Stergios Athanasoglou’s research while affiliated with Università degli Studi di Milano-Bicocca and other places

Suppose a group of agents are engaged in economic activity that produces emissions of pollutants. Emissions yield private benefits and impose negative externalities. The status-quo is assumed to be inefficient so that the agents are willing to negotiate an improved allocation of emissions. In this context, we are interested in allocations satisfying Pareto efficiency, individual rationality, and a principle of fairness that generalizes concepts that are encountered in practice. While the existence of such allocations is not guaranteed, we derive a necessary and sufficient condition for it. This condition is succinct and its verification is computationally tractable. Uniqueness will generally not hold, and so we describe a procedure that generates allocations with the desired properties and discuss ways of selecting from them. We apply our model to a setting of climate-change policy based on Nordhaus (2015). Our results show that it is possible to achieve a large reduction in global CO2 emissions that enhances every region’s welfare, while at the same time achieving Pareto efficiency and respecting norms of fairness.

We propose a novel framework for the economic assessment of environmental policy. Our main point of departure from existing work is the adoption of a satisficing , as opposed to optimizing, modeling approach. Along these lines, we place primary emphasis on the extent to which different policies meet a set of goals at a specific future date instead of their performance vis-a-vis some intertemporal objective function. Consistent to the nature of environmental policymaking, our model takes explicit account of model uncertainty. To this end, the decision criterion we propose is an analog of the well-known success-probability criterion adapted to settings characterized by model uncertainty. We apply our criterion to the climate-change context and the probability distributions constructed by Drouet et al. (2015) linking carbon budgets to future consumption. Insights from computational geometry facilitate computations considerably and allow for the efficient application of the model in high-dimensional settings.

The Europe 2020 Strategy was launched by the European Commission in 2010 to promote smart, sustainable, and inclusive growth across EU member states. As the strategy draws to a close in 2020 and is superseded by the Sustainable Development Goals and the Green Deal, this work aims to assess the progress made over the last decade, and to carry forward lessons for future endeavours. A composite indicator approach is adopted, which aggregates the distance of each country or region to politically-agreed targets. This allows a high-level summary of progress, but also examines detailed trends at national and regional levels, as well as by degree of urbanisation and by development. The results show that although the EU has moved forward as whole, some regions have lagged behind or even moved backwards, and within some countries their regions moving further away from one another. Progress has been particularly strong in education, but more work is needed in the environmental dimensions.

This paper is concerned with preference-aggregation rules satisfying desirable efficiency and solidarity requirements. We formulate weaker versions of existing solidarity axioms and show how they imply, in conjunction with strategy-proofness, the existence of reference outcomes holding privileged status. We propose a new class of rules, fixed-order status-quo rules, that can be productively contrasted to their closest counterparts in the literature, status-quo rules based on the least upper bound of a lattice. Fixed-order status-quo rules satisfy stronger efficiency requirements than lattice status-quo rules but have weaker, though still significant, solidarity properties. A subfamily based on lexicographic orders is analyzed further. Fixed-order status-quo rules are characterized by strategy-proofness, strong efficiency, and a third axiom, unanimity-basedness.

