Statistical evidence of the subjective work quality: the fairness drivers of the job satisfaction


Workers’ well-being appears to be strongly influenced by fairness concerns. A key question is how such a relationship could be dissected in order to get a picture where different dimensions of the Job Satisfaction (JS) (Intrinsic, Extrinsic, and Overall) are connected to each of the main fairness facets. Using a comprehensive Italian social cooperatives workers dataset, we try to give a statistical answer to this question. To do this, we first construct some JS and Work Intensity measures, using the Rasch Rating Scale Model, in order to divide the subjects within three homogeneous clusters. Second, for each group we select the most important items in terms of their impact on the different dimensions of the JS evaluated with a Variable Importance Indicator, constructed using the Random Forests. The main findings with our sample are that non-monetary components of fairness play a key role on the JS measure and that the importance attributed to different fairness items varies depending on the Work Intensity, then producing a non-trivial dependence of the JS measures from the fairness drivers.

DOI Code: 10.1285/i20705948v5n1p89

Keywords: Categorical PCA; Rasch Analysis; Rating Scale Model; Random Forest; Variable Importance; Work Intensity

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