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Abstract

The COVID-19 pandemic had varying impacts on social groups, depending on existing disadvantages, and it was widely believed that it triggered a rise in inequalities across different areas of life. Using indicators from the EU’s Multidimensional Inequality Monitoring Framework (MIMF), this report shows how inequality in the spheres of income, health, employment and education changed between 2010 and 2020. It also examines the main drivers of this change during the pandemic and explores the relationships between government policies in several domains and inequality.

Key findings

The first year of the COVID-19 crisis saw the fall in income inequality continue, confirming a levelling of inequality in the EU. However, those seeking work and people with low and medium education levels were most likely to experience a drop in income during the pandemic, highlighting that although income inequality overall may not have increased during COVID-19, it will be critical for policymakers to monitor this closely in the current cost-of-living crisis.

Health and income inequality are closely linked, with people in the lowest income quintile almost three times as likely to have a disability as people in the top 20%. During the pandemic, inequality in access to health services by income also increased: in 2020, the risk of having an unmet medical need for people in the lowest income quintile was 5.4 times higher than those in the top 20%, highlighting how policies focusing on reducing income inequalities can also reduce health inequalities.

Findings reveal that working from home during the pandemic may have created inequalities between low- and high-income groups, where temporary workers, young people and those in precarious employment emerged as more vulnerable to crises. To ensure this does not continue in the increasingly flexible world of work post-COVID, it will be crucial for policymakers to tackle precarious work and increase the transparency and predictability of working conditions.

During the pandemic, having adequate equipment to carry out online learning was more important than income, highlighting the importance of tackling the digital divide and access to technology for all over the long term. Parents and students living in rural areas, not needing to commute during this period, were also more likely to be satisfied with the quality of online schooling or education than those living in cities.

The ability to work from home created inequalities between low- and high-income groups, accentuating gender inequality in childcare and housework. In 2020, single mothers were most likely to reduce their working hours due to the closure of schools and childcare facilities – if women continue to work more hours of unpaid care than men, this may potentially widen the gender wage gap during recovery.

The report contains the following lists of tables and figures.

List of tables

Table 1: Indicators selected for the income inequality analysis
Table 2: OLS regression model exploring the relationship between government spending and inequality in making ends meet according to education level
Table 3: Panel OLS regression exploring general drivers of income inequality (1995–2020), EU27
Table 4: OLS regression model exploring drivers of income inequality between rural and urban households
Table 5: OLS regression model exploring income inequality by individual characteristics
Table 6: Logistic regressions on income inequality by individual characteristics
Table 7: Indicators selected for the health inequality analysis
Table 8: OLS regression model exploring the relationship between government expenditure and inequality in chronic disease
Table 9: Multilevel logit regression model on worsening health between 2019 and 2020
Table 10: Multilevel logit regression models on worsening health and mental health between 2019 and 2020
Table 11: Indicators selected for the employment inequality analysis
Table 12: OLS regression model exploring the relationship between government expenditure and inequality in opportunity in having a white-collar job
Table 13: OLS regression model exploring the relationship between gender inequality in occupations, childcare and paid leave at country level
Table 14: OLS regression model exploring the relationship between gender inequality in being employed, childcare and paid leave at country level
Table 15: Random effects within–between model showing the relationship between gender inequality in employment, over time and between countries
Table 16: Multilevel linear regression model on the number of hours worked in 2019 and 2020
Table 17: Multilevel linear regression model on the change in the number of hours worked between 2018 and 2019 and between 2019 and 2020
Table 18: Indicators selected for inequality in education analysis
Table 19: OLS regression model exploring the relationship between government spending and inequality in PISA scores
Table 20: Determinants of respondents’ satisfaction with the quality of their children’s online schooling (multilevel ordered logit model)

