Mapping EU Member States’ Quality of Life during COVID-19 Pandemic Crisis

Abstract

This study proposes an integrated methodology for the assessment and mapping of quality of life (QoL) among European Union member states in the period before and after the pandemic crisis of COVID-19. The assessment of quality of life was based on the development of composite criteria and Geographical Information Systems or GIS technology, using variables that assess quality of life. The composite criteria relate to the socioeconomic environment, employment conditions, economic conditions and health services. Each criterion was evaluated by a set of variables, and each variable was weighted based on relevant research by Greek experts. Criteria were also weighted and combined to assess overall quality of life. The methodology was applied in 27 EU member countries, and mapping led to the identification of countries with low and high quality of life. The results showed a change in the level of overall quality of life in the EU countries before and after the pandemic period, although on a limited scale, since there is a slight reclassification of the countries’ positions. The analysis also revealed the highest level of quality of life in four EU countries [Sweden, Denmark, the Netherlands and Luxembourg] that show an increased GDP per capita, combining a low level of arrears and a low level of inability to make ends meet, whereas four countries showed the lowest level of quality of life [Greece, Bulgaria, Romania and Croatia] in both periods.

1. Introduction

In the last quarter of 2019, an unknown virus that originated in Wuhan, China developed into a pandemic that became a danger not only to lives but also to the economies of every country. It did not take long for all countries around the world to realize that there is an urgent need to find a solution to this unprecedented threat of the 21st century. Until the end of the first quarter of 2020, in order to slow the spread of the coronavirus and protect the health and well-being of all Europeans, some social restrictions had to be put in place. Preparation time varied from country to country, with some governments acting immediately, whereas others needed time to take action and decide what strategy to pursue. Among the first measures taken were the establishment of coronavirus hospitals, the purchase of safe equipment for hospital staff and contact tracing procedures for the first cases. In addition, gatherings were banned, and dining and entertainment outlets were closed. The use of public transport was prohibited, with the exception of carrying out the professional activity of citizens. Restrictions on the movement of citizens between countries were also established. After the lifting of the quarantine, the borders were opened only on the indication of a negative disease test result and a 12-day quarantine of travelers. Even those affected by the pandemic were strengthened with state financial aid. The consequence of all this was that many countries had to readjust their annual state budgets, resulting in the creation of the first fiscal deficits. The governments of all countries had to choose which of the above strategies to follow to deal with the double crisis [pandemic and fiscal] in order to achieve the best results with the least fiscal cost. The prevailing strategy among countries was the implementation of a balanced policy between the protection of public health and the maintenance of fiscal balance, with the absolute protection of public health coming second and chosen mainly by eastern countries. With this data, the COVID-19 pandemic has had a significant impact on the quality of life (QoL), daily lifestyle, well-being and health of the citizens of the European Union.

Quality of life (QoL) is a subject of study that has attracted particular interest in the humanities, social sciences and economics. At the same time, it is difficult to define as a widely accepted concept due to its complex and multidimensional nature (Farquhar 1995; Sirgy et al. 2006; Anastasiou et al. 2023). The concept of QoL generally refers to the conditions of the environment in which people live, the qualities of the environment, the characteristics of a prosperous society and even the elements that make people feel satisfied and happy in life (Pacione 2003; Rose et al. 2009). Considering that the conditions of the environment (physical and living), people’s needs and their importance are constantly changing over time, the study of quality of life and the assessment of its level in a country or region is a subject of constant research interest (Faka 2020).The World Health Organization (WHO) defines QOL as “individuals’ perceptions of their position in life in the context of the culture and values systems in which they live and in relation to their goals, expectations, standards and concerns” (Kim and Kim 2020). Moreover, the study of quality of life requires a clear reference to the domains that define the concept of quality of life. Consequently, the study of the places where people live and work allows for the correlation of each domain involved in the definition of quality of life with specific characteristics of living conditions, such as economic conditions, the natural environment, working and educational conditions, housing needs, etc. (Linares et al. 2016; Mizgajski et al. 2014; Massam 2002; Kremastioti et al. 2018; Kopsidas et al. 2018; Komninos et al. 2020; Liargovas et al. 2020; Dermatis and Anastasiou 2020). With these data, the assessment of the quality of life consists of studying these characteristics through the development of composite indicators (UN-Habitat 2016), so that different indicators can be integrated and linked to each other in order to achieve the assessment of each sector and, consequently, the overall quality of life in a place (Faka 2020).

