Center for Studying Health System Change

Moderator: Paul Ginsburg

Panel Members:
Mark Pauly, University of Pennsylvania
Jack Hadley, The Urban Institute and HSC
Rob Cunningham, Health Affairs

Respondents:
Thomas R. Hefty, Blue Cross/Blue Shield United of Wisconsin
John Bertko, Humana

Mark Pauly: Thank you, Paul, and I’m delighted to be here as well. The individual or non-group insurance market, I was first asked to look at it by the organization formerly known as the Alpha Center about four years ago, to look at that market, and among other things, it proves something I always tell my graduate students, that almost anything is interesting if you look at it carefully enough.

Cosmically speaking, of course, the number of people insured--I don’t know this for a fact, but I’m speculating--in the individual insurance market is probably about equal to the number of people who buy pet insurance. But as Paul has already suggested, not only is it interesting in and of itself, but because the possibility of offering individuals tax credits to help them buy insurance in private markets, among which individual market would probably be prominent, because that’s now--I don’t know about traction, but at least it’s made it onto the political radar screen. That’s what we’re here.

And, again, in the list of your mission, should you choose to accept it, Len Nichols and I were both asked by John Iglehart first to see whether we could agree, and then if we could agree, to write a paper that would give the background for discussion of the subject of this conference, the individual insurance market, also known as the sacrificial victim. So what I plan to do here in the new few minutes is just give you the highlights of our paper.

Again, one message that I would convey--or two messages, actually. First, I found it a pleasure to work with Len on this, and we’ve agreed to disagree a lot over the years. In this case, we were able to agree to agree. And, second, it was also a pleasure to work with the wonderful community tracking survey data that was able to attach some numbers to things we always knew must be true. So you’ll see some of that.

Personally, as a researcher, as well as a person interested in policy, I think there are some surprising and also maybe some confirming things that came up in this exercise.

So here’s what I’m going to say quickly. I’ve already said a little bit about why the topic is controversial, and at least we tried to come up with what analysts could agree on about the individual insurance market. It’s probably too much to expect political types to agree. I’m always surprised at how much passion surrounds an issue like this. It seems to me to be just a fairly technical issue. But I suppose when the game is winning and losing elections, you attach passions to the strangest things.

Then, secondly, what analysts disagree about, well, we are going to talk about facts that analysts more or less agree about, but they disagree about facts and they disagree about values, and the hard part often is even for the authors to kind of separate those two. I will try as hard as I can to stick to facts until the end, when I’ll throw in my two cents’ worth about what I really think about individual insurance markets. And then there are policy disputes that follow from these markets.

Well, here is our quick summary of what we think the facts are, and I added, although it didn’t come out very well--it almost looks like a computer’s frown or something. But even the facts, it’ll be sort of "yes, but." And the most you can say about these facts is not that they’re absolutely true but that they’re very likely to be true. But at least these are the things we thought were reasonably uncontroversial.

First of all, just a description of the individual insurance market. I’ll show you a table on this in a moment and comment on it. But it’s small relative to the size of the health care sector, to even the total private sector, where the great bulk of Americans get their health insurance as part of their compensation for their job not in this market. But as Paul mentioned, some do get it in this market. But it would probably be used by subsidized uninsured, and it’s also shrinking and I’ll say a little bit about that.

The second factoid that I think there’s pretty much general agreement on is that, other things equal, if you buy the same insurance policy in the individual market as in the group market, much less in the public sector through Medicare or Medicaid, the portion of the premium that will have to go for administrative expenses, the main differential is not claims payment but it’s selling and billing expenses, is going to be higher for individual insurance. It’s a kind of custom-designed product, and it costs more than an off-the-rack suit.

The third is that although--I was going to say "yes, but." There are some people in some circumstances that may actually find a better deal in the individual insurance market than what at least somebody might guess they were being charged in the form of lower wages in the group insurance market, maybe; at least there’s arguments about that.

Thirdly, the risk of adverse selection is probably greater than in group insurance, especially where there’s regulated rating, whether it’s community rating, which requires insurers to ignore information about differences in risk levels that they can observe. Whether or not the problem is so serious if insurers were allowed to use all the information at hand is, I think, more controversial, but at least where they’re not--which happens fairly frequently--that’s a problem.

