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This article provides an overview of public spending effectiveness (performance) and efficiency across the European Union over a decade (2007–2019) using a composite indicator. The results show an improvement of public spending effectiveness over time and a deterioration of the efficiency for most countries. Policies should be geared towards increasing the efficiency of government spending. [ABSTRACT FROM AUTHOR]

• We offer a framework for the socioeconomic implications of aging populations. • We introduce a composite indicator: the Senior Economy Tracker (SET). • The SET assesses inter/intra-regional readiness in adapting to aging populations. • The SET guides policymaking and raises awareness of this demographic challenge. Aging constitutes the dominant demographic challenge globally. The demographic transition entails a paradigm shift in the economic model to accommodate economic structures to life-expectancy gains. The socioeconomic implications from this transition remain largely undefined conceptually from an integrated perspective and unrecognized in official statistics. This study introduces a multidimensional and multi-actor reference framework, and a composite indicator, the Senior Economy Tracker (SET), to measure national readiness and progress in adapting to the demographic transition, over time and across countries. We apply our indicator to 27 European countries in 2010-2021. Our study reveals crucial differences in pathways and stages of maturity in addressing the socioeconomic impacts of aging. The proposed indicator aims to guide action to adapt economic structures to longer life spans, assist organizational and individual decision making, facilitate the development of effective policy interventions and raise awareness of the demographic transition. [ABSTRACT FROM AUTHOR]

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

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

This research presents an innovative method for constructing composite indicators: the Subjective–objective method of maximizing extracted variance (Sommev). Sommev's hybrid weighting approach fills an important gap within a highly controversial area of the composite indicators' literature, which criticizes the statistical assignment of weights disconnected from theory and the errors and judgmental biases inherent in the expert opinion-based weighting approach. These innovations contribute to a more coherent and consistent operationalization of the theoretical framework of multidimensional phenomena, reconciling the non-compensability between sub-indicators and the maximum retention of original information through statistically defined weights, in which the expert's opinion is considered, but does not determine the sub-indicator's weights. Twenty simulations were carried out to analyze the application of the method in representing social exclusion in a Brazilian city. Composite indicators constructed by Sommev retain twice as much information as those constructed with equal weights or weights defined by experts. This increased informational capacity favors a more comprehensive representation of the multidimensional phenomenon, having a high potential for application in solving problems of a multidimensional nature in the social, economic, and environmental areas. [ABSTRACT FROM AUTHOR]

The study aims to assess household energy poverty in European Union (EU) countries, comparing them based on the Objective Energy Poverty Index and the Subjective Energy Poverty Index. The Objective Energy Poverty Index is derived from indicators such as energy expenditure share, risk-of-poverty rate, and electricity prices. The Subjective Energy Poverty Index includes indicators such as the inability to keep the home adequately warm, arrears on utility bills, and bad housing conditions. Both indices aggregate the indicators mentioned above using equal and non-equal weighting approaches. The analysis uses country-level data from 2019 to 2023 sourced from Eurostat. The findings indicate considerable variation in household energy poverty across the EU, with more pronounced inequalities in subjective indicators than objective ones. Additionally, the study reveals a weak correlation between the Objective Energy Poverty Index and the Subjective Energy Poverty Index, leading to differing country rankings based on these indices. However, the choice of weights in constructing the energy poverty indices does not significantly impact a country's energy poverty ranking. The paper also identifies countries where household energy poverty decreased in 2023 compared to 2019 and those where it increased. Regarding the Subjective Energy Poverty Index, Croatia and Hungary showed the most notable improvement in their rankings among European countries, while France, Germany, and Spain deteriorated their positions. According to the Objective Energy Poverty Index, Bulgaria, Croatia, Portugal, and Spain demonstrated the most significant improvement, whereas Greece experienced a considerable decline. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel use of four distance‐based methods for constructing composite indicators: namely, the maximizing deviations method, the weighted least‐square (WLD) deviation from the mean method, the WLD deviation from the ideal method, and the WLD dissimilarity method. The main advantages of these methods are that they have analytical solutions. Thus, there is no need to solve the underlying programming problem for each application. They also result in common (but not necessarily equal) weights that allow complete comparison and ranking. We explore the potential of the suggested weighting method by using it to re‐estimate the Sustainable Energy Index and the Sustainable Development Goal 7 composite indicator. [ABSTRACT FROM AUTHOR]

