Part Time Workers and the Affordable Care Act: A Proposal to Address the Real Issue

Part Time Workers as Share of Total Employed, Dec 2007 to Dec 2014

A.  Introduction

The Affordable Care Act (ACA, and also often referred to as ObamaCare) has been working well by any objective measure.  There are now more than 10 million additional Americans who have health insurance who could not get affordable health care before; the share of the uninsured in the US population is now a quarter less than what it was before the individual mandate of the Affordable Care Act went into effect; and this has been achieved at premium rates for the new plans that are reasonable and well less than opponents charged they would be.  Health care costs have also stabilized under Obama, both as a share of GDP and in terms of health prices relative to overall prices, in contrast to the relentless increases in both before.  And while some have criticized this, it is good that there are now minimum quality and coverage standards in health insurance plans.  Such standards are good in themselves.  And without such standards, purported health care “plans” which offer next to nothing (due, for example, to extremely high deductibles) and which can then cost next to nothing, would lead to a death spiral for genuine health care plans that cover costs when you are sick and need treatment.

Gains from the ACA are also reflected in the findings of a recently published report from The Commonwealth Fund.  The Commonwealth Fund has been organizing a periodic survey on health care coverage since 2001.  The most recent survey (for 2014) found that for the first time since the question was first asked in 2003, there was a reduction in the number of Americans avoiding (because of cost) health care services that they needed.  And for the first time since the question was first asked in 2005, the number reporting medical bill or debt problems also fell.  Personal financial distress due to medical problems has been reduced, due to greater access to health insurance and due to health insurance plans that now meet minimum standards.

Despite this (but not surprisingly given the position they staked out against the reform), the Republican Congress continues to vote to repeal, or at least weaken, the law.  The most recent vote was aimed at the provision in the Act which complements the individual mandate to purchase health insurance, with an employer mandate requiring firms with 100 full time equivalent employees or more from January 1 of this year (and with 50 or more from January 1, 2016) to offer health insurance to their full time employees or pay a fee.  The proposed Republican bill would change the definition of a full time worker from one who normally works 30 hours or more a week, to one who works 40 hours or more a week.

The supporters of the change charge that the prospect that employers (with 50 or 100 employees or more) will soon be required to offer health insurance to their full time employees has led firms to cut working hours of their employees, to shift them from full time to part time status, and hence avoid the employer mandate of the ACA.  As a Republican congressman from Texas said:  “We have heard story after story from every state in the union that employers are dropping workers’ hours from less than 39 hours a week to perhaps less than 29.”

This accusation is confused on several levels.  This post will first look at whether there is in fact any evidence that workers are being shifted from full time to part time status as a result of the ACA (or indeed for any other reason).  The answer is no, at least at the level of the overall economy.  Second, there has been a good deal of confusion in the discussion on what the issue really is with regard to part time workers, including by prominent congressmen such as Paul Ryan.  Either Ryan does not understand what the employer mandate is, or if he does, then he has deliberately mischaracterized it.

The public discussion has also avoided altogether the real issue.  It is not that firms with 50 workers or more would be required to offer health insurance to their employees (most do already), but that this insurance is only made available to their full time workers.  Part time workers get nothing, no matter what size firm they work at.  The final section of this blog post will discuss a way to resolve this equitably.

B.  What is the Evidence on Whether the ACA Has Increased the Ranks of Part Time Workers?

The opponents of ObamaCare assert that as a result of the employer mandate, firms have been shifting workers from full time to part time status.  E.g., instead of employing one worker for 40 hours, they are choosing to employ two workers for 20 hours each.  If true, the ratio of part time workers to the total employed will rise.

The chart at the top of this post shows this has not been the case.  It is based on data from the Bureau of Labor Statistics, from its Current Population Survey.  This monthly survey of households is used to determine the unemployment rate among other statistics.  The households surveyed are asked whether household members are employed full time or part time (if employed), and if part time, whether this is by choice (because they only want to work part time) or because they want a full time job but cannot find one.  The chart above shows the ratio of workers who are working part time not by choice but for economic reasons, to all workers employed.  Note that the BLS data defines a part time worker as one with fewer than 35 hours of work per week.  While this differs from the 30 hour standard in the ACA, as well as the 40 hour standard in the recently passed Republican legislation, the results in terms of the trends should be similar.  The BLS does not publish data with a different cutoff in terms of hours per week for what is considered part time work.

As in any economic downturn, the ratio rose rapidly in the economic collapse of the last year of the Bush administration.  Regular jobs were disappearing, with some of them shifting to part time status.  Indeed, the absolute number of part time jobs was increasing at the time, even as the total number of jobs was falling, thus leading to two reasons for the ratio to rise, and rise rapidly.

The ratio reached a peak soon after Obama took office, and began to fall about a year later.  Since then it has fallen at a fairly steady pace in terms of the trend.  There were sometimes relatively sharp month to month fluctuations in the data, but this can be on account of statistical noise.  The data comes from a limited sample of households, with only 5 to 6% or so of those employed on part time status (for economic reasons) for most of this period, so the statistical noise in a relative sense (month to month) will be large.  But the downward trend over time is clear, and at a similar downward pace for close to five years now.

What one does not see is any shift in this downward trend linked either to the signing of the Affordable Care Act in March 2010, or to the start of the individual health insurance mandate in January 2014, or to the anticipation of the start of the employer health insurance mandate in January 2015.  Note that since the classification of a worker as a full time or part time worker (and hence the classification of the firm as crossing the 100 or 50 full time worker standard) will be in a period of up to 12 months before the employer mandate goes into effect, one would have seen an impact in 2014 if the 2015 mandate mattered.  There is no indication of this.

The data cover the overall economy.  The figures refer to millions of workers as well as millions of employers.  The US is a large place.  Within such a large place, it will undoubtedly be possible to find particular cases where employers will say that they reduced worker hours to part time status so that they could avoid the health insurance employer mandate.  And one could indeed probably find a long list of firms making such statements.  It would be even easier to find a long list of firms and other entities where working hours were cut, whether or not there was any employer mandate pending.  In a dynamic economy, there will always be a large number of such cases (along with a large number of cases of firms going in the opposite direction, converting part time jobs to full time jobs).

Such anecdotal information, and even a long list of such anecdotes, is not evidence of an issue of substantial scale.  As seen above, there is no evidence of it in the overall numbers.  But one should still recognize that the issue could exist in particular cases.  The question, however, is what is the real issue here, and if there is one, how can it be addressed.

