to smooth consumption, then the poverty level during that month would jump from 1.6% to 18% – a major jump, albeit for a very short period of time. While these numbers are illustrative only, they demonstrate how misleading average loss per household can be.

These results are correlations, and cannot support definitive conclusions regarding causality. However, at least two insights suggest that these correlations stem from the fact that poorer households were more vulnerable to the 2015 flood, rather than that the differences in annual expenditures are due to the flood itself. First, most households report having fully recovered from the shock at the time of the survey, and the results are unchanged if the sample is restricted to the households that report having fully recovered. Second, the observed differences in annual expenditures represented in Figure 6 are much larger than what would be expected from the impact of the floods, considering the reported losses.

However, it is possible that the correlation between poverty and flood vulnerability identified here is at least partly due to the _cumulative_ effects of multiple floods and an amplifying feedback loop between poverty and vulnerability (Hallegatte et al., 2017). Our findings on the impact of risk perceptions on investment behaviors provide some evidence that flood risks can have a long-term effect on poverty that goes beyond what asset losses suggest (see Section 0). A firmer conclusion on this causality question would require survey data – if possible a panel – conducted before and after the event, or even the tracking of households over long periods of time.

# **7. Socioeconomic resilience: Boosting resilience requires more than increasing income**

The last element in our framework (see Figure 1) is socio-economic resilience. We define socioeconomic resilience as the ability of affected populations to cope with their losses — in this case, to recover from the impacts of floods without experiencing large well-being losses or long-term impacts (Hallegatte et al., 2017). Two years after the floods, 69% of affected households reported having fully recovered **.** Among these households, 54% reported having done so in less than one month. However, a significant fraction of the affected population took much longer to recover, or had not recovered after two years, making it important to understand the resources people have to cope with the losses and the tools they have to recover and rebuild.

# **7.1. The fundamental role of savings**

The main coping mechanisms used after the 2015 flood were tapping into savings and reducing non-essential consumption. Table 6 summarizes the _primary_ coping strategy that households employed to recover from the 2015 flood. While around 11% of affected households reduced food consumption, the fact that these households tend to be richer than the average suggests that this strategy did not include a reduction in consumption of essential goods. Overall, these results suggest that direct and indirect losses from the flood did not threaten the basic needs of most affected households.

The ability of households to use savings to cope with the flood seems to be independent of the level of expenditure. Rather, we find that resorting to savings depends on the source of income and on the gender of the household’s head (Table 7). Households with less stable sources of income, such as casual labor, are less likely to use savings. Households that have a stable business as a main source of income are less likely to use savings than employed workers. Female-headed households were significantly less likely than male-headed households to use saving as a coping strategy, as shown in Table 8. (A regression table on the use of savings as a primary coping strategy is provided in Table 26 in Appendix 8.) 19