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.     

Does Human Development Index Provide Rational Development Rankings? Evidence from Efficiency Rankings in Super Efficiency Model

The human development index (HDI) rankings have provided a referenced measure for people to choose a country in which to travel or live. This paper employs a superefficiency model to evaluate the rationality of the HDI rankings of 19 evaluated OECD countries in 2009. Compared to the HDI rankings, the efficiency rankings measured by the super-efficiency model have the following two advantages: (1) they consider the inputs that are used to generate the indicators for constructing the HDI, and decide the weights of inputs and outputs endogenously; (2) the input slacks measured by the super-efficiency model can evaluate whether the inputs are over-used and provide the improvement path of each country’s input variables. Empirical result shows that approximately 75 % of the evaluated countries had rather different results in the efficiency rankings and the HDI rankings. Additionally, the input slack shows that roughly 70 % of sample countries over-used their capital per labor relative to their existing outputs (or the HDI).     

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.     

Are Major Events Capable of Affecting Country Rankings? Validating Composite Indexes of Human Progress and Environmental Performance

A desired characteristic of composite indicators is sensitivity to major adverse events. This paper explores how major civil wars and the 2004 tsunami have influenced Human Development Index (HDI) and Environmental Performance Index (EPI) index values of the affected countries, respectively. The analysis shows that HDI and EPI scores have barely changed, being almost exclusively due to variations in GNI/capita for HDI and air quality for EPI. This casts doubt on the composite indexes’ usefulness and their ability to reflect major environmental and societal changes in the affected countries, or shows which dimensions are truly resilient to these events and can constitute a sustainable base for postwar/post-disaster recovery. Human progress and ecological indicators may need an overhaul, in order to account for the changes that actually happen at a point in time, in order to capture substantial changes in the socio-economic and ecological fabric of a country.     

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.     

Using Ranked Weights and Acceptability Analysis to Construct Composite Indicators: A Case Study of Regional Sustainable Society Index

A variety of published composite indicators, i.e., Energy Trilemma Index and Sustainable Society Index, are commonly aggregated with equal weights. However, this plausible scheme is criticized as eclecticism and ignores the discriminating power of the different indicators. Differing from the traditional methods that assign weights to each indicator for the purpose of aggregation, this paper proposes a new mechanism to construct composite indicators using ranked weights and stochastic multicriteria acceptability analysis. More specifically, this study comprehensively consider all possible preferences among the indicators. Under each preference, we develop a sophisticated mathematical transformation to calculate the least and most favorable scores of each entity, which formulates the lower and upper bounds of the intervals. Then an interval decision matrix, alternatively described as a stochastic decision problem, is formulated to construct the composite indicators. Holistic acceptability indices are generated and regarded as a new composite indicator, which is capable of providing a comprehensive and robust composite indicator with more discriminating power. We apply the proposed method to modify the regional sustainable society index and present the obtained results and comparisons.     

Data Envelopment Analysis for Composite Indicators: A Multiple Layer Model

The development of a composite indicator (CI) over a set of individual indicators is worthwhile in case the methodological aggregation process is sound and the results are clear. It can then be used as a powerful tool for performance evaluation, benchmarking, and decision making. In this respect, data envelopment analysis (DEA), as a self appraisal technique, has recently received considerable attention in the construction of CIs for policy analysis and public communication. However, due to the ever increasing complexity of numerous performance evaluation problems, more and more potential indicators might be developed to represent an evaluation activity in a more comprehensive way. These indicators might also belong to different categories and further be linked to one another constituting a multilayer hierarchical structure. Simply treating all the indicators to be in the same layer as is the case in the basic DEA model thereby ignores the information on their hierarchical structure, and further leads up to weak discriminating power and unrealistic weight allocations. To overcome this limitation, a multiple layer DEA-based CI model is developed in this study to embody a hierarchical structure of indicators in the DEA framework, and both its primal and dual form are realized. The proposed model is illustrated by constructing a composite road safety performance index for a set of European countries.     

Rank-Optimal Weighting or “How to be Best in the OECD Better Life Index?”

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.     

