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Purpose: This study aims to explore prevailing perceptions and practices related to well-being indexes within organizations, using the Better Life Index (BLI) as an example. Design/methodology/approach: This investigation consists of two surveys in Portugal. Study 1 (N = 311) explores public perceptions of well-being in business and its relationship with socio-demographic factors. Results show a highly positive attitude toward organizational well-being, with a preference for companies prioritizing well-being over higher salaries. Study 2 (N = 62) shifts focus to business characteristics linked to the intention of implementing well-being indexes and examines the impact of Study 1 findings on organizational representatives' responses. Findings: The findings reveal a positive and statistically significant correlation between the intention to adopt well-being indexes and both company size and sector. The dissemination of Study 1's results acted as a catalyst for organizational representatives, motivating them to adopt well-being indexes. Research limitations/implications: This research marks an initial step in incorporating well-being indexes in organizational settings. Future research should focus on identifying organizational factors that could hinder or encourage the adoption of well-being indexes. Practical implications: The results contribute to understanding which factors might be relevant when deciding whether and how to measure well-being at organizations. Originality/value: This study highlights the potential effectiveness of these indexes in promoting well-being within organizations, while also examining the feasibility of using the BLI to assess the impact of businesses on various well-being dimensions. [ABSTRACT FROM AUTHOR]

Copyright of Itobiad: Journal of the Human & Social Science Researches / İnsan ve Toplum Bilimleri Araştırmaları Dergisi is the property of Itobiad: Journal of the Human & Social Science Researches and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Beyond GDP approach to development is gaining widespread support from policy makers and researchers worldwide. While not formulated specifically for tourism activity, the approach serves as a guide to measuring the current and future well-being of destination residents associated with tourism development. Inspired by the Beyond GDP approach, a framework comprising the main dimensions of well-being is argued to provide valuable input into an action agenda to achieve sustainable tourism development. The paper discusses some key responsibilities of tourism industry stakeholders structured according to several different senses of 'beyond' that characterise the Beyond GDP research agenda. The paper concludes that research on destination tourism development can learn much from the Beyond GDP approach in respect of conceptual advance, industry practice and policy implementation. [ABSTRACT FROM AUTHOR]

Since 2011, the Organization for Economic Co-operation and Development (OECD) has maintained the Better Life Initiative, which proposes a quality-of-life index called Better Life Index (BLI), consisting of 11 dimensions. This paper presents a multivariate analysis approach that aims to reduce the BLI dimensions. For this purpose, we applied factor extraction by main components to reorganize BLI variables into three dimensions (factors): dimension 1 - personal development and support factors; dimension 2 - financial balance; and dimension 3 - insecurity with the labor market. These three factors were used as criteria for the PROMETHEE-SAPEVO-M1 multicriteria method. We applied the methodology to data from 38 countries (35 from OECD and 3 non-OECD economies). As a result, we verified that Denmark, Iceland and Switzerland stood out as the countries with the best performances after the proposed analysis. Among the 38 countries evaluated, 19 showed positive flows, allowing the distribution into two well-defined groups. Also, adopting this hybrid methodology of multivariate analysis and multicriteria was advantageous because it reduced the evaluation criteria that the decision-maker needs to evaluate. We compared the results obtained by PROMETHEE-SAPEVO-M1 with the ViseKriterijumska Optimizacija $i$ Kompromisno Resenje (VIKOR) and Elimination Et Choix Traduisant la Realité- Multicriteria Ordinal (ELECTRE-MOr) methods, with remarkably similar results. The main contribution of this study is to provide a hybrid methodology composed of a statistical structuring approach (factor analysis) in a problem with multiple conflicting criteria. After all, the approach proposed in this article represented a 94% reduction in the decision maker’s cognitive effort.

