There is growing recognition of the limitations of current data collection and the need to move beyond household-level data. The lack of individual data, and insight into within-household differences, is impacting the accuracy of global data on inequality and poverty, in terms of extent and who experiences it.
This is particularly salient in the context of gender data and a growing call to address gender gaps that hide information about the lives of women and girls, meaning policy and programming based on existing data are often gender-blind.
As Melinda Gates noted: “to close the gender gap, we must first close the gender data gap”.
Gender differences are also influenced by other factors that can exacerbate deprivation, such as age, disability and sexuality. Considering the impact of multiple factors simultaneously leads to an intersectional understanding of poverty. Conversely, not utilising an intersectional approach can lead to incorrect assumptions about gender inequality, as aggregating across (for example) urban and rural women, can mask the unique challenges that rural women may face compared to women in urban areas. In a context of growing concern about the lack of data in relation to disability and poverty, and an increasing focus on understanding the impact of intersecting sources of discrimination and marginalisation, the IDM has the potential to make an important contribution.
An intersectional approach is central to operationalising the core commitment to ‘leave no one behind’ in achieving the 2030 Agenda. The Global goals for Sustainable Development (SDGs) are a comprehensive agenda for the sustainable development of people and planet; the IDM is a multidimensional measure of poverty, so there is not full overlap in focus. Additionally, many of the SDG indicators are at the population level whereas the IDM measures at the individual level. That said, the IDM aligns with some 25% of the 53 gender-related indicators. It can also provide disaggregated data for some SDG indicators where this is not specifically required, consistent with the call for disaggregated data wherever possible.
A key potential contribution of the IDM is in relation to Sustainable Development Goal 1. The goal wording (‘To end poverty in all its forms everywhere’) anticipates a move beyond income-based, household-level poverty measurement. However, when the initial SDG indicators were agreed, there was no agreed method for individual-level, multidimensional poverty measurement. The IDM offers one approach. The 2030 agenda cannot be achieved without good data about the nature and scale of issues affecting individuals, including about the impact of intersecting characteristics on deprivation and inequality.
Increased attention to addressing gender data gaps, and to individual level poverty measurement, has seen significant interest from representatives of governments, international NGOs (INGOs) and civil society organisations when the IDM has been presented internationally. The goal is for the IDM to be available for use by a range of data producers including national statistics offices, national or district planning bodies, development agenciesand large INGOs, and for results to be available to diverse data users including policy makers and advocates for more and better data. The policy and protocol framework for data producers and users other than the IDM team will be developed as part of the current IDM program to ready the IDM for global use.