Furthermore, the definition of the concept of quality of life is a fundamental element in the process of studying quality of life, because of the subjective and objective criteria that are taken into account. The subjective dimension of the concept of quality of life refers to the perception of the satisfaction that individuals derive from their lives and the sense of well-being that they enjoy (Campbell 1976). On the other hand, objective assessment takes into account objective indicators (census data, secondary survey data, statistical data, geospatial data, etc.) that objectively measure the living conditions and environment in which people live, regardless of their perceptions of their standard of living (Pacione 1982). Nevertheless, the use of both methods is widely advocated in the literature, as there is no clear and conclusive evidence that one method is superior to the other (Pacione 2003; Eurofound 2017). The survey conducted by the OECD (2013) on measuring well-being and assessing quality of life addresses the issue by collecting data on 11 dimensions. The framework developed by the OECD [Better Lives Initiative] for measuring well-being distinguishes between current and future well-being. Current prosperity is measured in both material conditions and quality of life. The statistics presented in the respective OECD reports support the existence of significant progress in certain areas, such as income and wealth, education, the environment and subjective well-being. This progress must be sustained while statistical challenges remain in other areas of well-being.

Geographical Information Systems (GIS) are a useful tool for assessing various quality-of-life indicators. GIS is a computer program that uses spatial data to store, analyze and visualize information globally. It is widely used across various disciplines including environmental management, urban planning, public health, transportation and the social sciences to collect, manage, analyze and interpret data (Caruso and Reyes 2016). The application of GIS in quality-of-life analysis is particularly advantageous, since it permits the investigation of spatial patterns between distinct factors, which can help generate an understanding of how different aspects determine human well-being. Consequently, GIS technologies can be employed to analyze diverse dimensions of quality-of-life information through the mapping and visualization of spatial data.

The aim of this study is the development of a methodological approach that allows for the evaluation, comparative analysis and, by extension, the mapping of the quality of life in the EU member countries during the period before and after the pandemic crisis, in order to investigate the individual characteristics and living conditions that represent the degree of well-being of individuals. The methodology consisted of establishing a composite index of quality of life (QoL) that allowed for determining EU member states by the standard of living their inhabitants can enjoy. Then, the results were geographically mapped using Geographical Information Systems (GIS). The panel subjectively evaluated each indicator with the experts’ opinion to assess the overall weighted objective indicators for the development of composite QoL. The mapping of quality of life will thus be an important decision-making tool for identifying the factors that will contribute to improving the standard of living. The use of GIS will contribute to the assessment of quality of life by linking specific explanatory variables with spatial data, allowing for spatial analysis and the creation of data visualizations (Rinner 2007; Longley et al. 2005; Apparicio et al. 2008; Brereton et al. 2008; Haslauer et al. 2015; Li and Weng 2007; Malczewski and Liu 2014; Martinez 2019; Ram Mohan Rao et al. 2012; Shyy et al. 2007; Vizzari 2011; Dermatis et al. 2017, 2019a, 2019b, 2021).

2. Literature Review

The COVID-19 pandemic crisis and other related control measures have been the subject of numerous empirical studies focusing on how they may affect quality of life, lifestyle and public health, providing valuable insights into well-being at different societal levels. For example, the study of how disparate social groups are affected by the pandemic crisis and the perceptions formed is of particular research interest. The evolution of quality of life during the pandemic has also been the subject of research, in order to reveal the factors influencing this evolution. Generally speaking, studies conducted in this area can provide a source of information that allows for a better understanding of the effects and actions of the COVID-19 pandemic crisis.

More specifically, Himmler et al. (2023) studied European welfare patterns at the level of socioeconomic subgroups during the pandemic. Similarly, Cohrdes et al. (2024) investigated factors affecting quality of life in pandemics and their associations with sociodemographic aspects. The aim of the research was to gain new knowledge about the characteristics of certain groups and their differences in subjective well-being response patterns over time. Quality-of-life (QoL) course was also explored as an index of subjective well-being grouped by the identified latent classes from July 2020 to July 2021 based on monthly and pandemic phase follow-up data. The survey showed that two out of five people showed resilience (i.e., relative stability) or recovery (i.e., approaching pre-pandemic levels) over time. Also, Schilin (2023) examined various degrees of integration within the Economic and Monetary Union and how these affected attitudes towards the crisis among different groups. This analysis argues that the COVID-19 crisis has had different impacts on people’s lifestyles, actions and behavior. Furthermore, it highlights the importance of continued research in this area, as data monitoring and tool development can provide more effective strategies to directly address these issues. The results challenge deterministic assumptions about the self-reinforcing nature of differentiated DI integration in economic and monetary union and establish DI as a concept that structures elite perceptions.

