Tag Archives: Kaiser Family Foundation

The ACA’s transitional reinsurance tax: the numbers are funny again

Most sellers of health insurance in the United States outside of health insurance Exchanges will be forced to add $63 per member on to premiums for 2014 to cover a new tax imposed by the Affordable Care Act on the sale of such policies. That tax revenue coupled with $2 billion out of the federal treasury will go to subsidize individual policies sold on the federal Exchanges, probably lowering their gross premiums by about $525 per person.  If, however, enrollment in the federal Exchanges remains considerably lower than projected and enrollment in non-grandfathered, non-Exchange plans does not compensate for the reduction, the revenue collected from the tax is likely to be in excess of that which needs to be paid to support the statutory subsidies.  The $63 per member tax, which has precipitated considerable protest, thus might end up being overly high. And if the Executive branch can exercise its discretion to delay or waive taxes for one part of the ACA based on alleged new developments, why not for another?

The Center for Medicare & Medicaid Services (CMS)  has many options for addressing the surplus.  It might choose to to use the surplus tax revenue either to cut similar taxes in the subsequent years of the program or to rebate the excessive tax back to health plans and others who paid it. CMS might, I suppose, inflame people from both ends of the ideological spectrum by gifting insurers with more generous reinsurance this year.  Or CMS might simply squirrel the surplus away to provide reinsurance after the normal sunset of the program in 2016. I suspect, however, that  CMS is likely to use the surplus to increase the generosity of reinsurance provided in subsequent years of the program such as next year. Doing so could mask problems of adverse selection that could otherwise result in large premium increases. Such a choice would not  necessarily be a bad thing: it just highlights yet again the expense of the ACA, the fragility of attempts prior to its passage to model its effects, and the problems with thinking about its interlocking web of provisions in a linear, reductionist manner.

Here’s a more detailed explanation.

The Affordable Care Act subsidizes both insurance purchases made on the individual Exchanges and  individual policies still sold off the Exchange that conform with various ACA rules.  Doing so lowers the price of insurance and decreases the systematic risk associated with selling policies in a new regulatory environment in which the population of insureds may have different (and worse) health profiles than those previously composing the insurance pool.  A key way that the ACA does this is through a program of “transitional reinsurance” provided free of charge to insurers willing to write policies in the individual market — so long as those policies haven’t been exempted from the requirements of the ACA by being “grandfathered.” The program is “transitional” because it is supposed to end after three years. One way of thinking about all this is that free reinsurance lowers both the mean and the standard deviation of the net claims distribution faced by eligible insurers.

Under section 1341 of the ACA and the regulations CMS has developed to implement it, the transitional reinsurance program is ultimately supposed to break even. If tax revenues that fund it are less than the expenditures it requires, CMS has provided in 45 C.F.R. § 153.230(d) that reinsurance payments are cut in that year in order to prevent a deficit. If tax revenues that fund the transitional reinsurance program are greater than the expenditures it requires, CMS has stated in 45 C.F.R. § 153.235(b) that the surplus will be spent in subsequent years of the program on reinsurance benefits.  The program also works with a one year lag: money is collected and paid in each year is for claims made the preceding year.

The Center for Medicare and Medicaid Services has funded the transitional reinsurance program this year by levying (with the help of its IRS friends)  a $63 per insured life tax on most (but not all) health insurance policies sold in the United States this year. (The payments are deductible for for-profit enterprises). CMS says it is planning an exception to the tax for self-funded plans that are also self-administered, a rule that, as shown in the graphic below, CMS previously said (correctly) it lacked statutory authority to issue and that will significantly benefit labor unions. This tax revenue, coupled with a required $2 billion from the United States Treasury, is estimated to yield $12 billion to be paid in 2015 for claims arising in 2014.  CMS will use the the money to provide a form of stop-loss reinsurance that attaches at $45,000 of claims per member and that provides 80% reimbursement for claims up to $250,000. In earlier versions of the regulation, the attachment point was a less generous $60,000.

Comparison of regulations: March 11, 2013 v. October 30, 2013
Comparison of regulations: March 11, 2013 v. October 30, 2013

How would you spend $12 billion?  Well, using the “continuance tables” (statistical claims distributions) contained in CMS’s “Actuarial Value Calculator,” one can show that the expected payments under the reinsurance system created by CMS for 2014 will range from about $433 per member for a bronze plan up to about $597 for a platinum plan. The weighted average expected payment will be about  about $525. The enhanced size of this subsidy, rather than other miracles of Obamacare, may explain in part, by the way, why premiums on the Exchanges came in somewhat lower than some had projected. If CMS is planning on spending about $12 billion on transitional reinsurance and it spends $525 per insured person, simple division shows that it takes about 23 million people who might trigger the reinsurance obligation in order to exhaust the fund.

The problem, however, is that, given recent developments, there are unlikely to be 23 million persons in 2014  (a) who might trigger the reinsurance obligation (“reinsurance triggering”) and (b) who are insured by reinsurance-eligible insurers (“reinsurance eligible”). You could just take my word on this point and skip to the end of this entry or, better yet, follow the accounting done here.

