The Greater Accra Metropolitan Area is vulnerable to the consequences of perennial flooding (World Bank, forthcoming). The city’s rapid urbanization is characterized by a lack of urban planning and weak enforcement, which exacerbate its vulnerability to flooding (MESTI, 2016). Floods in the densely populated areas of Accra are induced by heavy rainfall primarily during the rainy season (May-June). The Odaw River, within the Korle-Chemu catchment area, drains most parts of the built-up area in central Accra. The river runs through the Odaw basin area, which covers 271 km<sup>2</sup> . The southern part of the basin is densely populated and includes the informal settlements Nima and Old Fadema, as well as the industrial and business areas in Kwame Nkrumah Circle and Kaneshie.

The Odaw basin was the area most affected by the flood in Accra on June 3, 2015. Rainfall recordings in the southern part of the basin indicate a rainfall of 130 mm in 6 hours, equivalent to a return period of 10 years (Klopstra et al., forthcoming). Based on estimates from NADMO and World Bank, the flood caused damages around 100 million USD. Total rainfall, in combination with the inadequate discharge capacity of the lined Odaw drain, were the main reasons for the flood. Impacts were worsened by the gates of an inceptor weir that could not be opened at the time of the event. In addition, accumulated solid waste behind the weir and several bridges along Odaw also contributed to the rising water levels. This event turned particularly tragic when a fire broke out at a gasoline pump where people were seeking refuge from the waters, resulting in about 150 casualties. Although the 2015 flood corresponds to just a 10-year return period flood, it is remembered as an extraordinary event by Accra residents.

# **4. Survey design of Accra Poverty-DRM Survey**

To understand how flood risk and the level of poverty affect household livelihoods and behaviors, the sample selection followed a four-step process to stratify the targeted slums by flood proneness and the level of poverty. First, we selected areas that are considered as informal settlements in Accra. Second, we designed our sampling strategy to ensure that we have in our sample a diversity of flood risk levels, using elevation as a (very imperfect) proxy for flood risks. Third, we categorized areas as low poverty and high poverty by using a neighborhood level poverty estimate created by Engstrom et al. (2017). Fourth, we selected households from the four strata – low elevation and low poverty, low elevation and high poverty, high elevation and high poverty and high elevation and low poverty. The key four steps of the sampling procedure are described in more detail below.

Note that the objective of the sampling was not to create a sample representative for the whole slum population of Accra, but to draw samples to contrast behavioral differences by the levels of poverty and flood proneness. Results can therefore be used to investigate the impact of poverty (and expenditure level) on the exposure, vulnerability, and socioeconomic resilience to the 2015 floods, but not to calculate total losses across the city or to map the risk in the city.

# **4.1. Sampling strategy**

The neighborhoods included in Table 1 are defined as slum areas based on the Accra Metropolitan Assembly (AMA), UN-HABITAT (2011) and a slum index developed by Engstrom et al. (2017). The slum index is a formula based on key characteristics of Enumeration Areas (EAs) that are highly correlated with “typical” slums – as defined by experts, using machine learning techniques. The sample was selected from EAs that are (i) fully inside the informal settlements defined by AMA and UN Habitat (2011) and (ii) have a slum index higher than 0.7, which is significantly above the average slum index among all EAs 7