Measuring World Better Life Frontier: A Composite Indicator for OECD Better Life Index

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.     

A Non-radial DEA Index for Peruvian Regional Competitiveness

In this paper, we propose a method to measure competitiveness performance at the subnational level, with an application to Peruvian regions. For this, we propose a benefit-of-the-doubt composite index that summarizes the information of several indicators that characterize competitiveness. It is based on an optimization approach, using data enveloping analysis (DEA) techniques, so that each indicator is weighted in an endogenous way, and each unit is evaluated in the most favourable light. Our proposed index is a non-radial variant of the typical DEA scores, which avoids the traditional pitfalls of DEA-based composite indices, such as unreasonable weights. Additionally, we propose a meta-frontier approach in order to compare the competitiveness performances across different periods of evaluation. Our assessments of the Peruvian regions’ competitiveness performance improve on the results of traditional DEA methods, which award high marks to regions with very heterogeneous performance (i.e., regions with very high scores in some indicators, and very poor in others). Additionally, the comparison of the performance across time shows a general decrease in the average competitiveness between 2008 and 2014 of the Peruvian regions.     

Media Use and Satisfaction with Democracy: Testing the Role of Political Interest

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.     

The Italian Regional Well-Being in a Multi-expert Non-additive Perspective

This paper designs a multidimensional index of well-being for 20 Italian regions, based on a set of 41 indicators organized in an original hierarchical structure, a decision-tree whose four main pillars are Economy, Society, Environment and Health. Our novel approach combines the objective dimension of the evaluation (a comprehensive set of statistical indicators) within a flexible non-additive aggregation model (the Choquet integral) characterized with the preferences of informed Italian stakeholders. Adopting the Choquet integral allows us to overcome the well-known limitations embedded in the linear models, by assigning a weight (capacity) to any coalitions of dimensions, and by allowing a different degree of substitutability within each decision node in the tree. The weights and the parameters for the aggregation are elicited through a computer-based nominal group technique, a method which reduces the occurrences of drastically dissenting valuations and the potential expert-selection bias. Our results show that experts’ perception of synergies and redundancies is quite heterogeneous between levels and nodes in the tree. Moreover, well-being measures are much influenced by the degree of substitutability embedded in the experts’ preferences. Overall, the Italian picture looks more heterogeneous when analysed through the Choquet integral, with respect to a linear model.     

A Measure of Well-Being Across the Italian Urban Areas: An Integrated DEA-Entropy Approach

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.     

Ranking and Prioritization of Emergency Departments Based on Multi-indicator Systems

In this work we discuss how Emergency Departments (EDs) can be ranked on the basis of multiple indicators. This problem is of absolute relevance due to the increasing importance of EDs in regional healthcare systems and it is also complex as the number of indicators that have been proposed in the literature to measure ED performance is very high. Current literature faces this problem using synthetic (or numerically aggregated) indicators of a set of performance measures but, although simple, this solution has a number of drawbacks that make this choice inefficient: a compensation effect among the indicators; a high degree of subjectivism in the indicators weighting; opacity in the decision making; all the EDs are considered to be comparable. Indeed, the situations in which EDs are comparable (i.e. when all the performance of one ED are not lower than the performance indicators of the other) are a minority and incomparability is by itself a source of information that should be used to identify situations for which different policy actions should be designed. In this work we propose to use non compensatory composite indicators and partial ordering theory to rank and compare EDs giving value to the reasons of such an incomparability. These methods are applied on a case study of 19 EDs in an administrative region in Italy.     

Has Livability of Japan Gotten Better for 1956–1990?: a Dea Approach

This paper presents an approach for time-series livability assessment using DEA (Data Envelopment Analysis), a mathematical programming technique for measuring the relative efficiency of DMUs (Decision Making Units) with multiple inputs and multiple outputs. Regarding each year as a separate DMU in DEA, and replacing the inputs and the outputs with negative and positive social indicators respectively, we evaluate Japan's livability for the period 1956–1990. Results of the analysis using eight social indicators identify 20 DEA livable years out of the 35 and find eight best-balanced years. It is concluded that DEA, which enables non-uniform, multi-dimensional and relative evaluation, can be a valuable analytic tool in quality-of-life research as well. This revised version was published online in August 2006 with corrections to the Cover Date.     

