Material deprivation statistics - early results


Data extracted in May 2019

Planned article update: May 2020

Highlights
The share of people unable to face unexpected expenses fell most between 2017 and 2018 in Bulgaria, Romania and Latvia.
The share of people unable to afford a week's annual holiday fell most between 2017 and 2018 in Bulgaria, Croatia and Romania.
The share of people unable to afford a meal with meat, fish, chicken or vegetarian equivalent every other day fell most between 2017 and 2018 in Hungary, Romania and Italy.

Population unable to face unexpected financial expenses, 2018 (early data)


Increased timeliness of the EU-SILC data

Since 2014 Eurostat disseminates early results for severe material deprivation rates so that trends in poverty levels can be tracked more closely. The coverage and the timeliness has increased over the years. Twenty-one European Union (EU) Member States and Norway have already submitted 2018 EU-SILC data (EU Statistics on Income and Living Conditions). Bulgaria, Latvia, Hungary, Austria and Finland have provided final data, while Belgium, Czechia, Denmark, Germany, Estonia, Greece, France, Croatia, Italy, Cyprus, Malta, the Netherlands, Portugal, Romania, Slovenia, the United Kingdom, and Norway have transmitted provisional data.

This article is based on data sent to Eurostat by early May 2019. Final EU-SILC cross-sectional data for 2018 are already available for 4 EU Member States (Denmark, Finland, Hungary and Latvia); 17 EU Member States and Norway provided provisional material deprivation and 'economic strain' data.


Full article

Material deprivation rate: country variations

Material deprivation rates gauge the proportion of people whose living conditions are severely affected by a lack of resources. The severe material deprivation rate represents the proportion of people living in households that cannot afford at least four of the following nine items:

  • mortgage or rent payments, utility bills, hire purchase instalments or other loan payments;
  • one week’s holiday away from home;
  • a meal with meat, chicken, fish or vegetarian equivalent every second day;
  • unexpected financial expenses;
  • a telephone (including mobile telephone);
  • a colour TV;
  • a washing machine;
  • a car; and
  • heating to keep the home adequately warm.

The severe material deprivation rate, broken down by sex, age group and household type, is the main indicator for material poverty in this article.

Table 1: Severe material deprivation rates, 2014-2018 (early data) - (% share of population) - Source: Eurostat (ilc_mddd11)

Since 2014, the rate of severe material deprivation in the EU-28 decreased from 8.9 % to 6.2 %, i.e. by 2.7 percentage points (pp). The rate varies significantly from country to country (see Table 1):

  • the lowest levels among the EU and EFTA countries were recorded in Sweden (0.8 % in 2016)and in Norway and Luxembourg (1.2 % in 2014 and in 2017 respectively).
  • the highest values were in Bulgaria (34.2 % in 2015), Romania (25.9 % in 2014) and Hungary (24.0 % in 2014).

The proportion of the materially deprived population decreased in almost all countries, or oscillated within a small range only. Between 2014 and 2018, the largest decreases in the proportion of persons lacking resources were observed in Hungary (-13.9 pp), Bulgaria (-12.2 pp) and Latvia (-9.7 pp), reflecting the improving material living conditions in those countries.


In 2018, for most of the countries that sent data to Eurostat, the early severe material deprivation rate decreased compared to 2017. The exceptions are Finland, where the rate increased by 0.7 pp (from 2.1 % in 2017 to 2.8 % in 2018), France (+ 0.6 pp increase, from 4.1 % to 4.7 %), the United Kingdom (+ 0.5 pp, from 4.1 % to 4.6 %), and Denmark (+ 0.3 pp, from 3.1 % to 3.4 %) . In Germany the rate remained stable at 3.4 %. The largest decreases were registered in Bulgaria (from 30.0 % in 2017 to 20.9 % in 2018, - 9.1 pp), followed by Greece (from 21.1 % to 16.7 %, - 4.4 pp), and Hungary (from 14.5 % to 10.1 %, - 4.4 pp).

