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The aim of this paper is to analyse the effect of COVID-19 on multidimensional well-being in the European population aged 50 and over by measuring changes in individual well-being before and after the pandemic outbreak. To capture the multidimensional nature of well-being, we consider different dimensions: economic well-being, health status, social connections and work status. We introduce new indices of change in individual well-being that measure non-directional, downward and upward movements. Individual indices are then aggregated by country and subgroup for comparison. The properties satisfied by the indices are also discussed. The empirical application is based on micro-data from waves 8 and 9 of the Survey of Health, Ageing and Retirement in Europe (SHARE), carried out for 24 European countries before the pandemic outbreak (regular survey) and in the first two years of the COVID-19 pandemic (June–August 2020 and June–August 2021). The findings suggest that employed and richer individuals suffered greater losses in well-being, while differences based on gender and education diverge from country to country. It also emerges that while the main driver of well-being changes in the first year of the pandemic was economics, the health dimension also strongly contributed to upward and downward well-being changes in the second year. [ABSTRACT FROM AUTHOR]

Over the years, the Human Development Index has become a reference measure of quality of life and well-being. Its growing importance has been accompanied by a lively debate in the literature concerning the pros and cons of this index. Many works have attempted to provide solutions to Human Development Index related problems. In this paper, we will focus on some of these problems, which are typical not only for the measurement of human development, but for the construction of composite indicators. We will try to provide an answer by proposing two new methodological tools, the M i n - B o D interval of synthesis and the mid aggregation point, which present interesting potentialities to be used in empirical analyses and for policy evaluations, not only in the human development measurement. The proposed tool have been applied to the Human Development Index data collected for 189 countries in 2019. [ABSTRACT FROM AUTHOR]

This study builds on the history of economic well-being (EWB) index construction to develop such an index for urban U.S. counties (population > 62,437). Unlike previous studies, we rely on external validation of economic well-being to construct a best-fit index, where our external validation approach follows the Tiebout Hypothesis. We estimate a best-fit, linear regression-based index, in which lagged features of economic well-being are weighted based on ability to explain subsequent county population change. Compared to an arbitrarily equally-weighted model using a composite index a model using lagged weighted EWB individual variables provide greater transparency while also explaining substantially more variation in population change across urban counties (19.9% vs. 15.7%). [ABSTRACT FROM AUTHOR]

This paper contributes to the research on the development of comparable composite indicators by introducing a Functional Weighted Malmquist Productive Index that allows for comparative trend analysis. In analogy with entropy-based weighted methods, this novel dynamic indicator is derived by measuring the degree of diversification of the single method through a family of diversity indices. The paper has the merit of proposing a new dynamic composite indicator that supplements the analysis with Functional Data Analysis (FDA) tools that provide us with useful information about the order and dynamics of the composite index trajectories. The simulation study set up in this paper raises doubts about the robustness of the entropy-based weighted methods while the application of the new index to well-being dataset highlights its practical appeal. [ABSTRACT FROM AUTHOR]

This article proposes a multidimensional analysis grid to assess regional disparities, transcending monetary considerations. This grid agrees overall with the common framework that prevails in the literature review that we have carried out. It is built around four dimensions of well-being: economy (development, labor market, human capital and innovation), social (health, living conditions and gender), environment and governance. Our analysis of regional disparities was based on the synthesis of fifteen indicators by constructing a Synthetic Index of Well-being (SIWB) by combining its four dimensions using an aggregative-compensative approach. This analysis covers Morocco, 35 of the OECD member countries and their 389 regions between 2000 and 2019. We have assessed the dynamics of Moroccan regions compared to those of the benchmark. Thus, we have highlighted the gaps to be made up in relation to the different areas of well-being as well as their thematic variations. [ABSTRACT FROM AUTHOR]

