Bardazzi et al (2023) argue that the complex nature of energy poverty prevents it from being captured by a single variable. For this reason, they recommend to policymakers that they monitor energy poverty by using a dashboard of metrics.
Picture by William Warby, Unsplash.
What is energy poverty?
A person is said to experience energy poverty (EP) if they are unable to access the energy services that are considered essential for a satisfactory standard of living and health. Although this qualitative definition is widely accepted, its quantitative equivalent is not. In the UK alone, for example, England, Scotland, Wales and Northern Ireland all have their own ways of measuring energy poverty. Furthermore, confusingly, the conclusions reached by using each metric don’t necessarily align with each other.
How can we measure it?
According to Bardazzi et al (2023), EP metrics can be categorized as objective, consensual, or multi-dimensional.
Objective metrics identify EP households by using data on households’ energy and total spending. The ones emphasized (and criticized) in the paper are mostly the following:
Ten Per Cent Rule. This was the first EP metric to emerge, and it was developed in the UK after the 1970s oil shock, when rocketing oil prices forced vulnerable families to spend an important part of their budget on energy bills. According to the indicator, a household experiences EP if it spends over 10% of its disposable income on energy. Although the metric has the advantage of being easy to communicate, Bardazzi et al (2023) highlight a couple of shortcomings. For one, the 10% threshold was derived using empirical data for the UK back in the 1980s, so it may no longer be a good benchmark. What’s more, it doesn’t capture those EP households which radically lower energy usage to afford more important necessities such as food.
M/2 indicator. According to this, households are likely to experience EP if their energy expenditure is below half the median of the reference population. This metric isn’t perfect either; low energy expenditure could be driven by factors such as high energy efficient dwellings, or specific rent contracts which include utility bills. And by capturing ‘relative’ expenditure, M/2 is arguably more of a measure of energy spending inequality rather than absolute poverty.
Low Income High Cost (LIHC) metric. This identifies EP when a household faces both high energy costs compared to the target population, and its residual disposable income (net of energy expenditure) is below the poverty line. Similarly to the ‘Ten Per Cent Rule’, the LIHC fails to capture those EP households which choose to lower their energy usage drastically.
Modified LIHC (M-LIHC) metric. This variation of the LIHC was proposed by Faiella and Lavecchia for Italy in 2014, and it expanded the LIHC EP identification criteria to include those households that have zero energy expenditure and are below the poverty line. Although an improvement from the LIHC, Bardazzi et al (2023) argue that the zero-expenditure threshold could be overly strict.
Consensual indicators, in contrast with objective metrics, do not use data on households’ expenditures, but instead they rely on relevant self-reported survey information. Two common examples are presented below:
Survey results on perceived ability to adequately heat the house in winter. An issue with this indicator is that people’s answers may be affected by cultural and demographic characteristics over what is considered an ‘adequate’ temperature. Deller et al (2021), for example, found that older households are less likely than younger ones to self-identify as unable to afford adequate warmth.
Self-reported information on whether a household was late to pay their energy bill. Similar to other metrics, this is unlikely to capture those EP households that avoid using energy services.
Lastly, multi-dimensional EP metrics use weighted averages to merge various objective and consensual metrics into a single indicator, to combine their insights. However, such indices can be hard to interpret, making them difficult to use.
Are energy poverty metrics interchangeable?
Bardazzi et al (2023) find that EP metrics don’t necessarily align when it comes to comparing the prevalence of energy poverty across countries. According to surveys on perceived ability to heat the home, for example, Italy had the 6th highest EP rate in the EU in 2018. In contrast, self-reported information on late payments of energy bills ranked Italy as low as 21st place (see Figure 1). Similarly, the M/2 metric indicates that Sweden had the highest EP rate in the EU in 2010, while the M-LIHC indicator positioned it at 20th place (see Figure 2).
Figure 1: Consensual metrics ranked Italy at differing positions for energy poverty.
Data: EU Household Budget Survey, Bardazzi et al (2023).
Figure 2: Objective metrics positioned Sweden at 1st and 20th place for EP.
Data: Statistics on Income and Living Conditions (EU-SILC data), Bardazzi et al (2023).
Conclusion
There is evidence of inconsistencies across indicators, at least when it comes to the level of EP across countries. At the same time, according to Bardazzi et al (2023), no metric is clearly superior to the others. As a result, they suggest that a dashboard approach of indicators is the best way to track trends in energy poverty.
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