An Indicator for the Measurement of Political Participation: The Case of Italy

In the modern globalized world, political participation is of paramount importance for balanced socio-economic growth and for human development. The Indagine Multiscopo sulle famiglie, a survey by Italian public institutions (ISTAT and CNEL), provides a wide range of data to evaluate specific aspects of Italian life. This work uses a set of data from the Indagine to analyse political participation in Italy at a regional level, by means of a composite indicator using parametric (Pena’s distance) and non-parametric (Mazziotta–Pareto Index) techniques. We have obtained a ranking that shows the level of political participation in different territorial contexts. The aim is to analyse the relation between Italian regions and the political behaviour of their communities: political discussions, participation in political meetings and marches, voluntary activity or donations to political parties and so on. The ranking is not correlated to voter turnout. We can assume that the politically engaged minority are unable to convince and involve the rest. At the same time, these small groups do not alter the general mistrust in parties and, generally speaking, in politics, which is spreading across Italy and also Europe.     

Interval-censored data with misclassification: a Bayesian approach

Survival data involving silent events are often subject to interval censoring (the event is known to occur within a time interval) and classification errors if a test with no perfect sensitivity and specificity is applied. Considering the nature of this data plays an important role in estimating the time distribution until the occurrence of the event. In this context, we incorporate validation subsets into the parametric proportional hazard model, and show that this additional data, combined with Bayesian inference, compensate the lack of knowledge about test sensitivity and specificity improving the parameter estimates. The proposed model is evaluated through simulation studies, and Bayesian analysis is conducted within a Gibbs sampling procedure. The posterior estimates obtained under validation subset models present lower bias and standard deviation compared to the scenario with no validation subset or the model that assumes perfect sensitivity and specificity. Finally, we illustrate the usefulness of the new methodology with an analysis of real data about HIV acquisition in female sex workers that have been discussed in the literature.     

Social Presence, Embarrassment, and Nonverbal Behavior

Nonverbal behaviors in response to viewing slides depicting nude males, nude females, erotic couples and neutral pictures, either alone or in the presence of two unfamiliar individuals, were studied in 22 female and 16 male university students. Participants were unaware of being videorecorded. Results revealed discrepancies between self-reported embarrassment and nonverbal behaviors supposedly expressive of embarrassment. Although self-reported embarrassment was higher when certain types of slides were viewed in the presence of others than when they were viewed alone, we observed significantly fewer lip movements, gaze shifts, face touches, downward gazes, and downward head movements in the presence of unfamiliar individuals than in the alone condition. We also compared behaviors during slide exposure and during the inter-slide intervals. For 9 out of 11 coded behaviors, frequencies were significantly higher during inter-slide intervals than during slide presentation. We argue that this is probably due to the fact that visual attention to the slides inhibited nonverbal behaviors. The results cast doubt on the possibility of inferring the internal state of an emotion such as embarrassment by analyzing nonverbal behaviors without taking account of the social setting in which such observations are made.     

The New International Migrations and the Changes in the Labour Market

Abstract. The article intends to point out some of the main changes that are taking place in the structure of the labour market in Europe and the role played by the recent waves of immigration from Third World countries. The main features of these new migratory flows are that they involve a much larger number of states, both as sending and as receiving countries. Secondly the majority of the migrants workers tend to hold secondary labour market jobs or to work in the informal sector. These two aspects make today's international migrations very different from the intraeuropean migratory waves that concerned a much lower number of countries and were directed towards industrial areas.     

