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Subjective well-being and the individual material situation in Central Europe: A Bayesian network approach

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    SYSNO ASEP0537125
    Document TypeV - Research Report
    R&D Document TypeThe record was not marked in the RIV
    TitleSubjective well-being and the individual material situation in Central Europe: A Bayesian network approach
    Author(s) Švorc, Jan (UTIA-B)
    Vomlel, Jiří (UTIA-B) RID
    Issue dataPraha: ÚTIA AV ČR, 2020
    SeriesResearch Report
    Series number2387
    Number of pages33 s.
    Publication formPrint - P
    Languageeng - English
    CountryCZ - Czech Republic
    KeywordsSubjective Well-Being ; Income ; Economic Strain ; Material Deprivation ; Bayesian Networks ; Central Europe
    Subject RIVBB - Applied Statistics, Operational Research
    OECD categoryStatistics and probability
    R&D ProjectsGA17-08182S GA ČR - Czech Science Foundation (CSF)
    Institutional supportUTIA-B - RVO:67985556
    AnnotationThe objective of this paper is to explore the associations between the subjective well-being (SWB) and the subjective and objective measures of the individual material situation in the four post-communist countries of Central Europe (the Czech Republic, Hungary, Poland, and Slovakia). The material situation is measured by income, relative income compared to others, relative income compared to one’s own past, perceived economic strain, financial problems, material deprivation, and housing problems. Our analysis is based on empirical data from the third wave of European Quality of Life Study conducted in 2011. Bayesian networks as a graphical representation of the relations between SWB and the material situation have been constructed in five versions. The models have been assessed using the Bayesian Information Criterion (BIC) and SWB prediction accuracy, and compared
    with Ordinal Logistic Regression (OLR). Expert knowledge, as well as three different algorithms (greedy, Gobnilp, and Tree-augmented Naive Bayes) were used for learning the network structures. Network parameters were learned using the EM algorithm. Parameters based on OLR were learned for a version of the expert model. The Gobnilp model, the Markov equivalent to the greedy model, is BIC optimal. The OLR predicts SWB slightly better than the other models. We conclude that the objective material conditions' influence on SWB is rather indirect, through the subjective situational assessment of various aspects related to the individual material conditions.
    WorkplaceInstitute of Information Theory and Automation
    ContactMarkéta Votavová,, Tel.: 266 052 201.
    Year of Publishing2021
Number of the records: 1