On the power of indicators: how the choice of fuel poverty indicator affects the identification of the target population

Abstract

In light of the creation of the EU Energy Poverty Observatory (EPOV) in January 2018 and the increase in debates on how fuel poverty is measured, we propose a critical analysis of fuel poverty indicators and demonstrate that choosing a given indicator is central to the identification of the fuel-poorpopulation. First, we conducted an inter-indicator analysis to show how profiles of fuel-poor households vary depending on the indicator selected. We designed a multidimensional approach based on a multiple correspondence analysis and a hierarchical and partitioning clustering analysis to study characteristics of fuel-poor households. We highlight the difficulty of identifying a fuel-poor 'typical profile' and show that the composition of the population depends on the choice of the indicator. Second, we applied an intra-indicator analysis using two objective expenditure-based indicators with thresholds. In particular, we conducted a sensitivity analysis based on a logit model including variables describing household and dwelling characteristics. We show that the profiles of fuel-poor households as well as the drivers of fuel poverty vary considerably with the chosen threshold level.Given these findings, we stress the need to review how we currently rely on conventional fuel poverty indicators to identify target groups and give some recommendations.

Fuel Poverty: Potentially Inconsistent Indicators and Where Next?

“…Like the present paper, Fizaine and Kahouli (2019) show that different FP indicators are associated with different types of households. However, rather than directly identifying the household characteristics associated with each FP indicator, they first perform cluster analysis to identify profiles of households falling within each type of FP.…”

“…Beyond the UK, expenditure-and perception-based FP are compared in Spain by Phimister et al (2015), in Greece by Papada and Kaliampakos (2016) and Ntaintasis et al (2019), in Belgium by Meyer et al (2018), in the US by Agbim et al (2020), in France by Fizaine and Kahouli (2019) and in the Republic of Ireland by Scott et al (2008). Direct comparison of the results is difficult due to the variations in the precise definitions of the expenditure-based indicators and different wording of the perception-based indicators, although all studies find a consistent lack of overlap between the two types of indicator.…”

“…It is difficult to provide direct comparisons with the regression results in Agbim et al (2020) and Fizaine and Kahouli (2019) because they use alternative FP definitions. Agbim et al analyse Texan data and identify household spending above 8% of income on electricity (roughly twice the local median) as "objectively energy burdened", while also considering whether (i) households had difficulty paying their electricity bill, and (ii) their electricity bill was causing them stress or mental discomfort.…”

Objective vs. subjective fuel poverty and self-assessed health

“…Moreover, Waddams and Deller (2017) for UK and Deller (2018) for the EU found that the identification of a common fuel poverty metric based solely on spending criteria is problematic due to heterogeneity between countries. More recently, Fizaine and Kahouli (2018) analyse the use of several objective and subjective measures to categorise fuel poverty and find differences in the profiles of the households depending on the measure and threshold utilised. They suggest exploring alternative approaches and particularly the combination of standard indicators, the exclusion of thresholds from expenditure-based measures, and innovative strategies based on more appropriate conceptual frameworks of fuel poverty.…”

Understanding regional energy poverty in Japan: A direct measurement approach

“…In recent years, there has been burgeoning interest in energy or fuel poverty in developed countries. 1 Many studies have been published in European countries such as Austria (e.g., Brunner et al, 2012), France (e.g., Legendre and Ricci, 2015;Imbert et al, 2016;Fizaine and Kahouli, 2019), Germany (e.g., Heindl, 2015;März, 2018), Greece (e.g., Santamouris et al, 2013;Papada and Kaliampakos, 2016), Italy (e.g., Fabbri, 2015), and Spain (e.g., Aristondo and Onaindia, 2018;Romero et al, 2018), to say nothing of the UK, a pioneer in this field (e.g., Boardman, 2010;Hills, 2012).…”