Transcript Document 7253642

Building Local Social Capital?
The Impact of
the Thai Social Investment Fund
and its contribution to regional learning
WB- NESDB Workshop
October 27, 2006
Rob Chase (EAPVP)
Social Capital Study
Advisors & Contributors
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At request of Khun
Paiboon
Wattanasiritham
Advisory Board:
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Dr. Maitree
Wasuntiwonse
Dr. Priyanut
Piboolsravut, NESDB
Khun Vichol
Manutausiri, MOI
Prof. Anuchart
Poungsamlee,
Mahidol Univ.
Khun Jirawan
Boopem, NSO
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Principal
Investigators:
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Assoc. Prof. Dr.
Napaporn Havanon,
Dr. Maniemai
Thongyou
Dr. Numchai
Supererkchaisakul
World Bank Team
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Gillian Brown
Rob Chase
Rikke Nording
Pamornrat
Tangsanguanwong
Overview
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“Community Driven Development (CDD)
builds social capital”
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Mixed method evaluation
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Thai experience contributes to regional
“Flagship”
Social capital dimensions in context
Separate selection and impact effects
Quantitative: propensity score matching
Qualitative: structured interviews to answer
“why?”
Results
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Picking villages with some strong social capital
characteristics
Strengthening some social capital dimensions
Research contributes to regional “East
Asia CDD Flagship” Study
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CDD hypotheses from available
data
1. CDD can reach poor communities
2. CDD involves communities in decision-making
and implementation
3. CDD delivers infrastructure in a cost-effective,
quality manner
4. CDD promotes systems for O&M that lead to
sustainable service delivery
5. CDD increase incomes of participant communities
6. CDD improve the dynamics of how
communities interact with local
government
Thai Social Capital Evaluation:
Goals
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Understand how social capital
operates in Thailand
Isolate effects of SIF on
communities, particularly with
regard to sustained changes in
social capital
Identify promising practical
approaches to enhance Thai social
capital
Thai Social Capital Dimensions:
Conceptual & Operational Framework
Stock
Channel
Solidarity
and Trust
Outcome
Social Cohesion
Cooperation and
Collective Action
.
Group and
Organization
.
Information
Sharing and
Communication
.
Network and
Linkages
Empowerment
Separating
Selection & Impact Effects
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Selection Effect:
“Communities with ex- Social Capital
ante higher social
Yi
capital participate more
readily in CDD
operations”
Impact Effect:
“The experience of
participating in a CDD
operation builds social
T0
capital”
Impact
Selection
T1
Time
Mixed Evaluative Methodology
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Lack of adequate baseline: ex-post evaluation
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Quantitative:
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Most likely case among development operations
Existing high-quality household data from before SIF
started: synthetic baseline from SES 1998
Match treatment and control communities within
provinces based on propensity score matching
Analyze scores derived from qualitative information
Qualitative:
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Augment matching within provinces
Conduct structured interviews
Understand social capital dimensions
Explore how SIF may have changed community SK
Propensity Score Matching
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Data source: Thailand SES 1998 and 2000
Sample characteristics:
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201 SIF villages (10% of the total villages)
SIF villages: More education, larger households, but
lower per capita expenditure
Propensity function variables (e.g., mean age,
education, assets, children, earnings)
Match 164 SIF villages with 6 nearest neighbors
within provinces
Thai research team selected 72 SIF treatment
villages and 72 matched comparison villages
Propensity Score Matching
Figure 1. Pre-match Kernel
Densities of participation
propensity
SIF Vil ages
Figure 2a. Post-match
Kernel Densities of
participation propensity
(Nearest neighbor)
Figure 2. Post-match
Kernel Densities of
participation propensity (6
nearest neighbor within
SIF Villages
Control Villages
provinces)
Control Vil ages
SIF Vil ages
Matched Control Vil ages
10.0645
4.74254
2.46848
O
O
SIF Villages
0
1.04508
SIF Villages
∆ Matched control
SIFvillages
Villages ∆ Matched
control villages
O
.00171
.015357
Pr(sif )
O
∆ Matched control
villages
∆ Non SIF Villages
-.053342
SIF Villages
-.053342
1.04508
Pr(sif )
-.053342
1.04508
Pr(sif )
Qualitative Field Work
Challenge: Capturing qualitative information from
144 villages so that the analysis was manageable
and the findings robust
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Selecting best match from six matching villages
Teams of three researchers spent several days in each
village
12 – 15 key informant and villager interviews in each
village
Subjectivity reduced by:
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Team members from different backgrounds
Workshops and training to reach common understanding
Anchoring vignettes
Individual interviewers scoring, checking consistency of
scores
Validation by six key informants in each village
Workshops during and after fieldwork to validate,
provide context, and interpret findings
Results:
Differences in Means
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Means between treatment and comparison
villages different to statistically significant degree
for 19 variables
Networks and linkages**
Solidarity: self-sacrifice for common benefits
Leadership: Diverse leadership capability
Capacity for organizational learning
Diversity of collective action
Tolerance of differences (negative)
Empowerment: effectiveness of villagers voice
Ability to sustain development achievements
Results:
OLS Regressions
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YN = α + β SES + γ SIF + ε
SES variables: mean expenditure, variance
expenditure, share of workers in agriculture, own
farm land, years of education
Robust differences from SIF participation:
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Networks and linkages **, self-sacrifice,
organizational leadership and learning, collective
action, villager’s voice, multi-party activity,
sustainability,
Organizational capacity, information availability
Interesting additional finding
+ Positive effect of share of workers in agriculture
+ Negative effect of share owning land
 Higher social capital among landless farm workers
Results:
Field Researcher’s Debriefing
Selection:
Impact:
Long-standing
characteristics
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Higher trust
Cooperation &
collective action
Norms of selfsacrifice
Evidence of recent
change
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Build networks
across villages
Reinforce norm of
collective action
Build leadership
Thailand Social Capital Implications
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Thai SIF selected poor villages with strong trust,
cooperation, and leadership characteristics
Some forms of social capital (trust, cooperation,
norms) are long-standing, inherent village
characteristics that are difficult to influence
Others (information flow, networking between
groups, local leadership) can be supported by
community driven project intervention
Social capital empowers communities and helps
them access and sustain development
Support for “bottom-up” efforts to improve
demand for effective local government services
reinforce “top-down” efforts to improve supply of
local government capacity
East Asia CDD Flagship Implications
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Poverty mapping techniques allow careful CDD
targeting to poor areas
With sufficient facilitation, CDD involves broad
participation, including disadvantaged groups
CDD delivers small scale infrastructure at
significant savings with acceptable quality
CDD approaches that link to local government
demonstrate better operations and maintenance
CDD demonstrate impressive returns to income
(economic internal rate of return)
CDD can increase transparency of
information, capacity of local associations,
and citizen’s influence over decision-making