How Can Disaggregated Data Support Inclusion?

Session and outcomes

This session highlighted the imperative of collecting disaggregated data to identify the differentiated impacts of disasters. HE Sally Mansfield summarized the session by stating that without comprehensive data, we can’t know who is being impacted, particularly in terms of women and individuals with disabilities.

The session addressed the challenges and barriers to collecting credible disaggregated data, particularly in crisis settings. Perspectives from Solomon Islands showed how countries with limited data can identify and mobilize marginalized and invisible populations, through engaging local communities to develop Community Profiles which accelerated recovery efforts.

As noted by the Australian Humanitarian Partnership Regional Coordinator, effective data collection can lead to the pre-disaster identification of vulnerable communities. This is exemplified in examples from Kerala, in which disaster related mortality for persons with disabilities was extremely low due to targeted preparedness work informed by comprehensive data. Gaps still exist in collecting data, as illustrated by UNFPA and by Practical Action Consulting. Vulnerable and hard to reach persons may not always be reflected in disaggregated quantitative data.

To leave no one behind, UNFPA suggested adopting Human Rights Based Approaches to data collection which can address the pitfalls of identifying the most marginalized. However, they issued a reminder that while attempting to collect disaggregated data for inclusion, this can often lead to exclusion due to political and social factors. Post disaster damage and loss assessments are seldom disaggregated by sex, age and disability, and are usually recorded in terms of productive resources, leading to a substantial undervaluation of the impact on women and other groups.

To bridge this gap, speakers highlighted the need for political will to turn commitments into action. A starting point is to build on good practices, using the means and modalities which were introduced during the session, such as the Kerala PDNA which highlighted how the inclusion of disaggregated data and the participation of marginalized populations can inform better recovery.

Utilizing open-source data and volunteerism to build self-reported maps and needs informed by include local participation. Implementing a 7-step approach to Gender and Age Inequality informed data on DRR as an outcome of the joint research for UNICEF and UN Women. The approach utilizes mixed data and targeted interviews to build a disaggregated data set that pieces together a complex puzzle of the differentiated impact of disaster.

Discussions from the audience raised the following:

  • UNICEF raised the need to amplify the discussion on disaggregated data to larger platforms.
  • Handicap International cautioned to include the needs of the elderly.
  • African Union Gender Advisor reminded us of the catastrophic impact of Cyclone Idai, and the necessity strengthen data collection across the continent.

Conclusion and action points

To better understand how different population groups are impacted by disasters, how they prepare, respond and recover from them, the collection and use of risk, disaster and recovery data is critical.

Disaggregation by sex, age and disability and key characteristics, play a crucial role, yet can lead to further exclusion from lifesaving information and resources. To identify missing voices, it is essential to gather adequate pre-disaster data.

Panelists concluded with the following action points:

  1. Promote the collection and sharing of disaggregated data as part of programme evaluations, to inform and guide humanitarian response and recovery.
  2. Promote investment in targeted inclusive programming pre-disaster.
  3. Advocate for the combination of disaggregated quantitative data with qualitative information on vulnerability, impacts and recovery, particularly reflecting the voices and leadership of marginalized communities.
  4. Systematically include vulnerable and hard to reach individuals who are not reflected in disaggregated quantitative data to ensure their needs and concerns are reflected.