An accounting

Let me concede, temporarily and for the sake of discussion, that there will be 6 million people on average in 2014 who are paying premiums based on policies purchased in the individual Exchanges.  That’s hard to believe given (a) that the number with a month to go is probably about 3.2 million (President Obama’s alleged 4 million enrollment reduced by 20% shrinkage for nonpayment); (b) that the number of insured in the Exchanges would have to be 7 million post March for there to have been 6 million on average during all of 2014; and (c) Vice President Joe Biden’s augury that 5 million would be a “heck of a start.”  I will grumpily concede it nonetheless.

How many off-Exchange purchasers should we then add?  Here the numbers are slippery too.  I am indebted, however, to some careful work by the Kaiser Family Foundation on this point.  You can read it here. The highest estimate I have seen for the number of nonelderely persons covered by  a plan purchased directly from an insurer at any one time in a calendar year is 19 million.  But many of these 19 million will (a) not have insurance the entire year; (b) will have insurance that is secondary to other insurance and thus unlikely to accumulate the $45,000 attachment point in claims; and (c) will be in grandfathered policies not eligible for reinsurance and persisting through 2014 only by dint of President Obama’s magic waiver of the terms of the ACA.  When one looks at the situation at any given point in time — which is the proper basis for figuring out an average — it looks as if there might be 13-14 million who have some form of individual health insurance and 10-11 million who have primary health insurance coverage of the sort that might trigger a reinsurance obligation.

So, should I add 11 million to the 6 million and say that there are 17 million insureds that might trigger a reinsurance obligation?  No! That would ignore two substitution effects.  We know from various studies that a lot (perhaps 65% – 89%) of the people purchasing policies on the Exchanges simply swapped non-Exchange policies that would not be eligible for the other big federal subsidy — premium tax credits — for Exchange policies.  So, even if we assume, contrary to the evidence, that only half of the Exchange purchasers came from the ranks of the uninsured, that means there are really only 3 million new purchasers of policies eligible for reinsurance. Moreover, the 10-11 million figure isn’t right anymore either.  For 2014, individual insurers have to choose. They can stop selling their policy altogether, they can expand benefits to conform with the tougher requirements of the ACA and obtain a right to reinsurance or, at least in some instances, they may be able to grandfather their policy and avoid many ACA mandates but forfeit a right to reinsurance. I have not seen any good statistics on how many of the 11 million will persist into 2014, but I would be surprised if more than 80% did.  So, rather than 11 million, it seems to be the better upper bound on the number of extant non-Exchange, reinsurance eligible policies is 9 million.

It thus seems to me as if the better upper bound on  the number of policies that might trigger a reinsurance obligation is 12 million: 3 million genuinely new policies plus 9 million sold outside the Exchange but eligible for reinsurance. This means, however, that if CMS’s estimates of claims under the ACA are correct, a reasonable upper bound on reinsurance payments under section 1341 of the ACA are likely to be at most $6.3 billion ($525 x 12 million) rather than $12 billion.

Given all this, there are two aspects of CMS’ s behavior that are a bit puzzling.  Why is CMS not adjusting the reinsurance benefit for this year say to provide 100% coverage rather than 80% coverage and/or removing the $250,000 cap on claims triggering reinsurance? Or, given the belief of the President that he has discretion to waive taxes in light of changed circumstances, why is CMS not waiving, say, half of the taxes that would otherwise be owed.  (Not that I think this is constitutional).

The answer to the puzzler, I suspect, is either a cognitive failure or a very clever strategy. It is possible that it has not dawned on CMS that changing enrollment patterns means that it will not be able to exhaust the $12 billion it expects to receive pursuant to section 1341. More likely, however, someone at CMS has done the math and has been delighted to discover a slush fund that it can use the money to provide extra generous reinsurance next year and thus keep the price of premiums down.  How will we know? If we see an announcement from CMS in the next few months changing the parameters for the 2015 reinsurance plan to be considerably more generous, believe that it is the result of collecting “too much” in taxes in 2014. In the meantime, however, we have another example of ACA “details” that don’t seem to stand up under close scrutiny.

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The Kaiser analysis of ACA enrollment has problems

On December 17, 2013, the Kaiser Family Foundation published an influential study that comforted many supporters of the Affordable Care Act who had been made nervous by early reports that the proportion of younger persons enrolling in Exchanges was significantly less than expected.  If true, such a disproportion could have created major stress on future premiums in the Exchanges because the private Exchange system under the ACA depends — or so it was thought — on younger persons subsidizing older persons. The Kaiser study asserted, however, that even if one cut the number of younger persons by 50%, insurer expenses would exceed insurer premiums by “only” 2.4%.  This finding under what it thought was a “worst case scenario” underpinned Kaiser’s conclusion that a “premium death spiral was highly unlikely.”

This post evaluates the Kaiser analysis. I do so in part because it disagreed a bit with my own prior findings, in part because it has gotten a lot of press, and because I have had a great deal of respect for Kaiser’s analyses in general.  I conclude that this Kaiser analysis rests, however, on an implausible assumption about the behavior of insurance purchasers and lacks much of a theoretical foundation. Once one eliminates this implausible assumption and employs a better theory of insurance purchasing, the threat of a death spiral becomes larger.

The reason for all this is a little complicated but try to bear with me and I will do my best to explain the problem.  Essentially, what Kaiser did was to run its simulation simply by lopping off people under the age of 34 and assuming that, for some reason, the disinclination of people to purchase health insurance on an Exchange would magically stop at age 34.  Thus, if an enrollment of, say, 2 million had been projected to come 800,000 from people age 18-34, 600,000 from middle aged people and 600,000 from the oldest group of enrollees, the “worst case” scenario Kaiser created (Scenario 2) would reduce enrollment to 1.6 million by having 400,000 come from people age 18-34, 600,000 from middle aged people, and 600,000 from the oldest group of enrollees. Thus, the youngest group would now constitute 25% of enrollees rather than 40%, and the other groups would constitute 37.5% of enrollees rather than 30%.

