Tag Archives: section 1342

Insurer losses in Exchanges of 10% not unlikely

Experts who have taken a look at the Affordable Care Act have separately considered the effects of three possible sources of unexpected losses by insurers selling policies in the individual Exchanges: purchasers being older than originally projected, more purchasers being women than originally projected, and purchasers having poorer health than originally projected.  And, at least with respect to the potential for age-based problems, the prestigious Kaiser Family Foundation has given supporters of the ACA considerable comfort by saying, worst case, older purchasers might result in only a 2.5% increase in insurer costs.  But no one to my knowledge — until now — has carefully considered the combined effects of these three sources of potential cost increases and, most likely, pressure for future premium increases.

I have now made an effort to consider the effects of these three sources of insurer losses acting together. Based on that effort, which represents the culmination of work over the past month, I believe it quite possible that insurer losses could amount to 10%, approximately 4% due to purchasers being older than expected, 1% due to greater purchases by women, particularly those in their 20s and 30s, and another 5% due to purchasers having poorer health than expected.

There are four major caveats that should be emphasized up front.  (1) These figures are estimates with large error bars; and anyone pretending to great exactitude in this field, particularly as much of the best data is not yet available, is, I suspect, likely pursuing more of a political agenda than a scholarly one. Losses could be close to zero; losses could be in the 15% range. Still, as I am going to show, significant losses are a serious possibility. (2) These losses are computed without consideration of “risk corridors” under section 1342 of the Affordable Care Act. That provision basically calls on taxpayers to pay insurers losing money on the Exchanges a significant subsidy. After consideration of Risk Corridors, average net insurer losses could range anywhere from close to zero to around 6%-7%. (3) These are national figures.  There are states such as West Virginia in which the age distribution is considerably worse right now than it is nationally.  One should not expect any of the rates of insurer losses (or profits) to be uniform across states or, indeed, across insurers. The figures developed here are an attempt at a  rough average. (4) The figures are based on the last full release of data by HHS on enrollment in the Exchanges; if matters change and, for example, the proportion of younger enrollees grows or the proportion of men grows, the loss rates I project here are likely to decline.

The graphic below summarizes my conclusions.  It shows insurer losses (or gains) as a function of a “health age differential” under two scenarios. By health age differential I mean the difference in ages between someone who has the expected health expenses of the actual enrollee and the chronological age of the enrollee.  Thus, if an enrollee was actually 53 but had the health expenses of an average 57 year old, their health age differential would be 4.  If they had the health expenses of a 50 year old, their health age differential would be negative 3. The yellow line shows insurer losses as a function of the health age differential assuming that the joint distribution of gender and age stays the way it was when HHS last released data.  The blue line shows insurer losses as a function of the health age differential assuming that the joint distribution of gender and age ends up the way it was originally projected to be.  As enrollment under the ACA increases and the proportion of younger enrollees increases, one might expect the ultimate relationship to head from the yellow line down to the blue line.  My assertion that losses could well be 10% is based on the assumption that the joint distribution of gender and age stays the way it is now but that the health of enrollees is equivalent, on average, to those 2 years older than their chronological age.  An assumption that enrollees could have health equivalent, on average, to those 4 years older than their chronological age, yields insurer losses of greater than 15% assuming the current joint distribution stays in place and about 10% assuming the original distribution ends up being correct.

The key graphic for this entry
 

The graphic above is useful because it gives what hitherto had been missing in discussions of problems in the individual Exchanges: some sense of the relative magnitude of problems created by age-based adverse selection (older people enrolling disproportionately) and health-based adverse selection (sicker people enrolling disproportionately). Roughly speaking, the degree of price increases induced by the current age and gender imbalances is roughly equivalent to what would occur if the health of the enrollees was, on average, equivalent to those of persons 2.5 years old than they actually are.

So what does it all mean?

At some point,  a journalist is likely to ask me what this all means?  Is there going to be a death spiral?  I would say we are right on the cusp.  Losses of 10% by insurers relative to expectations, coupled with whatever increase results from medical inflation, isn’t so enormous that I could say, yes, for sure we are heading into a death spiral. But neither is it such a small number that the risk can be ignored.  Moreover, as noted above, the 10% figure is a national average and we need to reduce it because of risk corridors.  In some states, however, where the age and gender figures may be worse or the health of enrollees is particularly problematic or where insurers just bid too low and the winner’s curse overtakes them, I still believe there is a substantial risk of a serious problem. In other states, where age and gender figures are better or insurers more accurately forecast the health of their enrollees, the risk of a death spiral is minimal. And, of course, the more people that actually end up purchasing policies in the Exchanges over the next few months, regardless of whether they come from the ranks of the previously uninsured or those who find that they can not keep their current policies, the more stable the system of insurance created by the ACA is likely to be.

