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  • Poverty and social exclusion of person or households are very serious problems affecting almost every country of the world. They are assessed from different points of view, such as the insufficient amount of income to procure basic life needs (income poverty) or from the point of view of the lack of at least three out of nine deprivation items (material deprivation). The third aspect of assessing poverty and social exclusion is the very low work intensity of person in the household (exclusion from the labor market). The aim of the article is the analysis of the third aspect of poverty measurement in the world within the context of the Europe 2030 strategy - very low work intensity. Using the data obtained from the EU-SILC 2021 statistical survey and using the LOGISTIC and GENMOD procedures within the SAS Enterprise Guide statistical software, we apply logistic regression methods and generalized linear models to quantify the effect of relevant categorical factors on the binary dependent variable very low work intensity of person in Slovak households. Using the LOGISTIC procedure will allow us to estimate a binary logistic regression model for the analyzed variable very low work intensity depending on factors such as economic activity, level of education, type of household, age, sex or region in which the person lives. By intervening in the programming code, we will extend the model with contrast analysis, in which we apply the CONTRAST statement, through which we identify hidden relationships between individual levels of factors and also the ESTIMATE statement, with which we estimate the probability that a person will face the risk of being excluded from the labor market depending on the selected levels factors. In the article, we will also show the estimation of the least squares means using the LSMEANS statement within the GENMOD procedure, based on which we will assess the existence of a non-significant difference in the least squares means of the logit of the chance of exclusion from the labor market between individual levels of the factor. In the case of non-significant differences, we merge the most similar categories into one newly created category, thus ensuring more accurate results of the entire model. (xsd:string)
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  • (SILC) (xsd:string)
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  • EU-SILC-Bibliography (xsd:string)
?:dateModified
  • 2023 (xsd:gyear)
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  • 2023 (xsd:gyear)
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  • 65 (xsd:string)
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  • english (xsd:string)
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  • Using of Logistic and Genmod Procedures by the Analysis of Exclusion from the Labour Market (xsd:string)
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  • inproceedings (xsd:string)
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  • 91st International Scientific Conference on Economic and Social Development (xsd:string)
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  • In 91st International Scientific Conference on Economic and Social Development, edited by Jelavic, Sanda Rasic and do Rosario Anjos, Maria and Tadic, Diana Plantic, 65-78, 2023 (xsd:string)
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  • European Union Statistics on Income and Living Conditions (EU-SILC) (xsd:string)
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  • 2023 (xsd:string)
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  • SILC (xsd:string)
  • SILC_input2023 (xsd:string)
  • SILC_pro (xsd:string)
  • english (xsd:string)
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  • 78 (xsd:string)
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