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The Findings and Experience of a Data Driven Faith-
Based Poverty Alleviation Program in the Philippines
Lincoln Lau PhD Director of Research
Background
• International Care Ministries (ICM) is a
faith-based NGO in the Philippines
– Core program is an adult education-based
poverty alleviation intervention called
Transform
– Target are households living in
‘ultrapoverty’ defined as <$0.50 USD per
person per day
• In the Philippines ~7Million
– Curriculum consists of three parts: 1)
Values, 2) Health & 3) Livelihood
2
Transform Program
• Communities of 30 households
• 975 Communities scheduled to receive VHL in 2017-2018
3
Data Collection Timeline
4
Participant
Selection
Transform Program
Long term
Follow-up
Survey
Pre Survey Post Survey
- Eligibility
determined by
Poverty Scorecard
- Screening
conducted by
partner pastors
- 35~40 screened
per community
- 2 Weeks prior to
the start of
Transform
- 120+ questions on
a variety of topics
- Part-time
enumerators (data
audited by third
party)
- Within 4 weeks
after the
completion of
Transform
- Matched survey to
the ‘Pre’
- For a subset at 6, 9,
12, 16, 18, 24, 36
months after the
end of Transform
• Approximately 9,500 surveys at the start and end of every ‘batch’ ~ almost 60,000
surveys per year
• Gradually leading to strategic research projects
Sample Studies conducted by ICM Research
• Randomized Controlled Trials (RCT)
– Formal vs Informal Values Training
– What is the Role of Religiosity in a Poverty Alleviation Program?
– How do trainer characteristics affect program outcomes?
• Embedded Study using Program & Evaluation Data
– How does ‘trust’ in key community leaders (faith or government) affect the outcomes of
a child malnutrition intervention?
• Social Network Analytics
– How do we capture the ‘social dimension’ of a development program?
Formal vs Informal Values Training - RCT
Design
- VHL is the regular Transform Program (formal values training)
- HLV is a Transform variant (informal values training)
Formal vs Informal Values Training - RCT
Results
Formal vs Informal Values Training - RCT
Formal vs Informal Values Training - RCT
What is the Role of Religiosity? - RCT
Design
• In partnership with Innovations in Poverty Action (IPA) and Yale University
• Programs: Feb 2015 to May 2015; Data collection: Jan 2015, Jun-Aug 2015 & Jun 2017
• Locations involved: Negros Occidental, Negros Oriental, General Santos & Koronadal
10
20 HL 20 V only20 Control20 VHL
Group B
(20 Pastors, 40 Communities)
Group A
(20 Pastors, 40 Communities)
Select 40 Pastors
(Each has 2 Communities, Total 80 Communities )
11
• ICM and IPA collected data separately. ICM results:
What is the Role of Religiosity? - RCT
12
What is the Role of Religiosity? - RCT
13
Design
• In partnership with IPA & Yale University
• ICM trainers were randomized to communities instead of regular assignments
• Characteristics of interest
– Education, duration of employment, age, sex, socioeconomic status, distance from office
• ICM trainers were all surveyed in September 2016
• Transform Program will run October 2016 – January 2017
• Data Collection: Sep 2016 & Feb 2017
How do trainer characteristics affect outcomes? - RCT
Trust and Malnutrition Intervention – Embedded Study
Does Social Capital affect Health Intervention Outcomes?
