Under traditional market assumptions, an individual’s demand for insurance is related to that individual’s level of risk. Higher risk individuals are more likely to purchase insurance and are more willing to purchase insurance at a higher cost. Conversely, low risk individuals are willing to pay less for coverage because their probable exposure is lower. In a situation of informational asymmetry, where the individual levels of risk are known to the consumer but not to the insurer, insurers are unable to price differentiate between high risk and low risk consumers.
Because of this, insurers are forced to price insurance at the average level of risk. This leads to low risk individuals self-selecting out of the market which in turn increases the average level of risk. Insurance companies understand this opt-out behavior and increase their premium rates which in turn causes the new set of low risk consumer to opt out. This cycle could repeat infinitely until the insurance market simply ceases to function. Alternatively, if insurers know individual risk factors but are prevented from using this information to price discriminate (for example, if insurers are prohibited from using information such as race, gender, age, or pre-existing conditions) it could lead to the same outcome.
Consider smoking as an example. Non-smokers, on average, live longer and have less medical problems than smokers. In either health or life insurance policies, if the insurance premium does not take into account the smoking status of the insured then smokers are more likely to purchase the policy—because they have a higher risk and therefore higher likelihood of using the insurance—and non-smokers are less likely to purchase the policy. This in turn creates a higher risk for the policy as a whole. The insurance company must increase rates, or it runs the risk of not having sufficient premiums to cover the claims made by policy holders. Increased rates push more non-smokers out of the market and the cycle continues.
Rectifying Adverse Selection
There are two predominate methods for insurers to counteract the issue of adverse selection. First, they can attempt to combat the informational asymmetry problem. This is done by requiring up front information from individuals seeking to buy a policy. Common examples include health questionnaires formerly used by health insurance companies and credit scores used by vehicle insurance companies. This gives the insurer more information which they can use to price discriminate according to individual risk.
But this method has potential drawbacks. In some cases, price discrimination is viewed unfavorably. Recent changes in the health care market brought about by the Affordable Care Act show demonstrate a societal distaste for charging more or refusing coverage of pre-existing conditions. Another drawback is that sometimes the information gained through this process isn’t accurate or complete. Those seeking insurance have an incentive to protect their informational advantage. This can lead to incomplete or inaccurate reports of information requested. When available, insurers combat this problem by including clauses that permit them to deny claims if the information is not provided accurately in good faith.
A second method of counteracting adverse selection is to modify the process through which insurance is offered or selected. For example, instead of offering health insurance on an individual basis it can be offered to a group of people based on characteristics other than health conditions. Health insurance offered through the workplace is an example of this type of behavior. While this process still allows for some selective behavior, it greatly reduces the individual incentive to opt-out of coverage. The individual shared responsibility provision—also referred to as the individual mandate—is based off of this approach to combatting adverse selection.