List of figures

Figure 1: Dimensions of life of the EU MIMF
Figure 2: Intersectional approach to effects of COVID-19 on inequality
Figure 3: Macro-, meso- and micro-level factors in income inequality during the COVID-19 pandemic
Figure 4: Heatmap showing the results of income inequality indicators by country, 2018–2019, EU27 and the UK
Figure 5: Income quintile share ratio (S80/S20) for equivalised disposable income, EU27
Figure 6: Gini coefficient of equivalised disposable income, EU27, Bulgaria, Greece and Poland
Figure 7: Odds ratio of a household having problems making ends meet (with versus without a tertiary education, 2018) against spending on education (2015, % of GDP), EU27 and the UK
Figure 8: Odds ratio of a household having problems making ends meet (with versus without a tertiary education, 2018) against spending on social protection (2015, % of GDP), EU27 and the UK
Figure 9: Scatterplot of government spending on social protection (% of GDP at time t–1) relative to the Gini index of disposable income at time t (1995–2020), EU27
Figure 10: Odds ratio of households having problems making ends meet (rural versus urban, 2018) against public investments in agricultural R&D (2015, % of GDP), EU27 and the UK
Figure 11: Households that reported that their income decreased in 2020 compared with the previous year by country (%), selected Member States
Figure 12: Households containing people aged 50+ that received financial support from the government due to the pandemic by country (%), selected European countries
Figure 13: Recipients of pandemic-related government support by country, EU27 (%)
Figure 14: Macro-, meso- and micro-level factors in health inequality during the COVID-19 pandemic
Figure 15: Heatmap presenting the results of health inequality indicators, 2018–2019, EU27 and the UK
Figure 16: Map of odds ratios of people reporting unmet medical care needs (women versus men, adjusted), 2018
Figure 17: Heatmap of odds ratio of feeling depressed for different social groups, 2018–2019, EU27 and the UK
Figure 18: Risk ratios of having a severe long-standing limitation in usual activities (disability) due to a health problem for various social groups (2010–2020), EU27
Figure 19: Risk ratios of having an unmet medical need due to high cost, distance to travel or waiting lists for various social groups (2010–2020), EU27
Figure 20: Government spending on education in 2002 (% of GDP) relative to ex ante inequality of opportunity in having two or more chronic diseases in 2019 (aged 50+), EU27
Figure 21: Macro-, meso- and micro-level factors in inequality in working life outcomes during the COVID-19 pandemic
Figure 22: Heatmap showing results of working life inequality indicators, 2018–2019, EU27 and the UK
Figure 23: Risk ratios of gender inequality in various dimensions of working life (2002–2020), EU27
Figure 24: Risk ratios of unemployment rates among various social groups (2002–2020), EU27
Figure 25: Risk ratios of employment rates among various social groups (2002–2020), EU27
Figure 26: Odds ratio of women being in employment versus men (2019) against the share of children under three years of age in formal childcare (2019, %), EU27
Figure 27: Average number of weekly hours worked in 2020 by country and contract type, selected EU Member States
Figure 28: Proportion of women who held second or third jobs by household type, 2020 (%)
Figure 29: Macro-, meso- and micro-level factors in inequality in education and learning during the COVID-19 pandemic
Figure 30: Heatmap showing results of education inequality indicators, 2018–2019, EU27 and the UK
Figure 31: Difference in tertiary education attainment as a whole in 55- to 74-year-olds and those with parents with a lower than tertiary education (2021)
Figure 32: Trends regarding inequality in education between women and men (2002–2020), EU27
Figure 33: Risk and odds ratios of NEET rates between various social groups (2004–2020), EU27
Figure 34: Government spending on education (2013, % of GDP) against P90/P10 PISA scores in mathematics (2018), EU27 and the UK
Figure 35: Parents’ satisfaction with the quality of online schooling for their children, EU27 (%)
Figure 36: Parents’ satisfaction with the quality of their children’s online schooling depending on whether they worked from home or not during the pandemic, EU27 (%)

Number of pages
102
Reference nº
EF22002
ISBN
978-92-897-2309-1
Catalogue nº
TJ-07-23-019-EN-N
DOI
10.2806/439913
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