mixed record in this regard. Their analysis is based on two successive rounds of the Russian Longitudinal Monitoring Survey (carried out with about 3,800 households in October 1996 and November 1998 respectively). In the crisis, mean household expenditure fell by about 20 percent and poverty headcount (national definition, expenditure) rose from 22 percent to 33 percent. On the other hand, inequality narrowed: the Gini coefficient for expenditure actually fell from 0.44 to 0.42; from 0.48 to 0.42 for income. Interestingly, there were on average expenditure gains by the poor between 1996 and 1998, and 42 percent of the 1996 poor had escaped from poverty two years later. The apparent paradox (gains by the poor, but increasing poverty headcount) is resolved by realizing that the overall increase in headcount reflected the very large number – over a quarter – of the previously non-poor that fell into poverty. Increases in poverty rates are lower if measured in terms of income rather than expenditure, and it seems that the expenditure responses reflected excessively precautionary behavior.

# _African crises of the 1990s_

Sizable short-run improvements in inequality were recorded in three African countries, Kenya, Uganda and Zambia, around the time that their banking crises broke in 1993-5 (Figure 2). Yet it may be unwise to jump to conclusions about the causality of such a correlation. For none of these three crises were associated with any large output dip (cf. the country entries in Caprio et al., 2005). The Uganda case relates to the decision to face up to the deep insolvency of a large state-owned bank at the center of the system. The Kenya and Zambia cases related to mismanagement and perhaps fraud at sizable banks unrelated to macroeconomic conditions. It is likely that the distributional changes that occurred in these countries during the mid-1990s were more closely related to terms of trade changes that were rather independent of the banking conditions.

These important cases highlight the possibility that banking crises can be accompanied by falling inequality. The next section examines the full set of documented banking crises to see what have been the associated movements of inequality.

# **4. Broad cross-country evidence**

In this section, we look at cross-country data on Gini coefficients of income or expenditure and on the distribution-related changes in poverty. A caveat is in order. It is not implied that Gini, or indeed relative inequality in general, is the most important aspect of the welfare impact of financial crises. Indeed, as mentioned above the Gini is very far from being a useful measure of welfare in this context, not least because an uniform percentage reduction in cash income may have a much greater impact on the welfare of the poor than that of the rich.<sup>15</sup> Instead, the purpose here is the analytical one of isolating distributional effects from the (larger) overall welfare effects which result especially from the across-the-board decline in mean income at all level of society which is typical of many crises.<sup>16</sup>

> 15 Despite the logical difficulties involved in making interpersonal comparisons, Atkinson’s index based on the perspective of an inequality-averse _social_ welfare function, or poverty measures such as the FGT class can be used to address the wider welfare questions.

> 16 The common presumption that poverty headcount invariably increases around crises is also contradicted by Povcal data (cf. Figure 3, where the dates and cases correspond to those used in Table 3b).   In most

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