Severe material deprivation by household type

The early severe material deprivation rates available for the year 2018 confirm a pattern seen in previous years, namely a higher incidence among:

  • people living in single person households with dependent children;
  • single person households; and
  • households with two adults and three or more children (see Table 2).
Table 2: Severe material deprivation by household type, 2018 (early data) - (% share of population) - Source: Eurostat (ilc_mddd13)

Households with two adults and one dependent child were the least affected by severe material deprivation in Bulgaria, Romania, Croatia, Italy, Latvia, Portugal and Malta. In Belgium, Czechia, Denmark, Germany, Greece, France, Cyprus, Hungary, Austria, Finland, the United Kingdom and Norway, the least affected were households with two adults, where at least one was aged 65 or more. In Estonia, the Netherlands and Slovenia the least affected households had two adults with three or more dependent children in 2018.

Severe material deprivation by age

In general, over the years the severe material deprivation is worst among persons below 18 years. Elderly persons (aged 65 and over) are less affected than working age adults (18 to 64 years). Since 2014, in most of the Member States for which early data is available, the rates decrease by age (see Table 3).

Table 3: Severe material deprivation rate by age, 2014-2018 (early data) - (% share of population) - Source: Eurostat (ilc_mddd11)

Between 2017 and 2018 the severe material deprivation rate rose for under-18-year-olds in Denmark and in the United Kingdom, both with +1.2 pp, Finland (0.9 p), France ( 0.6 pp), Belgium (0.5 pp) and Estonia (0.1 pp); but it fell by more than 1.5 pp in Bulgaria (-14.0 pp), Greece (-5.2 pp), Hungary (-4.0 pp), Latvia (-2.0 pp), Romania (-1.8 pp), Italy, Austria and Portugal (-1.7 pp each).

The rates for working age adults (aged 18 to 64) decreased from 2017 by 1.5 pp or more in Bulgaria (-9.7 pp), Greece (-4.8 pp), Hungary (-4.6 pp), Romania (-3.2 pp), Latvia (-2.0 pp), Croatia (-1.8 pp), and Italy (-1.5 pp). Year on year changes for the other countries are less significant.

In 2018, decreases of more than 1.5 pp were observed for elderly persons aged 65 and over in Hungary (-3.8 pp), Bulgaria (-3.6 pp), Romania (3.2 pp), Italy (-2.4 pp), Greece (-2.2 pp), Croatia (-2.0 pp) and Estonia (-1.6 pp). Year on year changes for the other countries are less notable.

Factors of material deprivation

As in previous years, the early data for 2018 show that severe material deprivation rates are determined mainly by changes in the ability to afford:

  • unexpected financial expenses;
  • a meal with meat, chicken or fish (or vegetarian equivalent) every second day; and
  • one week’s holiday away from home.

These items, for which deprivation rates are highest, are also those that are not durable investment items, but are largely covered (or not) by monetary income available for household expenditure. Rate changes for these items thus provide an early indication of changes in monetary income.

Figure 1: Population unable to face unexpected financial expenses, 2014, 2017 and 2018 (early data) - (% share of population) - Source: Eurostat (ilc_mdes04)

The percentage of people who said they were unable to face unexpected expenses fell, compared with 2014 data, in most of the countries for which early data are available (see Figure 1), with more than 10 pp in Hungary (-42.6 pp), Bulgaria (-17.5 pp), Czechia (-17.1 pp), Slovenia (-12.8 pp), Latvia (-12.1 pp), Malta (-10.9 pp), Croatia (-10.7 pp) and Cyprus (-10.2 pp). On the contrary, the early data show a worsening situation in this respect only in Belgium (0.4 pp). Other countries show small decreases in the percentage of people saying that they were not able to face unexpected expenses or a stable situation.

Compared with 2017 final data, in 2018, the percentage of people who said they were unable to face unexpected expenses fell in most of the countries for which early data are available. The largest decreases were observed in Bulgaria (-21.1 pp), Romania (-6.6 pp) and Latvia (-4.6 pp). In contrast, the early data show a worsening situation in this respect (more than 1 pp) in the United Kingdom (1.1 pp), Hungary and France (1.8 pp each) and in Norway (2.3 pp).

