moneylender interest rates increase with MFI competition, the evidence would be more consistent with the view that emphasizes the cream skimming effect of MFIs and fixed costs in informal lending.

Using two surveys from Bangladesh, this paper provides evidence on the effects of MFI penetration in the rural credit markets on moneylenders’ interest rates and households’ demand for informal loans. Bangladesh offers an excellent opportunity to understand the long run effects of MFI penetration on informal credit markets, because it is among the most mature MFI markets in the world with almost 40 years of microcredit lending. In 2011, there were 35 million MFI borrowers in Bangladesh with 248 billion taka in outstanding loans (Microfinance Regulatory Authority, Bangladesh Bank, 2009). According to estimates from various available data sources, approximately 40 percent of the households in rural areas are now MFI members (for example, HIES, 2010). We use two rich data sets for the empirical analysis: (i) an exceptionally large cross section data set that includes almost 800 villages in North-Western Bangladesh for the years 2006-2007, collected by the Institute of Microfinance (InM) in Dhaka, and (ii) a panel data set that covers from 2000 to 2007, collected by BIDS-BRAC.<sup>5</sup> The large cross-section data set with almost 800 villages provides adequate power to estimate the effects on village level moneylender interest rate with a measure of confidence, because there is ample variation in the degree of MFI penetration across different villages.

For identification of the effects of higher MFI coverage on the moneylender interest rate in a village, the main challenge is unobserved village-level heterogeneity. When we run an OLS regression of moneylender interest rates on MFI coverage, the estimated effect is likely to be biased, because the MFIs do not choose the location and intensity of credit programs across villages randomly.<sup>6</sup> The MFIs may target relatively well-off (productive) villages to ensure high enough repayment rates to attract or retain donor funding. If repayment is the overriding objective, the OLS estimate might find a spurious positive effect of MFI coverage on moneylender interest rates, driven by a higher aggregate demand for credit due to the higher productive potential of the village. On the other hand, their location choices might be primarily driven by poverty alleviation objectives and we would observe them to expand programs in relatively poor, less productive, and risk-prone villages. Under this alternative case, one might find a zero or even negative effect of MFI coverage on the interest rate in an OLS regression, even if the true causal effect is positive. For the identification of the effects of MFI membership on the demand for moneylender loans (or loans from informal sources, in general) by the households, we also have to worry about self-selection by the households. The MFI participants may be systematically different from the

> 5 The InM survey was led by Baqui Khalily and Abdul Latif, and the BIDS-BRAC surveys by Mahabub Hossain and his collaborators.

> 6 Note that the spatial heterogeneity observed in the MFI activities across villages in Bangladesh is an outcome of MFI choices, donor policy, historical accidents, and path dependence over a period of almost 40 years. This also implies that it may not be feasible to study the long-run effects of MFI competition by randomized interventions across villages.

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