Elsevier

Energy Policy

Volume 119, August 2018, Pages 168-182
Energy Policy

Energy affordability in the EU: The risks of metric driven policies

https://doi.org/10.1016/j.enpol.2018.03.033Get rights and content

Highlights

  • Energy expenditure shares are noticeably higher in New Member States than in the EU15.

  • A common pan-EU fuel/energy poverty metric is likely to be problematic.

  • Targeting high-level fuel poverty metrics could distort policymaking.

  • New survey questions on the lived experience of fuel poverty are suggested.

Abstract

This paper provides a pan-EU mapping of energy affordability using energy expenditure shares. Large variations in energy expenditure shares are identified, with the shares being significantly higher in New Member States than the EU15. First, these variations indicate that a single expenditure-based pan-EU fuel poverty metric is problematic; there is a trade-off between a metric identifying households in most need within individual Member States and one identifying households in a similar position across Member States. Second, household-level data from the UK, France and the Republic of Ireland are used to simulate the impact of ‘policy interventions’, involving energy expenditure reductions or income increases, on the recorded rate of fuel poverty. These simulations highlight that emphasising high-level fuel poverty metrics may distort policymakers’ choices towards improving the ‘picture’ of fuel poverty rather than maximising welfare improvements. Robust impact assessments identifying the fuel poverty interventions which deliver the greatest welfare increases for a given cost offer a better means of policy evaluation.

Introduction

Energy affordability has become an increasingly important issue in the EU1 with CEER-BEUC's 2020 Vision for Europe's Energy Customers2 including ‘Affordability’ as one of its four core principles to which energy regulators should adhere. There is also increased focus on energy poverty at the EU level with the establishment of The Energy Poverty Taskforce3 and the European Energy Poverty Observatory4 in 2016. The present paper expands the discourse on energy/fuel poverty5 towards the wider topic of energy affordability and the distribution of energy market outcomes across EU households. While fuel poverty is defined as “the phenomenon whereby a household struggles to afford adequate (energy) services”,6 the present paper assesses wider variations in the extent to which energy services are more or less affordable across household groups and Member States (MS). Here, fuel poverty is seen as a notable subset of the energy affordability topic.7

First, the paper maps differences in energy affordability across the EU using energy expenditure share (ENEXShr) data thereby complementing the existing literature which utilises European Union Statistics on Income and Living Conditions (EU-SILC)8 to compare households’ self-reported assessments of affordability. The average ENEXShr data presented in Section 5.1 emphasises a striking difference in energy affordability between the EU15 and New Member States (NMS). In 2010, the average ENEXShr across the EU15 was 4.6%, but among NMS it was 10.9%.

Second, the paper analyses individual household-level data from the UK, France and the Republic of Ireland (RoI) to highlight the main fuel poverty metrics provide only a picture of fuel poverty and households’ welfare. Labelling a particular metric ‘official’ risks encouraging policymakers to implement policies delivering the largest improvements in the official metric rather than policies delivering the greatest welfare improvements to the households in most need.

While gathering further evidence on the extent of fuel poverty is important, Section 6.3 argues this evidence should be seen ‘in the round’ and the levels of individual indicators should not be used as ‘targets’ against which policy performance is assessed. This recommendation goes against a conclusion of Hills (2012), when designing the UK's Low Income-High Cost (LIHC) metric, that there should be greater integration between high-level metrics and fuel poverty policies.9 Policymakers need to accept that most fuel poverty metrics are likely to be imperfect: metrics with desirable statistical characteristics may be difficult to communicate to non-specialists or require extensive, i.e. costly, data collection. Robust impact assessments comparing the energy expenditure (ENEX) reductions or welfare gains achieved against the costs of an intervention are a more direct way to assess the benefits of fuel poverty alleviation schemes.

The analysis for the UK, France and the RoI simulates the impact of ‘policy interventions’ on the percentage of households devoting at least 10% of their expenditure to energy (ENEX10).10 The ‘policy interventions’ involve ENEX reductions or income increases targeted at alternative household groups. The simulations, presented in Section 5.3, demonstrate that:

  • (i)

    even ‘large’ interventions reduce ENEX10 by relatively small amounts;

  • (ii)

    the ‘effectiveness’ of interventions in reducing ENEX10 depends on the ENEXShr distribution and average income in the target group;

  • (iii)

    and increasing household income has virtually no impact on ENEX10 despite welfare gains for households.