Relevance. Global demographic aging has actualized the need to find effective solutions to a wide range of problems related to the well-being of older citizens: from financial and economic to emotional and psychological. Focusing public attention on the problems of older people entailed the demand for institutional changes, the adoption of important social and political decisions to improve the quality of life of older citizens as the fastest growing social group, the improvement of traditional and the emergence of new tools for assessing and monitoring progress in this area both at individual level and at the level of regions, countries, international organizations. In addition to indices well known in international practice (Active Aging Index and Global AgeWatch), the Russian research arsenal includes the Russian Elderly Well-being Index (REWI), developed by researchers at the International Scientific and Educational Laboratory of Technologies for Improving the Well-Being of the Elderly at Tomsk Polytechnic University. REWI is objectively not free from the shortcomings inherent in the method as a whole, however, it can be used to track the dynamics of changes in the quality of life of the older generation both for Russia as a whole and for its individual regions, compare their ranking positions within one or several periods, put forward hypotheses about the causes of changes, analyze them, formulate proposals on measures that can improve the quality of life of older people in a particular region. Objective of the study is to analyze the changes in the assessment of the well-being of older people in the Tomsk region based on the REWI materials for 2014, 2016 and 2018 and formulate conclusions based on the results of three rounds of the all-Russian study in the context of the dynamics of the quality of life of older people. Methods: statistical analysis according to the original method for calculating the REWI based on microdata from the Federal State Statistics Service (Rosstat) survey «Comprehensive observation of living conditions of the population» for 2014, 2014 and 2018. Results. The analysis showed (1) the deterioration of values ​​for almost all domains of REWI (economy, society, health, infrastructure and quality of life in the regional dimension) in the whole of the Russian Federation for the entire observation period; (2) the deterioration of the rating positions of the Tomsk region according to the REWI in 2016 and 2018 relative to 2014, (3) a noticeable decrease in the scores for most domains and private index indicators for the Tomsk region in 2018. Conclusions. Based on the results of the analysis of the REWI values ​​for three observation periods, conclusions were formulated both about the possibilities and objective limitations in using this index to assess the quality of life and well-being of the older generation in the Russian Federation, and to analyze the dynamics of the corresponding processes in individual regions. Assumptions are put forward about the possible reasons for the negative dynamics of the Tomsk region in the REWI rating both in general and in its individual domains and indicators. Based on the data obtained, preliminary conclusions were drawn about the risks of non-implementation of the national action plan to improve the quality of life of older people and the incomplete coordination of actions carried out in our country with international programs in this area. Perhaps these problems will be able to attract more attention, efforts and resources for their solution against the background of the immediate prospects for the introduction of the Russian elderly well-being index which is built in accordance with the international methodology.

Over the last years there has been an increasing interest in measuring well-being at local level. This is mainly due to the fact that socio-economic indicators at country level do not provide a complete picture of the living conditions in a territory. Moreover, the temporal dimension is also a fundamental aspect that allows analysing the trends of local well-being over time. The aim of this paper is to provide a more in-depth analysis of territorial disparities, inequalities and divergences across the Italian territories. In particular, this paper is one of the first attempts to analyse the overtime trend of the Italian well-being at provincial level (NUTS3) using a subset of indicators recently provided by ISTAT to measure the Equitable and Sustainable Well-being (BES) at local level. We apply a Bayesian latent variable model to construct three composite indicators related to the three main pillars of well-being, namely economic, social, and environmental. These composite indicators have been estimated for all the years between 2004 and 2016 for each Italian province. Results suggest that in the period of analysis the economic well-being has worsened in almost all provinces, with weak signs of recovery starting from 2014. On the contrary, social well-being improved in almost all provinces, with some exceptions in the South. The environmental well-being has increased over time as well, more in the Northern and Central provinces than in the South. [ABSTRACT FROM AUTHOR]

