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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Gender equity and the Affordable Care Act

Much has been made here and elsewhere about how young people are subsidizing older people under the Affordable Care Act. While there is a substantial element of truth to this contention, at least young people generally get to become older people.  So, if the ACA were to last for decades, one could drive a small bit of comfort by viewing the arguable inequity as instead amounting to younger purchasers under the ACA just financing the health care they will receive at subsidized rates as they enter their 50s and beyond. The analogy doesn’t work terribly well because unlike something like a long term life insurance policy in which a similar “subsidy” exists, there is nothing that forces those insured later in life to have insured earlier on.  But at least youth is a “burden” that most of us share.

A closer look at the evidence, however, shows that the major determinant of whether someone is subsidizing another or being subsidized under the ACA is gender.  As shown here, gender is more important than age for purposes of ACA subsidization. And, for most of their adult lives males subsidize women under the ACA. Since gender is largely immutable, males never get the money back. While there are many factors that bear on whether this system is fair, the extent of subsidization is large enough to be worth considering.

Subsidization by gender and age
Subsidization by gender and age

The graphic above shows the extent of subsidization.  For each adult age (21-64) and each gender, I show the subsidy (positive or negative) the person receives under the ACA. The pink line shows the subsidy for women; the blue line shows the subsidy for men. Subsidization is the difference between the expected costs the person incurs and the person’s premiums under the ACA (without consideration of any government premium subsidies) normalized by dividing the difference by the person’s premiums.  Expected costs are calculated based on research by the Society of Actuaries and available in Excel data format from this web site. Premiums are calculated based on data provided by the Kaiser Family Foundation following its study of the ACA. To make sure that the units of of cost used by Kaiser and the Society of Actuaries match up, I apply a multiplicative correction factor to the premiums to ensure that the total level of subsidization is zero assuming that the estimated distribution of uninsured all enroll in ACA plans at an age-independent rate.  Use of more complicated assumptions about enrollment patterns, such as incorporation of the apparent fact that most of those purchasing policies in the individual Exchanges already had insurance, would result in a different correction factor but should not alter the basic conclusions of this post about cross-gender subsidization.

When one adds children into the mix, the picture becomes a bit more complex. As shown in the graphic below, insurers under the ACA appear heavily to subsidize children of both genders, although male children are subsidized somewhat more. The calculations here are based on an assumption that child-only policies cost 65.2% of the price for policies sold to 21 year olds.  (The 3:1 constraint on the ratio of premiums under the ACA applies only to adults (42 U.S.C. § 300gg(a)(1)(A)(iii)). This assumption was based on my sampling actual policies sold in the individual Exchanges under the ACA.

Subsidization by gender and age for all ages
Subsidization by gender and age for all ages

What is curious and perhaps somewhat comforting to those wanting to see the ACA succeed is the fact that, notwithstanding the significant differences in subsidization, women have not enrolled at rates way higher than men.  Overall, government statistics show that 54% of the enrollees are women and only 46% are men.  Nor are children forming a large part of the group enrolling in the individual Exchanges notwithstanding the high subsidization rates; they amount to just 6% of the total enrollees as of January 1, 2014. Now, part of this relative equality in enrollment rates by gender could be due to the masking effects of aggregation. It might be  that the female/male ratio is considerably higher among those ages 25-35, where the subsidization differential is quite large and the female/male ratio is much lower among those over age 60.  Thus, even if the overall ratio of enrollees was quite even, we could conceivably be seeing unequal enrollment patterns within age brackets.  As noted in an earlier post, neither the federal government nor any of the states have released data with the degree of detail that would be needed to confirm or refute this possibility and thus the actual joint distribution of enrollment by age and gender remains a matter for estimation using algebra and numeric methods rather than actual data. Still, it certainly appears that the rate of subsidization can not be the only factor affecting enrollment patterns; matters such as income, savings, risk aversion, as well as political, cultural and social factors are likely to be playing a role as well. How else can one, after all, explain the enormous differences in rates of enrollment across various states?

Now, is this “fair”?  That’s a difficult question. Most serious questions about insurance underwriting justice are difficult. (I’m going to include a short bibliography at the end of this post).  A large chunk of the difference between male and female healthcare expenses are based on the attribution of costs arising out of joint sexual activity to the female only.  It is, after all, the female’s body that is primarily affected by pregnancy. That attribution is based mostly on convenience, however, and, in many cases, the difficulty that would be created in trying to collect from a biological father. Moreover, it may be that subsidization in this area is compensatory, addressing countervailing subsidies of men in other government programs.