Our judgment is that the reforms, which generally have taken the form of reducing the extent of risk rating and the extent to which insurers have discretion over who they will insure in the individual market, has almost surely not increased the total number of people with insurance coverage. So if the ball that you want to keep your eye on is how do we get more Americans to have health insurance, you can do a lot of good things potentially with reform of the individual insurance market, but so far nobody has figured out how to hit that ball with this particular bat.

What you may be able to do, although even this turns out to be controversial, is given a total number of uninsured people with rating reforms like community rating, you may be able to move out of the uninsured, the high-risk people, and into the uninsured, the low-risk people, but we’re not even sure about that.

And then, of course, this is the fundamental message from economics, if only we needed to look at it again. All policy interventions produce tradeoffs. There’s no intrinsic good or evil in the individual insurance market. It’s just a matter of what tradeoffs you like and what values you place.

So here’s the kind of data that I mentioned, and I draw your attention to the last column. I think that’s the most interesting set of numbers. There was a decline in the percentage of--in the number of people enrolled in the individual insurance market between 1996 and 2001 of 11.5 percent. Of that, about 80 percent was attributable to the fact that the number of potential candidates to buy insurance in this market fell, so that fell by 8.6 percent, in part because of growth in employment-based insurance and in part because of growth in CHIP and Medicaid. But there was about 20 percent of it that was due to the lower likelihood of taking this insurance, given that you were stuck in that market. And so not exactly covered with glory in terms of performance in the last half-decade or so.

ere’s what analysts disagree about. That was just an illustration of factoid number one. And I think number one on this list is the most controversial. In the individual insurance market, some people face tough choices, and we hope you kind of know what that means.

I’ll say a little bit about it. But people actually do have quite different opinions of how tough is tough, and they also have quite different opinions, even if they agree on it, of how many people face those tough choices. So we tried to bring some information to bear, and actually I think this is helpful.

We at least were able to explain why it’s confusing because it kind of depends on what measure you use of tough, and maybe say something--and here we’re winging it a bit and out on a limb--about the answer to that question.

Then another thing that analysts disagree about, you get less discussion about this, I think, in the general public policy area, although you do among some of my friends, which is those people who attach value to imagining that people can get the insurance policy they want, not the policy that the boss’ benefits manager, who’s probably his son-in-law, anyway, picked for them, attach value to the individual insurance market. It, of course, doesn’t give you as many choices as you’d have if you were a federal employee, but compared to working at Gus & Otto’s Garage, even if Gus and Otto offer you group insurance, you’re going to have more choices in the individual insurance markets.

Then there do exist high-risk pools to pick up some of those people who have tough choices, and we comment on their effectiveness, or at least the debate about their effectiveness.

And then, as I’ve already mentioned, there’s controversy about both facts and values in terms of evaluating the effects of regulation of market performance.

So the number facing tough choices is tough to count, and we kind of--at least this is what you might be interested in counting. The truly uninsurable, we use the folk literature number of 1 percent of the population. I think that was quoted in testimony to Congress about 30 years ago, and it has never been updated. But we do bring to bear some data from a large insurer on what proportion of applicants they turn down, and it was about 4 percent. And if you assume that some of those people by scrambling would have got coverage, in the range of 1 to 3 percent is probably about where you would end up, my guess.

Then there are people who are insurable but they have important exclusions. If you have hay fever and you write that down on your application form or your doctor snitches on you, the insurer will probably not cover that. That can still mean that insurance is valuable to you for all the other things that have yet to happen, but it does exclude things.

But the most serious issue, probably, of tough-choice people are the people who are insurable but at very high prices, either an absolute sense or relative to income. And we observe people who don’t--and I need to say, and I think we show in the data, that there are people who don’t buy insurance even though they can afford it, and there are some people who buy insurance that it doesn’t look like they can afford. So it’s not just a matter of affordability, but it’s certainly a matter of consternation and confusion.