In Europe, the study conducted by Easterlin and O’Connor (2023) tried to analyze how the COVID-19 pandemic affected the level of life satisfaction. The analysis found that the fluctuations of the pandemic crisis [flares and recessions] corresponded to changes in life satisfaction across Europe. This finding contrasts with previous research, which was conducted in smaller areas and found a small decline on average in life satisfaction. The study by Polinesi et al. (2023) focused on the effects of COVID-19 on multi-dimensional well-being among people aged 50 and over in Europe, measuring changes in individual well-being before and after the outbreak of the pandemic. The multi-dimensional nature of well-being was expressed through the dimensions of economic well-being, health status, social connections and employment status. The results showed that workers and the wealthiest individuals suffered the largest losses in welfare, and differences by gender and education vary from country to country. It also emerged that in the first year of the pandemic, the main driver of changes in well-being was economics, and in the second year, the health dimension dominated the upward and downward changes in well-being.

Regarding the pandemic crisis of COVID-19, Mousazadeh et al. (2023) investigated how immigrants’ attitudes and behavior towards their sense of place (SOP) affected their level of quality of life. In this analysis, the researchers examined 120 Iranian nationals living in Budapest, Hungary regarding the impact of their sense of place attitude on their level of quality of life during the pandemic. The findings of this study revealed that evidence of SOP, such as location linkage, location identification and location dependency, was subject to change and based on quality of life during the pandemic. Violato et al. (2023) presented a combined approach with descriptive and regression analyses to investigate the association between the pandemic and changes in health-related quality of life (HRQoL) in the general population in 13 different countries. A composite measure of general health deterioration was obtained from the EQ-5D-5L instrument and its domains, such as mobility, self-care, usual activities, pain or discomfort and anxiety or depression. In addition to individual-level factors (socioeconomic status, clinical background and experience of COVID-19), national-level factors such as pandemic severity, government response and effectiveness were also examined for their association with health deterioration. The results showed that overall health worsened on average across countries for more than a third of the 15,480 participants, mostly in the health domain of anxiety/depression, especially for younger citizens.

The research of Unger et al. (2023) analyzed public opinion towards EU redistributive policies in Austria, Germany and Italy during the health and economic crisis. Specifically, they investigated whether citizens of the European Union support a common aid package, common debt and redistribution to those countries in greatest economic need. Through the study of three explanatory concepts—self-interest, justice attitudes and general support for European integration—it was found that all three explanatory concepts have predictive power. However, the strongest effect was observed for support for EU-level redistribution for citizens’ instrumental calculations of whether their country benefits from EU aid and general support for EU integration, rather than for justice attitudes. In another study published by Brooks et al. (2022), neo-functionalism was used as a theoretical framework to examine how the COVID-19 pandemic affected health policy and led to deeper integration in EU member states. As neofunctionalism might predict, member states have solved problems born of integration with more integration: maintaining the internal market, insuring against disasters, preventing border closures and strengthening the EU’s power to develop and supply vaccines. On the other hand, Xiong et al. (2020) sought to investigate the effects of COVID-19 on psychological outcomes in the general population and associated risk factors. The results showed relatively high rates of symptoms of anxiety, depression, post-traumatic stress disorder, psychological distress and stress in the general population during the COVID-19 pandemic in China, Spain, Italy, Iran, the USA, Turkey, Nepal and Denmark. Risk factors associated with distress measures include female gender, younger age group (≤40 years), the presence of chronic/psychiatric conditions, unemployment, student status and frequent exposure to social media/news about COVID-19. In addition, Tripoli et al. (2024) investigated the effects of the extension of the COVID-19 emergency on quality of life and lifestyle in a sample of 100 outpatients at the psychiatric unit at the University Hospital of Palermo, Italy. Quality of life was measured by a 12-item short-form survey on the impact of COVID-19 on quality of life. The majority of participants reported a major impact of COVID-19 on quality of life, and almost half reported a deterioration in lifestyle. Worse lifestyle was predictive of both poor mental and physical health-related quality of life.