Even if it is fair, however, to the extent potential enrollees are responding to the extent of subsidization, we need to be concerned that unisex rating is reducing the efficacy of the ACA in shrinking the number of uninsureds.  Remember all the ills created by lack of insurance that substantially motivated the ACA? Charging men “too much” leaves many of those ills untreated. If men are not signing up because they are being asked to pay too high a price, the goals of the ACA in reducing the number of uninsureds and improving individual health are compromised. Let us not forget as various politicians attempt to diminish expectations about the achievements of the ACA that it was heavily advertised as a program to reduce the number of uninsureds. Don’t believe me? Look here (32 million), here (34 million by 2019) and here for examples.

There are two additional pictures that may be helpful to those graphically minded in considering this issue. The first, shown below, shows the expected costs of males (blue) by age, the expected costs of females (pink) by age, and the unisex ACA premium (green)(normalized so that the overall subsidization rate would be zero if enrollment rates were age-independent).

Comparison of expected costs by gender and unisex premium
Comparison of expected costs by gender and unisex premium

The second graphic lets one compare the degree of age subsidization under the ACA.  The purple line (kind of a blend of blue and pink) shows the expected costs of enrollees assuming that 50% are male and 50% are female. The green line shows the unisex ACA premium, again normalized so that the overall subsidization rate would be zero if enrollment rates were age-independent among the previously uninsured population. (A different normalization metric should not dramatically change the picture). As one can see although there is a zone between ages 20 and 32 in which premiums are exceeding cost and a zone between ages 60 and 64 where costs are exceeding premiums, and, although as mentioned above, children are heavily subsidized, for most of adulthood, premiums track expected costs pretty closely.  This may help explain why neither under my analysis nor under that of the Kaiser Family Foundation do departures of the age distribution from those originally foreseen have a gigantic affect on the profitability of the system.  What might have a larger effect, if it were to occur, would be departures of the gender distribution of enrollees from those originally foreseen; but, as mentioned above, thus far this does not seem to be occurring.

Comparison of blended expected costs and ACA premiums
Comparison of blended expected costs and ACA premiums

I do need to add one critical note.  All of this assumes that the expected costs for each age come in as predicted.  This is hardly known for sure.  There are many reasons, including adverse selection, moral hazard, and others why those costs might depart seriously from that which was projected.

A “starter set” bibliography on insurance underwriting justice

Kenneth S. Abraham, Distributing Risk (1986) (the starting point for thinking about this issue)

Tom Baker, Containing the Promise of Insurance: Adverse Selection and Risk Classification, 9 Conn. Ins. L.J. 371 (2002-2003), available online here.

Seth J. Chandler, Insurance Underwriting with Two Dimensional Justice, available here.

Seth J. Chandler, Insurance Regulation, in the Encyclopedia of Law and Economics, available here.

City of Los Angeles Department of Water & Power v. Manhart, 435 U.S. 702 (1978) (available here)

Technical Note

The Mathematica notebook that underlies the analysis and graphics presented in this blog entry is available on Dropbox here.

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The distribution of individual market enrollees by age and gender combined

Earlier this month, the Department of Health and Human Services released more detailed information than it had before on the age distribution and gender distribution of enrollees in the individual markets serviced by the various Exchanges. What it did not do, however, and what needs to be done in order to better predict the likelihood of significant insurer losses in the Exchanges and, thus, greater pressure on premiums is to release data on the combined age and gender distribution of the enrollees. We don’t know, for example, how many woman aged 35-44 are enrolled in the Exchanges. This finer look at the data is important because, as discussed in a previous post, it is the combination of age and gender that bears a stronger statistical relationship to expected medical expenses.  And, while the ACA incompletely compensates for age in its premium rating scheme through dampened age rating, it does not compensate at all for gender.

