‘Children of the kitchen’: defining the household in Mali

Dean Karlan and Jacob Appel’s new book More Than Good Intentions – I’ll post a review soon – has a great example of how apparently straightforward survey questions can be anything but simple. Appel describes testing the wording of a questionnaire with a phone-card seller in Ghana, asking him how many people make up his household, or “share a single living space and take meals together”. As the conversation progresses and Appel probes deeper, the response changes from one person, to five, then eight, then seven, eventually settling at an extended family of (about) nine members.

Households in Mali are also often large, fluid and difficult to define. As in the Ghana example, definitions tend to be around those who share the provision of food. The Bambara word for ‘household members’ (guaden) even translates literally as ‘children of the kitchen’. However, this can still have different understandings for different people in different places, sometimes being taken to mean an extended family with a compound of many different buildings, sometimes interpreted as smaller sub-units of this. Instead of surveying simply by reading out exactly the same question to all respondents, my research assistant and I have decided that it is better to take the time to discuss and reach a common understanding at the start of each interview.

This matters not only for consistency within my own research sample, but also for making comparisons to other datasets. For example, I have used the questions from the Mali Poverty Scorecard to estimate the level of poverty of different households and villages – this is a simple set of ten indicators which correlate well with poverty levels (in terms of expenditure) based on data from the Mali Poverty Evaluation Survey (EMEP) of 2001. So to use this tool to help my research, the definition of the household that I use needs to match the definition from the EMEP.

Some researchers in Mali have gone further and investigated how changing the definition in the question changes the number of household members stated by the respondent. Beaman and Dillon used a randomised survey experiment to investigate the effect of using different household definitions in the Segou region of Mali. They found that adding more requirements to the definition given in the question – for example, requiring defined ‘household members’ to “work together on at least one agricultural plot or in one revenue generating activity” as well as living in the same lodging and recognising the same household head – actually increased the number of people listed as belonging to the household. This is counterintuitive because making the definition more strict should logically only result in a reduced number or the same number of defined household members. However, Beaman and Dillon suggest that perhaps the extra keywords in the stricter definitions prompted respondents to recall particular people that they might not otherwise have thought to include as household members.

This is important because these different definitions of household size can then in turn affect estimates of the poverty levels of households and subsequently wider regions, a good example of how key details of data collection can potentially have an impact on national statistics. For example, Delarue et al discuss in greater depth how data and methodology issues – including the definitions of ‘households’ used by surveyors in different regions – may be responsible for the “Sikasso paradox”, the phenomenon where official statistics suggest that cotton producers in the Sikasso region of Mali are poorer than other farmers, despite cotton-growing households and areas traditionally being thought of as richer.

As Chris Blattman puts it, getting inside the “sausage factory” of data collection is a humbling experience – and as these examples show, a very important one for understanding the possible effects of apparently small details.

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