So, after a lot of research, I feel more confident than ever in giving a lawyer’s answer —  it all depends — and a cliche — we’re not out of the woods yet.

Computation details

The results obtained here are based on essentially the same data as user by the Kaiser Family Foundation, which includes data on the relation between age and premium under typical plans, data from the Society of Actuaries (SOA), also used by Kaiser, on the relation between gender, age and expected medical expenses, and my own prior work attempting, based on data from the Department of Health and Human Services released earlier this month, to derive a joint distribution of enrollment in the individual Exchanges based on age and gender.  And, although the math can get a little complicated, the basic idea behind the computations is not all that difficult. It is essentially the computation of some complicated weighted averages.  Each combination of gender and age has some expected level of insurance cost (computed by the Society of Actuaries based on commercial insurance data) and some expected premium (computed by Kaiser based on a study of the ACA). Thus, if we know the joint distribution of gender and age, we can weight each of those costs and each of those premiums properly.

There are three areas of the computation that prove most challenging.  First, because HHS has not released all of the needed data, one must develop a plausible method of moving from the marginal distributions that were provided by HHS on enrollment by age and enrollment by gender into a joint distribution by gender and age. Second, one must calibrate the SOA cost data and the Kaiser premium data, which are expressed in somewhat different units,  such that, if the joint distribution of gender and age was as was originally expected an insurer would just break even.  And, third, one must develop a reasonable method of modeling insured populations that are drawn disproportionately from persons who have higher medical expenses. I believe I have now come up with reasonable solutions to all three issues.

Solution #1

The solution to the first issue, moving from a marginal distribution to a joint distribution, was detailed in my prior blog entry. In short, one finds a large sample of possible joint distributions that match the marginal distributions and scores them according to how well they match the property that people who are subsidized more likely to enroll.  One takes an average of a set of solutions that score best. There is an element of judgment in this process on the degree to which individuals respond to subsidization incentives and, all I can say, is that I believe my methodology is reasonable, avoiding the pitfall of thinking that subsidization is irrelevant or of thinking that it is the only factor that matters in determining enrollment rates. I present again what I believe to be the most likely joint distribution of enrollment by gender and age.

Plausible age/gender distribution of ACA enrollees
Plausible age/gender distribution of ACA enrollees

Solution #2

The solution to the second problem is obtained using calculus and numeric integration. One computes the expected costs and expected premiums given the original joint distribution of enrollees, which is taken to be a product distribution of which one distribution is a “Bernoulli Distribution” in which the probability of being a male or female is equal and the other is a “Mixture Distribution” in which the weights are those shown below (and taken from the  Kaiser Family Foundation web site) and the components are discrete uniform distributions over the associated age ranges.

Original estimate of age distribution of enrollees
Original estimate of age distribution of enrollees

The Society of Actuary data on the relationship between age, gender and medical costs is shown here.

Society of Actuaries data on gender, age and commercial insured expense
Society of Actuaries data on gender, age and commercial insured expense

The premiums under the ACA are shown here.

ACA Premiums
ACA Premiums

These two plots combined can give us a subsidization rate plot by gender and age.  It is shown below along with an associated plot showing the distribution of enrollees by age as was originally assumed and as appears to be the case.

Subsidization rates by gender and age along with anticipated and current age distribution of enrollees
Subsidization rates by gender and age along with anticipated and current age distribution of enrollees

Solution #3

To model adverse selection based on expensive medical conditions, I simply added a health age differential to the insureds.  That is, in computing expected medical costs, I assumed that people were their actual age plus or minus some factor.  (Ages after this addition were constrained to lie between 0 and 64). The graphic above showed insurer losses as a function of this “health age differential” under two scenarios.

