The 17 Sustainable Development Goals (SDGs) adopted by the United Nations are at the center of the global political agenda to eradicate extreme poverty, achieve universal education, promote gender equality and ensure environmental sustainability between others. These goals are organised in 169 indicators, which give an accurate perspective on the main dimensions related with country sustainable development. To gain insight into the relative position of involved countries, it is necessary to develop a composite index that summarises the global progress in the achievement of these goals, but considering possible conflicts and trade-offs between individual SDGs. The objective of this paper is to introduce a Goal Programming model to calculate a composite SDG index, capable of overcoming some of the limitations of celebrated approaches such as arithmetic and geometric averages. The proposed model balances between two extreme solutions: one which calculates a consensus index that reflects the majority trend of the SDGs, and another one which biases the estimated index towards those SDGs that show the most discrepancy with the rest. The model is applied on the EU-28 countries, and shows that the best performing countries regarding the sustainable development are Austria and Luxembourg, while Greece and Romania remain as the worst performers.

The environmental, social, and governance (ESG) rating of firms is a useful tool for stakeholders and investment decision-makers. This paper develops a rough set model to relate ESG scores to popular corporate financial performance measures. This methodology permits handling with information in an uncertain, ambiguous, and imperfect context. A large database was gathered, including ESG scores, as well as industry sector and financial variables for publicly traded European companies during the period 2013–2018. We carried out 500 simulations of the rough set model for different values in the discretization parameter and different grouping scenarios of firms regarding ESG scores. The results suggest that the variables considered are useful in the prediction of ESG rank when firms are clustered in three or four equally balanced groups. However, the prediction power vanishes when a larger number of groups is computed. This would suggest that industry sector and financial variables serve to find big differences across firms regarding ESG, but the significance of the model drops when small differences in ESG performance are scrutinized.

This paper presents the results of an efficiency study of Colombian public universities in 2012, conducted using the methodology of Data Envelopment Analysis (DEA) and the models CCR, BCC and SBM under output orientation. The main objective is to determine technical, pure technical, scale and mix efficiencies using data acquired from the Ministry of National Education. An analysis of the results shows the extent to which outputs of inefficient Higher Education Institutions (HEIs) could be improved and the possible cause of this inefficiency. The universities were also ranked using a Pareto efficient cross-efficiency model and a study was made of changes to overall productivity between 2011 and 2012. The results showed Tolima, Caldas and UNAD to be the best-performing universities, with Universidad del Pacífico as the worst performer. Malmquist index was applied to analyze the change in productivity from 2011 to 2012. The Universidad de La Guajira showed great improvement in technical efficiency between 2011 and 2012.

Mass appraisal, or the automatic valuation of a large number of real estate assets, has attracted the attention of many researchers, who have mainly approached this issue employing traditional econometric models such as Ordinary Least Squares (OLS). However, this method does not consider the hierarchical structure of the data and therefore assumes the unrealistic hypothesis of the independence of the individuals in the sample. This paper proposes the use of the Hierarchical Linear Model (HLM) to overcome this limitation. The HLM also gives valuable information on the percentage of the variance error caused by each level in the hierarchical model. In this study HLM was applied to a large dataset of 2,149 apartments, which included 17 variables belonging to two hierarchical levels: apartment and neighbourhood. The model obtained high goodness of fit and all the estimated variances of the parameters in HLM were lower than those calculated by OLS. It can be concluded as well that no further neighbourhood variables need be added to the model to improve the goodness of fit, since almost all the residual variance can be attributed to the first hierarchical level of the model, the apartment level.