In their research, Himmler et al. (2023) investigated patterns of well-being during the pandemic across Europe, with particular emphasis on relevant socioeconomic subgroups, using data from a repeated, cross-sectional, representative population survey with nine waves of data from seven European countries from April 2020 to January 2022, with the aim of understanding changes in well-being during the period of COVID-19 in Europe. Well-being is measured using the ICECAP-A, a multi-dimensional instrument to approach well-being capabilities. The study mainly focused on different socioeconomic subgroups, which provided a wealth of useful information. The results showed that Denmark, the Netherlands and France showed a U-shaped pattern in well-being, whereas well-being in the UK, Germany, Portugal and Italy followed an M-shape, with increases after April 2020 and a drop in the winter of 2020, rebounding in the summer of 2021 and falling in the winter of 2021. However, the observed average welfare declines were generally small. The largest declines were found in the attachment and enjoyment dimensions of well-being and among people with younger age, financial instability and poorer health. Mortality from COVID-19 was consistently negatively associated with competence well-being and its subdimensions, whereas severity and incidence rate were generally not significantly associated with well-being. König et al. (2023) examined the conditions surrounding the development of long-term health deterioration [“frailty”] in older adults as a result of COVID-19 and the underlying mechanisms and factors contributing to this development. A narrative review of the most relevant articles published on the association between COVID-19 and frailty was therefore undertaken up to January 2023. The results support the notion that there was indeed an increase in frailty in the elderly as a result of COVID-19. Regarding the underlying mechanisms, a multicausal genesis can be hypothesized, including both direct viral and indirect effects, particularly from imposed lockdowns with devastating consequences for the elderly: reduced physical activity, dietary change, sarcopenia, fatigue, social isolation, neurological problems. Bock et al. (2021), on the other hand, evaluated the teaching offered in oral and maxillofacial surgery at the university during the pandemic and investigated the students’ perceptions of the current situation. The results showed that the pandemic had a rather positive effect on the acquisition of theoretical skills and a negative effect on the acquisition of practical skills (p < 0.0001). Students declared high acceptance of digital learning forms and showed increased motivation to learn due to e-learning. The influence of the pandemic on the education of students was assessed ambivalently.

Another research study conducted by Andersen and Rocabado (2021) on a global scale sought to determine how COVID-19 had changed not only the duration but also the quality of life in 124 countries during the first year of the pandemic crisis. Changes in the quantity of life are measured as years of life lost due to COVID-19, including excess deaths not officially reported as deaths from COVID-19. Changes in quality of life correspond to the mean change in daily mobility, compared to the pre-COVID baseline. From the research results, it was found that there was a strong and negative relationship between the two, meaning that the countries with the greatest reductions in mobility are also the countries with the greatest loss of life years. It was estimated that around 48 million years of life were lost during the first year of the pandemic, which corresponds to 0.018% of all expected life years.

In addition, a population survey conducted by van Ballegooijen et al. (2021) during the initial period of the COVID-19 lockdown analyzed stress levels, worries, quality of life, access to healthcare and productivity, among other factors, during the first 8 weeks of the coronavirus lockdown in the general population in Belgium and the Netherlands. The results highlight the burden on society due to stress, lost medical resources and lost productivity. Danet (2021) also assessed the psychological impact among healthcare workers on the front lines of the SARS-CoV-2 crisis and compared it with other healthcare professionals through a systematic review of Western publications. European and American quantitative studies reported moderate and high levels of stress, anxiety, depression, sleep disturbances and burnout, with different coping strategies and more frequent and severe symptoms among women and nurses, without definitive results by age. On the front line, the psychological impact was greater than among the rest of health professionals and in the Asian countries.

Another group is described by Jabakhanji et al. (2022), who conducted a study between April 2020 and June 2021 in five European countries: France, Germany, Italy, Spain and Sweden. Their research aimed to understand the relationship between the evolution of the COVID-19 pandemic and sleep quality. The results support an increased impact on women, parents and young adults. In addition, they show that around half of the decline in sleep quality caused by the evolution of the pandemic can be attributed to lifestyle changes, worsening mental health and negative attitudes against COVID-19 and its management. In contrast, changes in SARS-CoV-2 infection status or sleep duration were not significant determinants of the relationship between COVID-19-related deaths and sleep quality.

Aslan and Zengin (2022) investigated the quality of life during the COVID-19 pandemic in Hungary, Slovakia, Latvia, Poland and Estonia, comparing it with Turkey. Their study also provided recommendations for policymakers. The results of the study indicate that the factors affecting the quality of life of the people during the pandemic differ between countries. In the study, it was determined that the countries with a high average of trust in government institutions and health systems also have high average scores of satisfaction and happiness. It is important for policymakers to have information about the factors affecting the quality of life of society to be prepared for pandemics. In addition, Sánchez (2022) conducted an analysis on the economic changes that took place in the European Union as a result of the global financial crisis (GFC) and the COVID-19 pandemic. The focus of the study was on changes in rates of poverty, extreme poverty and income inequality. The author argues that the pandemic has shown that both the personal and professional care infrastructure of societies is fundamental to economic, political, cultural and environmental life. He emphasized how a basic dimension of social justice is emotional equality, that is, equality in giving and receiving love, care and solidarity. In an important study, Jin (2022) investigated the impact of the COVID-19 pandemic on the health systems of each country in the European Union. The study also looked at the correlation between the pandemic and key indicators of economic convergence for the year 2020.

