Financial Footprints: A New Approach to Quantifying Fuel Poverty

By Dr. Torran Semple, University of Oxford

The cost-of-living and energy crises brought the issue of fuel poverty into sharp focus. For an increasing number of people, energy bills are a source of financial stress and insecurity; however, the full extent of fuel poverty is still poorly understood.

Background: The quantification crisis

The need to identify and quantify fuel poverty in England is more urgent than ever. Yet, existing definitions, like the Low Income High Costs (LIHC) and Low Income Low Energy Efficiency (LILEE) indicators, are known to significantly underestimate the true rate of fuel poverty. This inaccuracy poses a critical policy challenge: if fuel poverty is not measured accurately, it cannot be meaningfully alleviated or eradicated. 

This is where new data streams are particularly promising: the increasing availability of donated banking data has the potential to revolutionise poverty-related research. Given that traditional poverty quantification relies on relatively small, static survey samples, the sheer volume and dynamic nature of financial data could enhance, or potentially supplant, these traditional approaches.

The study: Unlocking the financial footprint for fuel poverty research

My ongoing collaboration with Smart Data Research UK’s Financial Data Service (FINDS) is putting this potential to the test. The study uses FINDS’ Income Volatility dataset, which includes approximately 1.11 million customers, to explore whether this vast financial footprint dataset can give us a clearer, more up-to-date picture of financial precarity, especially in the context of fuel poverty. 

The research involves estimating three fuel poverty-related socioeconomic indicators using the Income Volatility dataset:

• Financial Security: The proportion of a customer's total income used for committed or essential expenses. While a valuable baseline indicator, in isolation it does not capture the specific dynamic of fuel poverty.
• Potential Fuel Stress: Measures the proportion of essential expenses likely attributable to energy costs, based on postcode-level average energy costs. This provides valuable area-level insights into the potential strain of energy costs and how this might change dynamically (e.g., before and after updates to Ofgem’s energy price cap).
• Fuel Poverty Ratio: The proportion of total income likely attributable to energy costs. This indicator mirrors the structure of older (but still useful) poverty definitions but is calculated using real financial records.

The data granularity challenge

One key challenge is the discrepancy between traditional measurement approaches and indicators developed using modern data streams. Fuel poverty is traditionally measured as a household condition, whereas the financial records are derived at the individual level. Since we do not know household composition (occupant count) or dynamics (how utility bills are split), the financial data-based results are not directly comparable to existing, household-level statistics. This is a considerable limitation.

However, the proportional nature of these socioeconomic indicators mitigates this issue somewhat, as they focus on the individual’s financial capacity to absorb the typical energy costs in their area.

Initial findings and further research opportunities

The study’s initial results suggest that financial footprint data has considerable utility as a complementary means of quantifying fuel poverty. Despite this promise, the individual-level granularity of financial records means that household composition and dynamics cannot be accounted for.

The future of poverty quantification will likely rely on marrying these new streams of large-scale, dynamic data with robust traditional cross-sectional approaches. The analysis we are conducting with FINDS offers a fantastic opportunity to build upon these initial indicators and develop the tools necessary for real-time, granular targeting of financially vulnerable households. This capability is vital for governments’ poverty reduction and decarbonisation agendas as we navigate the uncertainties of the energy transition.

FINDS is a collaboration between Smart Data Foundry and the University of Edinburgh, funded through UKRI’s Smart Data Research UK programme.