Does the Village Fund Matter in Thailand?

1. Does the Village Fund Matter in Thailand? Regional Seminar on Poverty Monitoring and Evaluation Nanchang, China, May 11-12, 2007 Presenter: Shahid Khandker, World Bank

2. In 2001 the VRF was introduced in Thailand • Purpose was: • To provide 1 million Baht ($22,500) to every village and urban community • Stimulate local economy by targeting new activities • A total injection of $1.75 billion in 74,000 villages & 4,500 urban communities • By May 2005, the VRF disbursed $6.9 billion to 17.8 million borrowers (an average loan size of $387) • It is the largest micro-credit scheme • Thailand has also an extensive network of rural lending by BAAC

3. How does VRF work? • Village funds are locally run and have some discretion in setting interest rates, maximum loan amounts, and terms of loans. • Village Funds are a revolving fund managed by village fund committees who do not handle money directly. • Funds are handled by a number of intermediaries such as Government Savings Bank (GSB) mostly in urban areas, and BAAC in rural and semi-urban areas. • The village first sets up a local committee and gets it registered and opens an account with the intermediary. The government then deposits a million baht into the account. • By May 2005, 99.1% of all villages had a VRF in operation and 98.3% of the originally scheduled amount had been distributed.

4. Scope of the VRF impact study • What is the impact of VRF on participants? • Who benefits more from the VRF program and in what way? • Given the presence of BAAC, what additional role VRF played in Thailand? • What lessons to be learned from this experiment?

5. Data– SES 2004 • 34,843 households were interviewed from 2,044 municipalities and 1,596 villages • One sixth of 69,486 adult members borrowed from VRF at least once with higher proportion among the poor and rural areas • Interest rate was about 6 percent • Equal share (50%) of VRF loan in agriculture and non-agriculture • Loan default was about 8 percent • Some 70% among borrowers believed VRF helped improve their economic situation

6. Propensity-score Matching (PSM) Propensity-score matching match on the basis of the probability of participation. • Ideally we would match on the entire vector X of observed characteristics. However, this is practically impossible. X could be huge. • Rosenbaum and Rubin: match on the basis of the propensity score = • This assumes that participation is independent of outcomes given X. If no bias given X then no bias given P(X).

7. Steps in Score Matching • SES 2004 is representative, highly comparable, surveys of the non-participants and participants; • (ii)Estimate a logit/ probit model of program participation: • Predicted values are “propensity scores.” • (iii)Restrict samples to assure common support; • (iv) Failure of common support is an important • source of bias in observational studies • (Heckman et al.).

8. Density of scores for non-participants Density of scores for participants

9. (v) For each participant find a sample of non-participants that have similar propensity scores; (vi) Compare the outcome indicators. The difference is the estimate of the gain due to the program for that observation; (vii) Calculate the mean of these individual gains to obtain the average overall gain. -Various weighting schemes.

10. Summary of Propensity Score Matching results

11. Propensity score results • VRF borrowers are substantially poorer than non-borrowers • Borrowers are likely to be farmers, self-employed, come from North-West Region, have large family size and more earners per household • Given fixed amount of VRF per village, households living in a larger village are less likely to receive a VRF loan than those living in a small village • Education plays a positive role in obtaining VRF loans and participation

12. Average treatment effects of VRF participation • VRF borrowing helps increase per capita expenditure by 5.5% and per capita income by 4.4% • An average increase of income by 220 baht per month and expenditure by 200 baht • This rise in income implies a 17.6 percent rate of return to borrowing • Rates of increase in income and consumption are more for the poor than for the non-poor • Much of the effects are via farm income • Farm income rises by 40 percent compared to 9 percent in non-farm income • VRF is not a consumer credit nor a conduit necessarily for non-farm income growth • Our results are consistent with other findings based on panel of 960 households from rural Thailand (Kaboski and Townsend)

13. BAAC versus VRF • Of households covered in 2004, • ---23% borrowed from VRF only • ---15% from both VRF and BAAC • ---6% from BAAC only • Those who borrow BAAC are slightly poorer and substantially dependent on farm income • Borrowing from either BAAC or VRF only has small gains compared to borrowing from both sources • BAAC plus VRF raises expenditure by 9 percent and income by 8.5 percent. • Many BAAC households are likely to be credit constrained and thus, VRF plays an important complementary role

14. Means for BAAC vs. VRF

15. Propensity Score Matching Results for BAAC vs. VRF

16. Conclusions • No method is perfect • Alternative methods are desired to verify results • Difference-in-difference method to be applied using panel data • Instrumental variable method can also be applied • Results seem to be conclusive that VRF matters in Thailand, helps the poor more, and works more for rural than urban areas.