Climate-related vulnerability indices are increasingly being utilized to enhance the creation of better disaster management strategies and to better understand and anticipate the effects of disasters related to climate change. This study evaluates the climate change vulnerability of the cities in Turkey by focusing on their exposure, susceptibilities, and adaptive capacities to climate change. Data from social, economic, and environmental sub-indicators were assessed and the most relevant indicators were aggregated to construct a composite Climate Change Vulnerability Index (CCVI). The CCVI includes six forms of capital leading to socio-economic and environmental sustainability i.e. social, public utility and transport, economics, land cover, meteorology, atmospheric conditions, and natural disaster capitals, and will be assessed by combining each of these forms of capital and its three dimensions (exposure, susceptibility, and adaptive capacity). Stakeholder-driven structured methodology that discovers and ranks context-relevant indicators and sets weights for aggregating indicator scores by using the Best-Worst method (BWM) and stepwise weight assessment ratio analysis (SWARA) method are utilized. The indicators are aggregated through the application of the BWM and SWARA weights using a linear aggregation method. From BWM and SWARA, the highest weights were obtained for meteorological conditions and land cover which are more than 0.36 and 0.22, respectively. The lowest weights were assigned to social characteristics and economy both of which were smaller than 0.10. The findings indicated that the cities on the northern, western, and southern coasts as well as the cities in the southeast region are the most vulnerable to climate change. The construction of CCVI can be used as part of the decision-making process to minimize hazards and exposure to the risk of climate change for the cities of Turkey. [ABSTRACT FROM AUTHOR]

Evaluation of green development is crucial for policy analysis and decision‐making. To address the research gap on green development assessment systems, this study first presents the Super‐efficiency Bounded Adjusted Measure model to gage the green growth index and economic growth index. This analysis involves evaluation of the coupling coordination degree. Through the analysis of the coordination between green growth and economic growth leads to recommendations for each region under consideration in regards to promotion of the components of the sustainable growth. This study presents the model that melds the objectives of the green and economic growth and uses the results for constructing the sustainable development Lorenz curve and Gini coefficient for the analysis of the differences in the sustainable development levels across the regions. The empirical case is presented to show the possibilities for application of the proposed approach. [ABSTRACT FROM AUTHOR]

The performance of tax administrations (TAs) is usually described as their capacity to complete activities with the minimum of resources engaged. Accordingly, tax administration performance is a multifaceted phenomenon, and measuring and benchmarking its performance against other countries or regions remains a puzzle for researchers and practitioners. This paper introduces a new approach for measuring tax administration performance using the Composite I-Distance Indicator (CIDI) based on 11 individual performance measures from 35 European tax administrations over two consecutive years (2018–2019). For the given scores of tax administrations, we conducted a correlation analysis with (a) tax evasion loss and (b) the fiscal deficit of countries in which these tax administrations operate, aiming to assess the strength of the statistical relationship between these variables. The study highlights Denmark and the Netherlands as exemplary models for tax administration, with "Revenue Collection" being identified as a crucial driver of excellence and "Operational Performance" (such as "e-filing" and "on-time filing") forming critical aspects of TA efficiency. Also, the study finds a negative correlation between tax avoidance and tax administration performance. [ABSTRACT FROM AUTHOR]

This research provides an overview of the challenges in analyzing multidimensional social exclusion data using multiple indicators. It highlights the importance of composite indicators in simplifying the understanding of complex realities. Grounded in this literature, the research proposes a new approach to address the issues related to the multispatial and multitemporal analysis of composite indicators, contributing to the existing body of knowledge in this area. To illustrate its potential, social exclusion measures proposed by the Center for Studies and Mapping of Social Exclusion for Public Policies are used for two southern Brazil municipalities. This framework considers demographic, economic, educational, and household dimensions and fourteen variables. The proposed approach offers two significant contributions: firstly, it prevents outliers from heavily influencing the normalized sub-indicators and composite indicators during the scale transformation process. Secondly, it provides a solution compatible with the three-dimensional nature of the problem, thereby enhancing the multitemporal analysis of composite indicators. [ABSTRACT FROM AUTHOR]