There has been a growing consensus in recent years that development is a multidimensional concept that embodies the enhancement of several aspects of human life and, as a result, it is too complex to be captured by single indices. Composite Indicators have increasingly been recognised as useful tools in the measurement of this concept. In the absence of rigorous and comprehensive empirical studies in Greece on this topic, the paper assesses and reveals the developmental transformations of the regional economies at NUTS 2 and 3 levels in the period 1991–2011. In this way, this study provides a more comprehensive and integrative perspective of regional development in Greece presenting empirical evidence not only from a country with large and persistent regional inequalities but also from a cohesion country of the European Union for which regional policy has been of critical importance in the last decades. Moreover, the study adds to the literature shedding light on an under-researched topic; the importance of spatial heterogeneity in the construction of Composite Indicators. The results reveal a heterogeneous regional pattern of development for the Greek case. The findings can be used by policymakers as a way to better understand and improve the regional development process. [ABSTRACT FROM AUTHOR]

In the analysis of the difference in the distribution and profiles of the equitable and sustainable well-being, the territorial dimension is a fundamental reading-key for local policies since it allows the areas of advantage or relative deprivation to emerge more accurately. Specifically, in Italy the provincial level coincides with the administrative area of metropolitan cities, which are the subject of growing attention from European and national policies. The BES 2018 report by Italian National Institute of Statistics (ISTAT) has confirmed that from 2015 an improvement in many areas of well-being has been marked, even if territorial differences remain stable both in levels and dynamics. These differences appear in some cases as real structural differences between the North and South of Italy. Then, the measures of equitable and sustainable well-being in the territories allow, in various degrees, to deepen and specify this situation employing synthetic measures of well-being. In this work, we propose a statistical methodology focused on the simultaneous partial least squares structural equation modeling and simultaneous K-means clustering to obtain a composite indicator of Italian well-being and at the same time a classification of Italian territorial micro-areas by means of the just updated provincial data about BES 2018. In this way, the territorial differences of well-being can be more reliably and more exactly defined on the basis of the relationships among all elementary indicators and domains proposed in the analysis of well-being by ISTAT. [ABSTRACT FROM AUTHOR]

This article studies the dimensions that influence the well-being of Moroccan immigrants in Spain. In a first step, we build a composite indicator of immigrant well-being following the theoretical model by Bosswick and Heckmann (Integration and access to social rights of migrants: the contribution of local and regional authorities. Conceptual Framework Draft, Bamberg, 2006. https://www.eurofound.europa.eu/sites/default/files/ef%5fpublication/field%5fef%5fdocument/ef0622en.pdf) and adapted according to the methodological recommendations of the International Organization for Migration (IOM in Migration in the 2030 agenda, 2013. http://publications.iom.int/system/files/pdf/migration%5fin%5fthe%5f2030%5fagenda.pdf). The dimensions of the index are cultural well-being, structural well-being, community well-being and subjective well-being. In a second step, we consider this index as a dependent variable of immigrant-related social and economic characteristics. Results show that immigrants' well-being is related to the way they reached the country, the reasons they came, whether they arrived before or after the crisis, and their region of residence in Spain. The results underline the importance of local policies fostering Moroccan integration to increase immigrants' well-being. [ABSTRACT FROM AUTHOR]

The paper aims to shed light on the geography of well-being in Italian regions and explain the distance of a given region from an efficiency frontier. We build a composite regional well-being index over the period 2010–2015. Then using the index as output, we estimate a well-being generating function to rank Italian regions in terms of efficiency in attaining well-being. The rankings confirm the divide between Northern and Southern regions as regards overall well-being and efficiency. Our findings indicate that regions more dependent on external financing achieve lower efficiency scores. Current failures should not be used to reinforce selfish localism; they should rather stimulate the search for more effective policies to reduce disparities. [ABSTRACT FROM AUTHOR]

This letter assesses the impact of the Great Recession on well-being in Spanish provinces using two alternative composite indicators of objective well-being that include somewhat different dimensions. Whereas the crisis notably eroded economic well-being, its impact on overall well-being – which in addition to economic dimensions also includes non-economic ones – was imperceptible. This result points to the need to carefully define and assess well-being in empirical analyses. [ABSTRACT FROM AUTHOR]

