Exploring the Influences of Job Satisfaction for Europeans Aged 50 + from Ex-communist vs. Non-communist Countries

Daniel Homocianu (), Octavian Dospinescu and Napoleon-Alexandru Sireteanu
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Daniel Homocianu: Alexandru Ioan Cuza University
Octavian Dospinescu: Alexandru Ioan Cuza University
Napoleon-Alexandru Sireteanu: Alexandru Ioan Cuza University

Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, 2022, vol. 159, issue 1, No 10, 235-279

Abstract: Abstract The paper deals with the analysis of the influences of job satisfaction among Europeans aged 50 + (SHARE-ERIC’s data set-Wave7) filtered on main residences and education before 1989. Besides confirming the leading role of the workplace atmosphere and own efforts recognition (dual-core), it further validates the assumption that education and residence in former communist countries count when analyzing job satisfaction and brings two particular types of models. We used many methods based on data mining and variable selection, ordinal and binary logistic and probit regressions, cross-validations via LASSO and mixed-effects modeling with random effects on countries, average marginal effects, and logistic-based prediction nomograms. We discovered seven common influences that count the most when analyzing job satisfaction in these circumstances. It is about the dual-core above and the ones corresponding to older respondents, the better-educated ones (ISCED2011), those with computer skills, the ones endowed with thoroughness, and the ones having higher values of the CASP index of life quality. Depending on each of the two specific models, we discovered peculiarities related to the role of some economic (GDP and SMC to GDP) and institutional (WGI) indicators. For the ex-communist models, we found significant negative influences for both categories while, for non-communist ones, only the second category matters and has a positive role.

Keywords: Job satisfaction; Ex-communist vs. non-communist countries; Data mining and LASSO; Logistic and probit models; Mixed and marginal effects; Prediction capability (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11205-021-02754-z

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