Next, we compare MFI borrowers to those with other types of loans and report results in table 4. The three categories that show most significant and robust results are durables, home repairs, and temptation goods (all are significant at 1% and are of relatively large magnitude). Expenditures on festivals also show significant increase but of smaller magnitude. Interestingly, health expenditures are negative in all estimations, but only significant in naïve models. The total expenditures are not significant, as are nondurables and education.

To summarize, the comparison of MFI borrowers to those without loans and those borrowing from other sources yields some of the same results: increased durable purchases and home repairs are similarly significant in both cases, while the differences in festivals and temptation goods is slightly weaker when comparing MFI borrowers with other types of loans. These results make sense since MFI loans are often obtained to buy small durable goods, like an appliance (e.g., sewing machine, refrigerator, etc.), or can be used for small home repairs. In addition, MFI loans obtained for small business purposes are also likely to result in durables increase and home improvement. Health expenditures, however, show different patterns for these two comparison groups and a clear increase is only observable in the case of MFI borrowers versus nonborrowers.<sup>20</sup> The magnitudes of the effects appear large. We base our discussion on PSM estimates that we believe provide more conservative and better controlled estimates for results. Since our outcome variables are in log form, the estimated coefficients show a percentage increase in the variable. Thus, we find that durable goods purchases increase on average by 42% compared to those without loans and 41% compared to those with informal loans. The home repairs are increased by 90% in a group of MFI borrowers compared with nonborrowers, but only increased by about 50% compared to those with other informal loans. Festivals expenditures are increased by about 35% comparing to those without loans but only by about 20% relative to those with other sources of credit. Temptation goods expenditures also increase more relative to no loan group—by about 60%—while relative to other borrowers the increase is only about 30%.

Two points are worth noting here. First, the increase in magnitude is larger in comparing MFI borrowers to nonborrowers versus those borrowing from other sources. This is consistent with our argument that the two estimates (i.e., group with no loans and group with other loans) can be interpreted as the lower and upper bounds on the true effect because of the omitted variable bias due to unobservable entrepreneurial spirit or “spunk.” Second, while these appear as large numbers, recall that the categories increased are a relatively small percentage of total expenditures (e.g., durables, temptation goods).

Finally, we compare the magnitude of expenditure increase produced by our estimates to the average loan size. Appendix table A2 shows the average loan amounts for different categories of borrowers. For example, the median (mean) amount of outstanding Spandana or other MFI loan is Rs 10,000 (9,759). The amounts vary slightly with the stated loan purpose but are generally in the same range. It is clear that the MFI loan amounts are constrained by the maximum loan ceiling of Rs 10,000, which was the institutional constraint from Spandana. Thus, the non-MFI loan amounts are generally larger than MFI loans for various categories of expenditures. The difference is the largest for starting a new business: while the MFI loans are in the Rs 10,000 ballpark, the non-MFI loans are, on average, Rs 41,000 and median of Rs 18,000.

Next, we use the estimates from the PSM model and calculate the average change in annual expenditures implied by our estimates. Table A3 shows that the total increase in expenditures among the statistically significant categories adds up to about Rs 5,250. The difference with the average loan amounts is likely due to the other categories of expenditures that are not statistically

> 20. This could indicate reverse causality (i.e., those with a health crisis may turn to the MFIs for credit).

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