Yesterday, the federal government released its list of proposed gross premium increases for health insurers selling policies on the Exchanges. To many, particularly supporters of the ACA, the results released at healthcare.gov were jaw dropping. The median increase requested in New Mexico was 59%. In Pennsylvania, Highmark Health Insurance, the state’s “Blue Cross” insurer requested rate increases on many of its plans over 35%. In Illinois, Coventry Health Care, an Aetna subsidiary, requested rate increases of over 30% on several of its plans. In Oregon, PacificSource, the state’s third largest health insurer, sought increases of 29% and higher on several of its plans. In short, in many states, very large increases in gross premiums were requested by a diverse set of major and minor players.
Pundits, including me, have pointed out that one should not leap from a view of these numbers to the conclusion that policyholders in the Exchange markets should invariably expect double digit increases. The only companies in the data released yesterday are those requesting more than a 10% increase. As Larry Levitt, a top executive at the influential Kaiser Family Foundation, said, “Trying to gauge the average premium hike from just the biggest increases is like measuring the average height of the public by looking at N.B.A. players.”
In fact, however, the math of Obamacare means that many purchasing policies on the Exchange will actually experience larger netpremium increases than even the huge ones proposed by many insurers. This is so because of the way the Affordable Care Act computes the net premium paid by policyholders.
Let me take a quick example to illustrate the reason net premiums are going to go up even more than the numbers from healthcare.gov suggest. Take an individual who has an individual policy for which the gross annual premium is $4000. And suppose that the premium increase for that plan, as is proposed in many places, 25% up to $5,000. But suppose that the second lowest priced plan in the state, which was also charging $4,000 goes up only 5% to $4,200. What happens to net premiums.? Let’s make our individual a typical Exchange purchaser with an income equal to 250% of the federal poverty level. In 2015, that individual would be paying about $2,334 in net premiums. In 2016, because net premiums are pegged to the price of the second lowest silver plan, that individual would be paying about $3134 in net premiums.
In short, the policyholder experiences an increase not of 25% — bad enough — but of 34%, even worse. If the policyholder wants to keep its plan, and perhaps the network of medical practitioners that have developed an understanding of the policyholder’s medical conditions, it is going to require the policyholder to pay 34% more. To be sure there are complications that might tweak that number a bit, but the basic math is right.
It will be even worse for some. We know that in some states, a few plans are proposing reductions in their gross premiums. In our prior example, if the second lowest plan went down by 2%, the net premium of the plan the individual actually purchases will go up to $3414 per month, an increase of 46%.
Or, keep the assumption that the second lowest silver plan goes up by 5%, but have the purchaser have a income not of 250% of FPL but of 175% of FPL. Policies are supposed to be affordable for them too. Formerly they would have paid $1021 per year in net premiums. Now, they will pay $,1821 per year in net premiums, an increase of 78%. It turns out that keeping your healthcare plan is going to be an extremely expensive proposition.
So, yes, in some sense the gross premium increases released yesterday by the federal government are unrepresentatively large. But in terms of what people actually pay, they are, in many instances, unrepresentatively small. Of course, many people will be unwilling to pay increases of 34% or 46% or 78%. But to avoid those increases, they will increasingly need to flock to the second lowest silver plan. Doing otherwise will prove ever more expensive. And so, the promise of “choice” in healthcare plans contained in the ACA may be fulfilled significantly less than its proponents anticipated when the bill was passed. The architecture of Obamacare may induce yet more purchasers to converge on Silver HMO plans.
I’ve been doing a lot of research on the state of policies sold on the health insurance exchanges. It’s not easy because the Obama administration, as even its friends acknowledge, has not been forthcoming with information. It has, however, placed some useful information in the public domain: two large databases of the plans being sold on the “Federally Facilitated Marketplace.” That’s the health insurance Exchange for states, like Texas and many others, that declined to establish their own exchanges. With the help of the R computer language, I’ve been sorting through this database and have reached the following conclusions.
The change in premiums between 2014 and 2015 depends significantly on the metal level of the plan and whether it is a PPO or HMO.
Gross premiums for platinum plans are up significantly in price, 21%, whereas bronze plans and catastrophic plans are down over 11%. The high increase in gross premiums for platinum plans creates a serious potential for an adverse selection death spiral in that segment of the market.
