Of course, as mentioned, factors other than financial sector policy, including the economy’s ability to absorb shocks and safety net policies, are key determinants of the inequality consequences of a crisis. The equations presented remain quite underspecified.

# (b) Short-term effects

However, the same database allows us a somewhat clearer look at short-term impacts. First, there are 13 cases where we have an estimate both of the immediate pre-crisis Gini (not more than three years before the onset of the crisis) and that which is observed in the immediate aftermath of the end of the crisis. These are plotted in Figure 5. We immediately see that, once more, there is no general tendency for a reduction in the Gini in this time interval. Indeed, the mean Gini increases slightly (and without statistical significance) from 0.428 to 0.437. Sizable Gini falls are recorded for Zambia, Algeria, Yemen and Madagascar, but these are offset by increases in Nigeria, Costa Rica, Bangladesh and Nepal.

Perhaps more interesting is an even shorter time frame which looks at individual crises, as distinct from the entire country crisis history, and takes cases where a Gini estimate is available shortly (not more than 3 years) before the onset of the crisis and shortly (again not more than 3 years) after the onset. This provides a shorter interval and as such is less likely to be contaminated by secular trends. This data is available for twelve crises from eleven non-Transition countries and is shown in shown in Table 3 (panel b) and plotted in Figure 2. In this case too, there are both increases and declines in the Gini, but this time there is a small (still statistically insignificant) average decline in the Gini as predicted by the short-term hypothesis discussed above. Interestingly, there is a clear regional pattern to the increases and decreases. All but one of the increases in inequality during the crisis are in Latin America, all of the decreases are elsewhere (Africa and Asia). The small number of observations militate against worthwhile regression analysis: as illustrated in Regression Table 2, only the Africa dummy is significant.

# _Distributional effects on poverty near crises_

A third approach to selecting and sorting the cross-country information is to move to the careful decomposition prepared by Kraay (2004) of changes in poverty between the part attributable to changes in mean income (at a constant Lorenz curve) and the part attributable to distributional shifts. Kraay has conducted this decomposition for over 200 non-overlapping intervals for which sufficient data is available, and I am very grateful to him for making this data available to me before its publication. Of these intervals, some 42 span the onset of a banking crisis recorded in the Caprio et al. database in the sense that the first year of the crisis either coincides with the start of the data interval, or is after that start date and strictly before the end date of the data interval. Note that this choice of intervals is rather different to that employed above, but is conceptually somewhat close to the short-term individual crisis calculation. Nevertheless, this approach gives us a larger set of observations, 41 of which 16 relate to transition economies.

Regression analysis of the 25 non-transition cases suggests that, even when poverty increases with a fall in overall income following crisis, the impact of _distributional_ 15