Effects of income inequality on COVID-19 infections and deaths during the first wave of the pandemic: Evidence from European countries

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Vienna Yearbook of Population Research 2022 (Vol. 20), pp. 1–22
Effects of income inequality on COVID-19
infections and deaths during the first wave of the
pandemic: Evidence from European countries
David A. S´
anchez-P´
aez1
Abstract
Evidence from research on infectious diseases suggests that income inequality is
related to higher rates of infection and death in disadvantaged population groups.
Our objective is to examine whether there was an association between income
inequality and the numbers of cases and deaths during the first wave of the COVID-
19 pandemic in European countries. We determined the duration of the first wave
by first smoothing the number of daily cases, and then using a LOESS regression
to fit the smoothed trend. Next, we estimated quasi-Poisson regressions. Results
from the bivariate models suggest there was a moderate positive association between
the Gini index values and the cumulated number of infections and deaths during
the first wave, although the statistical significance of this association disappeared
when controls were included. Results from multivariate models suggest that higher
numbers of infections and deaths from COVID-19 were associated with countries
having more essential workers, larger elderly populations and lower health care
capacities.
Keywords: COVID-19; income inequality; first wave; European countries.
1 Introduction
In early 2020, a new coronavirus, SARS-CoV-2, also called COVID-19, arrived
in Europe from China. Mass outbreaks were first recorded in Italy and Spain, and
the virus then spread rapidly across the continent. Although European governments
adopted emergency measures to contain the pandemic’s advance, differences in the
1
Centre for Demographic Research (DEMO), Universit
´
e Catholique de Louvain. Louvain-la-Neuve,
Belgium
∗Correspondence to: David A. S´
anchez-P´
aez, david.sanchezpaez@uclouvain.be
https://doi.org/10.1553/populationyearbook2022.res1.1

2David A. S´anchez-P´aez
numbers of infections and deaths have been observed between countries. While
studies on the socioeconomic differences in the levels of COVID-19 infections and
deaths have been conducted in several European countries, none of these studies
compared these differences between countries.
The previous literature on this topic has pointed out that a disease can affect
societies differently depending on the vulnerability of their populations due to
conditions such as inequality or poverty. For instance, there is some evidence of
a positive association between income or wealth and self-reported health status
(Bor et al.,2017). Thus, health economists have argued that people with lower
socioeconomic status face worse health outcomes than their counterparts with
higher status, and that these differences can be explained in large part by two
mechanisms: health behaviour and access to health care (Bor et al.,2017;Santerre
and Neun,2012). The first mechanism refers to the tendency of poorer and less
educated people to be less well informed and less careful due to a lack of knowledge
and awareness of their health. The second mechanism refers to evidence that poorer
and less educated people tend to seek medical care less often, either because they
cannot stop working, or because they are concerned about the costs associated with
illness. Moreover, in the case of respiratory infectious diseases, social interaction
is a crucial determinant of the likelihood of becoming ill. When infected people
engage in economic or social activities, the risk of infection increases for healthy
individuals (Jung et al.,2020). In the current pandemic, wealthier individuals have
generally had more resources to self-isolate and telework, while people with lower
incomes have often been performing essential or manual work that cannot be
done remotely (Brown and Ravallion,2020;Jung et al.,2020;Lekfuangfu et al.,
2020;Papageorge,2020;Takian et al.,2020). Thus, the transmission pathways and
risk exposure levels have differed between socioeconomic groups. These societal
inequities have highlighted the vulnerability of the least favoured groups.
Income inequality is one of the non-biological factors that has been used to
explain adverse health outcomes, as it can affect the prevalence and consequences
of poor health within societies. Compared to middle- and high-income households,
low-income households tend to have lower life expectancy, higher mortality and
worse health status, even in developed countries (Bor et al.,2017;Jijiie et al.,2019;
Kawachi and Kennedy,1999;Krisberg,2016;Lynch et al.,1998,2000;Meara et al.,
2008;Neliss,1999;Olshans et al.,2012;Pickett and Wilkinson,2015;Rehnberg
et al.,2019;Shkolnikov et al.,2007;Villegas and Haberman,2014). Historically,
life expectancy and mortality have been unequal between the richest and the poorest
populations (Ahmed et al.,2020). In addition, more unequal societies tend to spend
less on income redistribution policies, such as strengthening health care systems
(Mello,2006).
Disparities arising from income inequality have also been observed in analyses of
the effects on populations of infectious respiratory diseases of viral origin. Studies
on the impact of seasonal influenza have found associations between socioeconomic
status and mortality, morbidity and symptom severity (Crighton et al.,2007;Tam
et al.,2014). Evidence from research on the Spanish flu, a pandemic comparable to

