In the current socio-economic scenario, affected by constant changes inthe labor market, employability found greater echo. Universities frequently adopt strategies aimed at improving the employability and usefulness of theareas of competence, motivation and interests of young graduates and workers. In this study a preliminary research was conducted on a sample of 84 university students who attended a consulting service at the University of Salento, the Career Service Office. The average age of the sample was 26.74 years (DS = 4.95), 63% women, 71.4% unemployed. The tool used was a questionnaire-interview. The aim of this research was to assess the role of employability and its influence on personal variables and active work behaviours, as job searching activities. Analyses were conducted through PLS-PM technique, a non-parametrical SEM modeling, and demonstrated that employability affects job search and personal efficacy, while personal efficacy had a non signicant relation with job search behaviours.

The Italian public universities are subsidised within a competitive framework that awards excellence, efficiency, and the capacity of universities to attract students from Italian regions other than its own. However, repeated cuts to public spending has increased the well-known Italian North-South divide. The most important student mobility (SM) flow is from the Southern to the Central-Northern regions--a phenomenon that has been magnified by an increasing number of outgoing students from Sicily over the last decade. In this paper, we rely upon micro-data of university enrolment and students' personal records for three cohorts of freshmen, in order to investigate preferential patterns of SM from Sicily toward universities in other regions. Indeed, our main goal is to eventually reveal the existence of chain migrations, through which students from a particular geographical area move towards a particular destination. We consider 38 clusters aggregating the 390 Sicilian municipalities, based on geographical proximity and socio-economic criteria. The data from each cohort is represented as a tripartite network with three sets of nodes, namely, clusters of Sicilian municipalities, students, and universities. The tripartite network is projected in a bipartite weighted network of clusters and universities, which is, then, filtered, in order to obtain a statistically validated bipartite network (SBVN). The SBVNs of the three cohorts suggest the existence and evolution of chain migration patterns over time, which are also gender specific.

In the last years, there is a widespread consensus that Science, Technology,Engineering and Math (STEM) education is crucial for long-term productivityand growth of a country. In this light, the paper aims to explore thephenomenon of mobility of Italian STEM students, namely the ows of graduatedstudents from the Southern regions who enrol in the universities ofNorthern/Central area and choose a STEM degree course. We exploit themicrodata of Italian Ministry of Education, University and Research (MIUR)referring to 8 cohorts (from a.y. 2008/2009 to a.y. 2015/16) of enrolled studentsin STEM elds residing in the south of Italy. The main results of ouranalysis show that the ow of STEM movers increases from year to year.This ow particularly aects the top performing students and, therefore, itis a threat to the socio-economic growth prospects of the Southern regionswhose gap with respect to the Central and Northern regions is expected togrow year by year.

Through the decades, the Higher Education System globally experimented a huge increase in the average marks that each student receives. Among several hypothesis, in this article the idea that grading is one of the tool that every department can use in order to attract a larger amount of students will be stressed. Regarding the Italian case, the speed in obtaining a degree is among the criteria considered by the Ministry of Education in order to evaluate universities, financing them proportionally. As a shortcoming, this can boost an artificial increase in marks. So, the number of students becomes important for those universities with the worst ranking positions, in order to finance themselves through fees. On the other side, it is reasonable to expect that a student emigrates toward places which offer higher chances of receiving a job. In other words, mobility might be driven by the search for better working conditions, and not by the 'ease' of the faculty. Testing this hypothesis, a Multinomial Conditional Logit Model will be implemented in order to measure the probability of choosing a certain destination depending on the harshness and reputation of a University and on the rates of unemployment at a regional level.

Migration is a permanent phenomenon rooted in history and recently involves high-skilled workers (HSWs). Among them, a crucial role is played by PhDs. HSWs face the risk to not find a job matching their skills and they can opt to accept to be overeducated for the job or move to another country or region. Mobility of HSW can be interpreted as a positive issue that can help to match jobs and skills. However, the emergence of a clear path between areas of countries or regions (e.g. from south to north Italy) highlights the risks of a drain of human capital from areas with low development to more developed ones. In this paper, we focus on a category of HSWs who have been almost neglected by the literature, the PhDs. The aim of the paper is to shed light on the mobility pattern of Italian PhDs, paying attention to PhDs from Southern Italy. This aim will be pursued by using microdata from the 2014 ISTAT Survey on the professional conditions of Italian PhDs at 4 and 6 years after the end of their studies.This work highlights that Southern PhDs have higher probability to move to other area of the countries, while Northern PhDs seem to prefer to move abroad thus confirming previous studies which identified a similar pattern for graduates. While the Northern part of the country compensate the drain of human capital with the mobility from the other part of Italy, the Southern face a relevant drain of ‘talents’.