C.  What the Employer Health Insurance Mandate Says

For better or worse, the US health care insurance system is built around health plans normally provided to workers through their place of employment, as part of their overall wage compensation package.  The system began during World War II and has expanded since, supported through substantial tax advantages.  By now, health insurance provision is close to universal among large employers, but substantially less so among small private firms:

Share of Private Firms Offering Health Insurance – 2013
< 10 employees 28.0%
10 to 24 employees 55.3%
25 to 99 employees 77.2%
100 to 999 employees 93.4%
≥ 1000 employees 99.3%
< 50 employees 34.8%
≥ 50 employees 95.7%
All private employees 84.9%
Source:  MEPS, Tables I.A.2 and I.B.2 (2013)

Overall, 84.9% of private sector employees are in firms that offer health insurance as part of their wage packages.  And 96% of firms with more than just 50 employees offer health insurance.

The Affordable Care Act built on this and did not replace it.  Liberals (including myself) would have preferred moving to a system where Medicare would be extended to cover the entire population rather than just those over age 65.  Medicare is an efficient and well managed program, and as an earlier post in this blog discussed, its administrative expenses come to only 2.1% of the benefits paid.  In contrast, administrative costs (including profits) of private health insurance are seven times higher at 14.0% of benefits paid, and an even higher 18.6% of benefits paid in the privately administered Medicare Advantage plans.

But Obama agreed instead to support an approach first proposed by the conservative Heritage Foundation, which was then put forward by Republicans in Congress as their alternative to the health reforms proposed by the Clinton administration (coming out of the task force Hillary Clinton chaired), and which was later adopted in Massachusetts when Mitt Romney was governor.  These plans were built around keeping the existing employer-based provision of health insurance for most of those employed, but to complement this with markets where individuals could purchase health insurance directly if they did not have employer-based coverage, coupled with an individual mandate to buy such health insurance.  The individual mandate is necessary to counter what would otherwise be a resulting death spiral of health insurance plans if everyone is granted access (including those with pre-existing conditions) but only the sick then purchased health insurance (for a description and discussion, see this earlier Econ 101 blog post).

It was not unreasonable to believe that the Republicans would not oppose a plan whose origins lies in their own earlier proposals, but that was not to be.

As noted, the individual mandate is necessary to avoid death spirals in health insurance plans for individuals.  Complementing this, an employer mandate to offer health insurance to their employees is necessary to counter what could otherwise be a “race to the bottom”.  If certain firms did not support such health insurance for their employees, thus reducing the cost to them of their workers, they could undercut competitors who did provide good health insurance support.  It could lead to a race to the bottom.  While not yet widespread in the US, especially for larger firms (see the table above), there has been increasing competitive pressure in the US over the last couple of decades to cut such health insurance support.  An increasing number of employers have done so.

Thus the ACA includes an employer mandate to complement the individual mandate.  However, while the individual mandate went into effect on January 1, 2014, the employer mandate has been twice delayed, and has now (as of January 1, 2015) gone into effect for firms employing 100 of more full time equivalent employees, and will go into effect on January 1, 2016, for firms employing 50 or more full time equivalent employees.  It is this provision that the Republicans in Congress are now trying to subvert.

The charge by Paul Ryan and others has been that medium to small size firms have been cutting the hours of their employees to shift the workers from a full-time classification to a part-time one.  The aim, they say, has been to reduce the number of their full time workers to below 50 so as to avoid the employer mandate.  For example, in a recent opinion piece published in USA Today, Congressman Ryan wrote:  “The law requires employers with more than 50 full-time employees to give them health insurance.  But because the law defines “full time” as 30 hours or more, employers are keeping employees below that threshold to avoid the mandate entirely.”

However, that is not what the law says.  Precisely to avoid such an incentive, the boundaries on the size of a firm subject to the employer mandate is defined in terms of full time equivalent workers (whether 50 or 100).  That is, if a job is split from one full time worker to two half time workers, the number of full time equivalent workers is unchanged.  The two half time workers count as one full time worker for the purposes of the statute.  Cutting back on the number of hours of individual workers to make them part time will not change the status of the firm when the total hours of labor to produce whatever the firm is producing remains unchanged.  And it would be foolish for a firm to produce and sell less when the demand exists for such sales, simply to avoid this mandate.

There is, however, a critically important issue here which Ryan and his colleagues have not discussed.  While splitting jobs of full time workers into multiple part time jobs will not change the status of the firm on whether it is subject to the employer mandate, shifting workers from full time to part time status does affect whether the firm would be required to include health insurance as part of their wage compensation package.  Firms subject to the mandate must offer an affordable health insurance plan available to at least 95% of full time (not full time equivalent) workers, or pay a fee.  The fee (of up to $2,000 per year per worker, less 30 workers per firm) is designed to partially offset (and only very partially offset) the cost of health insurance that they are shifting to others.

But such health insurance typically only is provided to full time workers.  This is true even for giant corporations.  Hence a firm can avoid making health insurance available to its workers by shifting them from full time to part time status.  This has always been the case, and is indeed a problem.

The Affordable Care Act addresses the issue only partially and tangentially.  By including a definition of what constitutes full time work at 30 hours a week or more, the ACA reduces the incentive to shift workers from the traditional 40 hours per week for full time work, to just under 40 hours in order to avoid providing health insurance cover.  A firm would need to cut a normal worker’s hours to below 30 hours per week to avoid providing health insurance, and is unlikely to do that for its regular work force.  But by moving the dividing line up to 40 hours per week, as the Republican legislation passed on January 8 would do, one opens up a loophole for firms to reduce worker hours from 40 to say 39 per week (or 39 1/2 or even 39.99 I would suppose).  Firms would be able easily to avoid offering health insurance to what are in reality their regular, full time, workers; use this to undercut competitors who do offer such insurance; and thus spark a race to the bottom on health insurance coverage in those industries.

D.  Addressing the Problem of Health Insurance for Part Time Workers

As noted above, the ACA does not do much to address the problem of part time workers receiving nothing from their employers for the health insurance everyone needs.  Setting the floor at 30 hours per week helps by ensuring workers close to the traditional 40 hour workweek will receive an employer contribution to their health insurance, and avoids the incentive to shift workers from 40 hours per week to just a bit below.  But part time workers of less than 30 hours per week will still normally receive nothing from their employer to help cover their health insurance.  And it creates an incentive for employers to structure positions as two workers at 20 hours per week, say, than one at 40.  While whether or not the firm was subject to the employer mandate would not be affected (since it is expressed in terms of full time equivalent workers), whether or not the firms would need to provide anything in terms of health insurance would be affected.