With the help of Mathematica, I have combined some algebra and some numeric methods to try and reverse engineer out combined distributions of age and enrollment that meet various constraints. I believe I have succeeded in finding a plausible combined distribution that can be used in developing plausible models of the likely extent of adverse selection in the individual health insurance markets under the ACA. I present the result in the table below and the chart below. I then have a “how it was done” technical appendix.   My work involves creation of a high dimensional polytope that satisfies the existing data and then a search for points on that polytope that appear most plausible. I have also posted a Mathematica notebook on Dropbox that shows the computation.

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

The pie chart above first groups the enrollees by gender. The inner ring shows males and the outer ring shows females. It then groups the enrollees by age bracket. As one can see, women outnumber men significantly in the 18-45 group, are about equal among minors and those between age 45 to 55, and are outnumbered by men in the 55-65 age group.

The graphic below shows the same data, but now age is the first grouping mechanism.

PlausibleGenderAgeDistributionTranspose

I also attempted to find the combined distribution that would satisfy the observed marginal distributions of age and gender but that would greatly reduce adverse selection. The graphic below thus presents pretty much of a  “best case” for the combined age-gender distribution in the Exchanges. Notice that now it is only in the 18-35 year old age brackets that there are substantial variations in the rates of male and female enrollment. I very much doubt that the actual statistics are as promising for ACA success as depicted in the graphic below, but I present them here to show the sensitivity of my methodology to various assumptions.

 

Distribution of enrollees by age and gender that would substantially reduce adverse selection
Distribution of enrollees by age and gender that would substantially reduce adverse selection

The next step

The next step in this process is to try to compute the difference between premiums and expenses based on these  combined age-gender distributions.  I will then compare it to the difference between premiums and expenses based on an age-gender distribution that might have been expected by those who earlier modeled the effects of the ACA.  The result should provide some insight into the magnitude of combined age-based and gender-based adverse selection.  It should be similar in spirit to the work I showed earlier on this blog here. I hope to have that analysis posted later this week or, I suppose more realistically given my ever pressing day job, early next week.

How it was done

I have essentially 12 variables we are trying to compute: the number of enrollees in the combination of two genders and six age brackets. I know 9 facts about the distribution based on data released by HHS. I know the total number of males and females and I know the total number of persons in each age bracket.  And I have 12 constraints on the values: they must all be positive. Using Mathematica’s “Reduce” command, I can use linear algebra to find the polytope that satisfies these equations and inequalities. I get an ugly expression, but it is one Mathematica can work with.

I can then sample 12-dimensions points on the polytope using Mathematica’s “FindInstance” command. I found 2400 points. Each of these points represents an allocation of enrollees among age and gender that satisfies the known constraints. I can then score each point based on its “distance” from my intuition about the strength of adverse selection. That intuition is expressed by “guesstimating” likely ratios between males and females for each of the six age groups.  I use a “p-Norm”  and Mathematica’s “Norm” command to measure the distance between the six male/female ratios generated by each of the 2400 points and my intuition.  I then take the 10 best 12-dimension points and thus obtain a 10x2x6 array. I take the average value of each of the 12 values over all 10 sample points.  It is that average that I show in the first two graphics above.

I then permitted the strength of adverse selection to vary by exponentiating the ratios in my intuition. By setting the exponent to zero, I basically try to minimize gender-based adverse selection and keep the gender ratios as close to each other as possible. The results of this effort are shown in the final graphic.

 

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Stunning report: only 11% of Exchange purchasers were uninsured

According to a report from reputable consulting firm McKinsey & Co. , only 11% of those purchasing health insurance in the individual health insurance market were previously uninsured. Although the report, discussed at length in a Wall Street Journal article, is the most extreme to date in examining whether the Affordable Care Act is, as promised, significantly reducing the number of uninsured or simply substituting one form of insurance for another, it is roughly in line with other surveys conducted recently. Michigan-based Priority Health reported that only 25% of its more than 1,000 enrollees surveyed in plans that comply with the law were previously uninsured. Health Markets Inc., an insurance agency that enrolled around 7,500 people in exchange plans, said 65% of its enrollees had prior coverage with, presumably, the remaining 35% uninsured.

The figures from McKinsey, coupled with the other survey data, are crucial to any evaluation of the success of the Affordable Care Act.  Its proponents like to brag that 10 million people have gained insurance as a result of the ACA.  As has already been pointed out by many, (see here and here) that figure is a grotesque exaggeration. But hitherto it had been assumed that at least a substantial portion of the individual Exchange purchasers were coming from the ranks of the uninsured. If the McKinsey report, which was based on a survey of over 4000 purchasers, holds up, it further reduces the number of people who have been helped in the most significant way by the ACA.

