Saturday 20 September 2014

Big trees, home insurance and adverse selection

One of the most difficult concepts we cover in ECON100 and ECON110 each semester is the problem of adverse selection. Adverse selection arises when there is information asymmetry - specifically, there is private information about some characteristics or attributes that are relevant to an agreement, that is known to one party to an agreement but not to others.

However, information asymmetry by itself is not enough for an adverse selection problem (e.g. I know whether I like the colour yellow or not, but that private information doesn't affect many market transactions I engage in - at least not to a large enough extent to cause market failure). The informed party must be able to benefit from keeping the private information secret - this is an example of pre-contractual opportunism on the part of the informed party.

An adverse selection problem arises because the uninformed party cannot tell those with 'good' attributes from those with 'bad' attributes. To minimise the risk to themselves of engaging in an unfavourable market transaction, it makes sense for the uninformed party to assume that everyone has 'bad' attributes. This leads to a pooling equilibrium - those with 'good' and 'bad' attributes are grouped together because they can't easily differentiate themselves. This creates a problem if it causes the market to fail.

In the case of insurance, the market failure may arise as follows (this explanation follows Stephen Landsburg's excellent book The Armchair Economist). Let's say you could rank every person from 1 to 10 in terms of risk (the least risky are 1's, and the most risky are 10's). The insurance company doesn't know who is high-risk or low-risk. Say that they price the premiums based on the 'average' risk ('5' perhaps). The low risk people (1's and 2's) would be paying too much for insurance relative to their risk, so they choose not to buy insurance. This raises the average risk of those who do buy insurance (to '6' perhaps). So, the insurance company has to raise premiums to compensate. This causes some of the medium risk people (3's and 4's) to drop out of the market. The average risk has gone up again, and so do the premiums. Eventually, either only high risk people (10's) buy insurance, or no one buys it at all. This is why we call the problem adverse selection - the insurance company would prefer to sell insurance to low risk people, but it's the high risk people who are most likely to buy.

Which brings me to the example of big trees and home insurance. One of my extramural ECON110 students asked me about this video for Youi insurance. It provides a good example of potential adverse selection, but one that is easily solved.

The insurance company doesn't know whether or not you have 'enormous trees that will fall and crush your house' (having big trees next to your house is private information). So, maybe they make an assumption that you do, and in order to compensate for the higher risk, they charge a higher insurance premium.

Of course, markets have developed ways of solving the adverse selection problem. If the informed party can find some way to credibly reveal the private information to the uninformed party, we call this signalling (I've previously written on signalling, in the context of wedding costs). If the uninformed party can find some was of revealing the private information, we call this screening.

In the case of the video, the screening solution to the big trees adverse selection problem is pretty simple. Just ask the person if they have big trees! [Of course, if they do have enormous trees next to their house, there is some incentive to misrepresent themselves and say 'no', but that's why you have a clause in the insurance contract that voids the contract if the insured person provides false information.]

Alternatively there is a signalling solution to the big trees adverse selection problem. The homeowner could take a photo of their house, demonstrating that there are no enormous trees next to it. Easy and credible.

[HT: Tracey from my ECON110(NET) class]

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