TY - JOUR

T1 - Количественный анализ данных о страховой грамотности и страховой защите: пределы и возможности

AU - Соколовская, Елена

N1 - Соколовская, Е. В. (2026) ‘Количественный анализ данных о страховой грамотности и страховой защите: пределы и возможности’, Вестник Санкт-Петербургского университета. Экономика, 42 (1), с. 126–146. EDN ZPRRJR

PY - 2026

Y1 - 2026

N2 - In the paper, we present the results of a quantitative assessment of the level of insurance protection of the population, taking into account the country's insurance system and the insurance literacy of citizens. The determination of the level of insurance literacy to a greater extent than the analysis of quantitative indicators of insurance market, is limited by the problem of spatiotemporal data incomparability. In this regard, this study aims to identify the potential of quantitative methods for the analysis of data of subjectively perceived level of insurance protection. Based on a study of the characteristics of accident and sickness insurance systems in OECD countries, and the components of insurance literacy, we determined a set of factors for further cluster analysis. The results of the analysis revealed that this set of parameters adequately characterizes the accident and sickness insurance system and thus can be used for cross-country comparisons. We presented the approach for measuring the subjectively perceived level of insurance protection using the k-nearest neighbors algorithm. The assessment of the quality of the models showed their greater accuracy when limiting the sample to respondents with secondary education or higher than secondary education, which correlates with the results of studies on insurance literacy, where the knowledge is the principal factor in the formation of insurance literacy of the population. The results demonstrate the possibility of using quantitative analysis algorithms under limited data to obtain predictive values of indicators of subjectively perceived level of insurance protection, which can be used in strategies for improving the financial and insurance literacy of the population.

AB - In the paper, we present the results of a quantitative assessment of the level of insurance protection of the population, taking into account the country's insurance system and the insurance literacy of citizens. The determination of the level of insurance literacy to a greater extent than the analysis of quantitative indicators of insurance market, is limited by the problem of spatiotemporal data incomparability. In this regard, this study aims to identify the potential of quantitative methods for the analysis of data of subjectively perceived level of insurance protection. Based on a study of the characteristics of accident and sickness insurance systems in OECD countries, and the components of insurance literacy, we determined a set of factors for further cluster analysis. The results of the analysis revealed that this set of parameters adequately characterizes the accident and sickness insurance system and thus can be used for cross-country comparisons. We presented the approach for measuring the subjectively perceived level of insurance protection using the k-nearest neighbors algorithm. The assessment of the quality of the models showed their greater accuracy when limiting the sample to respondents with secondary education or higher than secondary education, which correlates with the results of studies on insurance literacy, where the knowledge is the principal factor in the formation of insurance literacy of the population. The results demonstrate the possibility of using quantitative analysis algorithms under limited data to obtain predictive values of indicators of subjectively perceived level of insurance protection, which can be used in strategies for improving the financial and insurance literacy of the population.

UR - http://elibrary.ru/ZPRRJR

M3 - статья

VL - 42

SP - 126

EP - 146

JO - ВЕСТНИК САНКТ-ПЕТЕРБУРГСКОГО УНИВЕРСИТЕТА. ЭКОНОМИКА

JF - ВЕСТНИК САНКТ-ПЕТЕРБУРГСКОГО УНИВЕРСИТЕТА. ЭКОНОМИКА

SN - 1026-356X

IS - 1

ER -