stress by two to six percentage points in HIsub countries. Recall that, on average, HICs faced a decline in deposit growth of about 17 percentage points during the 2008 crisis (Figure 4). The most important control variables include the level of economic development (gnipc), macroeconomic stability (inflation), explicit deposit insurance (depins), banking sector stability (z-score).

Namely, more developed countries seem to have experienced a greater drop in deposit grown during the 2008 crisis consistently with the baseline results for the full sample. In contrast to the baseline result, macro-financial stability, as measured by the level of inflation, seems to have played an important role in mitigating declines in bank deposit growth. As we will see later, inflation, and thus macro-financial stability, was not a significant mitigating factor for MICs and LICs. It seems that inflation differentiates better the macro-financial conditions across HICs and upper-MICs, while the banking crisis dummy takes over this role for MICs and LICs. Further, explicit deposit insurance is consistently significant at the 10 percent level, suggesting that it can reduce declines in bank deposit growth by 8 to 16 percentage points. As we will see later this effect is larger than for MICs and also for LICs where it does not have a significant impact on propping bank deposit growth. Banking sector stability (z-score) is consistently the most significant variable of the regression for HIsub substantially mitigating the possibility of deposit withdrawals and reversals in deposit growth. In addition, greater exposure to external financing (loantodeposit) reduces declines in deposit growth, although it is not significant at common levels across all estimation methods. We will return to the economic interpretation of this effect when discussing the results for MICs for which this effect is more important.

# **_6.2. Middle Income Subsample (MIsub)_**

For MIsub (Table 2, column B), the parsimonious regression can explain around 50 percent of the variation in the dependent variable, with some indication that the fit of the regression could be even better than for HIsub when accounting for outliers (see the “Robust” column). It could thus appear that our model may be best fitted to MICs in which case the explanatory power of the variables on access to deposits could be also more tested. Concerning our main variable of interest, both access to deposit variables, Honohan(2008) and D-K&K(2012), are consistently significant with a coefficient magnitude similar to coefficient estimates from the baseline regression; reemphasizing possibly higher relevance of our model to MICs. Interestingly, the coefficient magnitude of D-K&K(2012) is notably higher than the coefficient magnitude of Honohan(2008). Assuming that D-K&K(2012) measures the _actual_ use of bank deposits whereas Honohan(2008) measures only access to deposits, the results could indicate that, in MICs, the actual use of deposits can deliver a more beneficial effect on the resilience of bank deposit funding in times of financial stress, compared with just access to deposits and their _potential_ use by people. A 10 percent increase in access to bank deposits can thus reduce declines in deposit growth during times of financial stress by three to seven percentage points in MICs. Recall that, on average, MICs faced declines in deposit growth of about 20 percentage points during the 2008 14