Although there is often nothing automatically wrong with this sort of “back of the envelope computation” — I have done many of them myself —  sometimes they give answers that are wrong in a meaningful way. And sometimes “meaningful” means a difference of just a few percentage points. Thus,  although the difference between 0.045 and 0.024 is not large on an absolute scale, this is one of these instances in which there could be a big difference between predicting premium increases augmented by 2.4% due to this particular form of adverse selection and predicting a premium increases augmented by 4.5% due to this particular form of adverse selection.   The first might be too small to lead to a quick adverse selection death spiral; the second, particularly if it combined with other factors increasing premiums, might be enough to start a problem. Death spirals are  a non-linear phenomenon a little like the “butterfly effect” in which small changes at one point in time can cascade into very large changes later on. What I feel comfortable saying is that the additional risk of a death spiral created by disproportionate enrollment of the an older demographic is greater than Kaiser asserts.

By simply lopping off the number of people under 35 who would enroll, the Kaiser model lacked a good theoretical foundation.  The model Kaiser should have run — “Scenario 3” —  is one in which the rate of enrollment is a sensible function of the degree of age-related subsidy (or anti-subsidy). Their two other scenarios could then be seen as special cases of that concept. Had they run such a “Scenario 3”, as I will show in a few paragraphs, the result is somewhat different.

Let me give you the idea behind what I think is a better model. I’m going to present the issue without the complications created by the messiness of data in this field.  We need, at the outset to know at least two things: (1)  the number of people of each age who might reasonably purchase health insurance if the subsidy were large enough (the age distribution of the purchasing pool); and (2) the subsidy (or negative subsidy) each person receives for purchasing health insurance as a function of age. By subsidy, I mean the ratio between the expected profit the insurer makes on the person divided by the expected expenses under the policy, all multiplied by negative one. The bigger the subsidy, the more money the insurer loses and the more likely the person is to purchase insurance.

Suppose, then, that the probability that a person will purchase health insurance is an “enrollment response function” of this subsidy. For any such enrollment response function, we can calculate at least three items: (1) the total number of people who will purchase insurance; (2) the age distribution of purchasers (including the “young invincible percentage” of purchasers between ages 18 and 35); and (3) — this is the biggie — the aggregate return on expenses made by the insurer.  Thus, some enrollment response function might result in 6.6 million adults purchasing insurance of whom 40% were “young invincibles” that generated a 1% profit for the insurer on adults while another enrollment response function might result in 2.9 million adults purchasing insurance of whom 20% were “young invincibles” that generated a 3% loss for the insurer on adults.

What we can then do is to create a family of possible enrollment response functions drawn from a reasonable functional form and find the member of that family that generates values matching the “baseline assumptions” made by both Kaiser and, apparently, by HHS about total enrollment and about the “young invincible percentage.” We can then calculate the aggregate return of the insurer on adults and call this the baseline return. What we can then do is assume different total enrollments and different young invincible percentages, find the member of the enrollment response function family that corresponds to that assumption, and then calculate the new revised return on adults. The difference between the baseline return and the new revised return on adults can be thought of as the loss resulting from this form of adverse selection. There are a lot of simplifications made in this analysis, but it is better, I believe, than either the back of the envelope computation by Kaiser that has gotten so much press and, frankly, the back of the envelope computation I did earlier on this blog.

Here’s a summary of the results.  When I (1) use the Kaiser/HHS age binning of the uninsured and indulge the simplifying assumption that the age distribution is uniform within each bin; (2) use Kaiser’s own estimate of the subsidy received by each age, (3) assume 7 million total purchasers ; and (4) assume 40% young invincibles with uniform age distribution within age bins, I find that the baseline return on adults is 1.0%. When I modify assumption (3) to have 3 million total purchasers and, as Kaiser did in Scenario 2, modify assumption (4) to have 20% young invincibles, the baseline return on adults is -3.5%.  Thus, a better computation of Kaiser’s worst case scenario is not a reduction in insurer profits of 2.4%, but rather a reduction of 4.5%.  

The graphics here compare enrollment rates, the age distribution of enrollees and various statistics for the baseline scenario and the scenario in which there are 3 million total purchasers and approximately 20% young invincibles.

Comparison of baseline scenario v. worst case using better assumptions
Comparison of baseline scenario v. worst case using better assumptions

We can use this methodology to run a variety of scenarios. I present them in the table below. A Mathematica notebook available here shows the computations underlying this blog entry in more detail. I am also making available a CDF version of the notebook and a PDF version of the notebook.

Various scenarios showing changes in insurer profits due to different enrollment response functions
Various scenarios showing changes in insurer profits due to different enrollment response functions

Please note that the computations engaged in here essentially ignore those under the age of 18.  This is unfortunate, but I do not have the data on the expected premiums and expenses of  children. It does not look as if Kaiser had that data either. Since children are expected to comprise only a small fraction of insured persons in the individual Exchanges, however, this omission probably does not change the results in a major way.