And then the main--one of the main facts we’ve found is that how bad you think things actually are for people who are high risk depends on how you measure high risk. So that’s what I’m going to talk about now. This is sort of the main research thing. Just give me a few minutes to talk about it because it’s exciting to researchers.

It turns out that what you would conclude about how easy it is, at least in terms of revealed behavior, not to mention all of the sleepless nights and the telephone calls and so forth, but in terms of what people finally are able to do, is it easier or harder for high-risk people to get individual insurance than low-risk people to get individual insurance? It’s hard for both groups to get individual insurance because it’s expensive insurance because of that high administrative cost. But it’s differentially difficult. It depends on how you measure health risk, and there we have three candidates there: self-reported health status, presence of chronic conditions, and age. And we say a little bit in the paper about individual versus family concept, but I won’t talk about that here.

I hope this is semi-legible to people in the back. What is worth paying attention to is the differentiation in the last two columns which talk about the non-group market--the first two columns talk about the group market--between the percentage of people, given income as measured by the band around poverty line, who would be candidates for non-group insurance who actually get non-group insurance. So running all the way down to the bottom, the Evil Knievel’s of health insurance there, the people who have incomes greater than 4 percent of the poverty line, but they’re still about--it depends on how you count it--about a fifth of the uninsured.

You can see that the percentage of people with a chronic condition at 60 percent who get this insurance is actually higher than the percentage of people in households where somebody has no chronic condition, which is about 40 percent. And you see the same qualitative difference, although, of course, the percentage falls as income falls. So at least if chronic conditions you measure, the people with chronic conditions actually are more likely to end up with this insurance, whatever else is going on, than the people without chronic conditions in their household. And this is a summary of that.

The ESI take-up, the employment-based insurance take-up is clearly higher than the individual take-up. At least personally I think that. This is an opinion. But I think that’s because of lower loading, but there could be other things going on, including corporate socialism and other favorite topics of Uwe Reinhardt.

Income is clearly important as well. No matter what, the higher your income, the more likely you are to have insurance.

People with chronic conditions, we think, would probably expect to see riders and higher premiums in the individual insurance market; nevertheless, they are more likely to end up with insurance than people in households without chronic conditions. And I guess that’s the punch line down there at the bottom.

Now, this shows, though--that’s one set of facts. You want a different set of facts to testify for the other side? Well, those exist, too, and it depends if you measure health status by this excellent, very good, good, fair, poor. Again, drawing your attention to the final two columns, you can see--I’ll just pick the rich uninsured: 33 percent of them, if they are in poor or fair health, only one-third of them end up with non-group insurance; whereas, if they’re in excellent on down to good health, more than half of them end up with non-group insurance. And, again, you get the same differentiation as income falls.

So it all depends on how you measure things, and this is in the nature of you open one door--you close one door, you open another door. What is the puzzle here? The puzzle is in some ways to reconcile the two findings that I just presented. How can we explain why people in poor health are less likely to have health insurance than people in good health? Well, one possibility is, of course, that insurers discriminate based on that. But then why didn’t they discriminate at least effectively based on chronic conditions?

The other possibility is--which is, actually, I think, the reason why we care about the uninsured. Not having insurance makes you sick, so maybe the causation goes the other way.

This looks at age, and unequivocally, age is an indicator of risk. And, again, you get the phenomenon closer to the chronic condition story, that holding income constant, the older people are, the higher risk they are, the more they’d have to pay in the individual market for sure, the more likely they are to buy insurance in that market. So you can see the conclusion there, which basically says what I just said.

We also did a multivariate model, and the message here is the computer told us what we could see in tables, which must mean it’s true. And so our overall conclusion about health status in the non-group insurance market is that the non-group market works passably well, even for high risks--I’ll take about one more minute here, Paul, if I can--particularly for--this is where we’re most out on a limb, so I’m sorry I have to talk so fast--particularly for the 40 percent of households who are not income constrained. And we hazard the guess that perhaps 80 percent or a lot of non-group households would have access to what we call actuarially acceptable premiums.

