Poverty data are incomplete. For example, despite increased recognition of the gender–poverty nexus within global development discourse, the conceptualisation and measurement of poverty remains insensitive to gender. The extensive evidence that speaks to the gendered nature of poverty is not yet reflected in global or comparable national data. UN Women’s Progress of the World’s Women 2015-2016 report noted that while “women’s socio-economic disadvantage is reflected in pervasive gender inequalities in earned income, property ownership, access to services and time use … [t]he absence of sex disaggregated data makes it difficult to establish if women are, across the board, more likely to live in poverty than men” (UN Women, 2015, p. 44[1]). It remains a challenge to turn evidence from the lived experience of individuals into the kind of information required at key decision-making tables, such as government budget committees. In allocating finite resources for greatest impact, decision makers require information that clearly captures and conveys:
Who is poor, in what ways, and to what extent;
How factors such as gender, age, ability/disability and rural/urban location influence circumstances; and
How these aspects interact to deepen deprivation.
Current poverty measures are limited in their ability to provide this information. A number of factors influence this, including the predominant focus on income or consumption and measurement at the household level. When multidimensional measures move beyond income, they still tend to be centred on a limited range of dimensions, such as health or education, and remain focused at the household level. These limitations matter because estimates indicate that around one-third of all inequality is within rather than between households (Kanbur, 2016[2]). While money is important, participatory research with people living in poverty indicates there are many other dimensions of life (social, environmental, etc.) that keep them poor and that should be included in a measure of multidimensional poverty (Wisor et al., 2014[3]).
Analyses of available household-level data offer important additional insights but are insufficient. Goal 1 of the Sustainable Development Goals (SDGs), “To end poverty in all its forms everywhere”, and the overall commitment of the 2030 Agenda to “leave no one behind”, requires multidimensional poverty data about individuals to enable policy‑relevant analysis of intersectional disadvantage.