Ex Ante Moral Hazard in Health Insurance and Innovation

Ex ante moral hazard is a significant concern in health insurance, particularly when it comes to innovation. This type of moral hazard occurs before a health event, where individuals may take on excessive risk or engage in unhealthy behaviors, knowing they'll be covered by insurance.

In the context of health insurance, ex ante moral hazard can lead to overconsumption of unhealthy behaviors, such as excessive drinking or smoking. This can result in increased healthcare costs and reduced overall well-being.

Ex ante moral hazard can also hinder innovation in the healthcare industry. For instance, if patients are not incentivized to take care of their health, they may be less likely to participate in clinical trials or adopt new, innovative treatments.

Moral hazard is a key concept in understanding ex ante moral hazard. Ex ante moral hazard arises if the agent can affect the probability of an accident.

Insurance policies can sometimes encourage people to take more risks, as they know they'll be covered if something goes wrong. Ex- ante moral hazard might also be manifested in diversion of the loan from business to household needs, hence lowering expected revenue.

A special case of ex ante moral hazard occurs when the loading factor is blown up by the moral hazard factor (1 + η). This is illustrated by Arrow's result, but with the added factor of moral hazard.

Ex ante moral hazard can lead to reduced consumption of preventive care, or changes in lifestyle, that result in an increased probability of requiring more expensive curative services. This is a real-world consequence of ex ante moral hazard.

Here are some examples of how ex ante moral hazard can manifest:

Ex ante moral hazard occurs because efforts to prevent diseases are non-contractible in an insurance policy or premiums can’t adjust for BMI choices and consumers are not compensated for the effects that their efforts at prevention have on expected benefits.

Health insurance can have a significant impact on innovation, particularly when it comes to addressing health issues like obesity. In the case of employer-provided health insurance, differences in wages between obese and non-obese workers can undo nominal risk pooling between workers, effectively eliminating the health insurance externality from obesity.

This is because the wage difference between obese and non-obese workers can undo the benefit of pooling, making it less likely for people to take advantage of health insurance. Without pooling, there is no health insurance externality from obesity.

The situation is different with public insurance, such as Medicare, where there is clearly pooling and the associated transfer from thinner to heavier individuals. This can lead to a welfare loss from the ex-ante externality, unless the subsidy induced by insurance causes someone to become heavier.

Research has shown that the elasticity of body weight with respect to the transfer from thinner to heavier individuals (induced by insurance) is a key factor in determining the size of the welfare loss. Unless the subsidy induced by insurance causes someone to become heavier, the insurance transaction is a costless transfer.

Here's a breakdown of the key factors that influence the size of the welfare loss:

In the case of Medicare, the share of the marginal health care expenditures paid by Medicare is approximately 50% for people aged 65 and over. This is based on data from the MEPS (Medical Expenditure Panel Survey).

The present value of the cumulative Medicare-induced public health insurance externality of obesity from initial age 18 to terminal age 80 is calculated using the formula:

∑t=min{18,65}80βt−18×m×[Tt(obese)−Tt(normal)]

where β is the discount factor, m is the share of the marginal health care expenditures paid by Medicare, and Tt (normal) and Tt (obese) are the average annual health care expenditures at age t for the normal weight and for the obese.

The results show that the positive cumulative innovation externality of obesity from pharmaceutical expenditures is much larger than the negative Medicare-induced public health insurance externality from pharmaceutical expenditures.

The model in question involves an innovator and N consumers. In stage 1, consumers simultaneously and non-cooperatively choose their level of prevention.

The innovator chooses the level of its R&D investments, which determines the probability μ that it's successful in developing a new medical care technology. This probability is crucial, as it affects the innovator's expected profit.

The innovator maximizes its expected profit Π (μ) = μR − C (μ), where R is the expected reward for success and C (μ) is a cost function. The expected reward for success is N times the combined illness benefit and monetary penalty from choosing the high level of prevention, SNORMAL.

Here's a breakdown of the cost function: C(μ) = cF + aμ + b2μ2, where cF ≥ 0, a > 0 and b > 0 are parameters. This captures the notion that firms take advantage of the most fertile research ideas first, but such ideas are scarce.

The innovator's optimum is to set μ = −ab + 1/bR, provided that for μ = −ab + 1/bR the properties Π (μ) ≥ Π (0) and μ ∈ [0, 1] hold.

The model we're discussing involves an innovator and N consumers. In the first stage, consumers simultaneously and non-cooperatively choose their level of prevention. This is a crucial decision that sets the stage for the rest of the model.

The innovator then chooses the level of R&D investments, which determines the probability μ that they'll be successful in developing a new medical care technology. This is a critical juncture in the model, as the success of these investments has a significant impact on the outcome.