A humbling thought

The more I engage in this analysis, the more I realize how difficult it is.  There are data issues and, more fundamentally, behavioral issues that we do not yet have a good handle on.  Neither my model nor Kaiser’s model can really explain, for example, why, as has recently been noted, enrollment rates are so much higher in states that support the ACA by having their own Exchange and with Medicaid expansion than in states that more greatly oppose the ACA.  As I have suggested before, there is a social aspect and political aspect to the ACA that is difficult for simple models to capture.  Moreover, as I noted above, this is an area where getting a number “close to right” may not be good enough.  Premium increases of, say, 9% might not trigger a death spiral; premium increases of 10% might be enough.  And neither my nor anyone else’s social science, I dare say, is precise enough to distinguish between 9% and 10% with much confidence.

So, longer though it makes sentences, and less dramatic as it makes analyses and headlines, the humbling truth is that we can and probably should engage in informed rough estimates as to the future course of the Affordable Care Act, but it is hard to do much more as to many of its features. I wish everyone engaged in this discussion would periodically concede that point.

Other Problems with Kaiser

There are  other issues with the Kaiser analysis. Let me list some of them here.

Even accepting Kaiser’s analysis premium hikes would likely be more than 2%

Kaiser’s discussion of insurer responses to losing money is inconsistent. Look, for example, at this sentence in the report: “[i]f this more extreme assumption of low enrollment among young adults holds, overall costs in individual market plans would be about 2.4% higher than premium revenues.”  Kaiser further reports “Insurers typically set their premiums to achieve a 3-4% profit margin, so a shortfall due to skewed enrollment by age could reduce the profit margin of insurers substantially in 2014.” I don’t have a quarrel with this sentence.  But then look at what the Kaiser report says. “But, even in the worst case, insurers would still be expected to earn profits, and would then likely raise premiums in 2015 to make up the shortfall,” No! According to Kaiser’s own work, “even in the worst case,” insurer costs would be 2.4% greater than premium revenues.  Since there is little float in health insurance and investment return rates are low these days, insurers would likely not earn profits.  Then it gets worse. “However, a one to two percent premium increase would be well below the level that would trigger a “death spiral.” Perhaps so, but if insurers need to earn 3-4% to keep their shareholders happy and they are losing 1-2%, a more logical response would not be a 1-2% increase in premiums but a 4-6% increase. And, as Kaiser points out, larger premium increases could trigger a premium death spiral in part because death spirals are like avalanches: they start out small, only a little snow moves, but once the process starts it can become very difficult to abort.

Logical Fallacies

The first paragraph of Kaiser’s report asserts:  “Enrollment of young adults is important, but not as important as conventional wisdom suggests since premiums are still permitted to vary substantially by age. Because of this, a premium “death spiral” is highly unlikely.” Even if the first sentence of this quote were correct — a point on which this entry has cast serious doubt — the second sentence does not follow.  To use a sports analogy, it would be like saying that,  the role of a baseball “closer” is important but not as important as conventional wisdom suggests. Therefore the Houston Astros, who lack a good closer, are highly unlikely to lose.  No!  There are multiple factors that could cause an adverse selection death spiral.  Just because one of them is not as strong as others make out, that does not mean that a death spiral is unlikely. That’s either sloppy writing or just a pure error in logic.

Other Factors

And, in fact, if we start to look at some of those other factors, the threat is very real.  As discussed here in more depth, I would not be surprised if adverse selection based on completely unrated gender places as much pressure on premiums as adverse selection based on imperfectly rated age. And, as I have discussed in an earlier blog entry, the transitional reinsurance that somewhat insulates insurers from the effects of adverse selection will be reduced in 2015. This will place additional pressure on premiums.

And, on the other hand, the individual mandate, assuming it is enforced, will triple in 2015 and risk adjustment measures in 42 U.S.C. § 18063, will likely provide greater protection for insurers.  These two factors are likely to dampen adverse selection pressures.

Notes on Methodology

There are a number of simplifying assumptions made in my analysis.  Some of them are based on data limitations. Here are a few of what I believe are the critical assumptions.

1. Functional form: I experimented with two functional forms, one based on the cumulative distribution function of the logistic distribution and the other based on the cumulative distribution function of the normal distribution.  These are both pretty conventional assumptions and make sure that the enrollment rate stays bounded between 0 and 1. The results did not vary greatly depending on which family of functions the enrollment response functions were drawn from.

2. Uniform distribution of ages within each age bin of potential purchasers. I believe this is the same assumption made by Kaiser and it results from the absence of any more granular data on the age distribution of the uninsured that I was able to find.

3. The enrollment rate depends on the subsidy rate standing alone and not other possibilities such as subsidy rate and age. The data on enrollment rates is very sparse and so it is difficult to use very complex functions.  Perhaps a more complex analysis would assert that enrollment depends on both subsidy rate and age, since age may be a bit of a proxy for the variability of health expenses and thus of risk.

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Gender could be as big a problem as age for the Affordable Care Act

Concerns about whether insurance sold on  the individual Exchanges under the Affordable Care Act will succumb to an adverse selection death spiral have focused mainly on the shortage of younger enrollees into the system. This shortage is potentially a problem because, due to section 1201 of the ACA, premiums for younger enrollees must be at least one third of that for older enrollees even though actuarial science tells us that younger enrollee expenses are perhaps just one fifth of those for older enrollees. Younger enrollees are needed in large numbers to subsidize the premiums of the older enrollees. But at least premiums under the ACA respond at least somewhat to age.