The concept of planetary boundaries (Rockström et al., 2009) and the need for social minima were integrated by Raworth (2012, 2017) into a ’doughnut-shaped’ framework, representing a ’safe and just space’ for humanity. Empirical assessments have revealed that no country currently falls within this ’doughnut’. However, to what extent do the results depend on the methodological assumptions, and could a less stringent metric, allowing trade-offs between indicators, improve these outcomes? Preserving the core of Raworth’s theoretical framework, we address these questions by constructing two separate sets of composite indicators for the social and environmental dimensions. Following an uncertainty-based approach, we obtain the two sets by combining alternative normalisation, weighting, and aggregation techniques. This approach yields a new, easily communicable, and robust metric for the ‘safe and just space’. Our analysis strengthens previous findings, showing that even with less stringent criteria, no country currently falls within the doughnut, underscoring the substantial gap to be addressed in both social and environmental policies.

In contemporary urban contexts, addressing challenges like urban sprawl, pollution, and heat islands is imperative. Understanding urban mechanisms and prioritizing proximity and sustainable mobility are crucial for meeting citizens' needs. This study presents a quantitative model with 21 composite indicators, aligning with the 15-minute city theory's dimensions (EDU, HSW, L&C, SUP), to measure resource accessibility. Focused on Valencia, the analysis of its 70 neighborhoods reveals significant disparities in indicators, mainly due to geographical distribution. Peripheries consistently score lower, while city centers and high-status neighborhoods demonstrate higher values. This underscores the importance of targeted interventions to promote equitable access to resources across urban areas.

The Environmentally Sensitive Areas Index (ESAI) is a comprehensive tool for assessing the susceptibility of areas to desertification. This index analyzes various parameters that are vital for environmental health. Through this index, factors such as human activities, geology, soil quality, vegetation and climate patterns are scrutinized. The analysis assigns weights to each participating factor. Thus, the index is derived from the aggregation of four categories (vegetation, climate, soil quality and management practices), and each of them is independently assessed to understand ecological health. In this way, the level of vulnerability to desertification is effectively measured. The application of the index in Greece (for a period of 20 years, 1984–2004) showed signs of environmental degradation and identified many areas with a high risk of desertification. Notably, there was a substantial increase in cultivated land within rural areas, contributing to shifts in the environmental landscape. Furthermore, this period is distinguished as the driest in the last century, with a peak between 1988 and 1993. The consequential rise in irrigation demand, driven by the simultaneous growth of crops and the intensification of agricultural practices, underscores the intricate interplay between human activities and environmental vulnerability.

Aggregate measures play an important role in the analysis of socio-economic phenomena. Various concepts for the construction of aggregate measures have been developed in the source literature. The relative taxonomy method proposed by Wydymus and its modification for the dynamic approach is the subject of interest in the presented article. Measures of central tendency are used in the construction of relative taxonomy method: in the classical version—the arithmetic mean, and in the positional version—the median. It was proposed to apply three robust measures of central tendency: trimmed mean, Winsorized mean, biweight mean. Robust location measures reduce the impact of extreme variable values on the aggregate measure values. The purpose of the article is to examine the usability of the measures of central tendency in ranking a set of objects based on the dynamic relative taxonomy. For this purpose, the degree of compatibility of the rankings of objects in terms of individual variables with the ranking based on the aggregate measure was assess. The rationale for this criterion is that the overall ranking is a resultant of rankings obtained for the individual variables. Kendall's tau correlation coefficient was applied as the measure of rankings compatibility. The empirical data on social cohesion and simulation data were used in the comparative analysis. For both the empirical data and all simulations, the findings collected with robust measures of central tendency are better than the ones obtained using the arithmetic mean. For the simulation data generated from the normal distribution, the highest degree of compatibility of the rankings of objects were achieved for the trimmed mean and the biweight mean. In turn, for the simulation data generated from three distributions (normal, lognormal, and Weibull), significantly better results in assessing the degree of compatibility of the rankings of objects were recorded for the median and the biweight mean. [ABSTRACT FROM AUTHOR]

