Band 18, Heft 2

This study addresses the critical public concern of effectively mitigating solvency risks in insurance companies through supervisory intervention. Building upon existing research, we extend our analysis to incorporate the effects of foreign investment and examine the impact of varying degrees of risk aversion among insurers. Furthermore, we investigate the optimal expected terminal utility in relation to initial insurance company premiums, integrating interest and exchange rates into our analytical framework. Additionally, we evaluate the impact of regulatory interventions, specifically capital injection schemes and risk-constrained asset allocation, on insurers’ default risk and certainty equivalent wealth using a utility-based approach. Our findings indicate that: (1) when policyholders exhibit higher degrees of risk aversion, a reduced propensity to allocate funds to riskier assets, such as stock index funds and foreign assets, is observed; (2) when exchange rates offer higher returns, an increased allocation to foreign assets is advisable; (3) conversely, when exchange rate returns exhibit greater uncertainty, a reduction in foreign asset allocation is recommended; (4) in scenarios where foreign risk-free rates yield higher returns, an increased allocation to foreign assets is suggested; and (5) under conditions of capital minimization, the influence of exchange rates and foreign interest rates on optimization becomes less discernible.

Previous studies have shown that a single social insurance project may improve subjective well-being. Yet, the effect of the overall social insurance system and the underlying mechanism are unknown. This study investigates the impact of the social insurance system on individuals’ subjective well-being and the impact mechanisms using the China Family Panel Studies (CFPS) conducted in four consecutive waves (2014, 2016, 2018, 2020). By applying the structural equation modeling approach, we construct a focused longitudinal path model and find that the social insurance system has a direct positive effect on individuals’ subjective well-being, measured by life satisfaction and job satisfaction. We explored three longitudinal mediation patterns including self-rated health, household income, and trust in the government, however, none of them are significant mediators. Through subgroup analysis, it is found that the male group and the age over 60 group benefit more from the social insurance system regarding life satisfaction improvement.

This study examines the economic stability of Korean Workers’ Compensation Insurance (WCI), highlighting the system’s vulnerability to triple moral hazard. Korean WCI features more generous worker compensation, lower employer premiums, and more active government involvement in accident prevention in addition to a higher industrial accident rate, compared to the system in Germany and Japan. The negotiation process of the WCI among workers, employers, and insurance managers often prioritizes short-term benefits, undermining long-term financial health. Workers seek greater compensation, employers push for lower premiums, and insurance managers may prioritize collective interests, exacerbating the system’s economic instability. Over recent years, applications for occupational disease recognition have surged, and premium rates have decreased, indicating increasing strain. To mitigate these issues, the study proposes enhancing transparency and rationality, involving non-political experts in decision-making, and clearly defining the government’s role in managing WCI. These measures aim to balance stakeholder interests and ensure the WCI system’s sustainability for future generations.

Insurance rating territory design and accurate estimation of territory risk relativities are fundamental aspects of auto insurance rate regulation. It is crucial to develop methodologies that can facilitate the effective design of rating territories and their risk relativities estimate, as they directly impact the rate filing and the decision support of the rate change review process. This article proposes a Gaussian Mixture Regression model clustering approach for territory design. The proposed method incorporates a linear regression model, taking spatial location as model covariates, which helps estimate the cluster mean more accurately. Also, to further enhance the estimation of territory risk relativities, we impose sparsity through sparse matrix decomposition of the membership coefficient matrix obtained from the Gaussian Mixture Regression model. By transitioning from the current hard clustering method to a soft approach, our methodology could improve the evaluation of territory risk for rate-making purposes. Moreover, using non-negative sparse matrix approximation ensures that the estimation of risk relativities for basic rating units remains smooth, effectively eliminating data noise from the territory risk relativity estimate. Overall, our novel methodology aims to significantly enhance the accuracy and reliability of risk analysis in auto insurance. Furthermore, the proposed method exhibits potential for extension to various other domains that involve spatial clustering of data, thereby broadening its applicability and expanding its usefulness beyond auto insurance rate regulation.