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This paper analyses the inequality between the regions of the Organization for Economic 13 Co-operation and Development (OECD) member countries by using stochastic multi- 14 objective acceptability analysis and the associated multivariate Gini index. By consider- 15 ing a large number of possible combinations of weights, the distribution of the potential 16 rankings for each region is used to measure multidimensional inequality both within and 17 between countries. Our results show that beyond the expected two clubs of rich and poor 18 countries, a third group of countries emerges that belongs neither to the top nor to the 19 bottom of the ranking, an outcome that can be attributed to the presence of significant 20 economic differences among regions within those countries. Most of the inequality lies 21 between countries, but regional well-being also significantly varies within the same 22 countries and we find an inverse U-shape connection between regional well-being and 23 its inequality within the OECD member countries.

This paper investigates how female presence in political institutions influences local governments’ cost efficiency, implementing a one-stage stochastic frontier model, based on a panel of Italian municipalities, with municipal fixed effects. The empirical results provide significant evidence for the presence of a gender effect in politics: higher efficiency is observed in municipalities with a female mayor and a greater presence of women in their councils. Further, the gender effect is confirmed taking into account the regional perspective, that is, the historic North–South Italian dualism, and performing several robustness checks. The empirical results support the usefulness of policies aimed at favouring female empowerment and at promoting gender equality in politics.

This paper proposes a novel method to analyze multidimensional poverty by using a large set of feasible weights to summarize the information about the poor, which enables remaining agnostic about the relative importance given to different poverty dimensions. This method allows for the calculation of the individual probability of being poor in a multidimensional perspective. The distribution of individual probabilities can then be combined with Generalized Lorenz dominance techniques to derive unanimous consent for a wide class of social welfare functions with a minimum load of value judgments. The innovations proposed here allow to move from a dual definition of poverty, where poor and non-poor individuals are classified in a mutually exclusive context, to a continuous measure of deprivation capturing both the extensive and intensive margin of multidimensional poverty. The empirical application of the method consists of measuring multidimensional poverty in ten selected countries using four waves of EU-SILC data (2008–2014).

There is widespread evidence that smoking is more spread among the least well-off, but few studies investigate the effects of the differentiation of smoking products. This paper provides new evidence on income related inequality of five different products using a database from the Italian Population Survey on Alcohol and other Drugs. In particular, we use the 2014 and 2018 surveys to estimate how the consumption of manufactured cigarettes, roll-your-own cigarettes, cigars, pipe, and e-cigarettes is distributed across the income scale. The results confirm the existing evidence that the use of manufactured and roll-your-own cigarettes is more concentrated among the poor, while the use of cigars and pipe tends to be more concentrated among richer individuals. On the other hand, an important result emerges that the use of electronic cigarettes appears to be more concentrated among the poor, giving support either to the possibility that e-cigs may induce entry into smoking at cheaper prices for less affluent people or that it helps downtrading and quitting of traditional smokers in the lowest part of the income distribution. In both cases, the policy implications, for example of how to tax these alternative products, are relevant, especially because individual socio-economic characteristics (gender, income, education, working condition and geographical location) play a significant and differentiated role across smoking products.

This paper proposes the use of a new technique, the Stochastic Multicriteria Acceptability Analysis (SMAA), to evaluate education quality at school level out of the PISA multidimensional database. SMAA produces rankings with Monte Carlo Generation of weights to estimate the probability that each school is in a certain position of the aggregate ranking, thus avoiding any arbitrary intervention of researchers. We use the rankings in 4 waves of PISA assessment to compare SMAA outcomes with Benefit of Doubt (BoD), showing that differentiation of weights matters. Considering the whole set of feasible weights by means of SMAA, we then estimate multidimensional inequality in education, and we disentangle inequality into a ‘within’ and a ‘between’ country component, in addition to a component due to overlapping, using the multidimensional ANOGI. We find that, over time, inequality within countries has increased substantially. Overlapping among countries, particularly in the upper part of the distribution has also increased quite substantially suggesting excellence is spreading among countries.