Findings from a site-based child malnutrition intervention in the
Philippines
Lincoln L Lau1,2, Han Qu1, Melinda Gill1, Donald C Cole2
1. International Care Ministries, Philippines
2. Dalla Lana School of Public Health, University of Toronto
14
Trust and Malnutrition Intervention – Embedded Study
• ICM implemented a child malnutrition intervention in 2012
and 2013
– Site based program
• Age <5 years
• Weight for height z-score <2 SD below WHO growth standards
• Food: rice based micronutrient fortified soy blended product
• All cases registered at the local Rural Health Unit (RHU)
• Deworming medication and Vitamin A supplementation provided at start
of intervention
15
Trust and Malnutrition Intervention – Embedded Study
16
De Silva M, Harpham T. Health & Place 13 (2013) 341-355
Trust and Malnutrition Intervention – Embedded Study
17
Site-Based Feeding
• 16 weeks of feeding
• Children gathered daily at a fixed
location
• Food prepared and feeding
monitored by ICM staff
• Anthropometrics at baseline and
endline
• Weight measured weekly
Children admitted into
the SBF program
n=2658
1403 female (52.8%)
Completed program
n=2216 (83.4%)
Recovered
n=2093 (94.4%)
Unrecovered
n=123 (5.6%)
Deaths
n=6 (0.2%)
Dropout
n=436 (16.4%)
Trust and Malnutrition Intervention – Embedded Study
18
Model (proportional odds) C1 TLB× geo C2 TPC× geo
Coefficient (s.e.) Coefficient (s.e.)
(Intercept) 0.32 (0.87) 0.78 (0.90)
Family Satisfaction -0.40* (0.19) -0.42* (0.19)
Trust Pastor Church -1.07*** (0.29) -1.47*** (0.34)
Trust Local Barangay Official 0.99** (0.33) 1.32*** (0.32)
geo – dense urban -3.12* (1.59) -3.72* (1.78)
geo – rural plain -1.23 (1.37) -2.25 (1.52)
geo – coastal -4.57** (1.68) -4.86** (1.55)
TLB× dense urban 0.82* (0.41) -
TLB× rural plain 0.29 (0.33) -
TLB× coastal 1.24** (0.45) -
TPC× dense urban - 0.92* (0.43)
TPC× rural plain - 0.52 (0.35)
TPC× coastal - 1.28** (0.41)
Trust and Malnutrition Intervention – Embedded Study
19
• Logistic model (dropout)
– Intensity of poverty
• Trust in religious leader or pastor [-1.47, p<0.001]
• Trust in local barangay [1.32, p<0.001]
• Discussion
– Limited capacity for ‘horizontal’ social networks?
– Complementarity?
ICM Social Network Analysis Pilot
20
ICM Social Network Analysis Pilot
21
• Social dimension of interventions
– Social capital, social cohesion, social connectivity1
– Social networks & social norms2
• Common measurement tools:
• Integrated Questionnaire for the Measurement of Social Capital (SC-IQ) –
World Bank (2003)
• Measuring Social Capital in 5 Communities – P. Bullen & J. Onyx (1998)
• Social Support Questionnaire 6 (SSQ6) – Sarason et al. (1987)
– Count-based (numbers, means, percentages)
1. Putnam 1995 & Kawachi et al. 2008
2. World Bank Group. World Development Report - Mind, Society, and Behavior. 2015
ICM Social Network Analysis Pilot
22
Question:
• Can we use Social Network Analytics to quantify the social
dimension (baseline or change)?
ICM Social Network Analysis Pilot
23
Ultra-poverty
- Low income
- Poor health
- Lack of access to
enterprise
Extreme-poverty
- Increase in income
- Improved health
- Gain access to
enterprise
Transform
Program
Increased Social
Capital?
Transform’s Theory of Change (theorized)
Social capital as a possible mechanism in the pathway to outcomes
ICM Social Network Analysis Pilot
24
Longitudinal data
• Does this accurately describe what is happening?
PRE POST
Item n (%) n (%) Percent Change
Number Surveyed 5984 NA 5001 NA
How much do you
trust your neighbors?