It is not enough that a few people have indeed been helped by the ACA. Billions of dollars of overhead have been spent on getting the individual Exchanges up and running.  Millions of people have been made to worry that their insurance coverage — imperfect as it may be — will be lost. Most likely, millions of individuals have already lost health insurance coverage as a result of the ACA. And, as I have discussed, millions of people dependent on small business as the source of their health insurance are likely to be further alarmed as those policies start to renew later this year. Many of them may lose advantageous coverage too.  There are many ways to improve people’s health with billions of dollars.   If the upside of Title I of the ACA — the part containing the elaborate individual Exchange mechanism is mostly a substitution of expensive ACA coverage — which, yes, has some additional benefits — for less expensive forms of coverage, then it those provisions are, to that extent, not making the sort of material improvement in people’s health that would constitute the only real justification for the expenditures.

The more modest ACA proponents (such as Jonathan Gruber on occasion)  have admitted that there will be losers as a result of the individual Exchange mechanism.  They have contended, however, that there will be a far larger number of winners. And ACA proponents have been quick to point to the 2.1 million (at last count) of enrollees in the individual Exchanges as amongst those winners.  If, however, 70-90% of those enrollees aren’t genuine winners but merely people cutting their losses, that is a very disturbing fact that must be given considerable attention in future debates over this landmark program.

Now, the McKinsey estimate is just that, only an estimate. Perhaps they have an axe to grind.  I don’t know. And, doubtless, we will see more work in this field in the months to come.  But it is hard to believe a reputable consulting company would fib or err by 30 or 40% on a statistic of this importance and that was bound to get a lot of publicity. Of all the things I have read, however, in the past month about the ACA, the fact that enrollment in the Exchanges may not even come close to equating with reductions in the number of uninsured is the most disturbing.

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New data on ACA enrollment shows problems in many states

The Department of Health and Human Services released updated data today on enrollment on the Exchanges including, for the first time, greater breakdowns on enrollment by several key categories: age, gender, and the metal level of purchase. The result of this long awaited and much requested data is, at first glance, very much a mixed picture. Some of the overall statistics do not look as problematic as some — including me — had feared they might be. But it looks as if there is a very serious potential for large adverse selection problems brewing in a number of states,  most notably West Virginia, Mississippi, Maryland and Washington State.

 

The good news for the ACA from the data

There are three major pieces of good news for those who support the goals of the ACA.

1. The overall gender distribution of enrollees, 54% female, 46% male does not appear on preliminary inspection to be sounding “red alert.” To be sure, the problem may be a little greater than would otherwise be suggested by the aggregated numbers if the middle age group is more heavily female and the oldest group of enrollees more heavily male that the aggregated numbers suggest.  And Mississippi is troubling with 61% female enrollment (and for other reasons, see below). But, overall, and if they hold up, these do not appear to be the the kind of numbers that would be way beyond what insurers likely expected or that, standing by themselves, would be devastating to an insurer on an Exchange.

2. Several states have total enrollments and the age distributions that should reduce the possibility of a serious death spiral getting started. New York and California are the two big states doing better than most.  Connecticut is doing very well also.

3. The metal tier distribution is 80% for Bronze and Silver policies and only 20% in Gold and Platinum.  That’s comforting for adverse selection. A higher proportion of enrollment in the more generous plans would have been a warning sign that enrollment was coming disproportionately from the sick.  There’s a footnote on this point later on — we are not out of the woods — but this is definitely better news for the ACA than a distribution of, say, only 50% Bronze and Silver purchases.

The bad news

Just because the ACA is doing better than some had forecast on an overall basis does not mean there will not be very serious problems in some states.  Given that the statute is presently unamendable as a practical matter, problems in just a few states can hurt a lot of people.  The data released by HHS today shows that there are a number of states in serious trouble.

The table below shows current enrollment in states that appear at first glance to be in significant trouble. (There may be states that are as bad as those shown in the table below; I kind of eyeballed the data, which was not provided in a nice electronic format, to select what appeared to be a problem).  I also show the projected number of purchasers, the percentage of current enrollees as a fraction of purchasers, the percentage of “young invincibles” as a fraction of the enrolled population and the percentage of the expensive 45-64 year old age group.

