WB- NESDB Workshop October 27, 2006 Rob Chase (EAPVP)

1. 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)

2. 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

3. 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

4. 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

5. 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

6. Thai Social Capital Dimensions:Conceptual & Operational Framework Stock Channel Outcome

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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