Statement by the Humanitarian Programme Cycle Steering Group on the limitations of mandatory population data disaggregation (Endorsed - 24 April 2024)

Commitment to impartiality, inclusion, and the role of disaggregated data

The members of the Humanitarian Programme Cycle Steering Group (HPC SG)1 are committed to impartiality in humanitarian responses, aiming to reach those most in need with appropriate and tailored assistance and protection. Disaggregated population data is important in informing impartial decision making and response delivery. It is a priority for the Emergency Relief Coordinator and the Inter-Agency Standing Committee, and it is an essential element to make progress on accountability to affected people. By collecting and using disaggregated data, humanitarian actors can understand the basic and specific needs of different groups impacted by a crisis, and from there design inclusive programmes. During implementation, monitoring disaggregated data allows humanitarian actors to assess the appropriateness of the response for different groups and, where necessary, improve it through programme adaptation.

All this helps humanitarian actors to reach those who face the greatest deprivations with adequate support. Therefore, in line with the commitment to the humanitarian principle of impartiality, and the Inclusive Data Charter, the members of the HPC SG agree that: all data should, wherever possible, be disaggregated in order to accurately describe all populations, with a particular emphasis on sex, age, disability status, geographic location, and displacement status.

Depending on the information needs and decisions to inform, disaggregated data can help to better understand how age, gender and diversity components feature in a given context and shape people’s experiences and needs.

The use of population estimates can be useful to inform initial programme design e.g., estimating the sex, age, disability and displacement status of crisis-affected communities for planning purposes. However, the same is not true for monitoring and reporting results: applying population estimates to results, with an assumption of equitable reach, masks possible inequalities and does not reflect who is actually reached in the response,5 and whether this changes over time and by location. During monitoring, disaggregated results reporting can only provide meaningful information if based on actual measurements which use harmonized and globally accepted methodologies, preferably on an intra-household level.

Challenges in disaggregating population data

While disaggregation is generally feasible for an activity or a project covering a limited population group (e.g. a refugee population in a specific site), it is often more difficult for larger groups with multiple demographic characteristics or spread over a large geographic area, where individual counting is not possible (e.g. a large IDP population living in an urban area, intermixed with a host community); this then calls for sampling methods.

Collecting population data entails respecting ethical standards, including on consent procedures.6 The collection of disaggregated data itself does not guarantee an equitable, quality response – rather, it is the subsequent analysis and appropriate use of this data in response programming which strengthens an equitable, quality response by addressing the disparities revealed by the data.

Using many disaggregation dimensions creates a multiplier effect on the number of data points to collect, store and analyse, leading humanitarian actors – and affected people themselves – to spend a lot of time filling out long data collection forms. When data is disaggregated by multiple factors, individual subgroups might have small sample sizes, making it difficult to draw statistically significant conclusions. As outlined in the IASC Operational Guidance on Data Responsibility in Humanitarian Action (April 2023), “Where feasible and appropriate, and without compromising these [data responsibility] Principles, organizations should strive to collect and analyze data by age, sex and disability disaggregation, as well as by other diversity characteristics as relevant to the defined purpose(s) of an activity.” Having additional data dimensions should be implemented on an as-needed/where relevant basis.

Disaggregated data collection and analysis often requires considerable resources (human resource, expertise, time, training, technology). A high volume of detailed population data calls for complex analysis, which requires analytical resources and commonly agreed frameworks that are not always present or available. In situations where resources are limited, it is difficult to find the right balance between dedicating them for additional disaggregation, or to programme implementation. Before embarking on such an analytical exercise, an organization should be cognizant of its capacities and available resources (e.g., human, financial, technological) to responsibly collect and process the data and derive meaningful conclusions, and should make every effort to ensure sufficient resources are available to carry out this data collection and analysis.

Humanitarian organizations must prioritize risk mitigation when planning to conduct disaggregated population data collection, as in certain circumstances, disaggregated data reporting may risk causing harm to the personnel collecting the data, and/or to affected people. Disaggregated data can be misused to stigmatize or discriminate against certain groups, requiring careful handling and ethical safeguards.

Disaggregated data collection should be complemented with qualitative data on the barriers faced by those left behind, in accessing humanitarian services and, at the monitoring stage, whether the collective response has been successful in tackling these barriers.

In some situations, collecting disaggregated data may simply not be possible, as a result of access restrictions. In such cases, humanitarian partners could consider innovative/alternative methodologies to strengthen the collection of baseline sex and age disaggregated data. In other situations, collecting disaggregated data may be irrelevant; for example, in humanitarian activities that are not directly linked to affected people, such as logistics interventions.

In challenging circumstances, mandatory disaggregated data reporting can be detrimental to the quality of life-saving programming. It may also lead to the counter-productive effect of actors reporting mere breakdown estimations, presented as valid collected data.

Position of the HPC Steering Group

With the aim of achieving impartial programming, and taking into account its commitment to data disaggregation together with the constraints of field realities, the members of the HPC Steering Group consider that disaggregated population data should be collected, analysed and reported with quality assurance, whenever the humanitarian community sees that the following criteria are met:

1. the disaggregation is relevant to the actions it aims to inform;
2. the disaggregation is feasible and commensurate with the available resources and timeline, proportional to the data’s expected use, and respects ethical standards;
3. there is a clear intention to use the disaggregated data for operational purposes and not just for reporting.

Where disaggregating data is not immediately feasible, measures should be undertaken to make it possible, while prioritizing the safety and dignity of affected people and the principle of 'do no harm’. Additionally, as disaggregated data is not the only way to determine the diverse needs and capacities of different groups of people impacted by crises, information may also be gathered through process indicators and qualitative data.

The HPC SG asks donors to work with humanitarian actors to set the focus on improving the quality of the humanitarian response, rather than on mandatory disaggregated data reporting.