# _Impacts of the floods_

The questionnaire is designed to estimate the monetary losses experienced by each household. We include housing repairs, asset losses, missed days of work and medical costs, but exclude non-monetary losses (e.g. deaths and injuries). Asset losses and labor income losses due to missed days of work were estimated combining household responses with complementary information, while housing repair and medical costs were directly estimated by the households.

- For asset losses, we collected price information on a number of commonly lost items from shops and households in the surveyed areas. We used this information to calculate asset losses per household. Since there is some variability in asset prices and richer households can be expected to own more expensive assets, asset losses for richer households are likely to be underestimated compared with poorer households’ asset losses.

- For labor losses, we first calculated the average daily wage in different occupations, based on both annual household expenditures and household member occupation information. Then, we multiplied the estimated daily wage by the number of missed days due to the flood to generate a total value of losses caused by missed days of work (see details in Appendix 6).

- Housing losses are calculated based on the cost of repairs, not actual damages. Since poorer households may not be in a financial position to (fully) repair the damages caused by the flood, their housing losses are likely to be underestimated compared with those of richer households.

- Similar issues apply to medical costs, which are imperfect proxies for health impacts.

The survey was conducted over a two-week period from the 27<sup>th</sup> of May to the 11<sup>th</sup> of June 2017 – two years after the 2015 flood. Field teams comprised of 22 enumerators and 4 supervisors administered questionnaires using Computer Assisted Personal Interviewing (CAPI). Each team had a tablet per enumerator, a GPS device for supervisors, power banks, notepads and modems with internet bundles to enable them to send data directly and regularly from the field.

To learn more about the communities where the household questionnaire was administered, a community questionnaire was also submitted to local leaders. The responses from this questionnaire inform the analysis based on the household responses and help us better understand patterns we observed in the data. Conclusions drawn from both the community and household analyses are incorporated into this report.

# **4.3. Sample Characteristics**

The SWIFT estimation shows that, in terms of expenditure, the Poverty-DRM sample covers all income groups of the city of Accra, despite the fact that the sample area only includes informal settlements. Average poverty headcount rates in the Poverty-DRM survey appear lower than in GLSS6 data for GAMA, but the difference is not statistically significant. According to GLSS6 data, the poverty headcount rate of GAMA was 2.1% in 2012/13, compared with 1.4% in the Poverty-DRM survey. (Descriptive statistics for the surveyed population are provided in Appendix 3.) These results may seem surprising because the Poverty-DRM survey draws sample households from urban slum areas only, but could be explained by economic growth between the two surveys. Also, GLSS6 data suggest that the difference in (monetary) poverty rates between non-slum and slum areas in Accra is quite small. Nevertheless, it remains important to note that the surveyed population includes households that are far from poverty, and has an expenditure distribution that is close to that of the full city.

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