Overview of the Quality of Life in Europe

Antonella D'Agostino, Giulio Ghellini, Maria Navarro and Angeles Sanchez


Over the past few decades, quality of life (QoL) has become a key issue in modern society and one of the most important goals for individuals. QoL implies, first, that the minimum conditions required for humans to thrive are met and, second, that opportunities and skills adequately match (Veenhoven, 2000). The process started in the more advanced democracies, replacing the search for mere material wealth and putting at the top of the public agenda new challenges for social organisation and, in particular, social policy. Within this framework, several international initiatives have been undertaken to improve the measurement of QoL (for a review, see Sanchez et al., 2018). In the context of the European Union (EU), in 2009 the European Commission initiated the project ‘GDP and Beyond: Measuring Progress in a Changing World’ starting with the idea that economic indicators, such as the gross domestic product (GDP), while important, do not completely indicate the effective level of well-being of a population and, consequently, how well we are operating (EU Commission, 2009).

Accordingly, the need to supplement the information provided by GDP with new data on other aspects of people’s lives has become a key issue and challenge for researchers, institutions and policymakers. In this vein, a great consensus has formed about the need to develop new data sources and new surveys so as to build innovative indicators of QoL, which are useful for measuring social progress from a different perspective (Mercy, 2015). These indicators should reflect the multi-dimensionality of the concept of QoL and, therefore, cover different aspects of individuals’ lives, such as living standards, social relationships, leisure and culture, good health, education and the environment (Michalos et al., 2011).

In the past few years, the relevance and the complexity of QoL measurement have driven methodological and empirical research, so, in addition to existing journals such as Social Indicators Research (published since 1974), new titles have emerged, including the Journal of Happiness Studies in 2000 and the Applied Research in Quality of Life in 2006 (McCrea et al., 2011). In this chapter, we focus on the measurement of QoL in Europe, considering QoL as a latent concept that can be studied as a foundational measurement model. That is, QoL is assumed to be defined by a set of indicators: (i) objective or social indicators that reflect people’s objective conditions within a given cultural or geographic area, and (ii) indicators of subjective well-being that reflect individual judgements of well-being (Diener & Suh, 1997). Our main goal is to show the utility of applying a fuzzy-set approach in this framework, using micro-data collected through survey data.

The rest of this chapter is structured as follows. The second section presents a brief literature review concerning the measurement of QoL and its recent development. The third section outlines the surveys generally used in monitoring QoL in Europe, as well as the main results of some empirical studies. The fourth section briefly describes the fuzzy methodology proposed by Betti et al. (2015) for building composite indicators of QoL, showing how this approach can be a useful methodological tool in this framework. Finally, some conclusions are offered in the last section.

Measuring Quality of Life: Literature Review

Over time, the global community has moved from viewing well-being or QoL1 as a narrow economic concept to one that encompasses people’s objective conditions and their subjective evaluation of these conditions. In this section, we chronologically review the main approaches used in studying people’s QoL.

In the development period since the Second World War, the dominant concept of well-being has been economic. The conditions that determined national development were defined on the basis of production in the highest possible monetary terms and the modification of productive activities when they were harmful. Economic welfare focuses on the material resources that people control, can use and dispose of, measured by income and, at the aggregate level, by national income or production per capita (Gough et al., 2007). However, since the 1950-1960s, some of the most respected economists have become aware of the limits of using GDP as a proxy for QoL - this was a criticism not only about the use of a single indicator for assessing social performance but also about the economic nature of the indicator used (see e.g. Galbraith, 1958; Mishan, 1967; Scitovsky, 1976; Sen, 1976).

In this context, from the 1970s, the social indicators movement (Andrews §c Withey, 1976) argued in favour of measuring QoL more broadly, on the basis of a long list of indicators, rather than relying only on GDP per capita. At the United Nations conference ‘Paris Biosphere Conference’ in 1968, many official agencies focused on the development of a comprehensive system for monitoring progress with economic, social and environmental indicators. Despite its impact, the main limitation of the social indicators movement was the absence until the 1980s of a theoretical foundation, which was provided by the capability approach (Sessa, 2016).

