(120,000 to 250,000 households) - see Table 5. This estimate is significantly higher than the numbers of around 12,000 affected households provided in the post-disaster assessments of the Red Cross.<sup>20</sup> This is partly due to different definitions of “being affected”. While the definition used by the Red Cross and the government is not provided in the note, it is possible that our definition is much broader. For example, many of the households experiencing water in the house are not severely affected, at least not monetarily. Consequently, exposure assessments hide an important heterogeneity in the way that households are affected by floods (see Section 3.1.3).

Analysis suggests that flood exposure is a widespread problem in Dar es Salaam. Different areas are affected at different times. Among the households that had water in the house during the flood in April 2018, only 31% had previously reported having ever had water in the house during initial data collection in 2017. This finding may explain why previous flood models seem to have underestimated the number of households exposed to floods in Dar es Salaam (see Kebede and Nicholls, 2011). Therefore, flood models may actually not be adequate in predicting which areas are exposed when considering policy options for flood mitigation.

_Table 5 - Number of people affected by the flood in April 2018 in Dar es Salaam (scaled up to city level using the sample weights)_

|**April 2018 flood:**|**Population affected lower**<br>**bound* **|**Population affected upper**<br>**bound****|
|---|---|---|
|Directly affected|550,000|1,040,000|
|Affected (incl. indirectly<br>affected)|900,000|1,730,000|
|_*assuming households that we_<br>_flood risk strata)_|_re not reached in follow up survey_|_were not affected (weights based on_|

_**assuming households that were not reached in follow up survey were affected in the same way as the ones with information (weights based on flood risk strata)_ According to these estimates, a large share of residents suffers from the impact of floods in Dar es Salaam, but impacts are not homogenous. Some households suffer significant monetary impacts, associated with housing repairs, asset losses and long periods of time each rainy season when everyday life is disrupted by floods. However, a large share of affected households only misses a few days of work or no work at all, leading to low or even no monetary losses, even though water may have reached their houses. Similarly, while rain causes delays in commuting, roads may still be accessible, and households are able to shift to different transportation methods. It is important to keep this in mind when assessing these numbers. The size of losses that were incurred to households will be addressed in greater details in Section 3.2.

# 3.1.3 Who are the exposed?

Poorer households are over-represented among those exposed to floods in Dar es Salaam. The proxy of per capita expenditure<sup>21</sup> is lower among affected households, indicating that poorer households tend to settle in areas with high risk. Affected households are also significantly more food insecure than non-affected households, confirming the correlation between poverty and exposure to floods. The pattern is consistent for households directly affected by the 2018 flood. However, in the latter case, results are only statistically significant for food insecurity. Figure 7 shows the distribution of the estimate used for expenditure in

> 20 <u>https://reliefweb.int/sites/reliefweb.int/files/resources/MDRTZ021do.pdf</u>

> 21 See Section 3.2.1 for details on the methodology implemented in here.

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