Addressing challenges like urban sprawl, pollution, and heat islands is imperative in contemporary urban contexts. Understanding urban mechanisms and prioritising proximity and sustainable mobility is crucial for meeting citizens' needs. This study presents a quantitative model with 21 composite indicators, aligning with the 15-minute city theory's dimensions (Education, Health and Social Welfare, Leisure and Culture, and Supply) to measure resource accessibility. Focused on Valencia, the analysis of its 70 neighbourhoods reveals significant disparities in indicators, mainly due to geographical distribution. Peripheries consistently score lower, while city centres and high-status neighbourhoods score higher. It underscores the importance of targeted interventions for equitable access to resources across urban areas. [ABSTRACT FROM AUTHOR]

The Environmentally Sensitive Areas Index (ESAI) is a comprehensive tool for assessing the susceptibility of areas to desertification. This index analyzes various parameters that are vital for environmental health. Through this index, factors such as human activities, geology, soil quality, vegetation and climate patterns are scrutinized. The analysis assigns weights to each participating factor. Thus, the index is derived from the aggregation of four categories (vegetation, climate, soil quality and management practices), and each of them is independently assessed to understand ecological health. In this way, the level of vulnerability to desertification is effectively measured. The application of the index in Greece (for a period of 20 years, 1984–2004) showed signs of environmental degradation and identified many areas with a high risk of desertification. Notably, there was a substantial increase in cultivated land within rural areas, contributing to shifts in the environmental landscape. Furthermore, this period is distinguished as the driest in the last century, with a peak between 1988 and 1993. The consequential rise in irrigation demand, driven by the simultaneous growth of crops and the intensification of agricultural practices, underscores the intricate interplay between human activities and environmental vulnerability. [ABSTRACT FROM AUTHOR]

The recent PROMETHEE‐Scoring approach offers a method for generating cardinal scores through an outranking procedure utilizing Stochastic Multicriteria Acceptability and σ$$ \sigma $$–μ$$ \mu $$ analysis. In this paper, we illustrate some undesirable effects of this method and explore possible approaches to address them. First, we modify the original LP by inserting a new constraint that limits negative impacts caused by alternatives with low flow mean and standard deviation. Also, clustering is used to identify outliers in the decision matrix that may induce undesirable effects. A toy example demonstrates the issues and explores the proposed implementations. In addition, a numerical application is presented where a set of countries is ordered based on criteria from an economic freedom assessment. Both the numerical application and the toy example show that the propositions can improve the consistency of the results and prevent inconsistent outputs. The results include comparisons between the rank of alternatives when using purely the PROMETHEE‐Scoring method, the proposed improvements, and the expected results from SMAA‐PROMETHEE. [ABSTRACT FROM AUTHOR]

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

Climate-related vulnerability indices are increasingly being utilized to enhance the creation of better disaster management strategies and to better understand and anticipate the effects of disasters related to climate change. This study evaluates the climate change vulnerability of the cities in Türkiye through focusing on their exposure, susceptibilities and adaptive capacities to climate change. Data from social, economic and environmental sub-indicators were assessed and most relevant indicators were aggregated with the goal of constructing a composite Climate Change Vulnerability Index (CCVI). The CCVI includes six forms of capital leading to socio-economic and environmental sustainability i.e. social, public utility and transport, economics, land cover, meteorology and atmospheric conditions, and natural disaster capitals, and will be assessed combining each of these forms of capitals and its three dimensions (exposure, susceptibility and adaptive capacity). Stakeholder-driven structured methodology that discovers and ranks context-relevant indicators and sets weights for aggregating indicator scores by using the Best-Worst method (BWM) and stepwise weight assessment ratio analysis (SWARA) method are utilised. The indicators are aggregated through application of the BWM and SWARA weights using a linear aggregation method. From BWM and SWARA, the highest weights were obtained for meteorological conditions and land cover which are more than 0.36 and 0.22, respectively. The lowest weights were assigned to social characteristics and economy both of which were smaller than 0.10. The findings indicated that the cities on the northern, western and southern coasts as well as the cities in south-east region are the most vulnerable to climate change. The construction of CCVI can be used as part of decision-making process to minimize hazards and exposure to risk of climate change for the cities of Türkiye.

