Composite Indices of Development and Poverty: An Application to MDGs

The measurement of development or poverty as multidimensional phenomena is very difficult because there are several theoretical, methodological and empirical problems involved. The literature of composite indicators offers a wide variety of aggregation methods, all with their pros and cons. In this paper, we propose a new, alternative composite index denoted as MPI (Mazziotta-Pareto Index) which, starting from a linear aggregation, introduces penalties for the countries or geographical areas with ‘unbalanced’ values of the indicators. As an example of application of the MPI, we consider a set of indicators in order to measure the Millennium Development Goals (MDGs) and we present a comparison between HDI (Human Development Index) methodology, HPI (Human Poverty Index) methodology and MPI.     

Who Makes the Most? Measuring the “Urban Environmental Virtuosity”

This paper advances a composite indicator called urban environmental virtuosity index (UEVI), in order to measure the efforts made by public local bodies in applying an ecosystem approach to urban management. UEVI employs the less exploited process-based selection criteria for representing the original concept of virtuosity, providing makes a cross cities comparison. In developing such a framework the main technical issue of constructing a composite indicator, involving the weighting and the aggregation phases will be overcame by using a multivariate approach.     

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.     

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.     

Away from Dissatisfaction,Closer to Well-Being: A Multidimensional Synthetic Measure

As part of the international debate on methods for measuring the social progress of a population, there has been increasing interest in individual subjective opinions about different aspects of quality of life (elementary indicators). In the literature, many methods have been introduced for producing measures of subjective well-being based on these opinions. Some of these methods aim to construct synthetic measures that allow us to consider all the aspects simultaneously. This topic often requires subjective methodological choices and/or distributional assumptions and, when the opinions are encoded by means of categorical ordinal values, the eventual quantification of the original variables. Here, starting from the Istat multipurpose survey on households’—aspects of daily life, we propose an original method for constructing a global satisfaction index. We introduce a variable based on the joint distribution of all the elementary indicators that is able to express the individual degree of global satisfaction. This approach allows us to maintain the original ordinal data scale and to avoid any aggregation formula. By comparing the observed distribution of the new variable and the theoretical one, which refers to the situation of overall dissatisfaction (all individuals are dissatisfied for every aspect), we propose three indices of global satisfaction. We also implemented two simulation studies that confirms both the efficacy and robustness of our method. We then applied it to measure the global satisfaction degree of the Italian population, using Istat multipurpose survey data for the year 2013.     

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.     

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 Gender Wellbeing Composite Indicator: The Best-Worst Global Evaluation Approach

In this paper we present a new global evaluation methodology for constructing composite indicators. Given the limitations in the construction of wellbeing indicators that include a gender perspective, this paper proposes separate measures for men and women, using new linear programming models to improve the traditional Data Envelopment Analysis models. We obtain composite indicators using common weights (which are most and least favourable) so that the indicators’ values are able to discriminate between all units and provide a global vision of each one. This approach does not require prior knowledge of the weights for initial indicators, nor does it require a normalization method. Thus, the new composite indicator is less subjective than others. For the practical implementation of this proposal, we use the countries of the European Union as an example.     

Assessment of Composite Indicators Using the ANOVA Model Combined with Multivariate Methods

The synthesis of a number of single observed indicators into a unique composite indicator involves various subjective choices related, for instance, to the type of combination (linear, non-linear) and to the aggregation method (simple average, geometric average) used in its construction. Thus, it is clearly important to analyse the variability of a composite indicator according to all possible alternatives before employing it in any decision-making process. Within such a framework, in this paper, we present a new approach based on a combination of explorative and confirmative analyses aiming to investigate the impact of different subjective choices on the variability of composite indicators. This new approach also allows the analysis of the related individual differences among the statistical units and the use of external information on the same units to enhance the interpretation of the final results.     

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.     

On the measurement of certain aspects of social development

An attempt has been made in this paper to measure social development. There has been some limited use of per capita national income as a measure of development. A number of social indicators from various societal areas may, alternatively, better represent social development of a region. A composite index of selected social indicators has been proposed as a measure of social development. Certain existing methods of construction of composite indexes have been examined and new methods proposed using the variance-covariance matrix and intercorrelation matrix of the indicators and the concept of (Euclidean) distance. Two examples of intertemporal and international comparisons have been presented to illustrate the proposed methods.The author is grateful to Prof. M. Mukherjee under whose supervision and guidance this work was carried out. However, the views expressed in this paper are those of the author and not of the institution to which he belongs.     

An Overview and Evaluation of Composite Indices of Development

The search for alternative indicators of development has witnessedthe development of a variety of composite indices of development. Theseindices integrate various social, political and economic aspects ofdevelopment in measurement. This paper presents an overview andevaluation of composite indices of development in terms of certain broaddimensions of measurement. In terms of method and technique, compositeindices are generally additive ones with equally weighted componentsconsisting of variables selected in an ad hoc manner. Numerouscriticisms have been leveled at these methods employed in compositeindexing. Composite indices are mainly quantitative insofar as theindices are all presented in numerical format. Composite indices aresubject to subjectivity despite the relative objectivity of the methodsemployed in composite indexing. Composite indices are of a cardinalnature, but remain ordinal insofar as differences in index values cannotbe interpreted meaningfully. The multidimensionality of compositeindices represents one of their main advantages. Indices representaggregate measures of a combination of complex development phenomena.The comparative application of composite indices of development overspace and time remains problematic. Composite indices generally combinemeasures of ends and means. In respect of method and technique,composite indexing is relatively complex. Composite indices arerelatively flexible, because changes in selection, scaling, weightingand aggregation can be effected readily, albeit at the cost ofcomparability. Composite indices perform relatively well in terms ofcross-national availability, but few indices perform well in terms ofinter-temporal availability. On the strength of the systematic positiveassociation between income and popular composite indices such as theHDI, many have claimed that these indices represent no real contributionto the literature on indicators research. Composite indices,furthermore, are often considered to be ideological statements ratherthan practically functional indicators. Yet, composite indices representuseful supplements to income-based development indicators. These indicesremain invaluable in terms of their ability to simplify complexmeasurement constructs, to focus attention and to catch the eye, thusenhancing their political appeal.     

