Mapping eu member states' quality of life during COVID-19 pandemic crisis

Dermatis, Zacharias; Kalligosfyris, Charalampos; Kalamara, Eleni; Anastasiou, Athanasios Article Mapping eu member states' quality of life during COVID-19 pandemic crisis Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Dermatis, Zacharias; Kalligosfyris, Charalampos; Kalamara, Eleni; Anastasiou, Athanasios (2024) : Mapping eu member states' quality of life during COVID-19 pandemic crisis, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 12, Iss. 7, pp. 1-18, https://doi.org/10.3390/economies12070158 This Version is available at: https://hdl.handle.net/10419/329084 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Economies 12: 158. https://doi.org/10.3390/ economies12070158 Academic Editor: Sergio Scicchitano Received: 27 March 2024 Revised: 4 June 2024 Accepted: 11 June 2024 Published: 24 June 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). economies Article Mapping EU Member States’ Quality of Life during COVID-19 Pandemic Crisis Zacharias Dermatis 1, Charalampos Kalligosfyris 1,2, Eleni Kalamara 2,3 and Athanasios Anastasiou 1,* 1Department of Management Science and Technology, University of Peloponnese, 221 00 Tripoli, Greece; [email protected] (Z.D.); [email protected] (C.K.) 2Greek Independent Authority for Public Revenue, 101 84 Athens, Greece; [email protected] 3Department of Economics, University of Peloponnese, 221 00 Tripoli, Greece *Correspondence: [email protected] 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. Keywords: quality of life; Geographical Information Systems; COVID-19; socioeconomic 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 Economies 2024,12, 158. https://doi.org/10.3390/economies12070158 https://www.mdpi.com/journal/economies Economies 2024,12, 158 2 of 18 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 qualityof-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 Economies 2024,12, 158 3 of 18 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 Economies 2024,12, 158 4 of 18 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 individuallevel 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 Economies 2024,12, 158 5 of 18 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 COVID19 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 Economies 2024,12, 158 6 of 18 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 =∑N k=1(wkaki) ∑N k=1wkj for i= 1, 2, 3, ..., m, where aki is the kindex of country i; wkis the weighting coefficient of the indicator k; Nis the number of indicators; m: the number of countries considered. Economies 2024,12, 158 7 of 18 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 1reveals 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 Economies 2024,12, 158 8 of 18 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. 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. 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 Xis calculated as follows: X∗=X−Xmax Xmin −Xmax where Xis the value of the variable; Xmin and Xmax 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). Economies 2024,12, 158 15 of 18 Economies2024,12,xFORPEERREVIEW15of18   FigureA2.Valuesofthescalevariables(2021). References (Anastasiouetal.2023)Anastasiou,Athanasios,NicholasApergis,andAthinaZervoyianni.2023.Pandemic,sentimentsover COVID-19,andEUconvergence.EmpiricalEconomics66:1683–707.https://doi.org/10.1007/s00181-023-02504-9. (AndersenandRocabado2021)Andersen,LykkeE.,andAlejandraGonzalesRocabado.2021.LifeandDeathDuringtheFirstYear oftheCOVID-19Pandemic:Ananalysisofcross-countrydifferencesinchangesinquantityandqualityoflife.Revista LatinoamericanadeDesarrolloEconómico19:9–57.https://doi.org/10.35319/lajed.202135438. (Apparicioetal.2008)Apparicio,Philippe,Anne-MarieSéguin,andDanielNaud.2008.Thequalityoftheurbanenvironmentaround