on nutrition
and IYCF issues. In the 2013 PE, CHV training was observed and reported to be generally participative
and clear, with clear learning objectives and sufficient training materials, and engaged classes. IYCF
refresher trainings were happening regularly, but suffered from the distances participants have to travel
154
(particularly in rainy season) and therefore low attendance. Trainings appeared to be engaging to those
who attend, and use similarly participative and clear teaching methods. Government IYCF trainers were
however sometimes not engaged with the aims of RAIN, and reverted to standard IYCF training rather
than relating content to RAIN objectives. Health-side training to this point had only been on IYCF, with
no attention to broader nutrition, sanitation or health issues, or conceptual or practical links to
agriculture. The 2013 PE recommended that “Concern needs to think through how the two sectors
overlap (for instance, around complementary feeding and maternal diet and time use), and put in place
some explicit activities at different levels… Concern needs to be explicit about whether the added value
is in the technical content of the trainings, or the training in, for instance, how to interact with women’s
groups, adult education skills, and problem solving skills”. In the knowledge assessment, CDF knowledge
on nutrition and health was generally poor. The knowledge of health supervisors was at a broadly
similar level to that of CHVs, which was adequate.
6. Gender training and inputs cascade- In the 2013 PE, the gender trainings provided by Women for
Change were almost universally acclaimed among respondents and there was enthusiasm for the
messages. It is likely that the model used by WfC was appropriate for the area, however, due to
inadequate monitoring by WfC or Concern, it was unclear how much of the community was covered by
these messages. At endline, 22% of households reported participating in RAIN community events,
including gender events. Some change in attitudes on gender issues can be seen in the endline
empowerment data, particularly in ‘social capital’, and a clear shift in women’s involvement in
agricultural decision-making, but there is still a long way to go.
Another side to the gender strategy was the provision of labor saving technology in the form of fuelefficient stoves, to reduce the burden of firewood collection. After difficulty finding community groups
to manufacture these, some stoves were made and distributed, but these did not cover all beneficiaries,
and there was no difference at endline in type of fuel used for cooking (though amount used was not
assessed).
7. Borehole rehabilitation and water access- Several boreholes were assessed and rehabilitated in the
project area at the start of the project. At baseline, around 63% of respondents in all study arms
reported using a borehole for drinking water; at endline, this had risen to 70% in the control area and
83% in the intervention areas, however differences were not statistically significant.
However, the 2013 PE noted that “the water issue in Mumbwa, as relates to home gardens, is bigger
than borehole rehabilitation; water availability generally has led some respondents in the evaluation to
question whether this was the best model for this agro-ecological area. Borehole rehabilitation is taking
longer than anticipated due to procurement issues (part of a broader procurement problem at CWZ),
without clarity that the result will be more water for gardens.”
8. Establishment of women’s groups- Women’s groups were established successfully in the project
areas. The 2013 PE noted that “Both monitoring data and the responses of focus group participants
suggest that women are joining the women’s groups; monitoring data is not sufficient to say whether
the number of women joining is as expected, or what proportion of women in different areas are not
joining”. There was no exit strategy for transitioning women out of the groups as their children aged, but
inputs were only provided once to each woman, and IYCF messaging would become repetitive over
time, so a natural fall-off of women was anticipated over time. Initially, there were also no plans for
ongoing enrollment of women beyond the first two years of enrollment rounds, but in planning for the
endline it became clear that without ongoing recruitment it would be likely that we would not see
enough beneficiary women with children aged 0-24 months in the endline survey, so additional rounds
of recruitment were added through 2014 and 2015.
155
In 2013, Concern undertook a Participation Survey in order to understand participation rates and
reasons in the absence of monitoring data. Purposively selecting 7 villages in two wards, this study
suggested that overall around 88% of eligible households were participating in RAIN, ranging from 100%
to 70% in the villages surveyed, with a majority of those not enrolled citing not being available at the
time enrollment was taking place. However, at endline only 35% of households with an eligible woman
reported having a member of a RAIN women’s group. Investigations by Concern are ongoing into flawed
Census numbers that may have led to an underestimation of eligible women in the project area; it is also
clear that the 2013 Participation Survey used a flawed methodology.
