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Abstract

La pandemia de COVID-19 tuvo diferentes repercusiones en los grupos sociales, en función de las desventajas existentes, y en general se considera que desencadenó un aumento de las desigualdades en diferentes ámbitos de la vida. A partir de los indicadores del marco de seguimiento de la desigualdad multidimensional para la UE (MFMI), en este informe se muestra cómo evolucionó la desigualdad en los ámbitos de la renta, la salud, el empleo y la educación entre 2010 y 2020. También se examinan los principales motores de este cambio durante la pandemia y se exploran las relaciones entre las políticas gubernamentales en diversos ámbitos y la desigualdad.

Key findings

Durante el primer año de la crisis de la COVID-19 el descenso de la desigualdad de ingresos continuó, lo que confirma una uniformidad de la desigualdad en la UE. Sin embargo, los demandantes de empleo y las personas con unos niveles de educación bajos y medios presentaron más probabilidades de experimentar una reducción de los ingresos durante la pandemia, lo que pone de relieve que, aunque la desigualdad de ingresos general pudo no haber aumentado durante la COVID-19, los responsables políticos tendrán que supervisar de cerca este parámetro en la actual crisis del coste de la vida.

La desigualdad en materia de salud guarda estrecha relación con la desigualdad de ingresos, ya que las personas encuadradas en el quintil de renta más bajo casi triplican las probabilidades de tener una discapacidad en comparación con las personas en el 20 % superior. Durante la pandemia, también aumentó la desigualdad en el acceso a los servicios sanitarios en función de los ingresos: en 2020, el riesgo de no haber visto atendida una necesidad médica entre las personas encuadradas en el quintil de renta más baja era 5,4 veces mayor que en el caso de quienes se encuentran en el 20 % superior, lo cual subraya hasta qué punto las políticas centradas en la reducción de las desigualdades en términos de ingresos también pueden reducir las desigualdades en materia de salud.

Las conclusiones revelan que trabajar desde casa durante la pandemia pudo haber generado desigualdades entre los grupos de renta baja y alta; los trabajadores temporales, los jóvenes y las personas con empleo precario se han revelado más vulnerables a las crisis. Para asegurarse de que esto no continúa en un contexto laboral cada vez más flexible tras la COVID-19, será fundamental que los responsables políticos aborden el trabajo precario y aumenten la transparencia y la previsibilidad de las condiciones de trabajo.

Durante la pandemia, disponer de equipos adecuados para el aprendizaje en línea se reveló más importante que el nivel de ingresos, lo cual pone de manifiesto la importancia de abordar a largo plazo la brecha digital y el acceso generalizado a la tecnología. La no necesidad de desplazarse durante este periodo redundó en que los padres y estudiantes residentes en zonas rurales se mostraran más satisfechos con la calidad de la educación en línea que los residentes en núcleos urbanos.

La capacidad de trabajar desde casa generó desigualdades entre los grupos de renta baja y alta, a la vez que acentuó problemas de desigualdad de género en relación con el cuidado de los hijos y el trabajo doméstico. En 2020, las mujeres al frente de familias monoparentales se vieron más expuestas a una reducción de su jornada laboral debido al cierre de colegios y guarderías; si las mujeres siguen trabajando más horas no remuneradas que los hombres en cuidados no remunerados, esto podría ampliar la brecha salarial de género durante la recuperación.

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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