But there is a way to address this, now that the individual health insurance marketplaces are operational under the ACA.  All firms could be required to contribute an amount for their part time workers proportional to the hours of such part time work to what full time work would be.  That is, if two workers are each working half time, the firm would contribute an amount of 50% (for each) of the cost of the employer contribution to the health insurance for one full time worker.  The total cost would be the same whether the firm employed one full time or two half time workers.  There would also then not be an incentive to split jobs from full time workers to multiple part time workers.

The employer contribution to the part time worker’s health insurance costs would then be paid, along with taxes such as for Social Security or Medicare, to the government in the name of the specific part time worker.  These funds would then be used as a partial pay down of the costs of that worker purchasing health insurance on the individual health insurance market exchanges set up under the ACA.  And while other splits could be considered, I would recommend that those funds would be split half and half between what the worker would need to pay on the exchange for his or her health plan, and what the government subsidy would provide.

A simple numerical example may help clarify this.  Using made up numbers, suppose the full monthly cost of a standard (Silver level) health insurance plan on the individual exchange where the worker resides is $400.  Assume also that at the current income level of this (part time) worker, the government subsidy for such insurance would be $200 per month, while the worker would pay $200 per month.  Now assume that firms would be required to pay proportional shares of what they provide to full time workers for their health insurance, and that this would come to $100 per month for this part time worker.  This would be split half and half between what the government subsidy would be and what the worker would pay, so under the new approach the government would provide $150, the worker would pay $150, and the funds coming from the firm would cover $100, summing to the $400 total cost.

A few specifics to note:  Many part time workers hold down multiple jobs.  They would receive for their “account” the total proportional amounts from all of their employers.  Many part time workers are also part of married couples.  There could be a household account into which all the sums were paid (for each family member), which could be used to purchase a family health plan on the exchanges.  In the event that the family was not purchasing insurance through the exchange (perhaps, for example, because the spouse worked at a firm providing family coverage), the amount paid by the firm for the part time worker would be returned to the firm (or canceled from the start).

And if the total amounts paid in from the full set of employers for that individual (or family) led to the government subsidy falling all the way to zero, any excess would be allocated to what the individual would pay for the insurance.  This could be common in cases where the family income of the part time worker was close to, or above, the income limit on which government subsidies are provided.

It is only with the advent of the individual health insurance exchanges that this method for covering part time workers became possible.  Previously, firms were not in a position to purchase half of an insurance policy for a half time worker.  But now they can contribute an amount equal to half the cost, with this then used to help purchase coverage on the individual marketplace exchanges.

Note also that with this reform, it would matter less whether full time work was defined as 30 hours per week or 40 hours per week or whatever.  I would recommend keeping the 30 hour per week boundary as it would be a factor in determining what the employer contribution would be.  But it would not be as critical as now, where the boundary determines whether 100% of the employer share of the health insurance cost is paid or 0% is paid.  There would be a smooth transition (a worker of 39 hours when 40 hours is defined as the standard would still receive 39/40 of the payment, and not zero), without a drop straight to zero.

There would also be no reason to limit this extension of the employer mandate only to firms with 50 (or 100) or more full time equivalent workers.  All firms should make such a contribution to covering the cost of their workers’ health insurance needs, just as they all make a contribution to Social Security and Medicare taxes.  Indeed firms of whatever size (although this will soon apply only to firms with less than 50 full time equivalent workers) that do not have any health insurance plan for their staff should participate.  The amounts paid could be set as a proportion to the cost of the medium Silver level plan available on the individual health insurance exchanges in their area.

Undoubtedly, there will be assertions by the Republicans that requiring such a contribution to health insurance costs for their part time workers will lead to an end to such jobs.  This would be similar to the arguments they have made that raising the minimum wage will lead to higher unemployment of lower paid workers, and arguments that were made earlier that paying Social Security taxes would lead to higher unemployment.  But as was discussed in an earlier blog post, there is no evidence that increases in the minimum wage in the magnitudes that have been discussed have led to such higher unemployment.  Ensuring firms contribute proportionally to the health insurance costs of their part time workers would not either.

The Cost of Health Care Has Stabilized Under Obama

Total National Health Expenditures as Share of GDP, 1980-2013

A.  Introduction

The Centers for Medicare and Medicaid Services (CMS) released in early December its regular annual estimate of overall health care expenditures in the US.  Their highly detailed tables start in 1960 and now go through 2013, and they provide the most reliable and complete regular figures on health care spending in the US.  While a number of news outlets noted that national health care expenditures had once again remained stable at 17.4% of GDP under Obama (for the fifth straight year now), there is much more that one can derive from these numbers that is of interest to anyone concerned with US health care expenditures.

B.  National Health Care Expenditures as a Share of GDP

The stability of total national health care expenditures at 17.4% of GDP under Obama is indeed significant.  But it is not unprecedented:  Health care expenditures were also stable as a share of GDP for an extended period during the Clinton administration.  But the general path has been strongly upward over recent decades, with the share now close to double what it was in 1980.  Large increases during the Reagan/Bush I and Bush II presidential terms were not offset by the stability during the Clinton and Obama years.  While I have not examined in detail the primary reasons for this difference, I would suspect that a factor has been the greater willingness during Democratic administrations to use government initiatives to hold down health costs.

But while the share of health expenditures in GDP in current prices has almost doubled over this period, the share expressed in terms of constant prices has been flat.  That line is also shown in the chart above, in red.  While there is no published estimate of a price deflator specifically for overall national health expenditures, it is reasonable to use the price deflator in the GDP accounts for personal consumption of health care.  The personal consumption figure accounts for about two-thirds of national health care expenditures, where the remainder will be for such items as investment in hospitals and equipment, for direct government expenditures on health care such as for doctors in the military and in the Veterans Administration, and for research.

Using this price deflator, the share of health expenditures in GDP in real terms in fact declined some over 1980 to 2000, rose by an equal amount between 2000 and 2009, and since then has been flat, to end in 2013 at the same share as it was in 1980 (8.9% of GDP in terms of the prices of 1980).  This is pretty remarkable.  Despite an aging population over this period, where older people require much more health care services than younger ones do, US spending on health care as a share of GDP would have been no higher in 2013 than it was in 1980 if the price of health care relative to overall prices (the GDP deflator) had not changed.

Note that this is not a result of the prices of 1980 as being something special.  The same result would have been found using the prices of any year.  And while not shown in the diagram above, the constancy of the share of health expenditures in GDP in real terms held back to the mid-1970s.  The share rose from the mid-1960s to the mid-1970s, in part due to the introduction of Medicare (the Medicare Act was passed during the Johnson administration in 1965, and the program started in 1966).  The increase in share over that period was by about a quarter (from a bit over 7% to a bit less than 9% of GDP, all in terms of 1980 prices).  It has since been relatively constant.