Of women who did join the RAIN groups, attendance was generally high. The 2013 PE found that
“General attendance is seasonal, with rainy/agricultural season attendance dwindling to nothing. The
monitoring data are not clear enough to say what proportion of beneficiaries is actually attending
regularly. The focus group data indicates that at least some women are attending the groups”. The 2014
PE found that “Enrolled women are attending groups and perceive them positively”. The 2013
Participation Survey found that of those households enrolled but not attending regularly, a majority
cited clashes with other household business, not benefiting from inputs, and being unaware of meeting
dates as key reasons. At endline, 50% of those who reported being a member of a RAIN group received
medium or high levels of program exposure, comprising attendance and delivery.
9. Frontline program delivery- The program was delivered in multiple streams (predominantly
agriculture, nutrition, and gender streams, but also labor-saving technology and water streams). Apart
from gender, which was a community-focused intervention, these streams came together at the level of
the RAIN groups, with intervention streams delivered according to study arm (ag-nutrition or ag-only),
with supplemental home visits planned from both agriculture and health frontline workers. On the
agriculture side, 90% of RAIN group meetings were attended by an SMF, and 50% of participating
households received home visits in the first half of 2015; overall, 39% of surveyed households reported
receiving any agricultural training, of which 61% was provided by RAIN. This tallies with the overall
participation rate of around 35% of households. On the health side, CHVs were present at around 40%
of RAIN group meetings- but notably were also present at around 40% of RAIN group meetings in agonly areas also. Only 14% of participating households received a home visit from a CHV, and 8% of
households did in the ag-only areas. The 2013 PE found that ”Little information is available on home
visits, either from monitoring data, frontline interviews, or focus groups- we are led to believe these are
not happening as intended”, which would appear to still be the case in 2015.
The 2013 PE found that “Frontline workers spend an average of 3-4 hours per day, 4-6 days per month
on community work, though there is quite a lot of variation within this. A majority of CHVs in both RAIN
and non-RAIN areas say that their workload has increased since one year ago (mostly due to increased
number of people to visit or an increased area to cover), but most SMFs do not; no RAIN workers say
they cannot manage their workload, but around a quarter of RAIN CHVs and 40% of non-RAIN CHVs say
it is too much. Time is a constraint to CHVs (but not so much to SMFs), with community workloads
affecting other responsibilities and vice-versa; other responsibilities include agriculture, childcare, and
domestic duties.” Thus motivation, confidence and enjoyment was generally high among frontline
workers, who are motivated by a combination of community spirit and personal development, but was
lower among health-side workers, who received fewer incentives and worked through government
systems. An explanation suggested by Concern for the low CHV engagement may have been that the
CHVs were not selected among the group members (as the SMFs were), but they were selected by the
health facility. This resulted in CHVs being either men, or much older than the group members. This may
have influenced the participation of the CHVs in the project.
156
The other key frontline role in RAIN was the CDF, a cadre of community development workers recruited
by MCDA to train, coordinate and supervise the SMFs and CHVs. There were originally 8 CDFs for the
entire project, which was increased to 12 when ongoing recruitment was established and beneficiary
numbers rose. Thus there were very few CDFs to coordinate the work of many frontline workers and
RAIN groups, and these were cited by some RAIN project workers as a bottleneck for the program. The
2013 PE found that “CDFs have been an issue for the project so far, with several replaced recently…
Frontline workers appear ambivalent to supervision structures, which neither excessively help nor hinder
them in their routine work.”
A key aspect of frontline delivery was the combined facilitation of groups by SMFs and CHVs in the agnutrition areas. At endline, while SMF participation in RAIN groups was high, CHV participation was low;
group members therefore had more opportunity to interact with trainers from the agriculture side than
from the health side. The 2013 PE found that strategic coordination between sectors was not
happening, from RAIN management down to field level: “CDFs appear to have stronger links to SMFs
than to CHVs, who report directly to government staff (in theory); this further underlines the
disconnected nature of the RAIN project’s agriculture and nutrition elements”. At this time, there was
little interaction between sectors, and a lack of planning for the harmonization of frontline worker
training, or beneficiary messaging at group meetings. The major issue identified for RAIN project
delivery related to a lack of harmonization of inputs, messages and information provided to groups
between the agriculture and health sectors of RAIN; this lack of coordination was seen at all levels from
management to manual design to training to frontline worker interaction, and was therefore not
surprising at the level of service delivery. By design, the burden of integration is placed at the level of
the program beneficiaries, who are left to make their own links between what the program advocates in
terms of agriculture, and in terms of child feeding; without integration in service delivery, the current
model requires mothers in women’s groups themselves to make the links between agriculture and
nutrition.
