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Early Childhood Development for the Poor: Impacting at Scale
Last registered on June 28, 2019

Pre-Trial

Trial Information
General Information
Title
Early Childhood Development for the Poor: Impacting at Scale
RCT ID
AEARCTR-0000958
Initial registration date
November 17, 2015
Last updated
June 28, 2019 8:07 AM EDT
Location(s)

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Primary Investigator
Affiliation
Yale University
Other Primary Investigator(s)
PI Affiliation
University of Pennsylvania
PI Affiliation
CECED
PI Affiliation
Institute for Fiscal Studies/UCL
PI Affiliation
Institute for Fiscal Studies, Centre for the Evaluation of Development Policies
PI Affiliation
Institute of Child Health
PI Affiliation
Institute for Fiscal Studies, Centre for the Evaluation of Development Policies
PI Affiliation
Pratham Education Foundation/ASER Centre
PI Affiliation
Institute forInstitute for Fiscal Studies, Centre for the Evaluation of Development Policies
PI Affiliation
Institute for Fiscal Studies
PI Affiliation
Institute for Fiscal Studies
Additional Trial Information
Status
On going
Start date
2015-09-01
End date
2021-06-30
Secondary IDs
Abstract
Neurobiological science has established that the first three years of life lay the basis for lifelong outcomes due to the rapid development of the brain. During this period, children are also vulnerable to negative influences of a number of biological and psychosocial factors such as malnutrition, illness and unstimulating environments. These factors may be induced by poverty and may have a detrimental effect on children’s health and development, including physical, cognitive, language and motor development. Research has shown that approaches to early childhood development (ECD) that involve psychosocial stimulation and/or nutrition are effective in mitigating influences of negative factors. Our aim is to understand the cost, sustainability and effects of such approach and the mechanisms needed for these early years’ interventions to be effective in poor populations. We need to know how the interventions change beliefs, knowledge and parenting practices as well as the specific investments (in time and resources) that parents and caregivers make in child rearing. We need to identify how specific investment choices are translated into individual child development outcomes. Our research agenda comprises evaluating two different modes of delivering an ECD intervention coupled with nutritional information through a randomised control trial in 192 villages in rural Odisha. Odisha provides an excellent setting for the study as it represents a prototypical context of low levels of stimulation, high malnutrition, poor developmental outcomes and extreme levels of poverty. The study will permit us to learn the extent to which an early childhood stimulation and nutrition intervention is feasible and effective in improving child health and development in low-income and disadvantaged environments.
External Link(s)
Registration Citation
Citation
Attanasio, Orazio et al. 2019. "Early Childhood Development for the Poor: Impacting at Scale." AEA RCT Registry. June 28. https://doi.org/10.1257/rct.958-2.0.
Former Citation
Attanasio, Orazio et al. 2019. "Early Childhood Development for the Poor: Impacting at Scale." AEA RCT Registry. June 28. https://www.socialscienceregistry.org/trials/958/history/48858.
Sponsors & Partners

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Experimental Details
Interventions
Intervention(s)
We will carry out a psycho-social stimulation programme coupled with nutrition education that aims to improve interactions between mothers or principal caregivers and their infants/children with the aim of achieving better child outcomes. The programme follows a systematic weekly curriculum based on the natural developmental stages of the child. Alternative service provisions include delivery of the curriculum by local women either (i) during weekly individual home visits to mother and child, or (ii) in a suitably modified way, within the context of weekly mother-child group meetings. The intervention will last for 24 months and will be evaluated using a randomised control trial. We will collect data on child development outcomes and detailed maternal and household level data before the start, half way through and at the end of the intervention. Our focus will be on communities in rural areas of Odisha and will involve children aged 7-16 months. We propose to implement and compare four variants of our intervention:
1. Provision of education on nutritional issues (hereafter referred to as “NE”): Regular visits to the home will be carried out by local women hired and trained for the project (henceforth referred to as educators) to deliver the nutritional education curriculum, which will be designed to produce positive changes in food choice, preparation and storage.
2. Individual Stimulation via Home Visits (IS) (+ NE): Weekly visits to the home will be carried out by local women hired and trained for the project (henceforth referred to as facilitators) to deliver the stimulation curriculum and involve mother and child in play and learning activities.
3. Group Stimulation (GS) (+ NE): In this variant, the facilitator will deliver a specially-designed version of the stimulation curriculum to a group of mothers and children. The group will meet weekly and is planned to have a maximum of 9 mothers and children.
4. Health and Nutritional Services Link (HNSL): A basic intervention that strengthens links with the existing services will be available to all study arms, including the control group. The aim of offering the basic service to all is to create a baseline where the current policy framework is well understood. We then measure our intervention over and above a status quo, which encourages take-up of policy as is now.
Intervention Start Date
2015-11-26
Intervention End Date
2018-01-01
Primary Outcomes
Primary Outcomes (end points)
Key Final Outcomes
1. Children’s child cognitive, language and motor development
2. Children’s nutritional status
3. Children’s morbidity

Key Intermediate Outcomes
4. Child rearing practices: level of stimulation in the home
5. Maternal knowledge of child rearing practices
Primary Outcomes (explanation)
Key Final Outcomes
1. We will assess cognitive, language and motor development at the time of the follow-up surveys using the third version of the Bayley Scales of Infant and Toddler Development (Bayley-III), suitably adapted for the context.
2. We will measure height and weight both at baseline and at the two follow ups, using standard methods. We will also collect information on feeding practices by food type, frequency and quantity.
3. We will collect data on the incidence of diarrhoea, fever and respiratory infections using the definitions of the WHO as measures of morbidity. We will also collect records of immunisations.