No Trust 53 1% 23 0% -48%
Tentative Trust 130 2% 63 1% -42%
Neutral 1720 29% 1159 23% -19%
Moderate 2682 45% 2143 43% -4%
Very Trusting 1313 22% 1519 30% 38%
Score 3.80 NA 3.96 NA 4%
ICM Social Network Analysis Pilot
25
• Dynamic Social Network Analysis
– Capture the social network at baseline (2 weeks prior) and at endline
(2 weeks following completing of the program)
– Measurement tool:
• Developed in with Petr Matous, The University of Syndey
• Similar to the ‘Name Generator’ [Matous et al. Field Methods. (2010)]
• ALL members within a community need to be captured (surveyed)
– Pilot of 10 communities
ICM Social Network Analysis Pilot
26
• 4 Questions:
ICM Social Network Analysis Pilot
27
SN1. Do you know _____________ ?
ICM Social Network Analysis Pilot
28
SN1. Do you know _____________ ?
ICM Social Network Analysis Pilot
29
SN2. Has ____ visited your house OR have you visited ____’s house in the last month ?
ICM Social Network Analysis Pilot
30
SN3. Do you know____ well enough to discuss personal health issues?
ICM Social Network Analysis Pilot
31
Statistics to describe the social dimension of network #3
PRE POST
Item n (%) n (%) % Change
Number Surveyed 25 NA 25 NA
How much do you trust
your neighbors?
No Trust 0 0 0 0 NA
Tentative Trust 2 0.08 0 0 -100.00%
Neutral 9 0.36 8 0.32 -11.11%
Moderate 12 0.48 17 0.68 41.67%
Very Trusting 2 0.08 0 0 -100.00%
Score 3.56 - 3.68 - 3%
ICM Social Network Analysis Pilot
32
Statistics to describe the social dimension of network #3
Item PRE POST % Change
SN1 - mean indegree/outdegree 10.24 15.28 49.2%
SN1 - centralization indegree 0.250 0.248 -0.68%
SN2 - mean indegree/outdegree 5.08 7.28 43.31%
SN2 - centralization indegree 0.257 0.335 30.44%
SN3 - mean indegree/outdegree 4.2 6.92 64.76%
SN3 - centralization indegree 0.165 0.264 60.04%
ICM Social Network Analysis Pilot
33
• Simple social network analytics
– Offers a different picture of social connections and networks
• Connectedness (indegree/outdegree or density)
• Harmony/Uniformity (centralization or reciprocity)
– Can analyze single communities
• Also can provide insight on individual participants
– Could be a metric to incorporate into logframes and ToCs
• Also can have implications on RBM of interventions
34
End
Role of Research within ICM Operations
35
DATA COLLECTION &
QUALITY
ACCESS & DATA SYSTEMS
DESIGN EVALUATION FRAMEWORK/SURVEYS
Research
Collaborate to
finalize
questionnaire and
methods
Database
(Project
Tomorrow,
Dropbox, Google
Drive, etc.)
ReportsMonitoring Research
Thrive
Jumpstart
Pre/Post
Survey
Values
Health
Livelihood
Metrics
Organize, hire and
schedule: 1)
Enumeration & 2)
Encoding
Managing
programs
EOYR/EOB Analysis
Strat 1/2/3
Program
Design
Transform
Prevail
Other
Studies
Data Collection around Transform
36
Transform
(assessing the program)
Monitoring
(Statistics)
Evaluation
(Metrics)Monthly Statistics
• Target: All participants
• Individual level data
• Collected by: ICM trainers
• Frequency: Weekly per Transform
community
Medical Statistics
• Target: Participants that receive a medical
package
• Individual level data
• Collected by: ICM PHE trainer
• Frequency: dependent on package
Livelihood Statistics
• Target: Participants that receive a Business
in a box (BiB)
• Collected by: ICM livelihood trainer
• Frequency: Daily
PRE/POST Survey
• Target: All participants
• Individual & household level data
• Demographic, Education, Social Capital,
Recent Illness, Sanitation Practices,
Income, Poverty Indicators, etc.