First wave of COVID-19 and income inequality: Evidence from European countries 3
COVID-19 in terms of its global reach, indicates that mortality rates were higher
among the poorest people (Bengtsson et al.,2018;Grantz et al.,2016;Mamelund,
2006;Murray et al.,2006;Sydenstricker,1931). However, no such mortality
differences by socioeconomic status were found in countries with low levels of
economic and social inequality (Rice,2005;Summers et al.,2014). The findings of
research on the effects of a more recent pandemic, the 2009 H1N1 influenza, were
similar. For example, several studies have found that H1N1 influenza mortality was
higher among the most deprived social groups in developed countries (Biggerstaff
et al.,2014;Lowcock et al.,2012;Rutter et al.,2012), while a cross-country
analysis showed that H1N1 influenza mortality was higher in low-income than in
high-income countries (Charu et al.,2011). The socioeconomic disparities in H1N1
influenza mortality and morbidity have been attributed to differences in levels of
exposure to the virus, susceptibility to the disease, and access to health care once
the disease had developed (Rutter et al.,2012).
The evidence that large income differences have damaging health and social
consequences is, therefore, strong. Moreover, it has been argued that the COVID-19
pandemic could exacerbate these differences, as inequality could increase the pace
of the spread of the disease (Ahmed et al.,2020;Brown and Ravallion,2020). For
instance, it has been observed that people in countries with greater income inequality
have been less likely to adopt preventive health measures, such as isolation,
physical distancing, and the use of masks and hand disinfection (Elgar et al.,2020;
Papageorge,2020;Pirisi,2000). In addition, initial findings on the effects of the
pandemic suggest that people in the lower socioeconomic groups have been facing
more severe consequences, and that income inequality might explain the differences
in the numbers of cases and deaths within and across countries. Results from the
United States show that infection and mortality rates from COVID-19 are higher
in the states and counties where income inequality or poverty levels are higher
(Brown and Ravallion,2020;Chen and Krieger,2020;Jung et al.,2020;Mollalo
et al.,2020;Mukherji,2020;Oronce et al.,2020). For Brazil, there is evidence
of a positive and significant correlation between income inequality and COVID-
19 mortality (Demenech et al.,2020;Martines et al.,2021). Studies conducted
in Germany, Israel and Spain have shown that infection rates in these countries
have varied based on income inequality, with socioeconomically disadvantaged
populations being more likely to be infected (AQuAS,2020;Arbel et al.,2020;
Wachtler et al.,2020). A comparative study of the 10 countries worldwide that have
been the most affected by the pandemic used a multidimensional index, including
income inequality, to show that the worse offa country is, the greater the impact of
COVID-19 has been (Ruiz Estrada,2020). A study comparing the number of deaths
per day in 80 countries concluded that mortality tends to increase more rapidly in
countries where inequality is greater (Elgar et al.,2020).
During the first pandemic wave, one of the measures governments used to
deal with the threat was the imposition of severe restrictions on mobility, which
in most cases meant that the population was ordered to stay home whenever
possible. Teleworking became widespread for all non-essential workers. However,

4David A. S´anchez-P´aez
essential workers, mostly in manual or machine-based activities, had to continue
working face-to-face and commuting to their workplaces, or risk losing their
jobs (Adams et al.,2020;Ahmed et al.,2020;Lekfuangfu et al.,2020). Studies
conducted in England and Wales and in Thailand found that the use of public
transport to commute to work was associated with increased risk of COVID-19
infection (Lekfuangfu et al.,2020;S´
a,2020). Analyses of geolocation data from the
United States showed that lower-income workers continued to move around during
lockdowns, while higher-income workers tended to stay at home and limit their
exposure (Buchanan et al.,2020). Another study concluded that the U.S. counties
with the highest levels of income inequality had higher rates of infection, as the
lower-income workers in these counties were less able to maintain social distancing
because of their work activities (Brown and Ravallion,2020).
The research to date has analysed the effects of income inequality on variations
in COVID-19 infections within countries. However, only a few cross-country
comparative studies have analysed how the COVID-19 pandemic has affected
countries depending on their socioeconomic differences, and none of these studies
has focused on Europe. Thus, our objective is to examine whether there was an
association between income inequality and the numbers of cases and deaths during
the first wave of COVID-19 in European countries. Although Europe is considered
to have lower inequality than other regions, evidence from past pandemics has
shown that even in European countries, there have been differences in health
outcomes associated with income distribution. Due to the rapid spread of the
virus, and to a lack of knowledge about how to combat it among both scientists
and the general public, governments did not have a plan for protecting the most
deprived social groups. Thus, analysing the effects of the first wave of the COVID-
19 pandemic on European countries can help us examine the differences in health
outcomes associated with socioeconomic inequities. More unequal countries were
already more likely to have adverse health outcomes and weaker health care systems.
Therefore, income inequality may have played a significant role in exacerbating
these existing vulnerabilities during the COVID-19 pandemic.
2 Data and methods
To conduct our analysis, we use as dependent variables the cumulated number of
infections and deaths at the end of the first wave. We have collected the daily
number of COVID-19 cases and deaths from Our World in Data (2020), one of the
specialized data repositories that has compiled global information on the evolution
of the pandemic.
It should be noted that although the virus spread rapidly through Europe, not all
countries were affected at the same time, and the evolution of the disease differed
from one country to another. Therefore, we have harmonized the analysis period by
estimating the duration of the first wave for each country using the reported number
of cases per day from January 2020 to January 2021. To do so, we first smoothed

