High-value datasets – measuring inequality in the EU
Publication Date/Time
2023-03-01T11:01:00+00:00
Exploring data sources to measure income inequality in Europe
This is part of a series of articles showcasing examples of high-value
datasets from their different thematic categories. High-value datasets
are defined by EU law based on their potential to provide essential
benefits to society, the environment and the economy. This series aims
to help readers find reliable and accurate information from official
sources relating to the availability of various high-value datasets,
and to present this information through data visualisation. You can
check out the article providing an overview of high-value datasets
here
[https://data.europa.eu/en/publications/datastories/high-value-datasets-overview-through-visualisation].

Only datasets specifically defined by law can be considered high-value
datasets, and as such the data presented in the articles does not
necessarily fall under that definition. Instead, the data has been
chosen to be thematically adjacent to high-value datasets and to
showcase what can be done with information made available by official
EU bodies and EU Member States. The official list of high-value
datasets adopted on 12 December 2022 can be found in the legal
documents
[https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=PI_COM:C(2022)9562]
that define them and their characteristics.

INCOME INEQUALITY AND HIGH-VALUE DATASETS

EU institutions acknowledge the significance of income inequality
indicators, which are considered highly valuable measurements. Some of
these measurements have been proposed as high-value datasets. The
‘statistics’ category includes datasets relating to inequality, as
outlined in Regulation 2019/1700 and Implementing Regulations
2019/2180, 2019/2181 and 2019/2242.

Specifically, the high-value dataset measurements include ‘yearly
inequality rate’ which has two key variables. The first variable is
the 80/20 ratio of total income received by the 20 % of the
population with the highest income (top quintile) to that received by
the 20 % of the population with the lowest income (lowest quintile).
The second variable is the Gini coefficient of equivalised disposable
income, which measures the extent to which the distribution of
disposable income after social transfers, adjusted for family size,
deviates from a perfectly equal distribution.

For the 80/20 income ratio, sex and age breakdowns are mandatory,
while regional breakdowns are only required for Member States when
possible, as outlined in the relevant regulation. The Gini coefficient
is a more comprehensive measure, and no specific breakdowns are
required.
[https://data.europa.eu/sites/default/files/img/media/3.income-inequality7-01.png]
INCOME INEQUALITY DATA FROM EUROSTAT

Eurostat produces one of the most comprehensive datasets
[https://ec.europa.eu/eurostat/databrowser/view/ilc_di01/default/table?lang=en]
about the distribution of income in EU Member States. It is based on
the European Union Statistics on Income and Living Conditions (EU-SILC
[https://ec.europa.eu/eurostat/web/microdata/european-union-statistics-on-income-and-living-conditions])
and the European Community Household Panel (ECHP
[https://ec.europa.eu/eurostat/web/microdata/european-community-household-panel])
surveys. This dataset assesses the distribution of income among
individuals by ranking them from lowest to highest earners, and
subsequently dividing the population into various sized
‘segments’. For example, this dataset can be used to see what
share of a Member State’s total income goes to the richest or
poorest 1 % of the population – the first and last
‘percentiles’ of income distribution. To gain a comprehensive
understanding of income distribution, the population can be split into
four equal groups, known as quartiles, each comprising 25 % of the
total population. By analysing the distribution of income among these
groups, we can gain insight into the living conditions of both the
wealthy and the less fortunate.

The following visualisation shows the distribution of income among the
top and bottom 1 % of earners in several EU Member States, along with
the general distribution of income in all four quartiles of the
population. The data displayed in the first visualisation presents
2 years (2005 and 2021) to provide an understanding of how income
distribution has evolved over time. These particular years were
selected as they offer the most extensive coverage, as data for all
Member States and years was not available. The second visualisation
focuses on 2021 data and allows for better comparison between EU
Member States. The graphic also includes some information about the
actual earnings in euro for certain groups.
[https://data.europa.eu/sites/default/files/img/media/3.income-inequality7-02.png]
[https://data.europa.eu/sites/default/files/img/media/3.income-inequality7-03.png]
INCOME INEQUALITY DATASETS FROM DATA.EUROPA.EU

The data.europa.eu portal hosts several datasets
[https://data.europa.eu/data/datasets?locale=en&query=income%20AND%20inequality&page=1]
on income inequality, including data on income inequality rates in
Ireland, household earnings in the Caribbean Netherlands – three
municipalities of the Netherlands in the Caribbean Sea – and more.
One particularly interesting dataset
[https://data.europa.eu/data/datasets/6lghmjcpw6t20inenvzeoa?locale=en],
also originally produced by Eurostat, examines the ratio between the
top and bottom 20 % of earners in EU Member States. When the ratio
increases, it indicates that the top 20 % of individuals are
receiving a larger share of income compared to the bottom 20 %. The
dataset also includes breakdowns by gender and age, which are
illustrated in the following graphic.
[https://data.europa.eu/sites/default/files/img/media/3.income-inequality7-04.png]
The data.europa.eu portal also offers
[https://data.europa.eu/data/datasets?query=gini] datasets on the Gini
coefficient, which measures the concentration of earnings in a given
population. A value of 1 indicates perfect equality in the
distribution of income (everybody earns the same) while a value of 0
indicates maximum inequality (all the existing income goes to a single
person).

