Why we need a multidimensional approach to measuring poverty that leaves no one behind

There are many ways in which multidimensional poverty can be measured. To leave no one behind, the best measures allow for disaggregated analysis and put the principle of inclusion at the heart of the data.

Poverty is about more than a lack of money

It’s tempting to want a straightforward answer to the question of who is living in poverty. The international extreme poverty line establishes a universal threshold based on daily income, which increased from US$1.90 to US$2.15 per day (purchasing power parity) in September 2022. People with incomes below this amount are categorised as living in extreme poverty, while those with incomes above it are not.

However, wherever you go, in different countries, communities and homes, it’s clear that poverty is not this binary. The experience of poverty affects many different aspects – or dimensions – of people’s lives, from health outcomes to political freedoms. Escaping poverty is not simply about earning a few extra dollars a week, just as national or social progress is about more than GDP growth.

A broad consensus now exists across the international development policy space that poverty is multidimensional, a concept that is examined in more detail in our briefing . The first Sustainable Development Goal (SDG) to ‘eradicate poverty in all its forms, everywhere’ explicitly recognises that poverty can take many forms and look different in different places. It is also just one goal, complementing 16 others that tackle a range of diverse needs that must be addressed to enable people and communities to enjoy decent lives.

Poverty measurement has improved and diversified

Fortunately, the data landscape for poverty measurement is much more nuanced now than it once was. We no longer need to rely predominantly on World Bank indicators for extreme poverty prevalence. Instead, a wealth of data, indicators, indices and insights are available to provide a better understanding of what poverty looks like for different people. There’s no ‘one size fits all’ approach, but there are a growing number of datasets that provide us with a range of tools to suit different contexts and purposes.

Using these multidimensional measures and tools improves our understanding of poverty and how it is experienced, which in turn enables policies and programmes to be better designed to consider:

• The root causes of poverty and the barriers people face to improving their lives.
• The dimensions of poverty that are most pernicious and neglected, including the blind spots of other development actors, such as people’s longer-term needs and resilience to shocks.
• The relationship between different drivers and outcomes of poverty that reinforce each other to leave people behind, necessitating a coordinated multi-sector policy or intervention.

Before using any measure to see who is left behind according to a certain dimension of poverty, it’s important to understand more about the data itself. DI’s ‘ data landscaping’ approach includes examining the quality, coverage, origin and systems governing data. Our guide reviews some of the main multidimensional poverty measures and indices and summarises the issues to consider when using them.

Different people experience poverty differently

One of the most important factors we consider in our guide is the extent to which poverty measures can be disaggregated by individual and group-based characteristics. This identifies how poverty can look different for different people; the answer to ‘who’ is living in multidimensional poverty can therefore have multiple answers depending on the dimension of poverty in question and the ways in which identities and associated inequalities intersect. These nuances are central to identifying and supporting the most excluded people with the most complex needs.

This inevitably introduces complexity to the data, requiring a range of dimensions of poverty to all be disaggregated by a range of personal characteristics. While this can be challenging to navigate and analyse, starting with a systematic data landscaping assessment of what data is available and what it can and can’t tell you, can highlight what analysis is possible.

Nothing about us, without us

At the heart of the leave no one behind commitment is the recognition that poverty can often be driven by exclusion and marginalisation, where people lack the power, influence and freedoms necessary to live a decent life and participate – economically and socially – on a level playing field. The process of collecting, processing, analysing and sharing data about people can play an important part in challenging the structural barriers that people face; if conducted without due care, it can reinforce them.

DI is a partner in the Data Values Project , which looks at data and data processes as a route to inclusion and highlights the ways that people can be included in the data lifecycle, beyond simply being subjects of poverty diagnostics. These include addressing issues of data ownership, security and privacy. Inclusion in the measurement process is particularly relevant to a multidimensional approach, as participatory processes can help identify the dimensions of poverty and inequality that matter to people based on their lived experience, as opposed to relying on top-down assumptions. We reviewed some of these participatory approaches in our guide.

Where to find out more

Our briefing examines the concept of multidimensional poverty – its origins, fundamental values and principles when adopting its approach.

Our guide details some participatory approaches to generating and using multidimensional measures, including our research in Somalia, where we used quantitative and qualitative methods to understand, from personal perspectives, the main drivers of marginalisation and the coping mechanisms associated with shocks that supported people’s wellbeing. The guide will support policymakers and practitioners to navigate multidimensional data and approaches.