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Weitere Publikationen: Stefan Angel (13 Treffer)

Karin Heitzmann, Stefan Angel
in: Gottfried Schweiger, Clemens Sedmak, Handbuch Philosophie und Armut
Buchbeiträge, J.B. Metzler'sche Verlagsbuchhandlung und C.E. Poeschel Verlag GmbH Stuttgart-Weimar, Stuttgart, März 2021, 7 Seiten, S.13-19, https://doi.org/10.1007/978-3-476-05740-2_2
Über Armut zu forschen, und damit Armut zu beschreiben und zu messen, ist ein schwieriges Unterfangen, auch weil es sich bei "Armut" um ein Konzept handelt, das – z. B. in der Alltagssprache – unterschiedliche Bedeutungen aufweisen kann (vgl. dazu auch Jacobs, 1995).
Journal of the Royal Statistical Society. Series A: Statistics in Society, 2019, 182, (4), 27 Seiten, S.1411-1437, https://doi.org/10.1111/rssa.12463
Review of Income and Wealth, 2019, 65, (3), 19 Seiten, S.495-513, https://doi.org/10.1111/roiw.12341
We take advantage of the fact that for the Austrian SILC 2008--2011, two data sources are available in parallel for the same households: register-based and survey-based income data. Thus, we aim to explain which households tend to under- or over-report their household income by estimating multinomial logit and OLS models with covariates referring to the interview situation, employment status and socio-demographic household characteristics. Furthermore, we analyze source-specific differences in the distribution of household income and how these differences affect aggregate poverty indicators based on household income. The analysis reveals an increase in the cross-sectional poverty rates for 2008--2011 and the longitudinal poverty rate if register data rather than survey data are used. These changes in the poverty rate are mainly driven by differences in employment income rather than sampling weights and other income components. Regression results show a pattern of mean-reverting errors when comparing household income between the two data sources. Furthermore, differences between data sources for both under-reporting and over-reporting slightly decrease with the number of panel waves in which a household participated. Among the other variables analyzed that are related to the interview situation (mode, proxy, interview month), only the number of proxy interviews was (weakly) positively correlated with the difference between data sources, although this outcome was not robust over different model specifications.
So far, research on the causes of over-indebtedness in Europe has predominantly focused on the characteristics of individuals or households. This article investigates to what extent country-level factors are associated with a European household's risk of being over-indebted. We examine variables that reflect policies aimed at combating over-indebtedness (the average level of economic literacy prevalent within a country and its classification into a specific debt-discharge regime) and variables that reflect other welfare-state policies (a country's affiliation to a specific employment regime and a summary measure referring to the net replacement rate in the case of long-term unemployment). The results, which are based on multilevel logistic regression analyses of European Union Statistics on Income and Living Conditions (EU-SILC) data for 27 European countries, suggest that all four country-level factors matter. This particularly applies to the variables reflecting other welfare-state policies, thus underlining the relevance of the design of social policy in fighting over-indebtedness.