A dataset
[https://data.europa.eu/data/datasets/8a7416cbf6b5bbec2398fb82b3a479ee8c4c3d67?locale=en]
produced by the Belgian Federal Planning Bureau shows how the Gini
coefficient has changed over time in Belgium since 2004, and allows
for comparison to the EU-27 since 2010. For more recent years,
regional breakdowns are available, which reveal disparities in income
inequality between the Brussels Region and the Flemish and Walloon
Regions.
[https://data.europa.eu/sites/default/files/img/media/3.income-inequality7-05.png]
A more detailed view of the income ratio between rich and poor earners
can be found in another dataset
[https://data.europa.eu/data/datasets/9dc70859-8195-5ddc-858a-b1a71a3015b7]
produced by the German Ministry of Labour,
[https://ec.europa.eu/eurostat/statistics-explained/index.php?oldid=528159]
Health and Social Affairs of the State of North Rhine-Westphalia. It
illustrates the ratio of the top and bottom 10 % of earners in the
region from 2005 to 2019, and also provides details on the actual
earnings of these groups.
[https://data.europa.eu/sites/default/files/img/media/3.income-inequality7-06.png]
OTHER DATA SOURCES ON INCOME INEQUALITY

Eurostat has a section
[https://ec.europa.eu/eurostat/web/experimental-statistics/income-inequality-and-poverty-indicators]
of its experimental statistics dedicated to income inequality and
poverty. These statistics include flash estimates
[https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Flash_estimate&oldid=176150]
that provide timelier information and were used to estimate the impact
of events like the COVID-19 pandemic on income inequality
[https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Early_estimates_of_income_inequalities]
and labour
[https://ec.europa.eu/eurostat/statistics-explained/index.php?title=COVID-19_labour_effects_across_the_income_distribution&stable=0].
The ‘Statistics explained’ section
[https://ec.europa.eu/eurostat/web/experimental-statistics/income-inequality-and-poverty-indicators]
on Eurostat’s website provides a more general overview of living
conditions, income distribution and inequality in the EU.

Another EU organisation that produces data and analysis on income
inequality is Eurofound, an agency that aims to ‘assist in the
development of better social, employment and work-related policies.
Eurofound’s website has a section
[https://www.eurofound.europa.eu/topic/inequality] with articles on
income inequality and related topics such as social and gender
inequality.

Other research on this topic includes the EU Science Hub’s project
[https://joint-research-centre.ec.europa.eu/crosscutting-activities/facts4eufuture-series-reports-future-europe/beyond-averages-fairness-economy-works-people_en]
‘Beyond averages – fairness in an economy that works for
people’, which includes articles on inequality, social mobility and
the perception of fairness among EU citizens.

CONCLUSION

As demonstrated by its inclusion in the list of high-value datasets,
data on income inequality is  deemed to be of high importance to
society, the environment and the economy in the EU. EU organisations
provide an extensive collection of data about income inequality that
is accessible to the general public. These datasets can provide
valuable insights and information about income inequality and its
impacts on individuals, communities and society. They can help
policymakers, researchers and other stakeholders to better understand
the root causes of income inequality and develop effective policies to
address it. In addition, these datasets can be used to track trends in
income inequality over time and to identify areas where intervention
may be needed to reduce disparities and promote greater economic
fairness. Overall, the availability of these datasets is an important
resource for anyone interested in studying or addressing issues
related to income inequality.

METHODOLOGICAL NOTES

In the second graphic, the actual earning values presented are in euro
and are adjusted for purchasing parity standard to ensure
comparability. However, they are not adjusted for inflation and should
not be used to measure the growth of earnings over time. The data in
the third and fourth graphics is to be understood as equivalised
disposable income
[https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Equivalised_disposable_income],
which is income after tax and monetary transfers adjusted to account
for family size.

 

To download the visualisations, click on the following: HVD overview
[https://gitlab.com/Giuseppeascone/data-provider-repository/-/blob/master/Data%20stories/3.0_HVD_overview.png],
income distribution 2005-2021
[https://gitlab.com/Giuseppeascone/data-provider-repository/-/blob/master/Data%20stories/3.1_income_distribution_2005-2021.png],
income distribution 2021
[https://gitlab.com/Giuseppeascone/data-provider-repository/-/blob/master/Data%20stories/3.2_income_distribution_2021.png],
income quintile share ratio
[https://gitlab.com/Giuseppeascone/data-provider-repository/-/blob/master/Data%20stories/3.3_income_quintile_share_ratio.png],
GINI index
[https://gitlab.com/Giuseppeascone/data-provider-repository/-/blob/master/Data%20stories/3.4_GINI_index.png],
90/10 ratio
[https://gitlab.com/Giuseppeascone/data-provider-repository/-/blob/master/Data%20stories/3.5_90.10_ratio.png].

To download the data behind the visualisations, click on the
following: inequality data
[https://gitlab.com/Giuseppeascone/data-provider-repository/-/blob/master/Data%20stories/income_inequality_data_to_share.xlsx]

 

_Article by Davide Mancino_

_Data visualisations by Federica Fragapane_

 