In the 1980s, Sen (1980) introduced the capability approach as a general way to evaluate the human condition, breaking with traditional welfare economics (Gough et al., 2007; Robeyns, 2005). The capability approach is a broad normative framework for the evaluation and assessment of individual well-being or the average well-being of members of a group and the design of policies (Robeyns, 2005). The capability approach focuses on the plural or multi-dimensional aspects of development and claims that income and resources do not provide a sufficient or satisfactory indicator of well-being, as they measure the means, rather than the ends. It is necessary to take into consideration what individuals can do not only with the technology they have but also, and most importantly, with their human capital or, to be more precise, their capabilities (Nussbaum, 2011; Sen, 1980, 1992). The capability approach also provided the theoretical foundations of the human development paradigm (Fukuda-Parr & Kumar, 2004). Since 1990, the United Nations Development Programme has calculated the Human Development Index at the country level on an annual basis. However, among political decision-makers, macroeconomic indicators continued to be the most used for measuring economic and social progress and for designing policy.

In recent decades, demand has grown in the academic and political sectors, as well as the general public, for the development of better ways to measure and monitor QoL more comprehensively. The main argument is that a single economic measure does not account for the social cost of economic development, such as the cost of urbanisation or pollution, and does not consider income distribution or significant assets, such as educational opportunities, employment opportunities, personal safety and political freedom (Neumayer, 2003; Nussbaum, 2011; OECD, 2017; Stiglitz et al., 2011; Van den Bergh, 2009). Moreover, it fails to include subjective aspects that influence QoL (Diener, 2002; Frey & Sutzer, 2002). The inclusion of self-reported indicators is crucial for properly measuring QoL because they are related to the nonmaterial aspects of human well-being, such as the influence of social relations, trust in people, autonomy and self-determination (Barcena-Martin et al., 2017; Bartolini & Sarracino, 2014; Dolan & Metcalfe, 2012; Frey & Stutzer, 2002; Stutzer & Frey, 2010). In practice, this new approach has been adopted, with several international initiatives stressing the importance of including data on self-reported well-being, as well as objective well-being, in larger-scale surveys undertaken by official statistical offices, because they all contribute to measuring QoL (see e.g. Eurofound, 2017; OECD, 2017; Stiglitz et al., 2011).

Social Indicators and Databases to Study Quality of Life in Europe

In the EU, as well as worldwide, social survey methods are the most common tool used to collect the variables needed for measuring objective and subjective indicators of QoL. In these surveys, QoL is often measured by asking respondents to report or evaluate various aspects of their lives split into QoL domains. At least four sample surveys are used in monitoring QoL in Europe: the Eurobarometer Survey, the European Values Study (EVS), the European Social Survey (ESS) and the European Quality of Life Survey (EQLS). Table 8.1 summarises the main characteristics of these surveys. The goal is to assess what these databases contain for studying QoL. Furthermore, in 2013 and 2018, Eurostat launched two ad-hoc modules of the European Union Statistics on Income and Living Conditions (EU-SILC) survey on material deprivation: well-being and housing challenges. These ad-hoc modules complement the variables permanently collected in EU-SILC by highlighting these domains of QoL. Therefore, the EU-SILC database for these two years is another useful source of information for studying QoL across Europe.

In this section, we focus on EQLS because this survey has some characteristics and advantages compared to other databases that better suit our conceptual approach to QoL. In particular, the European Foundation for the Improvement of Living and Working Conditions (Eurofound) contributes to monitoring QoL in Europe through the EQLS, which consists of nationally representative surveys conducted in several European countries, as shown in Table 8.1. It is a unique European survey that examines both the objective conditions of European inhabitants’ lives and how they feel about those conditions and their lives in general. EQLS has also developed a valuable set of indicators related to environmental and social aspects of progress, which complements traditional indicators of economic growth and living standards, such as GDP and income, and they are easily integrated into the decisionmaking process and considered in the public debate at the EU and national levels. After Eurofound (2012, 2017, 2019), as shown in Table 8.2, QoL can be measured with a set of social indicators arranged in different domains: subjective well-being, living standards and deprivation, housing conditions and quality, health and mental well-being, employment and work-life balance, family and social life (quality of relationships), social exclusion and community involvement, local environment, public services, trust in people and institutions, and access to services. Moreover, what is more relevant is that many questions in the second EQLS were posed in the Eurobarometer Survey in 2009, which enabled the study of trends in QoL in the EU from 2003 to 2009.