There’s the 40 percent who can afford it, and then there is 42 percent, about two-thirds of the remaining 60 percent, who would at least be charged premiums which, from the point of view of insurance, is about the best the insurer can do. And our data on applicants to the unnamed company found that 78 percent of them were offered insurance at premiums that were at most 25 percent more than the standard rate. This was my editorial comment, not Len’s. I think it’s government failure here. We fail to help out low-income people, not necessarily market failure. So that’s probably the most controversial conclusion, and I’ve put quotations around "works" because that’s what everybody will fuss about.

Then here’s one last factoid that we thought was interesting. There may be another kind of market failure in the non-group market in that the average expenses of people who don’t buy insurance are lower than the average expenses of people who do buy insurance. Well, that’s no surprise. That’s moral hazard. But they’re so much lower when you look at the data that it’s hard to believe it could be moral hazard alone. So that there must be something about the people who remain uninsured, particularly strong if you look at the people who are listed in fair or poor health. Either they’re not in such bad health as they seem, or they’re very stoic when it comes to seeking care. But the problem then with the market is, if the insurer bases their premiums on the expenses of the people who do buy insurance, they’ll end up offering something that looks like a terrible deal to the people in good health. So although the belief, mistaken belief in immortality probably explains why a lot of uninsured are uninsured, it may be also that when they actually look at the facts, the real facts about themselves, it is a pretty bad deal.

Other major disagreements, I’ve already commented on the feasibility of tailoring packages. The effects of high-risk pool, I think on balance they do good things, but they don’t do enough good things, and the values of effects of market reforms. The remaining policy disputes, I’ll just quickly summarize here. How well, when push comes to shove, does the non-group market work now? Here’s my two cents’ worth: Not as bad as you might have expected, but not as good as God would want it to. But we may not be able to improve it very easily. And how relevant is that to a world where we might do something dramatic, can only hope to do something dramatic--something dramatic about the uninsured. And if it was to offer fairly generous tax credits, the hypothesis that we discuss--of course, can’t prove because we haven’t been there yet--it is that that would change the way the non-group market functions. Certainly it should change to increase the--or likely it would change to increase the generosity of policies. So I’ve objected to trying to draw inferences about what the non-group market might be like in the nirvana based on what it’s like now on that hypothesis. And probably--and I can say more about this if somebody wants to fuss about it. My prediction is that if you subsidize this insurance so it sells itself, it will cause insurers to have to spend less on selling the stuff themselves and less on screening out the high risks.

Would the group market fall apart if the non-group market became heavily subsidized? The short answer to that is it depends on how you subsidize the non-group market, but not obviously true. But that’s what people fuss about. And are there any other policy interventions that are better? Is this the best of all possible worlds or is this the best of all thinkable worlds?

I think those are my comments.

[Applause.]

Paul Ginsburg: Jack Hadley?

Jack Hadley: Thank you, Paul, and Tad and Roland, for getting me up on the screen. I never could have done it myself, I know for sure.

The title of my presentation is "Health and the Cost of Non-Group Insurance." It’s based on work, on continuing work I’m doing with Jim Reschovsky at the Center for Health System Change.

One of the major concerns that people have about using tax credits as a policy option for expanding insurance coverage via the non-group market is that their value may be limited by certain structural characteristics of the non-group market. Mark touched on all of these in his presentation. Our presentation focuses on the first two: medical underwriting, primarily, and to a smaller extent, the issue of less generous benefits.

There are two basic policy questions that I’ll try to address in this presentation. One is: Will the tax credits be generous enough to make non-group insurance affordable for the target population, that is, uninsured people who don’t have access to either employer-sponsored insurance or public insurance? And, secondly, how many people would benefit from tax credits and what are the characteristics of those who don’t?

The main contribution of our analysis--and I hope it’s value-added--is that we adjust for the statistical problem of selection bias. And I’ll explain what I mean by that in just a moment. After making the adjustment, we project non-group premiums to the target population in our data set, and then assign them tax credit values using two well-known tax credit proposals. And then we’ll compare the proposals in terms of the amount of subsidy they provide, their implications for affordability, that is, the percentage of income that people would have to pay, both before and after a tax credit. And at the end I’ll show you some information comparing the benefits of non-group insurance and employer-sponsored insurance.