C.  Relative Prices Matter

The GDP share could only rise in current prices when it was flat in constant prices because the price of health care rose relative to the general price deflator for GDP.  This is just arithmetic.  It is therefore of interest to look more closely at what has happened to the relative price of health care.

For the period since 1980, health care prices have consistently out-paced the rise of overall prices until the last few years:

Change in Relative Price of Health Care vs. GDP, 1980-2013

 

The price index for GDP is a weighted average of the prices of all goods and services produced by the economy.  That is, and speaking loosely, a GDP price index rising by say 2% implies that about half (in weighted terms) of all prices rose by more than 2% while about half rose by less than 2% (including some that could have fallen).

What is unusual for the health care price index is that it has risen consistently faster than the overall GDP price index, until recent years.  The increase was particularly rapid during the Reagan / Bush I years, with the health care price index outpacing the GDP price index by 4.1% per year on average over this period.  For the more technically minded, the GDP deflator rose at an annual average rate of 3.9% over this period, while the health care price index rose at an annual average rate of 8.2%, so the relative price rose at the rate of 1.082/1.039, which equals 1.041, or 4.1% a year.

A 4.1% relative price growth compounded over 12 years (1980 to 1992) is huge:  At that rate, health care prices rose by 62% more than overall prices over that 12 year period.  And that is the immediate cause of health care rising as a share of GDP from 8.9% to over 13% in current prices over the period, despite a slowly falling share in real terms.  Real health care consumption relative to GDP fell, but total health care expenditures still rose relative to GDP in current dollar terms due to the higher relative prices for health care.

The relative price of health care relative to GDP then continued to rise, but at a much slower pace, during the Clinton years.  It then bounced back up some during the Bush II years (other than in 2005 and 2006, when the GDP deflator rose in the peak years of the housing bubble and then matched the increases in the price deflator for health care in those two years).

Under Obama, the relative price of health care came back down, and indeed was significantly negative in 2011 for the first time since before 1980.  This was then followed by two further years of zero or negative growth.  There have not been three consecutive years zero or negative growth in the relative price of health care in the US since 1946 to 1948, two-thirds of a century ago.

The Obamacare reforms account for at least some of this.  The Affordable Care Act (Obamacare) was passed in early 2010, and while the insurance coverage reforms (making health care insurance coverage available for all Americans) only went into effect in 2014, other health care reforms went into effect immediately.  These included a wide range of individually modest, but cumulatively significant, measures to bring down costs.  For example, the Medicare system for compensating hospitals now is set up to provide a financial incentive for good rather than poor quality care.  Under earlier systems, hospitals were paid more when the patient received poor quality care and got an avoidable infection, for example.  Such measures improved efficiency and brought down costs.

D.  Even At a Constant Share of GDP in Real Terms, Per Capita Consumption of Health Care Can Still Rise 

The relative price of health care has stabilized for three years now under Obama, while the share of health care expenditures in GDP, whether in real or nominal terms, has stabilized for five years.  But has this been achieved at the cost of reducing the availability and use of health care?  No:

Growth of Real Per Capita Personal Consumption of Health Care, and of Real GDP, 2001-2013

This diagram plots what has happened since 2001 to real per capita national health expenditures (from the same figures as used above from the CMS, but now converted into real per capita terms), real per capita personal consumption of health care services (from the GDP accounts), and real per capita GDP.  The figures are all scaled to equal 100 in 2008.  The national health expenditure and personal consumption of health care lines track each other fairly closely.  One could have used either.

As the graph shows, real per capita expenditures on (or use of) health care services have increased each year over this period.  There was still an increase, although at a slower pace, in the peak years of the economic downturn in 2009 and 2010.  And the increases continued, at a strong pace, in 2011 to 2013, when GDP was recovering as well.

When health expenditures stabilized as a share of GDP under Obama, some analysts at first speculated that this was due to lower consumption of health care services during the economic downturn.  Unemployment was high and many had less access to health insurance.  But use of health care services did not fall during the downturn.  And it then came back strongly in 2011 to 2013.  The stable share of GDP has been due to stable prices for health care since 2011, with real per capita health care expenditures then rising at a similar rate as rising real per capita GDP.

E.  Why Isn’t the Figure for National Health Expenditures Equal to 18% of GDP? 

An earlier post in this blog in the series on health reform stated that the US has been spending close to 18% of GDP on health care.  This was 50% more than the second highest spending OECD country (the Netherlands) and close to double the average spent of all OECD countries.  The figures were for 2011 and came from the then current OECD data for the US and other OECD countries (close to, but not quite the same as, the national health expenditure totals from the CMS for the US).  Why are the figures for the US now at 17.4% of GDP in 2011, as well as since?

The US health expenditure numbers have in fact not changed.  They are still expected to total $3 trillion in 2014.  The reason for the difference (aside from round-off:  they were a bit below 18% in the earlier numbers) is that the estimate of the denominator in the health expenditures to GDP ratio has changed.  In the summer of 2013, the BEA revised its methodology for estimating GDP, as it periodically does.  While there were several changes, the one with the largest impact was to revise the treatment of research and development expenditures.  The BEA had before treated such expenditures as what economists call an intermediate product (a good which is immediately used up as goods are produced, much like coking coal is used up in the production of steel).  They decided it was more appropriate to treat them as an investment product, which will last for several years (depreciating over time).  This was purely a methodology change.  But the effect was to revise estimated GDP up by about 3 to 3 1/2% in recent years.  This was not just applied to the GDP figures of recent years, but rather to the full GDP series going all the way back to 1929.  Hence the year to year growth rates were largely unaffected.

But a denominator which is now larger will lead to a health expenditure share in GDP which is lower.  By simple arithmetic, a share of 17.9% of GDP will fall to 17.4% of GDP if GDP is estimated to be 3% higher than before.

F.  Conclusion

Health care costs stabilized during Obama’s tenure, with health care costs as a share of GDP now flat (in both constant and in current prices) in contrast to the big increases (in current prices) before.  This has not come at the expense of falling availability or use of health care services.  They have continued to grow throughout his presidency, and especially since 2010.

Looking forward, 2014 may be different.  The Obamacare insurance reforms came into effect in 2014, and have reduced the ranks of the uninsured by more than 10 million Americans.  The share of the population without any health insurance fell by over 30%.  The newly insured are likely to make greater use of regular health care services in 2014, especially by those who previously had conditions which had been left untreated due to an inability to pay before.  However, this may be offset by fewer emergency room admissions by those who previously had no other option, where emergency room care is an especially expensive way to deliver health care services.

