Detragiache, 1998), financial deepening is associated with increased financial vulnerability. On the other hand, the expected impact of concentrated banking markets on insolvency risk is ambiguous. Market power may contribute to boost bank profits and strengthen capitalization thus reducing the risk of insolvency (Marcus, 1984; Chan et al., 1986; Keeley, 1990) yet more concentrated banking systems may lead to higher interest rates in the economy and make more problematic for borrowers to pay back their debt thus increasing the risk of bank instability (Boyd and De Nicoló, 2005). Introducing the M2 to GDP ratio as a proxy for financial deepening and the market share held by the three largest banks as a measure of concentration does not change the qualitative findings of our baseline specification (Table 3, column (5)).<sup>5</sup> Another check consists of controlling for the institutional environment. Countries with higher supervisory capacity are likely to show less risk of insolvency because problems are addressed at an early stage. The quality of bank supervision, in terms of compliance with international supervisory practices, can be approximated by the GDP per capita (Čihák and Tieman, 2008). The estimation results do not change (Table 3, column (6)). We also account for the quality of governance in the country by using the Worldwide Governance Indicators. These include six governance measures (voice and accountability, political stability, government effectiveness, regulatory quality, rule of law, and control for corruption) that we average into a single index per country. Again, our results for our baseline specification are not affected (Table 3, column (7)).<sup>6</sup> A final check consists of controlling for the impact of repeated incidence of bank insolvency. Banks that are technically insolvent often require extraordinary support from the government and/or shareholders even if they still meet minimum prudential requirements. They often struggle to improve their soundness and gain back the confidence of depositors and customers. In our model, this results in repeated observations for three of our ten insolvent banks. When the repeated observations are excluded (column (8), Table 3), the results of our baseline specification still hold, suggesting that our findings are not driven by the repetitive insolvency events.<sup>7</sup> All in all, the sensitivity tests performed show that our baseline model performs reasonably well. The main advantage of this model is that it uses widely available accounting-based CAMEL indicators which are helpful in predicting _technical_ insolvency in MENA. The statistical significance of these variables does not change much when other potentially relevant control variables are included or when a different estimation methodology is used.

# **4. In-sample and out-of-sample performance of the model**

It is customary to assess the predictive performance of EWSs by looking at the percentage of observations and insolvencies correctly called and the percentage of false alarms. This requires the specification of a cut-off or threshold probability above which the predicted probability can be interpreted as sending a signal of a pending insolvency.

> 5 Another popular measure of financial deepening, credit to the private sector as a share of GDP, is not appropriate for the MENA region, characterized by a large fraction of credit to the public sector, both government and stateowned enterprises. Hence we resort to M2 to GDP ratio.

> 6 We also experimented with the six governance indicators individually but found no significant impact. Results are available upon request.

> 7 In other words, we estimate the logit model where repeated observations are dropped.

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