Shedding light on energy in the EU - A guided tour of energy statistics — 2019 edition
Digital publication – updated with 2017 data

Energy is essential for our day-to-day life. Turning on our computers or starting our cars are actions that we take for granted, yet they represent the final stage of a complex process, from extraction to final consumption. Statistics can help to make the complex process of energy more understandable. This is the aim of the digital publication Shedding light on energy in the EU – A guided tour of energy statistics. This new type of publication aims to answer questions on energy most frequently asked by citizens in an original way. Answers are provided through short texts, dynamic infographics, maps, videos, graphs, photos etc. This digital publication provides the statistical background which helps to better understand the challenges faced by the Energy Union. Both those not so familiar with the energy sector and experts in the domain should find something of interest. [+]

Reconciliation of EU statistics on income and living conditions (EU-SILC) data with national accounts

The coherence of household survey data with national accounts has been studied extensively in recent years, following the “Beyond GDP” initiatives. This paper compares income aggregates in EU-SILC and national accounts, adjusts for the main conceptual differences, and discusses factors that could influence the observed discrepancies. Following a proposal by Atkinson, Guio and Marlier (2017), sensitivity of key social indicators to the micro/macro-discrepancies is then examined by adjusting the micro data totals to match the reconciled macro aggregates. Three adjustment methods are tested (simple proportional scaling, calibration to margins, Pareto imputation), and their impact on the measures of income inequality and at risk of poverty compared. In line with other studies, the micro/macro gaps are found to vary significantly across countries, and are more substantial in property and self-employment income compared to wages and salaries and transfers received. The observed gaps are likely to be mostly due to measurement errors and conceptual differences. Adjusting the micro data with the gaps results in significant increases in inequality and median income levels, but more subdued changes in at risk of poverty rates. The results are sensitive to the adjustment methods as well as proper assessment of the micro/macro gaps. Caution is warranted if distributional indicators are computed from macro-adjusted micro data. [+]