In recent times, composite indicators have gained astounding popularity in a wide variety of research areas. Their adoption by global institutions has further captured the attention of the media and policymakers around the globe, and their number of applications has surged ever since. This increase in their popularity has solicited a plethora of methodological contributions in response to the substantial criticism surrounding their underlying framework. In this paper, we put composite indicators under the spotlight, examining the wide variety of methodological approaches in existence. In this way, we offer a more recent outlook on the advances made in this field over the past years. Despite the large sequence of steps required in the construction of composite indicators, we focus particularly on two of them, namely weighting and aggregation. We find that these are where the paramount criticism appears and where a promising future lies. Finally, we review the last step of the robustness analysis that follows their construction, to which less attention has been paid despite its importance. Overall, this study aims to provide both academics and practitioners in the field of composite indices with a synopsis of the choices available alongside their recent advances. [ABSTRACT FROM AUTHOR]

Abstract Better Life Index (BLI), the measure of well-being proposed by the OECD, contains many metrics, which enable it to include a detailed overview of the social, economic, and environmental performances of different countries. However, this also increases the difficulty in evaluating the big picture. In order to overcome this, many composite BLI procedures have been proposed, but none of them takes into account societal priorities in the aggregation. One of the reasons for this is that at the moment there is no representative survey about the relative priorities of the BLI topics for each country. Using these priorities could help to design Composite Indices that better reflect the needs of the people. The largest collection of information about society is found in social media such as Twitter. This paper proposes a composite BLI based on the weighted average of the national performances in each dimension of the BLI, using the relative importance that the topics have on Twitter as weights. The idea is that the aggregate of millions of tweets may provide a representation of the priorities (the relative appreciations) among the eleven topics of the BLI, both at a general level and at a country-specific level. By combining topic performances and related Twitter trends, we produce new evidences about the relations between people's priorities and policy makers' activity in the BLI framework. Highlights • Better Life Index includes a multidimensional overview of well-being. • Composite Indices should consider societal priorities in the aggregation. • At the moment there is no representative survey about the societal priorities. • The aggregate of millions of tweets may provide a representation of the priorities. • We include priorities detected on Twitter into a Composite Index of well-being. [ABSTRACT FROM AUTHOR]

This paper provides a composite indicator of well-being for the 35 OECD countries, as well as South Africa, Russia and Brazil for the period 2013-2016, considering data on 10 different well-being domains from the OECD Better Life Index. In a first stage, countries are ranked according to their well-being indicator, constructed combining Data Envelopment Analysis with the Benefit-of-the-Doubt principle and Multi-Criteria-Decision-Making techniques. In a second stage, well-being clubs are identified using hierarchical cluster analysis, revealing that well-being is highly polarised. Moreover, as well-being affects people, population size is accounted for in the cluster analysis, showing that for the largest proportion of people in our sample, well-being is remarkably low. [ABSTRACT FROM AUTHOR]

This paper employs a multidimensional index for assessing women’s well-being in Iranian provinces using demographic and health survey macro data. Besides economic status as a traditional dimension for objective well-being, other factors such as health, education, risk, and technology as well as self-reported life satisfaction and happiness (psychological well-being) have been included as non-material well-being dimensions. We found that the values of women’s well-being are distributed unequally across the provinces. Southern provinces demonstrate the worst performances and central provinces have the best. Generally, Iranian women are in poor condition in terms of economic variables (e.g. income and participation in the job market), psychological well-being (life satisfaction), HIV literacy, use of the internet, and access to mass media. On the other hand, in recent years, they have made significant progress in education (such as the rate of literacy, high school degree), and health (safe pregnancy care and nutritional supplements). [ABSTRACT FROM AUTHOR]

