past effects of these variables on the probability of bank insolvency.<sup>1</sup> This approach also helps deal with potential endogeneity of regressors.

# _Data_

The paper uses annual data for 19 MENA countries—Algeria, Bahrain, Djibouti, Egypt, Iran, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Qatar, Saudi Arabia, Syria, Tunisia, UAE, West Bank and Gaza and Yemen—over the period 2000-12.

The main source of data is the Bankscope database, from which we extract financial information on 320 banks classified as commercial banks, savings banks, cooperative banks, real estate and mortgage banks, and Islamic banks. Due to a large number of missing entries, the initial sample was reduced to 198 banks in the econometric analysis. To minimize the loss of information, we focused on Bankscope’s consolidation code “ _Institutions_ ”, which includes both consolidated and unconsolidated data. We were nonetheless left with an unbalanced sample, reflecting different country data coverage and varied degree of financial development of the countries in the sample. Bank-level information is complemented by a number of control variables drawn from various sources. Appendix 1 presents a detailed list of variables and related sources.

The identification of bank insolvency in MENA is a very challenging exercise. First, systemic banking problems in the region are a rare event. According to the widely used Laeven and Valencia (2012) dataset, there have been only 10 crisis episodes in our sample economies over the period 1970-2012, and none in our sample period. Second, actual bank failures are also rare in MENA. The Bankscope database reports only two cases of receivership in our sample of banks over 2000-2012 and no episode of bankruptcy or liquidation. We therefore resort to a _technical_ definition of bank insolvency or _balance-sheet insolvency_ . In line with the existing literature (e.g., Hannan and Hanweck, 1988; Boyd et al, 1993), we define bank insolvency as a state where ( _car_ + _roa_ ) ≤ 0, where _car_ is the bank’s capital-asset ratio and _roa_ its return on assets. Using this strategy, we identify 14 insolvency events for 10 banks in 6 countries (see Table 1).

We use two-year lagged bank-level financial indicators to generate determinants of bank insolvency in the MENA region. In particular, our explanatory variables are selected based on the exiting literature, supervisory practices and data availability. Indicators from individual banks’ income statements and balance sheets are first identified to account for the categories included in the CAMEL rating system. We then check for additional potential determinants such as those related to bank ownership and business model, the institutional and regulatory framework, banking market structure, and the macroeconomic environment. A full list of variables with indication of the relative source is reported in Appendix 1.

The first covariate we use is the ratio of equity to total loans as a measure of bank capitalization. We use this indicator as opposed to the more standard equity to total assets ratio to avoid potential endogeneity problems, given that our dependent variable is constructed using the latter

> 1 Two-year lags are common in off-site surveillance models employed by supervisors. For example, since 1994 the Federal Reserve uses a EWS known as SEER or System to Estimate Examination Ratings to monitor the condition and performance of state member banks. The model estimates the probability that a bank will fail or become critically undercapitalized within the next two years.

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