In sum, the conceptual background of the EQLS (Eurofound, 2003) is based on a multi-dimensional approach that incorporates individual and societal perspectives and combines objective and subjective indicators. EQLS is also likely to be the most widely used survey by different researchers for monitoring the level and evolution of QoL in Europe following a multi-dimensional approach.

In particular, in the past few years, empirical evidence has been characterised by studies to measure the impact of the worldwide economic crisis on QoL. We present only some of the most relevant findings based on EQLS data.

Eurofound (2012) used descriptive and correlation methods to explore eight domains of QoL, taking into account the 2012 database. This report

Table 8.1 Main characteristics of surveys studying QoL in Europe




Sample design

Countries(last wave)

Domains of QoL






European Union member countries

General life satisfaction Living conditions Social security Environment Technolog)'

Health Family issues Social exclusion

European Values Study (EVS)


Every nine years




47 European countries/ regions


Work Environment Perceptions of life, politics and society, Religion and morality National identity

European Social Survey (ESS)


Every two years


19 European countries'1

Media and social trust Politics

Subjective well-being Household Human values Others'1

European Quality of Life Survey (EQLS)


Every four years


34 countries (27 EU members in 2003, Croatia, Island, North Macedonia, Montenegro, Serbia, Turkey and Kosovo)







Work-life balance Happiness Life satisfaction

Self-reported quality of their societies

Notes: •’ Not all the participating countries are the same in every round.b There are other topics, such as health care, welfare attitudes and work-life balance, but they are available in only one or two rounds.

Table 8.2 Indicators of QoL based on EQLS

Quality of life

Subjective well-being

Life satisfaction Happiness

Optimism about the future

Optimism about children’s or grandchildren’s future

Living standards

Satisfaction with living standards Difficulty making ends meet Material deprivation Economising on food

Housing conditions

Leaks, damp, or rot in housing Lack of bath/shower and toilet Ability to pav for heating

Health and mental well-being

Self-evaluation of health WHO Mental Well-being Risk of depression

Participation in sports or physical exercise

Work-life balance

Energy for household chores Difficulty fulfilling family responsibilities because of work

Difficulty concentrating at work because of family issues

Work hours do not fit with personal commitments

Quality of public services

Quality ratings of public services

Health care Education Public transport Child care Long-term care Social housing State pensions

Quality of society

Social insecurity

Job insecurity Housing insecurity Income insecurity in old age

Trust and tensions

Perceived tension between different racial or ethnic groups

Perceived tension between poor people and rich people

Feeling safe alone outdoors after dark Trust in people Trust in government Trust in local authorities

Participation and exclusion

Perceived social exclusion Involvement in civic and political activities Participation in voluntary work Participation in training

Note: Adapted from Eurofound (2019).

highlighted the different aspects of QoL that deteriorated during the financial and economic crisis, such as living and working conditions, with negative impacts on the everyday lives of people in several European countries. In addition, the report showed a clear division between the Nordic and Western European countries and the Southern and Eastern European countries, with a higher QoL in the former countries than in the latter countries. Betti et al. (2020a) found similar results, and we discuss it in detail in the next section.

Sommariba and Zarsosa (2019) constructed a proxy indicator for QoL using DP2 methodology using eight different dimensions with data from the 2012 EQLS. They also found a spatial pattern in which the Nordic countries, together with those in Central Europe, has a higher QoL than Eastern European countries. Specifically, their evidence showed that Bulgaria has the lowest QoL in Europe, whereas Denmark has the highest. Applying fuzzy- set theory with 2012 and 2007-2012 EQLS data, respectively, Betti (2016, 2017) found analogous results. Similar conclusions were reached by Rogge and Van Nijverseel (2019), despite using a different dataset (EU-SILC).

Eurofound (2014) examined the QoL of different types of families with children in the context of the economic crisis. The results are based on the 2007 and 2012 databases. In their report, simple statistical tools such as chi-square statistics and t-tests were used to identify statistically significant differences between categories and average values between different types of European families or different country groups. Eurofound (2017) used more than eight domains of QoL, the 2012 and 2016 databases, and descriptive methods. Their findings showed that QoL has improved less for Europeans in the lowest income quartile than for others, and, in general, the EU has experienced an improvement in some dimensions, such as overall health, the standard of living and the work-life balance, compared with pre-crisis levels.