The success of the R&D investments and the health status of each consumer are revealed in the next stage. This is a key moment in the model, as it allows consumers to make more informed decisions about their level of medical care.

Consumers choose the level of medical care in the final stage, taking into account the utility they derive from leisure, consumption, and the impact of illness on their well-being. The utility function is represented by expressions (5) and (6), which highlight the importance of these factors in the decision-making process.

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Consumers are a crucial part of The Model, making up a significant portion of its users.

They are typically individuals or households that purchase goods or services from businesses that are part of The Model.

The Model provides consumers with a convenient and efficient way to access a wide range of products and services, often at competitive prices.

Consumers can choose from a variety of payment options, including credit and debit cards, as well as digital payment methods.

The Model's focus on consumer convenience has led to a significant increase in online shopping, with many consumers opting to shop from the comfort of their own homes.

Businesses that are part of The Model benefit from the increased visibility and accessibility that it provides to consumers.

This, in turn, has led to a significant increase in sales and revenue for many businesses that are part of The Model.

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The innovator is a crucial part of the model, choosing the level of R&D investments to maximize its expected profit. This profit is determined by the probability of success and the costs associated with the investment.

The innovator's expected profit is given by the expression Π(μ) = μR − C(μ), where R is the expected reward for success and C(μ) is the cost function. The cost function can be represented by the expression C(μ) = cF + aμ + b^2μ^2, where cF ≥ 0, a > 0, and b > 0 are parameters.

The innovator's optimum is to set μ = -ab + 1/bR, provided that this value satisfies the properties Π(μ) ≥ Π(0) and μ ∈ [0, 1]. This result implies that the probability of innovation is increasing in the reward for success R.

The reward-elasticity of innovation, denoted by εμ, captures the responsiveness of the rate of innovation to changes in the reward for innovation. This concept is useful for empirical analyses of induced innovation and has two advantages: it is more intuitive than a specific cost function, and it is the object of interest in such analyses.

The reward-elasticity of innovation can be represented by the expression εμ ≡ dμ/dRRμ, which measures the change in the probability of innovation with respect to changes in the reward for innovation. This concept is essential for understanding the induced innovation effect and its dependence on the reward for innovation.

Incorporating marginal costs is a crucial aspect of our model, and it's essential to understand how it affects the reward for innovation and the optimal obesity subsidy.

The formal model initially ignores marginal production and marketing costs, but a consideration of these aspects changes the impact of a marginal increase in obesity on the reward for innovation.

This is where equation (29) comes in: t∗=[E(obese)−E(normal)]×RPATENT×(1−RMC). This equation takes into account the share of medical care expenditures that are spent on patent protected goods (RPATENT) and the share of medical care expenditures that covers marginal production and marketing costs (RMC).

The share of medical care expenditures that covers marginal production and marketing costs (RMC) is a critical factor in this equation.

By incorporating marginal costs, we can get a more accurate estimate of the optimal obesity subsidy, which is essential for making informed decisions.

The optimal policy is a crucial aspect of ex ante moral hazard, and it's defined by the social planner's goal to maximize total surplus. This is achieved by setting the obesity subsidy t to balance the innovator's expected profit with the expected consumer surplus.

The expected consumer surplus is calculated using the expression A−N×∫0BNORMALθF′(θ)dθ−N×πAVERAGE×CILLNESS, where A is a constant that depends on the number of people (N), the health benefits of prevention (H), the cost of obesity (SOBESE), and the cost of illness (CILLNESS).

A key factor in determining the optimal policy is the budget balancing condition, nOBESE × t = N × T. This ensures that the subsidy t is set in a way that maximizes the total surplus.

To find the optimal obesity subsidy, the social planner solves the optimization problem maxt{μR−C(μ)−N×∫0BNORMALθF′(θ)dθ−N×πAVERAGE×CILLNESS}. The solution to this problem is denoted by tTS∗.

Here's a summary of the key components involved in the optimal policy:

In economics, certain propositions can have a significant impact on how we approach problems like ex ante moral hazard.

The optimal obesity subsidy is higher than the increase in the reward for innovation from the lower level of prevention if consumers and the innovator capture a strictly positive share of the ex-post surplus from innovation.

This means that if both parties benefit from innovation, it's better to invest in obesity subsidies.

Proposition 1 is a crucial finding in our analysis, and it's actually quite straightforward. If consumers and the innovator capture a share of the surplus from innovation, the optimal obesity subsidy is higher than the increase in the reward for innovation from the lower level of prevention.

This means that if the innovator and consumers get a piece of the pie, so to speak, the optimal subsidy for obesity is actually greater than what we'd expect. In fact, it's only equal to the increase in the reward for innovation from the lower level of prevention if the innovator captures the entire surplus.