Maelstrom_Index

I also construct a “back-of-the-envelope”/quick and dirty “Maelstrom Index” that roughly measures the problems confronting the individual Exchanges in the state.  The maelstrom index is calibrated to run from 0 to 1 with a 0 score meaning the state is in excellent shape and a 1 meaning there is an extremely high risk of an adverse selection death spiral materializing.  It is not a probability measure.  The formula for the maelstrom index is shown at the end of this post.

Maelstrom_Index

As one can see, there are a number of states that have serious potential problems. West Virginia is problematic with 66% of its enrollees ages 45 to 64 and only 17% ages 18-34. Almost all experts would concede that this poses serious risk of adverse selection. And the overall fraction of enrollment relative to projections, just 20%, suggests that West Virginia’s insurance pool may be disproportionately populated by the sick. Mississippi is likewise a problem with enrollment only 14% of that projected, 58% in the age 45-64 bracket and, as mentioned above, 61% female enrollment. I have to believe that insurers writing in Mississippi are deeply concerned about these numbers.

Also  troubling are the numbers in Texas, Washington State, and Ohio simply because, as an absolute number, there is such a large absolute difference between the number of people projected to purchase and actual enrollment. I’ll speak more about all of these numbers and try to update the chart in the days ahead.

The unknown

There’s one key piece of data not found in the HHS release today. The number of “enrollees” who have actually purchased policies.  If this conversion rate is, as I fear, hardly 100%, the numbers presented today overstate the extent of purchases in the Exchanges and understate the dangers of adverse selection breaking out in many states.

The Maelstrom Index Formula

Max[0, (5*(1.4 – enrollmentPercentage – youngInvinciblePercentage))/7]

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Small business, the ACA and a second potential debacle

Small_Businesses_and_Obamacare___National_Review_OnlineThe following are excerpts of an article written by me and published in the National Review Online.  It’s available here. I recommend starting here, seeing if you are interested, and then clicking over to the National Review to read the entire article.

We could be about to see the same clumsy reconciliations of egalitarianism and freedom [that we see in the individual market provisions of the Affordable Care Act] ensnare the nation’s 6 million or so small businesses, the 40 million–plus people they employ, and the millions more spouses and children who depend on those employees. If only because the number of people involved is so much larger, the consequences and the stresses created could be even more serious than those we have seen playing out over the past few months in the individual market. The major points of tension here are (1) the prohibitions in section 1201 of the ACA on experience rating and medical underwriting in policies sold to small employers; (2) the requirement, also in section 1201, that, if a small business purchases group health insurance from a state-regulated insurer, it must provide the same sort of generous protections (including “essential health benefits”) as do individual policies; and (3) the effective tax that section 1421 of the ACA (section 45R of the Internal Revenue Code) places on wage increases and hiring by some small businesses that choose to offer health insurance.

What [various provisions of the ACA mean] is that there are an awful lot of employers who, if they want to provide health insurance to their employees and dependents, will now be able to purchase those policies at prices that do not take into account their abnormally high projected medical expenses.

A large number of these employers are likely to do so; even now 35 percent of employers with 50 or fewer employees provide some form of health insurance. Many small employers with lower-than-average projected health costs will strive to avoid being lumped in with their colleagues or competitors with higher costs. Instead, they will, if financially possible, “self-insure”: The section 1201 requirement of uniform premiums does not apply to arrangements whereby the employer (or union) itself nominally provides the medical benefits but throws off much of the financial risk onto reinsurers and many of the headaches of running a health plan onto “third-party administrators.” This option becomes even more attractive if employers can get away with the now-bandied-about “dumping strategy” of offering to pay their sickest employees enough so that they can purchase platinum health insurance in the individual exchanges and have money left over. Still other small employers may simply decide not to insure at all — reserving perhaps the delicious option of entering the exchange if some crucial employee or his dependents develop expensive medical conditions.