Net premiums will show larger percentage increases and decreases than gross premiums for many individuals. This is so because substantial parts of the premiums are paid via subsidies from the federal government.
PPO plans are up substantially in price, 8%, whereas HMO plans are down substantially, -18.%.
The combination of increases in the more generous platinum and PPO plans and decreases in the less generous bronze and HMO plans may start to divert Americans into healthcare plans that offer lower benefits and somewhat less choice, albeit at a lower price than was paid this past year.
Among plans that persisted between 2014 and 2015, the premium variations are less extreme: persistent bronze plans increased in price by 9.5% whereas persistent platinum plans increased in price by 14%. The larger variation in gross premiums overall is thus likely due to the exit of carriers who priced at extremes and low pricing by new entrants for bronze plans but very high prices for the more generous plans.
Cost sharing for the plans has increased somewhat, but many cost sharing arrangements have remained largely the same.
Competition, as measured by the number of unique issuers offering plans in each county, has increased substantially since 2014, but a market in which three or more insurers are actively competing is still a rarity, particularly for the plans that give consumers a greater amount of choice in selecting their doctor.
You can read a copy of the full report, which contains 29 tables of information, here.
One of the touted benefits of the Affordable Care Act was that, by fostering transparency, there would be greater competition in the health insurance market and that premiums would go down as a result. We now have data to help see whether competition within the various Exchanges has succeeded in reducing prices. This post, based on a scholarly talk I recently gave at the University of San Diego’s Workshop on Computation, Mathematics and Law, will suggest that the effect, if there is one, is small and subtle. It looks as if having just one seller of a product within a county may lead to somewhat higher prices, but the effect may not be robust. The methodology used here is a first cut. Whether other methodologies might tease out a larger relationship remains to be seen.
Note to ACA Death Spiral Fans: The USD conference mentioned above is one reason for the infrequent posts as of late. It’s been a busy period. Sorry. There’s A LOT to write about. Keeping track of Obamacare is at least a full time job.
The data for this project comes mostly from good old healthcare.gov, which, if one forages around a bit, actually contains a user-friendly database exportable in various standard formats such as CSV and JSON describing all 78,392 plans currently being sold in 2,512 counties via the federal Exchange. Each plan is described by 128 fields, including the metal tier of the plan, the name of the issuer of the plan, the type of plan (PPO, HMO, POS, EPO), the monthly gross premiums of the standard plan for various family types, the deductibles and cost sharing arrangements of the standard plan, and the deductibles and cost sharing arrangements of the variants of the plan that feature cost sharing reductions as described in 42 U.S.C. § 18071. The remaining data comes from the United States census.
The idea here is to consider each county of the United States as a market for health insurance and to find, for each county, the number of issuers selling plans on the Exchange, a representative measure of the price being charged by each issuer, and, therefore, a representative measure of the price charged within each county. If competition resulted in lower prices, one would expect to see — all other things being equal, which of course they are not — an inverse relationship between the number of issuers and the representative price charged within each county. We can also see, however, whether any such correlation is either spurious as a result of factors that correlate with both the number of issuers and the premiums charged or whether a stronger correlation might appear if other factors were controlled for. Here, the one other factor I took account of was county population density, the idea being that insurers might be less eager to enter counties in which the population density was low and that prices might be higher in such areas due to transportation costs.
Visualizing the Results
The “Distribution Chart” below shows a typical result from this data exploration. Here is the distribution of representative monthly premiums charged a couple in which the members are both 40 years old for a Silver PPO plan. The plot is broken down by the number of issuers within the count. If the insurer sells more than one Silver PPO plan within a county — which sometimes occurs — I take the median price for that insurer. And to determine the county price, I take the median price for all of the issuers.
The Distribution Chart works by using a dot to represent each gross monthly premium broken down by number of issuers. It applies different background colors that depend on the number of issuers within the county and shades each part of the background according to the density of premiums at that price level. Darker shades represent higher density.
We can run the same analysis for different purchasers, different metal levels, different types of plans and using different measures to move from issuer prices within a county to a single representative issuer price and to move from representative issuer prices to a representative county price. Here, for example, is the Distribution Chart for gold PPO plans purchased by couples age 40 with two children in which I use the minimum price offered by the issuer within each county and then use the 25th percentile price of those minimum prices to come up with a representative county price.