This paper investigates the association between income and the habit of smoking in 30 European countries. Using the European Health Interview Survey carried out between 2013 and 2015, the analysis focuses on the relationship among net monthly equivalised income of the household, the type of smoking behaviour, and the daily average number of cigarettes smoked. Income-related inequalities are estimated using the Erreygers Index. Results show that smoking is a habit which is mainly rooted in the lowest part of the income distribution both at individual and country level, regardless of the average level of per capita income. Considering that tobacco use worsens poverty conditions by diverting household spending from basic needs to tobacco itself, our results give support to a tax increase in order to discourage its use and to the implementation of educational and prevention programs aimed at helping people to quit smoking.

This paper evaluates multidimensional poverty in European countries introducing two main novelties compared with the previous literature: first, the dimensions of poverty are selected on the basis of the shared values included in the Charter of Fundamental Rights of the European Union; second, the whole space of feasible weights is used to summarise the multidimensional information, in order to remain agnostic about the importance given to the different deprivations. Using data from four waves of EU-SILC, the methodological innovations introduced here have allowed to produce a family of measures that capture the individual probability of being multidimensionally poor. Individual probabilities are then used to analyse the within and between distribution of multidimensional poverty in ten countries. Finally, they get combined with the generalised Lorenz dominance techniques in order to derive socially preferred distributions with the minimum load of value judgments. The novel methods proposed in this analysis allow to move from a dual definition of poverty, where poor and non-poor individuals are classified in a mutually exclusive context, to a continuous measure of deprivation, which allows to capture both the extensive and intensive margin of multidimensional poverty.

Vaccine hesitancy (VH) might represent a serious threat to the next COVID-19 mass immunization campaign. We use machine-learning algorithms to predict communities at a high risk of VH relying on area-level indicators easily available to policymakers. We illustrate our approach on data from child immunization campaigns for seven non-mandatory vaccines carried out in 6408 Italian municipalities in 2016. A battery of machine learning models is compared in terms of area under the Receiver Operating Characteristics (ROC) curve. We find that the Random Forest algorithm best predicts areas with a high risk of VH improving the unpredictable baseline level by 24% in terms of accuracy. Among the area-level indicators, the proportion of waste recycling and the employment rate are found to be the most powerful predictors of high VH. This can support policy makers to target area-level provaccine awareness campaigns.

In this paper we document the income-related inequality in gambling. We employ a novel database from 2014–2017 waves of the Italian Population Survey on Alcohol and other Drugs (IPSAD) which also include information on the preferences for games of chance. Following the Erryegers Index, our findings suggest that traditional lotteries are concentrated among the richest individuals, while betting and new generation games tend to be pro-poor games. The decomposition of income-related inequalities reveals that pro-rich inequality observed in traditional games is mainly driven by gender, age, and working condition. Higher components of the pro-poor inequality observed in betting and new generation games come instead from income and age. Since the pro-poor games are also the major contributors of the growth in gambling turnover and the increase in gambling disorders, our results indicate that a relevant part of increasing social costs associated to gambling are more likely to be paid by the less-well off, and potentially most vulnerable members of the society.

People who inject drugs (PWID) constitute the largest reservoir of hepatitis C virus (HCV). Although effective medications are available and access to care is universal in Italy, the proportion of PWID receiving appropriate care remains low.To identify the major barriers for PWID to HCV treatment we surveyed a large sample of practitioners working in outpatient addiction centres (SerDs). The survey was conducted in two stages and involved 30.3% of SerDs operating in Italy. In the first, SerD physicians completed a questionnaire designed with a Delphi structure. In the second, SerD practitioners completed a targeted questionnaire to identify barriers to four SerD services in HCV management: screening, referral, treatment and harm reduction.The first-stage questionnaire, in which a Delphi and RAND-UCLA method was used, revealed a lack of agreement among the physicians about barriers to health care. The more detailed second-stage questionnaire indicated the barriers to delivering specific SerD services. As regarded the delivery of all four services, the major reasons for treating50% of patients were: physician and nurse understaffing, technical, economic and logistic issues. In contrast, the practitioners who responded that they follow protocol recommendations often deliver all four services to50% of patients.HCV treatment remains out of reach for many PWID attending a drug treatment centre in Italy. To meet the World Health Organisation (WHO) target, there is a need to increase economic, technical and staff support at treatment centres using the protocols and the universal health care already in place.