First wave of COVID-19 and income inequality: Evidence from European countries 5
the daily number of infections using a seven-day moving average. Then, we used a
local polynomial regression – i.e., locally estimated scatterplot smoothing (LOESS)
– to fit the trend. As the result is a sinusoidal type pattern due to the multiple waves,
we considered the first wave to be the first hump of the LOESS fit. We defined the
onset as the day on which the 100th case was reported, and the end as the day on
which the slope of the fitting curve did not show a statistically significant decrease
after the number of cases per day was at least half that at the peak.
For illustrative purposes, Figure 1shows the smoothed trends and fitting curves
in several countries. For most European countries, the first wave lasted from mid-
March to late June, and it did not go beyond August 2020 in any European country.
Although the number of infections per day was already declining by the end of
January 2021 in Moldova and Ukraine, these two countries were excluded as they
showed no signs of having completed the first wave. Table 1displays the details of
the first wave.
Our variable of interest is income inequality. To measure income inequality,
we use the Gini index, which is distributed from zero, indicating totally equal
distribution, to 100, indicating totally unequal distribution. We collected the latest
reported Gini index results from the World Bank Open Data repository (World Bank,
2020). Figure 2displays the Gini index values across the countries included in our
sample. The Gini index values range from 24.2 to 40.4, and the sample mean is 31.7.
Europe is considered the most egalitarian continent in the world. At the regional
level, the Scandinavian and Eastern European countries generally have the most
egalitarian income distributions, while income inequality tends to be highest in the
Balkan countries.
Since recent studies have found that certain socioeconomic and demographic
characteristics can help to explain how COVID-19 has affected a particular country,
we include them in our analysis to control our estimates. Most of these studies
agree that the relevant characteristics include age structure, as age might reflect
the incidence of pre-existing health conditions (Brown and Ravallion,2020;Esteve
et al.,2020;Gardner et al.,2020;Kashnitsky and Aburto,2020;Nepomuceno et al.,
2020); poverty and education, as they are strong determinants of health outcomes
(Bor et al.,2017;Brown and Ravallion,2020;Santerre and Neun,2012); numbers
of essential workers, as these workers are more exposed to infection because they
use public transport and have face-to-face contact (Adams et al.,2020;Ahmed
et al.,2020;Lekfuangfu et al.,2020;S´
a,2020); population density, as infected and
uninfected individuals are more likely to interact in denser settings (Brown and
Ravallion,2020); social contact, as the risk of infection increases at higher levels of
social contact (Aparicio and Grossbard,2020;Cristini and Trivin,2020); and health
care capacities, as the pandemic has exposed vulnerabilities in health care systems
(Hopkins Tanne et al.,2020;Mollalo et al.,2020;Nepomuceno et al.,2020), and
health care capacities have played a role in how hard each country has been hit by
the disease. To include these controls in our analysis, we collected information from
various sources, while always using the latest reported data for each variable.

