Sull’uso del lavoro sommerso da parte delle imprese. La costruzione di un modello microeconometrico con i dati ispettivi INPS

The aim of this paper is to explore the use of administrative data to study the choices of firms relating to the utilization of regular and irregular workers. The data are collected by the INPS (National Social Security Administration) during inspection activity aimed to catch irregular workers and underground firms. Starting from INPS data we construct a microeconometric model in which the ratio between regular and irregular workers depends from characteristics of firm (size, sector, localization) and some measures of intensity and efficacy of inspection activity.

Il tirocinio tecnico-pratico nei corsi di studio in igiene dentale. II parte: standard del tirocinio e dell’insegnamento

Il Gruppo di lavoro della Commissione Nazionale dei Corsi di Studio in Igiene Dentale ha elaborato un documento di consensus con indicazioni volte a uniformare gli standard del tirocinio tecnico-pratico presso i Corsi di Studio in Igiene Dentale sul territorio nazionale

Il tirocinio tecnico-pratico nei Corsi di Studio in Igiene Dentale. I parte: obiettivi

Il Gruppo di lavoro della Commissione Nazionale dei Corsi di Studio in Igiene Dentale ha elaborato un documento di consensus con indicazioni volte a uniformare gli standard del tirocinio tecnico-pratico presso i Corsi di Studio in Igiene Dentale sul territorio nazionale

Climate and weather service provision: Economic appraisal of adaptation to health impacts

Abstract This paper seeks to demonstrate that the value of climate projection information can be used to derive quantitative estimates of both the costs and benefits of information-based measures introduced to reduce climate-related risks. Specifically, information relating to both longer term climate change and weather variability are combined to identify potential resource implications for health service planning when faced with higher frequencies of heatwaves. A range of climate projection-city combinations are explored in order to test the robustness of the economic justification for heatwave warning systems (HWWS) in Europe – London, Madrid and Prague. Our results demonstrate that in most cases the HWWS option can be justified in the current climate – it is therefore a "no/low regret" option. Our results also show that whilst costs increase slightly under climate change scenarios, benefits of HWWS are likely to increase more steeply in European contexts. However, whilst the majority of cost-benefit analysis (CBA) outcomes are found to be positive, (i.e. economic benefits are greater than economic costs), across alternative climate projection-city combinations, in sensitivity analyses it is possible to generate negative results in certain geographical contexts. Indeed, with respect to this climate change risk, this analysis has identified that the analysis of key uncertainties, such as effectiveness of HWWSs and the valuation of health improvements, is critical in strengthening the case for HWWS implementation

Geostatistical integration and uncertainty in pollutant concentration surface under preferential sampling

In this paper the focus is on environmental statistics, with the aim of estimating the concentration surface and related uncertainty of an air pollutant. We used air quality data recorded by a network of monitoring stations within a Bayesian framework to overcome difficulties in accounting for prediction uncertainty and to integrate information provided by deterministic models based on emissions meteorology and chemico-physical characteristics of the atmosphere. Several authors have proposed such integration, but all the proposed approaches rely on representativeness and completeness of existing air pollution monitoring networks. We considered the situation in which the spatial process of interest and the sampling locations are not independent. This is known in the literature as the preferential sampling problem, which if ignored in the analysis, can bias geostatistical inferences. We developed a Bayesian geostatistical model to account for preferential sampling with the main interest in statistical integration and uncertainty. We used PM10 data arising from the air quality network of the Environmental Protection Agency of Lombardy Region (Italy) and numerical outputs from the deterministic model. We specified an inhomogeneous Poisson process for the sampling locations intensities and a shared spatial random component model for the dependence between the spatial location of monitors and the pollution surface. We found greater predicted standard deviation differences in areas not properly covered by the air quality network. In conclusion, in this context inferences on prediction uncertainty may be misleading when geostatistical modelling does not take into account preferential sampling