Better Life Index (BLI), the measure of well-being proposed by the OECD, contains many metrics, which enable it to include a detailed overview of the social, economic, and environmental performances of different countries. However, this also increases the difficulty in evaluating the big picture. In order to overcome this, many composite BLI procedures have been proposed, but none of them takes into account societal priorities in the aggregation. One of the reasons for this is that at the moment there is no representative survey about the relative priorities of the BLI topics for each country. Using these priorities could help to design Composite Indices that better reflect the needs of the people. The largest collection of information about society is found in social media such as Twitter. This paper proposes a composite BLI based on the weighted average of the national performances in each dimension of the BLI, using the relative importance that the topics have on Twitter as weights. The idea is that the aggregate of millions of tweets may provide a representation of the priorities (the relative appreciations) among the eleven topics of the BLI, both at a general level and at a country-specific level. By combining topic performances and related Twitter trends, we produce new evidences about the relations between people's priorities and policy makers' activity in the BLI framework.

Cannabis accounts for the largest share of the illicit drug market, with a high prevalence of use even among adolescents. To tackle this longstanding problem, many kinds of reforms to national cannabis control policies have been implemented in Europe, but their effectiveness is still unclear. This paper analyses the association between selected categories of cannabis policy reforms and changes in perceived cannabis availability and patterns of use among adolescents. Data from 20 European countries across 15 years were drawn from a novel database of the European school Survey Project on Alcohol and other Drugs (ESPAD). Our analysis is based on a Difference-in-Differences design, which application is allowed by the fact that only thirteen out of the twenty countries included implemented policy changes. The results suggest that selected categories of reforms influence the availability and prevalence of cannabis use. In particular, some forms of restrictive intervention reduce the general prevalence of use and more liberal reforms seem linked to an increase in the share of students initiating use of cannabis. We find no evidence of an effect of policy changes on the share of frequent users, which are presumably those more likely to develop use-related health consequences.

One of the difficulties faced by policy makers during the COVID-19 outbreak in Italy was the monitoring of the virus diffusion. Due to changing criteria and insufficient resources to test all suspected cases, the number of ‘confirmed infected’ cases rapidly proved to be unreliably reported by official statistics. This limited the ability of epidemiologic models to predict the evolution of the infectious disease. This paper explores the possibility of using information obtained from Google Trends to supplement official statistics in order to predict when the number of deaths due to COVID-19 will peak in Italy. We estimate and regularize a panel model with regional and time fixed effects. Our preferred specification shows a positive and significant correlation between Google searches for commonly reported COVID-19 symptoms and deaths recorded. The analysis suggests that the social distancing measures implemented in early March in Italy were effective in slowing down the spread of the virus.

Since its lethality increases exponentially with age, the early 2020 COVID-19 shock unexpectedly raised the risk of corporate disruption at companies led by older CEOs. While normally unprepared successions might be beneficial by replacing entrenched CEOs, this systemic shock projected a possible crowding of older CEOs’ successions, with disruption costs dominating changeover benefits. Within this natural experiment, we find that stock returns and volatility worsened at S&P 500 listed companies with older CEOs when the COVID-19 lethal risk emerged. Our results resist various robustness checks. This advises companies to adopt contingency strategies of top managers’ replacement against possibly recurring pandemics.

This paper investigates the association between income and the habit of smoking in 30 European countries. Using the European Health Interview Survey carried out between 2013 and 2015, the analysis focuses on the relationship among net monthly equivalised income of the household, the type of smoking behaviour, and the daily average number of cigarettes smoked. Income-related inequalities are estimated using the Erreygers Index. Results show that smoking is a habit which is mainly rooted in the lowest part of the income distribution both at individual and country level, regardless of the average level of per capita income. Considering that tobacco use worsens poverty conditions by diverting household spending from basic needs to tobacco itself, our results give support to a tax increase in order to discourage its use and to the implementation of educational and prevention programs aimed at helping people to quit smoking.