6David A. S´anchez-P´aez
Figure 1:
Smoothing and fitting the number of infections per day in selected countries over a
180-day period
Spain United Kingdom
Germany Italy
Belgium France
13 06 09 0 120 150 180 13 06 09 0 120 150 180
0
5000
10000
0
2000
4000
0
1000
2000
3000
4000
5000
0
500
1000
1500
0
2000
4000
6000
0
2000
4000
6000
8000
Days since 100th case
Smoothed number of daily cases
End of first wave LOESS fit

First wave of COVID-19 and income inequality: Evidence from European countries 7
Table 1:
Details of the first wave of COVID-19 in European countries
First wave
Total Total
Country 1st case Start End Days cases deaths
Albania March 09 March 23 May 14 53 898 31
Austria February 25 March 08 May 22 76 16,436 635
Belarus February 28 March 30 August 17 141 69,589 613
Belgium February 04 March 06 June 19 106 60,476 9,695
Bosnia and March 05 March 22 May 28 68 2,462 153
Herzegovina
Bulgaria March 08 March 20 May 25 67 2,433 130
Croatia February 25 March 19 June 02 76 2,246 103
Cyprus March 09 March 23 June 23 93 990 19
Czechia March 01 March 13 May 20 69 8,721 304
Denmark February 27 March 10 June 24 107 12,815 603
Estonia February 27 March 14 July 04 113 1,993 69
Finland January 29 March 13 July 09 119 7,273 329
France January 24 February 29 June 05 98 192,450 29,114
Germany January 27 March 01 June 06 98 185,450 8,673
Greece February 26 March 13 May 28 77 2,906 175
Hungary March 04 March 21 July 03 105 4,172 588
Iceland February 28 March 12 May 23 73 1,804 10
Ireland February 29 March 14 June 30 109 25,473 1,736
Italy January 31 February 23 July 08 137 242,149 34,914
Latvia March 02 March 20 June 23 96 1,111 30
Lithuania February 28 March 22 June 09 80 1,727 72
Luxembourg February 29 March 17 May 23 68 3,990 109
Malta March 07 March 23 June 24 94 665 9
Montenegro March 17 March 31 May 29 60 324 9
Netherlands February 27 March 06 June 22 109 49,866 6,109
Norway February 26 March 06 June 28 115 8,855 249
Poland March 04 March 14 June 30 109 34,393 1,463
Portugal March 02 March 13 August 02 143 51,463 1,738
Romania February 26 March 14 June 07 86 20,479 1,333
Russia January 31 March 17 August 12 149 900,745 15,231
Serbia March 06 March 19 June 01 75 11,430 244
Slovakia March 06 March 18 June 02 77 1,522 28
Slovenia March 05 March 13 May 27 76 1,471 108
Spain February 01 March 02 June 11 102 242,707 27,136
Sweden February 01 March 06 August 29 177 83,958 5,821
Switzerland February 25 March 05 June 05 93 30,936 1,921
United Kingdom January 31 March 02 July 17 138 294,803 41,060

8David A. S´anchez-P´aez
Figure 2:
Gini index in European countries
25 30 35 40
Gini index
To account for (i) age structure, we use the latest projection of total population
from the World Population Prospects (United Nations,2020) to compute the share
of people aged 65 and older. For (ii) education, we use the share of population with
at least upper secondary school for the population aged 25 and older1(UNESCO,
2020). For (iii) essential workers, we use the share of people working in industry2
(ILO, 2020). For (iv) population density, we use the share of the population living
in urban areas (United Nations,2018). For (v) social contact, we use the number
of flight departures (domestic and international) (World Bank,2020). For health
capacities, (vi) we use the number of physicians – i.e., generalist and specialist
medical practitioners – per thousand inhabitants (World Bank,2020), and (vii)
1
Path to data is SDG/Sustainable Development Goals 1 and 4/Sustainable Development Goal 4/Target
4.4/Share of population by educational attainment.
2
This information can be found as part of the “Employment distribution by economic activity”
indicator.