3. Methodology

A composite quality-of-life indicator that is able to provide a true picture of the living conditions in a given area or country is a great advantage. On the whole, even though the most common form of a summary of economic activity includes price, unemployment and output indicators, composite indices of quality of life can also be used for this purpose. The consideration of what should form part of an economic index should not only be limited to various aspects but also to the peculiarities of each particular territory covered by a composite quality-of-life index. In turn, the methodological foundation for such an index is based on various statistical and spatial data sources (Giannias et al. 1999). Therefore, with respect to the measurement aspect, we consider that the operationalization of quality-of-life measures takes place as follows:

$$QoL=\frac{{{\displaystyle \sum }}_{k=1}^N\left(w_k{a}_{ki}\right)}{{{\displaystyle \sum }}_{k=1}^N\left(w_{kj}\right)}$$

• for i = 1, 2, 3, ..., m,
• where
• a_ki is the k index of country i;
• w_k is the weighting coefficient of the indicator k;
• N is the number of indicators;
• m: the number of countries considered.

Life quality evaluations have come up with a set of indicators, including the social environment, working conditions, education, housing, economy, health and lifespan (Sirgy et al. 2006; Brereton et al. 2008; Hagerty et al. 2001; Najafpour et al. 2014; Faka 2020). These areas have been identified by the European Union countries as the domains where quality of life is to be measured. Each criterion is measured through particular variables that explore individuals’ different dimensions of living standards. Among other features, the social environment is shaped by the structure of age distribution and citizen incomes—data typically represented in the form of unemployment levels, levels of education and so forth. Continuous education and training have a significant influence on an individual’s quality of life through increasing earnings and boosting employability, which consequently results in better living conditions (OECD 2013). The social environment is evaluated based on a number of factors, including the levels of unemployment, the number of people in low-work-intensity households, the level of the inactive population, the participation rate for education and training and the employment gap. It is also important to note that economic status is used to determine the degree of well-being among individuals, since the inability to meet basic requirements and feeling the pinch of material deprivation negatively impacts life (Rose et al. 2009). The Eurofound study (Eurofound 2013) found that low income levels as well as poor levels of educational achievement were significantly correlated with heightened rates of material deprivation. The study on the economic level of individuals takes into account the GDP per capita, unemployment rate, inability to meet basic needs, inability to handle unexpected expenses and the absence of debt related to financial companies and government organizations. In addition to this data, life expectancy will also affect the quality of living, and restricted access to healthcare facilities adversely affects the overall health, safety and well-being of people. The well-being of individuals can be assessed by considering indicators such as life expectancy and reports on unaddressed medical requirements.

Quality of life as an indicator of composite QoL in the above equation does not have the same weighting coefficients w_k in all countries, because the perception of people on the factors describing these indicators may vary. As a result, the quality-of-life index for any country will be based on the weights that are used to compute it. For instance, using the weights set by a consumer living in Italy, the value produced by the formula is how much that hypothetical Italian consumer would say represents the quality of life for country i. The weights, generally speaking, can be of any value. One of the common practices is to make them all 1/N, but one can also define the weights based on principal components or survey outcomes.

In this case, for the calculation of the quality-of-life indicators of the European countries, the weights defined by 30 Greek experts (health professionals, environmentalists, economic analysts, statistical analysts) who participated in a survey conducted between November and December 2022 were used. More specifically, we asked 30 Greek experts to rank the importance of each of the 11 variables for their quality of life and/or how well each of these 11 variables describes it. The average of the weights for each variable was used to calculate the weighted average. All experts were Greek and participated in research programs in environmental economics or other related sciences. For the calculation of the quality-of-life indicators of the European countries, the weights of the Greek experts were used. As a result,

Table 1. Weighting coefficients.

