Firm-level training in local economic systems: Complementarities in production and firm innovation strategies

The paper presents a conceptual review over the main aspects concerning the role of human capital investments and training activities within production processes, followed by empirical evidence on two local economic systems in Northern Italy, based on recent survey data. Theoretical and empirical considerations are brought together in order to provide new insights on the role of training and the factors associated to training activities at firm level.The potential driving factors of training here analysed compounds structural characteristics, labour demand dynamics, human resource management practices, workforce features, and firm performances. We observe that training activities emerge positively associated with high-performance practices, innovative labour demand features, workforce skill level, firm size, and are affected by labour flexibility in various directions. Empirical evidence confirms most previous results but also adds further important insights. The analysis suggests that a widening gap, between few innovatively evolving and many stagnant firms, could characterise the future dynamics of the Region. The high relevance of structural variables, labour demand factors and HRM/innovation practices shows that regional industrial policies and labour policies should be jointly implemented for increasing potential firm productivity. This is a key concern for the current debate on local systems economic development in the European and Italian environment.     

Point Estimates of Test Sensitivity and Specificity from Sample Means and Variances

In a wide variety of biomedical and clinical research studies, sample statistics from diagnostic marker measurements are presented as a means of distinguishing between two populations, such as with and without disease. Intuitively, a larger difference between the mean values of a marker for the two populations, and a smaller spread of values within each population, should lead to more reliable classification rules based on this marker. We formalize this intuitive notion by deriving practical, new, closed-form expressions for the sensitivity and specificity of three different discriminant tests defined in terms of the sample means and standard deviations of diagnostic marker measurements. The three discriminant tests evaluated are based, respectively, on the Euclidean distance and the Mahalanobis distance between means, and a likelihood ratio analysis. Expressions for the effects of measurement error are also presented. Our final expressions assume that the diagnostic markers follow independent normal distributions for the two populations, although it will be clear that other known distributions may be similarly analyzed. We then discuss applications drawn from the medical literature, although the formalism is clearly not restricted to that application.     

Earthquakes occurrences estimation through a parametric semi-Markov approach

The estimation of earthquakes’ occurrences prediction in seismic areas is a challenging problem in seismology and earthquake engineering. Indeed, the prevention and the quantification of possible damage provoked by destructive earthquakes are directly linked to this kind of prevision. In our paper, we adopt a parametric semi-Markov approach. This model assumes that a sequence of earthquakes is seen as a Markov process and besides it permits to take into consideration the more realistic assumption of events’ dependence in space and time. The elapsed time between two consecutive events is modeled as a general Weibull distribution. We determine then the transition probabilities and the so-called crossing states probabilities. We conclude then with a Monte Carlo simulation and the model is validated through a large database containing real data.     

Agent‐Based Recovery Model for Seismic Resilience Evaluation of Electrified Communities

In this article, an agent‐based framework to quantify the seismic resilience of an electric power supply system (EPSS) and the community it serves is presented. Within the framework, the loss and restoration of the EPSS power generation and delivery capacity and of the power demand from the served community are used to assess the electric power deficit during the damage absorption and recovery processes. Damage to the components of the EPSS and of the community‐built environment is evaluated using the seismic fragility functions. The restoration of the community electric power demand is evaluated using the seismic recovery functions. However, the postearthquake EPSS recovery process is modeled using an agent‐based model with two agents, the EPSS Operator and the Community Administrator. The resilience of the EPSS–community system is quantified using direct, EPSS‐related, societal, and community‐related indicators. Parametric studies are carried out to quantify the influence of different seismic hazard scenarios, agent characteristics, and power dispatch strategies on the EPSS–community seismic resilience. The use of the agent‐based modeling framework enabled a rational formulation of the postearthquake recovery phase and highlighted the interaction between the EPSS and the community in the recovery process not quantified in resilience models developed to date. Furthermore, it shows that the resilience of different community sectors can be enhanced by different power dispatch strategies. The proposed agent‐based EPSS–community system resilience quantification framework can be used to develop better community and infrastructure system risk governance policies.     

