Insurance loss coverage and social welfare

Restrictions on insurance risk classification may induce adverse selection, which is usually perceived as a bad outcome, both for insurers and for society. However, a social benefit of modest adverse selection is that it can lead to an increase in `loss coverage', defined as expected losses compensated by insurance for the whole population. We reconcile the concept of loss coverage to a utilitarian concept of social welfare commonly found in economic literature on risk classification. For iso-elastic insurance demand, ranking risk classification schemes by (observable) loss coverage always gives the same ordering as ranking by (unobservable) social welfare

Insurance loss coverage under restricted risk classification: The case of iso-elastic demand

This paper investigates equilibrium in an insurance market where risk classification is restricted. Insurance demand is characterised by an iso-elastic function with a single elasticity parameter. We characterise the equilibrium by three quantities: equilibrium premium; level of adverse selection (in the economist’s sense); and “loss coverage”, defined as the expected population losses compensated by insurance. We consider both equal elasticities for high and low risk-groups, and then different elasticities. In the equal elasticities case, adverse selection is always higher under pooling than under risk-differentiated premiums, while loss coverage first increases and then decreases with demand elasticity. We argue that loss coverage represents the efficacy of insurance for the whole population; and therefore that if demand elasticity is sufficiently low, adverse selection is not always a bad thing

On Technical Bases and Surplus in Life Insurance

We revisit surplus on general life insurance contracts, represented by Markov models. We classify technical bases in terms of boundary conditions in Thiele's equation(s), allowing more general regulations than Scandinavian-style `first-order/second-order' regimes, and replacing the traditional retrospective policy value. We propose a `canonical' model with three technical bases (premium, valuation, accumulation) and show how each pair of bases defines premium loadings and surplus. Along with a `true' or `real-world' experience basis, this expands fundamental results of Ramlau-Hansen (1988a). We conclude with two applications: lapse-supported business; and the retrospectively-oriented regime proposed by M{\o}ller & Steffensen (2007).Comment: 33 pages, 4 Figure

The effect of the COVID-19 health disruptions on breast cancer mortality for older women: A semi-Markov modelling approach

We propose a methodology to quantify the impact on breast cancer mortality of diagnostic delays caused by public health measures introduced as a response to the COVID-19 pandemic. These measures affected cancer pathways by halting cancer screening, delaying diagnostic tests, and reducing the numbers of patients starting treatment. We introduce a semi-Markov model, to quantify the impact of the pandemic based on publicly available population data for women age 65{89 years in England and relevant medical literature. We quantify age-specific excess deaths, for a period up to 5 years, along with years of life expectancy lost and change in cancer mortality by cancer stage. Our analysis suggests a 3-6% increase in breast cancer deaths, corresponding to more than 40 extra deaths, per 100,000 women, after age 65 years old over 5 years, and a 4-6% increase in registrations of advanced (Stage 4) breast cancer. Our modelling approach exhibits consistent results in sensitivity analyses, providing a model that can account for changes in breast cancer diagnostic and treatment services

Insurance pricing for breast cancer under different multiple state models

In this paper we consider pricing of insurance contracts for breast cancer risk based on three multiple state models. Using population data in England and data from the medical literature, we calibrate a collection of semi-Markov and Markov models. Considering an industry-based Markov model as a baseline model, we demonstrate the strengths of a more detailed model while showing the importance of accounting for duration dependence in transition rates. We quantify age-specific cancer incidence and cancer survival by stage along with type-specific mortality rates based on the semi-Markov model which accounts for unobserved breast cancer cases and progression through breast cancer stages. Using the developed models, we obtain actuarial net premiums for a specialised critical illness and life insurance product. Our analysis shows that the semi-Markov model leads to results aligned with empirical evidence. Our findings point out the importance of accounting for the time spent with diagnosed or undiagnosed pre-metastatic breast cancer in actuarial applications