In this paper we analyze the predictability of the bankruptcy of 7795 Italian municipalities in the period 2009–2016. The prediction task is extremely hard due to the small number of bankruptcy cases, on which learning is possible. Besides historical financial data for each municipality, we use alternative institutional data along with the socio-demographic and economic context. The predictability is analyzed through the performance of the statistical and machine learning models with a receiver operating characteristic curve and the precision-recall curve. Our results suggest that it is possible to make out-of-sample predictions with a high true positive rate and low false-positive rate. The model shows that some non-financial features (e.g. geographical area) are more important than many financial features to predict the default of municipalities. Among financial indicators, the important features are mainly connected to the Deficit and the Debt of Municipalities. Among the socio-demographic characteristics of administrators, the gender and the age of members in council are among the top 10 features in terms of importance for predicting municipal defaults.

It has been shown that the development of a country's people stems from its wealth, which is characterized by human, social, and natural capital, but an assessment of the Human Development Index adjusted for wealth is still missing in the literature. There are large differentiations in the productive bases of countries, and the Human Development Index alone does not allow for encompassing the systems' ability for fostering human development from the endowment of resources available. The input-to-output link between the resources and human development reflects the systems' ability to optimise assets. This paper introduces an evaluation of the Human Development Index adjusted for the comprehensive wealth of countries using an ordinal non-compensatory technique that is robust to outliers, and which allows for the consideration of environmental factors and non-discretional inputs. Results show relevant changes in ranking when Human Development Indexes is adjusted for wealth. Decreases in the Human Development Index when wealth is considered, are observed in North America, the Middle East & North Africa, while increases in the Human Development Index when wealth is considered, are observed in most Sub-Saharan African and South Asian countries. The unexplored link between wealth and human development reveals an unprecedented heterogeneity among countries that is affected by economic, social, and institutional factors. The gaps in these factors must be further investigated if the increase of human development is to be implemented because efficient long-term management of natural resources is necessary for sustainability.

In this paper, we use the standardized mortality rates for 21 mutual exclusive causes of death to propose a composite index of US county-level health performances in 1980-2014 interval. We aggregate mortality rates by the Stochastic Multi-Criteria Acceptability Analysis (SMAA), in order to avoid any a priori judgement on the importance given to a specific cause of death. The total observed inequality among counties is then decomposed to estimate the variability between and within states by means of the Theil index on SMAA outcomes. On average, there has been a decrease in the Composite Index of mortality from 1980 to 2014, but while the majority of counties had an increase in health conditions, some counties have shown a decrease in health performances in the same interval. This may be the reason of a persistent increase of total inequality among counties, with inequality within states constantly higher than inequality between states, both responsible of the growing inequality levels of health performances in the period analysed.

This paper proposes the use of a new technique, the Stochastic Multi-criteria Acceptability Analysis (SMAA), to evaluate education quality at school level out of the PISA multidimensional database. SMAA produces rankings with Monte Carlo Generation of weights to estimate the probability that each school is in a certain position of the aggregate ranking, thus avoiding any arbitrary intervention of researchers. We use the rankings in 4 waves of PISA assessment to compare SMAA outcomes with Benefit of Doubt (BoD), showing that differentiation of weights matters. Considering the whole set of feasible weights by means of SMAA, we then estimate multidimensional inequality in education, and we disentangle inequality into a ‘within’ and a ‘between’ country component, in addition to a component due to overlapping, using the multidimensional ANOGI. We find that, over time, inequality within countries has increased substantially. Overlapping among countries, particularly in the upper part of the distribution has also increased quite substantially suggesting excellence is spreading among countries.