No free lunch,buddy: past housing transfers and informal care later in life

Previous empirical literature on the relation between intergenerational transfer of assets and services has mostly focused on contemporary exchanges. By contrast, we provide novel evidence showing that parents who helped their adult children in the past are rewarded by higher chances of receiving informal care later in life. To this end we use Italian data containing precise retrospective information about the help with housing that couples received from their parents when they got married, such as a real estate donation or down payment. Our estimates show that this type of past help is positively associated with the current provision of informal care to the parents. This result is robust to controlling for a large set of individual and family characteristics and is only partially due to increased geographical proximity. We suggest that this finding can be explained by mixed self-interest motives, related to theories based on either bilateral exchange or the presence of a third generation (grandchildren), such as the demonstration effect model or the family constitution model.     

Some maximum-indifference estimators for the slope of a univariate linear model

As known, the least-squares estimator of the slope of a univariate linear model sets to zero the covariance between the regression residuals and the values of the explanatory variable. To prevent the estimation process from being influenced by outliers, which can be theoretically modelled by a heavy-tailed distribution for the error term, one can substitute covariance with some robust measures of association, for example Kendall's tau in the popular Theil–Sen estimator. In a scarcely known Italian paper, Cifarelli [(1978), ‘La Stima del Coefficiente di Regressione Mediante l'Indice di Cograduazione di Gini’, Rivista di matematica per le scienze economiche e sociali, 1, 7–38. A translation into English is available at http://arxiv.org/abs/1411.4809 and will appear in Decisions in Economics and Finance] shows that a gain of efficiency can be obtained by using Gini's cograduation index instead of Kendall's tau. This paper introduces a new estimator, derived from another association measure recently proposed. Such a measure is strongly related to Gini's cograduation index, as they are both built to vanish in the general framework of indifference. The newly proposed estimator is shown to be unbiased and asymptotically normally distributed. Moreover, all considered estimators are compared via their asymptotic relative efficiency and a small simulation study. Finally, some indications about the performance of the considered estimators in the presence of contaminated normal data are provided.     

Personality and psychopathological profiles in individuals exposed to mobbing

Increasingly, mental health and medical professionals have been asked to assess claims of psychological harm arising from harassment at the workplace, or "mobbing." This study assessed the personality and psychopathological profiles of 146 individuals exposed to mobbing using validity, clinical, and content scales of the Minnesota Multiphasic Personality Inventory 2. Profiles and factor analyses were obtained. Two major dimensions emerged among those exposed to mobbing: (a) depressed mood, difficulty in making decisions, change-related anguish, and passive-aggressive traits (b) somatic symptoms, and need for attention and affection. This cross-sectional pilot study provides evidence that personality profiles of mobbing victims and psychological damage resulting from mobbing may be evaluated using standardized assessments, though a longitudinal study is needed to delineate cause-and-effect relationships.     

SIMPCA: a framework for rotating and sparsifying principal components

We propose an algorithmic framework for computing sparse components from rotated principal components. This methodology, called SIMPCA, is useful to replace the unreliable practice of ignoring small coefficients of rotated components when interpreting them. The algorithm computes genuinely sparse components by projecting rotated principal components onto subsets of variables. The so simplified components are highly correlated with the corresponding components. By choosing different simplification strategies different sparse solutions can be obtained which can be used to compare alternative interpretations of the principal components. We give some examples of how effective simplified solutions can be achieved with SIMPCA using some publicly available data sets.     

Model selection in high-dimensional noisy data: a simulation study

In many practical applications, high-dimensional regression analyses have to take into account measurement error in the covariates. It is thus necessary to extend regularization methods, that can handle the situation where the number of covariates p largely exceed the sample size n, to the case in which covariates are also mismeasured. A variety of methods are available in this context, but many of them rely on knowledge about the measurement error and the structure of its covariance matrix. In this paper, we set the goal to compare some of these methods, focusing on situations relevant for practical applications. In particular, we will evaluate these methods in setups in which the measurement error distribution and dependence structure are not known and have to be estimated from data. Our focus is on variable selection, and the evaluation is based on extensive simulations.