Technical Note

A Mathematica notebook containing the computations used in this blog entry is available . here on Dropbox. I’m also adding a PDF version  of the notebook here. I want to thank Sjoerd C. de Vries for coming up with an elegant method within Mathematica of describing the joint distribution used in the computations of various integrals.  I am responsible for any mistakes in implementation of this method and my use of Mr. de Vries idea implies nothing about whether he agrees, disagrees or does not care about any of the analyses or opinions in this post.

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The Risk Corridor Calculator: How the government plans to use fictitious profits to shovel more money to insurers

Snapshot of the Risk Corridor Calculator
Snapshot of the Risk Corridor Calculator

This is a different kind of blog entry.  There isn’t going to be too much text here. Instead, I want to direct you to a spreadsheet I created (The Risk Corridors Calculator) available on Google Docs and the first (click here to watch it on YouTube) of two videos I’ll be making that explain

(1) how Risk Corridors work under the regulations originally proposed by the Department of Health and Human Services (HHS)

(2) how the insurance industry could lose money notwithstanding Risk Corridors as a result of President Obama changing his mind and conditionally permitting certain insurers for one year to “uncancel” certain  policies that the Affordable Care Act would otherwise have have prohibited starting in 2014; and

(3) how the proposed revisions to the Risk Corridor regulations will shovel money to many insurers and could put them back in the same position they would have been had President Obama not changed his mind.

[Note from 8:32 a.m. 12/6/2014: I discovered a small error in the Risk Corridors Calculator. It has been fixed.  It does not affect anything essential in this blog. Unfortunately, I will need to conform the video to the Calculator, which is likely not to happen until later today. So, if you watch the video today, it is conceptually fine, but just be aware that one of the formulas was off.]

In essence, however, the proposed HHS regulations impute fictitious “profits” to insurers that they then get to subtract from their net premiums.  As a result, it will look to the Risk Corridors program as if the insurer is losing more money in an Exchange plan and therefore entitled to greater government assistance.  (The government has now acknowledged that, although the Congressional Budget Office scored it as costing nothing, Risk Corridors need not be budget neutral.) Another way of thinking about the proposal is that it creates phantom costs that affect the apparent (though not the real) profitability of the insurer and then shovels money to insurers based in part on those phantom costs. It is little different than the government insisting that the insurer lost money due to claims that it actually did not pay and is therefore entitled — even under a formula that is formally unchanged — to greater payments from the government.  Viewed yet another way, it is almost as if the proposed regulations treat what President Obama did as a “tort,” and remedy the wrong by licensing the aggrieved insurers to use contorted accounting to place themselves back in the same position they would have been in had the President not, in effect, interfered with the prospective economic advantage they thought they had in the Exchanges.

Neither this blog entry nor the video will address whether the proposed regulations are permissible as a matter of administrative law or separation of powers. Nor will I explore today whether the regulatory changes can be seen as a necessarily evil. Exposing what is actually going on here, however, must create some serious concerns for all concerned about the rule of law. When section 1342 of the Affordable Care Act (42 U.S.C. § 18062) speaks of “allowable costs,” one would initially think it referred to costs actually incurred by the insurer as a result of running its program. Those costs might be paying claims, paying the electric bill, marketing costs and, perhaps, some reasonable allowance for profit — such as the 3% of after tax premiums actually placed in the original regulations.

But it is going to take some work to show that, by “allowable costs,” the statute meant costs that the insurer did not actually incur in running its program. The burden will be even higher due to the fact that the proposed regulations apparently contemplate varying this heightened profit allowance from state to state. This will be done not in response to different rates of return on capital in the different states, but only to take account of differential losses to insurers caused by different state responses to President Obama’s about-face on whether certain plans that violate ACA requirements could continue to be sold outside of the Exchanges.

In short, the increase in “profit” sure looks like a book-keeping entry whose sole purpose has nothing to do with anything in the statute but is instead designed to restore the insurer to the position it would have been in had federal policy not changed. It is as if the insurers are being given some sort of entitlement to the profits they would otherwise have made and the administration is looking for any term in the statute not glued down (such as an 80% reimbursement rate on certain losses) in order to accomplish this goal.

Fleshing out  more fully these matters of statutory interpretation, separation of powers, and administrative law will be left for later, however, along with a fuller explanation of what is going on inside the Risk Corridor Calculator that I created. For now, play with the spreadsheet and enjoy the video.

Resources

Society of Actuaries, Health Watch: Risk Corridors under the Affordable Care Act — A Bridge over Troubled Waters, but the Devil’s in the Details

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