Figure 2: Population unable to afford to go for a week's annual holiday, away from home, 2014, 2017 and 2018 (early data) - (% share of population) - Source: Eurostat (ilc_mdes02)

Compared with 2014, the early data for 2018 show that the percentage of the population that cannot afford to go on a week’s annual holiday decreased in almost all countries for which those data are available. The largest improvements (more than 10 pp) were recorded for Malta (-22.0 pp), Bulgaria (-19.4 pp), Croatia (-18.8 pp), Hungary and Czechia (-16.6 pp each), Portugal (-14.3 pp) and Latvia (-12.1 pp) (see Figure 2). Only for Greece was there a small increase of 1 pp in the percentage of the population that cannot afford a one week's holiday per year.

Compared with 2017, the early data for 2018 demonstrate that the percentage of the population that cannot afford to go on a week’s annual holiday decreased considerably, by more than 4 pp, in Bulgaria (-22.1 pp), Croatia (-6.9 pp), Romania (-6.1 pp), Hungary (-5.2 pp), Latvia (-4.5 pp) and Czechia (-4.3 pp) (see Figure 2).

Figure 3: Population unable to afford a meal with meat, fish, chicken or a vegetarian equivalent every second day, 2014, 2017 and 2018 (early data) - (% share of population) - Source: Eurostat (ilc_mdes03)

Since 2014, the percentage of people who said they could not afford a meal with meat, fish, chicken or a vegetarian equivalent every second day decreased substantially, by more than 7 pp, in Hungary (-15.4 pp), Malta (-9.9 pp), Bulgaria (-8.1 pp), Czechia (-7.5 pp) and Latvia (-7.3 pp). It increased in Belgium by 0.4 pp, in Finland by 0.5 pp and in Norway by 0.9 pp (see Figure 3).

In 2018, the percentage of people who said they could not afford a meal with meat, fish, chicken or a vegetarian equivalent every second day decreased compared with 2017 the most in Hungary (-4.1 pp), Romania (-2.9 pp), Italy (-2.5 pp), Czechia and Cyprus (-1.8 pp each). It increased in the Netherlands and the United Kingdom (0.1 pp each) and Finland (0.6 pp). The remaining Member States showed smaller changes (see Figure 3).

Making ends meet

The economic strain variable ‘making ends meet’ is included in the early transmission of data. It is related to current income and enables the timely detection of trends in poverty.

The 2018 data are broadly consistent with the situation in previous years (see Table 4); in other words:

  • in most countries, the highest proportion of people said that they had either ‘some difficulties’ or that it was ‘fairly easy’ for them to make ends meet ('Medium' ability to make ends meet);
  • Greece, Bulgaria and Cyprus were the countries with the highest proportion of people declaring ‘difficulties’ or ‘great difficulties’ in making ends meet ('Low' ability to make ends meet); and
  • the highest proportion of people who could make ends meet ‘easily’ or ‘fairly easily’ ('High' ability to make ends meet) were found in the Netherlands and Norway.
Table 4: Ability to make ends meet, 2014-2018 (early data) - (% share of population) - Source: Eurostat (ilc_mdes09)

Significant changes in 2018 compared to 2017:

  • major decreases in the proportion of people who had ‘difficulty’ or ‘great difficulty’ in making ends meet ('Low' ability) in Latvia (-8.4 pp), Hungary (-6.5 pp), and Czechia (-5.8 pp);
  • in the proportion of people who could make ends meet ‘easily’ or ‘very easily‘ ('High' ability) a decrease in Norway (-0.2 pp) and a minor decrease for Denmark and Germany (-0.1 pp each). Increases of more than 1 pp were recorded in Finland (7.3 pp), Estonia (4.3 pp), Czechia (4.0 pp), the United Kingdom (3.2 pp), Cyprus (2.8 pp), Italy (2.2 pp), Latvia (2.1 pp), Slovenia (2.0 pp), Malta (1.6 pp), Belgium, the Netherlands and Austria (1.4 pp each).

Source data for tables and graphs

Data sources

In Eurostat’s online database, provisional indicators are flagged ‘p’ (provisional) to distinguish them from final data. The difference between provisional data and final data is explained in the section on ‘Data sources’. For the countries for which only provisional data is available, the analysis is merely indicative: in some cases, there may be discrepancies between provisional and final data.

