This paper investigates an operation mechanism for mutual aid platforms to develop more sustainably and profitably. A mutual aid platform is an online risk-sharing platform for risk-heterogeneous participants, and the platform extracts revenues by charging participants commission and subscription fees. A modeling framework is proposed to identify the optimal commissions and subscriptions for mutual aid platforms. Participants are divided into different types based on their loss probabilities and values derived from the platform. We present how these commissions and subscriptions should be set in a mutual aid plan to maximize the platform’s revenues. Our analysis emphasized the importance of accounting for risk heterogeneity in mutual aid platforms. Specifically, different types of participants should be charged different commissions/subscriptions depending on their loss probabilities and values on the platform. Participants’ shared costs should be determined based on their loss probabilities. Adverse selection occurs on the platform if participants with different risks pay the same shared costs. Our results also show that the platform’s maximum revenue will be lower if the platform charges the same fee to all participants. The numerical results of a practical example illustrate that the optimal commission/subscription scheme and risk-sharing rule result in considerable improvements in platform revenue over the current scheme implemented by the platform.

Many economies have recently adopted the defined-contribution retirement financing system, but one disadvantage of this system is that retirees have to bear longevity risk. As a result, several economies have also introduced the public annuity plans. We analyze the similarities and differences between voluntary public annuity with ceiling (VPAc) plan and mandatory public annuity with flexibility (MPAf) plan that are empirically observed. Introducing either plan reduces the severity of adverse selection in public annuities, but further distorts the private annuity market. These two plans have systematically different effects on retirees’ utility levels: the good health group is adversely affected and the average health group benefits. On the other hand, the poor health group benefits from the VPAc plan but may be adversely affected under the MPAf plan.

Mutual benefit societies evolved as the major provider for sickness, accident and life insurance in the late nineteenth and early twentieth centuries on both sides of the Atlantic. One of the major problems facing insurers was the risk of adverse selection, i.e. that unhealthy individuals had more incentives than healthy individuals to insure when priced for the average risk. By empirically examining whether longevity among insured individuals in a nationwide mutual health society was different from a matched sample of uninsured individuals, we seek to identify the presence of adverse selection. We find no compelling evidence showing that unhealthy individuals were more likely to insure, or reasons to believe that problems related to adverse selection would have been a major reason for government intervention in the health insurance market in Sweden.

We propose a macroeconomic model in which adverse selection in investment amplifies macroeconomic fluctuations, in line with the prominent role played by the credit crunch during the financial crisis. Endogenous lending standards emerge due to an informational asymmetry between borrowers and lenders about the riskiness of borrowers. By using loan approval probability as a screening device, banks ration credit following increases in lending risk, generating large endogenous movements in TFP, explaining why productivity often falls during crises. Furthermore, the mechanism implies that financial instability is heightened when interest rates are low.

Bonus-malus systems (BMSs) are widely used in actuarial sciences. These systems are applied by insurance companies to distinguish the policyholders by their risks. The most known application of BMS is in automobile third-party liability insurance. In BMS, there are several classes, and the premium of a policyholder depends on the class he/she is assigned to. The classification of policyholders over the periods of the insurance depends on the transition rules. In general, optimization of these systems involves the calculation of an appropriate premium scale considering the number of classes and transition rules as external parameters. Usually, the stationary distribution is used in the optimization process. In this article, we present a mixed integer linear programming (MILP) formulation for determining the premium scale and the transition rules. We present two versions of the model, one with the calculation of stationary probabilities and another with the consideration of multiple periods of the insurance. Furthermore, numerical examples will also be given to demonstrate that the MILP technique is suitable for handling existing BMSs.

During the past decade, genetics research has allowed scientists and clinicians to explore the human genome in detail and reveal many thousands of common genetic variants associated with disease. Genetic risk scores, known as polygenic risk scores (PRSs), aggregate risk information from the most important genetic variants into a single score that describes an individual’s genetic predisposition to a given disease. This article reviews recent developments in the predictive utility of PRSs in relation to a person’s susceptibility to breast cancer and coronary artery disease. Prognostic models for these disorders are built using data from the UK Biobank, controlling for typical clinical and underwriting risk factors. Furthermore, we explore the possibility of adverse selection where genetic information about multifactorial disorders is available for insurance purchasers but not for underwriters. We demonstrate that prediction of multifactorial diseases, using PRSs, provides population risk information additional to that captured by normal underwriting risk factors. This research using the UK Biobank is in the public interest as it contributes to our understanding of predicting risk of disease in the population. Further research is imperative to understand how PRSs could cause adverse selection if consumers use this information to alter their insurance purchasing behaviour.

In this article, we investigate differences in the profiles of patients within the Australian mixed public-private maternal health system to examine the extent of adverse selection. There are conflicting influences on adverse selection within the private health sector in Australia due to government regulations that incentivise lower risk segments of the population to purchase community-rated private health insurance. We use a two-phase modelling methodology that incorporates statistical learning and logistic regression on a dataset that links administrative and longitudinal survey data for a large cohort of women. We find that the key predictor of private patient status is having private health insurance, which itself is largely driven by sociodemographic factors rather than health-or pregnancy-related factors. Additionally, transitioning between the public-private systems for a subsequent pregnancy is uncommon; however, it is primarily driven by changes in private health insurance when it occurs. Other significant factors when transitioning to the private system for a second pregnancy are hypertension, increased access to specialists and stress related to previous motherhood experiences. Consequently, there is limited evidence of adverse selection in this market, with targeted financial incentives likely outweighing the impact of community rating even during childbearing years where private health service use increases.

In comparison with other organizational forms, cooperatives have been found to offer poor product quality and suffer from low reputation. The main reasons discussed in the literature are information asymmetries, which leads to adverse selection and moral hazard, as well as the absence of profit orientation due to poorly specified and diluted property rights. However, although, in reality, many cooperatives indeed perform poorly, and some are apparently able to completely avoid or at least reduce the problems that are considered prototypical for this hybrid organizational form. Therefore, the purpose of this study is to identify the characteristics required to reduce the problems resulting from poorly specified property rights and information asymmetries and, hence, to successfully compete in the market. The data we use to identify the characteristics that separate “good” and “bad” cooperatives encompass 136 organizations in Austria, Germany, and northern Italy. Our logistic regression estimations show that older and larger cooperatives (in terms of acreage) have a significantly higher probability to be listed in at least one of two highly respected wine guides. Moreover, German cooperatives have a significantly lower probability of being listed than either Austrian or northern Italian cooperatives. The findings suggest that differences in performance can be explained by the management of cooperatives. (JEL Classifications: D23, D82, D86, J54, L22, L25, L66, P13, Q13)

Although they have often been found to be technically inefficient, cooperatives not only have survived in the wine industry but continue to play major roles in most European countries. Because the specific advantages of their “organizational architecture” (resource pooling and decentralization) seem to outweigh the disadvantages (vaguely defined property rights and high monitoring costs), this paper discusses organizational measures to improve the performance of cooperatives by addressing three different aspects of “organizational design” (managing entry and exit, motivating members, and investing in corporate culture). (JEL Classifications: D22, D23, L14, L21, L31)