The lesser studied potential source of  adverse selection problems, however, is the fact that medical expenses of women for many ages are essentially double those of men and yet the ACA forbids rating based on gender.  In a rational world, one would therefore expect women of most of the ages eligible for coverage in the individual Exchanges to enroll in plans on the Exchange at a higher rate than men. But, since the women have higher than average expenses than men, premiums based on the average expenses of men and women will prove too low, creating pressure on insurers to raise prices. And, of course, there could also be some disproportionate enrollment by older men who have higher medical expenses than women of equal age. While I welcome contrary arguments in what I regard as a fairly new area of study involving the ACA, gender-based adverse selection would certainly appear to be  a real problem created by the structure of that law.  To me, it looks to be potentially as large a problem as age-based adverse selection. It is certainly one that needs continuing and careful evaluation.

Caveats

I see only three limited factors that reduce what would otherwise appear to be a significant additional source for significant adverse selection. As set forth below, however, I do not believe that any of these factors are likely to materially reduce the problem.

1. Ignorance

The first is ignorance. Adverse selection emerges only if individuals can accurately foretell their future medical expenses with some accuracy. To the extent, therefore, that men and women are ignorant of the effect of gender on their projected medical expenses, adverse selection is potentially diminished. I say “potentially,” however, because of a subtlety: people don’t have to know why their expenses are what they are in order for adverse selection to emerge; they only have to be somewhat accurate in their guess. Thus, even if men and women don’t make the cognitive leap from seeing lower (or higher) medical expenses to issues of gender, but they still on balance get it right, adverse selection can exist. Thus, I end up doubting that ignorance of the correlation between gender and medical expense is going to retard adverse selection problems very much.

2. Correlation between gender and expense is lower for those 50-65.

The second factor that might reduce adverse selection based on gender is, curiously enough, adverse selection based on age. The difference between male and female medical expenses diminishes as one exits the middle 40s and heads into the 60s. Indeed, somewhere in the late 50s, the rates cross and men have slightly higher average medical expenses than women. Therefore, to the extent that it is the 50-65 set that is disproportionately purchasing coverage in the individual Exchanges, the potential for gender-based adverse selection is diminished — but only somewhat .  I say “but only somewhat” because if males over the age of about 55 or 58 enroll at higher rates than women of similar ages there will actually be adverse selection pressures due to the higher medical expenses of men that age. On the other hand, to the extents efforts are made to reduce age-based adverse selection by promoting coverage to the younger (potentially child-bearing) set, the potential for most forms of gender-based adverse selection increases.

3. Gender-correlated risk aversion

The third factor that could in theory reduce adverse selection problems is if men are more risk averse than women with respect to medical expenses and therefore purchase health insurance at equivalent rates even though their risk is objectively lower. Men could conceivably be somewhat more risk averse due to prevailing gender roles in the economy: on average it is possible that health problems among men may affect the family’s income more than health problems among women.  Although as an academic I feel I would be remiss in failing to at least mention this possibility, in the end I doubt it amounts to very much. The roles of men and women in the family economy are complex and variegated. And the sources of risk aversion with respect to health are likewise multifold, having a lot to due with individual psychology, family history and family structure. And, of course, it could be that middle aged men are less risk averse than women, in which case the effects of adverse selection are worse.

The data

How do we know about the effects of gender? The graphics below show two studies on the topic. The first is from the Society of Actuaries and was relied on by the Kaiser Family Foundation in its recent study of the effect of age rating. Look at the solid blue (male) and pink (female) lines. (Cute, Kaiser). One can see that until age 18, the costs for men and women in the commercial market has been about the same. By the time we get to, say, age 32, the cost for women is about 2.5 times that for men. The gap then shrinks so that by the time we get to age 58 or so, men’s costs actually start to somewhat exceed women’s.

Society of Actuaries report on gender and healthcare expenses
Society of Actuaries report on gender and healthcare expenses

A study by the respected Milliman actuarial firm, although differing in detail, shows roughly the same pattern. At age 30 or so, female expenses (blue) and about double those of males (green). The gap shrinks until about age 55, at which point male expenses exceed female expenses.  (I’m not sure why Milliman shows female expenses being so much higher than male expenses for the age bracket marked “to 25” unless by “to 25” they mean ages 18-25.)

The_young_are_the_restless__Demographic_changes_under_health_reform_-_Milliman_Insight

Is Gender-Based Adverse Selection Actually Happening?

As to whether the theoretical possibility of gender-based adverse selection is actually materializing, there is yet strikingly little evidence. I have scoured the Internet and found almost nothing on the gender of enrollees. In some sense this is not surprising since, unlike age, on which we have a trickle of data from CMS, which somehow is just unable to compile and release more complete information, gender is completely irrelevant to premium rates. On the other hand, as shown below, the federal application asks about gender, as do a few other state applications such as California, Kentucky and Washington State. So, in theory we should be able to get the information at some point.  In the meantime, if anyone has information on this issue, I would love to see it. What we really need is a breakdown of enrollees based on both age and gender because the ratio’s role varies depending on whether enrollees below age 55 or so are involved or whether enrollees above age 55 are involved.

