With respect to the fulfillment of the objectives of the Europe 2020 strategy, the threat of poverty and social exclusion has not been sufficiently reduced in the European Union (EU) over the past decade, and large regional disparities persist. Young people are the most affected by the problems of income poverty, material deprivation and labour market exclusion, which are the three dimensions of poverty and social exclusion. In this article, we focus on comparing the EU countries in terms of the three listed dimensions, while revealing similarities and differences in the incidence and severity of these social phenomena among youth. In addition to measuring dimensions by the currently used AROPE (at risk of poverty or social exclusion) rate, we also use a larger spectrum of relevant indicators for a more comprehensive analysis. While the AROPE aggregate indicator uses the same methodology for the population of young people as for the whole population, our approach includes indicators that are specific to young people. We assume that all dimensions affect each other, so we apply multidimensional statistical methods such as principal components and cluster analysis to analyse them. These methods have revealed that some dimensions affect poverty and social exclusion to a greater extent and others to a lesser extent than might appear to be the case, based on AROPE’s partial rates. Moreover, we present quantified integral indicators that together with the results of the multivariate methods, provide a rather complex picture concerning the geographical distribution of poverty and social exclusion, as well as their dimensions in the EU, for the population of persons aged 18–24 years in 2008 and 2017.
The aim of the paper is to assess the impact of socio-economic and socio-demographic factors on the risk of poverty or social exclusion. The paper focuses on the analysis of the probability of social exclusion of the Slovak population from 4 perspectives, from being at risk of poverty or social exclusion, at risk of poverty, severely materially deprived, and living in a (quasi-)jobless household. The least-square means analysis and contrast analysis linked to logit models were used to identify risk groups, and to estimate the social exclusion probabilities. Based on the EU-SILC 2020 database, unemployed persons with low education and persons from single-parent and multi-child households had the greatest risk of social exclusion in Slovakia. Under ceteris paribus conditions, the risk decreased with increasing age and improving health status. The riskiest marital status was divorced. Analyses revealed regional disparities from the point of view of all 4 perspectives, with people living in South-Center and Eastern Slovakia and people living in sparsely and moderately populated areas having the greatest risk. Since economic activity status, household type, and educational attainment level showed as the most relevant factors, the article pays special attention to the assessment of the mutual influence of these factors. Although the pattern of the risk of social exclusion of persons broken down by household type and education for the unemployed and employed is similar, the riskiness of the most vulnerable groups of people is more pronounced for employed persons.
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.
The paper introduces the proposal of the measurement model for insurance and reinsurance contracts in accordance with the new standard IFRS 17 Insurance contracts that will be effective as of January 1, 2023. The Standard does not contain formulas, but it is principle-based, which is why the selected method of general model measurement is a scientific benefit for the measurement of the insurance product. The application of the GMM method is not the same as that of the insurance company and the reinsurance company perspective, despite the same chosen actuarial assumptions. The scope of changes, which the new Standard offers, is comprehensive and brings new challenges, even for scientific purposes.
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