EU-SILC, established under ‘framework’ Regulation (EC) No 1177/2003, is the reference source for statistics and indicators on income and living conditions. It is a multi-purpose instrument that focuses mainly on income, collecting detailed income components at household and individual level, but also gathers information on social exclusion, material deprivation, housing conditions, labour market participation, education and health.

Monetary income is one of the most relevant factors for assessing poverty and inequality, but wealth and consumption levels are also relevant, linked as they are to material deprivation, i.e. the inability to afford goods and services and/or to engage in activities seen by society as ‘ordinary’ or ‘necessities’. Currently, by the time statistics have been processed and indicators been released, EU-SILC final data have a lag of almost two years in the case of monetary income data and 1 to 1 1/2 years in the case of non-monetary information. This has profound implications for EU-SILC’s usefulness for policy purposes, especially in times of rapid economic changes.

Since the March 2000 Lisbon Summit, Member States and the Commission have cooperated in the field of social policy on the basis of the ‘open method of coordination’ (OMC). To monitor the social OMC, the EU and its Member States have adopted commonly agreed indicators, including in the area of material deprivation. In particular, the severe material deprivation rate is a component of the Europe 2020 ‘at risk of poverty or social exclusion’ headline indicator, calculated as the total number of persons at risk of poverty, severely materially deprived or living in households with very low work intensity.

More recently, the need for early estimates of material deprivation was highlighted in the Commission Communication Towards Social Investment for Growth and Cohesion — including implementing the European Social Fund 2014-20 (COM (2013) 83 final). Nine Member States sent Eurostat the first provisional material deprivation and economic strain data for 2012, so that it could carry out a feasibility study on calculating relevant variables and indicators. Its main conclusions were that the computation of provisional material deprivation indicators was feasible and that preliminary indicators were close to final material deprivation values. It found that there were two main reasons for discrepancies between early and final values:

  • the application of preliminary cross sectional EU-SILC weightings, as not all information needed to work out final weightings is available at the end of the data collection year; and
  • for some Member States, data editing could be finalised only partially by the early data submission date.

All EU-SILC microdata transmitted to Eurostat must contain individual and household weightings. In all household surveys, mainly because of non-response, some groups are over-represented and others under-represented in the raw data. These imbalances are usually dealt with by attaching a compensatory weighting to members of sub-groups thought to be over- or under-represented in the survey data.

All survey estimates are calculated using these weightings. Data are calibrated to align totals from the survey to known totals from reliable external sources such as recent population statistics, including information on age, gender, regional breakdowns, the labour force, etc. All these variables might not be fully available to national statistical institutes at the time the early material deprivation variables have to be transmitted, i.e. at the beginning of the year after data collection. The information used to construct the cross sectional household and individual weightings is specific to each Member State and decided at national level [1]. Several procedures are applied to construct the provisional weightings, which might (as the study showed) come very close to the final weightings.

Context

The recent economic and financial crisis has thrown up a number of challenges for official statistics and social statistics in particular, where the timeliness of data and indicators has become a key issue in the debate.

Eurostat (together with the Member States) initiated the transmission of early data on material deprivation and economic strain variables collected through the EU-SILC survey in response to the urgent needs of social policymaking. Although monetary poverty is one of the most relevant factors when assessing poverty and social inclusion, material deprivation is also an important descriptor of the difficulties households face in achieving the living standards considered by society to be normal.

Material deprivation data and indicators are absolute measures that can be used to analyse and compare aspects of poverty in and across Member States. Currently, data are made available during the spring of the year following the survey year and cover three quarters of the Member States. To supplement the early data on material deprivation Eurostat develops flash estimates of the main EU-SILC indicators as experimental statistics.

The aim is to disseminate early non-income data, eventually for all Member States, at the end of the survey collection (reference) year or at the very beginning of the next year. It is not possible to bring forward the provision of comprehensive monetary income data in the same way, as this takes more time to make them available in a majority of countries. Meantime the flash estimates could feed the preliminary discussions and analysis until the final EU-SILC data is released

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Income and living conditions (t_ilc)
Material deprivation (t_ilc_md)


Income and living conditions (ilc)
Material deprivation (ilc_md) (Implementation of changes in variables)
Material deprivation by dimension (ilc_mddd)
Economic strain (ilc_mdes)

Notes

  1. More information can be found in the national quality reports