We can also aggregate matters. Here is the Distribution Chart for all Bronze plans of all types (HMO, PPO, POS, EPO) in which I take the median of multiple plans issued by a single issuer and then take the median value of all issuers to derive a county price. I do this for a single adult, age 30.
Here’s an analysis examining all types of Bronze plans but using a variant of the visualization. The individual dots are suppressed and we now have little histograms for situations in which there is 1 issuer through 8 issuers.
When I eyeball this data and many more permutations that I have produced, I at least do not see any dramatic and widespread relationship between the number of issuers within a county and the representative gross premium being charged. For some combination of parameters, one occasionally sees higher prices when there is only one issuer in the county, but generally the picture, at least the naked eye is quite blurry. The one thing I can say with some certainty is that the family-type of the purchaser — individual, couple, family with children — does not appear to affect matters. Premiums appear quite uniformly scaled across these groups.
What I do consistently is, as noted here and here, that there are many counties in which there is only one issuer of a particular level and type of plan. For Silver PPO plans, for example, in which one wants a medium level of cost sharing but wants at least some freedom in selecting a provider, of the 2,512 counties, 20% of the counties have no issuers with such a plan while another 36.6% have only one such issuer. Only 13% of the counties have three or more issuers of these plans. The pie chart below shows the distribution of issuers.
Or, suppose one simply wants a bronze plan of any sort. What we see is that 16.2% of the counties apparently have no such plan, 27.9% have only one issuer and 31% have 2. Thus, only about one third of the counties have 3 or more choices for a simple bronze plan. The pie chart below shows the result.
Sometimes the human eye and the human brain, magnificent as those organs are, do not see patterns that in fact emerge when studied through the lens of statistics or machine learning. Modern computers and statistical activities make it easy to go beyond eyeballing data. What I have done, therefore is to merge representative premium data with data on the population density of each county and see if any statistically significant relationship emerges between the number of issuers within each county and the county representative price.
I want to start with the simplest model: a linear relationship between the number of issuers and the county representative premium. I will do the analysis at first for my baseline Silver PPO purchased by a couple age 40 where I use the median price of the issuer if they sell more than one Silver PPO within the county and the median price of issuers . The graphic below shows the results. There is a statistically significant relationship between the number of issuers and the premium. For each additional issuer, the gross premium goes down by about $16. The model overall, however, accounts for only 2.1% of the variation in representative county prices, meaning, roughly speaking, that 98% of the variation in premiums is correlated with factors other than the number of issuers.
The problem with leaping from this finding to an attempted vindication of claims about the virtues of the ACA is that the result, even weak as it is, depends a bit on specification of the model. This gets a little technical, but unless one assumes a priori that there is some good reason to think that the relationship between number of issuers and price is in fact a linear one, restricting the regression to a simple linear model is potentially misleading. Here, for example, I regress the same data on n (the number of issuers), n-squared and the log of n. All of the coefficients in front of the various terms are still significant, but if one looks at the picture one gets a much more complex story. It appears that having one issuer does lead to high prices and that having two issuers may minimize the number of prices. As one increases the number of prices beyond two prices go up again until we peak at four issuers. This model explains almost 9% of the variance in pricing, which is considerably better than the simplest linear model but still not very good. Clearly, pricing is determined by much more than the number of issuers within a county.
The observed pattern when this more complex regression model is used appears roughly to persist for all metal types of HMOs and PPOs except platinum PPOs where we see the price increase as the number of issuers within a county increases. The family type of the purchaser appears not to affect the general shape of the relationship. I am never able to explain more than about 12% of the variance in premium pricing when I use just the number of issuers within the county as my single explanatory variable.
I have some sense that the population density of a county might have an effect on pricing. Perhaps lower density counties are more expensive. Or, it could be the case that higher density counties, which may have fancier equipment, are more expensive. The regression below shows a simple linear regression using two variables: number of issuers within the county and population density of the county. As one can see, the results are little changed. Both variables have effects that are statistically significant but small. As one goes from 1 to 2 issuers, the price drops by about $17 per month. As one goes from a county in which the population density is 4.3 (which would put it in the 10th percentile) to a county in which the population density is 491 (which would put it in the 90th percentile), the price goes up by $7 per month. The model still does not explain much (adjusted R-squared <0.03). Here are the results in more detail.