Copyright of Turkish Studies - Economics, Finance, Politics is the property of Electronic Turkish Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Organization for Economic Co-operation and Development (OECD) has developed the Better Life Index (BLI), which is a multidimensional approach to well-being. This study aims to investigate the impact of health and social expenditures as well as the economic freedom index on well-being measured by BLI among 34 OECD countries for 2013–2017 period. We estimated panel models to observe the social and health expenditure effects according to their sources and the overall impact of economic changes among countries. We find that public and private social expenditures have a positive and statistically significant impact on BLI at 1% significance level. Additionally, whereas public health expenditure has a positive and statistically significant impact on BLI at 1% significance level, the effects of private health expenditure are statistically significant at 10% significance level. The economic freedom index has a positive and statistically significant impact on BLI in both models. Both social and health expenditures should take a prominent position in policy debates for a better life. [ABSTRACT FROM AUTHOR]

Socio-economic issues in countries all over the world affect the entrepreneurship ecosystem. The impact of socio-economic issues and developments on individuals' lives conceptualizes welfare. In this sense, the relationship between the welfare of the countries and the entrepreneurial climate is the object of interest. This study aims to investigate the impact of socio-economic welfare on entrepreneurial climate in OECD countries with the PLS-SEM method by using OECD's Better Life Index and Worldbank Ease of Doing Business Index for 2020. This study contributes to previous literature by reversing the analysis of the relationships between entrepreneurship and well-being. The model established with the assumption that all sub-indicators of the better life index positively impact the ease of doing business, only correlated with income, employment, and education indicators. However, there is only a positive and significant relationship between the education variable and ease of doing business at p

Life satisfaction (LS) measures how people assess their lives as a whole, not their present emotions. Measuring emotions can be very subjective, but it is still a useful completion to more objective data when comparing quality of life across countries. Many questionnaires are used to measure especially LS and happiness. The Partial Least Squares Discriminant Analysis (PLSDA) is a statistical method for classification and includes an ordinary Partial Least Squares Regression, where the dependent variable is categorical that represents each observation's class membership. In this study, the purpose is to classify 35 OECD countries correctly to their predefined classes (above or below the average LS level of OECD) by using year 2017 Better Life Index data. In the analyses PLSDA, a flexible supervised classification method, is used. PLSDA is a preferable alternative method in case of some assumptions not satisfied for classical discriminant analysis. The results showed that PLSDA has a satisfying classification performance and self-reported health (SH) is only effective variable in determining the LS levels of countries

Copyright of Journal of Social Security / SGD-Sosyal Güvenlik Dergisi is the property of Journal of Social Security / SGD-Sosyal Güvenlik Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The former consensus on a link between energy consumption and improved well-being of a country has been scrutinized by scholars for decades and is, succinctly, for the time being a contested thesis. Until the 1970s the relation of energy to well-being was defined as linearly proportional, and in a later period still as an increasing logarithmic function. Recent empirical research falsified the assumption of an overt link between well-being and increased energy consumption in countries with high per-capita energy usage. Going forward, our research shows for the first time a possible proof for a negative correlation between energy consumption and well-being after a Well-being Turning Point (WTP). We used a world data set, not limited to high-income countries but including 176 nations with available data sets. Our findings could support both the logarithmic growth of well-being together with energy, as well as the contradicting saturation theses supporting degrowth, and thus opens the discussion in all directions. In this paper Energy Footprint data within Eora database and Global Multi Regional Input Output methodology have been calculated, which includes also the energy embodied in imported/exported products and services (also known as consumption-based energy accounts). The use of footprint accounts has been demonstrated in our previous research to be necessary when analysing global energy consumption trends; as it records the energy consumption reality better than the usually used Total Primary Energy Supply which is provided by the International Energy Agency. In this paper, we provide in a novel way, some statistical support for an Energy WTP, i.e. a high-energy threshold after which a further consumption increase results in a reduced Human Development Index. We tested our results for possible biases (e.g. excluding outlier countries and including a factor for considering the weight of high population countries) and concluded that a WTP is one possible interpretation of the data. Thus, we would like to open a discussion about how energy consumption could end up having a negative effect on well-being, considering its indirect impacts in citizens of a country. Unlabelled Image • First evidences for a Well-being Turning Point (WTP), supporting a well-being degrowth beyond high-energy footprint threshold. • The WTP is a novel argument to support degrowth from energy perspective. • The WTP can be neither fully proven nor falsified with current data. • The WTP is not visible when the fitting was population-weighted. • The WTP is not visible if oil exporting countries and some small countries are excluded. [ABSTRACT FROM AUTHOR]