Quality-of-Life Factor	Weighting Factor
GDP per capita	67.5
Inability to make ends meet	68.0
Unemployment rates	58.7
Persons living in households with very low work intensity	46.2
Inactive population as a percentage of the total population	36.0
Life expectancy	71.0
Self-reported unmet needs for medical examination	48.7
Participation rate in education and training (last 4 weeks)	72.0
Inability to face unexpected financial expenses	69.0
Arrears (mortgage or rent, utility bills or hire purchase)	52.0
Gender employment gap	41.0

Source: Authors’ calculations.

reveals the preferences of Greek life and, in particular, shows how Greek consumers see the quality of life in other European countries.

In this analysis, we assume that the ranking based on quality of life is equivalent to a ranking based on the maximum utility that a representative consumer can enjoy in each of the countries considered. Consequently, the two rankings will not be equivalent if each country’s quality of life is calculated using the weights of its own representative consumer—for example, the quality of life for Spain is calculated using the weights of a representative Spanish consumer, the quality of life for Germany is calculated using with the burdens of a representative German consumer, etc. Therefore, for the purposes of the study, it is assumed that consumers in well-defined homogeneous regions [such as EU countries] have identical preferences and skills, are fully mobile within their region and choose optimal locations so that they cannot improve their position by moving to another country. Another important point is that the weights are not necessarily time-invariant. However, for the same reason that we use the same weights to calculate quality-of-life indices for all countries, we must use the same weights to calculate quality-of-life indices for all time periods considered. Our analysis and results therefore show how a specific group of (Greek) consumers [experts] view quality-of-life issues between countries in the EU for the period before and after the COVID-19 pandemic crisis. The above assumptions constitute the limitations of the analysis.

The preferred framework for evaluating the quality of life in the EU countries based on the above variables relies on factors that have been extensively used in similar studies presented in the literature review (Polinesi et al. 2023; Violato et al. 2023; Unger et al. 2023; Himmler et al. 2023; van Ballegooijen et al. 2021; Sánchez 2022).

With this data, the quality-of-life index in a country is defined as the weighted average of the scale variables. The scale value, X*, of a variable X is calculated as follows:

$$X^{*}=\frac{X-X_{max}}{X_{min}-X_{max}}$$

where

• X is the value of the variable;
• X_min and X_max are the minimum and maximum values, respectively;
• X* is the scaled value of the variable;
• The range of the scaled value X* of a variable is 0–100.

The variables considered to determine quality of life in our analysis include the following: GDP per capita, inability to make ends meet, unemployment rates, persons living in households with very low work intensity, inactive population as a percentage of the total population, life expectancy, self-reported unmet needs for medical examination, participation rate in education and training (last 4 weeks), inability to face unexpected financial expenses, arrears (mortgage or rent, utility bills or hire purchase) and gender employment gap. Unfortunately, due to a lack of data, environmental quality, crime and public services, as well as measures of income distribution, are not included in our index and analysis. The survey data are for the time periods 2019 and 2021, i.e., before and after the COVID-19 crisis, and they are derived from secondary public sources (OECD, IMF, World Bank).

The values of the scaled variables X* are presented in Appendix A.

4. Results and Discussions

As mentioned above, the aim of the study is to assess the quality of life of the 27 EU member states in the period of 2019–2021, i.e., before and after the COVID-19 pandemic crisis, using a composite QoL indicator based on a set of individual QoL factors as analyzed in detail above. The results will then be geographically mapped using GIS, and useful conclusions will be drawn about QoL in the EU and its determinants.

At this stage, it is appropriate to present initial GIS data on the percentage change in the individual factors that make up the composite quality-of-life index in the EU countries. Figure 1 below shows the data representing the percentage change in the values of the quality-of-life factors selected for inclusion in the survey. The percentage change in the values of quality-of-life factors is represented by the magnitude of change in each factor in the pie chart presented for each country. The graphs are dominated by the elements of the factors that show the greatest change in the period of 2019–2021, whereas the rest of the factors either did not change, or their change was very small. For example, in Italy and Spain, there is a significant increase in the need for medical examination, and in Greece, there is a significant change in the ability of citizens to cope with their obligations [to make ends meet] as well as an increase in the number of inactive citizens. In addition, a significant number of countries are experiencing a change in the unemployment rate.

From Figure 1, it can be seen that in the period under consideration, 2019–2021, a significant change was recorded in the factors related to gross domestic product, unemployment rate, citizens’ ability to meet their living needs, the need for medical examination and the amount of overdue debts of citizens.

