Data to manage uncertainty and risk that leaves no one behind

The eruption of natural disasters, conflict, diseases and economic shocks can result in dramatic changes to poverty and inequality. The Covid-19 pandemic demonstrated this clearly with the sudden reversal of global poverty trends in 2020. Working with Concern , Development Initiatives (DI) has investigated what kinds of data could help us understand the potential impact of these risks on those who might be vulnerable. This information can be used to inform actions and investments for preparedness and prevention that leave no one behind, rather than responding only once the damage is done.

Data has to be timely

Using data to systematically measure multiple aspects of people's wellbeing, their access to resources and services, along with their individual characteristics and identities, can provide empirical evidence of who is left behind, moving us beyond broad assumptions. The timeliness of the data we use for this is extremely important. We know that things can change quickly, particularly for people who are already vulnerable or marginalised people in the most fragile places, so we must have up-to-date data to build a full and complete picture of need.

Unfortunately, much of the data that we use to measure poverty and inequality isn’t very timely. In particular, national survey or census data is often published years after it was originally collected. Moreover, to prevent a rise in poverty and inequality that may be caused by a shock or crisis , we need forward-looking information that measures existing vulnerabilities as well as future hazards.

Why consider future hazards?

The need for this forward-looking perspective has never been more apparent. Between 2019 and 2020 alone, the Covid-19 pandemic saw the number of people living in extreme poverty increase by an estimated 50 million . Catastrophic climate change events such as floods, droughts and heatwaves have affected millions of people, with hunger now on the increase. The dramatic escalation of conflict in Ukraine , with tragic consequences for both immediate humanitarian and longer-term development needs, as well as broader global impacts on energy and food prices and associated hunger, demonstrates how situations can rapidly deteriorate and fundamentally change poverty dynamics.

DI and Concern surveyed data that was readily accessible on the internet for multiple countries, and found many sources of information on who would likely be affected by potential or emerging shocks and hazards around the world. This included data on the probability of shocks occurring, early warning systems as well as real-time crisis monitoring and humanitarian needs assessments.

Is the current data reaching its potential?

As an organisation that works in some of the most volatile countries in the world and recognises how risk plays into poverty dynamics, Concern were interested in how to optimise data use to actively manage risk and prevent the worst outcomes for those people who are already vulnerable. Together, we identified a few reasons why the risk data that we surveyed might not be being used to its full potential.

Limited national data ownership

The vast majority of the data we found was generated by organisations based in the Global North, which raises a number of questions about:

• the ownership of this data
• the role, or lack thereof, of National Statistical Offices
• the interoperability and usefulness of this data for national actors
• the extent to which the data takes into account intersecting national risks.

More broadly, it also suggests that implementation of the Paris Declaration is particularly lacking in this context. National actors leading and coordinating risk management and supporting preparedness and prevention planning and investments for the future would likely benefit from greater ownership of risk data, as well as the collection of more and better-disaggregated data in future. International actors can support this effort with joined-up and coherent approaches to supporting and strengthening national data ecosystems and through greater demand and use of nationally generated data.

Insufficient disaggregation to identify the most vulnerable

In many sources, the data was disaggregated by relatively small geographic regions, enabling responses to be spatially targeted. Yet it was less common to find data that could identify the age, gender, disability status or other characteristics of those most likely to be affected.

Data on the impacts of Covid-19 in many countries, for example, identified significant inequalities in the health and economic impacts on different groups; differences that were not factored into initial policy responses . However, the collection, management and use of data with more identifiers require active steps to address associated data governance risks. Strong data security and protection measures are needed, particularly to protect marginalised individuals or groups in fragile settings.

Inherent uncertainty in managing ‘risk’

Whatever the data suggests about who may be left behind tomorrow, existing poverty still needs to be addressed today. While investment in prevention may indeed be more cost effective and morally just than waiting until need emerges, upfront investment is difficult to prioritise where the need isn’t certain. For example, DI’s analysis of disaster risk reduction spending in Ethiopia found that resources allocated for prevention were less likely to be spent, as they were understandably diverted to current humanitarian needs, particularly where resources were slower to be allocated to programmes.

A flexible, dynamic approach

But choosing whether to address current or future needs need doesn’t have to be a trade-off. Ideally, spending on current needs, such as social protection payments to the most vulnerable, can not only strengthen resilience once a shock emerges but also be designed to flex and adapt when this happens. However, this isn’t always the case, particularly where there is the potential for emerging risks to impact newly vulnerable groups, who are different from people who are chronically poor.

It’s clear that a dynamic view of who is or may be left behind is a powerful tool to enable effective risk management to mitigate the worst outcomes for people experiencing vulnerability. At DI, we will continue to explore how data can support efforts to ensure that policies and investment decisions leave no one behind in the future.

Concern’s Réiseal Ni Chéilleachair and Anushree Rao provided input to this blog post