In recent years, the definition and measurement of the well-being levels of nations have been among the most studied topics by researchers. After World War II, national income was used for this purpose; however, due to the impact of globalization, the inadequacy of this approach was identified, and a human-oriented development approach was adopted. Thus, the Human Development Index (HDI), first introduced in 1990, was presented as an important indicator. The need to define and measure well-being in more detail based on changing conditions resulted in the calculation of the Better Life Index (BLI) by the Organization for Economic Cooperation and Development (OECD) in 2011. In the present study, BLI 2017 data for 35 OECD member countries and 3 non-member states were analyzed with Gray Relational Analysis (GRA). In Multicriteria Decision Making (MCDM) analyses, determination of the weight is an important and critical issue that directly affects the results. Therefore, four objective weight determination methods, including mean-weights (MW) and standard deviation (SD), entropy, and CRITIC, were used in the study. Countries were ranked based on gray coefficient scores determined in the analysis. Thus, it was determined that the countries with the highest scores included Norway, Australia, USA, Canada, Iceland, Switzerland, Denmark, and, Sweden while the countries with the lowest scores were South Africa, Turkey, Mexico, Greece, and. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Academic Inquiries / Akademik Incelemeler Dergisi is the property of Akademik Incelemeler Dergisi (Journal of Academic Inquiries) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Life satisfaction (LS) measures how people assess their lives as a whole, not their present emotions. Measuring emotions can be very subjective, but it is still a useful completion to more objective data when comparing quality of life across countries. Many questionnaires are used to measure especially LS and happiness. The Partial Least Squares Discriminant Analysis (PLSDA) is a statistical method for classification and includes an ordinary Partial Least Squares Regression, where the dependent variable is categorical that represents each observation's class membership. In this study, the purpose is to classify 35 OECD countries correctly to their predefined classes (above or below the average LS level of OECD) by using year 2017 Better Life Index data. In the analyses PLSDA, a flexible supervised classification method, is used. PLSDA is a preferable alternative method in case of some assumptions not satisfied for classical discriminant analysis. The results showed that PLSDA has a satisfying classification performance and self-reported health (SH) is only effective variable in determining the LS levels of countries. [ABSTRACT FROM AUTHOR]

This paper uses recent multidimensional well-being measurements to examine multidimensional well-being and inequality across the European regions in 2000 and 2014 with the use of eleven well-being indicators from the OECD Better Life Index. We use generalized mean aggregation method with alternative parameters to allow different substitutability and complementarity levels between well-being dimensions, which range between perfect substitutability and some degree of complementarity between the dimensions, to examine well-being and inequality across the European regions. Accounting for the interactions between the well-being dimensions matters for the multidimensional well-being and inequality across the European regions. The results show that the multidimensional well-being across the European regions are relatively lower when the dimensions are more seen as complements compared to the case when they are considered to be perfect substitutes. Furthermore, there is also a higher degree of multidimensional inequality across the European regions when the dimensions are considered to have some complementarity. Changes in well-being dimensions between 2000 and 2014 indicates that multidimensional well-being improved and inequality decreased in the personal and community well-being categories, but remained unchanged in material well-being category across the European regions irrespective of interaction levels between well-being dimensions. Policy implications of these multidimensional well-being indices are also evaluated by using these indices to determine the eligible regions for the European Union structural funds where the number eligible regions shows some variation depending on whether the dimensions are perfect substitutes or more of complements. [ABSTRACT FROM AUTHOR]

Ever since the inception of OECD's Better Life Index in 2011, a string of literature have emerged offering different aggregation procedures for the 11 dimensions of the index encompassing the broad categories of material living standards and quality of life. What is the most optimistic weighting scheme that allows more countries to achieve better measured well-being outcomes? What is the most pessimistic weighting scheme that worsens outcomes for more countries? Stochastic dominance efficiency is a data driven aggregation method that allows us to answer such questions which may be beneficial to policy makers and researchers. We offer rankings of countries across dimensions as well as rankings based on a single composite index aggregating all dimensional indicators. This type of analysis not only presents an opportunity to examine the sensitivity associated with re-weighting indicators, but this approach also reveals which indicators are driving overall improvement in measured well-being and which ones are hindering it. We find that the worst-case scenario rankings are generally more correlated with the equal-weighting scheme. And the best-case scenario weights offer a far more equal distribution of achievements across countries. [ABSTRACT FROM AUTHOR]