In recent years, there has been an increasing proliferation of initiatives focusing on the concept of quality of life and well-being. At the centre of these studies there is the recognizing that the GDP offers only a partial perspective of factors affecting people's lives. Following this line of the research, this paper is aimed at computing the well-being efficiencies of a sample of Italian Province capital cities, using a methodological approach that combines data envelopment analysis (DEA) with Shannon's entropy formula. To avoid subjectivity in choosing a representative set of variables that proxy the phenomenon under study, we rely on the theoretical framework adopted by the Italian National Institute of Statistics (ISTAT) within the equitable and sustainable well-being (BES) project. The dashboard of indicators included in the analysis are related to the Ur-BES initiative, promoted by ISTAT to implement the BES framework at cities level. In a first step of the analysis, an immediate focus on separate dimensions of urban well-being is obtained by summarizing the plurality of available indicators through the building of composite indices. Next, the adopted integrated DEA-Shannon entropy approach has permitted to increase the discriminatory power of DEA procedure and attain a more reliable profiling of Italian Province capital cities well-being efficiencies. The results show a marked duality between the Northern and Southern cities, highlighting important differences in many aspects of human and ecosystem well-being. [ABSTRACT FROM AUTHOR]

An interesting measure for equitable and sustainable well-being has been proposed recently by the National Institute of Statistics in Italy and the National Council for Economy and Labour. It is called BES (from the Italian Benessere Equo e Sostenibile). A set of indicators, partitioned into several domains and themes, is used for measuring the BES. Taking into account prior knowledge of both the structure of this set of indicators and the relationships among them, the paper proposes a hierarchical composite model for measuring and modeling the BES of the Italian provinces. This hierarchical model allows us to synthesize individual indicators into single indexes in order to construct composite indicators at a global and a partial level. Moreover, we analyze the relationships among the different domains and themes as well as the effects of these on equitable and sustainable well-being, in order to search for strongly influential factors. In order to estimate the parameters of the model, we use both Partial Least Squares path modeling and a new method, called Quantile Composite-based path modeling. In particular, Partial Least Squares path modeling is used to estimate average effects in the network of relationships between variables, while with Quantile Composite-based path modeling we investigate whether the magnitude of these effects changes across different parts of the variable distributions, providing a more complete picture and uncovering specific local leveraging factors for improvement. A final ranking of the Italian provinces, according to the BES composite indicator, is also provided at the national level and for different geographic areas of Italy. [ABSTRACT FROM AUTHOR]

Composite indicators (CIs), in the social sciences, are used more and more for measuring very complex phenomena as the poverty, the progress and the well-being. Using an approach Model Based in to build CIs, instead of an approach Data Driven, it is possible to consider the role (formative and reflective) of the manifest variables (MVs) and to model the relationships among the CIs. In this article, we begin introducing structural equation modeling (SEM) as a tool for building Model Based CIs. Secondly, among the several methods developed to estimate SEM, we show Partial Least Squares Path Modeling (PLS-PM), due to the key role that estimation of the latent variables (i.e. the CIs) plays in the estimation process. Moreover, we present some recent developments in PLS-PM for the treatment of non metric data, hierarchical data, longitudinal data and multi-block data. Finally, we demonstrate how these recent developments can strongly help in the building of CIs. It is easy to realize, for example, that as a consequence of considering nominal and ordinal data, the knowledge about a phenomenon synthesized by a CI is considerably extended and improved especially for operational use. In order to highlight the potentiality of the proposed approach, the construction of a CI is discussed. In particular, a CI of Social Cohesion, developed by using European Values Study data, will be described in detail. [ABSTRACT FROM AUTHOR]

We present a method of rank-optimal weighting which can be used to explore the best possible position of a subject in a ranking based on a composite indicator by means of a mathematical optimization problem. As an example, we explore the dataset of the OECD Better Life Index and compute for each country a weight vector which brings it as far up in the ranking as possible with the greatest advance of the immediate rivals. The method is able to answer the question 'What is the best possible rank a country can achieve with a given set of weighted indicators?' Typically, weights in composite indicators are justified normatively and not empirically. Our approach helps to give bounds on what is achievable by such normative judgments from a purely output-oriented and strongly competitive perspective. The method can serve as a basis for exact bounds in sensitivity analysis focused on ranking positions. In the OECD Better Life Index data we find that 19 out the 36 countries in the OECD Better Life Index 2014 can be brought to the top of the ranking by specific weights. We give a table of weights for each country which brings it to its highest possible position. Many countries achieve their best rank by focusing on their strong dimensions and setting the weights of many others to zero. Although setting dimensions to zero is possible in the OECD's online tool, this contradicts the idea of better life being multidimensional in essence. We discuss modifications of the optimization problem which could take this into account, e.g. by allowing only a minimal weight of one. Methods to find rank-optimal weights can be useful for various multidimensional datasets like the ones used to rank universities or employers. [ABSTRACT FROM AUTHOR]