This next slide gives you a very quick summary of the data we used from the community tracking study, and one of the things you’ll probably come away with at the end of the presentation is that, even though we use the same data that Mark and Len used, we don’t actually go the same way and come up with somewhat different results, although at the end of the day, I think we wind up pretty much in the same place.

So let me talk about selection bias. Basically it refers to the possibility that people covered by non-group insurance are not representative of the uninsured and that they’re healthier, possibly, in both observable and unobservable ways, and, therefore, it’s making inferences from the cost of non-group insurance based on what people with coverage--who actually have that coverage have to the uninsured is biased. We call it selection bias because it works through two mechanisms. One is the self-selection of healthier and richer people into the non-group market. The other is based on insurers’ offering and pricing decisions that possibly screen out sicker and poorer people.

With our data we can actually look at one element of this, the health characteristics, observable health characteristics of the people based on the kind of insurance coverage they have. And one difference you can see right off the bat between our analysis and Mark’s and Len’s is that we use bigger numbers. So I think you can probably see them a little more easily.

The first two rows measure adults’ self-reported health status. The bottom two rows measure a couple of family health characteristics. And I think it’s pretty clear as you compare non-group to employer-sponsored to uninsured that, in fact, people with non-group coverage really are--appear to be much healthier, both in terms of the proportion who say their health is fair or poor and the proportion who have a child in fair or poor health. So without going into any details about our statistical analysis, let me just say that what we do is estimate a relationship between premiums charged for the non-group policies in our data set as a function of personal and family health characteristics, also controlling for age and family structure, costs of medical care in the community, several measures of state insurance regulation, and the presence of high-risk pools.

To adjust for the effects of selection bias, we use what’s known in the trade as a Heckman selection model, which is named after James Heckman, who won the Nobel Prize in economics a couple of years ago. So it’s a method that has some pedigree, although there are people who still think it’s all hocus-pocus.

This slide summarizes the main results of our analysis, and let me explain the columns first, and then I’ll talk about the rows.

Each of the columns reports percentage differences in premiums predicted by our model. The first column incorporates the Heckman selection adjustment. The second column, the one labeled "unadjusted," does not.

Now, for the rows, the first five rows measure the health of the policy holder relative to a person who’s in excellent health with no chronic conditions, the reference group. And then each subsequent entry shows you--predicts how much higher the premium would be as the policy holder’s health deteriorates.

The last three rows measure family health characteristics, and there the entries indicate, again, how much higher or lower the premium would be if you cover a spouse who has chronic conditions, if one of the people covered is a female of child-bearing age, or if you cover a child who’s in fair or poor health.

Let me start with the unadjusted column. The main story there is that the numbers are all small. Some are negative. None are significantly different from zero. So the implication from the unadjusted approach is that there is little or no medical underwriting in the non-group market.

However, if you use the selection-adjusted estimates, they suggest a very different story, namely, that as health deteriorates, premiums do show a pretty sharp upward increase. In fact, for a policy holder who’s in very good or good health but has two or more chronic conditions or someone who’s in fair or poor health, our model estimates the premium would be about 40 percent higher.

Now, for those of you who are number-challenged and like to look at pictures instead, this slide shows essentially the same thing. It plots the premium that we predict for a family of four, varying only the health of the policy holder and the age of the adults. And what you see is that the two unadjusted lines are essentially flat or show no clear relationship between premium and health status, while the ones that use the Heckman selection adjustment, or selection correction, the green and the red lines, show a clear upward increase in premium.

So what we do next then is take the selection-adjusted premiums, and we project them onto the uninsured families that are in our data set, and then we use the parameters of two tax credit proposals to assign tax credits to the people as well. And the two proposals we use are the one that was put forward by the Bush administration and the Senate bipartisan REACH proposal.

What we find is that both proposals would provide credits to a large number of people in the target population. The REACH proposal would provide some credit for about 95 percent of the population, the Bush proposal about 85 percent of the population. The REACH proposal is more generous, but it’s also more costly, and you can, you know, sort of do what you want with that, whether you like it or not.

