Educational efficiency in a dea-bootstrap approach

We use the PISA 2006 results to analyse the students' proficiencies in 24 European Countries with regard to two indexes that represent the educational resources available at home and the family background of students. Many factors affect the proficiencies and therefore, using a DEA-bootstrap method, we intend to measure the efficiency of the European educational systems as capability to ensure high students' competencies despite adverse conditions about the educational resources available in students' home and the family background.PISA, educational achievement, efficiency analysis.

Tourism attractiveness in Italy: Regional empirical evidence using a pairwise comparisons modelling approach

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Chapter Big data analysis and labour market: an analysis of Italian online job vacancies data

Economists and social scientists are increasingly making use of web data to address socio-economic issues and to integrate existing sources of information. The data produced by online platforms and websites could produce a lot of useful and multidimensional information with a variety of potential applications in socio-economic analysis. In this respect, with the internet growth and knowledge, many aspects of job search have been transformed due to the availability of online tools for job searching, candidate searching and job matching. In European countries there is growing interest in designing and implementing real labour market information system applications for internet labour market data in order to support policy design and evaluation through evidence-based decision-making. The analysis of labour market web data could provide useful information for policy-makers to define labour market strategies as big data, jointly with official statistics, support policy makers in a pressing policy question namely “How to tackle the mismatch between jobs and skills?”. In this regard, the topic of skills gap, how to measure it and how to bridge it with education and continuous training have been tackled by using the big data collection, such as the Cedefop (European Center for the Development of Vocational Training) initiative and the Wollybi Project (made by Burning Glass). In this framework, this contribution focuses on the issues arising from the use (and the usefulness) of on-line job vacancy data to analyse the Italian labour market by using the Wollybi data available for the years 2019 and 2020. Furthermore, the availability of data for the year 2020, will allow us to evaluate whether there has been an impact of COVID19 in terms of needed skills and required occupations in the online job vacancies

Chapter A statistical information system in support of job policies orientation

A significant problem for labour market policies relies on the individuation of the most advisable skills to have and to enhance through focused training offers. Vocational training systems and institutions are called to answer the question posed by every person looking for a new job or professional opportunities: which are the skills-to-have to enhance the professional profile? Many efforts have been made to answer this question, mainly designing predictive models; however, these models are often limited to specific economic sectors and usually don’t adopt a country-specific perspective. This paper proposes a recommendation system oriented to specific users: once that the user has described his/her skills profile, the system suggests the skills that, once got, will fit with the most frequent job vacancies. In this proposal perspective, the skills are proposed regardless of the economic sector, and they are compatible with the characteristics of the specific country labour market. In this contribution, we will focus on the Italian market; the recommendation system is based on the job ads published by Italian companies on various websites for both 2019 and 2020 after the skills required for each job offer have been mapped to one of the skills presented in the classification of European Skills/ competence, qualifications ad Occupations (ESCO)

A discrete- time hazard model for loans: some evidence from Italian Banking System

Problem statement: The probability of default, PD, is a crucial problem for banks. In the last years international accords (Basel, Basel 2 and Basel 3) have incentived banks to adopt objectives systems to evaluating and monitoring risk of default in order to predict PD for new loans based on borrower's characteristics. The aim of this study is to introduce a discrete survival model to study the risk of default and to propose the empirical evidence by the Italian banking system. Approach: Survival analysis is used if we are interested in whether and when an event occurs. In this context the event occurrence represents a borrower's transition from one state, loan in bonis that is not in default, to another state, the default. In this study through a survival model (in particular a discrete-time hazard model) it is possible verify when the probability of default is the highest considering, for each group of loans, a set of explanatory variables as risk factors of PD. Results: The empirical application obtained through a discrete time hazard model have provided clear evidence that time when the default occurs is an important element to predict the probability of default in time. Regarding Italian data the hazard model shows that explanatory variables (i.e., territorial area, productive economic sector, size of loan and generation of belonging) have effects both on if and on when loan bankrupts. Conclusion: The hazard model estimated for a population of loans involve different probability of default considering conjointly the explanatory variables and the time when the default occurs. Considering jointly the time and the risk factors a probability of default has been modelled for two main groups of loans: "Good borrowers" for which the risk of default is the lowest and "bad borrowers" for which this risk is the highest

Measuring territory student-attractiveness in Italy. Longitudinal evidence

The aim of this paper is to investigate the factors affecting university student mobility in Italy in a longitudinal perspective, by considering the flows across competing territorial areas supplying tertiary education programs. The Bradley-Terry modelling approach based on pair comparisons has been adopted to define the attractiveness of competing territories and a range of determinants related to the socio-economic characteristics of the areas as well as universities’ resources. Data released by the Italian Ministry of Education (MiUR) are analysed for the academic years 2010/2011-2014/2015. The modelling approach considers score values for each territory and year, allowing to evaluate whether attractiveness improves or deteriorates over time, and to rank areas according to their attractiveness. To this end, an index based on ranking changes, appropriately weighed with the differences in score values, is proposed. Empirical findings highlight that attractiveness depends not only on the educational programs, but also on territories’ socio-economic factors, reflecting the well-known North-South divide that persists in time