In particular, there was an increase in the level of overdue payments among citizens in Spain, Germany, Italy, France, Portugal, Ireland, Austria and Sweden, whereas a decrease was observed in Belgium, Bulgaria, the Czech Republic, Estonia, the Netherlands, Romania, Slovenia, Lithuania, Finland and Denmark. There is also a worsening in the inability of citizens to meet unexpected expenses in Germany, Luxembourg, Romania, Hungary and Malta, whereas the Netherlands, Slovenia, Lithuania, Ireland, Denmark, the Czech Republic, Poland, Sweden, Finland and Belgium show an improvement in the values of this indicator over the period of 2019–2021.

In addition, all EU countries under review experienced a decrease in life expectancy, mainly due to an increase in deaths from COVID-19 complications, with the largest decreases in Bulgaria, Slovenia, Romania, Poland, Latvia, Lithuania, Hungary, Estonia, Croatia and the Czech Republic. It should also be noted that Spain, Malta, Slovenia, Luxembourg and France recorded a notable change in the indicator related to reports of unmet needs for medical examination, in contrast to Sweden and Finland, where a relative decrease was observed.

On the other hand, most EU countries were also affected by the pandemic at the socioeconomic level, with a significant increase in the unemployment rate, with the exception of Greece, Italy, France, Malta and Luxembourg, where the indicator shows a slight improvement. The largest increases were observed in Romania, Austria, Denmark, the Czech Republic, Estonia, Denmark and Bulgaria. There is also a significant increase in GDP per capita in countries such as Bulgaria, Denmark, Estonia, Croatia, Latvia, Lithuania, Romania, the Netherlands, Sweden, Finland and Denmark. On the other hand, GDP per capita fell in Spain, Cyprus and Portugal.

Table 2. Quality-of-life ranking.

	2019	2021
Sweden	1	2
Denmark	2	4
Netherlands	3	1
Luxembourg	4	3
Finland	5	5
Austria	6	8
Germany	7	10
Malta	8	6
France	9	16
Czechia	10	11
Estonia	11	9
Slovenia	12	7
Portugal	13	14
Belgium	14	12
Ireland	15	13
Spain	16	21
Poland	17	15
Lithuania	18	17
Slovakia	19	19
Italy	20	22
Hungary	21	20
Cyprus	22	18
Latvia	23	23
Croatia	24	24
Romania	25	26
Bulgaria	26	25
Greece	27	27

Source: Authors’ calculations.

below presents data on the ranking of the quality-of-life indicators of the EU countries between 2019 and 2021 and the relative position of the countries in question.

The ranking based on the composite index of quality of life (QoL) shows that Sweden occupies the first position in 2019 and the second position in 2021. The Netherlands also occupies the first position among EU countries in terms of quality of life in 2021, showing a significant improvement compared to 2019, when it occupied the third position. In the period of 2019–2021, Sweden, Denmark, the Netherlands, Luxembourg and Finland consistently occupy the top five positions in terms of living standards in the EU. At the other end of the scale, Greece, Bulgaria and Romania occupy the lowest positions over this period. Moreover, between 2019 and 2021, Luxembourg [from 4th to 3rd], Malta [from 8th to 6th], Estonia [from 11th to 9th] and Slovenia [from 12th to 7th] improved their positions in the ranking table, as did Belgium [from 14th to 12th position], Ireland [from 15th to 13th position], Poland [from 17th to 16th position], Lithuania [from 18th to 17th position], Hungary [from 21st to 20th position], Cyprus [from 22nd to 18th position] and Bulgaria [from 26th to 25th position]. The biggest improvement is observed in Slovenia, with five positions, followed by Cyprus, with four positions.

On the contrary, declines were observed in the relative position of Denmark [from 2nd to 4th position], Austria [from 6th to 8th position], Germany [from 7th to 10th position], France [from 9th to 16th position], the Czech Republic [from 10th to 11th position], Portugal [from 13th to 14th position], Spain [from 16th to 21st position], Italy [from 20th to 22nd position] and Romania [from 25th to 26th position]. The most serious deterioration is in France, down seven places, followed by Spain (five places) and Germany (three places). The others remained unchanged.

The increase in the position of the Netherlands in 2021 [1st position] compared to 2019 [3rd position] is due to an improvement in the values of the factors relating to GDP per capita, the inability of citizens to make ends meet, the number of persons living in very low labor-intensive households, the inactive population as a percentage of the total population, the participation rate in education and training, the inability to cope with unexpected financial costs, the level of outstanding debts of citizens and the gender employment gap. In contrast, Sweden experienced a deterioration in the values of the factors related to the level of unemployment, the number of persons living in very low-work-intensity households, the level of people’s outstanding debts and the gender employment gap in 2021, and it therefore ranks second in 2021.

