Action on poverty and inequality needs locally owned data

In October 2022, the Organisation for Economic Co-operation and Development (OECD) Development Assistance Committee’s (DAC) Community of Practice on Poverty and Inequalities (CoP-PI), of which Development Initiatives (DI) is a member, held its second annual meeting to discuss the challenges of addressing poverty and inequality amid rising food insecurity.

The context for this meeting felt bleak. In its latest Poverty and Shared Prosperity report, the World Bank made it clear that the target to eradicate extreme poverty by 2030 would not be met. Compounding crises of Covid-19, conflict, energy and food price inflation, alongside an increase in the frequency and severity of climate hazards, are making life harder for many millions of people globally; reducing national and international capacities to manage the consequences.

As participants of the CoP-PI, we at DI are thinking about how data on people, risk and finance can support development efforts which can help address these challenges, including – but also looking beyond – food insecurity.

As analysts and data scientists, we always want to see more and better data which:

• Is timely, comprehensive and accessible
• Tells us about people and their lives so that interventions are effectively targeted
• Measures whether or not progress is being made
• Is responsible and inclusive across the data-value chain, from design and production to dissemination and use.

But more than that, this year’s meeting prompted us to further reflect on how the international donor community can better support data and data systems to be nationally owned. This can enable sustainable and locally led responses to crises – including through locally owned social protection systems – and means that data can be used to its full potential in local contexts.

In adherence to the Bern principles (which set out the need for statistical support to align with country needs), here’s what this means in concrete terms:

1. Timely, detailed and transparent international finance data. A near real-time understanding of where money is being spent – for example, the extent to which nutrition is targeted or resources are directed towards social protection – can not only identify finance gaps, but can also provide clarity to national governments on how international finance for these sectors may change over time and in response to crises. The OECD’s DAC statistics database, including its marker on nutrition and its reporting code on social protection, can play an important role in identifying where money is spent. However, to be effective, these markers and reporting codes require rigorous criteria on how they are created, clear methodologies, consensus on how they will be used and alignment with national priorities.
2. Use of timely and accessible data on multidimensional poverty. To put poverty at the centre of development efforts, data and evidence on poverty in all its dimensions, including hunger, should be built into planning and programming by national and international development actors. This is particularly relevant for key sources of development finance such as Official Development Assistance (ODA), which is uniquely positioned to support those left furthest behind.
3. Investment and attention to international and national data on risk. For development efforts to be forward-looking, strengthen resilience and prevent the most catastrophic impacts of different crises, we need more than just historical data. A range of data can be useful to help understand different risks and the impact that they may have on people and their outcomes, from early warning systems for hunger to meteorological data. Many of these risks go beyond national borders, making it all the more important that this data is shared at both regional and international levels.
4. Improving the quality of national administrative data. Administrative data collected at the point of service provision is a critical source of information; it can be used to directly inform the delivery of essential services that have the potential to benefit the furthest behind most. Inclusive data that can be disaggregated to identify different people’s vulnerabilities and inequalities in access to services can facilitate a more appropriate response.

DI looks forward to continuing to work with the CoP-PI, applying our expertise to the use of locally owned and locally relevant data and evidence to tackle rising poverty and inequality.