The energy crisis that recently hit Europe has made energy poverty a highly relevant topic in the EU. Consequently, the crisis created the need to improve the ways that energy poverty is measured and monitored. Existing EU data collections already provide energy related indicators for household income and consumption expenditure separately. To get a better picture of the impact of higher energy prices on EU citizens, Eurostat started an experiment to measure energy poverty by using the joint income, consumption and wealth (ICW) dataset, which provides the possibility to analyse overlaps of energy related indicators originating from different social statistics. The ICW dataset combines microdata on income, household composition and individual weights from the EU statistics on income and living conditions (EU-SILC) with household consumption expenditure from the household budget survey (HBS).
Energy poverty indicators
This article shows the results of Eurostat’s analysis on energy poverty based on indicators derived from the ICW dataset focussing on overlaps and breakdowns of the following indicators:
- Income-based indicator arrears on utility bills (AUB). This variable comes from the EU-SILC. It is a qualitative item with yes/no answer options and may reflect the subjective perception of the respondent that may not be linked to the household income.
- Income-based indicator inability to keep home warm (IKHW). This variable comes from the EU-SILC. It is a qualitative item with yes/no answer options and it is based on the respondent’s perception. Therefore, like AUB, it has some level of subjectivity, independently from the income reported by the same household.
- Expenditure-based indicator high share of income on energy expenditure (2M), which shows the percentage of people living in households whose share of expenditure on residential energy in the household’s equivalised disposable income is more than two times higher than the national median share of residential energy in equivalised disposable income. For this indicator, consumption expenditure on residential energy (COICOP group 04.5) is derived from the matched HBS and EU-SILC dataset where the income data come from the EU-SILC.
- Expenditure-based indicator low absolute energy expenditure (M/2), which shows the percentage of persons living in households whose expenditure on residential energy is more than 2 times lower than the national median expenditure on residential energy. In this case, the energy expenditure is derived from the matched HBS and EU-SILC micro dataset where the household composition is based on the EU-SILC. M/2 is an indicator of hidden energy poverty, aiming to identify households with abnormally low energy expenditure.
The indicators in this article are based on household data that are converted to individual level, and they are presented as percentage shares of the total population.
Overlaps
A Venn diagram (Figure 1) shows the overlaps of the 4 energy poverty indicators (AUB, 2M, M/2, and IKHW). The percentage share of the energy poor population measured as the intersection of all 4 indicators was close to 0%;when looking at the intersections covering 3 indicators, the percentage ranged from 0.1% to 0.6%.
A possible explanation for the low values for overlaps of all 4 or even 3 indicators is that they target different parts of the population. For this reason, not only the mere intersections but also different combinations of the different indicators should be considered. Despite the slightly higher percentage shares of energy poor when measured as intersections for pairs of indicators, the 2-indicator overlaps were still quite low, ranging from 0.1% for the pair 2M-M/2, to the 1.1% for the pair 2M-IKHW. For individual indicators without overlaps, the values ranged from 2.7% to 12.8%.
Figure 2 shows the share of the population living in households that are energy poor for each overlap category: no intersections and intersections of 2, 3 or 4 indicators. As observed in Figure 1, the overlaps based on 4 or 3 indicators were extremely low, with the former close to zero. When considering only 2 indicators, the percentage was higher, with a median for the aggregate of available EU countries being just below 5%. In contrast, about 29% of the population appeared to be energy poor when all individual indicators were considered together.
The lower shares of the energy poor population measured from intersections of 2 or more indicators, relate to the fact that the different energy poverty measures shown in this article may target different parts of the population. This was particularly valid for the expenditure-based quantitative indicators – 2M and M/2 – showing a low value for their overlap. In other words, the same household was rarely energy poor simultaneously according to both 2M and M/2.
Indicators based on income, consumption and wealth (ICW)
From another perspective, the individual energy poverty indicators could be analysed in relation to the characteristics of the population, considering breakdowns such as income deciles, tenure status and degree of urbanisation.
Breakdown by income decile
Looking at income deciles (Figure 3 and 4) can be useful for determining the impact of energy costs on different household groups according to their equivalised disposable income. As expected, the highest percentages of the population living in households considered energy poor fell into the lowest income deciles. This was observed for all countries for which data were available, particularly for the 2 income-based indicators (AUB and IKHW, Figure 3 top and bottom panel, respectively) and the expenditure-based 2M measure (Figure 4 top panel).
Regarding the M/2 indicator, the same pattern was observable for most of the countries, while for others, such as Czechia, Germany, Estonia, Malta and Slovenia, energy poverty appeared to be more equally distributed among income deciles. In general, higher-income households may be able to afford higher energy efficiency measures (including investments), which could finally lead to lower energy costs.
Another reflection can be made on the geographical comparison of these indicators. On one hand, a high variability in terms of maximum values was observed in the 2 qualitative income-based indicators (AUB and IKHW). On the other hand, the 2 expenditure-based indicators presented higher comparability across countries.
Breakdown by tenure status
Figures 5 and 6 present the breakdown of the different indicators by tenure status of households. Considering the AUB and IKHW indicators (Figure 5, top and bottom panels respectively), 2 patterns were identified:
- Belgium, Denmark, Germany, France, the Netherlands and Austria showed a higher presence of energy poverty among tenants.
- For all other countries, energy poverty mainly affected homeowners.
The different levels across countries for these two indicators broken down by the tenure status counterbalanced to result in similar levels for both measures for the EU aggregate.
For the expenditure-based indicators (2M and M/2), a higher burden of energy costs fell on homeowners. This could partially be explained by the implicit inclusion (all or in part) of the energy costs in rental payments, which may result in lower levels reported as energy expenditure in the HBS.