Greece’s bottom ranking over the period is explained by its particularly poor performance on unemployment, the number of people with outstanding debts, the number of people living in very low-work-intensity households, the share of inactive people in the total population, the inability to cope with unexpected financial costs and the gender employment gap. Figure 2 and Figure 3 below show the results of the relative ranking of quality of life in EU countries for the period of 2019–2021, using GIS mapping.

Generally, the results showed a change in the level of overall quality of life in the EU countries before and after the pandemic period, although on a limited scale, since there is a slight reclassification of the countries’ positions. This conclusion is in agreement with previous studies regarding the change in the quality of life in the EU during the pandemic crisis such as the works of Brooks et al. (2022), Easterlin and O’Connor (2023) and Himmler et al. (2023). Also, the analysis revealed the highest level of quality of life in four EU countries [Sweden, Denmark, the Netherlands and Luxembourg] that provided an increased GDP per capita, participation rate in education and training and life expectancy, combining a low level of arrears and a low level of inability to face unexpected financial expenses, whereas four countries show the lowest level of quality of life [Greece, Bulgaria, Romania and Croatia] in both periods, as they present low GDP per capita in combination with a high inability to face unexpected financial expenses and inability to make ends meet, high self-reported unmet needs for medical examination and high levels of arrears. From the above analysis, it follows that in shaping the level of the quality of life of EU citizens, economic factors are dominant.

5. Conclusions

Using the standard of living as a measure for social policy allows for the level of quality of life to be identified and quantified. Such measurement helps in setting priorities and goals related to improving living conditions, which is particularly relevant for countries with low standards. Comprehensive improvements in the quality of life must encompass a range of factors, and GIS is among the tools to be used to create efficient and effective solutions. The purpose of this research is to employ the data provided to enable people and decision makers in EU countries to make better-informed choices for special interventions, progress-related initiatives and the allocation of funding resources with a view to increasing the quality of life. Additionally, this report considers modern problems such as the pandemic crisis, scarce financial resources, high costs of energy supplies and the necessity of improving the quality of living. The survey disclosed the changing patterns of living conditions in different EU countries amid the COVID-19 crisis, pointing to certain aspects of the quality of life that call for immediate action as a step to improve the well-being of citizens. The analysis reported in this paper is about the quality of life of European Union member states, both before and after the COVID-19 pandemic crisis. The methodology involved constructing a composite index on the quality of life based on objective indicators as well as subjective assessments by experts. GIS (Geographical Information Systems) technology was used to create a map of the living standards in various countries. This serves as an important instrument for decision making to identify factors that could increase the quality of life.

The analysis showed that there are also differences between EU countries in relation to quality-of-life indicators. Although some countries show positive development in factors such as the ability to pay unexpected expenses and meet living needs, others have fared worse, including increased levels of overdue debts, high rates of inactive citizens, low levels of per capita income and challenges to meeting living needs. The study showed that looking at individual characteristics and living conditions is crucial to assessing well-being and highlighted the importance of targeting specific areas for intervention. Τhe results showed a change in the level of overall quality of life in the EU countries before and after the pandemic period, although on a limited scale, since there is a slight reclassification of the countries’ positions. The analysis also revealed the highest level of quality of life in four EU countries [Sweden, Denmark, the Netherlands and Luxembourg] that show an increased GDP per capita, participation rate in education and training and life expectancy, combining a low level of arrears and a low level of inability to face unexpected financial expenses, where four countries show the lowest level quality of life [Greece, Bulgaria, Romania and Croatia] in both periods, as they present low GDP per capita in combination with a high inability to face unexpected financial expenses and inability to make ends meet, high self-reported unmet needs for medical examination and high levels of arrears. From the above analysis, it follows that in shaping the level of the quality of life of EU citizens, economic factors are dominant.

The contribution of the research focuses on identifying key areas of action aimed at enhancing well-being by monitoring changes in living standards during the pandemic crisis. Such targeted, effective interventions need to be implemented to address these identified challenges as well as to promote an improved quality of life in all EU member states. Also, through the research, the value of adopting an extensive and multidimensional methodology regarding the evaluation of quality of life is highlighted, in order not to overlook objective facts and subjective judgments. The study adopted GIS technology and composite indicators as tools to assess and map quality of life, thus offering a suitable tool to guide decision making and resource distribution. Thus, the implications showed that there are critical areas to target improvements to enhance the well-being of people and communities, especially during the pandemic crisis and economic challenges. Future additional research is needed to determine whether the above analysis and results would change if expert opinion surveys from the remaining European countries were used.