First wave of COVID-19 and income inequality: Evidence from European countries 9
the number of hospital beds per 10,000 inhabitants (WHO, 2020). In addition, to
account for any possible effects of a government’s response to the crisis, we include
two controls: the number of days between the first case and the localized or national
lockdown (Dunford et al.,2020), and the ideological orientation of the government
(CIDOB,2021). In the first case, we consider the possibility that a late response
could have contributed to the pandemic hitting the country harder. It should be noted
that only Belarus did not adopt a lockdown policy. Therefore, we use the duration of
the first wave as the number of days. In the second case, we consider the possibility
that the ideological orientation of the government may have had an effect on the
dependent variables and the variable of interest through the unobserved preferences
(of individuals or governing parties) regarding income redistribution, or through
measures taken to control the pandemic. To account for this possibility, we include
a dichotomous variable that takes the value of one when the ideology is right or
centre-right, and a value of zero for other ideologies.
We use data from all European countries with complete information. Thus, we
include 37 European countries in our study, and our sample covers 94% of Europe’s
population.
We first estimate a bivariate model for each dependent variable, including only the
Gini index as an explanatory variable. Second, we estimate multivariate models that
include the controls specified above. The reported numbers of cases and deaths are
the count data. Poisson distribution is used for modelling the number of times an
event occurs in an interval of time or space. Poisson regression assumes that the
logarithm of its expected value can be modelled by a linear combination of its
parameters:
log(E(Y|X)) =Xβ
E(Y|X)=eXβ
where Xis a vector of independent variables, and βis the set of parameters. While
a Poisson model assumes that the variance (var(Y)) is equal to the mean (E(Y|X)=
µ), this assumption does not always hold true. When the variance is greater than
the mean – i.e., when there is overdispersion – either quasi-Poisson or negative
binomial regression models are more appropriate (Ver Hoef and Boveng,2007).
Quasi-Poisson models assume that the variance is a linear function of the mean,
var(Y)=θµ, where θis an overdispersion parameter. Negative binomial models
assume that the variance is a quadratic function of the mean, var(Y)=µ+αµ2,
where the overdispersion is the multiplicative factor 1 +αµ. Overdispersion tests on
our sample showed that the null hypothesis var(Y)=µis rejected. Then, following
Ver Hoef and Boveng (2007), we have performed a diagnostic analysis (not shown)
plotting the fit of the variance, using averaged squared residuals, to the mean. The
results suggest that the quasi-Poisson model fits the variance-mean relationship
better.
Finally, it should be noted that the values of the number of infections and deaths
vary widely across countries due to their different population sizes. Thus, we include

10 David A. S´anchez-P´aez
in all regressions the log of total population as an offset,
log(E(Y|X)) =log(pop)+Xβ
then,
log(E(Y|X)) −log(pop)=log E(Y|X)
pop !=Xβ
3 Results
In Europe, the first wave lasted an average of 98 days (see Table 1). During this time
period, there were 2,581,181 confirmed COVID-19 cases and 190,564 confirmed
deaths from the disease in the 37 countries included in our study. The longest first
waves were in Sweden (177 days), Russia (149 days) and Portugal (143 days);
while the shortest first waves were in Albania (53 days), Montenegro (60 days) and
Bulgaria (67 days).
The upper panel of Figure 3shows the cumulated number of infections per million
population (p.m.p.) during the first wave by country. The solid line represents the
average of the sample, which was 3,707.5 infections p.m.p. It is not a coincidence
that the countries with the highest numbers of infections were Sweden (8,313.3
infections p.m.p.) and Belarus (7,364.4 infections p.m.p.). In both countries, no
measures were taken to restrict social contact, which also explains why Sweden
had the longest first wave. The countries with the lowest numbers of infections,
coinciding with the shortest first wave durations, were Slovakia and Greece (both
with 279 infections p.m.p.), followed by Albania (312 p.m.p.) and Bulgaria (350.1
p.m.p.).
The lower panel of Figure 3displays the cumulated number of deaths during the
first wave of COVID-19. The solid line shows the average in our sample, at 273.7
deaths p.m.p. Belgium had the highest mortality rate by far, at 836.5 deaths p.m.p.,
followed by the United Kingdom (604.8 deaths p.m.p.), Spain (580.4 deaths p.m.p.),
Italy (577.5 deaths p.m.p.), Sweden (576.4 deaths p.m.p.) and France (446 deaths
p.m.p.). Except in Sweden, a higher infection rate in a country did not necessarily
predict higher mortality. Among the possible explanations for this finding are that
complications from infections might have been exacerbated by vulnerabilities at the
individual level, and that the responsiveness of the countries’ hospital systems could
have varied.
The upper panel of Figure 4plots the Gini index and the number of infections.
Pearson’s correlation estimation suggests that there was a moderate positive asso-
ciation of 0.287 (95% CI =0.076–0.474) between these two variables. The per
capita risk of infection increased by 1.08 (95% CI =1.03–1.14, se =0.028) for
every unit of increase in the Gini index (see column [1] of Table 2). After including
controls (see column [2] of Table 2), the association became weaker (1.04), such
that the confidence interval now included one (95% CI =0.98–1.09, se =0.027).