Two other notes

1. Someone might, I suppose, think that since the role of gender reverses at about age 55, the effects of gender on adverse selection cancel each other out. This would be totally wrong.  If women have higher medical expenses than men up to about age 55 and if women therefore enroll at higher rates, that can cause adverse selection and premium pressures for enrollees of those ages. And if men have have higher medical expenses than women after about age 55 and if men therefore enroll at higher rates, that can cause adverse selection and premium pressures for enrollees of those ages. The effects are cumulative and not offsetting.

2. Does this mean I am opposed to unisex rating? No, not necessarily. First, women face higher medical expenses than men from about 20 to 50 significantly because of childbearing expenses. A family law expert on my faculty confirms what I suspected, which is that there is certainly no routine cause of action by the pregnant female against the prospective father for prenatal maternity expenses. We currently ascribe these expenses to the woman even though a male generally has contributed to those expenses through consensual sex. One could argue that unisex rating offsets this proxy for responsibility.

Second, if there are adverse selection problems caused by unisex rating, they can, in theory, be addressed by programs that that subsidize insurers for female enrollees. Impolitic as it might be to say so, one could treat being a fertile woman as a “risk factor” in the same way that section 1343 of the ACA currently treats medical conditions such as heart disease.  The cost of the subsidies resulting therefrom could be seen as compensating somewhat for the transaction costs of figuring out which childbearing expenses the male partner has contributed to as well as tracking down the male partner and trying to hold him financially responsible.

What I am concerned about, however, is ignoring the issues created by unisex rating. Since it is not currently corrected for by section 1343 of the ACA and corrected for only in a very indirect and partial way by sections 1341 and 1342 of the ACA, there is the potential for the absence of gender rating to destabilize and ultimately shrink the insurance markets in ways that do few people any good. Wishing that a problem would go away or hoping that people don’t see the opportunities to optimize their behavior is seldom a recipe for successful government programs.

 

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Middle class not selecting plans in the individual Exchanges

No more than 1% of those with household incomes between 139% and 400% and eligible to select a plan on the individual Exchanges have thus far done so. This is the information about those with middle incomes and lower-middle incomes one can derive from statistics released this week by Health and Human Services.   The rate of plan selection among those with incomes over 400% of the federal poverty level is at least 3 times higher than that of persons with middle and lower-middle incomes. It could well be 4 times greater.

No matter how you fine tune the computations, I believe it is fair to say that the middle class is finding the carrots too small and the sticks too small. Some of this may be due to difficulties with the enrollment process rather than the underlying architecture of incentives under the ACA, but either way, most of those eligible to do so, are, at least for now, rejecting the benefit theoretically available to them on the individual Exchanges under the Affordable Care Act.

Visualization

Here are two figures showing the results of my calculations in more detail.

Rates of Take Up

The first graph shows the absolute rates of take up (selection of a plan) among with lower-middle and middle incomes (the lower surface) and the wealthier (the higher surface).  The x-axis of the graph shows the assumption one makes about those reasonable eligible to purchase policies.  When x is low, one assumes the income distribution of the eligible pool most closely resembles that of persons currently without health insurance. When x is high, one assumes the income distribution of the eligible pool most closely resembles that of persons currently with health insurance form their employer. The y-axis of the graph shows the assumption one makes about the number of persons current with health insurance from their employer who might reasonably be considered eligible to purchase insurance on a health insurance Exchange. A low value of y means that very few of these people should be considered eligible. A high value of y means that 10% of these people should be considered eligible. The z-axis (vertical) shows the fraction of people eligible to do so who have to date selected a policy on an Exchange.

Take Up Rates among the Wealthier (top surface) and the Lower Middle and Middle Income Group (lower surface)
Take Up Rates among the Wealthier (top surface) and the Lower Middle and Middle Income Group (lower surface)

As one can see the values are always less than 1% for the lower-middle and middle incomes. The values for the wealthier depends on the assumptions made but for  all values are below 6% and is frequently below 4%. And these are values for selection of a plan, not for actual purchase of a policy. Those numbers are likely to be even smaller due to many people leaving items in their “shopping cart” without paying at the check out counter.

Take Up Ratios

The second graphic shows the ratio between the take up rates among the wealthier and the take up rates among the lower-middle and middle income group. The x and y axes are the same as before.  A value of 3.4 on the z-axis means that the take up rate among the wealthy is 3.4 times what it is among the lower-middle and middle income groups. As one can again see the ratio is above 3 for almost all assumptions one could make and is frequently above 4.

Take Up Ratios
Take Up Ratios

Show me the calculation

How do I get to these figures? Algebra. Some of it is very nasty algebra, but I have the world’s best computer algebra system, Mathematica, at my disposal to make the problem much easier. Rather than include the somewhat complex computations directly in this blog post, I’m going to include a PDF file showing the computations and a CDF file (a Mathematica file format). You can read the CDF file either with Mathematica itself or with the free CDF Player available here. The data, by the way comes from a combination of  this tidbit of information found on page 7 of the report released  by HHS on  December 11, 2013, and data from the Urban Institute and Kaiser Foundation.

Page 7 of the HHS Report
Page 7 of the HHS Report

Sources

Distribution of the Nonelderly Uninsured by Federal Poverty Level (FPL)

Distribution of the Nonelderly with Employer Coverage by Federal Poverty Level (FPL)

HEALTH INSURANCE MARKETPLACE: DECEMBER ENROLLMENT REPORT For the period: October 1 – November 30 (December 11, 2013)

 

 

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Could the American Enterprise Institute possibly be right there is this massive second wave of cancellations coming?

