bibtype V - Research Report
ARLID 0537125
utime 20240103225053.2
mtime 20210108235959.9
title (primary) (eng) Subjective well-being and the individual material situation in Central Europe: A Bayesian network approach
publisher
place Praha
name ÚTIA AV ČR
pub_time 2020
specification
page_count 33 s.
media_type P
edition
name Research Report
volume_id 2387
keyword Subjective Well-Being
keyword Income
keyword Economic Strain
keyword Material Deprivation
keyword Bayesian Networks
keyword Central Europe
author (primary)
ARLID cav_un_auth*0361639
name1 Švorc
name2 Jan
institution UTIA-B
full_dept (cz) Matematická teorie rozhodování
full_dept (eng) Department of Decision Making Theory
department (cz) MTR
department (eng) MTR
full_dept Department of Decision Making Theory
country CZ
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0101228
name1 Vomlel
name2 Jiří
institution UTIA-B
full_dept (cz) Matematická teorie rozhodování
full_dept Department of Decision Making Theory
department (cz) MTR
department MTR
full_dept Department of Decision Making Theory
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2020/MTR/svorc-0537125.pdf
cas_special
project
project_id GA17-08182S
agency GA ČR
ARLID cav_un_auth*0348851
project
project_id GA19-06569S
agency GA ČR
country CZ
ARLID cav_un_auth*0380559
abstract (eng) The 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\nwith 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.
RIV BB
FORD0 10000
FORD1 10100
FORD2 10103
reportyear 2021
mrcbC52 4 O 4o 20231122145431.9
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0314873
confidential S
arlyear 2020
mrcbTft \nSoubory v repozitáři: 0537125.pdf
mrcbU10 2020
mrcbU10 Praha ÚTIA AV ČR