Key Intermediate Outcomes
1. The presence of toys and learning materials in the house will be assessed together with parental involvement with the child, the child’s routines and organisation of the child’s time inside and outside the family house. This will be assessed using the Family Care Indicators, developed by UNICEF, and possibly selected subscales of the Home Observation for the Measurement of the Environment (HOME).
2. We will collect information on the mother’s knowledge of nutrition and stimulation, and her beliefs regarding the importance of these for children’s development. To test knowledge, we will rely on a selection of items from the Knowledge of Infant Development (KIDI).
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The evaluation design will be based on a cluster randomized trial in which a target sample of 192 villages with the selected facilitators/educators will be allocated randomly to intervention variants 1 to 3 described above and a control group. In total, there will be 48 villages in each intervention arm, including the control.

Within each village we intend to treat and collect data on all children aged 7-16 months at the start of the intervention. We estimated an average of 7.5 children in this age group per community based on data from our sanitation project in similar villages. However, in our final sample of 192 villages, this average was around 14. Since our budget did not allow for treatment of so many more children, we adopted the following approach:
- In villages with 8 children or less (one third of the 192 villages), we took all children to be part of the study.
- In the remaining villages, we chose one eligible child at random and thereafter included in the sample those additional seven children that live geographically speaking closest to the randomly chosen child.
This approach ensures that even in villages with relatively large populations and spread, our target children would come from the same neighbourhood, hence facilitating intervention implementation. Similarly, it allows to have a clear criteria for inclusion into (exclusion out of) the intervention. On the one hand, we have a clear age cut-off, on the other hand a geographical clustering. It may also improve the quality of the intervention if all mothers of children in a particular age group in the villages interact and provide social support for each other regarding child-rearing practices. Finally, this will be consistent with scaling-up the program to full coverage.
Villages are randomised to the different intervention arms as we expect the interventions to have spillover within villages. By randomizing across villages we allow for possible spillovers within villages that may increase the overall impacts.
Indeed part of our evaluation design is to measure such spillovers, which if there may be an important conduit for propagating and reinforcing good practice in child rearing. To do so, we will in addition to our target children, interview households of mothers with children below and above the target age range (2-6 months and 17-20 months). In total, 767 such spillover-households will be included in the sample.
To summarize, the total sample sizes will be 1,451 families with children in the 7-16 month age range at project start and 767 children outside the treatment age range, distributed among three groups of 48 intervention villages and one group of 48 control villages.
Experimental Design Details
Not available
Randomization Method
Randomization will be done in office by a computer using the statistical software stata, version 13.1.
Randomization Unit
Randomization will be carried out at the village level.
Treatment will be clustered at the village level, hand in hand with the choice of unit of randomization.
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
192 villages (clusters), 48 per study arm, including control
Sample size: planned number of observations
The total sample sizes will be 1,451 families with children in the 7-16 month age range at project start and 767 children outside the treatment age range, distributed among three groups of 48 intervention villages and one group of 48 control villages.
Sample size (or number of clusters) by treatment arms
48 communities in each treatment group (1-3, i.e. 144). 48 communities in control group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We have assumed a power of 80% and a significance level of 5% for each test. We have also assumed perfect compliance, which is what we hope to come close to. However, note that a small degree of non-compliance will not hurt power much, which is more sensitive to the number of clusters/communities than to the number of individuals within the cluster. We will make every effort to avoid non-compliance so as to avoid introducing any bias. On this basis and our study design, we will be able to detect improvements larger than 21%-32% of a standard score (for cognition or other similar outcomes – for boys and girls taken together) for any pairwise comparison with the control. For each gender separately the minimum detectable effect lies between 28% and 36% of a standard score. We have presented MDEs on the basis of a low spatial correlation (0.04), which is the number we found in the rural communities of Colombia (where members of the team implemented a home-visiting intervention) and on the basis of a relatively high spatial correlation of 0.3. Moreover, we have not taken into account that in estimating the impacts we will include baseline variables in the regressions, which can reduce the noise substantially and therefore increase precision. Thus, we view the minimum detectable effects as an upper bound of what we will achieve: in practice we expect to be able to detect even smaller effects.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Institute for Financial Management and Research (IFMR), Human Subjects Committee
IRB Approval Date
2014-08-24
IRB Approval Number
IRB00007107; FWA00014616; IORG0005894
IRB Name
Yale University Human Subjects Committee
IRB Approval Date
2014-09-01
IRB Approval Number
1112009492
IRB Name
University College London (UCL), UCL Ethics Committee
IRB Approval Date
2012-02-24
IRB Approval Number
2168/002
IRB Name
Indian Council of Medical Research (ICMR)
IRB Approval Date
2013-03-20
IRB Approval Number
5/7/822/2012/RCH
IRB Name
University of Pennsylvania (Penn), Office of Regulatory Affairs
IRB Approval Date
2014-01-02
IRB Approval Number
815027 IRB#8
IRB Name
Pratham Education Foundation (Pratham), FWA for the Protection of Human Subjects
IRB Approval Date
2013-01-31
IRB Approval Number
FWA00019832