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Vol. 6 No. 1 (2018)
Social Entrepreneurship in the Global Context

Other Articles

Work Intensity in Slovakia in Relationship with Socio-Economic Characteristics of Households

https://doi.org/10.15678/EBER.2018.060107

Published 2018-03-28

  • Erik Šoltés
    • ,
  • Mária Vojtková

Erik Šoltés
University of Economics in Bratislava

Mária Vojtková
University of Economics in Bratislava

Abstract

Objective: The aim of this article is to evaluate the relationship between work intensity and educational attainment of the household head and household type in Slovakia.

Research Design & Methods:Statistical analyses were carried out in analytics software SAS Base and SAS Enterprise Guide by means of contingency, correspondence analysis and multinomial logistic regression. Empirical analyses are based on data from the survey EU-SILC 2015.

Findings: The article provides estimates of the probabilities of individual degrees of households’ work intensity depending on the household type and educational attainment level of the household head, while simultaneously in both cases households are broken down by economic activity of the household head. The presented analysis revealed categories of households which are the most and the least threatened by labour market exclusion from the point of view of the considered factors.

Implications & Recommendations: While the social inclusion monitor in Europe says that in 2012 (quasi-) joblessness was typical for households with three or more children, our analysis for 2014 did not confirm this. The exclusion from the labour market in 2014 was the most typical in Slovakia for households without dependent children, where there is no more than one person in productive age.

Contribution & Value Added: The article is not limited only to the very low work intensity which is used to assess the progress in the reduction of (quasi-) joblessness, but focuses on all the levels of work intensity (very low, low, medium, high and very high).

Keywords

SILC − European Union Statistics on Income and Living Conditions, correspondence analysis, multinomial logistic regression

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