publichousingbuildingsinMontréal:AnobjectiveapproachbasedonGISandmultivariatestatisticalanalysis.SocialIndicators Research86:355–80. (AslanandZengin2022)Aslan,Yasemin,andOrhanZengin.2022.COVID-19PandemiDönemindeTürkiyeveDoğuAvrupa ÜlkelerininYaşamKalitelerineDairKapsamlıBirDeğerlendirme.ODÜSosyalBilimlerAraştırmalarıDergisi13:763–84. https://doi.org/10.48146/odusobiad.1140891. (Bocketal.2021)Bock,Anna,FlorianPeters,PhilippWinnand,KristianKniha,MariusHeitzer,MartinLemos,FrankHölzle,andAli Modabber.2021.OneyearofCOVID-19pandemic:Acrosssectionalstudyonteachingoralandmaxillofacialsurgery.Head& FaceMedicine17:51. (Breretonetal.2008)Brereton,Finbarr,J.PeterClinch,andSusanaFerreira.2008.Happiness,geographyandtheenvironment. EcologicalEconomics65:386–96. MainGDP aggregate sper capita Inability tomake ends meet‐EU ‐ SILC survey Unemplo yment ratesby sex,age and education al attainme ntlevel Persons livingin househol dswith verylow work intensity byage andsex populatio nasa percentag eofthe total populatio n,bysex andage (%) Life expectan cybyage andsex Self‐ reported unmet needsfor medical examinati onbysex, age,main reason Participat ionrate in education and training (last4 weeks)by sex,age Inability toface unexpect ed financial expenses‐ EU‐SILC survey Arrears (mortgag eorrent, utilitybills orhire purchase) from 2003 onwards‐ Gender employm entgap Belgium 54,06329 12,43243 27,5 86,66667 71,5 90,83333 46,66667 31,93548 22,8125 6,470588 35,26316 Bulgaria 0,001736 31,62162 19,16667 47,77778 60 3,333333 20 ‐0,32258 66,875 54,11765 38,94737 Czechia 19,51341 3,243243 ‐1,66667 15,55556 37 53,33333 0 11,93548 9,6875 1,176471 0,757895 Denmark 62,27841 2,162162 ‐1,66667 63,33333 22 86,66667 13,33333 66,12903 14,0625 12,64706 31,05263 Germany 52,07527 0 5 64,44444 26,5 82,5 3,333333 20 52,8125 12,05882 33,15789 Estonia 19,45308 0 26,66667 10 24,5 49,16667 6,666667 48,3871 37,8125 10,88235 14,21053 Ireland 42,71291 11,08108 26,66667 100 47 98,3333 3,333333 40,64516 45,9375 34,11765 47,36842 Greece 14,87282 100 97,5 100 83,5 77,5 93,33333 7,096774 97,8125 101,1765 98,94737 Spain 25,91761 18,37838 98,33333 85,55556 51,5 102,5 3,333333 40,32258 57,8125 36,17647 50,52632 France 45,76005 9,72973 40,83333 77,77778 55 95,83333 60 31,29032 39,375 23,82353 27,36842 Croatia 12,2502 15,67568 38,33333 52,22222 76,5 48,33333 10 9,354839 98,4375 42,94118 50 Italy 37,86266 19,18919 54,16667 85,55556 97,5 99,16667 33,33333 22,90323 55,3125 16,76471 95,78947 Cyprus 34,39187 25,40541 37,5 24,44444 36,5 90 3,333333 17,74194 88,75 45 58,94737 Latvia 13,34838 8,918919 38,33333 27,77778 41 20 56,66667 19,03226 83,4375 15,29412 20 Lithuania 31,67159 1,891892 34,16667 47,77778 29 29,16667 3,333333 19,03226 66,875 13,52941 2,105263 Luxembourg 99,99913 4,864865 19,16667 26,66667 54 98,33333 23,33333 51,29032 19,6875 13,52941 33,68421 Hungary 6,006164 21,08108 9,166667 17,77778 39 29,16667 6,666667 10,96774 61,875 27,05882 50,52632 Malta 23,239 5,135135 3,333333 13,33333 29 99,16667 3,333333 32,90323 2,1875 17,05882 81,05263 Netherlands 54,29985 ‐1,08108 10 54,44444 1,5 87,5 3,333333 82,25806 0,3125 1,764706 37,89474 Austr ia 55,49093 5,405405 26,66667 50 34 85,83333 6,666667 36,12903 11,25 8,235294 40 Poland 17,14645 3,513514 3,333333 3,333333 56 38,33333 13,33333 10,32258 29,6875 14,70588 68,42105 Portugal 23,12267 25,13514 30 17,77778 44 85 50 37,41935 50,625 14,11765 25,78947 Romania 17,95859 24,59459 21,66667 10 92 15,83333 73,33333 7,419355 100,9375 21,76471 100,5263 Slovenia 25,63634 2,162162 15 4,444444 45 82,5 3,333333 56,45161 30 20,58824 30 Slovakia 10,46141 10,27027 31,66667 11,11111 47 31,66667 16,66667 9,354839 46,875 24,11765 39,47368 Finland 50,95625 0,27027 39,16667 61,11111 26 91,66667 0 86,12903 26,5625 18,23529 5,263158 Sweden 53,01241 3,243243 48,33333 58,88889 5,5 101,6667 0 99,67742 10,625 9,411765 22,63158 Figure A2. 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