A listing of the variables used in estimating the propensity scores for 2004 is given in Table 3 (except for the provincial dummy variables). The first thing to note is that on average VRF borrowers are substantially poorer than those who do not borrow from the VRF, whether measured by monthly expenditure per capita (2,549 baht vs. 4,286 baht) or income per capita (3,209 baht vs. 6,088 baht), or by access to subsidized medical care (93% have a 30 baht medical card, vs. 77% for non-borrowers). Compared to non-borrowers, those who borrow from the VRF are more than twice as likely to be farmers and to be self-employed, they are more likely to live in the Northeast region, they have larger families, and there are more earners per household. The important point here is that borrowers differ appreciably from nonborrowers, at least unless one conditions on the covariates.

The estimate of the probit propensity score equation for the full sample is also shown in Table 3. The equation fits well enough and, as noted above, appears to be adequately balanced. One of the more influential variables is the inverse of the number of households per village (or block): The Thai Village Fund initially provided a fixed amount to every village, irrespective of size, which means that households living in a large village are less likely to have access to these loans than those in a small village. This effect shows clearly in the estimates of the propensity score equation reported in Table 3.

# Basic Results

Given the propensity scores, it is then possible to match each treatment case with a nearby comparison case, and hence to estimate the impact of VRF borrowing. The results are summarized in Table 4; the upper half of the table refers to 2004 (with separate propensity score equations for the full sample, for rural households only, and for the panel), and the bottom half to 2002.

When propensity score matching is used with the full sample of households surveyed in 2004, VRF borrowing is associated with a statistically significant 3.3% more expenditure per capita and a not-quitesignificant 1.9% higher income per capita. Translated into average increases (at the mean) this implies a rise in per capita spending of 84 baht per month and of income of 61 baht per month. A reasonable interpretation is that VRF loans are partly, but not exclusively, functioning as consumer credit; they also appear to be working through the effect on income. The results based on the 2002 data are comparable: VRF borrowing is associated with a 3.1% rise in income (t=1.90) and a 2.6% rise in expenditure (t=2.15). To put these numbers into perspective, the mean size of a VRF loan was 16,183 baht (Table 2), and mean monthly income per person was 4,987 baht (Table 1) in 2004.

The increases reported in Table 4 are plausible. The boost to income in 2004 represents an annualized rate of return of 4.5% on the amount borrowed (which averaged 16,183 baht). However, these effects are only found when expenditure (or income) per capita is shown in log form; when measured in levels, the VRF has no statistically significant impact in these cases. The use of the log of income (rather than _Village Fund, draft of June 29, 2009_ _Page 11 of 34_