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.

     

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.     

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.     

The Unbalance Penalisation Method for Metrics of Social Progress

This study contributes to the debate on the development of aggregate metrics of societal progress. Summarising societal progress into a single number poses various methodological challenges, including the choice of indicators, normalisation, weighting and aggregation. This paper addresses the issue of aggregation in the case of metrics of well-being and uses as a case study the European Union regional Social Progress Index—EU-SPI—published by the European Commission. The index is an aggregate measure of 55 social and environmental indicators observed for all the European regions grouped into 12 components. In metrics of this type, while complete substitutability among components is rarely acceptable, controlling their level of substitutability is highly desirable. To this aim, we adopt a modified version of the unbalance penalisation approach originally proposed by Casadio Tarabusi and Guarini (Soc Indic Res 112:9–45, 2013). A penalisation is applied to the regions whose performance across the index components is unbalanced, that is when they perform well on some components but worse on others. The penalisation applied by this approach depends on two parameters that, in its original formulation, are generally arbitrarily chosen. We design a data-driven approach allowing for an informed choice of the penalisation parameters. The comparison between the EU-SPI original and penalised scores shows that the penalisation effect is particularly evident for regions with a strongly unbalanced profile across the components. The proposed method allows for adjusting the level of substitutability between components when constructing an aggregate metric, an important functionality especially when measuring societal progress for policy-making.

     

The Influence of the Human Rights to Water and Sanitation Normative Content in Measuring the Level of Service

Human Rights to Water and Sanitation (HRWS) have been consolidated as relevant frameworks to measure different levels of services. It is essential to move forward with specific initiatives that interpret the content of these human rights and operationalize them through specific metrics. However, some critical issues emerge in attempting this. Different approaches are proposed in this article to tackle this challenge: (1) utilizing a participatory technique to discuss the relative importance of the normative criteria to define water and sanitation services, (2) defining a short list of key indicators to measure the different dimensions of HRWS, and (3) assessing the impact of different weighting systems in the constructing an aggregated index, which has been proposed as a useful tool to monitor water, sanitation, and hygiene (WASH) from a rights perspective. Two municipalities (in Mozambique and Nicaragua) were selected as initial case studies. The results suggest that there is a common understanding among the experts about prioritization of the HRWS criteria. Differences in the relative importance given to the HRWS criteria can be explained due to the particularities of the local context. Further, the research suggests that expert opinions may be partially conditioned by targets and indicators proposed at the international level. Although the influence of weighting techniques on aggregated measures and their utilization in the decision-making process are limited, this methodology has a great potential for adapting specific WASH metrics to different regional, national, and/or local contexts taking into account the HRWS normative content.     

Highlighting Methodological Limitations in the Steps of Composite Indicators Construction

The paper opens the debate on the need to find a stable methodological framework in the construction of composite indicators (CIs) in order to address the methodological challenges including those of sensitivity and uncertainties related to methods used. As CIs are well-known to be essential in public debate, their methodological construction must be known by a large public. Illustrating CIs’ construction steps by a simple indicator, the paper aims to “democratize” this disciplinary field which is still a black box for some researchers but also to show how composite scores are sensitive to methods used and then, its impacts on policies. For example, in the Sustainable Development Indicator case, the geometric aggregation system is favorable to emerging countries which lead the ranking table whereas high income countries (which are leaders in the linear and equal weight system) except Australia, are misclassified. Uncertainty and sensitivity analysis confirm these results showing that the indexes’ scores seem to be influenced by the orientation (implied theoretical framework) given by its sponsors including policy makers. Regarding the validity of the index, correlation tests with some lights and well known indicators, reveal very consistent results.     

The European Gender Equality Index: Conceptual and Analytical Issues

This article presents a composite indicator designed to measure and compare existing structural gender equality in the countries of the European Union. The construction of an index is always a complex task which requires making a great many important conceptual, analytical and empirical decisions. This complexity explains the wide variety of gender equality indices created during the last 25?years. All the proposed indices attempt to measure the same thing, gender (in)equality, but none of them define the concept they want to measure in exactly the same way, nor do they operationalize measurement in the same manner. Taking into account the methodology of these preceding approaches, this article explains the basis for the most important analytical and conceptual decisions made in constructing the European Gender Equality Index. The article also includes the overall scores obtained by the 27 European countries on this index, on its three basic dimensions (education, work and power), and on the eighteen indicators that make up its structure. With this data, corresponding to 2009, an adequate analysis of gender equality in Europe can be established.     

An Unbalance Adjustment Method for Development Indicators

This paper analyzes some aggregation aspects of the procedure for constructing a composite index on a multidimensional socio-economic phenomenon such as development, the main focus being on the unbalance among individual dimensions. First a theoretical framework is set up for the unbalance adjustment of the index. Then an aggregation function is proposed that takes unbalance among development dimensions into account; a separate index is also introduced that measures the unbalance itself. Finally the dataset of the Index of African Governance for the year 2007 is used to test this method and compare it against the weighted arithmetic mean of variables with relation to the measured values of unbalance, yielding significantly different results for ratings and rankings, which in addition show negative correlations with the unbalance adjustment values. The changes ensuing from the adjustment are commented for some countries.