First wave of COVID-19 and income inequality: Evidence from European countries 11
Figure 3:
Cumulated number of infections and deaths per million population (p.m.p.) during
the first wave of COVID-19
Slovakia
Greece
Albania
Bulgaria
Hungary
Montenegro
Croatia
Latvia
Lithuania
Slovenia
Bosnia−Herz.
Czechia
Poland
Romania
Cyprus
Finland
Estonia
Malta
Norway
Serbia
Austria
Denmark
Germany
Netherlands
France
Switzerland
Italy
United Kingdom
Portugal
Ireland
Spain
Belgium
Iceland
Russia
Luxembourg
Belarus
Sweden
0 2000 4000 6000 8000
Cases p.m.p.
Country
Slovakia
Albania
Montenegro
Latvia
Greece
Bulgaria
Malta
Cyprus
Croatia
Lithuania
Czechia
Iceland
Serbia
Poland
Norway
Bosnia−Herz.
Slovenia
Estonia
Finland
Hungary
Belarus
Romania
Austria
Germany
Denmark
Russia
Portugal
Luxembourg
Switzerland
Ireland
Netherlands
France
Sweden
Italy
Spain
United Kingdom
Belgium
0 200 400 600 800
Deaths p.m.p.
Country
EU average

12 David A. S´anchez-P´aez
Figure 4:
Number of infections and deaths per million population (p.m.p.) during the first wave
of COVID-19 and the Gini index
Sweden
Belarus Luxembourg
Russia
Iceland
Belgium Spain
Ireland
Portugal
United Kingdom
Italy
Switzerland
France
Netherlands
Germany
Denmark Austria Serbia
Norway
Malta Estonia
Finland Cyprus
Romania
Poland
Czechia
Bosnia and Herzegovina
Slovenia
Lithuania
Latvia
Croatia
Montenegro
Hungary Bulgaria
Albania
Greece
Slovakia
Pearson correlation = 0.287
6
7
8
9
Cases p.m.p. (log−transformed)
Belgium United Kingdom
Spain Italy
Sweden
France
Netherlands
Ireland
Switzerland Luxembourg
Portugal Russia
Denmark Germany
Austria Romania
Belarus Hungary
Finland
Estonia
Slovenia Bosnia and Herzegovina
Norway Poland Serbia
Iceland
Czechia Lithuania
Croatia
Cyprus
Malta Bulgaria
Greece
Latvia Montenegro
Albania
Slovakia
Pearson correlation = 0.236
2
3
4
5
6
25 30 35 40
25 30 35 40
Deaths p.m.p. (log−transformed)
Gini index
The lower panel of Figure 4shows a positive correlation between the Gini index
and the number of deaths, although it was weaker than the correlation found for
infections. The Pearson’s correlation estimation was 0.236 (95% CI =0.02–0.43).
The per capita risk of death increased by 1.01 (95% CI =0.93–1.10, se =0.043) for
every unit of increase in the Gini index (see column [3] of Table 2). In this case, the
per capita risk increased to 1.05 after the controls were included (see column [4]
of Table 2), but the confidence interval still included one (95% CI =0.97–1.14,
se =0.042).
The results for the other covariates are presented in columns [2] and [4] of Table 2.
A higher share of the population with at least upper secondary school was connected
to lower per capita risk. Our results indicate that the share of better educated people
was associated with a reduction in the risk of infection of 0.99 (95% CI =0.97–
0.99, se =0.008), and with a reduction in the risk of death of 0.98 (95% CI =0.96–
0.99, se =0.011). Consistent with increased exposure to risk, the per capita risk of
infection increased by 1.04 (95% CI =1.01–1.09, se =0.027) with the proportion
of industrial workers. However, the evidence does not necessarily suggest that the
proportion of industrial workers was related to the risk of death.
The more people who travelled, whether internationally or domestically, the faster
the virus spread. Our results show that the risk of infection was 1.15 (95% CI =
1.01–1.34, se =0.070) higher in countries where more flights departed. Similarly,