Featured Application: The method proposed in this paper is the first step towards predicting and scoring life satisfaction using a machine-learning model. Our method could help forecast the survival of future generations and, in particular, the nation, and can further aid individuals the immigration process. For many years there has been a focus on individual welfare and societal advancement. In addition to the economic system, diverse experiences and the habitats of people are crucial factors that contribute to the well-being and progress of the nation. The predictor of quality of life called the Better Life Index (BLI) visualizes and compares key elements—environment, jobs, health, civic engagement, governance, education, access to services, housing, community, and income—that contribute to well-being in different countries. This paper presents a supervised machine-learning analytical model that predicts the life satisfaction score of any specific country based on these given parameters. This work is a stacked generalization based on a novel approach that combines different machine-learning approaches to generate a meta-machine-learning model that further aids in maximizing prediction accuracy. The work utilized an Organization for Economic Cooperation and Development (OECD) regional statistics dataset with four years of data, from 2014 to 2017. The novel model achieved a high root mean squared error (RMSE) value of 0.3 with 10-fold cross-validation on the balanced class data. Compared to base models, the ensemble model based on the stacked generalization framework was a significantly better predictor of the life satisfaction of a nation. It is clear from the results that the ensemble model presents more precise and consistent predictions in comparison to the base learners. [ABSTRACT FROM AUTHOR]

An Alternative: Better Life Index as a Measure of Multidimensional Development in Indonesia The purpose of this study is to get value-BLI Better Life Index as a measure of success of development in Indonesia, and to analyze the relationship between BLI with the human development index HDI, index IPR regional development, and economic growth. BLI formation method through three stages: normalization, weighting, and aggregation. The results show that Indonesia’s BLI is in the lower-middle class. Province of Jakarta, East Kalimantan, North Sulawesi, Riau and South Sumatra with the highest value of BLI. BLI size has a positive and significant correlation with IPM and IPR compiled by the BPS-Statistics Indonesia. However, BLI was significantly negatively correlated with economic growth. Keywords: Dimension of Development; Better Life Index; Human Development Index; Regional Development Index; Index Economic Growth Abstrak Tujuan penelitian ini adalah mendapatkan nilai Better Life Index (BLI) sebagai ukuran keberhasilan pembangunan di Indonesia, serta menganalisis hubungan antara BLI dengan Indeks Pembangunan Manusia (IPM), Indeks Pembangunan Regional (IPR), dan pertumbuhan ekonomi. Metode pembentukan BLI melalui tiga tahap: normalisasi, pembobotan, dan agregasi. Hasil penelitian ini menunjukkan bahwa nilai BLI Indonesia termasuk kategori menengah bawah. Provinsi Jakarta, Kalimantan Timur, Sulawesi Utara, Riau, dan Sumatera Selatan dengan nilai BLI tertinggi. Ukuran BLI ini mempunyai korelasi yang positif dan signifikan dengan IPM dan IPR yang disusun oleh Badan Pusat Statistik (BPS). Namun demikian, BLI berkorelasi negatif secara signifikan dengan pertumbuhan ekonomi.

Indexing for measuring the quality of life have been developed by various countries or organizations. Probably the most comprehensive among these indexes is the Better Life Index, which is developed by OECD. The sampling of the study consists of 34 countries that are the members of OECD, and two countries that are not members of OECD. The data are obtained from the 2017 criteria of OECD life index. In this way, the welfare levels of the countries were compared through the 11 life index criteria defined by OECD. In the study, correlation and regression analyses were performed to reveal the relationships between the OECD life criteria and to measure the degree of these relationships. Thus, it was attempted to demonstrate to what extent the index criteria, particularly the safety criterion, affect a society's quality of life. When we evaluate these analyses in general sense, it was observed that there is a significant and positive relationship between the safety criterion and other parameters. However, a negative relationship between the safety and the satisfaction criteria was found according to another result of the analyses. This is because of the fact that, after a certain threshold level, an individual won't have a positive attitude towards the interventions to the living space. [ABSTRACT FROM AUTHOR]