This paper aims at investigating the effect ofEconomic Socialand Cultural Status (ESCS) on the educational performances of students, using two waves of the Programme for International Student Assessment (PISA) survey of OECD (2009, 2012). The analysis is conducted at student level for all countries included in the PISA sample. The estimates are based on the ConditionalData Envelopment Analysis(DEA), applied for the first time in the Slack Based Measure (SBM) version. This method allows a detailed evaluation of the additional effort the students should do when they are operating in an ESCS that has a comparative disadvantage. The unprecedented use of a conditional non-radial model of efficiency, provides evidences of a significant effect of the ESCS on performances, with a strong heterogeneity among students, and countries. It follows that some problems with education may not be due to the education systems themselves, but to the economic, social and cultural gaps, which determine a persistence ofinequalityof opportunity. Thus,public policiesare needed to foster virtuous paths to reduce disparities among students with different socioeconomic background both between and within systems.

This paper proposes a novel approach to evaluate European countries on Sustainable Development Goals (SDG) by means of Hierarchical Stochastic Multicriteria Acceptability Analysis (HSMAA). HSMAA produces rankings with Monte Carlo generation of weights, overcoming the need to choose one specific set of weights. The main contribution of this paper lies in the possibility of quantifying the probability by which each country receives a given ranking. Furthermore, HSMAA allows to take into account the hierarchical nature of SDG measurement given that each of the 17 Goals also consists of several indicators. Our results show that Denmark outperforms other European countries, while lower levels of performance are observed in Romania and Bulgaria. In between bottom and top performers, we also find that many countries’ rankings vary widely by the chosen set of weights, exemplifying the need to rank countries based on multiple weightings and to quantify the probabilities of each ranking.

Over the last two decades, due to strong decentralization and widespread budget constraints, the Italian co-payment for health care has become a way to finance public health. This phenomenon has provoked a continuous increase of private costs of public health and an evident regional heterogeneity. As a result, a pervasive spatial inequality of access to public health care is becoming increasingly clear. The aim of this paper is to measure this inequality, mainly determined by the differences among regional co-payment prices. Access, equity, and needs are all part of the phenomenon ‘inequality of access’, and they are difficult to define and measure in health care. For this reason, most of the previously proposed measurement methods have inherent limitations and have prompted us to use an innovative approach focused exclusively on the supply side. In particular, we focus only on the cost of health benefits (co-payment). From a methodological perspective, we use a recent new version of the Stochastic Multiobjective Acceptability Analysis (SMAA), which is a methodology mainly used to build composite indicators of multidimensional phenomena out of the market. In order to deal with the hierarchical structure of the Italian health care system, we use the Hierarchy Stochastic Multiobjective Acceptability Analysis (HSMAA), which takes into account the uncertainty with respect to the weights assigned to the considered criteria, as in the standard SMAA, but also the uncertainty with respect to the weights assigned to the considered sub-criteria. Applying for the first time HSMAA to measure inequality allows us to create a unique index for each region and then to make a classification among them. The results show that, since there are different prices for the same health benefits among different regions, there are strong spatial inequalities in the cost of the Essential Levels of health care in Italy.

Advances in machine learning and Big Data research offer great potential for international development agencies to leverage the vast information generated from accountability mechanisms to gain new insights, providing analytics that can improve decision-making. From a knowledge management perspective, project operational reports are crucial historical records as they tell the story of a project, compile data generated throughout the project cycle, discuss achievements of intended development objectives, and provide learning that can inform future operations. Taking the International Fund for Agricultural Development (IFAD) as a case study, this paper explores how machine learning can harness existing project data to uncover latent information about food systems dynamics, which is already present in documentation but has not yet been investigated. Specifically, we aim to provide evidence on the evolution of food system dimensions within IFAD-funded projects through the application of supervised text mining, network analysis and LASSO regression to project documents collected from hundreds of projects spanning the whole of IFAD's investment portfolio in the 1981-2019 interval. Findings show an increase in reporting against food system dimensions and consolidate the applicability of machine learning analytics to uncover historical trends about international agencies’ activities and accelerate knowledge generation around strategic themes.