The following advice was given to Concern in the 2013 PE report: “The structures that are in place for
reporting and the pressure to implement fast are not conducive to strategic planning that is vital for the
achievement of RAIN aims. Concern needs to think explicitly about reconciling the two implicit aims for
RAIN- to provide services to beneficiaries, and to produce evidence on agriculture interventions for
nutrition; these aims are not mutually exclusive, but certain elements of the project (such as capacitybuilding of local partners, and HR processes) need to be addressed with this explicit lens. While the
theory underlying the RAIN interventions is clear, this is not being translated well in implementation, in
its current form; it is important to get service delivery right, otherwise impact at the level of households
is unlikely to be realized. Tweaks made now will likely greatly improve the quality of the intervention,
increase exposure and utilization of services, and increase adherence to recommended behaviors.”
10. Agriculture knowledge- Monitoring data show that agriculture was covered in trainings at an
average of 80% of group meetings by mid-2015, making agricultural knowledge and skill dissemination
one of the strongest elements of the project. Concern’s Irish Aid Programme Funding (IAPF) evaluation
found that many women reported using new skills such as seed saving, and many beneficiaries noted
that they will continue to practice the new approaches to agriculture, nutrition and gender equality that
they have learned through the program; many have built relationships with their SMFs who continue to
live within the community. In the second round of process evaluation in 2014, women in intervention
areas were much more confident that they could maintain a garden than their counterparts in the
control area (over 90% in intervention groups, compared to 60% in control). The IAPF evaluation also
noted, however, that it was clear that some of the beneficiaries were at different levels in terms of
knowledge and practices, and that those beneficiaries with the highest level of dependency on the
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project are often those who need the most support maintaining these practices. As monitoring data
show the proportion of SMFs maintaining teaching gardens fluctuated from less than 30% to over 80% in
different months (2014 and 2015), and not all eligible women were reached by the project, the overall
population impact on agricultural knowledge and skills among women was not as strong as it could have
been if all were reached and all had access to demonstration plots.
11. Health and nutrition knowledge- At endline, patterns in changes of IYCF knowledge were
inconsistent, in both intent to treat and per-protocol analyses. Overall, IYCF knowledge had increased
over time in the ag-nutrition group compared to control on some information and decreased on other
information. Hygiene knowledge was significantly different among study arms at endline for many
aspects relating to hand washing; protecting children from worms; and making drinking water safer, but
the direction of difference was not consistently favorable to a single study arm, or to the RAIN
intervention arms. In the 2013 PE, there was a question around the added value of RAIN trainings over
routine government trainings for health volunteers, where the curriculum and training materials used
are those provided and used by the government. It is therefore possible that the RAIN nutrition and
health messaging was not adding additional information, and that women were receiving this
information through government channels already. This would suggest that linking CHVs to a group
model did not add value to the program.
12. Establishment of gardens and vegetable production- At endline (in the dry season) around 13% of
households used some land for the establishment of a garden, as opposed to 6% in control areas. The
2013 PE (in the wet season) found that “most women participating in FGDs [sampled of beneficiaries
and SMFs] had established gardens. However, it is important to note that women distinguish between
rainy season and dry season gardens; the latter are harder to establish because of water supply issues.”
There was a significant increase over time and compared to control of households producing any
vegetables and fruits (34% in ag-only and 41% in ag-nutrition groups at endline compared to 22% in
control), and more intervention households were producing vitamin-A rich plant foods, and for more
months of the year.
13. Establishment of small animal production- Procurement and distribution of small animals was
problematic in the RAIN project. It was difficult to procure sufficient numbers of goats locally; chickens
frequently died of Newcastle disease; and the pass-on schemes were slow and caused tension among
some beneficiaries. The 2013 PE found that “While construction of chicken houses is reported to be
common, goat houses are less frequent because of the expected delays in receiving goats through the
pass-on system.” At endline, there were no significant differences between study arms in ownership of
animals or production of animal products, though intervention arms did have more months producing
dairy.