This self-segregation of small employers based on the projected health-care expenses of their employees will pressure small-group health insurers to raise prices. …

Of course, the curious thing about the looming debacle in the small-group market is that its possible contraction might be the one thing that could rescue the individual market from the probable death spiral. Right now, the individual markets are in danger as a result of lower-than-predicted enrollment and disproportionate enrollment of those over age 50. If small employers actually stop offering coverage — either because the costs of ACA-compliant policies prove too high or because of a death spiral in the SHOP exchanges (or both), they may end up just sending people to the individual exchanges. That won’t do much for President Obama’s promise that people could keep their health plans, and it won’t constitute a “silver lining” for people who want to reduce government’s role in health insurance, but it will do what many conservatives have wanted to do for years: undo the ideology that has previously tied the labor and health-insurance markets together.

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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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No, 10 million have not gained coverage through the ACA

A blog entry by Josh Marshall on the Talking Points blog and largely repeated by Ezra Klein on the Washington Post WonkBlog contends that 9 or 10 million people have obtained coverage through the Affordable Care Act. This statistic, which I am frightened will be repeated by those predisposed to the Affordable Care Act until such time as it is deemed true, is just nonsense.  There is something called “causation” and just because A occurred and then B occurred does not mean that A caused B. There is also this arithmetic operation called “subtraction,” and while one can make a pile of numbers look bigger by neglecting to subtract off the ones that make the result smaller, such an omission corrupts the resulting sum.

Where does the 10 million figure come from?

The 9-10 million figure is comprised from 3.1 million people under the age of 26 who have coverage, 2.1 million people who have allegedly obtained coverage in the individual Exchanges, and 4.4 million who have allegedly obtained coverage through Medicaid expansion.  The graphic shows the computation. Each of the constituent numbers has serious problems.  And there are negative numbers that Marshall and Klein have neglected to take into account.

Marshall-Klein addition
Marshall-Klein addition

The 2.1 million counts people who have not paid for their policies

The 2.1 million figure has problems. It uses the “enrollment” number rather than the “paid for” number.  We don’t yet know the conversion rate between putting an item in one’s shopping cart — perhaps to preserve the right to obtain retroactive coverage — and actually paying for coverage.  Early conversion rates in some states, as discussed on this blog, have been less than two thirds. So, until we know how many people actually purchased policies, the 2.1 million represents an upper bound on coverage, not the actual number.

Mr. Marshall asserts, by the way, that it is “deep and intense form of denial” to say that people won’t pay for their policies.  All I can say is, “let’s see.”  I promise I will post on this blog a very unfun entry titled “I was wrong” if it is shown that at least 80% of the 2.1 million that have enrolled thus far actually get coverage in the Exchanges pursuant to the ACA.  Let’s see if Mr. Marshall is willing to make a similar promise if more than 20% don’t get coverage.

The 4.3 million Medicaid number counts people who would have obtained Medicaid without the ACA

As Klein though not Marshall acknowledges, the 4.4 million number is high because there would have been an expansion in the number of people in Medicaid even without the ACA provisions taking effect in 2014.  Moreover, as Klein has the honesty to concede, “some states are also counting people who’re simply renewing existing Medicaid policies.” So what’s the real number. Klein says he doesn’t know and I can’t say I do either. But, according to data from the Kaiser Family Foundation,  Medicaid enrollment increased by 3.4 million between 2008 and 2009, by 3.4 million between 2009 and 2010, by 2.4 million between 2010 and 2011 and by 1.3 million between 2011 and 2012.  Wouldn’t a fair minded person thus subtract  at least 1 million from the 4.4 million figure? Wouldn’t a fair minded person want to at least mention the issue?

By the way, I know that we are just counting people covered “because of” the ACA, but while we’re at it perhaps we should remember that more people are on Medicaid may not be this unalloyed wonderful thing. Many may be on Medicaid as a result of increased poverty or may be substituting Medicaid other health insurance coverage that they earlier had.

[Note: Following my publication of the original version of this blog entry, Sean Trende published on RealClearPolitics.com a far more detailed and, frankly, better analysis of this number that I have did here. He notes that much of the expansion in Medicaid numbers comes from states that did not in fact expand Medicaid.  His estimate is that the correct number of persons who received Medicaid coverage because of the Affordable Care Act is about 10% of the Marshall-Klein number, perhaps 380,000.]