Kristapsone and Bruna (2019), using eight domains of QoL from the 2012 and 2016 EQLS database, and descriptive and inferential statistical analysis, analysed changes in QoL between both years. They found that, after the economic crisis, in the EU only the indicators related to satisfaction with the present state of the economy in a country increased between 2012 and 2016, whereas the other seven indicators of QoL remained the same in both years, so satisfaction in the other domains did not change over this period. They also concluded that EQLS data show that economic growth, social and economic reforms, and the efficacy of social security in the period surveyed in the EU post-crisis have not significantly contributed to their personal assessment of QoL.

Fuzzy and Multi-dimensional Approach for Studying the Quality of Life

As we discussed previously, the empirical findings on QoL are characterised by a multiplicity of approaches because the concept varies widely and is complex because of its multifaceted nature, which is not easy to define and measure (Glatzer, 2006; Mauro et al., 2018). Thus, we concentrate on a specific methodological approach that can be used in this framework, namely, the fuzzy and multi-dimensional approach introduced by Betti et al. (2016) for studying QoL in Macedonia. This approach was later applied by Betti (2017) and Betti et al. (2020a), using a more appropriate statistical approach for measuring the effect of the economic crisis in QoL in Europe with EQLS data. Table 8.3 summarises the eight domains of QoL and the 48 social indicators described by Betti (2016, 2017), which, in turn, are based on the seminal contributions of Nussbaum and Sen (1993), Phillips (2006) and Eurofound (2003, 2010), outlined in Table 8.2.

This methodology is based on two main hypotheses:

Hypothesis 1: the theoretical concept of OoL is not directly observable; rather, it is latent, and observed social indicators can be used as partial/ imperfect measures of this underlying theoretical concept

Hypothesis 2: OoL is a vague concept with different shades and degrees, rather than an attribute that is simply present or absent for individuals in society.

HI applies this approach in a formative measurement model as well as DEA-BoD, DP2 and MPI (Jimenez-Fernandez & Ruiz-Martos, 2020). H2 is, indeed, a new feature in a formative measurement approach.

The empirical application of this approach is, obviously, strictly related to the availability of a set of social indicators organised into several QoL domains. However, the selection of meaningful and useful social indicators for the analysis is a non-trivial task. It is strictly dependent on the conceptual framework (Maggino, 2017) of the phenomenon studied and limited by data availability (see e.g. Guio & Marlier, 2017, in the framework of poverty analysis). Nevertheless, EQLS data offer a great opportunity for the empirical implementation of this approach because this survey collected several social indicators that can be organised into different domains. We briefly introduce the main steps that characterise the multi-dimensional and fuzzy approach.

In general, each social indicator is measured on a scale of 1 to a maximum (e.g. in EQLS data, the high end of the scale is generally 10) needs to be converted into the interval [0,1] according to fuzzy logic. The transformation used can be found in Betti and Lemmi (2013) and in Betti et al. (2015,2020b).

Let lk be the &th social indicator converted into the interval [0,1]. Explorative factor analysis (EFA) and confirmative factor analysis (CFA) are typically used to group these indicators into dimensions that represent specific aspects of QoL. Basically, each dimension s can be composed of a different number of individual indicators lk [k = 1,..., Ks) on the basis of the results of factor analysis. For instance, in Betti et al. (2020a), eight different dimensions were individualised (see Table 8.3). A function related to each

Table 8.3 Domains and individual social indicators of quality of life

QoLl quality of relations


Poor and rich people


Management and workers


Men and women


Old people and young people


Different racial and ethnic groups


Different religious groups


trust in people and institutions


The parliament


The legal system


The press


The police


The government


In general, would you say that most people can be trusted or that you can’t be too careful in dealing with people?


access to services


Distance to doctor’s office/hospital/medical centre


Delay in getting an appointment


Waiting time to see a doctor on the day of an appointment


Cost of seeing the doctor


quality of public services


Health care




Public transport


Child care


State pension system





Your education


Your present standard of living


Your housing


Your family life


Your social life


I feel left out of society


Life satisfaction




Housing quality


Keeping your home adequately warm


Rot in windows, doors, or floors

q 19c

Damp or leaks in walls or roof


Lack of indoor flush toilet


Lack of bath or shower


Lack of a place to sit outside (e.g. garden, balcony, terrace)


standard of living


Shortage of space


Paying for a week’s annual holiday away from home


Replacing any worn-out furniture


Table 8.3 Continued


A meal with meat, chicken or fish every second day if you want it


Buying new, rather than second-hand, clothes


Having friends or family in for a drink or meal at least once a month


Rent or mortgage payments for housing


Utility bills, such as electricity, water and gas

inc ind

Income deciles

Q0L8 health


Could you please tell me how satisfied you are with vour health, on a scale of 1 to 10?


