Did you sleep here last night? The impact of the household definition in sample surveys: a Tanzanian case study
Tiziana Leone, London School of Economics and Political Science (LSE)
Ernestina E. Coast, London School of Economics and Political Science (LSE)
Sara Randall, University College London
Household sample surveys are integral to planning for development in most poor countries and there is growing demand for data to inform development strategies. The practicalities of data collection require a social unit to be defined, generally referred to as a household, although households as defined by survey practitioners may differ considerably from the social units that many people live in. Most survey and census definitions of a household rely on some combination of 3 factors: sleeping in the household the night before the interview; eating from a common cooking pot; and sharing economic resources. Using Tanzania as a case study, this paper firstly analyses the impact of different household definitions on key socio-demographic indicators. Secondly it explores how to overcome the limitations of the definition both in the data collection and analysis. The aim is to highlight shortcomings of household data and to investigate the possible impact that the outcomes might have on policy-making. This study uses the 2004 Tanzanian Demographic and Health Survey (DHS) (n=9735 households) and primary in-depth (n=52) case study interviews with Tanzanians in four different settings. Analyses use sensitivity analysis to plot possible scenarios for a set of socio-demographic indicators (dependency ratio, sex ratio, median education level, mean household size, and sex of household head), indicators which are used frequently as proxies or correlates of development. Results show that the household age and sex structure change considerably if the approach of the definition changes. For example both the dependency ratio and the proportion of female-headed households increase if more stress is given to the sleep in definition. This study has two implications. Firstly, that household survey instruments might be adjusted to better capture a range of lived realities. Secondly, information that will help survey analysts to better understand and interpret household survey data.
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Session 30: The trustworthiness of demographic data