1,721,007 research outputs found
Sulle relazioni tra dimensione, ambiente e performance finanziaria dei comuni italiani
No full textNel presente lavoro la dimensione dei comuni italiani non è posta in relazione all’efficienza bensì alla performance finanziaria. L'analisi proposta si svolge in due stadi. Nel primo, si utilizza un approccio di valutazione multi-criteriale (Ishizaka e Nemery, 2013; Greco et al., 2016) combinando le tecniche introdotte da Cohen et al. (2012) e Galariotis et al. (2016), al fine di indagare le performance finanziarie dei comuni italiani utilizzando gli indicatori finanziari ISTAT (2019). In particolare, si è seguito lo stesso approccio già adottato in Paradiso et al. (2019), al quale si rimanda per una diffusa esposizione, ma modificato per consentire l’analisi specifica della dimensione demografica dei comuni. Nel secondo stadio (esplicativo), gli indici ottenuti dal primo stadio sono stati analizzati per ottenere informazioni sulle relazioni strutturali tra le performance finanziarie e diversi fattori ambientali (interni ed esterni) che caratterizzano i comuni italiani. In particolare, sono stati considerati sia specifici fattori interni riconducibili al contesto politico-istituzionale, sia fattori esterni riconducibili al contesto socio-economico, incidenti sulle performance finanziarie dei comuni
Gender equality disclosure nell'economia d'azienda
No full textGender equality is one of the 17 Sustainable Development Goals of the United Nations 2030 Agenda. It has become a topical issue for business entities and institutions. In parallel, gender governance and social sustainability reporting and disclosure has received increasing attention in specialized literature. This article contributes to this body of literature by proposing the results of the case study of public mega-universities in Italy. The performance plan documents of Italian mega-universities were analyzed using text mining techniques. These techniques can harness existing data to uncover latent information about gender equality, which may be present in the documents. The results show that the number of words related to gender equality in non-financial reporting and disclosure has consistently grown over time: words referring to gender equality in 2020-22 are almost double that of 2019-21, which, in turn, are much higher than in 2018-20. This study highlights that machine learning and Big Data research offer a great potential for universities to leverage the vast information generated from accountability mechanisms to gain new insights to improve decision-making on gender equality and social sustainability disclosure
Miti e realtà della condizione finanziaria dei comuni italiani: un approccio multi-criteriale
No full textThis paper proposed a Multi-Criteria Benchmarking (Ishizaka, Nemery, 2013; Greco et al. 2016) combing the techniques introduced by Cohen et al. (2012) and Galariotis et al. (2016) to study the financial performances of the Italian municipalities using the ISTAT (2019) financial indicators. This Multi-Criteria Benchmarking is used to measure financial performances in a peer assessment context over time. This approach allows to summarise a multitude of indicators in order to identify the strengths and financial problems of municipalities
La parità di genere: uno studio sui Piani Integrati delle Performance delle Università Italiane
No full textHow does Economic Social and Cultural Status affect the efficiency of educational attainments? A comparative analysis on PISA results
No full textMachine learning prediction of academic collaboration networks
No full textWe investigate the different roles played by nodes’ network and non-network attributes in explaining the formation of European university collaborations from 2011 to 2016, in three European Research Council (ERC) domains: Social Sciences and Humanities (SSH), Physical and Engineering Sciences (PE), Life Sciences (LS), as well as multidisciplinary collaborations. On link formation in collaboration networks, existing research has not yet compared and simultaneously examined both network and non-network attributes. Using four machine learning predictive algorithms (LASSO, Neural Network, Gradient Boosting, and Random Forest) our results show that, over various model specifications: (i) best model link formation accuracy is larger than 80%, (ii) among the non-network attributes, public funding plays an important role in PE and LS, (iii) network attributes count more than non-network attributes for the formation, sensibly increasing accuracy, (iv) feature-importance scores show a different ordering in the four domains, thus signalling different modes of knowledge production and transmission taking place within these different scientific communities
Measuring health inequality in US: a composite index approach
No full textIn 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
Predicting bankruptcy of local government: A machine learning approach
No full textIn 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
Income-related unmet needs in the European countries
No full textThis paper proposes an assessment of the association between income and unmet health needs in 29 European countries included in the European Health Interview Survey (EHIS) in 2015. Income-related inequalities for four categories of unmet needs are estimated by the Erreygers Index (EI). Unmet needs are organised into two categories: on the one side, unmet needs directly associated with households’ budget constraints; on the other side, unmet needs not directly associated with budget constraints, as waiting lists and transportation problems. Results show that all categories of unmet needs fall on lower-income people for most European systems. Furthermore, when analysing the determinants of unmet needs, those directly associated with budget constraints are led by the economic drivers, while income-related unmet needs due to long waiting lists and distance or transportation problems are more related to institutional factors such as the low quality of government
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