The present report has been commissioned by the Consumers Directorate of DG Justice and Consumers from the Joint Research Centre’s Econometrics and Applied Statistics Unit as part of a broader collaborative effort that aims to extend and revise the statistical indicators, methodology, and presentation of information that underpin both the ‘Consumer Markets’ and the ‘Consumer Conditions’ Scoreboards, within the more general framework of internal market integration. The present document has been conceived to address the refinement and further development of the Consumer Markets Scoreboard (CMS). As a result, the main objectives of this report are twofold: 1. To provide a comprehensive review of the theoretical framework and methodology behind the CMS, and to assess the statistical soundness and robustness of the existing Market Performance Indicator (MPI). 2. To review the empirical tools that can be used to analyse micro-level data on market performance, as perceived and reported by the experienced consumers responding to the Market Monitoring Survey (MMS). In this light, the report is structured in two parts. The first part commences with a survey of the literature about consumer satisfaction and market performance studies (chapter 1.1), which is divided in three main sections. The first section focuses on the different theoretical approaches and behavioural models that have been proposed to analyse the consumer decision-making process and subsequent satisfaction outcomes. The second section of the literature review focuses on the issue of how to empirically assess consumer satisfaction. Finally, the third section addresses the links between consumer satisfaction and profitability and economic growth. After the literature review, chapter 1.2 discusses the methodological framework for the assessment of consumer market satisfaction. The theoretical foundations of the assessment and the prospective list of indicators have been revised in line with the findings from the literature review. Additionally, a new component based on the individuals’ subjective perception of the perceived value of the goods or services available in the market has been proposed to further refine the conceptual framework underlying the CMS. In chapter 1.3, the statistical soundness of the 2014 Market Performance Indicator (MPI) is reviewed and assessed. For that purpose, both descriptive statistics and correlation structure analyses of the components aggregated within the indicator are performed. The results confirm that the statistical structure of the MPI is fairly balanced and that, as expected, the countries’ scores obtained in terms of aggregate markets (goods and services) are correlated. In the section devoted to the uncertainty and sensitivity analysis of the MPI (chapter 1.4), the starting point for the analysis has been to check to what extent the scores and rankings resulting from the indicator are sensitive to subjective modelling choices, in particular to the choice of weights and the aggregation scheme. As explained therein, the results presented in the current report—based on aggregate data from the CMS 2014—tend to confirm the robustness of the scores and rankings of the MPI across countries and across markets. The first part of the report concludes with a section (chapter 1.5) which aims to set in a wider context the results from the previous sections and to discuss possible avenues for further analysis. The general objective of the second part of the report is to explore the heterogeneity in consumer experiences across consumer markets. The first section of the second part (chapter 2.1) presents an overview of studies exploring the sources of heterogeneity in consumer motivation, preferences and behaviour. From chapter 2.2 to chapter 2.4, individual level data from the MMS 2015 are used for the empirical investigation of consumer markets performance as subjectively perceived by consumers themselves. Econometric tools such as multiple linear regression models, logistic regression and multinomial logistic regression have been used to perform the analyses. Explanatory variables in those models are in line with the literature, and include socio-demographic characteristics of survey respondents such as age, gender, education, occupation, internet usage, mother tongue and income. Additional explanatory variables accounting for market specific conditions and cross-country or cross- cultural differences have also been included in the models. The results obtained therein show that socio-demographic characteristics shape consumer assessment of market performance. Regional and cross-country differences have also a significant impact on the results. Furthermore, there is significant evidence that consumers are influenced by the specific conditions encountered in the different markets and assess their consumption experiences within them accordingly. When looking at socio-demographic trends, results of the multivariate analyses performed on the overall MPI scores indicate that women are statistically significantly more positive than men. The middle age group (35-54 year-olds) is negatively associated with higher MPI scores. People with higher levels of education tend to give significantly more negative scores. Ratings are significantly higher when respondents belong to the categories of housepersons and pensioners, and conversely seem to be the lowest when respondents are self-employed. Both those who never use the internet and those who use it very frequently (daily) assign significantly more positive ratings. Those whose mother tongue is not an official language tend to be more negative in their overall market assessments. Furthermore, negative associations have also been found to be very intense for those consumers in a very difficult financial situation. With regard to regional differences, overall ratings are significantly lower in the Eastern and Southern regions. On the other hand, ratings appear to be significantly higher when considering Eurozone countries and New Member States. When looking at the different markets and market groupings, goods markets perform significantly better than services when assessed through the overall MPI scores. In general, the assessment of market performance is found to be significantly poorer for those services markets related to clusters such as banking, utilities and telecoms. However, when analysing the results obtained from the MMS 2015 data, we must also highlight that the situation may differ heavily across the individual components of the MPI (comparability, trust, problems and detriment, expectations and choices). Additionally, outside the realm of the MPI, complaints and switching behaviour are two additional dimensions of market performance included in the MMS 2015 and assessed by survey respondents. Heterogeneity in consumer assessment has also been found in the empirical analyses undertaken on these dimensions. The second part of the report concludes with a section devoted to summarise the main findings of the empirical analyses. Finally, a brief summary of overall results and conclusions is presented at the end of the report.

The latest edition of the Environmental Performance Index (EPI) was presented and discussed on January 2014 during the World Economic Forum (WEF) Annual Meeting in Davos. The EPI is released biannually since 2006 by Yale and Columbia Universities, in collaboration with the Samuel Foundation and the WEF. The EPI ranks how well countries perform on high-priority environmental issues concerning the policy areas of environmental health and ecosystem vitality. The JRC’s Econometrics and Applied Statistics Unit was invited for a fifth consecutive time to perform a statistical audit the EPI, focusing on two main questions: 1) Is the EPI multi-level structure statistically coherent? 2) What is the impact of modelling assumptions on the 2014 EPI ranking? The 2014 EPI was found to be well-balanced with respect to its two policy objectives , which were also adequately correlated to justify their aggregation into an overall index. Satisfactory correlations were observed between indicators and respective EPI issue areas, implying meaningful indicator contributions to the variance of the aggregate scores. Possible refinements of the index mainly concern the issue areas of Forests, Fisheries and Agriculture, which do not seem to contribute significantly to the EPI ranking. The JRC’s uncertainty analysis investigated the robustness of EPI country ranks to two key choices: policy objective weights and aggregation function. The choice of aggregation function at the policy objectives level was found to be the main driver of the variation in country ranks, accounting for a much greater share of the observed variance in country ranks. This suggested that future deliberations on the index’s methodology should focus primarily on the choice of aggregation function for the two policy objectives and much less on their weights.