The quality of work dimensions. Results of a multivariate analysis from the Third Isfol Survey on Quality of work in Italy

This paper starts with an overview of the theoretical framework on quality of work and identifies five relevant dimensions, in line with Gallino & La Rosa: ergonomic, complexity, autonomy, control and economic dimensions. The above dimensions are described and measured by means of multivariate analysis to detect differences in terms of the factors affecting the level of the quality of work dimensions achieved. The data set that we use for this purpose is the Third Isfol Survey on Quality of Work (IsfolQdL) that has been carried out in 2010 on a sample of 5,000 workers and operationalizes the five dimensions of the quality of work. The results of the multivariate analysis confirm the worse achievements in terms of quality of work by temporary workers and lower skilled workers and lower level of achievements by women in the economic and autonomy dimensions. Women are also more likely to be found in part-time work positions and the latter show an improvement in the ergonomic dimension (that includes also work life balance) at the expenses of the economic and autonomy dimensions

Synthetic indicators to analyze work-related physical and psychosocial risk factors: evidence from the European Working Conditions Survey

In modern workplaces, alongside physical, chemical, and biological hazards, other risks are linked to the organisation of work and to the nature of the work itself. This paper investigates the association between workers’ well-being and both psychosocial and physical risk factors at work proposing a synthetic measure suitable to generate insights on well-being at work and on individual risk factors. Exploiting data from the European Working Conditions Survey, we select as response variable the “self-assessed health”. As this proxy of well-being is measured on a Likert scale, Ordered Probit analyses are run, and respondents’ profiles are illustrated. Then, a Principal Component Analysis is carried out to build two synthetic measures summarising the selected risk determinants. The resulting first principal components are subsequently used as synthetic indicators in further, simplified, Ordered Probit models to explain the impact of different sets of risks on perceived health. Such a methodology allows for a straightforward interpretation of the results since many different risk drivers are replaced by two continuous synthetic indicators. Our findings, in line with existing research, confirm that both types of risk factors do exert a substantial impact on workers’ health, although the psychosocial determinants seem to be more prominent

Come valutare il lavoro occasionale di tipo accessorio? Alcune proposte metodologiche.

Il documento illustra due diverse proposte metodologiche per la valutazione del “Lavoro occasionale accessorio”. In particolare i due approcci illustrati, uno qualitativo e l’altro quantitativo, puntano a rilevare informazioni relative a due momenti diversi del ciclo di vita della policy. In un caso, si illustra la metodologia predisposta per la valutazione di processo dell’istituto analizzando le modalità con cui gli attori coinvolti lo hanno implementato e applicato, ricorrendo a metodi di consultazione diretta tramite intervista nel quadro di una indagine di campo pilota. Nell’altro si descrive un modello per la valutazione d’impatto dello strumento, che può impiegare metodi statistici e basi di dati amministrativi o provenienti da indagini campionarie condotte ad hoc

Deriving a Composite Indicator to Measure Well-being at Work in European Union Countries

The objective of this paper is to display how the exposure to psychosocial risk factors impact on workers’ well-being as much as the physical risk factors, in particular showing the strength of the effect of exposure to selected risk factors existing in the workplace on workers’ well-being. Following the framework proposed by EU-OSHA (2013) and applying statistical procedures to the European Working Conditions Survey data, we analysed how and to what extent physical risk factors and psychosocial risk factors impact on health and well-being of workers in European workplaces. More in particular, we carried out Ordered Probit models to measure the effect of specific items operationalising the physical and the psychosocial risk factors and subsequently run a Principal Component Analysis (PCA) to identify two separate synthetic indicators of physical risk factors and psychosocial risk factors respectively, to analyse their impact on health and well-being

Investigating well-being at work via composite indicators

The paper investigates the effect on workers’ well-being of selected risk factors existing in the workplace. Following the framework proposed by EU-OSHA (2013), the two categories of risk factors under investigation will be on the one hand the physical risk factors and on the other hand the psychosocial ones. More in particular, our objective is to display how psychosocial risk factors can affect workers’ health conditions as much as the physical risk factors, using some of the evidence of the European Working Conditions Survey (Eurofound, 2017). As a proxy of workers’ well-being, the variable of interest, that is the self-assessed health (SAH), stems from question Q75: “How is your health in general? Would you say it is (..:)” measured on a 5-point Likert scale (1 Very good; 2 Good; 3 Fair; 4 Bad; 5 Very bad). Current literature, as summarized in OECD handbook (2008), emphasizes the stages to achieve effective and consistent composite indicator. In order to build a model-based composite indicator for the SAH, the methodology chosen for this paper involved a number of steps. First, due to the ordinal variable of interest, Ordered Probit analyses were run based on explanatory variables describing the physical risks at work, the psychosocial risks and some individual characteristics. Then, a Principal Components Analysis was carried out to build a composite indicator summarising the selected variables, and the resulting Principal Components (PCs) have subsequently been used as explanatory variables in further Ordered Probit analyses. Moreover, results from the latter models are compared to measure the intensity of the relationship between SAH and variables identifying physical and psychosocial risks, highlighting those more relevant in influencing SAH. The results display that both types of risk factors do exert a significant impact on workers’ health, and in both cases the synthetic indicator (i.e. the first PC) accounts for most of the variance providing and effective synthesis of the data. When included in Ordered Probit models to measure the strength of their effect on the self-reported health, the indicators built for the two sets of risks turn out to be significant, both together and alone. The benefit of building these synthetic indicators relies on that they allow for simplifying a model-based analysis and may help in disentangling specific drivers of work-related well-being, as long as they are actually carriers of information, with the additional advantage of removing redundant information, obtaining more robust models

RESEARCH ARTICLE OPEN ACCESS The Use of Optical Remote Sensing For Mapping Flooded Areas

Flood maps are a crucial tool to support emergency management, disaster recovery and risk reduction planning. Traditional flood mapping methods are time-consuming, labor intensive, and costly. Our goal in this paper is to introduce a novel technique to aggregate knowledge and information to map coastal flooded areas. We proposed a Difference of Normalized Difference Water Indices (DNDWI) derived from two LANDSAT-5/TM surface reflectance product acquired before and after the passage of Hurricane Ike, for Upper Texas in September of 2008. The reference flooded area was delineated interpolating the maximum surge in each location using a spline with barriers method with high tension and a 30 meter Digital Elevation Model (DEM). It was noticed that NDWI values decreased after the hurricane landfall on average from 0.226 to 0.122 for flooded area. However for the non-flooded areas it increased from 0.292 to 0.300. Results from the Monte Carlo simulation showed that mapping flooded areas with DNDWI got an accuracy of 85.68 % while the non-flooded areas got an accuracy of 92.13%. Thus, DNDWI is promising tool for mapping flooded areas since it is a cheaper and simple technique which can be applied rapidly for several areas of the planet

Employment conditions in the international road haulage sector

Abstract The market integration and internationalisation of the road transport sector has implications for the social protection of its workers. This Policy Department A study aims to provide the EMPL Committee with information about trends in the employment conditions of drivers in this sector. In particular, it aims to review whether the current regulatory framework is achieving the desired balance between market integration and social protection of workers, and what steps can be taken to ensure this balance in the future