Functional network changes and cognitive control in schizophrenia

Cognitive control is a cognitive and neural mechanism that contributes to managing the complex demands of day-to-day life. Studies have suggested that functional impairments in cognitive control associated brain circuitry contribute to a broad range of higher cognitive deficits in schizophrenia. To examine this issue, we assessed functional connectivity networks in healthy adults and individuals with schizophrenia performing tasks from two distinct cognitive domains that varied in demands for cognitive control, the RiSE episodic memory task and DPX goal maintenance task. We characterized general and cognitive control-specific effects of schizophrenia on functional connectivity within an expanded frontal parietal network (FPN) and quantified network topology properties using graph analysis. Using the network based statistic (NBS), we observed greater network functional connectivity in cognitive control demanding conditions during both tasks in both groups in the FPN, and demonstrated cognitive control FPN specificity against a task independent auditory network. NBS analyses also revealed widespread connectivity deficits in schizophrenia patients across all tasks. Furthermore, quantitative changes in network topology associated with diagnostic status and task demand were observed. The present findings, in an analysis that was limited to correct trials only, ensuring that subjects are on task, provide critical insights into network connections crucial for cognitive control and the manner in which brain networks reorganize to support such control. Impairments in this mechanism are present in schizophrenia and these results highlight how cognitive control deficits contribute to the pathophysiology of this illness

When is utilitarian welfare higher under insurance risk pooling?

This paper focuses on the effects of bans on insurance risk classification on utilitarian social welfare. We consider two regimes: full risk classification, where insurers charge the actuarially fair premium for each risk, and pooling, where risk classification is banned and for institutional or regulatory reasons, insurers do not attempt to separate risk classes, but charge a common premium for all risks. For the case of iso-elastic insurance demand, we derive sufficient conditions on higher and lower risks’ demand elasticities which ensure that utilitarian social welfare is higher under pooling than under full risk classification. Empirical evidence suggests that these conditions may be realistic for some insurance markets

When is utilitarian welfare higher under insurance risk pooling?

This paper considers the effect of bans on insurance risk classification on utilitarian social welfare. We consider two regimes: full risk classification, where insurers charge the actuarially fair premium for each risk, and pooling, where risk classification is banned and for institutional or regulatory reasons, insurers do not attempt to separate risk classes, but charge a common premium for all risks. For iso-elastic insurance demand, we derive sufficient conditions on higher and lower risks' demand elasticities which ensure that utilitarian social welfare is higher under pooling than under full risk classification. Using the concept of arc elasticity of demand, we extend the results to a form applicable to more general demand functions. Empirical evidence suggests that the required elasticity conditions for social welfare to be increased by a ban may be realistic for some insurance markets

Will genetic test results be monetized in life insurance?

If life insurers are not permitted to use genetic test results in underwriting, they may face adverse selection. It is sometimes claimed that applicants will choose abnormally high sums insured as a form of financial gamble, possibly financed by life settlement companies (LSCs). The latter possibility is given some credence by the recent experience of “stranger‐originated life insurance” (STOLI) in the United States. We examine these claims, and find them unconvincing for four reasons. First, apparently high mortality implies surprisingly high probabilities of surviving for decades, so the gamble faces long odds. Second, LSCs would have to adopt a different business model, involving much longer time horizons. Third, STOLI is being effectively dealt with by the U.S. courts. Fourth, the gamble would be predicated upon a deep understanding of the genetic epidemiology, which is evolving, subject to uncertain biases, and cannot predict the emergence of effective treatments

Both unmedicated and medicated individuals with schizophrenia show impairments across a wide array of cognitive and reinforcement learning tasks

BACKGROUND: Schizophrenia is a disorder characterized by pervasive deficits in cognitive functioning. However, few well-powered studies have examined the degree to which cognitive performance is impaired even among individuals with schizophrenia not currently on antipsychotic medications using a wide range of cognitive and reinforcement learning measures derived from cognitive neuroscience. Such research is particularly needed in the domain of reinforcement learning, given the central role of dopamine in reinforcement learning, and the potential impact of antipsychotic medications on dopamine function. METHODS: The present study sought to fill this gap by examining healthy controls (N = 75), unmedicated (N = 48) and medicated (N = 148) individuals with schizophrenia. Participants were recruited across five sites as part of the CNTRaCS Consortium to complete tasks assessing processing speed, cognitive control, working memory, verbal learning, relational encoding and retrieval, visual integration and reinforcement learning. RESULTS: Individuals with schizophrenia who were not taking antipsychotic medications, as well as those taking antipsychotic medications, showed pervasive deficits across cognitive domains including reinforcement learning, processing speed, cognitive control, working memory, verbal learning and relational encoding and retrieval. Further, we found that chlorpromazine equivalency rates were significantly related to processing speed and working memory, while there were no significant relationships between anticholinergic load and performance on other tasks. CONCLUSIONS: These findings add to a body of literature suggesting that cognitive deficits are an enduring aspect of schizophrenia, present in those off antipsychotic medications as well as those taking antipsychotic medications