The most significant requirement for people to maintain their lives comfortably, is to have a specific level of welfare. Therefore, a country having a high level of welfare is preferred as a livable country. This has significant influences on the economies of countries. One of most significant researches conducted for determining the welfare levels of countries is the Better Life Index (BLI) research conducted by Organization for Economic Co-operation and Development (OECD) every year on a regular basis. With this research, 38 countries are ranked based on main criteria and subcriteria, but a general ranking is not made. The aim of this study is to rank the countries by using Better Life Index data created by OECD. In the study, the criterion weights were determined and then, the countries were ranked with MULTIMOORA method. Then, the relationship between ranking of countries and their Human Development Index (HDI) rankings were investigated with Spearman's Rank Correlation Coefficient and the results were interpreted. [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]

Copyright of Afyon Kocatepe University Journal of Social Sciences / Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi is the property of Afyon Kocatepe University (AKU) Sosyal Bilimler Enstitusu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Iş, Güç: The Journal of Industrial Relations & Human Resources is the property of Is, Guc: The Journal of Industrial Relations & Human Resources and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The OECD Better Life Index is an interactive composite index that aggregates a country's well-being outcomes through the weights defined by online users. This paper analyses these weights by analysing the responses given by close to 88,000 users since 2011 to date. The contribution of this paper is threefold. First, it investigates the factors shaping users' preferences over a set of 11 well-being dimensions, while most of the previous empirical works in the area have focused on factors affecting support for a specific well-being domain (e.g. redistribution, environmental concerns) at a time. Second, it provides insights into users' preferences for a large group of countries, which differ in terms of culture and living conditions. Third, a finite mixture model (FMM) approach is used to test for heterogeneity in the effect of satisfaction levels on the weight attached to a given BLI dimension across sub-population groups. Various empirical models are used to identify responses' patterns and see whether they can be accounted for respondents' characteristics and their perceived level of well-being. The paper finds that health, education and life satisfaction are the aspects that matter the most in OECD countries. Descriptive statistics show that men assign more importance to material conditions than women; while women in general value quality of life more than men. Environment, housing, civic engagement, safety and health become more important with age, while life satisfaction, education, work-life balance, jobs and income are particularly important for those younger than 35. There are also regional patterns in users' findings, for instance civic engagement is particularly important in South America, while safety and work-life balance matter tremendously in Asia-Pacific. Furthermore, an additional analysis carried out on a subset of observations finds that for several well-being dimensions (i.e. jobs, housing, community, health, education, civic engagement, safety, life satisfaction and work-life balance) there is a positive and linear relationship between individual preferences and self-reported satisfaction in those dimensions. Finally, the check for heterogeneity in the relationship of satisfaction to preferences in well-being dimensions, via an FMM analysis, reveals that, in the case of income and education, two classes of individuals with distinct effects of satisfaction levels on preferences are identified. [ABSTRACT FROM AUTHOR]

For many years there has been a focus on individual welfare and societal advancement. In addition to the economic system, diverse experiences and the habitats of people are crucial factors that contribute to the well-being and progress of the nation. The predictor of quality of life called the Better Life Index (BLI) visualizes and compares key elements—environment, jobs, health, civic engagement, governance, education, access to services, housing, community, and income—that contribute to well-being in different countries. This paper presents a supervised machine-learning analytical model that predicts the life satisfaction score of any specific country based on these given parameters. This work is a stacked generalization based on a novel approach that combines different machine-learning approaches to generate a meta-machine-learning model that further aids in maximizing prediction accuracy. The work utilized an Organization for Economic Cooperation and Development (OECD) regional statistics dataset with four years of data, from 2014 to 2017. The novel model achieved a high root mean squared error (RMSE) value of 0.3 with 10-fold cross-validation on the balanced class data. Compared to base models, the ensemble model based on the stacked generalization framework was a significantly better predictor of the life satisfaction of a nation. It is clear from the results that the ensemble model presents more precise and consistent predictions in comparison to the base learners.