Sustainable development of countries necessitates parallel enhancements of societal wellbeing and reductions of environmental impacts. To track how countries perform to achieve these twin objectives, we introduce and calculate a novel metric called the Just Transition Score (JTS) that aims to assess countries' relative sustainability of social progress. Based on the content of environmental impacts per unit of progress, we calculate the JTS for 161 countries from 2011 to 2019. Additionally, we break down the aggregate indicator into two main components, namely the Carbon Just Transition Score, and the Material Just Transition Score. The results show significant disparities across countries both in terms of the overall JTS and its components. Countries with the best JTS performance, such as Portugal, Spain, and Costa Rica achieve the most sustainable social progress, while countries with the lowest JTS scores either fall short on social progress (the poorest countries) or create high environmental damage (such as the Gulf countries). The relative sustainability of social progress of poorer countries can be improved by increasing societal wellbeing while limiting growth of their environmental impacts. In richer countries, improvements would require more aggressive measures aimed at reducing the extent of environmental damage.

This study computes a flexicurity index for the EU28 countries for 2001-2019 following the European Commission’s four components of flexicurity model. The index allows the ex-post assessment of flexicurity efforts and efficiency. Following the computation of the index, we compare its values against the theoretical flexicurity typologies and against other empirical flexicurity groupings to assess their (dis)similarities. Even though Northern and Western countries generally have higher flexicurity scores than Southern and Eastern states, the study shows some countries deviate from their theoretical performance. Thus, some of the Continental and Mediterranean countries have flexicurity values like those of the Nordic group. Moreover, the flexicurity regimes are not static as the theoretical typology suggests: while Denmark and France are always in the top performers’ group, other countries change their performance throughout the 2001-2019 period. The flexicurity index correlates highly with empirical country groupings in the literature. The highest correlation is with country groupings using the European Commission’s four components of flexicurity model, followed by the Golden Danish Triangle, and lastly, the Wilthagen and Tros’ flexicurity matrix. In the end, we compare EU countries’ performance in the flexicurity index scores with their performance in selected employment and unemployment rates, labor productivity, and poverty rates. Results suggest that higher flexicurity performance correlates generally with better labor market and social outcomes, the highest correlations being in the case of labor productivity rates.

• Municipal solid waste management performance of 167 NUTS-2 regions in EU states. • Performance measurement developed through 4 BoD models inspired by the DEA approach. • Best performers are registered in Central and Northern EU NUTS-2 regions. • Eastern and Southern EU NUTS-2 regions performed generally worse. Achieving a Circular Economy requires effective waste management. Monitoring the transformation path taken by the European Union (EU) and its Member States is crucial for assessing the impact and effectiveness of current European waste legislation. Even though numerous studies have shown positive performance and convergence in waste treatment, regional efficiency evaluation studies are still lacking. This research aims to benchmark the performance of municipal solid waste management in 167 regions – at level 2 of the Nomenclature of Territorial Units for Statistics - across European Member States during the period from 2008 to 2013. It looks to assess whether there is any positive convergence among these regions. The efficiency assessment employs four different Data Envelopment Analysis models. One of these models, the Benefit-of-Doubt, is a non-parametric approach and represents a special case within the Data Envelopment Analysis framework. Our findings indicate a yearly reduction in the coefficient of variation of 3.6%. Despite this progressive convergence, substantial differences in municipal solid waste management performance exist between EU-20 regions, even within the same country. Particularly, the analysis demonstrates that the best performers are registered in the Central and Northern EU regions, while the Eastern and Southern EU regions performed worse. [ABSTRACT FROM AUTHOR]

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

This paper assesses the environmental sustainability of Japan by applying the environmental sustainability gap (ESGAP) framework, which builds on the concepts of strong sustainability, critical natural capital, environmental functions, and science-based reference values. The assessment is carried out using two indices of environmental sustainability (Strong Environmental Sustainability Index (SESI) and Strong Environmental Sustainability Progress Index (SESPI)) that provide a snapshot and a trend perspective on environmental sustainability performance and on progress toward it. The results reveal that Japan has not experienced significant changes in terms of aggregate environmental sustainability throughout the 2011–2017 period, but this is primarily a consequence of the mutually offsetting movements of different indicators. The country performs best for the human health and other welfare indicators, but worst for the sink function indicators such as the per-capita CO2 emissions and the eutrophication of fresh water. The indices also expose the main policy areas that Japan needs to strengthen to improve its environmental performance. They include issues such as tropospheric ozone pollution, which has long been discussed in scientific literature but never been a primal policy focus of the government until very recently. [ABSTRACT FROM AUTHOR]