Short answer: The AEI estimate looks high but, yes, a massive second wave of cancellations is coming

The American Enterprise Institute (AEI) has received considerable press over the past 24 hours for asserting that the Affordable Care Act will generate a massive second wave of insurance cancellations this summer as small employers (and their employees) will be compelled to abandon policies that do not provide “Essential Health Benefits” and meet other standards of the Affordable Care Act.  Fox News has asserted that the AEI statement means that up to 100 million people could be canceled next year.  Other news sources and  at least one influential conservative radio talk show host are making similar claims.

If this were true, it would obviously be a subject of considerable importance.  Anyone doubting this point should consider the firestorm that erupted over the recent cancellations of a much lower number of individual health insurance policies as a result of the Affordable Care Act’s insistence that health insurance meet its full standards starting in 2014 and the tough limitations on “grandfathering” exemptions for older health insurance plans.

But, is it true?  Is it really true that there could be a large number of cancellations?  Could we really be talking about 100 million people? Could the very conservative AEI  be making political hay rather than something more factual? Let’s look at the argument.  It’s part legal and part statistical. I’m going to break the argument down into pieces and see how it holds up.

1. Legal Basis

The legal part stems from the claim that although large businesses (more than 100 employees) are not required to provide “Essential Health Benefits” under the Affordable Care Act for all insurance plans beginning after January 2, 2014, small businesses are.  That appears to be true.  Section 1201 of the Affordable Care Act, which, among other things, amends section 2707 of the Public Health Service Act, reads as follows: “A health insurance issuer that offers health insurance coverage in the individual or small group market shall ensure that such coverage includes the essential health benefits package required under section 1302(a) of the Patient Protection and Affordable Care.”  (emphasis added. It does not say “in the individual, small group or large group market” but rather “in the individual or small group market.”  And if one goes through the statutory labyrinth from Section 1304(a)(3) of the ACA to 1304(b), one learns that, at least until 2016, the small group market means insurance purchased by employers with 100 or fewer employees.

There is, however, an exemption for grandfathered plans.  Section 1251(a)(2) makes clear that almost all of the provisions of the Subtitle that contains section 1201 of the ACA doe not apply to “to a group health plan or health insurance coverage in which an individual was enrolled on the date of enactment of this Act.” There’s an exception to the exemption, but it does not apply to this situation.

So, it sure looks to me as if all non-grandfathered plans issued in by 100 of fewer workers will, beginning for plan years that begin after January 1, 2014, be compelled to provide “Essential Health Benefits” along with other requirements of the ACA.

2. How many policies are we talking about?

The Census Bureau keeps track of how many employees are employed by firms of different sizes. The last time they looked, 2010, there were roughly 39 million people employed in such firms.  So, an upper bound on the number of policies — note, policies, not persons — affected is 39 million.

The 39 million policy figure must be reduced, however, in figuring out how many cancellation notices are likely to go out in 2014. This is so for several reasons (two of which I will confess to having forgotten about during a very transitory first posting of this blog entry).

The first reason the 39 million figure is too high is that not all small employers provide health benefits.  According to the Kaiser Family Foundation’s 2013 Annual Survey of Employer Health Benefits (page 39), about 57% of employees in firms with under 200 employees provide health benefits.  It doesn’t have data on firms under 100 employees, but if one eyeballs the data that is provided, I don’t think one would be too far off estimating that about 50% of firms with fewer than 100 employees provide health benefits. So, this takes us down to about 19.5 million employees.

But the 19.5 million employee figure needs to be reduced because not all employees accept health insurance even when it is offered. According to Kaiser (same report as above, page 49), the take up rate among those with fewer than 200 employees is 62%.  It doesn’t look like it varies too much according to firm size in that range, so we’ll say there are roughly 12 million employees in small firms who get health insurance through their jobs.

But the 12 million figure needs to be yet further reduced because some policies will remain grandfathered and thus exempt from the Essential Health Benefits requirement.  According to the same Kaiser report  (page 223), about 49% of employees in firms with under 200 employees were in grandfathered plans.  It doesn’t have data on firms under 100 employees, but if one eyeballs the data that is provided, I think it is fair to say that about 50% of employees in firms with under 200 employees were in grandfathered plans as of 2013. This figure needs to be reduced, however, to take account of the decay in the proportion of plans that can remain grandfathered as time goes on.  From 2011 to 2012, for example, the percentage of workers in smallish firms in  non-grandfathered plans grew from 37% to 46%. And from 2012 to 2013,  the percentage of workers in smallish firms in  non-grandfathered plans grew from 46% to 51%.  So, it’s not unreasonable to believe that something like 56% of workers in firms with 100 or fewer workers will be in non-grandfathered plans at some point during 2014.  Could be a few percentage points higher, could be a few percentage points lower.

If we do the multiplication, however, that means that we are at roughly 7 million policies that will be required to provide Essential Health Benefits at some point during 2014.  But we need to do a little more subtraction because, surely, there must be some of these policies that are essentially in compliance with the ACA right now.  There might be “cancellation notices” with respect to these policies but if the policy content and prices doesn’t change as a result, few people will care.  How many such compliant policies are there?