First wave of COVID-19 and income inequality: Evidence from European countries 13
Table 2:
Per capita risk of the number of infections and deaths during the first wave of
COVID-19. Quasi-Poisson regressions including log of population as an offset.
Standard errors are in parentheses, and 95% confidence intervals are in brackets
Cases Deaths
Variable [1] [2] [3] [4]
Gini 1.08 [1.03–1.14] 1.04 [0.98–1.09] 1.01 [0.93–1.10] 1.05 [0.97–1.14]
(0.028) (0.027) (0.043) (0.042)
Education 0.99 [0.97–0.99] 0.98 [0.96–0.99]
(0.008) (0.011)
Workers 1.04 [1.01–1.09] 1.00 [0.93–1.08]
(0.027) (0.038)
65+0.83 [0.77–0.90] 1.07 [1.01–1.13]
(0.038) (0.049)
Urbanization 1.03 [1.01–1.06] 1.05 [1.02–1.09]
(0.013) (0.017)
Flights 1.15 [1.01–1.34] 1.30 [1.02–1.79]
(0.070) (0.144)
Physicians 1.32 [1.06–1.64] 0.57 [0.39–0.79]
(0.112) (0.179)
Beds 0.99 [0.98–1.00] 0.99 [0.98–0.99]
(0.005) (0.006)
Lockdown 1.01 [1.00–1.01] 1.00 [0.97–1.02]
(0.004) (0.011)
Right party 0.74 [0.50–1.09] 0.65 [0.34–1.18]
(0.201) (0.312)
Goodness of fit
Deviance 704,869.71 195,864.68 147,246.63 20,676.46
Dispersion 20,177.68 7,048.83 4,126.82 851.25
Chi sq. 706,218.67 183,269.47 14,4438.64 22,132.6
the decision to impose restrictions on movement helped to slow the spread of the
virus. According to our estimates, each additional day that a government delayed
taking measures to restrict movement, such as lockdowns, increased the risk of
infection by 1.01 (95% CI =1.00–1.01, se =0.004). On the other hand, having a
right-wing or centre-right government was associated with a lower risk of infection,
at 0.74 (95% CI =0.50–1.09, se =0.201), and of death, at 0.65 (95% CI =0.50–
1.09, se =0.312).

14 David A. S´anchez-P´aez
Per capita risk increased with urbanization. As in the case of infections, a higher
share of the population living in urban areas was associated with the virus spreading
more rapidly. In our sample, the risk increased by 1.03 (95% CI =1.01–1.06, se =
0.013) for each additional percentage point of urbanization. The higher risk of death
(1.05, 95% CI =1.02–1.09, se =0.017) may be explained by the saturation that
existed in hospitals during the peak of the pandemic. The countries where a higher
proportion of the population was aged 65 and older had a lower risk of infection,
at 0.83 (95% CI =0.77–0.90, se =0.038), but a higher risk of death, at 1.07 (95%
CI =1.01–1.13, se =0.049). These findings show the two faces of this pandemic:
i.e., most of those infected with COVID-19 were under age 50, while mortality was
concentrated among the elderly.
The COVID-19 pandemic has tested the capacities of countries’ health care
systems, and has revealed weaknesses in many of them. Increasing one hospital
bed per 10,000 inhabitants slightly decreased the risk of death from COVID-19
by 0.99 (95% CI =0.98–0.99, se =0.006). Of all of the variables included in
our analysis, we found that the highest per capita risk was associated with the
number of doctors. Increasing one physician per thousand population decreased
the risk of death by 0.57 (95% CI =0.39–0.79, se =0.179). However, the presence
of more physicians was associated with a higher risk of infections, at 1.32 (95%
CI =1.06–1.64, se =0.112). One possible explanation for this result is that the
presence of more physicians increased the likelihood of detecting infections, either
because there was a greater capacity to test for COVID-19 when tests were
carried out in physician practices, or because there was an increase in the number
of doctor visits by symptomatic individuals who were subsequently referred to
testing.
4 Discussion
Evidence from past pandemics has shown that the rates of infection and mortality
tend to be higher in the most vulnerable socioeconomic status groups, especially
in countries with higher levels of social inequality (Bengtsson et al.,2018;Grantz
et al.,2016;Mamelund,2006;Murray et al.,2006;Sydenstricker,1931). Moreover,
evidence from recent country case studies has suggested that this pattern has
persisted during the COVID-19 pandemic. Our cross-country study focused on
the question of whether varying levels of income inequality were associated with
differences in the numbers of infections and deaths across European countries
during the first wave of the pandemic.
Unlike other studies that analysed the effects of COVID-19 during its first stage,
we did not use an ad-hoc analysis period. Instead, we developed a method to
determine the duration of the first wave of the pandemic. To do this, we started
our analysis period on the day on which the first case was reported, and ended it on
the last day for which we could update the data (January 2021). Thus, our potential
study period covered one year. Then, by smoothing the daily cases and fitting the