Selective industrial policies have been increasingly used by governments to achieve desired normative goals. However, they have been revealed to be complex and vulnerable interventions, demanding robust tools able to justify choices and mitigate potential 'government failures'. In light of the emerging challenges and potential disruptions that might threaten our economies and societies, we contend that postshock industry resilience can be a valuable analytical framework to understand how different sectors react to unforeseen shocks. Accordingly, we present a methodology that measures postshock industry resilience and apply it to the Italian case in the aftermath of the 2008 shock. Particular attention is devoted to tourism-related industries. Main findings show that the industries reacted heterogeneously to the 2008 shock. For tourism-related industries, the results suggest following an ad hoc approach to the analysis of each tourism-focused industry to avoid generalizations that might lead to incorrect policy interpretations. [ABSTRACT FROM AUTHOR]

A Composite Indicator (CI) is a useful tool to synthesize information on a multidimensional phenomenon and make policy decisions. Multidimensional phenomena are often modeled by hierarchical latent structures that reconstruct relationships between variables. In this paper, we propose an exploratory, simultaneous model for building a hierarchical CI system to synthesize a multidimensional phenomenon and analyze its several facets. The proposal, called the Ultrametric Composite Indicator (UCI) model, reconstructs the hierarchical relationships among manifest variables detected by the correlation matrix via an extended ultrametric correlation matrix. The latter has the feature of being one-to-one associated with a hierarchy of latent concepts. Furthermore, the proposal introduces a test to unravel relevant dimensions in the hierarchy and retain statistically significant higher-level CIs. A simulation study is illustrated to compare the proposal with other existing methodologies. Finally, the UCI model is applied to study Italian municipalities' behavior toward waste management and to provide a tool to guide their councils in policy decisions. [ABSTRACT FROM AUTHOR]

This research offers a solution to a highly recognized and controversial problem within the composite indicator literature: sub-indicators weighting. The research proposes a novel hybrid weighting method that maximizes the discriminating power of the composite indicator with objectively defined weights. It considers the experts' uncertainty concerning the conceptual importance of sub-indicators in the multidimensional phenomenon, setting maximum and minimum weights (constraints) in the optimization function. The hybrid weighting scheme, known as the SAW-Max-Entropy method, avoids attributing weights that are incompatible with the multidimensional phenomenon's theoretical framework. At the same time, it reduces the influence of assessment errors and judgment biases on composite indicator scores. The research results show that the SAW-Max-Entropy weighting scheme achieves greater discriminating power than weighting schemes based on the Entropy Index, Expert Opinion, and Equal Weights. The SAW-Max-Entropy method has high application potential due to the increasing use of composite indicators across diverse areas of knowledge. Additionally, the method represents a robust response to the challenge of constructing composite indicators with superior discriminating power. [ABSTRACT FROM AUTHOR]

In this paper, we analyse the changes made to the basic EU directive on waste and assess its impact on the waste legislation of EU members. We then examine the Slovak waste strategies/programs that have implemented the EU directive on waste, namely the Waste Prevention Program, the Waste Management Program, and the Envirostrategy 2030. Based on EU waste legislation, the Environmental Strategy 2030 sets the waste treatment aims for Slovakia until 2030. However, it is questionable whether Slovakia will achieve the set goals. Our research indicates that as of 2021, Slovakia's rate of waste incineration with energy recovery and landfilling rate of municipal waste are below the EU average, while the recycling rate, both for materials and composting and digestion, is higher. In our quantitative analysis, we examine the progress of waste management performance in Slovakia from 2017 to 2021, focusing on the LAU-1 districts. We estimate composite efficiency indicators using the techniques of Data Envelopment Analysis and Malmquist Indices. In accordance with the hierarchy of waste treatment methods, the applied models consider desirable waste operations variables (recycling and incineration with energy recovery) and undesirable waste operation variables (landfilling). Our results reveal significant variations in efficiency across the LAU-1 districts. The average technical efficiency of the 72 districts has improved from 0.714 in 2017 to 0.852 in 2021, indicating that the performance of districts is generally improving and catching up with the best-performing districts. The total performance, as measured by the Malmquist index, has improved by 45.5 %. Districts with access to waste incineration facilities with energy recovery have exhibited higher efficiency scores, benefitting from this advantage. [ABSTRACT FROM AUTHOR]

