reflects the findings of existing research and policy practice<sup>7</sup> as well as an effort to achieve broad country coverage.

The first dimension, financial resilience, is measured using proxies for solvency of financial institutions, their liquidity positions, and exposure to credit risk. The first sub-category is quantified by using the capital ratio (percentage of capital in total assets) and the Z-Score. Both measures gauge the solvency of national banking systems. The capital ratio is a well-known measure that, as suggested for example by the Basel Committee on Banking Supervision (2010) is linked negatively to the probability of occurrence and the severity of distress. The Z-Score is a more comprehensive measure that combines information on leverage (equity to assets) with performance (return on assets) and risk (standard deviation of return on assets) to more fully approximate the likelihood of insolvency in the banking sector (Mare et al., 2015).<sup>8</sup> Liquidity standards are also associated with the reduction in the probability of crises (Basel Committee on Banking Supervision, 2010). Therefore, in the second sub-category, we quantify the exposure to liquidity risk by examining the risks associated with a mismatch between assets and liabilities. Specifically, to capture the liquidity risk exposure, we use the ratio of credit provisioning to bank deposits and the ratio of the most liquid assets to short term funding (Bologna, 2015). The third sub-category of nonperforming loans (NPLs) accounts for two different aspects. The ratio of nonperforming loans to total loans is widely used as a (lagging) measure of credit risk exposure (Delis et al., 2014). The ratio of provisions to nonperforming loans gives an indication on adequate provisioning taking into account past performance and expected losses (Abedifar et al., 2013).

The second dimension, volatility, is quantified through the standard deviation in credit growth and deposits growth at the country level. We consider both price and volume growth. We first compute the year on year growth rate at the country level in bank lending rate, commercial banks outstanding loans, deposit interest rate and commercial banks outstanding deposits. We then compute the standard deviation of the growth rate at the country level. In this way we are able to quantify the uncertainty and risk deriving by both the variability in the cost and provisioning of credit, and the cost and volume of funding.

The third dimension captures the cost of crises, calculated using data from Laeven and Valencia (2013). We consider measures of banking crises including the output loss (the cumulative loss in income relative to pre-crisis trends), the costs of government intervention to mitigate and resolve the crises (direct fiscal outlays due to financial sector rescue packages), and the peak level of realized credit risk (the peak ratio of NPLs to total loans).

Figure 3 reports the distribution of the various indicators used to measure financial stability arranged by country income-group. It also highlights the median, the minimum and the maximum of the distribution by income group.

The distributions of the financial stability indicators show that the most advanced countries score relatively worse than the other countries in some indicators. For instance, the median value of high income countries appears the lowest for capital ratios and the highest for the variability in the deposit rate growth.

> 7 See for instance, Lowe (2002) for a discussion of macroeconomic consideration for risk measurement, Danielsson et al. (2015) for the link between volatility and financial crises, and Acharya et al. (2010) for the importance of measuring tail risks.

> 8 While the Z-score contains equity to assets, which is very similar to the capital ratio, the two indicators deliver statistically different information. They are uncorrelated based on the computed correlation matrix (Annex 2).

7