This analysis complements the work of Heindl and Schuessler (2015), by extending fuel poverty simulations to additional MS and highlighting the factors affecting the ability to improve recorded fuel poverty.

Given the ENEXShr variations reported in Section 5.1, adopting a common ENEX based fuel poverty metric across the EU is likely to be problematic. If a common fixed ENEX threshold, such as ENEX10, were adopted, in some NMS such a high proportion of the population would be identified as fuel poor that the classification's usefulness for targeting within the MS would be lost. Equally, if a ‘relative’ ENEX metric was selected, the nature of households labelled as ‘fuel poor’ would vary considerably between MS. The large variations also suggest that a ‘rational’ EU-wide fuel poverty policy would require significant cross-border transfers, something which could face political obstacles. This paper's evidence provides support to the European Commission's position, expressed by Vice-President Maroš Šefčovič, that there should not be a common EU definition of energy/fuel poverty due to the differing circumstances of each MS.11

The current paper stands in contrast to Thomson’s et al. (2016) arguments that a common EU fuel poverty definition would be beneficial by increasing fuel poverty's prominence12 and clarifying the term's meaning. As any fuel poverty definition incorporates value judgements, and social policy is the responsibility of MS, it seems appropriate for democratically elected national governments to choose their preferred fuel poverty definition and policy. Also, having a common definition which is not optimised for specific MS's circumstances risks misdirecting resources. Nevertheless, the current paper agrees with Thomson et al. (2016) that the EU has a legitimate role in enabling policy synergies across MS. The EU can support synergies by increasing the availability of high-quality pan-EU affordability data, as argued by Thomson et al. (2017), and by collating robust impact assessments that identify effective policy interventions.

Section 5.2 also highlights how tracking EU-SILC indicators through time draws attention to challenges in these indicators’ interpretation. The discussion of this point in Section 6.2 adds to Thomson et al. (2017) and Tirado Herrero (2017) by specifying lived experience indicators which capture tightly defined situations faced by fuel poor households.

Section snippets

Background – energy affordability indicators

There are a variety of ways to assess energy affordability and fuel poverty, as discussed by Thomson et al. (2017) and Tirado Herrero (2017). Fig. 1 shows how these indicators broadly fall into three categories: (a) ENEX-based indicators, (b) self-reports of the lived experience,13 and (c) proxy indicators. As argued by Tirado Herrero (2017), it is difficult to identify a single ‘best’

Methodology

Two main methods are followed: (i) analysis of descriptive statistics utilising high-level data from Eurostat data tables, and (ii) simulations of alternative policies on ENEX-based fuel poverty rates harnessing micro-data from France, the RoI and the UK.

Eurostat data

A pan-EU perspective on energy affordability is possible using two data sources: (i) Eurostat's collated household budget survey database25 and (ii) EU-SILC data.26 (i) enables comparison of average ENEXShr, while (ii) includes the percentage reporting an ‘inability to keep your home adequately

Results and discussion

Results from three distinct, but linked, pieces of analysis are reported. In Section 5.1 the Eurostat ENEXShr data is used to argue an ENEX-based fuel poverty indicator is unlikely to be suited to the specific purpose of providing a single pan-EU fuel poverty indicator. The simulations reported in Section 5.3 widen the critique of ENEX-based fuel poverty indicators to the general policymaking setting by emphasising these indicators only ever provide a ‘picture’ of fuel poverty. Unless used with

The role of energy expenditure share distributions

The simulations’ main insight is that, if a policymaker wants to improve the picture of fuel poverty given by high-level ENEX metrics, understanding ENEXShr distributions among different household groups is valuable. Understanding the ENEXShr distribution has two impacts, one positive and one that can distort policy. The positive effect is to ensure a target group has a high proportion of fuel poor individuals. The potential distortion is that to achieve the greatest improvement in a high-level

Acknowledgements

The financial support provided by Microsoft, EDF Energy, the Utility Regulator Northern Ireland and E-Control, the Austrian energy regulator, through the Centre on Regulation in Europe (CERRE) is gratefully acknowledged. The author thanks participants at a CERRE workshop and a DG Ener presentation for their comments, in particular, Claire Milne. Catherine Waddams is thanked for her leadership of the CERRE project and valuable comments. The reviewers of this article are thanked for their

Funding

This work was supported by Microsoft, EDF Energy, the Utility Regulator Northern Ireland and E-Control, the Austrian energy regulator, through the Centre on Regulation in Europe (CERRE).

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