Call centres and job satisfaction in Italy: employment conditions and socio-biographical patterns

ABSTRACT

The article examines job satisfaction in 21 Italian call centres. The results of research carried out on 1715 handlers indicate how dissatisfaction prevails among call centre representatives (CCRs) and how it is influenced by aspects related to some organisational characteristics (service delivered, size and organisational typology), on one side, and to different aspects of working conditions (contract, wage and tenure) and participants’ biographical and working profiles of CCRs (gender, age, educational attainment), on the others. However, the most interesting finding emerges by distinguishing different dimensions of job satisfaction (extrinsic and intrinsic-relational). In particular, the relationship between type of contract and job satisfaction is rather interesting. For non-permanent workers, in fact, the probability of being dissatisfied is decidedly greater if we consider the extrinsic dimension of job satisfaction. Instead, when the intrinsic-relational dimension is taking into account, atypical workers are no more dissatisfied than the permanent ones. Job insecurity and limited perspectives in terms of work alternatives, safeguards and rewards, seem to be the source of greatest dissatisfaction for Italian CCRs. This certainly does not surprise considering the Italian development model and its dualistic labour market, highly segmented between insiders and outsiders.     

Semi‐conditional cash transfers in the form of family allowances for children and adolescents in the informal economy in Argentina

In 2009, Argentina introduced a new transfer programme for children and adolescents younger than age 18 (Universal Child Allowance) that extended coverage under the contributory programme for family allowances to include families in the informal economy and families of unemployed persons. This article describes this innovative programme, compares it with similar programmes in Latin America and analyses its impact on coverage and its possible effects on the welfare of the population. The results indicate that the extension of access to this type of benefit has reduced considerably the coverage gap for the poor and indigent and supports efforts to consolidate the operations of different and poorly coordinated transfer programmes.     

Clustering microarray data using model-based double K-means

The microarray technology allows the measurement of expression levels of thousands of genes simultaneously. The dimension and complexity of gene expression data obtained by microarrays create challenging data analysis and management problems ranging from the analysis of images produced by microarray experiments to biological interpretation of results. Therefore, statistical and computational approaches are beginning to assume a substantial position within the molecular biology area. We consider the problem of simultaneously clustering genes and tissue samples (in general conditions) of a microarray data set. This can be useful for revealing groups of genes involved in the same molecular process as well as groups of conditions where this process takes place. The need of finding a subset of genes and tissue samples defining a homogeneous block had led to the application of double clustering techniques on gene expression data. Here, we focus on an extension of standard K-means to simultaneously cluster observations and features of a data matrix, namely double K-means introduced by Vichi (2000). We introduce this model in a probabilistic framework and discuss the advantages of using this approach. We also develop a coordinate ascent algorithm and test its performance via simulation studies and real data set. Finally, we validate the results obtained on the real data set by building resampling confidence intervals for block centroids.     

And yet they Co-move! Public capital and productivity in OECD

In this paper, we add to the debate on the public capital–productivity link by applying very recent developments in the panel time series literature that take into account cross sectional correlation in non-stationary panels. In particular, we evaluate the productive effect of public capital by estimating various production functions on a panel of 21 OECD countries over the period 1975–2002. Our results suggest that public capital has a positive long run impact on output, with elasticities that range between 0.05 and 0.15, depending on model specification. These findings are robust to the existence of spillover effects from public capital investments in other countries and to the inclusion of other productivity determinants, like human capital, the stock of patents and R&D capital. Finally, we do not find any important effect of public capital on GDP in the short run: this suggests that public infrastructure investments might not be a powerful countercyclical policy instrument.     

The MIMIC–CUB Model for the Prediction of the Economic Public Opinions in Europe

Social Indicators Research - To study the Europeans’ perception on the economic conditions, a model that combine Multiple Indicators Multiple Causes (MIMIC) and Combination of Uniform and...     

Large-scale commonness is the best predictor of bird species presence in European cities

Urban Ecosystems - Urban bird communities are homogenized across large spatial scales, suggesting that the urban environment acts as an environmental filter. We hypothesize that large scale...     

Adaptive multiple importance sampling for Gaussian processes

In applications of Gaussian processes (GPs) where quantification of uncertainty is a strict requirement, it is necessary to accurately characterize the posterior distribution over Gaussian process covariance parameters. This is normally done by means of standard Markov chain Monte Carlo (MCMC) algorithms, which require repeated expensive calculations involving the marginal likelihood. Motivated by the desire to avoid the inefficiencies of MCMC algorithms rejecting a considerable amount of expensive proposals, this paper develops an alternative inference framework based on adaptive multiple importance sampling (AMIS). In particular, this paper studies the application of AMIS for GPs in the case of a Gaussian likelihood, and proposes a novel pseudo-marginal-based AMIS algorithm for non-Gaussian likelihoods, where the marginal likelihood is unbiasedly estimated. The results suggest that the proposed framework outperforms MCMC-based inference of covariance parameters in a wide range of scenarios.