The aim of this paper is to propose a class of composite indicators for measuring well-being at the local level, which takes into account the variability between and within the local units. Although we believe that well-being is a multidimensional concept and cannot be reduced to a single measure, we also stress the importance of aggregating the information of several well-being indicators into a reduced number of composite indicators, one for each well-being domain, which play a crucial role in policymaking and benchmarking. As an application, we focus on the Equitable and Sustainable Well-being of the Italian Provinces. In particular, based on a dataset containing 41 elementary indicators, we aggregate them by domain, comparing different aggregative approaches and illustrating the difference in the rankings of the Italian Provinces they produce. Finally, as an illustrative example, we focus on a member of the class of composite indicators that accounts both for vertical variability (between the Provinces) and horizontal variability (within the Provinces). The construction of a composite index for each domain allows us to evaluate and compare multidimensional well-being among the Italian Provinces. [ABSTRACT FROM AUTHOR]

The OECD recently released a comprehensive set of 11 well-being indicators, the so-called Better Life Index (BLI), for 36 countries. The BLI covers a wide range of socio-economic aspects of life, which are essential to well-being. This well-being dataset allows us to compare countries' overall well-being. However, in spite of the BLI's wider coverage of variables, it fails to consider sustainability concerns. This study provides a practical proposal for comparing overall well-being by incorporating sustainability concerns. Using the World Bank's adjusted net savings data as a sustainability indicator, we add an extra dimension to the BLI. Then, we apply a composite indicator and aggregate these 12 indicators for each country into a single number. Moreover, we improve the current method for constructing composite indicators by adopting corrected convex non-parametric least squares (CNLS). It is a typical problem in a non-parametric approach based on linear programming for countries' scores of composite indicators to become equal and their performance cannot be distinguished. This becomes even more severe if the number of sample countries is small or the number of aggregated indicators is large, which is the case of the present study dealing with 12 indicators for 36 countries. The use of CNLS, which is based on quadratic programming, overcomes this problem and allows us to order all countries in the sample completely. The empirical results show that the introduction of a sustainability indicator for comparisons does not change countries' overall rankings significantly. However, it certainly changes the ranking of some countries in both directions. [ABSTRACT FROM AUTHOR]

The aim of the present paper is to propose an innovative method to construct composite indicators, starting from an attractive frontier approach called Multi-directional Efficiency Analysis (MEA), and making it suitable to aggregate simple indicators. The main advantage of this method is the separation of the benchmark selection issue from that of efficiency measurement. The novel method, called Multi-directional Benefit of the Doubt , combines the MEA with the Benefit of the Doubt approach adding some valuable properties to the obtained composite indicator. The proposed method is tested on simulated data for illustrative purposes, and then, an empirical application on OECD "Better life index" is provided. Results suggest that the method could be a good tool for local policy makers alongside the OECD BLI to investigate and improve some non-performing well-being dimensions. • An innovative method to construct composite indicators is proposed. • Benchmarks are selected proportionally to simple indicators potential improvements. • Weights and preference structure are data-driven and specific for each unit. • Specific sub-indicators' scores permit to provide an individual improvement plan. • It could be a good tool for local policy makers in well-being framework. [ABSTRACT FROM AUTHOR]

