# **9. Discussion and policy implications**

This research supports the idea that poor people suffer disproportionally from floods, and therefore that flood management can be particularly beneficial for them. Flood management could be considered as a component of the poverty-reduction strategy in the city of Accra. This is particularly true because the impacts of floods seem to go beyond asset losses to affect behavior, potentially slowing down asset accumulation and poverty reduction among affected households (ODI and GFDRR 2015).

But most importantly for policy design, it shows that building resilience is not only about increasing income: monetary poverty (defined by low annual expenditures) does not appear as a strong driver of the ability to recover from the losses due to the floods. Instead, access to coping and recovery mechanisms – such as assistance and financial tools – seems more important. This means that traditional poverty reduction instruments – such as cash transfers with targeting based on poverty indicators – may not be able to prevent all long-term impacts of natural disasters. Flood management programs need to be designed to target _low-resilience_ households, such as those with little access to coping and recovery mechanisms, even if they are not living in poverty before the shock.

Third, the large heterogeneity of vulnerability and resilience across households makes the targeting of flood risk and impact mitigation and post-flood support particularly challenging. With constrained budgets, local or national authorities may want to target interventions to minimize the risk of floods toward the households who are the most vulnerable, i.e. who would be losing the most if they were affected in the future, or toward the households who are the least resilient, i.e. who would struggle to recover from a flood. Could the insights from the 2015 flood guide how to perform such a targeting?

A first obstacle is the fact that our survey applies to a single event, which may not be representative of flood risks in the entire city. Also, we have focused our data collection in informal settlements, and could not perform a city-wide representative sampling. While focusing on informal settlements makes sense due to the high vulnerability of these areas, the limited sampling makes it impossible to draw conclusions regarding the full distributional impact of floods in the city. An extension of the survey to the rest of the city would of course be an obvious next step in this research activity.

But even assuming that the 2015 event is representative of flood risks in Accra and that households’ vulnerability and resilience to future floods is equivalent to the vulnerability observed in 2015, using the survey results to target disaster risk management interventions would be challenging. Our results suggest that it will be difficult to “predict” which households are most likely to lose a large fraction of their annual expenditures or to struggle to recover after a shock, based on the characteristics available before the event (for instance based on Census data). Even though having low annual expenditures makes it more likely for a household to lose a large share of its annual expenditure, many other observed and unobserved factors contribute. These households cannot be easily identified based on their characteristics like access to services, housing quality and characteristics, type of toilet or waste collection. Available household characteristics only explain a small fraction of the variance of flood losses across households.

In the face of these difficulties, one option that merits more in-depth analysis is the use of self-targeting instruments, such as providing access to loans (with or without subsidies) to affected households who may not have access to borrowing, or public work programs that can offer an alternative source of income to affected people.

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