Disaster Poverty Household Survey 2017-2018, Dar es Salaam [Tanzania]

The Disaster Poverty Household Survey (DPHS) is designed to collect information to assess the relationship between disaster risk (exposure, vulnerability, and capacity to recover) and poverty in the urban environment. The data can be used to explore policy-relevant research topics related to climate change adaptation, urbanization, urban poverty, and more.

DPHS data contains information on household characteristics, household expenditure, living conditions and household experience with disasters. Household characteristics include household size and member level information on religion, education, and labor. Household expenditure is collected using the Survey of Well-being via Instant and Frequent Tracking (SWIFT) methodology, which estimates household income (or consumption expenditure) based on non-monetary variables that are highly correlated with poverty. Information on living conditions covers housing quality, asset ownership, access to services and jobs, rent and housing costs and tenure arrangements. Information on experiences with disasters includes direct and indirect impacts of historic disasters on household assets, education, health and labor access, as well as impacts on public services. There is also information on coping behaviors and perception of risk of future exposure. The Disaster-Poverty survey can be customized to collect information on different disasters. So far, it has mainly focused on the impacts of urban flooding.

 The DPHS in Dar es Salaam was conducted in 2 rounds in November-December 2017 and in September 2018, with the objective to assess the role of poverty and other social factors in urban flooding in the city. The survey data collected in 2017 focused on exposure to frequent flooding, while the follow up survey in 2018, targeting the same households, focused on the impact of a flood event that happened in April 2018. During the follow up survey in 2018, additional households were also added to the sample. The data collected is representative at the city level and overrepresented in areas that are flood prone.