The Ex-Ante Evaluation of Achieving Sustainable Development Goals

This paper describes the methodology and main results from an overall assessment on future achievement of sustainable development goals. The proposed approach consists of a model-based, looking forward composite sustainable development index—FEEM sustainability index—projected to the future. It represents a first experiment to reproduce the future dynamics of sustainable development indicators over time and worldwide and to assess future sustainability under different scenarios. The assessment presented here is relevant under different viewpoints. First, it has a very broad nature in terms of both geographical coverage and meaningfulness: it considers the multi-dimensional structure of sustainable development by combining relevant indicators belonging to economic, social and environmental pillars for the whole world. Second, the modelling framework to compute future trends of indicators relies upon a recursive-dynamic computable general equilibrium model. This is an ideal tool to look simultaneously at the development of many indicators, their potential interactions and trade-offs, and more in general to the consequences of economic development and/or policies aiming to increase performance in one or more indicators; it allows measuring the overall sustainability under alternative scenarios, across countries and over time. Finally, regarding the construction of the composite indicator, the application of fuzzy measures and Choquet integral increases substantially the model capability allowing taking into account the interactions that exist among the three main pillars of sustainability and the considered indicators.     

Choosing Aggregation Rules for Composite Indicators

From a formal point of view, a composite indicator is an aggregate of all dimensions, objectives, individual indicators and variables used for its construction. This implies that what defines a composite indicator is the set of properties underlying its mathematical aggregation convention. In this article, I try to revise the theoretical debate on aggregation rules by looking at contributions from both voting theory and multi-criteria decision analysis. This cross-fertilization helps in clarifying many ambiguous issues still present in the literature and allows discussing the key assumptions that may change the evaluation of an aggregation rule easily, when a composite indicator has to be constructed.     

An Introduction to ‘Benefit of the Doubt’ Composite Indicators

Despite their increasing use, composite indicators remain controversial. The undesirable dependence of countries’ rankings on the preliminary normalization stage, and the disagreement among experts/stakeholders on the specific weighting scheme used to aggregate sub-indicators, are often invoked to undermine the credibility of composite indicators. Data envelopment analysis may be instrumental in overcoming these limitations. One part of its appeal in the composite indicator context stems from its invariance to measurement units, which entails that a normalization stage can be skipped. Secondly, it fills the informational gap in the ‘right’ set of weights by generating flexible ‘benefit of the doubt’-weights for each evaluated country. The ease of interpretation is a third advantage of the specific model that is the main focus of this paper. In sum, the method may help to neutralize some recurring sources of criticism on composite indicators, allowing one to shift the focus to other, and perhaps more essential stages of their construction. An abridged version of this paper was presented at the Workshop on European Indicators and Scoreboards, organised by DG Education and the Joint Research Centre within the auspices of CRELL, in Brussels, October 24–25, 2005.     

An Economic Wellbeing Index for the Spanish Provinces: A Data Envelopment Analysis Approach

 This article presents the estimation of a synthetic economic wellbeing index using Data Envelopment Analysis (DEA). The DEA is a multidimensional technique that has its origins in efficiency analysis, but its usage within the social indicators context is particularly appropriate. It allows the researcher to take advantage of the inherent flexibility of DEA when assigning weights to the factors. The model itself carries out the aggregation and weighting of 8 partial indicators, which attempt to describe the four components of economic wellbeing suggested by Osberg (Royal Commision on the Economic Union and Development Prospects for Canada (University of Toronto Press, 1985)), in order to assess the economic wellbeing of the 50 Spanish provinces. By using the index obtained in the analysis a “ranking” of the provinces is obtained. This ranking proves to be relatively similar to the one that corresponds to per capita income, although there are significant differences.     

