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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). At request of Khun Paiboon Wattanasiritham Advisory Board: Dr. Maitree Wasuntiwonse

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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
At request of Khun Paiboon Wattanasiritham

Advisory Board:

Dr. Maitree Wasuntiwonse

Dr. Priyanut Piboolsravut, NESDB

Khun Vichol Manutausiri, MOI

Prof. Anuchart Poungsamlee, Mahidol Univ.

Khun Jirawan Boopem, NSO

Principal Investigators:

Assoc. Prof. Dr. Napaporn Havanon,

Dr. Maniemai Thongyou

Dr. Numchai Supererkchaisakul

World Bank Team

Gillian Brown

Rob Chase

Rikke Nording

Pamornrat Tangsanguanwong

Social Capital Study Advisors & Contributors
overview
Overview
  • “Community Driven Development (CDD) builds social capital”
    • Thai experience contributes to regional “Flagship”
    • Social capital dimensions in context
    • Separate selection and impact effects
  • Mixed method evaluation
    • Quantitative: propensity score matching
    • Qualitative: structured interviews to answer “why?”
  • Results
    • Picking villages with some strong social capital characteristics
    • Strengthening some social capital dimensions
research contributes to regional east asia cdd flagship study
Research contributes to regional “East Asia CDD Flagship” Study
  • 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
Thai Social Capital Evaluation: Goals
  • 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
separating selection impact effects
Separating Selection & Impact Effects
  • Selection Effect: “Communities with ex-ante higher social capital participate more readily in CDD operations”
  • Impact Effect:

“The experience of participating in a CDD operation builds social capital”

Social Capital

Yi

Impact

Selection

T0

T1

Time

mixed evaluative methodology
Mixed Evaluative Methodology
  • Lack of adequate baseline: ex-post evaluation
    • Most likely case among development operations
  • Quantitative:
    • 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:
    • Augment matching within provinces
    • Conduct structured interviews
    • Understand social capital dimensions
    • Explore how SIF may have changed community SK
propensity score matching
Propensity Score Matching
  • Data source: Thailand SES 1998 and 2000
  • Sample characteristics:
    • 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 matching1
Propensity Score Matching

Figure 1. Pre-match Kernel Densities of participation propensity

Figure 2a. Post-match Kernel Densities of participation propensity (Nearest neighbor)

Figure 2. Post-match Kernel Densities of participation propensity (6 nearest neighbor within provinces)

O SIF Villages

∆ Matched control villages

O SIF Villages

∆ Matched control villages

O SIF Villages

∆ Non SIF Villages

O SIF Villages ∆ Matched control villages

qualitative field work
Qualitative Field Work

Challenge: Capturing qualitative information from 144 villages so that the analysis was manageable and the findings robust

  • 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:
    • 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
Results: Differences in Means
  • 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
Results: OLS Regressions
  • 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:
    • 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:

Long-standing characteristics

Higher trust

Cooperation & collective action

Norms of self-sacrifice

Impact:

Evidence of recent change

Build networks across villages

Reinforce norm of collective action

Build leadership

Results:Field Researcher’s Debriefing
thailand social capital implications
Thailand Social Capital Implications
  • 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
East Asia CDD Flagship Implications
  • 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