Well-being is a multidimensional concept that cannot be described using a single indicator. By the synthesis of different dimensions it is possible to obtain composite indicators (CIs). Principal Components Analysis (PCA) is one of the most popular multivariate statistical techniques used building CIs. However, the fact that PCA does not take into account the spatial dimension of the phenomenon makes it unsuitable for studying the well-being of urban areas. In this paper, we propose to use Spatial Principal Component Analysis (SPCA) for measuring well-being. The SPCA technique provides principal component scores that summarize both the spatial variability and the spatial autocorrelation structure among the statistical units. In this paper, the SPCA is used to construct well-being CIs for the Italian provinces. • We propose a Composite Indicator based on Spatial Principal Component Analysis considering the presence of spatial autocorrelation. • The application concerns 103 Italian Province capital cities and employs Urban BES considering 64 indicators belonging to 8 dimensions. • The addressed approach is useful for policymakers in the promotion of well-being for public policy. • Results contribute to a better understanding of spatial variations and disparities across Italian provinces. [ABSTRACT FROM AUTHOR]

Economic indicators, such as GDP, do not measure all dimensions of society's progress. Hundreds of alternative measurement systems have been proposed to measure sustainable development. This paper argues for harmonization of these methods, and facilitates this discussion by providing a detailed comparison of country-level measures. The analysis includes 55 systems (12 composite indicators and 43 sustainable development indicator (SDI) sets). The analysis shows that SDI sets are more common than composite indicators. This paper also shows that composite indicators generally focus on environmental themes, while SDI sets are broader in scope through inclusion of more social themes. Overall, we find a great degree of agreement on the most popular indicators and themes in SDI sets. However, we do find that certain indicators are more prevalent in higher and lower income countries. The article emphasizes the need for harmonization of measurement systems, but also shows the importance of flexibility at the country level. Copyright © 2015 John Wiley & Sons, Ltd and ERP Environment [ABSTRACT FROM AUTHOR]

In the last decades, a growing quantity of literature has been written about concepts such as quality of life and well-being as a proxy for evaluating societal prosperity and progress. This paper analyses the theoretical foundations and the empirical validity of multi-dimensional technical tools that can be used for well-being measurement. There is no doubt that there is a lot of complexity and fuzziness inherent in multi-dimensional concepts such as well-being. A possible reduction of this complexity, a pre-condition for policy-making, introduces the problem of the descriptors used: indicators and indexes. In a multi-dimensional framework, multi-criteria evaluation is a consistent approach for the construction of so-called composite indicators. Often, some individual indicators improve while others deteriorate. This is the classical conflictual situation dealt with in multi-criteria evaluation; in particular non-compensatory methods are quite relevant, since compensability implies complete substitutability between different types of capital. Uncertainty and sensitivity analysis can estimate the robustness of the results obtained and help the framing of the debate around the conceptual framework used, that is, which representation of reality (and thus which societal values and interests) has been considered. [ABSTRACT FROM AUTHOR]

This article presents the design, process of construction, content and validation of the Socioemotional Well- Being Index. This index is a composite indicator of subjective well- being, and has been designed with the aim of providing a measurement device for the sociological analysis of the subjective components of quality of life and social quality. Two spheres of knowledge have been combined in its construction: research in social indicators, the recent development of which has been oriented toward the elaboration of composite indicators, and the theoretical content developed in recent decades by the sociology of emotions. As a composite indicator, the index presented in this article offers a hierarchical and multidimensional alternative to the univariate scales measuring happiness and satisfaction most often used in social research. In addition, in comparison to measures of subjective well-being grounded in cognitive evaluations, this index is based on the evaluation of a series of emotional states recently experienced by individuals. The conceptual definition of socioemotional well-being is based on Thomas Kemper's social interactional theory of emotions and Randall Collins' theory of interaction ritual chains. A '4 factor, 10 variable' solution has been obtained by applying common factor analysis to the data of the European Social Survey, 2006. [ABSTRACT FROM AUTHOR]