A Common Weight Approach to Construct Composite Indicators: The Evaluation of Fourteen Emerging Markets

This study presents an ongoing project, emerging market (EM) evaluation project, of the Taiwan Institute of Economic Research (TIER). The purpose of this project is to construct a composite indicator (CI) named as growth potential index (GPI) for selecting the promising EMs, in which to begin new or expand existing business is attractive to governments, firms, and investors. However, weight determination is one of the most difficult tasks in the construction process of a CI. A new approach inspired by the Z score and rooted in data envelopment analysis (DEA) is proposed to objectively determine the common weights for constructing the GPI without requiring data normalisation beforehand. The same dataset is used to compare the proposed common weight approach with the equal weighting method (currently used by the TIER), the widely used DEA-CI model, and the first common weight DEA-CI model. Spearman’s rank correlation test revealed a high positive correlation between the GPIs obtained by the proposed approach and each considered method. The major findings include: (1) China is the most promising EM; (2) Argentina, China, Malaysia, Poland, and Russia are above-average EMs; (3) India, Indonesia, Saudi Arabia, South Africa, and Thailand are below-average EMs; and (4) of the so-called BRIC countries (Brazil, Russia, India, and China), China is the best EM, and India is the worst EM.     

CONSTRUCTING A HEALTH AND SOCIAL INDICATOR FRAMEWORK FOR INDIGENOUS COMMUNITY HEALTH RESEARCH

Health and social indicators that capture the distinct historical, social, and cultural contexts of Indigenous communities can play an important role in informing the planning and delivery of community interventions. There is currently considerable interest in cataloguing and vetting meaningful community-level health and social indicators that could be applied to research and health promotion activities in Indigenous communities in Australia, Canada, and New Zealand, inclusive of conventional indicators as well as measures developed specifically for use in or with Indigenous communities. To avoid haphazard selection of indicators, and to assure the comprehensiveness and relevance of any given set of indicators, a framework that can accommodate and conceptually classify indicators representing a full range of domains is required. We report here on the development of a conceptual framework, by which Indigenous community indicators, and more general community-level social indicators, can be sorted, catalogued, and systematically classified within four hierarchical levels. The indicator framework was developed across Canada, Australia and New Zealand in consultation with academic researchers and Indigenous community stakeholders, building from established health and social indicator systems. The Indigenous indicator framework permits Indigenous communities, public health researchers, and funding agencies to compare and select the most appropriate indicators for application in specific contexts from the multitude of existing indicators.     

Education Enrollment Level and Income Inequality: A Case of SAARC Economies

This article introduces ‘critical open-mindedness’ as a new sociological construct, which can be employed particularly in the studies of social attitudes and attitude change, social values, social identities, cross-cultural relations and social discrimination. By drawing on the data collected through the 2005 World Values Survey in Australia, we have operationalized the construct into an integrative social index, called ‘critical open-mindedness index’ consisting of five dimensional composite indicators (CIs; i.e. the social, political, cultural, economic, and environmental). We have adopted an integrative approach to constructing these composite indicators in which we pragmatically select and incorporate a variety of techniques with the purpose of maximizing the validity of the end results. The findings with respect to Australians’ critical open-mindedness, both in general and in reference to its five dimensions are discussed. We have also developed and examined a social psychological index of ‘socio-cognitive open-mindedness’ inspired by a number of commonly used international scales and by drawing on the same dataset. We have shown that these two types of open-mindedness are qualitatively different. Our analysis does not support the idea that individuals’ social psychological open-mindedness determines their critical open-mindedness. It is rather the opposite which is the case.     

Country Performance Evaluation: The DEA Model Approach

This study employs four data envelopment analysis (DEA) models to evaluate the performance efficiency of 21 OECD countries and assess whether the undesirable outputs are over-produced relative to desirable outputs. In evaluating the performance of OECD countries via super-efficiency models, this study focuses on two aspects. First, employing the concept of the Sharpe ratio, we propose another method to deal with undesirable outputs (the unemployment rate, inflation, and air pollution) in DEA. This approach can reveal the relative importance of desirable outputs and undesirable outputs, detect whether undesirable outputs are over-produced, and obtain more accurate efficiency scores. Second, we examine whether knowledge capital can improve a country’s efficiency scores. Our empirical results support the above arguments. In addition, research and development (R&D) expenditures, the proxy variable for knowledge capital, can indeed improve countries’ efficiency scores, implying that the endogenous growth theory is supported in OECD countries. Evidently, whether the undesirable outputs are included in the DEA models and are properly treated is crucial in the evaluation of efficiency values.

