Exaggerating the "Market Failure" in Health Insurance

Here is an interesting little trick, which became crystal clear to me after reading a recent post by David Henderson. It is a trick that pundits and policy wonks sometimes use to exaggerate the case for a market failure in health insurance. Henderson lampoons it beautifully. (The term "wonk" usually implies an attention to analytical detail and economic understanding, both of which are unfortunately missing when people commit this fallacy.)

Henderson's piece links to this piece at the Institute for New Economic Thinking. Here is the relevant quote.

The Centers for Medicare and Medicaid Services projects that per capita spending on health care in the US will average $10,800 in 2017. But the cost for the most expensive 10 percent of patients will average $54,000 per person, compared to an average of just $6,000 for everyone else. The cost for the healthiest 50 percent of patients averages under $700 per person.

The implication is that for 10% of us, health insurance is completely unaffordable. You'd have to charge at least $54,000 to cover this unhealthy 10% of the population, plus whatever it costs to administer the policy, handle the claims, underwrite the insureds, etc.

This is a mistake. The mistake is to group people by their actual expenses, known after-the-fact, as opposed to grouping people by their expected expenses, before you know who actually gets sick (has a heart attack, get cancer, and so on).

I'll use an auto insurance example to illustrate how absurd this is. Suppose we're talking about auto liability coverage for property damage. If you hit someone's car with your car, your insurance policy will pay for the damage. (Unless you drive around without auto insurance, in which case shame on you.) The chance of an accident varies depending on the exact demographic (age, gender, mileage driven, credit history), but the overall average is, say, about 5% per year. So, paraphrasing the piece linked to above:

The per capita spending on auto property damage is $150 per capita. But the cost for the most expensive 5% of motorists is $3,000 per person, compared to an average of just $0 for everyone else.

Obviously something is wrong here. You don't see 95% of the market paying a $0 premium on their auto PD coverage, with the unlucky 5% paying $3,000. The problem comes from grouping people by their known claims, after-the-fact. Insurance premiums aren't determined according to actual claims. If they were, there would be no insurance market. Everyone would just be paying for every dollar of expense they incur, so it would make little sense to have these intermediaries (insurers) handling our money for us. In fact, premiums are determined by expected claims. Someone gathers all the historical data on insurance claims and runs a big statistical model, the output of which tells the user the average cost of an accident for every potential insurance customer. (That someone happens to be me. This is what I do for a living, so please take my point.) When you go to an insurance agent, they punch your information into a computer and in the background the statistical algorithm calculates the expected cost of insuring you. Everyone pays something. The people with the highest premium may pay a great deal more than the people with the lowest premium, but the Institute for New Economic Thinking piece is grossly overstating the magnitude of that disparity. You would not have 10% of the population paying $54,000 for health insurance. Likely you wouldn't have anyone at all paying that much. Nobody's expected expenses are that high, even if some people end up having expenses that high. Insurance is priced and sold before the die is cast. The $54,000 figure is something that is only known after the die has been cast.

Suppose you calculated everyone's expected insurance cost, then divided the population into 20 groups. (Such groupings are generically called "quantiles." If there are 20 quantiles, they are called "vingtiles". There's your word for the day. "Deciles" for 10 groupings, "quintiles" for 5, "percentiles" for 100. See the pattern?) It would look something like this:

The blue line is the share of accidents for each quantile (scale is on the left vertical axis); the red line is the average cost by quantile (scale is on the right vertical axis). Quantiles are on the horizontal axis; obviously 1 is best, 20 is worst. This is based on some made-up data, but it's not fundamentally different from what a you'd see from real data.

Most people have an affordable premium in the $50-$200 range, with a few people (at the 90th to 100th percentiles) paying upwards of $600. Big differences, for sure, but still affordable. (The uptick at the very end is a typical result for this kind of quantile plot. 90% of the population shows a gentle, gradually rising slope, but the very worst 10% or so of risks tend to curve up sharply. It's like most people vary along a pretty smooth continuum, but then you get to the 10th percentile and you see all the raging alcoholics with multiple DUIs and impulse-control problems.)

If I were to naively group people by their actual claims history, it would look more like this:

Everyone without an actual claim would be represented as having "zero cost." The 5% of people with claims would have an average cost of about $3,000. Obviously this is retrospective, not prospective. It is useless for determining future expected costs, in the sense that it implies free insurance coverage for 95% of the population and an absurdly high prospective cost for the other 5%. Something is wrong here. But the policy wonks who comment on health policy routinely make this error. Maybe they are just confused. Maybe they are thoughtlessly throwing out a number that seems to bolster the case for government regulation of insurance markets. Or maybe it really is a cynical attempt to exaggerate the market failure in health insurance, crafted by people who have (for whatever reason) already made up their minds that they want a lot of government intervention. Whatever the motive, don't fall for this trick. There may be a small number of people whose premiums in a competitive market would be so expensive as to be completely unaffordable, but it's nowhere near 10% of the population.

By the way, I could make my above example far more stark by using auto bodily injury coverage: "0.5% of insurance customers are responsible for 100% of the claims!" Or term life insurance: "0.1% of customers are driving 100% of the costs!" Dear lord, these people are obviously uninsurable! The only reason you can do this with health insurance without instantly sounding ridiculous is that most people actually have a few little healthcare costs in any given year.