Can adverse selection increase social welfare?

This talk will focus 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 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

How can adverse selection increase social welfare?

This talk will focus 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

Are health care inequalities unfair? A study on public attitudes in 23 countries

Background: In this article we focus on the following aims: (1) to analyze national and welfare state variations in the public perception of income-related health care inequalities, (2) to analyze associations of sociodemographic, socioeconomic, health-related, and health care factors with the perception of health care inequalities. Methods: Data were taken from the International Social Survey Programme (ISSP), an annually repeated cross-sectional survey based on nationally representative samples. 23 countries (N = 37,228) were included and assigned to six welfare states. Attitude towards income-related health care inequalities was assessed by asking: "Is it fair or unfair that people with higher incomes can afford better health care than people with lower incomes?" with response categories ranging from "very fair" (1) to "very unfair" (5). On the individual level, sociodemographic (gender, age), socioeconomic (income, education) health-related (self-rated health), and health care factors (health insurance coverage, financial barriers to health care) were introduced. Results: About two-thirds of the respondents in all countries think that it is unfair when people with higher incomes can afford better health care than people with lower incomes. Percentages vary between 42.8 in Taiwan and 84 in Slovenia. In terms of welfare states, this proportion is higher in Conservative, South European, and East European regimes than in East Asian, Liberal, and Social-Democratic regimes. Multilevel logistic regression analyses show that women, people affected by a low socioeconomic status, poor health, insufficient insurance coverage, and foregone care are more likely to perceive income-related health care inequalities as unfair. Conclusions: In most countries a majority of the population perceives income-related health care inequalities as unfair. Large differences between countries were observed. Welfare regime classification is important for explaining the variation across countries

Utilitarian Social Welfare and Insurance Loss Coverage Under Restricted Risk Classification

This thesis considers the effect of restrictions on insurance risk classification on utilitarian social welfare and insurance loss coverage. First, 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. Next, we consider scenarios where the regulator does not ban risk classification, but instead imposes a price collar, i.e. a limit on the ratio of premiums for high risks relative to those for low risks. Pooling and full risk classification could be considered as limiting cases of a price collar. A regulator imposed price collar would force insurers to use partial risk classification - where some risk-groups might be merged to pay the same premium. We find that for iso-elastic demand, a price collar can give higher loss coverage than either pooling or full risk classification, but only if high and low risks have certain combinations of demand elasticities (both greater than one)

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

Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making

We draw attention to an important, yet largely overlooked aspect of evaluating fairness for automated decision making systems---namely risk and welfare considerations. Our proposed family of measures corresponds to the long-established formulations of cardinal social welfare in economics, and is justified by the Rawlsian conception of fairness behind a veil of ignorance. The convex formulation of our welfare-based measures of fairness allows us to integrate them as a constraint into any convex loss minimization pipeline. Our empirical analysis reveals interesting trade-offs between our proposal and (a) prediction accuracy, (b) group discrimination, and (c) Dwork et al.'s notion of individual fairness. Furthermore and perhaps most importantly, our work provides both heuristic justification and empirical evidence suggesting that a lower-bound on our measures often leads to bounded inequality in algorithmic outcomes; hence presenting the first computationally feasible mechanism for bounding individual-level inequality.Comment: Conference: Thirty-second Conference on Neural Information Processing Systems (NIPS 2018