In this article we consider the estimation of the stress-strength reliability parameter, R = P(X < Y ) when the stress (X) and the strength (Y ) are dependent random variables distributed as bivariate Lomax model. The maximum likelihood, moment and Bayes estimators are derived. We obtained Bayes estimators using symmetric and asymmetric loss functions via squared error loss and Linex loss functions respectively. Since there are no closed forms for the Bayes estimators, we used an approximation based on Lindley's method to obtain Bayes estimators under these loss functions. An extensive computer simulation is used to compare the performance of the proposed estimators using three criteria, namely, relative bias, mean squared error and Pitman nearness (PN) probability. Real data application is provided to illustrate the performance of our proposed estimators.

Over the past 50 years, extensive research have been carried out to understand how clustering work in classifying data into meaningful groups. Various clustering algorithms and cluster validity indexes have been proposedand improvised to obtain the best clustering result. However, there is noclustering method that is able to give consistent results on similar structureof a dataset. An alternative mechanism to control the variation of resultsand improved the quality of traditional clustering is through consensus clustering. In this paper, we generate multiple partitions of consensus clusteringthrough a resampling method by employing q-fold cross-validation approach.q-fold cross-validation approach is able to speed-up the consensus partitionsprocedure with qth iterations. To encounter with different number of cluster labels occur in the partitions, we employed voting-based method in the second stage of consensus clustering to obtain optimal consensus partition.The performance of optimal consensus partitions is evaluated from Silhouetteplot

Forecasting is an important exercise in Time series analysis. For a statio-nary time series, there are theoretically strong forecasting methods which canprovide most accurate forecasts for the future (Karlin and Taylor (1975)).For most non stationary time series Box Jenkins methodology is a usefulforecasting technique. Essentially, the Box Jenkins methodology assumesthat any non stationarity time series can be conveniently modeled as anAutoregressive Intregrated Moving Averages (ARIMA) model with sucientnumber of unit roots in the linear stochastic dierence equation generatingthe time series. The non stationarity in such time series is then removed bysuccessively dierencing of the series until one obtains a stationary series,for which optimal forecasts can be computed. The forecasts for the originalseries are then computed by `inverting' the dierence operators that wereused ( Makridakis et al. (1998)) on the forecasts computed for the statio-nary series. The main objective of this study is to demonstrate that the BoxJenkins methodology is not useful, especially in large time series, when thenon stationarity in the time series is due to `explosive' roots. An alternativemethod is proposed in such a situation and its performance is assessed bothon a simulated as well as on a real life data.

Job Satisfaction is a set of favorable or unfavorable feelings and emotions linked to how employees view their work environment, and supervisors need to be attentive to employee satisfaction levels. If employees are not satisfied with their jobs, the overall progress of the entire system is affected. This paper reports on a teacher job satisfaction study that examined a sample of 362 teachers. The Common Assessment Framework & Education questionnaire was used to collect data. The aim of the study is to identify, by a Structural equation model, the factors that most influence Job Satisfaction taking into account age, total years of service and gender. The results underlines a significant difference between male/female in Job Satisfaction model.

Length of stay in hospitals are mostly characterized as asymmetric, right skewed and leptokurtic in nature. Earlier studies have considered parametric distributions like gamma, Pareto, lognormal for studying length of stay of patients in hospitals. However, in this study we have proposed transformed distributions to be the best choice for characterizing the length of stay. For this study, we have considered paediatric asthma dataset and identified that transformed Weibull-Pareto as the best fit. For a comparative purpose we have also provided the results of gamma, lognormal, and Pareto distribution. Maximum likelihood approach is considered to estimate the unknown parameters of the Transformed distribution followed by goodness of fit tests to examine the suitability of the fitted distributions. The results provide a direction for modelling the length of stay in hospitals due to different medical problems which require hospitalization.