A Comparison of the Well-Being of Agricultural and Non Agricultural Households Using a Multicriterial Approach

The territorial approach of rural development requires taking into account economic, social and environmental features. Therefore, among the goals of the rural development policy the well-being of the target population cannot be disregarded. However, well-being is a multidimensional phenomenon and its measure could be complicated since many indicators (with different scale, units of measure, burden, trends) have to be estimated and analyzed. With a composite indicator which summarizes a bulk of information this concern can be overcome. A multicriteria approach of aggregation is proposed because cardinal aggregation involving in undesirable compensatory property.     

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.

     

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.     

Quality of Life and Well-Being Ranking of Selected Districts of Khyber Pakhtunkhwa,Pakistan

Quality of life (QoL) is a multidimensional concept encompassing many aspects of an individual’s existence. It is not just a measure of material resources but of intangible environments which affect quality of life. The study concentrates on the province of Khyber Pakhtunkhwa, Pakistan, and looks at the QoL and well-being in thirteen districts representing the province’s major population. It followed an integrative approach to measuring quality of life. Principal component analysis was used to tackle with multidimensionality. Weighted factor scores were used as an index for ranking QoL and well-being on the basis of social indicators chosen in different life domains. The results show that districts having high urban disposition top the ranks in QoL in Khyber Pakhtunkhwa, like Peshawar, Mardan, Swat and Charsadda, while Bannu, Kohat and Lower Dir fall amongst the bottom districts in terms of QoL. The rural/urban ranking also follows similar patterns with a few exceptions.     

Employing the Components of the Human Development Index to Drive Resources to Educational Policies

A new form of composition of the indicators employed to generate the United Nations Human Development Index (HDI) is presented here. This form of composition is based on the assumption that random errors affect the measurement of each indicator. This assumption allows for replacing the vector of evaluations according to each indicator by vectors of probabilities of being the best or the worst according to such attribute. The probabilistic composition of such probabilities of preference according to each indicator into probabilities of being the best or the worst according to all of them generates indices that may unveil, on one hand, performances to be followed and, on the other hand, extreme conditions that an additive composition would hide. Differences between the results of application of the diverse forms of composition are examined in the case of the HDI and in the case of the districts version of the HDI employed to compare Brazilian municipalities. It is verified that the smallest correlation between the education enrolment rate and the other indicators in the Brazilian case enlarges such differences.     

How to Integrate Macro and Micro Perspectives: An Example on Human Development and Multidimensional Poverty

When designing indicators of well being attention is focused on its multidimensionality and on aggregation across different dimensions. However, two other aspects need consideration: distribution of each indicator within its dimension (inequality) and joint distribution of individual achievements or—viceversa—of deprivations. Aim of this paper is to suggest a correction to the composite well-being index; correction that takes into account some aspects concerning the joint micro distribution of achievements across dimensions. As an example we correct the human development index by means of the multidimensional poverty index.     

Measurement of social development: an international comparison

PQLI and HDI are the two most popular measures of development, besides per capita income. Over the years, PQLI appears to be not much in use for regional comparisons, especially after the introduction of HDI. While PQLI considers only the physical variables—adult literacy, life expectancy at birth and infant survival rate, HDI has life expectancy at birth, educational attainment and real GDP per capita (PPP$). PQLI and HDI are similar, the main difference between the two being the inclusion of income in HDI and exclusion of the same from PQLI. In a sense, HDI represents both physical and financial attributes of development and PQLI has only the physical aspects of life. The present author took the lines of PQLI to express development in terms of physical variables and considering development as a multidimensional phenomenon, Ray (1989) [Ray, A. K. (1989). On the measurement of certain aspects of social development, Social Indicators Research (Vol. 21, pp. 35–92). The Netherlands: Kluwer Academic Publishers.] included as many as 13 physical variables to represent social development across 40 countries; no financial variable was included in the construction of composite index, termed as the Social Development Index, SDI. Incidentally, like PQLI, SDI was introduced before HDI. Unlike PQLI and HDI, SDI considers (i) a large number of indicators representing various concern areas and (ii) a set of objective methods for combining the development indicators as a composite index. Ray (1989) has been restated and updated in this article with newer cross-country information. In the present study, SDI has been constructed for over 102 countries, including 21 OECD countries, using 10 development indicators, instead of 13 indicators in the past. Apart from presenting objective methods for combining indicators into SDI, the present study asserts that SDI works better than HDI as a measure of development for an international comparison. The views expressed in the article are those of the author and not of the institution he serves.     

