Propaganda y publicidad

Table 6.7 shows the results when we use the Heckman (1978) two-stage treatment effect procedure that includes the Inverse Mill ratio for all the five different capital structure measures. The Wald chi-square test indicates that we cannot leave all explanatory variables out of our regression specifications. The Inverse Mill’s ratio denoted as Lambda is not significant in any of the regressions except in the borrowing model at 10% significant level, The Inverse Mills ratio tells us that our estimations does not suffer from any sample selection bias. The results in both sections show that the correction for self-selection bias does not significantly alter our earlier results as the sign of the coefficients are almost similar, but the significant results are fewer in this case.

Results for the Heckman (1978) approach in Table 6.7 shows that female director on MFIs board are not significantly related with leverage. Although the sign of the coefficient is positive, the result is consistent with the findings of Matsa and Miller (2013) who find no change in firm leverage after the introduction of female boardroom representation quota system in Norway. This means that risk aversion may not be a distinctive part of women approach to corporate decision making. By contrast, Berger et al (2014) find that an increase in the proportion female bank directors result in increased portfolio risk.

After splitting leverage into borrowings and deposits, findings suggest that female board of directors have positive but not statistically significant relationship with borrowing. We find a significant positive relationship between female directors and deposits. This result is consistent with earlier result in Table 6.5. With this result, we can forward the argument that female directors attract more deposits to MFIs due to the better match between female leadership and its female clients. Lending credence to matching or sorting argument proposed in Becker’s (1973) model for marriage market. Becker gave several examples of matching such as the “optimal sorting of more informed customers and more honest shopkeepers”. For instance,

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Ghatak (2000) applies Becker’s model in microfinance settings and show how good borrowers are matched in a group lending scheme. This finding helps improve our understanding on the match between female directors and MFI clients.

The study also finds that female directors are not significantly associated with equity capital, but the coefficients are negative and consistent with previous results in Table 6.5. Thus, it is observed that female directors are significantly and positively related to subsidies at 10% significant level. MFIs that have one or more female directors on board are having 1% increase in subsidised funding than those without. This result is consistent with Pfeffer and Salancik (1978) argument that board of directors seek to link organizations to the most beneficial resources in their external environment. The finding supports such arguments in the sense that female directors seem to play a networking role between they MFIs board and other external organizations that provide them with the most beneficial resources source of funding in the microfinance sector, in this case subsidies.

The results on the control variables are consistent with previous findings that suggest firms with larger boards tend to be more leveraged (Jensen, 1986; Wen et al., 2002; and Alvez et al., 2015). Furthermore, board size is not associated with subsidies. Larger boards are associated with high deposits and less equity. Another important MFI-level control variable is the ROA. The result suggests that there is no relationship between ROA and all the capital structure measures. However, the sign of the coefficients is similar with previous results.

130 Table 6.7

Results of a two-stage regressions on female directors and capital structure

Variables LEV BORR DEP EQUI SUBS

D_female director 0.017 0.001 0.016** -0.014 0.013* (1.28) (0.09) (2.49) (-1.11) (1.69) MFI controls ROA -0.309 -0.271 0.019 0.310 -0.282 (-1.52) (-1.07) (0.56) (1.55) (-1.36) Board size 0.034** -0.021 0.019*** -0.031** -0.008 (2.36) (-1.62) (2.88) (-2.24) (-0.80) Size 0.034*** 0.008 0.017*** -0.035*** -0.012*** (4.77) (1.17) (4.71) (-4.66) (-3.58) Age -0.010 0.029 -0.012 -0.007 0.002 (-0.50) (1.36) (-1.20) (-0.44) (0.15) Risk 0.007 0.009 -0.061** 0.001 0.001 (0.13) (0.14) (-2.44) (0.02) (0.02) County controls Regulation 0.063*** -0.152*** 0.238*** -0.068*** -0.038** (2.67) (-5.63) (10.45) (-2.91) (-2.33) GDP 0.038 0.144 0.105 -0.041 -0.214 (0.28) (0.78) (1.42) (-0.32) (-0.72) HDI -0.089 -0.214 -0.181 0.080 -0.061 (-0.58) (-1.15) (-1.26) (0.53) (-0.50) Lambda -0.134 0.411* -0.023 0.030 0.049 (-0.63) (1.72) (-0.23) (0.14) (0.32) CONSTANT 0.284* 0.445* -0.154 0.780*** 0.244** (1.72) (1.71) (-0.91) (4.86) (1.99)

Regional Dummies Included Included Included Included Included

Time Dummies Included Included Included Included Included

Obs 1423 1448 1400 1457 1071

Adjusted 𝑅2 _{0.202 0.193 0.358 0.214 0.155}

Wald 𝑥2 _{135.10*** 159.42*** 327.62*** 158.76*** 94.34***}

Instruments: fitted probabilities from a probit explaining binary variable for female director. Capital structure in terms of Leverage, Borrowings, Deposits, Equity, subsidies regressed on female directorship, MFI and country controls using IV. As instruments, fitted probabilities from probit analysis explaining the dummy female director have been used in the dummy endogenous variable model of Heckman (1978). Significance levels based on heteroskedastic and autocorrelation-corrected standard errors clustered at the MFI level. ***,**,* indicates that the coefficient estimates are significantly different from zero at 1%, 5%, 10% levels.

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Prudential regulations have negative impact on borrowings, equity and subsidies, but has a positive effect on deposits. Being regulated enables, MFIs to diversify their funding choices and thus reduce their dependence on subsidies and grants. The study also finds a significant positive relationship between regulation and both leverage and deposits. Regulation in this sense helps to protect depositors and restrict MFIs from excessive borrowing. The asset size of MFIs is positively and significantly related to leverage and deposits and negatively related to equity and subsidies. This evidence is consistent with previous studies (Rajan and Zingales, 1995), which suggests that larger MFIs with big asset size can easily leverage their assets compared to smaller MFIs with small asset size that mostly depend on grants and subsidies. The country effects are neither surprising in view of other capital structure evidences.