Putting data at the heart of the Leave No One Behind agenda: areas for action
Good data is vital for ending poverty. How can we maximise its benefits, while mitigating its risks and limitations, to ensure it is fit for purpose in our efforts to leave no one behind?
Six years ago, governments signing up to the UN Agenda 2030 made the commitment to Leave No One Behind. It was a big achievement to get countries to agree to this radical agenda that puts the people that are furthest behind first and seeks to tackle structural inequalities. This week at the UN’s General Assembly, governments have been discussing how we are all doing on this agenda.
There has been some important progress. Before Covid-19, the number and percentage of people living in extreme poverty around the world had been falling. We’ve also seen advances in the disaggregation of data and broad recognition at national and international levels of the importance of structural inequalities to understanding drivers of poverty and exclusion.
Despite this progress however, reaching the target to end extreme poverty by 2030 was unlikely, with poverty still increasing in some countries, particularly those affected by conflict and fragility. Important data gaps on who remains in poverty persist, particularly in recognising the most marginalised groups in society and their intersecting characteristics. The Covid-19 pandemic has compounded these challenges, reminding us in a very stark way how fragile progress on poverty and inequality reduction can be and how much risk and uncertainty underpins the lives and livelihoods of people living in poverty.
In the space of 18 months, over 4 million people around the world are estimated to have lost their lives. The global economy contracted by 3.3% in 2020, a reduction in trade and consumption, hitting many people’s businesses and incomes. Upcoming analysis from DI tells us that the pandemic increased the number of people living in extreme poverty by an estimated 50 million between 2019 and 2020, reversing the trend experienced for decades of global poverty reduction. Policies to reduce mixing and travel have shut down entire sectors, formal and informal, cutting off livelihoods for many, and leaving people isolated from family and friends for months on end. Children have faced a huge disruption to their schooling, affecting their academic attainment as well as their mental health and social lives.
Development Initiatives (DI) works to encourage and support the use of data to operationalise the commitment to Leave No One Behind. However, recent events have thrown the role of data into sharp focus, and not always in a positive way. On the one hand we have the clear narrative for good − in this constantly changing context of a global pandemic, getting trustworthy, timely and disaggregated data can be vital for informing both short term response to crises, but also to build longer term resilience for people and communities to respond to shocks in the future and sustainably reduce poverty and inequality. But on the other hand, the crisis situations in Afghanistan and Myanmar have shown, with awful consequences, that disaggregated data in the wrong hands can exacerbate harm and that we need a greater focus on data governance, data protection and data rights.
There are several things that we think can be helpful in this context.
Inclusive and responsible approaches to data
Data about people and their lives is best developed when people have a role to play throughout the data collection, management and analysis processes, to ensure the data has meaning and relevance and respects the rights and interests of those people and communities. DI employs participatory methods to ‘centre the voices’ of individuals and communities at greatest risk of marginalisation through community-driven data processes. DI is a data partner in the Making Voices Heard and Count project, which aims to give voice and agency to marginalised groups that are at risk of being overlooked in the implementation of the Sustainable Development Goals (SDGs), through combining locally-owned data generation with advocacy for this approach at the national and international levels.
We know that more granular and detailed data about people also comes with risks attached, with respect to privacy and how that data is accessed and used. We have a responsibility to consider these risks throughout the data collection, management and analysis processes. We should reduce the amount and types of data being collected in certain situations as well as being clear about who is responsible for data governance and data protection. This is why we need to go beyond discussions about disaggregated analysis and ask ourselves the difficult questions about true inclusion and where our data values lie.
There are no simple answers to these questions which is why DI is proud to be co-leading track one of the Data Values Project on ‘Data as a Route to Inclusion and Equity’, working alongside a diverse set of people and organisations from the Global Partnership for Sustainable Development Data.
DI’s data landscaping work takes a systematic approach to look at the coverage, accessibility and quality of data available, as well as the systems and structures that collect, store and manage data, including that which is community generated. Our work with the Asia Foundation in Nepal for example, included an assessment of current data systems for ongoing monitoring of the SDGs, as well as building a data-driven portal for data mapping for disasters. Data landscaping can identify the most relevant data that can be used in the fast moving context of a pandemic to understand how people are doing and inform targeted responses as well as identifying gaps in the data, where it may be necessary to develop new data collection tools. The landscaping approach also considers the political economy of data within a country as well as the structures, standards and policies that govern the systems themselves and the culture that drives the demand for and use of data. All too often this approach highlights the tensions between the data required to monitor the SDGs at a global level and the data infrastructure needed to deliver the SDGs nationally and locally.
Disaggregated poverty and inequality analysis
DI’s approach to analysing available data on poverty and inequality is to go beyond averages to understand intersectional inequalities and exclusion. Covid-19 has clearly highlighted how different people have been affected and how health, economic and social impacts may have been compounded for people with multiple intersecting identities. Our work in Benin for example uses DI’s P20 approach, to compare key outcomes of income, nutrition, income and birth registration of the poorest 20% of people with the rest of the population, making explicit how different groups experience different outcomes. Disaggregated data analysis can help identify the particular groups most vulnerable as crises unfold and therefore where to focus response and recovery efforts to prevent the most severe impacts of the crisis on people’s lives and livelihoods.
Distributional analysis of policies and spending
Data can also be used to provide a distributional understanding of the targeting and impacts of policies and spending. Our work in Kenya for example, as part of the Inclusive Futures consortium, analysed the budget of five counties in Kenya to see how much was spent on disability inclusion and which departments were responsible for this spending. An estimated US$12 trillion is estimated to have so far been spent globally on the Covid-19 response, but least developed countries spent 580 times less per person than in developed countries. This makes it even more crucial that there is a clear understanding of exactly who is benefiting from response and recovery efforts to ensure that no one is left behind.
The way forward
While the impacts of Covid-19 have been unprecedented in many ways, people living in poverty face profound challenges every day, challenges that can be better understood with well governed, robust, timely and disaggregated data about people and their lives. Risks and shocks from conflict and climate change introduce further vulnerabilities, requiring a dynamic understanding of risk, poverty and resilience – again, good data can help here. While data alone cannot mitigate the effects of these crises, it can be used to inform the policies and politics needed to address them and target the people in most need.
It is with this in mind that there is an even greater need to use robust, timely and disaggregated data on how people are doing − especially in the wake of the pandemic, as the health, economic and social impacts are likely to be felt most severely by those already marginalised and excluded.
We must not shy away from the difficult conversations that are needed on the risks and limitations of data, and we must not let these difficult conversations reduce our focus on the need for nationally owned, well governed, inclusive foundational data systems that will enable us to achieve the commitment to Leave No One Behind.
Enabling evidence-informed decision-making at the local level in Nepal
Read more about the Data for Development (D4D) programme, the impact it has had in Nepal and our priorities for the future.
Disability inclusion in Western Kenya: Key findings from county budget data
This series of publications tracks the disability inclusivity of budgets in five counties in Kenya across five financial years.
The P20 in Benin: From consultation to consensus
This report presents current trends among the poorest 20% of people (P20) in Benin and provides recommendations for delivering on the Sustainable Development Goals (SDGs) pledge to leave no one behind.