     

Measuring Well-Being Over Time: The Adjusted Mazziotta–Pareto Index Versus Other Non-compensatory Indices

Most of the socio-economic phenomena such as development, well-being, and quality of life have a multidimensional nature and require the definition of a set of individual indicators to be properly assessed. Often, individual indicators are summarized and a composite index is created. One of the main problems in constructing composite indices is the choice of a method which allows time comparisons. In this paper, we consider the Adjusted Mazziotta–Pareto Index, a non-compensatory composite index used by the Italian National Institute of Statistics for measuring “Equitable and Sustainable Well-being” in Italy. An empirical comparison with some traditional non-compensatory indices is presented and an Influence Analysis is, for the first time, performed in order to assess the robustness of the index.     

Application of the “Benefit-Of-the-Doubt” Approach for the Construction of a Digital Access Indicator: A Revaluation of the “Digital Access Index”

The use of composite indicators as a tool for ranking and making decisions is ever increasing in a world marked by the inequalities and competition in all domains. However, the dependence of countries ranking on the weighing scheme used to aggregate individual indices or sub-indicators, most of the time set by experts/stakeholders may weaken the credibility of composite indicators. One method which is able to overcome these limits is the “Data Envelopment Analysis” approach, particularly named “Benefit-Of-the-Doubt” in the context of composite indicators’ construction. We propose a revaluation of the Digital Access Index given that Information technology is the most important factor driving improvement in a wide array of areas critical for the quality of life for individuals as well as societies. We have shown that this method is more suitable for identifying trends and drawing attention to particular issues and also for setting policy priorities. In fact, the weights of the individual indices, used to compute the composite indicators are based on the data itself and are proper for the country under consideration. These weights contain adequate information in order to help policy-makers to better understand the nature of the new innovation economy and the types of public policies needed to drive innovation, productivity and broad-based prosperity for its citizens.     

Comparing MCDA Aggregation Methods in Constructing Composite Indicators Using the Shannon-Spearman Measure

Composite indicators have been increasingly recognized as a useful tool for performance monitoring, benchmarking comparisons and public communication in a wide range of fields. The usefulness of a composite indicator depends heavily on the underlying data aggregation scheme where multiple criteria decision analysis (MCDA) is commonly used. A problem in this application is the determination of an appropriate MCDA aggregation method. Of the many criteria for comparing MCDA methods, the Shannon-Spearman measure (SSM) is one that compares alternative MCDA aggregation methods in constructing composite indicators based on the information loss concept. This paper assesses the effectiveness of the SSM using Monte Carlo approach-based uncertain analysis and variance-based sensitivity analysis techniques. It is found that most of the variation in the SSM arises from the uncertainty in choosing an aggregation method. Therefore, the SSM can be considered as an effective measure for comparing MCDA aggregation methods in constructing composite indicators. We also use the SSM to evaluate five MCDA aggregation methods in constructing composite indicators and present the findings.     

Modeling urbanism: Economic,social and environmental stress in cities

The time has come for urban social indicator research to converge with the basic substantive efforts of urban researchers. Such a convergence may propel both basic and applied researchers toward more fruitful outcomes. This paper argues that the traditional model of urbanism provides the medium for the convergence. When urbanism is conceptualized to be multidimensional, seemingly discreet indicators of demographic, economic, social, and environmental conditions in cities may be incorporated into a more general model of urban structure and change. Specifically, using social indicators for 195 cities from ZPG's Children's Stress Index and the 1990 U.S. Census, we show empirically: (1) Urbanism is a complex factor with four distinct dimensions: demographic scale, economic stress, social stress, and environmental stress. (2) These four dimensions of urbanism may be reliably measured with standard composite variables used in today's social indicator research. (3) Within the Urbanism factor there are causal connections among the separate dimensions, the most basic of which is that asserted by arguments from the traditional theory of urbanism; specifically, that population size, density, and social heterogeneity are causally linked to stress in economic, social, and environmental systems of the city.