In an earlier post, we examined how insurance markets work and how companies and individuals both view risk. This post breaks down who actually ends up purchasing insurance and how this affects the entire market, and ultimately the cost of insurance.
Let’s consider the same scenario from the last post, where a homeowner was considering purchasing a fire insurance policy for their $500,000 apartment. The anticipated losses were $7,000 over a five year period but the insurance policy would cost them $10,000.
One key assumption in this simple model is that the probability of fire is known. In reality, the probability of fire varies from individual to individual.
Let’s make our model more realistic and say that the probabilities in the above example only represent the population averages for how likely each person is to have a fire. But the homeowner in question knows (or thinks) that their probability of having a fire is less, let’s say half as much. Should they still buy the insurance?
With the probability of fire cut in half, their expected loss would only be $3,500. But the insurer doesn’t take this into account so they would still charge the full $10,000 for the policy.
Some very risk-averse people might still purchase the policy, but many would not.
On the other side of the market, you have a reckless homeowner who has a much higher chance of fire, let’s say double. This person would then have an expected loss of $14,000, so the $10,000 policy would seem like a great deal. Of course some people still wouldn’t purchase it for a number of reasons including their ability to pay, risk-seeking behavior, etc.
Generally speaking, though, people who are more at risk and have higher expected losses are more likely to want insurance. This is true in almost all insurance markets (health, car, corporate).
So, what does this mean for the insurance company? Imagine that the market is made up equally of people like the first homeowner (low chance of fire) and people like the second homeowner (high chance of fire). The population averages for how likely each person is to have a fire remain the same, but we know that the people who are actually buying insurance will have a higher rate of fire.
Because of this, the actual payouts for the policies would probably average closer to $14,000 instead of the $7,000 payout used to calculate the policy cost and the company wouldn't be able to sustain their prices. Given the higher payouts, they would have to raise their total policy cost to $17,000 to cover their risk premium and administrative costs.
Expected Payout | $14,000 |
Risk Premium | $500 |
Administrative Costs | $2,500 |
Total Cost | $17,000 |
Of course, as the cost of insurance goes up, the number of people who want to (or can afford to) buy insurance will continue to drop. And the people who do buy insurance will be the people who are most at risk. So the cycle would continue with the cost of insurance rising until only the very highest risk customers will buy a policy.
This effect comes solely from the mismatch of information between the seller (insurer) and buyer (individual). Generally speaking, this mismatch in information is called information asymmetry and is at the heart of a number of economic phenomena. This specific effect is called adverse selection.
Adverse selection refers to any process where the party with more information selects transactions that are more beneficial to them, at the cost of the less-informed party. In this case, the individual buying the insurance is the “selector” and has more information. They use this information to selectively participate in transactions (i.e. buying insurance).
Economists have spent a lot of time looking for ways to avoid these adverse selection situations, including:
Signaling
Here the party with more information reveals their type. For example, a healthy person might get a note from a doctor saying that they are healthy. Or a company might reveal some of its financial statements to remove some of the uncertainty around their value. A classic example of signaling is education.
Screening
Here the party with less information creates a process whereby the other side is forced to reveal information about themselves through their choices. For example, an insurance company might have different types of plans. They might have a more expensive “full coverage” plan and a less expensive “limited coverage” plan. In this way, the more at-risk customers will buy the full-coverage and the less at-risk customers will buy the limited coverage.
Universal healthcare
One solution to adverse selection is to not allow selection at all. For example, forcing everyone to have healthcare will keep costs down and eliminate the issue of adverse selection. In this situation, people who are not at-risk are subsidizing the costs of people who are at-risk. Of course, there are many other considerations when talking about healthcare policy.
