controls. The OLS estimate, reported in column (1) of Table 1, shows a statistically significant and positive correlation. This positive ‘effect’, however, could result from common unobserved village characteristics. If better infrastructure and higher productivity of a village lead to both a higher informal interest rate and better coverage of MFIs, then one would expect this correlation to weaken when we add controls for village productivity and infrastructure.

In the next specification, we add several controls for village productivity and risk characteristics which can also potentially affect MFI placement. Access to markets and other services is measured by average distances to bazar (market), bus stop and secondary school. Distance to formal bank branch is introduced to capture potential competition from and linkages to the formal financial sector (Bell (1993)). Irrigation increases productivity and reduces risk of agricultural production, affecting both risk and returns in the credit market. Accordingly, we include percentage of households using irrigation as a control. We also include the number of households surveyed in a village as a scale variable. Vulnerable Group Development (VGD) is a major public safety net program targeting the poor in Bangladesh; many NGOs also use the VGD cards as an indicator of moderate poverty. For example, the BRAC excludes a household from its ultra-poor program (CFPR/TUP) if it has a VGD card. We use percentage of households with VGD cards as an indicator of moderate poverty in the village.<sup>26</sup> Land ownership is used by most of the MFIs as a salient selection criterion. While many MFIs including BRAC, Grameen Bank, and BRDB in principle lend only to households owning less than 50 decimal of land, mis-targeting due to both type 1 and type 2 errors is not uncommon. In particular, the evidence indicates that landless (owning less than 10 decimal of land) are largely excluded from the standard MFI lending programs. Thus the landless constitutes an important clientele of moneylenders. We include the percentage of landless (less than 10 decimal) in the village to capture this effect. When these controls are added to the specification, the results (column 2) indicate a much _larger_ effect of MFI coverage in the interest rate regression. In columns (3) and (4), we add district and upazilla fixed effects as catch-all controls for time-invariant unobserved village heterogeneity respectively. The coefficient of MFI coverage becomes slightly larger in column (4) compared with column (1). Both estimates (columns (3) and (4)) are statistically significant at the 1 percent level. What is striking though is the fact that instead of weakening, the partial correlation between informal interest rate and MFI coverage has become numerically and statistically more significant when village productivity controls are added. This suggests that, in our application, MFI location choices are driven largely by poverty alleviation objectives, and thus OLS coefficients are likely to be biased downward.

> 26 We emphasize again here that we are using VGD card instead of a village level “poverty line” to define the extent of moderate poverty, because the MFI loan officers do not rely on village level poverty line estimates (if available).

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