Big Data and Social Indicators: Actual Trends and New Perspectives

Big Data are a top subject in international research articles and a vast debate is taking place on their actual capability of being used to complement or even substitute official statistics surveys and social indicators in particular. In this paper we analyse the metadata of the Scopus database of academic articles on Big Data and we show that most of the existing and intensively growing literature is focused on software and computational issues whilst articles that are specifically focused on statistical issues and on the procedures to build social indicators from Big Data are a much smaller share of this vast production. Nevertheless the works that focus on these topics show promising results because in developed countries Big Data seem to be a good information base to create reliable proxies of social indicators, whereas in developing countries their use (for instance using satellite images) may be a viable alternative to traditional surveys. However, Big Data based social indicators deeply suffer of a number of open issues that affect their actual use: they do not correspond to any sampling scheme and they are often representative of particular segments of the population; they generally are private process-produced data whose access by national statistical offices is rarely possible although the intrinsic value of the information contained in Big Data has a social importance that should be shared with the whole community; Big Data lack the socio-economic background on which social indicators have been founded and their help to policy makers in their decision process is a fully open point. Therefore Big Data may be a big opportunity for the definition of traditional or new social indicators but their statistical reliability should be further investigated and their availability and use should be internationally coordinated.     

Degradation data analysis for samples under unequal operating conditions: a case study on train wheels

Traditionally, reliability assessment of devices has been based on life tests (LTs) or accelerated life tests (ALTs). However, these approaches are not practical for high-reliability devices which are not likely to fail in experiments of reasonable length. For these devices, LTs or ALTs will end up with a high censoring rate compromising the traditional estimation methods. An alternative approach is to monitor the devices for a period of time and assess their reliability from the changes in performance (degradation) observed during the experiment. In this paper, we present a model to evaluate the problem of train wheel degradation, which is related to the failure modes of train derailments. We first identify the most significant working conditions affecting the wheel wear using a nonlinear mixed-effects (NLME) model where the log-rate of wear is a linear function of some working conditions such as side, truck and axle positions. Next, we estimate the failure time distribution by working condition analytically. Point and interval estimates of reliability figures by working condition are also obtained. We compare the results of the analysis via an NLME to the ones obtained by an approximate degradation analysis.     

Practical modeling strategies for unbalanced longitudinal data analysis

In practice, data are often measured repeatedly on the same individual at several points in time. Main interest often relies in characterizing the way the response changes in time, and the predictors of that change. Marginal, mixed and transition are frequently considered to be the main models for continuous longitudinal data analysis. These approaches are proposed primarily for balanced longitudinal design. However, in clinic studies, data are usually not balanced and some restrictions are necessary in order to use these models. This paper was motivated by a data set related to longitudinal height measurements in children of HIV-infected mothers that was recorded at the university hospital of the Federal University in Minas Gerais, Brazil. This data set is severely unbalanced. The goal of this paper is to assess the application of continuous longitudinal models for the analysis of unbalanced data set.     

Preliminary Data Errors and Their Impact on the Forecast Error of Simultaneous-Equations Models

Econometric models, especially when designed for forecasting purposes, tend to use updated economic series, the last figures(s) of which embed first-published or preliminary data errors. This article identifies and assesses the contribution of errors in preliminary data to the forecast error and to the forecast error variance of linear dynamic simultaneous equation models. The effect of preliminary data errors is shown to be pervasive, although not necessarily weighty. The suggested decomposition of the forecast error is applied to a small macroeconometric model of the Italian economy.     

On the superposition of overlapping Poisson processes and nonparametric estimation of their intensity function

It is shown that, when measuring time in the Total Time on Test scale, the superposition of overlapping realizations of a nonhomogeneous Poisson process is also a Poisson process and is sufficient for inferential purposes. Hence, many nonparametric procedures which are available when only one realization is observed can be easily extended for the overlapping realizations setup. These include, for instance, the constrained maximum likelihood estimator of a monotonic intensity and bootstrap confidence bands based on Kernel estimates of the intensity. The kernel estimate proposed here performs the smoothing in the Total Time on Test scale and it is shown to behave approximately as a usual kernel estimate but with a variable bandwidth which is inversely proportional to the number of realizations at-risk. Likewise, bootstrap samples can be obtained from the single realization of the superimposed process. The methods are illustrated using a real data set consisting of the failure histories of 40 electrical power transformers.