Environmental-economic perspectives are recently taking growing momentum in the analysis of country-level performances, but few studies take into account the multi-dimensional nature of the phenomenon. The leading measure of pollution still remains only CO2 emission or, at best, CO2 equivalent emission. The few works taking a multi-criteria perspective are mainly related to specific case studies, urban areas, or the localization of renewable power plants. In this paper, we propose a multi-criteria analysis for a macro-perspective assessment of 10 different pollutants in 30 European countries over the period 2008–2015. Methodologically, we propose a new hybrid tool allowing us to merge the consolidated procedure usually employed for environmental evaluation: PROMETHEE, with a more flexible weighing process inspired by Data Envelopment Analysis. The unprecedented use of this tool for developing a macro perspective allows the clustering of European countries according their relative performances without any exogenous assumption about the weights given to the specific pollutants. In addition, the empirical evidence emerging from the analysis provides a comprehensive picture of patterns followed by European economies, which highlights the relative strengths and flaws at country-level for the considered pollutants.

Online ratings provide valuable information to operators. However, the use of this information for revenue maximization purposes remains at a moot point. This article proposes a new method to decompose ratings based on their relevance to hotel performance. From a methodological standpoint, we propose a multi-criteria decision analysis approach. The empirical validation includes two independent data sources, online ratings from Booking.com and RevPAR data from STR. By means of pairwise comparisons in PROMETHEE, the findings reveal the different weights of individual ratings, helping operators to understand the weight of each rating attribute in terms of revenue maximization. In particular, apart from the importance of the location, the role of staff and facilities emerge as central in terms of revenue maximization. The proposed model offers new theoretical insights on the relevant dimensions, helping hoteliers to prioritize when making trade-off decisions.

One of the most influential measures of multidimensional well-being, the Better Life Index, launched by OECD in 2011, contains a detailed overview of the social, economic, and environmental performances of different countries. Since its launch, a relevant number of empirical studies have been proposed on these data, but the role played by the distance between societal priorities and country-level performance in Better Life Index as well as in multidimensional well-being remains underexplored. We propose to address this issue by means of a multidimensional spatial model. We position the countries in the Euclidean K-dimensional space in which each dimension is a specific aspect of well-being, and we consider each individual's opinion on the same dimensions to calculate the personal optimal point. The distance between the optimal point of well-being and the actual observed point at individual level is the individuals' loss in well-being. We show that the societal loss at country-level is negatively related to the overall well-being and the main indices of quality of democracy. Based on the above evidence, we would argue that a multidimensional spatial framework represents a promising tool for the analysis of the whole class of multidimensional measures of well-being in which a group of individuals expresses the weights individually assigned to a set of dimensions within a pre-established range.

This paper introduces the Best-Worst PROMETHEE method, which avoids the rank reversal problem of other PROMETHEE methods. We use this new technique to rank schools in the Programme for International Student Assessment (PISA). We consider three separate outputs, provided by average student attainments in mathematics, language, and sciences. Our sample comprises 16,500 schools in 66 countries, and our results show significant differences between school performance both within and between countries. The top half of the ranking mainly comprises European and Asian countries, while the bottom half includes many North African, Middle Eastern, and South American countries. We find no strong association between inequality between schools and the country-level performance, suggesting the absence of a trade-off between equity and performance in education. The Best-Worst PROMETHEE is a generic method that can be used to support decisions in strategic sectors with multidimensional outcomes.