Again, I can use a more complex specification. Below I show the results of using linear, quadratic and logarithmic terms for both number of issuers and population density. What we see is a complex picture in which having just one issuer appears to persist in causing somewhat higher prices and in which population density plays a small role. But we are still able to explain less than 10% in the variation of premiums. Again, whatever is going on in premium pricing models, is a lot more complex.
A Foray into Machine Learning
I also attempted to see whether a computer could find a formula that predicted county representative gross premiums any better than my statistical models when given free rein to do so. To do this, I loaded the data into a program called Eureqa from Nutonian .com, which basically uses “genetic programming” to find models that predict well. The basic idea is to treat mathematical formulae kind of like strands of genetic material and permit mathematical formulae that perform better to evolve via mutation and “sex” to produce what may be yet formulae. Sometimes it produces amazing results and — well — sometimes it does not. Either way, however, genetic programming and other methods of machine learning are a useful complement to traditional techniques. They help one check whether the apparent incapacity of traditional methods such as regression are an artifact of limited specifications or the result of unavoidable noise in the data.
In this case, Eureka basically found little. It found some functional forms a human might not come up with such as the one below, which appeared to predict decently, but in fact did not do any better than the models I developed by hand. The foray into machine learning suggests, then, that the limited ability of our our statistical models to predict well is not the result of a failure to specify the model correctly but rather the result of noise in the data and unobserved variables.
Unfortunately, perhaps, the results shown here are not the sort one writes home about or that get on the front page of either scholarly publications or news reports. They are kind of “meh” results. Maybe market concentration has an effect, but, at least as revealed by the data here it is small. So, why might this be?
1. Perhaps the number of insurers in the Exchange is not as relevant anymore as might be thought. Given the availability of individual policies off the Exchange in some states, the number of individual polices within the Exchange may not be as important. I don’t have the data on off-Exchange policies and neither, so far as I know, does anyone else.
2. Maybe pricing is determined more by the identity of the insurer than the number of insurers. Suppose, for example — and I do not say this is true — that Blue Cross made different assumptions about adverse selection and moral hazard with the purchasing population than did, say, United Healthcare. Markets that Blue Cross entered aggressively might thus have lower representative county prices than markets in which they did not. Or suppose that Blue Cross was able to use market power and/or superior skill to create narrower networks that nonetheless satisfied regulators. This might account for markets in which Blue Cross was present exhibiting lower prices. Or suppose that Humana was more willing to take a loss the first year in order to supposedly lock in business than was Blue Cross. This too might explain lower pricing. This suggests another experiment in which one looks at pricing as a contest and seeing how each of the competitors fared against each other.
3. Maybe consumers are very sophisticated such that “Silver PPO plans” are not comparable. If consumers, for example, value the precise package of benefits and providers offered by, say, Blue Cross in a county as being quite different from the precise package of benefits and providers offered by, say, Humana, then we can’t just count issuers in determining the level of competition in a county.
4. Population density isn’t the right variable to include. Maybe what we need is some measure of medical pricing by counties. Or maybe, as the Wall Street Journal suggested, we need to include some measure of income or income inequality. Sadly, it may be that healthcare costs more in poorer counties, perhaps because the poor have more serious health problems. At the moment I have not included those variables. Future examinations of this area should probably do so insofar as the data permits.
Ordinarily, it would be my practice to make the Mathematica notebooks used to conduct this analysis fully available. I very much believe in transparency. Unfortunately, this analysis was conducted using features in a beta version of Mathematica 10 and I have signed a non-disclosure agreement with respect to that software. While I received consent to show certain results from use of that software, I did not request or receive consent to show code. Moreover, the code would not work on computers that do not have Mathematica 10. I commit to releasing the code as soon as Mathematica 10 is out of beta. I don’t think my NDA stops me from saying, however, that Mathematica 10 looks somewhere between absolutely spectacular and completely mind-blowing.
Exploring the likely implosion of the Affordable Care Act