Breakdown by degree of urbanisation
Figures 7 and 8 present the breakdown of the individual energy poverty indicators by degree of urbanisation. Energy poverty seemed to be equally distributed across locations according to their degree of urbanisation, with a few exceptions where there was a higher presence of energy-poor households in the urban areas of the different countries. This pattern was well reflected in the EU aggregate, where the share of city-dwellers living in energy-poor households according to the AUB indicator (Figure 7 top panel) was 2.5% (1.9% of that in towns and suburbs), compared with 1.8% for the rural population. Considering the IKHW indicator (Figure 7 bottom panel), energy poverty affected 2.8% of the population living in cities, 2% in towns and suburbs and 1.6% in rural areas.
Although the shares of the energy poor population derived from the expenditure-based indicators were relatively higher than the income-based indicators, their distributions are quite similar. Looking at 2M and M/2 respectively (Figure 8 top and bottom panels), the figures show that:
- 5.9% and 4.9% of the people living in cities were energy poor.
- 5.2% and 4.1% of the people living in towns and suburbs were energy poor.
- 4.6% and 4.0% of the people living in rural areas were energy poor.
Methodology and data preparation
Matched ICW dataset
The indicators in this article were calculated with reference to a dataset containing all household members and their equivalised disposable income and expenditure, based on statistical matching at household level. In the matched ICW data, the data coming from EU-SILC correspond to the HBS reference year (see Table 1). The household characteristics and some other variables refer to the EU-SILC survey year, which is the year after the reference period. In the current exercise, the reference year by country is the one reported within the HBS 2020 wave.
Data preparation for analysis of overlaps of energy poverty indicators
While negative income and expenditure values observed in the household surveys may be explained by debt or energy costs included in the rent, the negative values from both income and expenditure data were excluded in this analysis, following the recommendations of the methodology of the Energy Poverty Observatory (EPOV) Indicator Dashboard. According to the ICW methodology and in line with the previously published ICW-based affordability indicators, households with energy consumption expenditure equal to zero have been excluded from the dataset, due to the impossibility of distinguishing between real zeros and non-response entries. In contrast, the zero values for income are considered reliable and are therefore kept in the analysis.
Source data for tables and graphs
Feedback
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Data sources and availability
Eurostat's experimental income, consumption and wealth statistics are based on the statistical matching of EU statistics on income and living conditions (EU-SILC) for income, the Household Budget Survey (HBS) data for consumption and Household Finance and Consumption Survey (HFCS) data for wealth.
The annual collection of EU-SILC was launched in 2003 and is governed by Regulation (EU) No 2019/1700 (previously: Regulation (EC) No 1177/2003) of the European Parliament and of the Council. Household disposable income is established by adding up all monetary incomes received from any source by all members of the household (including income from work, investment and social benefits) — plus income received at household level — and deducting taxes and social contributions paid. In order to reflect differences in household size and composition, this total is divided by the number of equivalent adults using a standard equivalence scale, the modified OECD scale, which attributes a weight of 1.0 to the first adult in the household, 0.5 to each subsequent member of the household aged 14 and over, and 0.3 to household members aged less than 14. The resulting figure (equivalised disposable income) is attributed to each member of the household.
The Household Budget Survey (HBS) is a survey conducted every 5 years on the basis of a gentlemen's agreement between Eurostat, the EU Member States and the EFTA countries. Data are collected using national questionnaires and, in most cases, expenditure diaries that respondents are asked to keep over a certain period of time. The last three waves were collected around 2010, around 2015 and around 2020. Consumption is described according to the Classification of individual consumption by purpose (COICOP) for each household. Total consumption is obtained by adding up all COICOP items and (as with income) this total is divided by the number of equivalent adults using the same modified OECD scale. The resulting figures are used to compute equivalised expenditures, which are attributed to each member of the household, in order to compute the low levels of expenditure indicator.
Information on assets and liabilities is from the Household Finance and Consumption Survey (HFCS), in particular the first, second and fourth waves conducted in 2010, 2014 and 2021. The HFCS is run by national central banks and coordinated by the European Central Bank. It collects information on assets, liabilities, and to a limited extent, income and consumption, of households.
Context
In order to support its agenda for social fairness and a good balance between economic and social goals, the European Commission has stressed the need to bring social indicators up to a par with macroeconomic indicators within the EU's reinforced macroeconomic governance. To this end, it is important to ensure the availability of harmonised statistics at EU level that cover the distributional aspects of household income, consumption and wealth (ICW).
In September 2016, the Directorates General of the National Statistical Institutes (DGINS) conference[1] in Vienna stressed the importance of ICW statistics shedding light on people's material well-being and on inequality. The conference concluded that there was a need for a harmonised statistical framework on ICW based on a multi-source approach integrating existing sources of data (EU-SILC, Household Budget Survey (HBS) and the Household Finance and Consumption Survey (HFCS). These data are the first outcome of a data integration effort that will be pursued and improved in the coming years.
In the meantime, Eurostat has launched a section on its website dedicated to the dissemination of experimental statistics. These statistics use new data sources and methods in an effort to expand and improve Eurostat's response to its users' needs. Since the statistics presented in this article come from experimental data processing and are based on statistical assumptions, they belong to this section until they reach a sufficient level of maturity.
Notes
- ↑ The DGINS conference is held once a year and is aimed at gathering the Directors General of the National Statistical Institutes to discuss topics related to the statistical program.
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Methodology
- Income, consumption and wealth - experimental statistics (icw) (ESMS metadata file — icw_esms)