Formally, this is represented by the equation tTS∗=μ×(πOBESE−πNORMAL)×s×(D1−D0)+εμ×1−ss×μ×(πOBESE−πNORMAL)×s×(D1−D0).

Proposition 2 is a ballot measure that will have a significant impact on the state's agricultural industry. It proposes to establish a right to repair for farmers and consumers, allowing them to repair and maintain their own equipment and products.

This proposition is a response to the growing trend of companies designing products with planned obsolescence, making it difficult for people to repair and maintain them. In fact, some companies have been known to use proprietary screws and other design elements to prevent repairs.

The right to repair will not only save farmers and consumers money but also reduce electronic waste and promote sustainability. By allowing people to repair and maintain their own products, we can reduce the need for new, resource-intensive products.

Some of the key provisions of Proposition 2 include allowing farmers to repair and maintain their own equipment, requiring companies to provide repair information and tools, and prohibiting companies from designing products with planned obsolescence.

If this caught your attention, see: How Do Insurance Companies Reduce the Risk of Moral Hazard

We used data from the Medical Expenditure Panel Survey (MEPS) to measure pharmaceutical expenditures. The data spans from 2002 to 2005.

The MEPS data allowed us to examine pharmaceutical expenditures by age and Body-Mass Index (BMI) group. This timeframe was chosen to eliminate any potential time effects in pharmaceutical expenditures.

We only used MEPS data from 2002 to 2005, as this period is available and suitable for our analysis.

We use the Medical Expenditure Panel Survey (MEPS) data from years 2002–2005 to measure pharmaceutical expenditures and total health care expenditures by age and Body-Mass Index (BMI) group.

The MEPS data is available from 1996, but we only use the data from 2002–2005 to eliminate concern over possible time effects in the pharmaceutical expenditures.

We focus on a specific time period to ensure our results are reliable and accurate, avoiding any potential biases that might come from using data from earlier years.

Figure 2 reveals a significant finding about the present value of cumulative innovation and insurance externalities of obesity.

This result demonstrates that another moral hazard in health can be just as important as the ex-ante moral hazard examined by Ehrlich and Becker in 1972, a concept that has been central in health economics for decades.

The Medicare-induced implicit pooled health insurance subsidy for obesity is roughly equal to the optimal subsidy for obesity that is implied by the innovation externality of obesity from pharmaceutical expenditures.

The presence of this Medicare-induced public health insurance externality of obesity is not enough to justify "soda taxes", "fat taxes" or other penalties aimed at increasing the personal costs of obesity.

The exact value of the innovation externality of obesity is sensitive to the assumptions about the parameters, but the conclusion that the two opposing externalities of obesity are of the same order of magnitude appears robust.

We likely underestimate the true magnitude of the innovation externality in this analysis because we ignore the innovation externality from other medical expenditures than pharmaceutical expenditures.

The calculations relied on the derived lower bound for the innovation externality rather than on the derived expression for the exact externality.

The relationship between obesity and health care expenditures is complex, and understanding the impact of obesity on innovation and insurance externalities is crucial.

Bhattacharya and Sood (2007) show that in pooled health insurance, if the elasticity of body weight with respect to the transfer from thinner to heavier individuals (induced by insurance) is zero, there is no welfare loss from the ex-ante externality.

The induced innovation hypothesis suggests that the effect of extending drug insurance on welfare through induced innovation can be significant.

Empirical investigations of the induced innovation hypothesis in the pharmaceutical industry find support for the hypothesis, with studies such as Acemoglu and Linn (2004), Finkelstein (2004), Lichtenberg and Waldfogel (2003), and Yin (2008) all finding evidence of induced innovation.

The present value of the cumulative innovation externality of obesity from pharmaceutical innovation can be calculated using the expression ∑t=t0Tβt−t0×Innovation_Externalityt, where β is the discount factor and Innovation_Externalityt is the innovation externality of obesity at age t.

In contrast, the present value of the cumulative Medicare-induced public health insurance externality of obesity from initial age t0 to terminal age T is ∑t=min{t0,65}Tβt−t0×m×[Tt(obese)−Tt(normal)], where m is the share of the marginal health care expenditures paid by Medicare.

Using data from the MEPS, the share of health care expenditures covered by Medicare for people aged 65 and over is approximately 50%.

The results show that the positive cumulative innovation externality of obesity from pharmaceutical expenditures is much larger than the negative Medicare-induced public health insurance externality from pharmaceutical expenditures.

Here's a comparison of the calculated cumulative externalities for a person with terminal age 80:

Note: The exact values of X, Y, and Z are not provided in the article section, but the comparison suggests that the positive cumulative innovation externality from pharmaceutical expenditures is of the same order of magnitude as the negative cumulative Medicare-induced public health insurance externality from all health care expenditures.