First wave of COVID-19 and income inequality: Evidence from European countries 15
smoothed trend, we determined the duration of the first wave for each country. To
the best of our knowledge, this is the first study that has used this approach to
homogenize the comparisons between countries.
After analysing the bivariate relationships, we found a moderate positive associa-
tion between income inequality, as measured by the Gini index, and the numbers of
infections and deaths during the first wave of COVID-19. To some extent, the Gini
index captured the presence of groups living under vulnerable conditions within
a given population. Previous evidence indicates that deprived groups tend to have
worse health outcomes (Bor et al.,2017;Santerre and Neun,2012). The positive
relationship we found in the bivariate models suggests that the pandemic had a
disproportionate impact on disadvantaged populations.
Based on our results, we draw several conclusions. First, unlike other known
pandemics, the COVID-19 pandemic triggered a simultaneous global response
aimed at stopping the spread of the virus. Thus, governments around the world
imposed restrictions on movement and closed borders. In Europe, the pandemic-
related lockdowns lasted approximately three months, and began an average of
20 days after the first case was reported. It appears that these measures protected
countries with the highest levels of social vulnerability from the effects of the
pandemic during the first wave. Indeed, there is evidence that the infection and
death rates were higher during the second and third waves (Our World in Data,
2020), when the mobility restrictions were milder. We will analyse these differences
in further research.
Second, methods for collecting the number of deaths varied from one country to
another, which has led to underreporting in some cases (Harries,2020;Hirsch and
Martuscelli,2020). In other words, the observed number of deaths varied across
countries depending on the (unobserved) reporting policy, which may have led to
biases. We intend to test our hypothesis using excess mortality as the dependent
variable once data for all European countries (and for less developed countries)
become available. Similarly, the number of infections may have been affected by
differences in testing policies between countries. Testing levels were lower during
the first wave than they were during subsequent waves.
Third, one of the characteristics of this pandemic has been the rapid speed of the
spread of the virus across populations. Although the proportion of people infected
with COVID-19 during the first wave who became severely and critically ill can
be considered low, given the large numbers of people who were infected, this
relatively small proportion resulted in high absolute numbers of critically ill people,
which, in turn, placed great pressure on health care systems. In general, European
countries have public and universal health care systems, which may reduce the
effects of social inequity. However, our results show that even in Europe, there were
differences between countries in the risk of death associated with more doctors and
greater hospital capacity during the first pandemic wave. A potential explanation for
this finding is that more unequal societies devote fewer resources to redistributive
policies, such as health care (Mello,2006).

16 David A. S´anchez-P´aez
Fourth, during the first pandemic wave, not everyone had the option to stay home
and telework. Essential workers continued to commute to their workplaces, and
were more exposed to the virus than white-collar workers (Adams et al.,2020;
Ahmed et al.,2020;Lekfuangfu et al.,2020;S´
a,2020). In turn, the work activities
of these individuals increased the risk of infection for their cohabitants (Aparicio
and Grossbard,2020). Our estimates show a clear relationship between infections
and the proportion of the population working in essential activities. Given that most
of these workers had lower incomes, our results show another dimension of the link
between income inequality and the pandemic.
In summary, we found a moderate positive association between income inequality
and the numbers of COVID-19 infections and deaths in our models without controls.
However, after the controls were included, the statistical significance of this associa-
tion disappeared. Thus, the link between socioeconomic inequalities and infectious
diseases was no longer obvious once the correlations among multiple covariates
were accounted for (Brown and Ravallion,2020). Our results are consistent with
previous evidence showing that the effects of socioeconomic inequalities on health
outcomes tend to be smaller in countries that already had relatively low levels of
social and economic inequality prior to the onset of the pandemic (Rice,2005;
Summers et al.,2014). In further research, we intend to explore this association
at the subnational level (e.g., NUTS II level), or at the individual level.
Turning to the policy implications of our findings, we recommend that govern-
ments constantly prioritize the protection of vulnerable groups in their contingency
plans. On the other hand, further research is needed about, among other pandemic-
related topics, the effects of lockdowns. For instance, the closure of non-essential
businesses across Europe has contributed to increased unemployment, poverty
and inequality. Moreover, the impact on mental health of remaining isolated, of
increased uncertainty, and of feeling vulnerable when social interactions are re-
established should be assessed.
Acknowledgments
The author would like to thank Ashira Menashe-Oren, Benjamin-Samuel Schl¨
uter
and Bruno Schoumaker for their comments on a previous version of this manuscript;
and Damien Courtney, Alexia F¨
urnkranz-Prskawetz and Miguel S´
anchez-Romero
for providing useful feedback during the presentation of this research at the
Wittgenstein Centre Conference 2020.
ORCID
David A. S´anchez-P´aez https://orcid.org/0000-0002-7828-8193

First wave of COVID-19 and income inequality: Evidence from European countries 17
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