However one wants to approach the issue, the raw material will be the change in real income and expenditure of different income deciles, and for this one must consult the evidence of household surveys.

For a wide cross-country analysis of movements in poverty and inequality around the time of financial crises, the Povcal database is an inevitable choice. It contains such data for 95 countries and between one and 14 different years (an average of 4-5 observations for each country). Matching this with the Caprio-Klingebiel banking crisis data set giving details on 180 crises in 149 countries (including advanced economies) offers the prospect of assessing correlations in a useful way (Caprio et al. 2005).

# _Timing_

Yet we have to be careful. Timing of the crises is one of the most uncertain aspects of the crisis database, yet issues of timing are crucial to our analysis of the effects of crisis on poverty and inequality, if we are to capture what (as argued above) are potentially contrasting short-term and long-term effects.

The only systematic econometric cross-country study of which I am aware that attempts to detect the impact of banking crises on inequality is Lopez (2004).<sup>17</sup> The goal of his paper is to explore the impact of different policy approaches on inequality and growth in the short run and in the long run. He uses a somewhat larger database, that used by Dollar and Kraay (2002) (updated), and focuses on changes in inequality over five-year intervals. Lopez’s solution to the timing problem is to take non-overlapping five-year intervals and choose the latest available Gini coefficient in each interval. For banking crises (only one of his explanatory variables), he uses the number of years in the relevant five-year period for which a crisis is ongoing.<sup>18</sup> Lopez employs the Arellano-Bond GMM econometric methodology which involves double differencing of this data, and this arguably places a premium on data accuracy.<sup>19</sup> Interestingly, he finds that the crisis variable is highly significant, with a t-statistic of over 4.<sup>20</sup> Five-year periods in which countries, however, poverty headcount is, however, on a downward medium-term trend which somewhat complicates the separate identification of the effects of shocks. The problem is illustrated in the data of Table 4, where a sharp fall in the poverty headcount in Ghana is the most dominant feature. Adjusting for the different trends in different countries uses up most of the available degrees of freedom.

17 Panizza (2004) also provides some evidence, but limits himself to the Latin American cases also studied by Lustig (2000).

18 Note the implicit assumption here that, if a country is not reported in the crisis database as having an ongoing crisis, then it is crisis-free for that period. However the authors of the crisis database have warned that it is an incomplete catalog of crises, and does not declare as crisis-free the countries and dates that are not mentioned.

19 And also reduces the degrees of freedom because of the need to exclude countries with fewer than three observations. From 953 observations on 137 countries, the double differencing reduces the effective sample size to 194 observations on 61 countries. Further merging with the policy and growth data reduces Lopez sample further to 134 observations on 41 countries.

20 His other policy variables include: initial GDP per capita, initial output gap, education (secondary enrolment), financial depth, trade openness, government consumption as share GDP, public infrastructure (telephone lines) price stability, governance (first principal component of the ICRG indicators), cyclical volatility, external imbalances, and the growth in the terms of trade. All but the last four are found to be significant in explaining changes in the Gini. The inclusion of several variables likely to be correlated with 12