The average subsidy, that is, the percentage of the premium that will be covered by the credit, is about almost 70 percent under the REACH proposal and about 46 percent under the Bush proposal.

This next slide shows the distribution of people by the amount of subsidy that they would receive, and, again, under both proposals substantial proportions of people would receive large subsidies of 50 percent or more, 46 percent under the Bush proposal, 73 percent under the REACH proposal.

Well, what about affordability? How much do people have to pay relative to their incomes? In this slide, we see that without a tax credit--and this may be one of the areas where we disagree a little bit with what Mark and Len conclude--a relatively small proportion of people, only about 30 percent, would pay less than 8 percent of their income, and about 45 percent would pay more than 16 percent of their income to buy non-group policies in the target population.

However, with the tax credits, those numbers change pretty dramatically, and under Bush, more than half, 55 percent, would fall into the, quote, affordable range, and under the REACH proposal, about two-thirds. So this is the glass is half-full or the glass is about two-thirds-full picture. What about the other 17 to 23 percent for whom non-group insurance would be still pretty expensive? And I’ll speed things up a little bit here. Here we look at the characteristics of the people who are tax credit winners and losers under the REACH proposal, which is the more generous of the two, and what we conclude is that those who don’t do well or don’t benefit very much from the tax credit are, in fact, much sicker, and from this slide they also tend to be much older and poorer.

The right-hand column indicates that almost 70 percent of those who find policies unaffordable are in the--have incomes below poverty.

I said I’d tell you a little bit about benefits. When you look at the cost-sharing structure of non-group policies compared to employer-sponsored policies, what we see is that, in fact, the structures look pretty similar, that most policies have copays of less than $20; most have coinsurance of less than 20 percent; and the great majority have deductibles of less than $500.

However, this information doesn’t tell you anything about exclusions or limits or services that are not covered. An indirect way of looking at that is to take a look at people’s out-of-pocket expenses, depending on the kind of insurance they have. And we do that here focusing on single-person families and comparing their out-of-pocket expenses over the year. And what we see is that if you have non-group insurance, your out-of-pocket spending is from one and a half to two times larger than if you have employer-sponsored insurance. And given that, not surprisingly, when we asked people directly how satisfied are you with the amount your plan pays relative to the benefits, we see that those with non-group insurance are much less satisfied.

So, to summarize, as I indicated, I think we actually do wind up at pretty much the same place that Mark and Len did, which is to say that tax credits have the potential to help many people, 46 to 73 percent receive large subsidies, 55 to two-thirds would find that the cost of insurance would fall to less than 8 percent of their income.

On the flip side, however, the tax credits based only on income don’t help everyone, and our estimate of those who, what we would say, face impossible choices is roughly 17 to 23 percent; tough choices, about an equal proportion, 16 to 22 percent; and those people tend to be sicker, older, and poorer. And it does appear to be the case that non-group benefits are less generous.

So, to sum up, what are the implications of this? I guess this is where the rest of the conference will be going today. I think no big surprises here. Varying credits with age would probably help since health is correlated with age. Increasing the size of the credit for poor people would help. Expanding eligibility for public insurance as a complementary policy would help. And possibly expanding subsidized high-risk pools could be an asset as well.

Thank you.

[Applause.]

Rob Cunningham: Thank you. Good morning, everybody. Can you hear me? Is it off?

Paul Ginsburg: I think it’s on.

Rob Cunningham: This program and the package of papers that’s--no mike? What do we need?

Rob Cunningham: In today’s meeting and in the package of papers that Health Affairs is publishing today, there’s a high focus on a discrete subject, the non-group market, which is complex and difficult, and you’re going to be asked to really have a workout on looking at the details. This piece of the program is sort of a stretching exercise where you can stop doing that for a minute and prepare yourself, exhale for the next round of high-focus presentations. I want to talk a little bit about the contextual factors Mark mentioned, that there’s a lot of passion about the non-group market that, when you look at the non-group market, you say why do people get passionate about this.

What’s happened over here? Uh-oh. I’m denied access to my slides.