This study computes a flexicurity index for the EU28 countries for 2001-2019 following the European Commission's four components of flexicurity model. The index allows the ex-post assessment of flexicurity efforts and efficiency. Following the computation of the index, we compare its values against the theoretical flexicurity typologies and against other empirical flexicurity groupings to assess their (dis)similarities. Even though Northern and Western countries generally have higher flexicurity scores than Southern and Eastern states, the study shows some countries deviate from their theoretical performance. Thus, some of the Continental and Mediterranean countries have flexicurity values like those of the Nordic group. Moreover, the flexicurity regimes are not static as the theoretical typology suggests: while Denmark and France are always in the top performers' group, other countries change their performance throughout the 2001-2019 period. The flexicurity index correlates highly with empirical country groupings in the literature. The highest correlation is with country groupings using the European Commission's four components of flexicurity model, followed by the Golden Danish Triangle, and lastly, the Wilthagen and Tros' flexicurity matrix. In the end, we compare EU countries' performance in the flexicurity index scores with their performance in selected employment and unemployment rates, labor productivity, and poverty rates. Results suggest that higher flexicurity performance correlates generally with better labor market and social outcomes, the highest correlations being in the case of labor productivity rates. [ABSTRACT FROM AUTHOR]

Objective: To construct an indicator for assessing the complexity of UK veterans’ needs. Study design: Cross-sectional, secondary analysis. Methods: The study applied principal component (PCA) analysis as the method to determine the weights of different needs based on their interactions with each other, the effectiveness of the model was evaluated using bootstrapping. The dataset on UK veterans’ support provided by the “Soldiers, Sailors, Airmen and Families Associations” (SSAFA) (N = 35,208) was considered. The grant applications for different categories of support were used as indicators of different needs. The dimensions of breadth (number of different needs) and depth (number of grant applications to address the need) were incorporated in the assessment of complexity. Results: The complex needs indicator for the current sample was validated. The majority of cases had a complexity score of 1 or less. Conclusions: The research suggested and tested an assessment method for the complexity of veterans’ needs, that may be positively associated with higher risk of adverse health outcomes. This indicator can be used by decision-makers for risk stratification of the veteran population, thus supporting the allocation of resources in a more effective way.

Composite indicators are one-dimensional measures that help decision-makers understand complex realities. Existing software builds composite indicators using different methods, also providing a measure of the stability of their structure. Considering this, it is possible to point out that there is a gap for software that provides more quality measures and composite indicators capable of guiding decision-makers on their usability. The software for building and measuring the quality of composite indicators using Ordered Weighted Averaging, so-called S-CI-OWA, fills this gap. The S-CI-OWA is based on Ordered Weighted Averaging, which assigns weights according to the input value, solving hierarchical evaluation problems considering the fuzzy nature of scoring, weighting, and aggregation operations. The S-CI-OWA offers three new features over existing software: one, versatility in driving composite indicator scores up or down; two, flexibility in defining this intensity of emphasis, allowing more positive or negative indicators (sub-indicators) to be highlighted in decision-making units; three a broader set of quality measures of composite indicators, notably: ranking uncertainty, ratio of atypical measurements, discriminating, explanatory and informational power.

