Tag Archives: Rate review

Many will experience premium hikes even larger than requested rate increases

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 net premium 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.

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Government data shows potentially scary ACA premium increases for 2016

Under the implementation of the Affordable Care Act promulgated by the Obama administration, the federal government publishes a list each June 1 of health insurers seeking to increase their premiums by over 10% from one year to the next.  Today, the Obama administration released their data for 2016. There are a lot of insurance plans and a lot of very high requested increases on the list.

My examination of the data this afternoon shows 661 insurance plans in which a rate increase of over 10% is being requested.  And the increases requested by these insurers is often way over 10%.  The median increase requested by insurers on the list it varies from a low of 12% in New Jersey to 59% in New Mexico.  Median means half the numbers are below the median and half are above the median.  Thus a median increase of 32% in Pennsylvania means that half the insurers there on the list are asking for more than a 32% increase in premiums.

An aggregation of the data is also revealing. If one looks at the median increase in each state, the “median of the median” is 19%. Half of the states are seeing median increases of less than 19% and half are seeing median increases of more than 19%.

Most of the analyses of this data thus far have looked at particular states and found them troubling.  Taken as a whole, however, the widespread significant increases should be disturbing to those who were confident that the Affordable Care Act would continue to result in low premiums.

Moreover, the median figures cited above are by no means the maximum increases requested by insurers. Let us start with some heavily populated states and take a look at some representative high increase requests.  In Texas, Time Insurance is requesting a 65% increase. In Florida, Time Insurance is asking for 63% on one of its products; the better known UnitedHealthcare is asking for 31%.  In Illinois, Blue Cross is asking for a 38% increase on one of its plans; Coventry, also a good sized player, is asking for 34% on another.  In Pennsylvania, a Geisinger plan is asking for 58%; Geisinger is a significant player in that state.  The list goes on and on.

The table

The table below shows the data I was able to mine from healthcare.gov on the rate increases.

State Number of plans reporting Median Rate Increase (Conditional on Rate Increase > 10%) Rank
Alabama 14 24 13
Alaska 13 24 14
Arizona 24 20 17
Arkansas 3 21 16
California 0 N/A
Colorado 0 N/A
Delaware 26 16 28
District of Columbia 8 14 38
Florida 13 18 25
Georgia 27 16 29
Hawaii 6 18 22
Idaho 57 19 20
Illinois 16 15 31
Iowa 30 25 11
Kansas 15 35 3
Kentucky 0 N/A
Louisiana 15 18 26
Maine 0 N/A
Maryland 8 30 6
Massachusetts 0 N/A
Michigan 12 15 33
Minnesota 0 N/A
Mississippi 6 26 10
Missouri 13 16 30
Montana 12 34 4
Nebraska 12 15 32
Nevada 25 14 36
New Hampshire 11 44 2
New Jersey 7 12 40
New Mexico 3 59 1
New York 0 N/A
North Carolina 17 26 8
North Dakota 3 18 23
Ohio 15 14 34
Oklahoma 8 28 7
Oregon 23 20 18
Pennsylvania 51 32 5
Rhode Island 0 N/A
South Carolina 10 24 12
South Dakota 18 17 27
Tennessee 12 14 35
Texas 22 26 9
Utah 31 19 21
Vermont 0 N/A
Virginia 19 14 37
Washington 24 13 39
West Virginia 14 19 19
Wisconsin 12 18 24
Wyoming 6 23 15


All of that said, the figures should not be misinterpreted.  The following caveats must be considered.

1. The data only lists those insurers that requested an increase of more than 10%.  There are many plans that requested increases less than that amount.  So it is incorrect to say that the average or median increase in insurance prices is going to be 19%. If a lot of big insurers are requesting increases less than 10%, the average increase will be less than 19%.  On the other hand, if the big insurers are over 19% and it is mostly small insurers that are submitting rate increase requests of under 10%, then the 19% figure is too low.

2. The data is not weighted by the number of policies sold by an insurer.  With all respect to small insurers (and small states), in the grand scheme of things it does not matter much if a small insurer in a small state is raising its rates 40%.  Of course it will affect the people involved, but it is not a good bellwether of the performance of the ACA.  On the other hand, if a big insurer in a big state, like Scott & White in Texas, is requesting increases (as is the case) of 32%, that is a very big deal. Until we have an estimate of the number of policies sold by each insurer, a secret that seems to be more tightly guarded than many diplomatic communications, it is hard to know perfectly what the numbers in the list actually mean.

3. The data for some important states is missing.  We have no data for New York and California, for example, and no data from about seven other states. Does that mean that there are no insurers there requesting more than a 10% increase, that the data is just delayed, or is there another explanation?  Until this mystery is resolved, it’s hard to know fully what the numbers published today imply.

4. Ask does not equal get. All we have right now are the rate increases requested by insurers.  There now follows a review process in which the reasonableness of the rate increases are examined.  If the federal government or, in some instances, the states find the rate increases unreasonable, then they do not go into effect.  Of course, insurers who see their rate increases denied, may decline to sell the policies, which results in less competition and leaves many insureds without any continuity in coverage. Yes, it is possible that some insurers are bluffing and requesting pie in the sky.  The risk in calling that bluff by denying or modifying a rate increase is that the insurer may pull out.

5. I basically did this analysis by hand because CMS has not released the data in a form (such as Excel, CSV, JSON or others) that would facilitate machine analysis.  I tried to do the work carefully, but I am an imperfect human.  I am doubtful, however, that any errors materially affect the conclusions here.



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