Analysis of work intensity in Slovakia using testing and estimation of linear combinations of GLM parameters
Keywords:work intensity, exclusion from the labour market, poverty and social exclusion, general linear model (GLM), least square means, contrast analysis
AbstractNot only unemployment itself but also the reduced work intensity of a household has a major impact on the social exclusion of a person. The work intensity of households is currently being monitored in Europe mainly for purposes of identifying those people or households that are excluded from the labour market. The households’ work intensity directly affects the inclusion or exclusion from the labour market, which is one of the three social exclusion dimensions. Moreover, it also, as confirmed by several studies, fundamentally affects the other two dimensions of social exclusion, namely income poverty and material deprivation. The aim of the paper was to assess which factors in interaction with the economic activity status of a person significantly affect the household’s work intensity and, depending on these factors, to estimate the household’s work intensity. For this purpose, the general linear model and the associated analysis of marginal means and the contrast analysis were used. The analyses are based on a database EU-SILC 2020 for the Slovak Republic and performed in the SAS Enterprise Guide and by means of PROC GLM in the SAS programming language using CONTRAST and ESTIMATE statements. The article examines between which levels of significant factors there is a significant difference in terms of a household’s work intensity and in particular provides estimates of work intensity depending on the household type, educational attainment level and the age of a person. At the same time, in all three cases households are broken down by the economic activity status of the person. The presented analyses revealed categories of persons that are the most and the least threatened by labour market exclusion from the point of view of the considered factors.
Copyright (c) 2023 Erik Šoltés, Silvia Komara, Tatiana Šoltésová, Martin Mišút
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