The life satisfaction approach (LSA) enables researchers to attach monetary values to nonmarket circumstances that affect people's life satisfaction. Previous studies employ LSA to examine how the environment, health, and social capital, etc. affect life satisfaction. This study focuses on the OECD Better Life Index and intends to evaluate more comprehensive socioeconomic characteristics. Considering comprehensive factors about life enables us to avoid biased estimation. This study also considers more consistent estimation methods. Previous studies tend to attach monetary valuations in different settings. Thus, it is difficult to compare the effects of different socioeconomic circumstances. This study employs LSA to estimate the happiness functions of Japanese survey respondents by incorporating extensive socioeconomic characteristics as explanatory variables. Controlling for multiple factors affecting subjective wellbeing, we more accurately attach monetary values to each factor within a consistent analytical framework. By doing so, we assess factors' relative comparable influence on subjective wellbeing. We adopt three estimation procedures to check robustness against model specifications. Results indicate that respondents value small changes in their socioeconomic circumstances more highly than other factors. Especially, circumstances surrounding quality of life carry higher equivalent monetary valuations than material living standards. [ABSTRACT FROM AUTHOR]

It is by now common knowledge that in switching from GDP to alternative, multidimensional, measures of collective well-being one can provide a better account of a country's socio-economic conditions. Such a gain, however, comes at the price of losing output-to-input type of link between well-being and the resources necessary to make it available. Since well-being measures are not meant to be only an exercise in documentation, but also to inform policies and priorities, we propose a method to build a measure of well-being in the form of a single index, as for GDP, which takes into account: (1) the social and environmental costs, not considered in the GDP, and (2) the use of conventional resources (capital and labour), not considered in the currently available multidimensional measures of well-being. We use a Data Envelopment Analysis type of model, integrated with Principal Component Analysis, to evaluate OECD countries' relative efficiency in providing well-being. Our results show that the costs of producing well-being have a large and significant impact on the resulting index of well-being. Therefore, high efficiency in providing well-being and high income cannot be considered a proxy to each other. In addition, it is shown that countries react differently to the different costs of well-being: poor countries are, on average, more efficient in terms of conventional inputs (labour and capital), while rich countries have higher efficiency indices relative to social and environmental costs. The close to zero correlation between GDP and well-being indices for rich countries provides new support to the 'Easterlin paradox'. [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 Better Life Index was introduced by the OECD as a tool to chart the multidimensional well-being of its member countries. However, the Better Life Index relies only on aggregate country-level indicators, and hence is insensitive to how multidimensional well-being is distributed within countries. This paper discusses how a distribution-sensitive Better Life Index could be designed. A broad family of distribution-sensitive Better Life Indices is discussed and decomposed in interpretable building blocks. While a rich and comprehensive micro-level data set is necessary to implement the distribution-sensitive Better Life Index, no such data set is currently available for all OECD member countries. The paper constructs therefore a 'synthetic' data set that relies on information about macro-level indicators and micro-level data from the Gallup World Poll. The implementation of the distribution-sensitive Better Life Index is illustrated with this synthetic data set. The illustration indicates that, when taking the distribution of well-being into account, Nordic countries are top-ranked whereas Greece, the Russian Federation and Turkey occupy the bottom positions. The results indicate considerable losses due to multidimensional inequality for OECD member countries. In addition, sizeable differences are found in the level and composition of multidimensional inequality. [ABSTRACT FROM AUTHOR]

With the growing interest in evaluation of quality of life, emerging number of methods are presented. Each contribution varies depending on the matter of interest, and all of them address the issue of subjective weighting factors. The objective of this paper is to explore possibilities to enhance Better Life ranking methodology, available from the Better Life initiative website, using I-distance method. The result was twofold: firstly, we pointed out potential shortcomings of subjectively chosen weights of Better Life ranking methodology by employing our I-distance approach. Secondly, we provided detailed information on how each Better Life indicator contributes to the final position and emphasize the essential indicators in the process of ranking. We have collected the latest available data for 2014, including all 24 indicators of the Better Life composite index. After that we have compared the two ways of rankings, i.e. the I-distance ranking and the Better Life ranking, emphasizing the improvement offered by the I-distance methodology. Further, through iterative exclusion of indicators based on the level of their significance, we have reached the highest quality of the model. That model includes the following six indicators: personal earnings, water quality, life satisfaction, household net adjusted disposable income, employment rate, rooms per person. Hereby, we have compared and presented ranking changes at each iteration for the top 10 countries, which offer a level of consistency in their rank. In addition, one of the objectives is to help policymakers focus on the key indicators in order to improve the ranking of the country, showing governments and administrations which indicators are the most important to invest into. Moreover, our approach could be the foundation for impartial framework of the quality of life's assessment, independent of subjectively formed weighting factors. [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]