Social Workers’ Causal Attributions for Poverty: Does the Level of Spatial Concentration of Disadvantages Matter?

Ildiko Husz (), Marianna Kopasz and Márton Medgyesi ()
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Marianna Kopasz: Centre for Social Sciences
Márton Medgyesi: Centre for Social Sciences

Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, 2022, vol. 162, issue 3, No 6, 1069-1091

Abstract: Abstract Social workers may play an important role in the implementation of welfare policies targeted at the poor. Their norms, beliefs, and attitudes form local anti-poverty programmes and affect discretionary practices with their clients. Despite this, we know little about how social workers’ exposure to poverty shapes their attitudes towards poverty and their causal attributions for poverty. This study investigates social workers’ poverty explanations and the extent to which they depend on the level of local poverty. Data from a survey conducted among Hungarian social workers were analysed using multilevel linear regression models. To measure local poverty, we used a composite index of poverty, as well as a subjective measure of exposure to poverty. Our analysis revealed that most social workers explained poverty with structural causes, but individual blame was also frequent. Contrary to our hypothesis, the level of local poverty did not significantly increase the adoption of structural explanations but did raise the occurrence of individualistic ones. However, the effect of local poverty was non-linear: social workers tended to blame the poor for their poverty in the poorest municipalities, where multiple disadvantages are concentrated, while moderate poverty did not lead to such opinions. Our results suggest that efforts should be made to improve the poverty indicator framework to better understand the phenomenon of spatial concentration of multiple disadvantages and its consequences for the poor.

Keywords: Attitudes; Attributions for poverty; Spatial concentration of disadvantages; Post-socialist countries; Social workers; Multilevel linear regression (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11205-021-02868-4

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