In general, would you say your health is ...


Chronic physical or mental health problems, illness or disability


I am optimistic about the future

Note: Adapted from Betti et al. (2020a).

dimension s is then calculated, called the membership function, which is a quantitative specification of the individual degree of QoL. Accordingly, the value of the membership function is 0 for the lowest level of quality of life and 1 for the highest level. Let OoLj< ; indicate the membership function of dimension s for individual;'(/' = 1 ... n) in the sample. Therefore, as the value increases from 0 to 1, the well-being of individual j for the corresponding dimension increases. OoL is calculated as:

~ (sir

where w{s)k is the weight of /(.in dimension s. In turn, w[s)k is computed as the product of two components that take into account both the dispersion of indicator lk in the dimension s and its correlation with the other indicators in the same dimension s.

Finally, a comprehensive measure of the QoL of each individual j can be obtained as the unweighted mean over the S dimensions of the dimension- specific OoL{s)j:

The outcome of this procedure allows us to obtain very simple synthesis measures. Indeed, the (sample) weighted means (OoL^, s = 1, ..., S

and QoL ) of Equations (8.1) and (8.2) give the measures of the degree of QoL observed in each dimension s and for all dimensions as a whole, respectively.

Following this approach, Betti et al. (2020a) found that the negative effect of the crisis on QoL was very high, especially in Greece, Malta, Ireland, Cyprus, the Czech Republic and Poland. However, they found the opposite result in Macedonia. Moreover, they pointed out the heterogeneous impacts of the economic crisis on the QoL in European countries with respect to the country and the type of dimension observed. Indeed, some dimensions seem significantly lower in many countries, whereas in others, the changes have been positive, albeit to a smaller extent. Specifically, they concluded that the economic crisis had a negative impact on the overall standard of living, on trust in institutions and on some aspects of health care, but it has led individuals to increase their connections with friends and family in order to receive some kind of help and support.


In this chapter, we discuss the utility of a multi-dimensional and fuzzy approach to measuring QoL, which implies measuring QoL using a formative measurement model framework. In other words, we treat QoL as a multi-dimensional latent concept that can be explained with objective and subjective social indicators arranged in different domains of QoL. Moreover, the fuzzy measure also preserves the richness of the latent concept, which is helpful because QoL is a difficult and vague concept to define. The value of the approach proposed in this chapter can be summarised in terms of at least three very interesting features, as discussed in Betti et al. (2020b).

First, the latent dimensions of QoL are not predefined a priori; rather, they are identified by EFA and then validated by CFA. Second, the aggregation of social indicators into a domain is performed with a statistically based weighting system that takes into account measurement errors, redundancies and other characteristics of the social indicators involved based on a ‘prevalence correlation’ (i.e. taking into account the dispersion of a social indicator - prevalence weights - and its correlation with the other social indicators in a given domain - correlation weights). Third, the approach has the advantage of calculating a composite indicator of QoL, simultaneously maintaining its multi-dimensionality by including different composite indicators in each domain.

Flowever, like other formative measurement models, this methodology involves stages in which judgements must be made - for example, concerning the selection of the social indicators and the weighting system. Indeed, the notion of measuring QoL could include the measurement of practically anything of interest to anyone, and everyone could find arguments to support the selection of partially different sets of social indicators. Therefore, the empirical analysis has to be conducted with transparent steps to achieve comprehensible and meaningful results.


1 Following Michalos et al. (2011) and Veenhoven (2017), in this chapter we use

the terms ‘well-being’ and ‘quality of life’ interchangeably, because both terms

are interpreted in the broad sense of living a good life.


Andrew, F.M., Sc Withey, S.B. (1976). Social Indicators of Well-Being. New York: Plenum.

Barcena-Martin, E., Cortes-Aguilar, A., Sc Moro-Egido, A. (2017). Social comparisons on subjective well-being: The role of social and cultural capital. Journal of Happiness Studies, 18(4), 1121-1145.

