Microfinance Banks and Household Access to Finance

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multivariate results confirm that the volume effect is substantially weaker in regions where aretail bank is present.[Insert Table 7 here]The analysis presented in Table 6 and 7 does not take into account that the decision ofbanks to open new branches is endogenous. The positive correlation observed between newProCredit branches and the increase in use of bank account presented in our univariate andmultivariate analysis may thus be biased estimates of the actual volume effect of microfinancebanks. Our empirical concern is that ProCredit Bank may have opened branches in thoseregions which experienced a strong aggregate increase in economic activity and use of retailbank accounts and thus that the observed correlation in Tables 6 and 7 may be due to reversecausality.Note that positive correlation between the aggregate economic potential of a region and theopening of a microfinance bank branch is only partly in line with the location effect predictedin section 2. There we showed that conditional on the presence of a retail bank themicrofinance bank should be more likely to open a branch in locations with a large share oflow-income and moderate-income households. Thus an improvement in economic conditionsin a region should only spur an expansion of the ProCredit branch network if thisimprovement takes place at the bottom end of the income ladder. ProCredit should beparticularly interested in opening new branches where there are many households with verylow, but increasing income levels. By contrast in regions where income improvements areconcentrated among high-income households the microfinance bank should be less likely toopen new branches as these households would be served by ordinary retail banks.To examine the location effect of microfinance banks and to yield unbiased estimates oftheir volume effect we employ a two-stage instrumental variable analysis. In the first stage we21
estimate the location decision of ProCredit Bank. The dependent variable is thus D.ProCreditclose. In the second stage of our IV analysis we examine the volume effect by regressing ourindicator of the change in bank account use (D.Account) on the predicted values ofD.ProCredit close derived from the first stage and individual household characteristics. 7As instruments in the first stage we use the 2010 PSU averages of Low Income, Informal,Female, Muslim as well as the variable Town close as indicators of the potential demand forbank accounts in a region. The averages are calculated for each individual household as theaverage of the respective variable across all other households in the same PSU. The economicrationale behind these instruments follows from our model which suggests that the averagesocioeconomic conditions in a PSU should affect the location decision of the microfinancebank. As we use average PSU indicators for 2010 we account for the expected futureeconomic conditions in a region which should drive the banks location decision between 2006and 2010. At the same time we assume that the average socioeconomic structure of otherhouseholds in a region only indirectly affect the propensity of an individual household in thatPSU to open a bank account via their impact on ProCredit’s location decision.[Insert Table 8 here]Table 8 reports results of our IV estimation which we conduct separately for our twosubsamples of household-pairs: those pairs which are close to a retail bank (columns 1-2) andthose which are not close to a retail bank (columns 3-4). The first-stage results reported incolumn (1) provide mixed support for a location effect. Considering only those locationswhich are close to a retail bank we find that ProCredit is more likely to open a new branch in7 We resort to estimating linear probability models because interpreting marginal effects of interaction terms innon-linear regressions is less straightforward (see Ai and Norton, 2003). Besides, the linear probability modelshould be a fairly good approximation as our interest primarily lies in the effects around the means of thedistributions of our main explanatory variables (see Table 4 which shows that our distance cut-off of fivekilometres is relatively close to the mean of the continuous distance variables).22
Page 1 and 2: Microfinance Banks and Household Ac
Page 4 and 5: stronger increase in the use of ban
Page 6 and 7: and Strøm (2010) examine data for
Page 8 and 9: of this extra interest in terms of
Page 10 and 11: households which have low transacti
Page 13 and 14: 3. Data and Methodology3.1. Life in
Page 15 and 16: anches in our sample increased from
Page 17 and 18: eluctant to use commercial banking
Page 19 and 20: more likely to open new branches in
Page 21: The results presented in Appendix 3
Page 25 and 26: etail bank is already present, ProC
Page 27 and 28: household types with the interactio
Page 29 and 30: investment in microfinance banks se
Page 31 and 32: Table 2. Variable definitions and s
Page 33 and 34: Table 4. Summary statistics of all
Page 35 and 36: Table 6. Volume effect: Univariate
Page 37 and 38: Table 8. Location and volume effect
Page 39 and 40: Table 10. Composition effect: Multi
Page 41 and 42: Appendix 2a. Albania: Bank and hous
Page 43 and 44: Appendix 2c. Macedonia: Bank and ho
Page 45: Appendix 3. Volume effect: Robustne

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