Policy paper

Inclusive Data Charter Action Plan

Updated 6 March 2019

This was published under the 2015 to 2016 Cameron Conservative government

Introduction

The vision of the Sustainable Development Goals and Agenda 2030 is a world where the Global Goals have been met for everyone, where we have reached zero poverty, there is shared prosperity and security, and no one is left behind. The Global Goals aim for a world where all women and men, girls and boys, at all stages of their lives, have equal opportunities to achieve their potential and live in dignity, free from extreme poverty, exclusion, stigma, violence and discrimination.

The UK’s Aid Strategy sets out the government’s high level of ambition on the Global Goals, paying particular attention to the UK’s role on Leave No One Behind. Our approach covers 3 areas: Understand, Empower and Include. For DFID, ‘Understand’ means strengthening our knowledge of whom and where people are, or at risk of being left behind and the analysis of why. Improving the collection and use of disaggregated data to inform decisions and continuing to build evidence of what works in different contexts is essential to enable us to empower and include those at risk of being left behind.

On 3 May 2016 DFID hosted a conference on age data disaggregation together with partners from Civil Society and the United Nations. This led to the development of DFID’s Data Disaggregation Action Plan, launched by the UK National Statistician at the World Data Forum in Cape Town in January 2017.

Our 2017 Data Disaggregation Action Plan set out the initial practical steps that we would take to move the agenda forward within our own organisation and through the global system – with a focus on disaggregating data by sex, geography, disability and age. Our new Inclusive Data Charter Action Plan builds upon the earlier plan with more detail on our approach, and a strengthened ambition following our commitment to the Inclusive Data Charter vision and principles (See Box 1).

Inclusive Data Charter

DFID is an anchor member of the Global Partnership for Sustainable Development Data and has been part of the development of the Inclusive Data Charter concept. DFID signed up to the Inclusive Data Charter at the Global Disability Summit in July 2018.

The Inclusive Data Charter Action Plan primarily relates to DFID’s programme data and lays out the actions and steps we will take over the short (over the next 18 months), medium term (over the next 2 to 3 years) and long term (over the next 3 to 5 years) to better understand the situation of the poorest and most marginalised, and make better decisions that positively impact all people’s lives.

Key to achieving the ambitions in this action plan will be strengthening the culture within DFID of disaggregated and inclusive data. This will involve shifting mindsets and improving our capability and capacity.

This Action Plan is a living document and as such will be monitored, reviewed, refreshed and updated every two years in advance of the World Data forums. We will invite feedback from our partners to help us collectively better address the challenges of inclusive data. We will continue the spirit of peer review in which this action plan was developed and seek to meet with our peers to help us assess the individual and collective impact of these Inclusive Data Charter Action Plans over the coming years.

Box 1: Inclusive Data Charter Principles

• Principle 1: All populations must be included in the data
• Principle 2: All data should, wherever possible, be disaggregated in order to accurately describe all populations
• Principle 3: Data should be drawn from all available sources
• Principle 4: Those responsible for the collection of data and production of statistics must be accountable
• Principle 5: Human and technical capacity to collect, analyse and use disaggregated data needs to be improved, including through adequate and sustainable financing

Principle 1: All populations must be included in the data

The absence of reliable internationally comparable data means governments can underestimate the scale of who is being left behind and the impact of different policies or may lack the political will or even deliberately exclude certain groups. We know that some parts of society remain outside official statistics by statistical design (such as those living in institutions, slums, the homeless, those above a certain age), due to legislation (LGBT is illegal in some countries) or by their own choice to avoid stigma and discrimination (people living with HIV may fear stigmatisation).

Ensuring we do no harm, we will continue to learn from and work with others to understand and share knowledge on how to cover known and unknown missing populations – including for example experience of removing age limits on international surveys, developing age specific surveys, how to capture information on those outside of traditional household structures. In the short term, this will include;

• developing minimum standards for data collection and reporting and working with our partners and suppliers over the medium term on implementing them
• facilitating conversations with others already doing work in this space to understand and share knowledge on how to cover known and unknown missing populations
• maintaining an active role in the Titchfield City Group on Ageing and Age disaggregation, feeding learning from the group on age specific surveys back into DFID work
• continuing to advocate for and support the use of the Washington Group disability status question sets for disaggregation, especially where we support national censuses, surveys and administrative systems. We will go beyond disaggregation to collect further relevant data for disability inclusion, such as enablers and barriers to inclusion

Principle 2: All data should, wherever possible, be disaggregated to accurately describe all populations

No data exist for two thirds of Sustainable Development Goals (SDGs) indicators^{[footnote 1]} and to track progress at the subnational, national, regional and global level, a large amount of high quality, timely and disaggregated data is needed – as referenced in Target 17.18 (Box 2) of the Global Goals.