• Collected by: Contacted Enumerators
• Frequency: Before and after each Transform
Learning
(Research)
Ongoing/Previous Projects
• Malnutrition – correlates & predictors of
successful treatment
• Pregnancy Package – correlates of negative
outcomes

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Lincoln-Lau-Session-3A-CCIH-2017

  • 1. The Findings and Experience of a Data Driven Faith- Based Poverty Alleviation Program in the Philippines Lincoln Lau PhD Director of Research
  • 2. Background • International Care Ministries (ICM) is a faith-based NGO in the Philippines – Core program is an adult education-based poverty alleviation intervention called Transform – Target are households living in ‘ultrapoverty’ defined as <$0.50 USD per person per day • In the Philippines ~7Million – Curriculum consists of three parts: 1) Values, 2) Health & 3) Livelihood 2
  • 3. Transform Program • Communities of 30 households • 975 Communities scheduled to receive VHL in 2017-2018 3
  • 4. Data Collection Timeline 4 Participant Selection Transform Program Long term Follow-up Survey Pre Survey Post Survey - Eligibility determined by Poverty Scorecard - Screening conducted by partner pastors - 35~40 screened per community - 2 Weeks prior to the start of Transform - 120+ questions on a variety of topics - Part-time enumerators (data audited by third party) - Within 4 weeks after the completion of Transform - Matched survey to the ‘Pre’ - For a subset at 6, 9, 12, 16, 18, 24, 36 months after the end of Transform • Approximately 9,500 surveys at the start and end of every ‘batch’ ~ almost 60,000 surveys per year • Gradually leading to strategic research projects
  • 5. Sample Studies conducted by ICM Research • Randomized Controlled Trials (RCT) – Formal vs Informal Values Training – What is the Role of Religiosity in a Poverty Alleviation Program? – How do trainer characteristics affect program outcomes? • Embedded Study using Program & Evaluation Data – How does ‘trust’ in key community leaders (faith or government) affect the outcomes of a child malnutrition intervention? • Social Network Analytics – How do we capture the ‘social dimension’ of a development program?
  • 6. Formal vs Informal Values Training - RCT Design - VHL is the regular Transform Program (formal values training) - HLV is a Transform variant (informal values training)
  • 7. Formal vs Informal Values Training - RCT Results
  • 8. Formal vs Informal Values Training - RCT
  • 9. Formal vs Informal Values Training - RCT
  • 10. What is the Role of Religiosity? - RCT Design • In partnership with Innovations in Poverty Action (IPA) and Yale University • Programs: Feb 2015 to May 2015; Data collection: Jan 2015, Jun-Aug 2015 & Jun 2017 • Locations involved: Negros Occidental, Negros Oriental, General Santos & Koronadal 10 20 HL 20 V only20 Control20 VHL Group B (20 Pastors, 40 Communities) Group A (20 Pastors, 40 Communities) Select 40 Pastors (Each has 2 Communities, Total 80 Communities )
  • 11. 11 • ICM and IPA collected data separately. ICM results: What is the Role of Religiosity? - RCT
  • 12. 12 What is the Role of Religiosity? - RCT
  • 13. 13 Design • In partnership with IPA & Yale University • ICM trainers were randomized to communities instead of regular assignments • Characteristics of interest – Education, duration of employment, age, sex, socioeconomic status, distance from office • ICM trainers were all surveyed in September 2016 • Transform Program will run October 2016 – January 2017 • Data Collection: Sep 2016 & Feb 2017 How do trainer characteristics affect outcomes? - RCT
  • 14. Trust and Malnutrition Intervention – Embedded Study Does Social Capital affect Health Intervention Outcomes? Findings from a site-based child malnutrition intervention in the Philippines Lincoln L Lau1,2, Han Qu1, Melinda Gill1, Donald C Cole2 1. International Care Ministries, Philippines 2. Dalla Lana School of Public Health, University of Toronto 14