Labor force composition and underemployment trends, 1969–1980

Underemployment indicators are both ‘objective’ indicators of individual well-being and social welfare and ‘normative’ indicators for programmatic use. Components of an underemployment indicator framework, the Labor Utilization Framework (LUF), are operationally defined and shown to be closely related to a family of proposed alternatives. Using CPS data, a 12-year time series of LUF indicators is developed for the U.S. labor force and its key subgroups. The across-time heterogeneity of the labor force is analyzed in relation to a ‘basic’ demographic group-by-LUF-by-time contingency, showing how the complexity of labor force structure that emerges from a multi-state indicator of underemployment can be rigorously modeled. Standard loglinear models, which focus on the temporal aspects of data from repeated cross-sectional surveys, provide the analytic technique. Changes in the demographic composition of the labor force account for about 30% of the overall period variability, and age structure has made the most important contribution to the compositional change.     

Application of Different Techniques of Factor Analysis to Develop Neighborhood Social Capital Rating Scale

Composite indicators are widely used to determine the ranking of countries, organizations or individuals in terms of overall performance on multiple criteria. Their calculation requires standardization of the individual statistical criteria and aggregation of the standardized indicators. These operations introduce a potential propagation effect of extreme values on the calculation of the composite indicator of all entities. In this paper, we propose robust composite indicators for which this propagation effect is limited. The approach uses winsorization based on a robust estimate of the distribution of the sub-indicators. It is designed such that the winsorization affects only the composite indicator rank but has no effect on the entities ranking in each sub-indicator. The simulation study documents the benefits of distribution-based winsorization in the presence of outliers. It leads to a ranking that is closer to the clean data ranking when compared to the ranking obtained using either no winsorization or the traditional winsorization based on empirical quantiles. In the empirical application, we illustrate the use of winsorization for ranking countries based on the United Nations Industrial Development Organization’s Competitive Industrial Performance index. We show that even though the sub-indicator ranking does not change, the robust winsorization approach has a material impact on the ranking of the composite indicator for countries with large discrepancies in the scores of the sub-indicators.

     

Living Near to Attractive Nature? A Well-Being Indicator for Ranking Dutch,Danish, and German Functional Urban Areas

While nature is widely acknowledged to contribute to people’s well-being, nature based well-being indicators at city-level appear to be underprovided. This study aims at filling this gap by introducing a novel indicator based on the proximity of city-residents to nature that is of high-amenity. High-amenity nature is operationalized by combining unique systematic data on people’s perceptions of what are the locations of attractive natural areas with data on natural land cover. The proposed indicator departs from the usual assumption of equal well-being from any nature, as it approximates the ‘actual’ subjective quality of nature near people’s homes in a spatially explicit way. Such indicator is used to rank 148 ‘cities’ in the Netherlands, Denmark, and Germany. International comparability of the indicator is enhanced by the use of a definition of cities as functional urban areas (FUAs), which are consistently identified across countries. Results demonstrate that the average ‘nearness’ of FUA populations to high amenity nature varies widely across the observed FUAs. A key finding, that complements insights from existing city-level indicators, is that while populations of FUAs with higher population densities may live relatively far from nature in general, they also live, on average, closer to high-amenity nature than inhabitants of lower density FUAs. Our results may stimulate policy-debates on how to combine urban agglomeration with access to natural amenities in order to account for people’s wellbeing.     

Measuring longevity achievements under welfare interdependencies: a case for joint life expectancy indicators

Whereas period life expectancy constitutes an intuitive indicator of the survival conditions prevailing at a particular period, this paper argues that, given the existence of welfare interdependencies, that widespread indicator is nonetheless an incomplete measure of the longevity achievements relevant for human well-being. The central importance of coexistence for human-beings implies that usual life expectancy measures should be complemented by joint life expectancy indicators, which measure the average coexistence time under particular survival conditions. After a study of the theoretical foundations of ‘single’ and ‘joint’ life expectancy indicators, it is shown that joint life expectancy measures tend to enrich significantly the comparison of longevity achievements across countries and periods. Moreover, the introduction of joint life expectancy indicators—as a complement to conventional life expectancy measures—into multi-variable indexes such as the United Nations’ HDI is also shown to affect international rankings of standards of living to a non negligible extent.