# **NOTES**

1 Among studies of banks and credit during the Great Depression, see for instance Bernanke (1983), Haubrich (1990), and Calomiris and Mason (1997). Gorton (1988) uses a sample of banking crises from the US National Banking Era (1863-1914) to test whether panics were caused by depositors’ reaction to a forthcoming economic downturn or by self-fulfilling beliefs.

2 Some examples of case studies include Garcia-Herrero (1997), Drees and Pazarbasioglu (1998), Jaramillo (2000), Gonzáles-Hermosillo et al. (1997), Ramos (1998), and Schumacher (2000). Among papers drawing general lessons, see Davis (1995), Gavin and Hausman (1995), Goldstein and Turner (1996), Mishkin (1996), Rojas-Suarez and Weisbrod (1995), and Sheng (1995).

3 The authors use an ‘adjusted’ version of the noise-to-signal ratio, computed as the ratio of the probability of false alarms (type II error) to one minus the probability of a missing a crisis (type I error).

4 As in Demirgüç-Kunt and Detragiache (1998), we estimate the model without country fixed effects because we want to include non-crisis countries as controls. In the new regressions, however, we allow for the error terms to be correlated within each country by clustering the errors by country. In the 1998 paper we just used robust standard errors.

5 Also, including the fiscal deficit in the regressions markedly reduces the number of observations.

6 See also Bell and Pain (2000) for a recent review of leading indicator models of banking crisis.

7 CAMEL stands for Capital Adequacy, Asset Quality, Management, Earnings and Liquidity.

8 Borio and Lowe (2002 and 2005, this volume) also present a model based on the signals approach. In a related paper, Boyd, Gomis, Kwak and Smith (2000) focus on the cost of crisis and present a detailed review of macro conditions before, during and after crises, for more than 50 crisis countries, basing their discussion on a general equilibrium model. They highlight the great diversity of economic conditions that precede crises, drawing the conclusion that it is difficult to rule out sunspots, i.e. random events, as the cause of many crises.

9 Using a variant of the multivariate logit model, in which the crisis dummy takes the value of one in the year before the crisis and the value of two in the year of the crisis, Hardy and Pazarbasioglu (1999) also find that macroeconomic indicators were of limited value in predicting the Asian crises. In none of these countries was the precrisis period identified as problematic. They conclude that the best warning signs for these crises were proxies for the vulnerability of the banking and corporate sector.

10 See Demirgüç-Kunt (1989) for a review of this early literature.

11 See Eichengreen and Fishlow (1998) for a review of this literature.

12 This study does not address the question of whether foreign entry leads to a less concentrated banking system, however.

13 It is not possible to control for the quality of regulation and supervision in a panel of data, such as is typically used on banking crisis regressions, because measures of these dimensions are only available after 1999. Results from cross-sectional tests show that countries with more generous deposit insurance design are likely to have experienced crises since the 1980s, even after controlling for supervision and regulation.

14 An additional problem is that changes in the aggregate stock of real credit to the private sector are not a good measure of the flow of credit available to the economy, especially around banking crises, because of valuation effects caused by inflation or exchange rate changes. Also, a decline in the stock of credit may result from restructuring operations that transfer non-performing loans to agencies outside the banking system (Demirgüç-Kunt, _et al._ , forthcoming).