It is not clear what the net effect will be.  Preliminary quarterly GDP data (for the first three-quarters of 2014) do not show a rise in the share for personal consumption of health care (there was a growth in real terms similar to the growth in real GDP).  But these numbers are still early and preliminary.  And the full national health expenditure numbers for 2014 will not be out until next December.

But so far, health expenditures as a share of GDP have stabilized under Obama, and the preliminary indication is that this is continuing in 2014.  This is a major achievement.  But they have stabilized at what is still a very high share of GDP, far higher than what is spent on health in other OECD countries.  Much more aggressive and fundamental reform will be necessary to bring the share down to the far lower levels of what other countries spend, and yet obtain  health outcome results that are similar to or better than the outcomes in the US.

Transparency of Quality is Essential for a Well-Functioning Health Care System

New York State CABG Mortality, with distribution, 1989-2011

A.  Introduction

Prospective patients will try to assess the quality of the medical care provided by the doctors or hospitals where they might go, when deciding where to seek treatment.  They seek out recommendations from friends and family, they look at publicly available rankings such as those of US News and World Report, and they have their own past experience with some doctor or hospital.  More recently, more information has become available on the internet, allowing prospective patients to look up personal histories on medical providers (where they went to medical school, their age, what languages they speak), as well as to view consumer comments and ratings on dedicated medical websites as well as websites such as Yelp.  There may also be reputational ratings (where doctors are asked what other doctors they would recommend), such as those conducted by the Washingtonian magazine in the Washington, DC, area.

But such information is limited, possibly biased, and superficial.  Recommendations of friends and family, your own experience, and comments and ratings on sites such as Yelp, are really just anecdotal, based on a very limited number of cases.  Individuals will also not always know whether the care they received was in fact high quality or not (there may have been complications, but they will normally not know if they were avoidable).  Rankings in reports such as that of US News and World Report have been criticized for being based on a small set of statistics (limited to those that the publication can obtain) which might have limited relevance.  And reputational ratings can be self-reinforcing, as those being surveyed rate some doctor or hospital highly simply because they have been highly rated in the past.  They may well have no real basis for making an assessment.

Most fundamentally, this information does not focus on what one really wants to know:  Does the doctor or hospital provide good quality care that will cure the patient?  Information such as that above has little on whether the doctors or hospitals are in fact any good at what they do.  Rather, the information is mostly on inputs (where did the doctor go to medical school, for example), or on superficial factors (was the receptionist pleasant when one checked in).

As a result, one can find out more on the quality of a $500 television that one is looking to buy, than on the quality of a doctor who will perform a coronary artery bypass surgery on you.

But information on actual results of doctors and hospitals, in terms of success rates (was the condition cured) and mortality rates, the frequency of medical complications, and other such measures, in fact exist.  The problem is that most of this information, with some exceptions noted below, is kept secret from the public.  Especially limited is information on the performance of specific doctors.  But the information is collected.  There are mandatory reports filed with government and regulatory authorities (both at the federal and state levels in the US).  Insurance companies (including Medicare) will know for the population they cover whether the treatment actually worked or required additional attempts or changes in approach.  Insurance will also know whether there were complications that then had to be treated (with the resulting expenses then filed).  And they will know all this at the level of the individual doctor and medical facility, and for the well defined specific medical procedures which were performed.

The information therefore exists.  The problem is that it is not made publicly available.  The normal rationale provided for this secrecy is that the information is complex and can be difficult to interpret by someone other than a medical professional.  But that is a lame excuse.  The information could be released in a form which adjusts for such factors as the underlying riskiness of the particular cases a doctor has dealt with (there are standard statistical ways to do this), and with accompanying information on the degree of uncertainty (derived statistically) in the information being provided.  One would also expect that if such information were made publicly available, then specialized firms would develop who would take such information and assess it.  Based on their technical analysis, they would sell their findings to insurance companies and firms, as well as interested individuals, on which doctors and facilities performed the best for specific medical procedures.  Government entities interested in good quality care (such as Medicare, in the public interest and also because good quality care costs less in the end) could also assess and make such information available, for free.

The real reason such information on outcomes is in general not made publicly available is rather that the results can be embarrassing for the doctors and hospitals.  And more than simply embarrassing, there could be huge financial implications as well.  Patients would avoid the doctors and hospitals who had poor medical outcomes.  With close to $3 trillion now being spent each year on medical care in the US, this means there are huge vested interests in keeping this information secret from the public.

This is starting to change, however.  As noted above, there are exceptions as well as experiments underway to provide such information to the public.  But it has been fragmented, partial, and highly limited.  The limited information that has been provided so far has been primarily at the level of hospitals, although there have been some experiments with data also being provided on the performance of individual doctors in certain specialties.

From these trials and experiments, we know that widespread availability of such information in an easily accessible form could have profound impacts on the practice of American medicine.

B.  The Impact of Transparency – A New York Experiment

The oldest and longest lasting experiment has been in New York.  Starting with data from 1989 (made publicly available in 1990), the New York State Public Health Commissioner has released the risk-adjusted 30-day in-hospital mortality rates of those undergoing coronary artery bypass graft (CABG, or simply heart bypass) surgery, by specific hospital.  They started to release physician specific mortality rates (on a three-year rolling basis) from December 1992.  There have been a number of good descriptions of, and analyses of the impacts of, the New York program.  Sources I have used include the articles here, here, here, and here.  In addition, a good description is provided as the third chapter in the excellent book by Dr. Marty Makary, Unaccountable, a source I will make further use of below.  Dr. Marty Makary is a physician at The Johns Hopkins Hospital, specializing in pancreatic surgery.  In addition to his many medical research publications, Dr. Makary has undertaken research on how to improve the quality of medical care delivery.

The chart at the top of this post shows what happened to 30-day in-hospital mortality rates following heart bypass surgery since 1989, across hospitals in New York State performing this procedure.  Only hospitals doing 70 or more such surgeries in any given year are included in the chart.  This was to reduce the statistical noise arising from small samples (and there were only a few exclusions:  two hospitals were excluded in two of the 23 years of data, and only one or zero in all of the other years).  A total of 28 hospitals were covered in the 1989 set, with the number rising over time to 38 in 2011.