  • 15. Trust and Malnutrition Intervention – Embedded Study • ICM implemented a child malnutrition intervention in 2012 and 2013 – Site based program • Age <5 years • Weight for height z-score <2 SD below WHO growth standards • Food: rice based micronutrient fortified soy blended product • All cases registered at the local Rural Health Unit (RHU) • Deworming medication and Vitamin A supplementation provided at start of intervention 15
  • 16. Trust and Malnutrition Intervention – Embedded Study 16 De Silva M, Harpham T. Health & Place 13 (2013) 341-355
  • 17. Trust and Malnutrition Intervention – Embedded Study 17 Site-Based Feeding • 16 weeks of feeding • Children gathered daily at a fixed location • Food prepared and feeding monitored by ICM staff • Anthropometrics at baseline and endline • Weight measured weekly Children admitted into the SBF program n=2658 1403 female (52.8%) Completed program n=2216 (83.4%) Recovered n=2093 (94.4%) Unrecovered n=123 (5.6%) Deaths n=6 (0.2%) Dropout n=436 (16.4%)
  • 18. Trust and Malnutrition Intervention – Embedded Study 18 Model (proportional odds) C1 TLB× geo C2 TPC× geo Coefficient (s.e.) Coefficient (s.e.) (Intercept) 0.32 (0.87) 0.78 (0.90) Family Satisfaction -0.40* (0.19) -0.42* (0.19) Trust Pastor Church -1.07*** (0.29) -1.47*** (0.34) Trust Local Barangay Official 0.99** (0.33) 1.32*** (0.32) geo – dense urban -3.12* (1.59) -3.72* (1.78) geo – rural plain -1.23 (1.37) -2.25 (1.52) geo – coastal -4.57** (1.68) -4.86** (1.55) TLB× dense urban 0.82* (0.41) - TLB× rural plain 0.29 (0.33) - TLB× coastal 1.24** (0.45) - TPC× dense urban - 0.92* (0.43) TPC× rural plain - 0.52 (0.35) TPC× coastal - 1.28** (0.41)
  • 19. Trust and Malnutrition Intervention – Embedded Study 19 • Logistic model (dropout) – Intensity of poverty • Trust in religious leader or pastor [-1.47, p<0.001] • Trust in local barangay [1.32, p<0.001] • Discussion – Limited capacity for ‘horizontal’ social networks? – Complementarity?
  • 20. ICM Social Network Analysis Pilot 20
  • 21. ICM Social Network Analysis Pilot 21 • Social dimension of interventions – Social capital, social cohesion, social connectivity1 – Social networks & social norms2 • Common measurement tools: • Integrated Questionnaire for the Measurement of Social Capital (SC-IQ) – World Bank (2003) • Measuring Social Capital in 5 Communities – P. Bullen & J. Onyx (1998) • Social Support Questionnaire 6 (SSQ6) – Sarason et al. (1987) – Count-based (numbers, means, percentages) 1. Putnam 1995 & Kawachi et al. 2008 2. World Bank Group. World Development Report - Mind, Society, and Behavior. 2015
  • 22. ICM Social Network Analysis Pilot 22 Question: • Can we use Social Network Analytics to quantify the social dimension (baseline or change)?
  • 23. ICM Social Network Analysis Pilot 23 Ultra-poverty - Low income - Poor health - Lack of access to enterprise Extreme-poverty - Increase in income - Improved health - Gain access to enterprise Transform Program Increased Social Capital? Transform’s Theory of Change (theorized) Social capital as a possible mechanism in the pathway to outcomes
  • 24. ICM Social Network Analysis Pilot 24 Longitudinal data • Does this accurately describe what is happening? PRE POST Item n (%) n (%) Percent Change Number Surveyed 5984 NA 5001 NA How much do you trust your neighbors? No Trust 53 1% 23 0% -48% Tentative Trust 130 2% 63 1% -42% Neutral 1720 29% 1159 23% -19% Moderate 2682 45% 2143 43% -4% Very Trusting 1313 22% 1519 30% 38% Score 3.80 NA 3.96 NA 4%
  • 25. ICM Social Network Analysis Pilot 25 • Dynamic Social Network Analysis – Capture the social network at baseline (2 weeks prior) and at endline (2 weeks following completing of the program) – Measurement tool: • Developed in with Petr Matous, The University of Syndey • Similar to the ‘Name Generator’ [Matous et al. Field Methods. (2010)] • ALL members within a community need to be captured (surveyed) – Pilot of 10 communities
  • 26. ICM Social Network Analysis Pilot 26 • 4 Questions:
  • 27. ICM Social Network Analysis Pilot 27 SN1. Do you know _____________ ?
