nonparticipants in the same village in terms of unobserved characteristics such as entrepreneurial ability and risk aversion. The unobserved village and household level heterogeneity can bias the estimated effects of MFI competition on moneylender interest rate, and on household’s demand for informal loans, and it is not in general possible to pin down the directions of such bias from theoretical reasoning alone.

A standard approach to tackling the omitted variables bias is to design an instrumental variables strategy. However, it is extremely difficult, if not impossible, to find credible exclusion restrictions required for the instrumental variables approach in our application, and there has been increasing skepticism about the validity of the exclusion restrictions imposed in many related contexts. We thus take advantage of advances in econometrics that provide alternative ways to address omitted variables bias without imposing exclusion restrictions; in particular, we implement the minimum-biased (MB) propensity score reweighting estimator proposed by Millimet and Tchernis (forthcoming), and the heteroskedasticity based identification scheme developed by Klein and Vella (2009a). While the propensity score reweighting estimators (e.g., IPW) rely on the conditional independence assumption (CIA), the MB estimator is attractive because it minimizes the bias arising from possible violation of the CIA due to selection on unobservables. Building on an insight originally due to Wright (1928), heteroskedasticity based identification approach was developed in a series of papers by Rigobon (2003), Klein and Vella (2009a, 2010) and Lewbel (2012). The intuition behind heteroskedasticity-based identification is that when there is substantial heteroskedasticity in the treatment equation, the changing variance in the residual acts as a “probabilistic shifter” of the treatment status, similar to the shifts induced by a standard instrumental variable satisfying exclusion restriction (for an excellent discussion, see Rigobon (2003)).<sup>7</sup> The observed heteroskedasticity in the treatment equation in our application has clear theoretical foundations; the heteroskedasticity results from interactions between fixed costs in establishing a new branch and private information of loan officers on potential borrowers. For the household level analysis, we exploit a two-round panel data set spanning seven years, and combine a difference-in-difference model with household fixed effects (DID-FE), and then implement different estimators including matching and MB estimator in the DID-FE model.<sup>8</sup> Our main findings are as follows. The evidence strongly suggests that penetration of microfinance in a village increases the moneylender interest rate when the MFI coverage is high enough. At low levels of MFI coverage, there is no perceptible effect on the moneylender interest rate. The ‘proponent’s view’ that competition from MFIs brings down the ‘exploitative’ interest rates thus seems to be contradicted.

> 7 For recent applications of heteroskedasticity based identification,  see Rigobon (2003), Rigobon and Rodrik (2005), Maurer et al. (2012), Klein and Vella (2009b), Farre et al. (2012, 2013), Schroeder (2010), Gilchrist and Zakrajsek (2012), Emran and Hou (2013), and Emran and Shilpi (2012), Emran et al. (forthcoming), among others.

> 8 For discussions on the advantages of combining matching with a DID design, see Heckman et al. (1998) and Blundell and Costa-Dias (2009).

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