The data were drawn from the annual reports issued by the New York State Department of Health.  Reports for 1994 to 2011 (the most recent report issued) are available on their web site.  Reports for earlier years were provided to me by a helpful staff member (whom I would like to thank), and the figures for the first half of 1989 were published in a December 1990 article in the Journal of the American Medical Association.  All the mortality rates shown are risk-adjusted rates, as estimated by the New York Department of Health, which controls for the relative riskiness of the patients (compared to the others in New York State that year) that were treated in the facility.

The chart depicts a remarkable improvement in mortality rates once it became known that the figures would be gathered and made publicly available, with individual hospitals named.  The chart shows the fall over time of the average rate across the state (note this is not the median rate, but rather the mean), as well as the minimum and maximum rates across all hospitals with 70 or more CABG procedures in the year.  The ranges at the 90th and 10th percentiles are also shown.  Among the points to note:

1)  The average risk-adjusted mortality rate fell sharply in the early years, and since then has continued to improve.  Furthermore, the underlying improvement was in fact greater than what it appears to be in these figures.  The average mortality rates shown in the chart are for the mix of patients (by riskiness of their health status) in each given year.  But especially in the early years, when angioplasty and coronary stent procedures were developing and found to be suitable for lower risk patients, the pool of patients for whom coronary bypass surgery was needed became a riskier mix.  Taking this into account, while the overall average mortality rate fell by a very significant 21% between 1989 and 1992, once one accounts for the higher risk of the patients operated on in 1992, the fall in the cross year risk-adjusted mortality rate was an even larger 41% over just this three year period.  Technology for CABG procedures did not change over this period.  Transparency did.

2)  The improvement in the coronary artery bypass surgery mortality rate in New York is especially impressive as New York was starting from a rate which was already in 1989 better than the average across all US states.  And by 1992, the rate in New York was the best across all US states.

3)  What is perhaps even more interesting and important, not only did the average rate in New York improve, but also the dispersion in mortality rates across hospitals was dramatically reduced.  The maximum (worst) mortality rate dropped from almost 18% in the first half of 1989 to under 6% by 1992.  The minimum rate was 2.1% in 1989H1, and fell to zero in 9 of the 12 most recent years.  One sees this narrowing in dispersion also in the range between the 90th and 10th percentile bands.

Publication of the mortality results got a good deal of media attention in the early years, and led to pressure, especially on the poor performers, to improve.  Note that the information being gathered was not anything new.  State health authorities long had reports on death rates by hospitals.  What was new was to make this information publicly available, with hospitals named.

Hospitals with poor records then scrambled to improve.  A range of actions were taken.  Some might have seemed obvious, but even so, were not undertaken until the mortality rates by hospital were made publicly available.  For example, hospitals with poor records began to create cardiac specific teams of nurses and other staff, rather than draw on staff from a pool who could be assigned to a wide range of different medical conditions.  Such specialization allowed them to learn better what was needed in cardiac surgery, and to work better as teams.  Such a reorganization at Winthrop Hospital, which included bringing in a new Chief of Cardiac Surgery who led the effort, led to a drop in its mortality rate from 9.2% in 1989 (close to the worst in the state in that year) to 4.6% in 1990 and to 2.3% in 1991 (better than the state wide average that year of 3.1%).

Other issues were highly hospital specific.  For example, one hospital (St. Peter’s in Albany) saw that its mortality rates for pre-scheduled elective and even urgent CABG surgery cases were similar to those elsewhere in New York.  But it had especially poor rates for emergency cases, which raised its overall average.  After reviewing the data, its doctors concluded that they were not stabilizing sufficiently the emergency patients before the surgery.   After it corrected this, its mortality rates fell sharply.  They were among the highest in New York in 1991 and 1992 (at 6.6% and 5.8%), but the rates then fell to 2.5% in 1993 and 1.4% in 1994 (when the New York average rate was 2.5%).  Mortality in emergency cases fell from 26% in 1992 (11 of 42 cases) to 0% in 1993 (zero in 54 emergency cases).

Another hospital (Strong Memorial) also found that its mortality rates for routine elective cases were similar to the New York average, but very high for the emergency cases, bringing up its overall average.  The problem was that while they had a good adult cardiac surgeon, he was always fully booked with routine cases, and hence was not available when an emergency case came in.  They then used one of two doctors who were not trained in adult cardiac surgery to handle the emergencies (one was a vascular surgeon, and the other a specialist in pediatric cardiac surgery).  By hiring a new adult cardiac surgeon and then better balancing the schedule, the rates soon dropped to normal.

American health care has traditionally relied on state regulators, armed with reports on hospital and indeed surgeon specific practices and outcomes, to impose safety and good practice measures.  But there is no way a central regulator can know all that might be underlying the causes of poor outcomes, or what actions should be taken to remedy the problem.  They also will not focus on hospitals with relatively good, or even average, mortality rates, even though such institutions could often still improve.  By releasing the data to the public, hospitals with poor records will be under great pressure to improve, while even those with relatively good records will see the need to get better if they are to stay competitive.  And the actions taken will often be actions that no central regulator would have been able to see, much less require.

C.  Staff Surveys as Another Indicator of Quality

Outcome indicators, up to and including mortality rates, are one set of measures which could have a profound impact on the quality of health care delivery if made publicly available.  An additional type of measure has been developed by Dr. Marty Makary, tested with a number of hospitals, and is now routinely used in hospitals across the US.  But the results are then typically kept secret from the public.

Specifically, Dr. Makary developed a simple staff survey (see here and here, in addition to his book Unaccountable referenced above) with some key questions.  The survey goes to all staff in a hospital, and asks questions such as whether the respondent would feel comfortable having their own care performed in the hospital unit in which they work.

In the original test, the surveys were sent to all staff at 60 hospitals across the US.  They got a 77% response rate, which is quite good.  What is most interesting was the wide range they found in the results across the hospitals.  For example, on the question of whether the staff member would want their own care performed at the hospital unit in which they work, there were two hospitals where close to 100% of the staff said they would, but also one hospital in which only 16% said they would.  There was a fairly even spread between these two extremes, and in about half of the hospitals surveyed, less than half of the staff said they would want their own care performed there:

Makary Hospital Staff Survey - Care in Own Unit.003

This would be powerful information to have as a patient.  The insiders are really the ones who know best what quality of care is being provided.  If even they would not want their health care needs met at their hospital, one knows where one would want to avoid.

It is recognized that the original Makary survey was done with the promise that the identities of the individual hospitals would not be revealed.  Should such surveys be made publicly available, the staff responding might well be less negative.  But the identities of the individual staff members would still be kept confidential (with the data gathered by an independent third party, and anonymously over the web).  There would certainly still be some dispersion in results across hospitals, and one could take into account the possible biases when judging the results.  And if a hospital is rated poorly by its staff even when they know the results will be made public, one knows which hospitals to avoid.  One would expect such hospitals then to scramble to improve the quality of the care they provide.