Bartolini, S., Sc Sarracino, F. (2014). Happy for how long? How social capital and economic growth relate to happiness over time? Ecological Economics, 108, 242-256.

Betti, G. (2016). Fuzzy measures of quality of life: A multidimensional and comparative approach. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 24(1), 25-37.

Betti, G. (2017). What impact has the economic crisis had on quality of life in Europe? A multidimensional and fuzzy approach. Quality & Quantity, 51(1), 351-364.

Betti, G., D’Agostino, A., Gagliardi, F., Sc Giusti, C. (2020b). The integrated fuzzy and relative index for poverty analysis: A review of applications in the social sciences. Studies of Applied Economics - Estudios de Economta Aplicada, 38(1), pp. 1—14.

Betti, G., D’Agostino, A., Ghellini, G., Sc Longobardi, S. (2020a). Measuring the impact of financial crisis on quality of life in Europe. Economics Bulletin, 40(1), 292-307.

Betti, G., Gagliardi, F., Lemmi, A., Sc Verma, V. (2015). Comparative measures of multidimensional deprivation in the European Union. Empirical Economics, 49(3), 1071-1100.

Betti G., Sc Lemmi A. eds. (2013). Poverty and Social Exclusion: New Methods of Analysis. London: Routledge.

Betti, G., Soldi, R., Sc Talev, I. (2016). Fuzzy multidimensional indicators of quality of life: The empirical case of Macedonia. Social Indicators Research, 127(1), 39-53.

Diener, E. (2002). Will money increase subjective well-being? Social Indicators Research, 57, 119-169.

Diener, E., Sc Suh, E. (1997). Measuring quality of life: Economic, social, and subjective indicators. Social Indicators Research, 40(1-2), 189-216.

Dolan, P., Sc Metcalfe, R. (2012). Measuring subjective well-being: Recommendations on measures for use by national governments. Journal of Social Policy, 41(2), 409-427.

EU Commission (2009). GDP and Beyond: Measuring Progress in a Changing World. COM, 433. Brussels.

Eurofound (2003). Monitoring Quality of Life in Europe. Luxembourg: Office for Official Publications of the European Communities.

Eurofound (2010). Subjective Well-Being in Europe. Luxembourg: Office for Official Publications of the European Communities.

Eurofound (2012). Third European Quality of Life Survey - Quality of Life in Europe: Impacts of the Crisis. Luxembourg: Office for Official Publications of the European Communities.

Eurofound (2014). Third European Quality of Life Survey - Quality of Life in Europe: Families in the Economic Crisis. Luxemburg: Publications Office of the European Union.

Eurofound (2017). European Quality of Life Survey 2016: Quality of Life, Quality of Public Services, and Quality of Society. Luxembourg: Office for Official Publications of the European Communities.

Eurofound (2019). European Quality of Life 2016: Life and Society in the EU Candidate Countries. Luxembourg: Publications Office of the European Union, doi: 10.2806/663872

Frey, B.S., 6c Stutzer, A. (2002). The economics of happiness. World Economics, 3(1), 1-17.

Fukuda-Parr, S., 8c Kumar, A.S. (2004). Readings in Human Development: Concepts, Measures and Policies for a Development Paradigm. New York: Oxford University Press.

Galbraith, J.K. (1958). The Affluent Society. Boston: Houghton Mifflin.

Glatzer, W. (2006). Quality of life in the European Union and the United States of America: Evidence from comprehensive indices. Applied Research in Quality of Life, 1(2), 169-188.

Gough, I., McGregor, A.J., 6c Camfield, L. (2007). Theorising wellbeing in international development. In I. Gough 6C A.J. McGregor (Eds.), Wellbeing in Developing Countries: From Theory to Research (pp. 3-44). Cambridge: Cambridge University Press.

Guio, A.C., 6C Marlier, E. (2017). Amending the EU material deprivation indicator: Impact on size and composition of deprived population. In A.B. Atkinson, A.-C. Guio, 6c E. Marlier (Eds.), Monitoring Social Europe (pp. 193-207). Luxembourg: Publications Office of the European Union.

Jimenez-Fernandez, E., 6C Ruiz-Martos, M. (2020). Review of some statistical methods for constructing composite indicators. Estudios de Economia Aplicada/ Studies of Applied Economics, 38(1), 1-15.