Box 2- Agenda 2030

Data, monitoring and accountability

SDG Target 17.18: By 2020, enhance capacity-building support to developing countries, including for least developed countries and small island developing States, to increase significantly the availability of high-quality, timely and reliable data disaggregated by income, gender, age, race, ethnicity, migratory status, disability, geographic location and other characteristics relevant in national contexts.

DFID remains committed to the goal of full data disaggregation for all social groups under the Global Goals, but our approach will be iterative, in line with our DFID’s Data Disaggregation Action Plan and we will progressively realise our ambitions on data disaggregation while working to bring stakeholders and suppliers along with us on each step of our journey.

We focused on 4 variables - sex, age, disability status and geography - in the first instance to enable DFID to reorient our approach and raise our ambition whilst we worked across the international system to develop tools, methods and guidance on the wider Global Goal disaggregation variables.

In the short to medium term, we will:

• use tools for disaggregation, such as the Washington Group questions for disability status and work with others to test the feasibility of and develop guidance for using these tools in administrative systems and programme M&E processes

• share best practice on the impact and use of disaggregated data for programme purposes, alongside evidence and knowledge of when it is and when it is not useful to disaggregate

As we do this, we will learn and share our knowledge on the challenges of gathering and using data that intersect across the different disaggregation dimensions to understand and respond to the multiple layers of discriminations people experience which may foster exclusion. We will also explore options of presenting the data, for example through data visualisation and analytical tools, such as Power BI.

We retain our ambition to be able to report key headline results disaggregated by at least sex, age, disability status and geography where relevant/appropriate under our Single Departmental Plan by 2022. In 2019, we will undertake analysis of our Single Departmental Plan results that are disaggregated and share that analysis widely. This will be our baseline to improve upon, and we will use this to push for greater disaggregation across the organisation.

In the longer term, we will move towards additional disaggregation variables; we expect this to include income, race and ethnicity.

We will consider how we can best influence multilateral and international financing institutions to support more and better disaggregation of data. We will also learn from our core funding payment-by-results arrangements with a number of United Nations humanitarian agencies where the collection and use of disaggregated data (particularly for disability) plays a key role.

Principle 3: Data should be drawn from all available sources

There are weak incentives for governments in developing countries to fill the known gaps in official statistics. As a result, solely relying on official sources may provide an incomplete picture. For example, we know that 44% of countries worldwide do not have comprehensive births and death registration data^{[footnote 2]}.

The UN Global Working Group on Big Data for Official Statistics has demonstrated the benefits of combining unconventional data sources with more traditional data sources such as censuses and surveys.

To improve the accessibility and availability of timely data, we will:

• support user-generated or user led data, as a vital component of a data eco-system. In the short term, we will continue to work through partners such as the Global Partnership for Sustainable Development Data on citizen-generated data and will feed learning from the group back into DFID work
• both advocate for and support partner countries to undertake national censuses and build their Civil Registration and Vital Statistics (CRVS) systems as a key source of national data. We will use our influence to encourage partners to do the same
• extend our ambition to work towards making all DFID’s generated programme related data open, free of charge, and available in structured and standardized formats to support interoperability, traceability, and effective reuse

Principle 4: Those responsible for the collection of data and production of statistics must be accountable

DFID will continue to collaborate with international initiatives that promote aid transparency, driving greater use of aid transparency data for greater accountability and effectiveness. We already require all our implementing partners to publish to the International Aid Transparency Initiative (IATI) or other relevant international transparency standards, and to pass the same expectations down their delivery chains. We will continue to ensure we hold and use data responsibly in line with Data Protection guidelines. When we gather data from beneficiaries, we will ensure that they are aware of how the information will be used and will ensure their personal information is protected.

To strengthen our accountability in the short to medium term, we will:

• look at how to improve the user experience of both internal and external data that will maximise its value and use
• develop a DFID approach to quality assurance of analysis based on the AQuA Book

Principle 5: Human and technical capacity to collect, analyse and use disaggregated data needs to be improved, including through adequate and sustainable financing

To support the shift to a data-driven organisation, we will need to continue to enhance the skills and capability of staff across DFID.

In the long term, this will be complemented by developing better systems to gather, capture, store and use disaggregated results data from implementing partners for our programme use. This may mean a radical redesign of our data model and the tools (logframes) we use to capture results. This may include using open standards such as IATI, Humanitarian Exchange Language (HXL), learning from others that are already doing this such as the Humanitarian Data Exchange and IOM Data Portal.

DFID will also continue to play a leadership role working through partners such as the Global Partnership for Sustainable Development Data to strengthen data systems in a coordinated, strategic way; and ensure that they deliver data that is disaggregated and therefore meaningful to those most often left behind or harder to reach. We will work with others to ensure that partner countries have the support and capacity to generate the statistical data needed to support development.

1. OECD (2017) Development Cooperation Report 2017: Data for Development Highlights ↩

2. OECD (2017) Making better use of results data in development co-operation. Development Cooperation Report 2017: Data for Development, OECD Publishing, Paris. ↩