This paper explores the relevance of the variables that define well-being and human progress and makes a quantitative inquiry into the validity of three of the well-known and well-documented composite indicators of well-being: the Human Development Index (HDI), the Legatum Prosperity Index (LPI) and the Happy Planet Index (HPI). After choosing the key variables that describe most of the objective and subjective dimensions of well-being, we perform cluster analysis to come up with an optimal grouping of countries based on their multidimensional performance on well-being. A comparison of the classifications obtained with the three indexes invalidates the HPI, confirms results obtained for the HDI, and validates for the first time the LPI as a reliable measure of well-being. The optimal cluster structure yields robust results, which correct the rank discrepancies between the HDI and LPI for a large number of countries. It also proves that a robust ranking of countries based on multidimensional well-being can be achieved with a relatively small number of variables, which mitigates the risk of including variables that are not reliable and/or not available for a significant number of countries. The fact that cluster analysis generates results based on similarities between observations and not on computed values based on the aggregation of variables helps overcome problems that may occur due to the distribution of variables and increases its value as a validation method. Therefore, validation results achieved through cluster analysis are more robust and help to achieve a good check of the validity and relevance of the composite indexes, provide an objective perspective that can guide policy-makers and the public in making a fair assessment of actual levels of well-being, and avoid unfounded claims that may overstate it and delay or postpone measures to increase it. [ABSTRACT FROM AUTHOR]

The OECD Better Life initiative recently released a comprehensive set of 11 indicators of well-being covering a group of countries. Each individual indicator corresponds to a key topic that is essential to well-being. However, the problem of aggregating them is left to users of this dataset. Using these as individual indicators, we propose a composite indicator of overall well-being, which is intended to measure the performance of each country in terms of providing well-being to its people. The 'benefit of the doubt' approach (BOD), a well-known aggregation tool based on a weighed sum, assigns the most favourable weights for each entity under investigation. BOD may also be considered to evaluate the performance of each entity in terms of its efficiency. Regarding individual indicators as outputs, it constructs the benchmark production frontier from observed individual indicators. A composite indicator based on BOD equals the distance between each entity's individual indicator and the production frontier, indicating its efficiency. It is widely considered that the well-being of a country's people stems from its productive base, which is characterized by capital assets and social infrastructures. Thus, the productive base can be considered the input used to produce well-being, which is reflected by individual indicators. Therefore, when we apply BOD to aggregate individual well-being indicators across countries, we implicitly assume that all countries have the same productive base, as BOD addresses only the output and neglects the input. This inaccurate assumption leads to a distorted performance measure. Data envelopment analysis (DEA), in which BOD has its roots, is a tool to measure the efficiency of each entity by allowing for differences in inputs as well as outputs across entities. DEA also measures efficiency by using the distance to the production frontier; however, unlike BOD, DEA constructs the production frontier more accurately by utilizing the information of inputs as well as outputs, leading to a better performance measure. We apply DEA to aggregate 11 individual well-being indicators into a composite indicator using the World Bank's estimates of each country's productive base. The composite indicator based on BOD is distributed similarly to and is highly correlated with the existing Human Development Indicator (HDI). It is also positively correlated with GDP per capita. On the other hand, we show that the composite indicator based on DEA is negatively correlated with HDI as well as GDP per capita. [ABSTRACT FROM AUTHOR]

This paper presents some of the main features of recent OECD work on people's well-being and societies' progress. After discussing the main limitations of GDP as a measure of 'welfare', the paper presents the multidimensional framework and rationale underpinning the OECD Better Life Initiative, and describes how this framework aims to overcome the limitations of GDP. The paper also presents some of the evidence from the OECD 'Better Life Index', an interactive tool that allows citizens to compare countries according to their own views and preferences on the relative importance of the different aspects of human well-being. [ABSTRACT FROM AUTHOR]

Composite indicators capable of summarizing a large and complex information such as the one in the well-being field, has become a tool with a great potential for the evaluation and management of public policies. Given the limitations in the construction of well-being indicators which include a gender perspective, this paper proposes the use of a new methodology for constructing a measurement. We propose new linear programming models to improve the traditional DEA models. For the practical implementation of this proposal we take the countries of the European Union as an example. [ABSTRACT FROM AUTHOR]