Background and Aims: Families who live in a disadvantaged socioeconomic situation frequently face substandard housing, unsafe neighborhoods, inadequate schools and more stress in their daily lives than more affluent families, with a host of psychological and developmental consequences that can hinder their children’s development in many ways. However, the measurement of socioeconomic status among youth and its link with different forms of illicit substance use is challenging and still unclear. This paper extends existing research on the relationship between socioeconomic status and illicit drug use among adolescents by focusing on three different patterns of use (experimental, episodic and frequent) and making use of two indicators to improve the measurement of individual socioeconomic characteristics in a big sample of European students. Methods: Data were drawn from the European school Survey Project on Alcohol and other Drugs (ESPAD), which, since 1995, collects comparable data among 15-to-16-year-old students to monitor trends in drug use and other risk behaviors across Europe. The sample comes from 28 countries that participated in the 2015 data collection. The consumption of cannabis, cocaine and heroin are considered, and the related patterns are identified based on the frequency of use. Family characteristics at student level are defined through two dimensions: parental educational level and perceived socioeconomic status. Multivariate multilevel mixed-effects logistic regression was performed in order to measure the association between individual characteristics and vulnerability for drug use. Results: Some patterns of use, episodic and frequent in particular, were found strongly associated with a lower socioeconomic status and lower parental education. Conclusions: Our results suggest that drug policies should be combined with actions aimed at removing barriers to social inclusion that are attributable to the socioeconomic background of adolescents.

The public and private sectors are different in terms of ownership, funding, and control. The degree of publicness influences the decision-making processes, strategies, and actions, thus severely impacting the performance levels. This study contributes to the existing literature on managerial efficiency environmental factors by proposing a framework and methodology for estimating the influence of the “publicness effect.” A new method is tested, relying on the combined integration of three different data envelopment analysis models in the Italian waste management sector. Results show that the “publicness effect” accounts for 33.87% of managerial efficiency for public companies and 11.6% for mixed ones.

It is by now common knowledge that in switching from GDP to alternative, multidimensional, measures of collective well-being one can provide a better account of a country’s socio-economic conditions. Such a gain, however, comes at the price of losing output-to-input type of link between well-being and the resources necessary to make it available. Since well-being measures are not meant to be only an exercise in documentation, but also to inform policies and priorities, we propose a method to build a measure of well-being in the form of a single index, as for GDP, which takes into account: (1) the social and environmental costs, not considered in the GDP, and (2) the use of conventional resources (capital and labour), not considered in the currently available multidimensional measures of well-being. We use a Data Envelopment Analysis type of model, integrated with Principal Component Analysis, to evaluate OECD countries’ relative efficiency in providing well-being. Our results show that the costs of producing well-being have a large and significant impact on the resulting index of well-being. Therefore, high efficiency in providing well-being and high income cannot be considered a proxy to each other. In addition, it is shown that countries react differently to the different costs of well-being: poor countries are, on average, more efficient in terms of conventional inputs (labour and capital), while rich countries have higher efficiency indices relative to social and environmental costs. The close to zero correlation between GDP and well-being indices for rich countries provides new support to the “Easterlin paradox”.

Conventional measures of competitiveness in terms of final prices shed little light on how those measures are affected by public expenditure. By taking productivity as a common factor to any index of competitiveness we propose to assess the Italian Public Sector contribution by its productivity in providing public services. Integration of Data Envelopment Analysis with Principal Component Analysis provides a consistent methodology to face the problem of high dimensions, which is a characterising feature of public services provision. Results show a large geographical variability in Public Sector productivity and a significant differentiation in terms of both layers of government and types of services. Thus offering evidence to identify areas, services and tiers of government lacking efficiency and constituting a potential obstacle for growth. A East–West divide emerges on top of the Country’s traditional, though less marked than commonly thought, North–South divide.

Nel valutare l’eco-efficienza con Data Envelopment Analisys è necessario scegliere uno tra gli approccio presenti in letteratura. Ognuno di essi si differenzia dagli altri sia per il modo di trattare il bad output sia per i risultati ottenuti. La valutazione risulta pertanto condizionata direttamente dalla metodologia che si decide di utilizzare e si mostra perciò quanto più importante conoscerne le peculiarità. Questo lavoro è volto ad analizzare le caratteristiche tecniche degli approcci più rilevanti ed evidenziandone le differenze, sia nella modalità in cui viene trattato il bad output, sia nei risultati. Nella parte finale tali diversità sono messe in evidenza riportando un caso di studio sui dati di 22 paesi dell’OECD.