I will confess that I don’t know how many small group policies already comply with the requirements of the ACA and would thus likely not change substantially if they needed to be cancelled. But my guess is that the number is rather small.  The Robert Wood Johnson Foundation noted several years back that a lot of individual and small group policies did not provide Essential Health Benefits such as substance abuse benefits. The independent research firm HealthPocket found recently that only 2% of individual health insurance plans covered all Essential Health Benefits and that the average plan covered about 76% of those benefits.  HealthPocket did not, however, study small group policies.

In the absence of great evidence, I am going to assume, probably quite liberally, that 1/3 of the plans that will be required to provide Essential Health Benefits either already provide them or provide something sufficiently close to them that any cancellation of those policies will not require significant alteration of the plan. This means, however, there are — just to keep the numbers round — 5 million small group policies that will be cancelled in 2014 and that will need to be altered significantly as a result of the ACA’s EHB requirement.

3. How many people are we talking about?

But policies do not equal people.  There is often more than one person on a policy: a spouse and a dependent or two. This means that while 5 million is a plausible lower bound on the number of people who will be getting potentially unwelcome cancellation notices in 2014, it is likely to low an estimate. And on this point, we have decent data. A 2009 report by America’s Health Insurance Plans found that the average policy covered 3.03 lives.  There is no reason to think that this number has either materially changed over the past few years or that small group plans are different from other plans.

So, again doing some rounding, if we do the multiplication of 5 million policies by 3 lives per policy, that means that 15 million or so Americans now getting health insurance through a small employer are likely to get meaningful cancellation notices this coming year. Another 6 million Americans now getting health insurance through a small employer will get cancellation notices but might receive similar coverage without large disruption. 

4. Conclusion

Is the claim true?

Bottom line: so far as I can see at this time, the American Enterprise Institute statement is truthy but somewhat exaggerated. The 100 million figure looks very high to me, but the real number of something like 15 million Americans (many of whom will be voting in Congressional elections right after receiving the notice) should be high enough to get the nation’s attention. Indeed, if my figures on the number of already-compliant policies is overly generous, the real number might be as high as 21 million Americans.

Does it matter?

To be sure, some of the plans into which these displaced Americans may end up may be better than those they have presently. Not being able to keep your health insurance doesn’t always make you worse off.  Some of the adjustments that need to be made to bring the policies into compliance may be relatively small and relatively inexpensive.  Many of the policies will not have been the sort of “junk” that can exist in the individual market. and thus transitioning to compliant plans, though initially stressful, may not end up being permanently traumatic. Moreover, under section 1421 of the ACA (26 U.S.C. § 45R), for some employers with 25 or fewer (not well paid) employees there will be tax credits of up to 50% to help them purchase insurance.

But the fact that the cancellation notices may not be calamitous for some does not mean that they will not pose serious problems for millions of employers and employees. For the many employees in firms with more than 25 employees or who are in firms with fewer than 25 employees but who are somewhat better paid, the tax credit provision offers no relief.  For the many small businesses whose policies were close to compliant, even having to pay a little more for “better” policies may be a big deal.  If the experience of these 15 million policyholders is similar to those of the millions of those with recently ACA-cancelled individual policies, many of them are going to find that the better insurance policies mandated by the ACA comes with a significant price tag that they or their employer, or a combination of the two, are going to pay.

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Risk Corridors making it to the mainstream media

More people are starting to pay attention to the stealth “Risk Corridors” provision contained in section 1342 of the Affordable Care Act (18 U.S.C. § 18062) . They are looking at whether that provision of the law, which calls for transfers by the Secretary of HHS from profitable exchange insurers to unprofitable ones, might persuade insurers to retain greater enthusiasm about participation in the Exchanges and whether the financial bill for section 1342 might not be the zero projected by the Congressional Budget Office.  One of those paying attention is the influential CNN.

CNN report
CNN report on risk corridors

In a story this evening titled “Obamacare fix could add millions of collars in government costs” by Adam Aigner-Treworgy, CNN pretty much tracks the analysis offered on this blog in previous days.  It quotes the Kaiser Family Foundation, usually a pretty reliable source on the finances of the ACA as saying that the difference between the amount previously thought owed under Risk Corridors and the amount that might be owed as a result of recent developments “could be tens of millions or even hundreds of millions of dollars.” The story likewise quotes Melinda Buntin, a former deputy director for health at the Congressional Budget Office and current chair of the Department of Health Policy at Vanderbilt University:

To the extent that the risk pool changes in ways that were not foreseen by the insurers because of the announcement yesterday and they are not included in the bids that they proposed to the government and that their selection is riskier and more adverse that they anticipated then that could be an additional cost to the government.

Risk corridors was also the topic of remarks today by White House press secretary Jay Carney.  As reported on the blog, The Hill, Carney said the following today. “If the costs are higher, then [the Department of Health and Human Services] can mitigate those costs with insurers,” Carney said at a briefing. “If costs come in significantly lower, then the insurers will replenish the fund by passing back some of those profits.”

The question again is where does the money come from?  Just saying “the treasury,” as law professor and ACA zealot Timothy Jost is quoted as saying in the CNN report, is not precise enough. Someone needs to find a specific appropriation that can be used for this purpose.  On the other hand, if the Kaiser Foundation is right and we are talking about sums less than a billion dollars, that may be a very small amount for President Obama to dig up from somewhere in order to salvage his signature domestic achievement. I guess I’m a little less confident that the bill is going to be that small  — unless, of course, enrollment in the Exchanges continues to be minuscule.

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