D.  While a Number of Transparency Initiatives Are Underway, They Remain Fragmented and Partial

Patients have always sought information on the quality of the care they will need, and have made decisions on where to go based on what they can find out.  But the information that they have been able to obtain has been only partial, highly fragmented, and far from what they really need to know to make a wise decision.

People will also find measures that are easily observed, but not necessarily terribly important to the quality of the care they will receive.  For example, they may find out whether parking is free and convenient, but this should not normally be a driving factor for their decision.  More relevant, and obviously something they will know, will be geographic location:  Is the facility close to them, or further away?  But they will normally have little basis for determining whether it is worthwhile to go a facility that is further away.

There has been a substantial expansion in recent years in the amount of information one can find on providers.  While still limited, one can find out more now than before.  There is the New York experiment described above, which New York soon extended from hospitals to individual surgeons, and also to angioplasty and cardiac stent procedures.  New York has also brought together on one web site easy access to a wide range of health topic data sets.  These include data sets on outcomes and quality of care indicators (such as the most recent CABG mortality rates by hospital and by surgeon, for example) but also many others (such as the most common baby names chosen).

The Obama administration has also expanded substantially the public availability of information on hospital quality measures.  The Centers for Medicare and Medicaid Services (CMS) now makes available at its Medicare Hospital Compare site results at the hospital level, drawn primarily from the data they have for Medicare patients, on such outcome measures as mortality rates, complications, hospital readmission rates, and other indicators.  However, they are still partial, and instead of showing, for example, actual and historical figures by hospital for indicators such as the rate of complications or mortality, they simply show whether the rates are similar to the national norm, or better or worse by a statistically significant margin (at the 95% significance level).

With the clear positive impact of the New York experiment, other states have also begun to implement similar programs.  But they remain partial and fragmented, and do not provide the comprehensive picture a patient really needs if they are to make a wise choice.

In addition, many professional medical societies have begun to collect similar data from their members, and then calculate risk-adjusted measures.  However, they have then kept the individual results secret, with identifying information by hospital or physician not made available.  Individual hospitals and physicians could release them if they so chose, and some have.  But one can safely assume that only those with good results will release the information, while those with poor results will not.

The same is true for hospital staff surveys, such as the one described above pioneered by Dr. Makary.  Such surveys are now widely used.  Dr. Makary reports in Unaccountable (published in 2012) that approximately 1,500 hospitals were then undertaking such surveys.  The number is certainly higher now.  But the results are in general kept secret.  Some hospitals make them publicly available, but one can again safely assume that these will be the ones with the better results.  Without the others for comparison, it is difficult to judge how meaningful the individual figures are.

So the relevant data are often collected already.  It is only a matter of making them public.  There is not a question of feasibility in collecting such data, but rather a question of willingness to make them public.

E.  What a Transparent System of Information on Quality Should Include

As noted above, people will gather what information they can.  But what they can gather now is limited.  What is needed is hard data on actual outcomes, identified by hospital and by individual doctor.  As the New York experiment discussed above indicates, the result could have a profound impact on quality of care.

Specifically, there should be easy access to the following specific measures:

a)  Volume:  While not directly an outcome measure, it is now well established in the literature that a higher frequency of a doctor undertaking some specific medical procedure, or that is done by all the doctors at some hospital or medical facility, is positively associated with better outcomes.  A doctor that undertakes a procedure a hundred times a year, or more, will on average have better outcomes than one who does the procedure only a dozen times a year (i.e. once a month).  And volume can be easily measured.  The problem is in obtaining easy access to the information, and at the relevant level of detail (i.e. by individual doctor, and for the procedure actually being considered for the patient, not just of some standard benchmark procedure).

b)  Success rates:  While many of the outcome measures being used in various trials and experiments are negative measures (mortality rates; complication rates), a more useful starting point would be risk-adjusted success rates.  What percentage of the procedures undertaken by the individual doctor or at the medical facility for some condition actually leads to a cure of the condition?  How success is defined will vary by the medical issue, but standard ones are available.  If the risk-adjusted success rate is 80% for one doctor and 99% for another, the choice should be clear.  Yet I have never seen a trial or experiment where such success rates by medical facility, much less at the level of individual doctors, were made publicly available.

c)  Success rates without complications:  A more stringent measure would be not only that the procedure was a success, but that it was achieved without a noteworthy complication such as an infection.

d)  Complication rates:  Moving to negative measures, one wants to see minimized the complications associated with some procedure.  The medical profession has identified the complications often found as a result of some medical procedure, and significant complications will be reported.  They can also normally be identified from medical insurance records, as they require treatment.  As with mortality rates, these should be published on a risk-adjusted basis.

e)  Mortality rates:  The ultimate “complication” is mortality.  As discussed extensively above, these should be made available by medical procedure and by individual doctor on a risk-adjusted basis.  The 30 day mortality might be appropriate for most medical procedures, but for others the 60 day or 90 day rates might be more appropriate.  Medical societies can work out what makes most sense for a given procedure.  But everyone should then be required to use the same measure, to allow comparability.

f)  Bounceback rates:  Bounceback rates are the percentage of patients undergoing some procedure, who then need to be readmitted back to a hospital (the original one or some other) within some period following release, usually 90 days.  Readmission rates are regularly collected by hospitals, and they can also be risk adjusted when made publicly available.  They are a good indication that some problem developed.  Some rate of readmission might well be expected for certain procedures.  They are not risk free.  But one wants to see if the bounceback rates are especially high, or low, for the physician or medical facility being considered.

g)  Never events:  Never events are events that should never occur.  While a certain rate of complications will normally be expected, one should never see an operation done on the wrong side of the body, or sponges or medical instruments left in the body after the surgeon has sewn up.  Hospitals know these and keep track of them (as such never events often lead to expensive lawsuits), but not surprisingly want to keep them secret.

h)  Hospital Staff Surveys:  As discussed above, Dr. Marty Makary developed a survey that would go to all hospital staff, which asks a series of questions on the quality of care being provided at the facility.  While approximately 1,500 hospitals were already administering the survey in 2012 (when his book Unaccountable was published), they are voluntary and in general not made publicly available.  They should be.

While the surveys can cover a long list of questions, Dr. Makary recommends (Unaccountable, page 216) that the percentage of hospital staff responding “yes” to the following three questions, at least, should be made public:

–  “Would you have your operation at the hospital in which you work?”

–  “Do you feel comfortable speaking up when you have a safety concern?”

