Disaggregated data: the only way to leave no one behind

This post is written by Natalie Sharples, Senior Policy Advisor, Health Poverty Action, and Claire Thomas, Deputy Director, Minority Rights Group


Fulani men sell flipflops in Kanua market - Carsten ten Brink

Fulani men sell flipflops in Kanua market – Carsten ten Brink – CC BY NC ND

Leave No One Behind was the main clarion call throughout the process of developing the Sustainable Development Goals. Dubbed the ‘people’s agenda’ by the UN Secretary General, the new Global Goals were to be met “for all nations, all peoples and for all segments of society”.

While that rhetoric remains, it risks being undermined by the detail of the indicators; the current framework is falling far short of meeting the ambition of the goals and targets.

As is well recognised, delivering an inclusive agenda requires data broken down by a wide range of social groups. This was addressed in the goals and targets with a clear commitment to disaggregate data by age, sex, disability, race, ethnicity, origin, religion or economic or other status.

While the Independent Expert Agency Group tasked with creating the indicators, have expressed their commitment to disaggregation by all the categories listed in the targets as an overarching principle, this is undermined by a focus only on targets that reference particular population groups.  In some cases, even those are lacking.  Take target 10.2. Whilst the target embodies the spirit of the SDGs to “empower and promote the social, economic and political inclusion of all, irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other status,” the indicator proposed to measure it does not even list five of the groups in the target, rendering it meaningless.

The stakes are high. How can we deliver a ‘people’s agenda’ if we pick and choose the people?

To use the example of ethnicity. Indigenous women, and those from other cultural and ethnic minorities, face multiple and intersecting forms of discrimination. Last year’s briefing from ODI highlights that across 16 countries in the global South, the poorest women from disadvantaged ethnic groups were the most likely to have missed out on progress in education and health. Their research published last month again emphasises ethnicity as a key marker of social exclusion. One of the more stark examples shows that in Nigeria, the Fulani are eight times less likely than Yoruba to have access to sanitation, three times less likely to have had a substantial education and more than twice as likely to belong to the bottom wealth quintile. That people’s chances of accessing basic services is dependent on their ethnicity is unacceptable.

Fulani men sell flipflops in Kanua market - Carsten ten Brink

Fulani men sell flipflops in Kanua market – Carsten ten Brink

The reason for proposing indicators that fail to live up to the commitment to data disaggregation agreed by states, is not clear. Whilst challenges in collecting data do exist, our experience shows that disaggregation by ethnicity can be straightforward. In many cases the data is already available, it is just not being analysed.  For example many of the indicators will rely on data from household surveys, Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). These already contain questions pertaining to ethnicity or proxies such as language; they simply require that they are used and analysed.

One simple thing that both national and donor governments can do is to request disaggregated data to provide the incentive needed to drive it forward at all levels.

Some concerns about collecting ethnic data are wholly valid.  These include the fear of fuelling ethnic tensions, particularly in states which have experience of ethnic conflict. There are however practical steps that can be taken to overcome concerns and minimise risks.  In some cases this might be using proxies such as religion, language or location, and in all cases upholding human rights standards including clear safeguards on data privacy, self-identification and ensuring the participation of marginalised groups in data definition, collection and analysis.

There are also concerns about the burden disaggregation places on countries. However disaggregation is not expected to happen immediately, but realised progressively, allowing states time to develop the necessary statistical capacities. And – as highlighted –  in many cases simply using the methods and data that already exist.

After years of consultations, negotiations and campaigns focussing on addressing inequality, and widespread commitment to Leave No One Behind, it’s vital the indicators do not diminish the goal framework.

Disaggregation is on the agenda for discussion by the Independent Expert Agency Group in the coming days. We hope members will address these concerns and deliver indicators that measure progress for all groups. This is the only way to fulfil the promise of the Global Goals  that These goals are for everyone, everywhere.

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