further improving access to deposits. We thus investigate the relationship between access to deposits and bank funding stability separately for countries in different income groups next.

# **6. Income-Group Splits**

Our sample of countries includes 21 HICs, 59MICs, and 15 LICs when we estimate the baseline regression. To investigate whether, and how, the positive impact of access to bank deposits on stability of bank deposit funding varies across different levels of development, we run separate regressions for three subsamples of high, middle, and low income countries in our sample. Note that the high income subsample combines HICs and upper-MICs, the middle income sample contains only MICs, and the low income sample includes a mixture of lower-MICs and LICs. We use overlapping subsamples because of small degrees of freedom for LICs and HICs and to make sure that the three subsamples are balanced in size.<sup>4</sup> Because the subsamples are considerably smaller than the total sample employed in the main regression, we use bootstrapped standard errors to conduct inference for the estimated coefficients of subsample regressions. Bootstrapped standard errors are more attentive to the possible challenges posed by small sample estimation by using the empirical distribution of residuals rather than the assumed empirical one.

Table 2 presents the estimation results for high (column A), middle (column B), and low (column C) income countries focusing on the parsimonious regression results. The estimation methods and approaches explained in Table 1 were analogously applied to the subsamples of high, middle, and low income countries. The parsimonious regressions were again obtained by maximizing the adjusted R-squared.

## **_6.1. High Income Subsample (HIsub)_**

For the subsample of high income (HIsub) countries (Table 2, column A), the parsimonious regression explains around 50 percent of the variation in the dependent variable, which is almost twice as high explanatory power as that of the full regression for all countries. The access to deposit variables are significant at 10 percent level for all regressions including Honohan(2008) and when we use D-K&K(2012), with both coefficient estimates slightly lower than those for the full sample of countries (Table 1). When using D-K&K(2012), we also observe that, not only is the coefficient estimated more precisely than that of Honohan(2008), but its magnitude is about twice as large. This could point to greater ability of the demand side data to measure financial inclusion in deposits that is relevant for stability of bank deposit funding. A 10 percent increase in access to bank deposits can thus mitigate declines in deposit growth during times of financial

> 4 We have also run regressions merging HICs and LICs into one subs-sample to verify some of our findings and conjectures. Because of its diversity, this merged subsample was only used to illustrate the contrasting environment of MICs that are becoming financially developed and integrated with the rest of the world, but at the same time the trust in banks is being still tested and access to deposits needs further improvements. These estimation results concur with our hypothesis that MICs can benefit most from the synergetic effect that reforms to support financial inclusion in deposits can have on banking sector stability. The results are available upon request.

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