The 3.1 million number counts people who already had coverage

The 3.1 million number apparently counts everyone under the age of 26 who has coverage under their parent’s policy. But what would the number be “but for” the Affordable Care Act? How many of the 3.1 million are insured “because of” the ACA. First, many insurers were already covering dependents up until age 25 or close thereto.  Two thirds of the states had laws required that they do so. Thank the states, not the ACA. Second, much of the effect is substitution.  Not all, but a good number of these young adults could have obtained coverage on their own through their job or otherwise but, because of the peculiar way many group policies obtained through an employer work, found it cheaper to enroll on their parents plan.  All the ACA does, then, with respect to these people is reallocate where people get their insurance and the costs different types of insurers face.  Actual scholarship conducted by the National Bureau of Economic Research found that found that early implementation of the ACA increased young adult dependent coverage by 5.3 percentage points and resulted in a 3.5 percentage point decline in their uninsured rate.  The National Bureau of Economic Research thus estimated the reduction in uninsured young adults caused by the ACA at least in 2010 at well less than one million.  Nothing to sneeze at, but not the 3.1 million claimed.

By the way, in case you mistrust the National Bureau of Economic Research, take a look at the work of the Employee Benefit Research Institute.  It too found that some young adults were substituting parental coverage for coverage they might have had to pay for through their jobs.  It too found that the ACA had increased the number of young adults with health insurance coverage, but not nearly to the same extent as the claim of 3.1 million made by these bloggers.

The ACA has also caused people to lose coverage

Marshall and Klein may be good at adding fake numbers, but they appear to have forgotten about subtraction (or how to add negative numbers).  There are a number of people who have lost health insurance coverage as a result of the ACA. There are likely to be a yet larger number who lose it when small business has to renew policies later in 2014 and finds those policies considerably more expensive. (I’ll be talking about this issue more in the next month or two). No one knows exactly how many people have lost coverage so far or how many will lose it in “the second wave.” Estimates of the first number range from half a million and up and I have estimated the second number as being many millions.  One would think an honest assessment of the effects of the ACA would not just ignore these negative consequences.  Even President Obama, by giving at least some of those people, a (possibly unlawful) exemption from the individual mandate has not gone that far.

And finally …

The Affordable Care Act can not be defended with the glib “it’s worth it if even just one person got health care coverage as a result.” There are a lot of ways to give people health care coverage and to improve people’s health. How that’s done can determine how much money it costs the government and what sort of a burden it places on individuals and businesses. That’s why it does in fact matter how many people are helped by the ACA and how they are helped.  That’s why it galls me that the grossly exaggerated 10 million figure is likely to get considerable play. If it were true, the figure would matter.  The problem is that it is neither true nor calculated in a way likely to get at the truth. So, when we assess the ACA, could we please stop the nonsense, add up real numbers, and remember about subtraction!

[Note: Following the publication of this blog entry, the Washington Post rated the assertion that 9 million people have gained coverage through the ACA a “Two Pinnochio lie.” It reserved the right to adjust (upwards, I presume) the number of Pinnochios, however, if it turns out that the 4 million Medicaid number isn’t right either.  I believe Sean Trende’s analysis (see above) makes pretty darned clear that the 4 million figure is a serious exaggeration.  I thus expect no fewer than “Three Pinnochios” being attached to the assertion by the time all is said and done.]

[Note: I just checked (February 5, 2014) and darned if the Washington Post didn’t upgrade the lie to Three Pinnochio status — “Significant factual error and/or obvious contradictions.”  See here and here. Good for the Washington Post!]

In fairness …

There are, actually, two things I like about the Marshall/Klein blog entries. The first is that Marshall points readers to the “Gaba spreadsheet.” This is one of several attempts to actually track enrollments under the Affordable Care Act.  It is a useful resource that, in conjunction with other data, should help people speak objectively about the ACA.  The second is their point that the decrease in the number of uninsured would be a lot higher if all states had agreed to expand Medicaid.  Yes, Medicaid would have cost a lot more for the federal government and, possibly, a bit more for the states, and, yes, there are ways other than provision of insurance to give people access to medical care or improve their health,  but the reduction in the number of the uninsured caused by the refusal to expand Medicaid is a point opponents of the ACA need to deal with.  I have this wish that people could stop treating the ACA as this monolith that is either all wonderful or all awful. Disentangling it may prove impossible and improving it may prove very difficult and/or very expensive, but, in the long run, misleading presentations of the facts do not help anyone’s health.

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