assessment tool, Survey of Wellbeing via Instant and Frequent Tracking (SWIFT),<sup>5</sup> which makes use of the latest ML techniques to estimate expenditure levels and the incidence of poverty in 10 to 15 simple questions. SWIFT offers two key advantages. First, the poverty estimates are fully consistent with the official poverty estimates of the country. Second, producing poverty estimates traditionally requires one to two hours of interview and more than six months of data processing, but SWIFT needs only five to ten minutes of interview and less than a week of data processing. Both significantly reduce the time and costs needed to produce poverty estimates. This method is woven into a DRM (disaster risk management) questionnaire. With both SWIFT and DRM data, the team can assess direct and indirect impacts of floods and how these are related to poverty. Gaining insights in these areas is crucial for TURP as the project helps the government create an evidence-driven disaster risk management strategy, which promotes better targeting and informs investment decisions in disaster prevention. In addition, the questionnaire also collects information on food security status of the households. The Coping Strategies Index (CPI index) was developed by WFP and CARE International as an indicator of food insecurity. In Dar es Salaam, DRM interviews were completed with the comparative (reduced) CSI. It is composed of 5 questions on food access and coping strategies to deal with limited food access. It produces a score between 0-56, where a higher score means higher food insecurity.

# 2.2.2 Follow-up interviews over the phone

Follow-up interviews with 701 households were carried out in September 2018. After the floods of April 2018, it was decided to implement a follow-up phone interview to assess the impacts on the households included in the original sample. The follow-up interviews were carried out by a call-center in Dar es Salaam from September 3-11, 2018. Among the 730 households for which phone numbers had been obtained in 2017, 419 were reached and agreed to the follow up interview, which corresponds to a response rate of around 60%. 17% of households could not be reached, 20% were associated with a wrong number or a number no longer in service, and only 5% refused to participate in the phone interview. At the same time, data collection was complemented with the addition of 282 households to the sample. These additional households were also asked about impacts of the 2018 flood.

In total, the 701 households included in the follow-up rounds differ somewhat from the households that did not respond.<sup>6</sup> Income, for example, is 19% lower among the households that responded to the follow-up interview. In addition, households that responded to the follow-up interview seem to be located in slightly more risky areas. On average, the elevation is lower and household perception of future risk of floods is higher. These differences may affect the results in several ways: for example, the conclusions reached after comparison between affected and non-affected households could differ from the baseline in the case of the 2018 flood, since both groups are on average slightly poorer. Consequently, the data from the follow up interviews are primarily used in the analysis of vulnerability, in which comparison is primarily among the affected, and when calculating total losses of the flood in 2018.

> 5 Survey of Wellbeing via Instant and Frequent Tracking, methodology found here: <u>http://documents.worldbank.org/curated/en/591711545170814297/Survey-of-Wellbeing-via-Instant-and-FrequentTracking-SWIFT-Data-Collection-Guidelines</u>

> 6 See Table 10 in Appendix 1 for comparison between the households that responded in the follow-up interview with the ones that did not.

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