Kristapsone, S., 6C Bruna, S. (2019, 16-18 May). Changes in the subjective assessment of quality of life in Latvia and the European Union: Results of European quality of life survey (2016). In New Challenges of Economic and Business Development-2019: Incentives for Sustainable Economic Growth [Proceedings!. University of Latvia, Riga, Latvia.

Maggino, F. (2017). Developing indicators and managing the complexity. In F. Maggino (ed.), Complexity in Society: From Indicators Construction to Their Synthesis (pp. 87-114). New York: Springer.

Mauro, V., Biggeri, M., 6C Maggino, F. (2018). Measuring and monitoring poverty and well-being: A new approach for the synthesis of multidimensionality. Social Indicators Research, 135(1), 75-89.

McCrea, R., Marans, R.W., Stimson, R., 6c Western, J. (2011). Subjective measurement of quality of life using primary data collection and the analysis of survey data. In R.W. Marans 6c R.J. Stimson (Eds.), Investigating Quality of Urban Life (pp. 55-75). Dordrecht: Springer.

Mercy, J.L. (Ed.). (2015). Quality of Life: Facts and Views. Luxembourg: Publications Office of the European Union.

Michalos, A.C., Smale, B., Labonte, R., Muharjarine, N., Scott, K., Moore, K., Swystun, L., Holden, B., Bernardin, H., Dunning, B., Graham, P., Guhn, M., Gadermann, A.M., Zumbo, B.D., Morgan, A., Brooker, A.-S., 6c Hyman, I. (2011). The Canadian Index of Wellbeing. Technical Report 1.0. Waterloo, ON: Canadian Index of Wellbeing and the University of Waterloo.

Mishan, E.J. (1967). The Cost of Economic Growth. London: Staples Press.

Neumayer, E. (2003). Beyond income: Convergence in living standards, big time. Structural Change and Economic Dynamics, 14, 275-296.

Nussbaum, M.C. (2011). Creating Capabilities: The Human Development Approach. Cambridge: Belknap Press of Harvard University Press.

Nussbaum, M.C., Sc Sen, A. (Eds.). (1993). The Quality of Life. Oxford: Oxford University Press.

OECD. (2017). How’s Lifef Measuring Well-Being. Paris: OECD.

Phillips, D. (2006). Quality of Life: Concept, Quality, Practice. London: Routledge.

Robeyns, I. (2005). The capability approach: A theoretical survey. Journal of Human Development, 6(1), 93-114.

Rogge, N., 8c Van Nijverseel, I. (2019). Quality of life in the European Union: A multidimensional analysis. Social Indicators Research, 141(2), 765-789.

Sanchez, A., Chica-Olmo, J., 8c Jimenez-Aguilera, J.D. (2018). A space-time study for mapping quality of life in Andalusia during the crisis. Social Indicators Research, 135(2), 699-728.

Scitovsky, T. (1976). The Joyless Economy. New York: Oxford University Press.

Sen, A. (1976). Real national income. Review of Economic Studies 43(1), 19-39.

Sen, A. (1980). Equality of what? In S. McMurrin (Ed.), Tanner Lectures on Human Values (Vol. 1, pp. 197-220). Cambridge: Cambridge University Press.

Sen, A. (1992). Inequality Re-Examined. New York: Oxford University Press.

Sessa, M. (2016). Measuring and monitoring the achievements of the millennium development goals through dynamic composite indices. Social Indicators Research, 127, 469-503.

Somarriba, N., 8c Zarzosa, P. (2019). Quality of life in the European Union: An econometric analysis from a gender perspective. Social Indicators Research, 142, 179-200.

Stiglitz, J., Sen, A., 8c Fitoussi, J. (2011). Mismeasuring Our Lives: Why GDP Doesn’t Add Up. New York: New Press.

Stutzer, A., 8c Frey, B.S. (2010). Recent advances in the economics of individual subjective well-being. Social Research: An International Quarterly, 77(2), 679-714.

Van den Bergh, J. (2009). The GDP paradox. Journal of Economic Psychology, 30(2), 117-135.

Veenhoven, R. (2000). The four qualities of life. Journal of Happiness Studies, 1, 1-39.

Veenhoven, R. (2017). Greater happiness for a greater number: Did the promise of enlightenment come true? Social Indicators Research, 130, 9-25.

< Prev   CONTENTS   Source   Next >