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

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  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  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 StockChannelOutcome

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” Time Social Capital Y i T0T0 T1T1 Selection Impact

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  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 Matching Figure 1. Pre-match Kernel Densities of participation propensity O SIF Villages ∆ Non SIF Villages O SIF Villages ∆ Matched control villages O SIF Villages ∆ Matched control villages O SIF Villages ∆ Matched control villages Figure 2. Post-match Kernel Densities of participation propensity (6 nearest neighbor within provinces) Figure 2a. Post-match Kernel Densities of participation propensity (Nearest neighbor)

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  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  Y N = 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

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  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