Rob Cunningham: Okay. I can’t hardly see it. There. I have to put on a different pair of glasses to do this. I can’t see the arrow.

Robert Cunningham: The passion comes sometimes from confusing contextual factors with the issues that arise and the difficulties that arise in the non-group market per se. And I think we’re better off if we can recognize and identify the contextual factors that sometimes put a load on our concerns about the questions of the non-group market. So I want to try to identify a few of them in a very non-researchy kind of way to help us be aware of the things that may make it more difficult than it has to be to understand what we want to get.

The first is that the non-group market is not the only arena in which tax-based subsidies have to operate. Let me summarize briefly, and we’ll also look at the non-group market is not necessarily being offered as a replacement for employment-sponsored insurance, although there are some voices that occasionally suggest that it is and that subsidies for the non-group market, insuring of the non-group market are a first step towards ultimately replacing the employment-based system, which we have a lot of discontents with. And I think sometimes that’s a reason that people get passionate about this a little bit prematurely.

The third point is the ideological conflict between public and private solutions to the uninsured, which always looms large but we may need to look at perhaps more creatively than we have and, again, not load up our deliberations about the problems of the non-group market and what to do about them with larger questions that in some ways are misapplied.

The Bush administration proposal essentially, which set the agenda to some extent, although it’s not live in Congress right now, makes the non-group the exclusive arena for subsidies or virtually the exclusive arena. The primary argument here, one is to foster choice, but the other is that it’s unfair to provide a subsidy to the employment-based market because it already enjoys a large subsidy as it is in its double-dipping, in effect.

A number of tax credit proposals address both the non-group and employment-sponsored insurance. The REACH proposal that Jack mentioned is one of them. The way they cope with the inequity and the double-dipping factor is by having a smaller subsidy--I think it’s 25 percent in the REACH proposal--that’s available to income-eligible workers to purchase at the workplace.

The Granger-Wynn proposal that came out this past summer has a similar notion, a lesser subsidy to employment-based coverage, so that you’re covering both.

These are administratively complex. The REACH proposal is huge. That’s partly because they don’t attempt to prevent crowd-out effects. They think that it’s legitimate to give these subsidies for non-group and group market and for people who already have coverage. It’s not designed exclusively for the uninsured. So it represents tax relief essentially for low- and moderate-income workers. But it is possible to use tax-based subsidies for employment-based coverage, and we know that the majority of the uninsured are in small employment. It’s not simple to visualize how these subsidies would flow. Employers who don’t offer would have to somehow realize and be moved by the fact that their employees had a little more money to deal with cost sharing if they had non-generous policies or something like that. But it would take a couple of steps, but shoring up the non-group market with subsidies is also not a slam-dunk, so shoring up the small-group market with subsidies is something that we needn’t rule out and we needn’t think of the non-group market as an option that excludes the possibility of tax-based subsidies for a small-group market.

Let me add that those proposals have considerable support, and there’s been a number of--well, the Chamber of Commerce, the National Association of Health Underwriters have expressed support for the Granger-Wynn proposal. Health Care Leadership Council also is a supporter. Stuart Butler, who’s a long-time advocate of tax credits, criticized the Bush proposal for not including the employment-based market. So these are not sort of left-field ideas.

The second point stretches a little bit further out of the immediate context. The notion that the non-group market is going to eventually--should eventually replace employment-based insurance is--everyone is free to argue it. There are, however, some sort of truisms that have been thrown around increasingly, I think, in the past few years that are misleading in this respect, and I want to address a couple of them because I happen to have come across this subject with some specialized knowledge of my own from studying the history of Blue Cross and Blue Shield and the origins of the health insurance system.

We have in particular the myth of the accidental system. We hear this a lot of the time, that employment-based insurance is the unintended consequence of wartime wage and price policies that led to the tax preference for employment-based coverage, and we wouldn’t really have this stuff if it hadn’t been for those policies. This is simply not correct, historically. I haven’t done exhaustive research on the subject. I’ve been getting a lot of calls about this since my paper on which this presentation is based appeared in the Health Affairs website a month or so ago.