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Material Deprivation in Poland and Slovakia – a Comparative Analysis

100%
Šoltés E., Ulman P.
Zeszyty Naukowe Uniwersytetu Ekonomicznego w Krakowie
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2015
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issue 11(947)
19-36
EN
One of the key targets of the EU’s 2020 Strategy is to substantially reduce the number of people at risk of poverty or social exclusion. The EU seeks to reduce poverty by lifting at least 20 million people out of the risk of poverty or social exclusion by 2020. The monitoring of progress towards this target is based on the headline indicator AROPE – people at risk of poverty or social exclusion. The indicator applies to people either at risk of poverty or severely materially deprived or living in a household with a very low work intensity. This article focuses on material deprivation, one of the three components monitored to evaluate the social situation in the EU. The article deals with material deprivation and severe material deprivation in Slovak and Polish households. The two main goals of this article are to examine which factors have a significant effect on material deprivation and to determine the influence of those relevant factors on material deprivation of Slovak and Polish households. The article provides a comparative analysis of the material deprivation of Slovak and Polish households. We used microdata from EU SILC 2012 from Poland and Slovakia and association analysis including logistic regression. These statistical methods were applied using SAS Enterprise Guide.
PL
Jednym z kluczowych celów Unii Europejskiej zawartym w strategii Unii Europejskiej „Europa 2020” jest zasadnicza redukcja liczby osób zagrożonych ubóstwem lub wykluczeniem społecznym. Unia Europejska poszukuje możliwości zmniejszenia problemu ubóstwa poprzez ograniczenie do 2020 r. liczby osób zagrożonych ubóstwem lub wykluczeniem społecznym o co najmniej 20 milionów. Monitorowanie procesu redukcji ubóstwa zostało oparte przede wszystkim na wskaźniku AROPE, który odnosi się do sytuacji osób zagrożonych ubóstwem lub doświadczających poważnej materialnej deprywacji albo żyjących w gospodarstwach domowych utrzymujących się przede wszystkim z innych źródeł niż praca. Artykuł jest poświęcony problemowi materialnej deprywacji jako jednemu z trzech komponentów monitorowanych w ramach ewaluacji sytuacji społecznej w Unii Europejskiej. Wspomniana deprywacja została w nim ujęta w stopniu ogólnym i intensywnym. Celem opracowania jest odpowiedź na pytanie, jakie czynniki i w jaki sposób istotnie wpływają na materialną deprywację gospodarstw domowych w Polsce i na Słowacji. Poddanie badaniu gospodarstw ze wspomnianych państw pozwoliło na przeprowadzenie analizy porównawczej w zarysowanym powyżej obszarze badawczym. Dane statystyczne w postaci zbiorów indywidualnych obserwacji gospodarstw domowych zaczerpnięto z badania EU SILC przeprowadzonego w Polsce i na Słowacji w 2012 r. Do realizacji celu pracy wykorzystano metody analizy zależności, w tym modele regresji logistycznej, natomiast obliczenia przeprowadzono przy użyciu SAS Enterprise Guide.
2
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General linear model – an effective tool for analysis of claim severity in motor third party liability insurance

81%
Šoltés E., Zelinová S., Bilíková M.
Statistics in Transition new series
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2019
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vol. 20
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issue 4
13-31
EN
The paper focuses on the analysis of claim severity in motor third party liability insurance under the general linear model. The general linear model combines the analyses of variance and regression and makes it possible to measure the influence of categorical factors as well as the numerical explanatory variables on the target variable. In the paper, simple, main and interaction effects of relevant factors have been quantified using estimated regression coefficients and least squares means. Statistical inferences about least-squares means are essential in creating tariff classes and uncovering the impact of categorical factors, so the authors used the LSMEANS, CONTRAST and ESTIMATE statements in the GLM procedure of the Statistical Analysis Software (SAS). The study was based on a set of anonymised data of an insurance company operating in Slovakia; however, because each insurance company has its own portfolio subject to changes over time, the results of this research will not apply to all insurance companies. In this context, the authors feel that what is most valuable in their work, is the demonstration of practical applications that could be used by actuaries to estimate both the claim severity and the claim frequency, and, consequently, to determine net premiums for motor insurance (regardless of whether for motor third party liability insurance or casco insurance.
3
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Comparison of household work intensity in Slovakia and Czechia through least squares means analysis based on GLM

81%
Šoltés E., Komara S., Košíková M., Šoltésová T.
Statistics in Transition new series
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2024
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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.
4
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Segmentation and estimation of claim severity in motor third-party liability insurance through contrast analysis

71%
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
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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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