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Comparison of household work intensity in Slovakia and Czechia through least squares means analysis based on GLM

100%
Šoltés E., Komara S., Košíková M., Šoltésová T.
Statistics in Transition new series
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2024
|
vol. 25
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issue 4
1-26
EN
The work intensity (WI) of a household is primarily monitored in order to identify (quasi-)jobless (QJ) households. QJ households are those households whose members use less than 20% of their work potential. Individuals in such households, together with incomepoor and severely materially and socially deprived persons are included in the Europe 2030 Strategy as socially excluded who need to be targeted by social policies. The aim of the paper is to assess the impact of relevant factors and their interactions on the WI of households in Slovakia and Czechia. For this purpose, general linear models, contrast analysis and estimates of marginal means are employed. The presented analyses are based on the EU-SILC 2021 survey and carried out separately for Slovakia and Czechia. The paper reveals the common and different features of these countries in terms of the WI of households. Particular attention is devoted to the identification of the profiles of persons at high risk of living in QJ households.
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Segmentation and estimation of claim severity in motor third-party liability insurance through contrast analysis

88%
Reiff M., Šoltés E., Komara S., Šoltésová T., Zelinová S.
Equilibrium. Quarterly Journal of Economics and Economic Policy
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2022
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vol. 17
|
issue 3
803-842
EN
Research background: Using the marginal means and contrast analysis of the target variable, e.g., claim severity (CS), the actuary can perform an in-depth analysis of the portfolio and fully use the general linear models potential. These analyses are mainly used in natural sciences, medicine, and psychology, but so far, it has not been given adequate attention in the actuarial field. Purpose of the article: The article's primary purpose is to point out the possibilities of contrast analysis for the segmentation of policyholders and estimation of CS in motor third-party liability insurance. The article focuses on using contrast analysis to redefine individual relevant factors to ensure the segmentation of policyholders in terms of actuarial fairness and statistical correctness. The aim of the article is also to reveal the possibilities of using contrast analysis for adequate segmentation in case of interaction of factors and the subsequent estimation of CS. Methods: The article uses the general linear model and associated least squares means. Contrast analysis is being implemented through testing and estimating linear combinations of model parameters. Equations of estimable functions reveal how to interpret the results correctly. Findings & value added: The article shows that contrast analysis is a valuable tool for segmenting policyholders in motor insurance. The segmentation's validity is statistically verifiable and is well applicable to the main effects. Suppose the significance of cross effects is proved during segmentation. In that case, the actuary must take into account the risk that even if the partial segmentation factors are set adequately, statistically proven, this may not apply to the interaction of these factors. The article also provides a procedure for segmentation in case of interaction of factors and the procedure for estimation of the segment's CS. Empirical research has shown that CS is significantly influenced by weight, engine power, age and brand of the car, policyholder's age, and district. The pattern of age's influence on CS differs in different categories of car brands. The significantly highest CS was revealed in the youngest age category and the category of luxury car brands.
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