The study aims to assess household energy poverty in European Union (EU) countries, comparing them based on the Objective Energy Poverty Index and the Subjective Energy Poverty Index. The Objective Energy Poverty Index is derived from indicators such as energy expenditure share, risk-of-poverty rate, and electricity prices. The Subjective Energy Poverty Index includes indicators such as the inability to keep the home adequately warm, arrears on utility bills, and bad housing conditions. Both indices aggregate the indicators mentioned above using equal and non-equal weighting approaches. The analysis uses country-level data from 2019 to 2023 sourced from Eurostat. The findings indicate considerable variation in household energy poverty across the EU, with more pronounced inequalities in subjective indicators than objective ones. Additionally, the study reveals a weak correlation between the Objective Energy Poverty Index and the Subjective Energy Poverty Index, leading to differing country rankings based on these indices. However, the choice of weights in constructing the energy poverty indices does not significantly impact a country’s energy poverty ranking. The paper also identifies countries where household energy poverty decreased in 2023 compared to 2019 and those where it increased. Regarding the Subjective Energy Poverty Index, Croatia and Hungary showed the most notable improvement in their rankings among European countries, while France, Germany, and Spain deteriorated their positions. According to the Objective Energy Poverty Index, Bulgaria, Croatia, Portugal, and Spain demonstrated the most significant improvement, whereas Greece experienced a considerable decline.

The performance of tax administrations (TAs) is usually described as their capacity to complete activities with the minimum of resources engaged. Accordingly, tax administration performance is a multifaceted phenomenon, and measuring and benchmarking its performance against other countries or regions remains a puzzle for researchers and practitioners. This paper introduces a new approach for measuring tax administration performance using the Composite I-Distance Indicator (CIDI) based on 11 individual performance measures from 35 European tax administrations over two consecutive years (2018–2019). For the given scores of tax administrations, we conducted a correlation analysis with (a) tax evasion loss and (b) the fiscal deficit of countries in which these tax administrations operate, aiming to assess the strength of the statistical relationship between these variables. The study highlights Denmark and the Netherlands as exemplary models for tax administration, with “Revenue Collection” being identified as a crucial driver of excellence and “Operational Performance” (such as “e-filing” and “on-time filing”) forming critical aspects of TA efficiency. Also, the study finds a negative correlation between tax avoidance and tax administration performance.

Abstract This research paper introduces a methodology to assess the robustness of the Global Innovation Index (GII), by comparing the rankings provided in it with those achieved using alternative data-driven methodologies such as data envelopment analysis (DEA) and principal component analysis (PCA). With it, the paper aims to reduce the level of subjectivity in the construction of composite indicators regarding weight generation and indicator aggregation. The paper relies on PCA as a weighting-aggregation scheme to reproduce the 21 sub-pillars of the GII before the application of DEA to calculate the relative efficiency score for every country. By using the PCA-DEA model, a final ranking is produced for all countries. The random forests (RF) classification is used examine the robustness of the new rank. The comparison between the new rank and that of the GII suggests that the countries positioned at the top or the bottom of the GII rank are less sensitive toward the modification than those in the middle of the GII, the rank of which is not robust against the modification of the construction method. The PCA-DEA model introduced in this paper provides policymakers with an effective tool to monitor the performance of national innovation policies from the perspective of their relative efficiency. Ultimately, the contribution made in this paper could be instrumental to enhance the effectiveness and the efficiency of the practice of innovation management at the national level.

In this paper, we analyse the changes made to the basic EU directive on waste and assess its impact on the waste legislation of EU members. We then examine the Slovak waste strategies/programs that have implemented the EU directive on waste, namely the Waste Prevention Program, the Waste Management Program, and the Envirostrategy 2030. Based on EU waste legislation, the Environmental Strategy 2030 sets the waste treatment aims for Slovakia until 2030. However, it is questionable whether Slovakia will achieve the set goals. Our research indicates that as of 2021, Slovakia’s rate of waste incineration with energy recovery and landfilling rate of municipal waste are below the EU average, while the recycling rate, both for materials and composting and digestion, is higher. In our quantitative analysis, we examine the progress of waste management performance in Slovakia from 2017 to 2021, focusing on the LAU-1 districts. We estimate composite efficiency indicators using the techniques of Data Envelopment Analysis and Malmquist Indices. In accordance with the hierarchy of waste treatment methods, the applied models consider desirable waste operations variables (recycling and incineration with energy recovery) and undesirable waste operation variables (landfilling). Our results reveal significant variations in efficiency across the LAU-1 districts. The average technical efficiency of the 72 districts has improved from 0.714 in 2017 to 0.852 in 2021, indicating that the performance of districts is generally improving and catching up with the best-performing districts. The total performance, as measured by the Malmquist index, has improved by 45.5 %. Districts with access to waste incineration facilities with energy recovery have exhibited higher efficiency scores, benefitting from this advantage.