14. Improved post-harvest management- The main strategy for post-harvest management was drying
of vegetable foods to extend the season for which these were available to eat, and drying of seeds for
re-use (though many seeds provided by the project were hybrid variety, and therefore would not yield
so well in following years). The 2013 PE [sampling banaficiaries and SMFs] found that “Use of solar
dryers and seed banking techniques was widely reported in FGDs, and seems to be going well, although
not all types of seeds are kept: this is determined by availability/ type of seed (hybrid seed cannot be reused)”. However only 8% of women in intervention areas reported having access to a solar dryer at
endline, suggesting that coverage/access to these did not keep pace with beneficiary recruitment.
15. Change in resources controlled by women- At endline, impacts on women’s economic
empowerment were not consistent. Women’s overall financial empowerment changed little since
baseline, and was slightly worse in intervention areas. Access to assets for women was slightly better in
intervention areas however, and in specific domains more closely related to the project’s aims
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(ownership of animals) and long-term female-specific savings (ownership of jewelry) the intervention
areas were doing significantly better. In agriculture specifically, women in intervention areas had
significantly less input on decisions relating to use of money from the sale of animals but slightly more
on money from the sale of crops.
16. Change in women’s decision making- In the 2014 PE [purposive sample of beneficiaries], women in
RAIN intervention areas were confident in both producing and feeding the foods children need. At
endline, decision-making and equality scores were no better than control. More broadly that decision
making however, women’s social capital scores in terms of interactions with her spouse, social
interaction, support, and information sharing were significantly improved in RAIN intervention areas
compared to control, likely due to interaction facilitated by RAIN groups.
17. Change in women’s time use- At endline, there were no significant impacts on women’s time useeither positive or negative.
18. Improved food security and diets- Overall, respondent’s perception of their household food security
decreased significantly over time for all groups, in both the full sample, and among confirmed RAIN
beneficiaries, though there was a significant positive program impact on the level of “severe hunger”. At
the same time, the Ag-Nutrition group had significant positive impacts on household dietary diversity,
with an increase of about 1 food group, based on a 12 food group scale. There were significant
improvements in maternal dietary diversity (measured out of a total of 7 food groups) within study arms
over time, in both intent-to-treat, and per-protocol analysis, but there were no differential changes over
time, in favor of any study group, in impact analyses.
19. Improved health practices- RAIN did not try to change health practices beyond hygiene. At endline,
vitamin A supplementation and deworming coverage were fairly high in intervention areas, but these
differences were present at baseline. Overall child morbidity improved in all study arms over time, but
was worse in the ag-only arm compared to the other arms. There were no clear patterns in reduction of
disease over time.
20. Improved IYCF practices- There were no differences between groups, at endline, in any of the core
WHO IYCF indicators. . There were significant increases in several complementary feeding indicators
(with increases ranging from 6 to 12 pp for different indicators, in the RAIN intervention groups), but
these increases were not differential in favor of any group, either compared to each other, or the
control group. In general, all breastfeeding-related IYCF indicators were high at endline, across all three
study groups. Complementary feeding practices were sub-optimal ranging from approximately 25-30%
for the minimum acceptable diet indicator, to 60% for the minimum meal frequency indicator. Concern’s
2013 Barrier Analysis suggests that perceived self-efficacy, perceived positive consequences, perceived
social norms, perceived action efficacy, and perceived risk are the key determinants for exclusive
breastfeeding among the target population, and that perceived self-efficacy and perceived social norms
are the key determinants for dietary diversity during complementary feeding. Access to foods was
mentioned as a barrier to maternal but not child dietary diversity.
21- Change in stunting rates- Between baseline and endline, the prevalence of stunting decreased
significantly in all three study groups, but decreased more in the control group. Conversely, the
prevalence of wasting increased in all three study arms, but increased less in RAIN intervention areas.
While we do not have data to explain the overall changes in prevalence, the presence of a control group
allows us to look at differential changes related to the project. By this metric, the RAIN project had no
impact on stunting, and a potentially protective effect on wasting.
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rain project: impact evaluation report

Transcription

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