  • 28. ICM Social Network Analysis Pilot 28 SN1. Do you know _____________ ?
  • 29. ICM Social Network Analysis Pilot 29 SN2. Has ____ visited your house OR have you visited ____’s house in the last month ?
  • 30. ICM Social Network Analysis Pilot 30 SN3. Do you know____ well enough to discuss personal health issues?
  • 31. ICM Social Network Analysis Pilot 31 Statistics to describe the social dimension of network #3 PRE POST Item n (%) n (%) % Change Number Surveyed 25 NA 25 NA How much do you trust your neighbors? No Trust 0 0 0 0 NA Tentative Trust 2 0.08 0 0 -100.00% Neutral 9 0.36 8 0.32 -11.11% Moderate 12 0.48 17 0.68 41.67% Very Trusting 2 0.08 0 0 -100.00% Score 3.56 - 3.68 - 3%
  • 32. ICM Social Network Analysis Pilot 32 Statistics to describe the social dimension of network #3 Item PRE POST % Change SN1 - mean indegree/outdegree 10.24 15.28 49.2% SN1 - centralization indegree 0.250 0.248 -0.68% SN2 - mean indegree/outdegree 5.08 7.28 43.31% SN2 - centralization indegree 0.257 0.335 30.44% SN3 - mean indegree/outdegree 4.2 6.92 64.76% SN3 - centralization indegree 0.165 0.264 60.04%
  • 33. ICM Social Network Analysis Pilot 33 • Simple social network analytics – Offers a different picture of social connections and networks • Connectedness (indegree/outdegree or density) • Harmony/Uniformity (centralization or reciprocity) – Can analyze single communities • Also can provide insight on individual participants – Could be a metric to incorporate into logframes and ToCs • Also can have implications on RBM of interventions
  • 35. Role of Research within ICM Operations 35 DATA COLLECTION & QUALITY ACCESS & DATA SYSTEMS DESIGN EVALUATION FRAMEWORK/SURVEYS Research Collaborate to finalize questionnaire and methods Database (Project Tomorrow, Dropbox, Google Drive, etc.) ReportsMonitoring Research Thrive Jumpstart Pre/Post Survey Values Health Livelihood Metrics Organize, hire and schedule: 1) Enumeration & 2) Encoding Managing programs EOYR/EOB Analysis Strat 1/2/3 Program Design Transform Prevail Other Studies
  • 36. Data Collection around Transform 36 Transform (assessing the program) Monitoring (Statistics) Evaluation (Metrics)Monthly Statistics • Target: All participants • Individual level data • Collected by: ICM trainers • Frequency: Weekly per Transform community Medical Statistics • Target: Participants that receive a medical package • Individual level data • Collected by: ICM PHE trainer • Frequency: dependent on package Livelihood Statistics • Target: Participants that receive a Business in a box (BiB) • Collected by: ICM livelihood trainer • Frequency: Daily PRE/POST Survey • Target: All participants • Individual & household level data • Demographic, Education, Social Capital, Recent Illness, Sanitation Practices, Income, Poverty Indicators, etc. • Collected by: Contacted Enumerators • Frequency: Before and after each Transform Learning (Research) Ongoing/Previous Projects • Malnutrition – correlates & predictors of successful treatment • Pregnancy Package – correlates of negative outcomes