–  “Does the teamwork here promote doing what’s right for the patient?”

F.  Conclusion

There are of course many other measures of quality one could examine, and there has been some movement in recent years to making more available.  These include results from patient surveys (“were you content with your experience at the hospital?”, “were the rooms kept clean?”), as well as the percentage of cases where certain established medical best practices were followed (“was aspirin given within 24 hours of a suspected heart attack?”).

Such additional measures might well be useful in particular cases.  It will depend on the individual, their particular condition, and what specifically is important to them.  People should have a choice, and do the research they personally wish to do.

But until hard measures on actual outcomes, such as those described above, are made widely available, and on a comprehensive rather than partial and fragmented basis, it will not be possible to make a well informed and wise choice on which doctor and medical facility to go to.  Without this, there can be no effective competition across providers.  There will be little pressure on the poor quality providers either to improve their performance, or drop out and let providers who can deliver better quality care treat the patients.

The impact on the quality of health care services provided would be profound.

More on the Widely Varying Charges for Common Health Procedures: Price Variation for Standard Blood Tests

Blood Test Prices in California - Lipid Panel

A.  Widely Varying Prices Charged Even for Standard Blood Tests

This post is an addition to an earlier post on this blog that looked at the widely varying prices being charged in the US for common health procedures.  As that post noted, such differences in prices for what are fundamentally the same services are a clear indication that the market is not working.  The prices would be similar if the market was working, with differences that are relatively small and explainable by factors such as geography.  But that is not the case.

That post looked at data from a number of studies (including my own simple research on the prices that I would be charged in the Washington, DC, area, for a common surgical procedure).  Prices could vary by a factor of 10, and indeed often even more.  And as that post showed in a series of charts, the prices actually paid in the US (at the rates negotiated by insurers) are not only widely varying, but also consistently far higher than the prices paid for the same procedures in other countries.

A criticism of studies that examine the prices being charged for health care procedures is that individual cases can differ, with some more complex than others.  Thus prices might vary for that reason.  Even though it is difficult to see how costs can vary by a factor of ten or more even with differing levels of complexity for some standard procedure (such as a hip or knee replacement, for example), one can recognize that differing degrees of complexity might explain at least some of the price differences.

Thus a study published last week in the BMJOpen, an open-access on-line journal affiliated with the British Medical Journal, is of interest as it addresses the question of whether such price variation is found also for procedures where case complexity does not enter.  The lead author is Dr. Renee Hsia, of the Department of Emergency Medicine at the University of California – San Francisco.  In an earlier study, summarized in the blog post cited above on health care price variation, Dr. Hsia had looked at the prices charged by hospitals in California for an uncomplicated but urgent appendectomy.  She found that the prices varied by a factor of 120, between the lowest rate charged and the highest.

In the current study, Dr. Hsia with her colleagues looked at the prices charged by California hospitals for ten common blood tests.  The prices reviewed are the so-called “chargemaster” rates, or the list prices at the hospitals for the tests.  The actual price paid will then normally be a lower rate negotiated with the hospital by your insurer (if you have insurance), but the chargemaster rate is the starting point.  Why this matters will be discussed below.

Dr. Hsia was able to obtain the data for California because hospitals there are required to report to state authorities the average prices they charged for a number of common procedures.  Since routine blood tests are standard, and are not more or less complicated for one patient vs. another (the blood is drawn, brought to a standard machine, and the results then read), one cannot argue that the price variation observed might be a consequence of different degrees of case complexity.

The results from one of the blood tests examined, that of a standard lipid test (which measures blood cholesterol levels), is shown graphically at the top of this post.  Data was available from 178 hospitals, and each hospital reported the average price it charged for this test over the course of 2011.  The price charged at one hospital was only $10 per test.  The average price charged at a different hospital, for the exact same blood test, was $10,169 per test, or over 1,000 times as much.  Such variation is absurd.

These are, of course, the extremes.  But even if one focusses on observations in the middle of the distribution, it is impossible to see how such variation in prices charged can be justified.  The price at the 5th percentile (meaning 5% of the hospitals charged this price or less) was $76.  The price charged at the 95th percentile (meaning 5% charged this price or more) was $602.  This is almost 8 times higher than the price at the 5th percentile.  The results for the other nine blood tests examined were broadly similar (with ratios between the prices at the 95th and 5th percentiles varying from a high of 12 times and a low of 6.8 times).

B.  Chargemaster Rates Matter

What can justify such a spread?  Nothing that I can see.  The tests are standard, use standard machines, and all use similarly drawn blood.  The response of a spokeswoman for the California Hospital Association was that the prices reviewed in the study are “meaningless”, since virtually no one (she states) pay these rates.  As noted above, the rates reviewed in the study, as in the earlier study of the prices charged for appendectomies, are the chargemaster rates of the hospitals.  These are the regular list prices for the procedures, which are then typically discounted in negotiations with individual insurers.

But there are still several problems with this, including:

1)  How much the prices are negotiated down will vary according to the bargaining strength of the patient’s individual insurer in the region.  In the bargaining process discussed in an earlier post in this series on health reform, insurers will bargain with hospitals on what the rates will be.  Their relative bargaining strength will depend on how concentrated the local market is in terms of hospitals (if there is only one hospital, or one chain of hospitals all owned by the same entity, but a number of insurers, the bargaining power of the hospital will be great) versus insurers (in one insurer dominates in the market, while there are many hospitals, that insurer will have great bargaining power).  If you have insurance with an insurer who does not command great market share in the region, the price you will have to pay may be close to the chargemaster rate.

2)  If you do not have insurance (and many could not get health insurance, prior to the reforms of Obamacare), you will be charged the chargemaster rate.  You might then try to bargain individually with the hospital, but the starting point will be the chargemaster rate.  And many hospitals will insist, unless you are poor, that you have to pay that chargemaster rate.

3)  You may well have insurance, but if the particular hospital you are in is not in your insurance network (perhaps because you were brought by an ambulance to the nearest hospital in an emergency), you will be charged the chargemaster rate.  Your insurance company might pay a portion of this at what they consider to be a “reasonable rate”, but this is likely to be close to what your insurer has negotiated with others, and as we have discussed in the earlier blog posts cited above, this might be only one-tenth of the chargemaster rate.  You will then still be responsible for the other 90%.  This can be a lot, if you are at the hospital where a simple lipid panel blood test is charged at over $10,000.

4)  You may well again have insurance, and be in a facility that is in-network for your insurer, but your insurer might disagree on whether some standard blood or other test ordered by your doctor was really needed.  Your insurer will then refuse to cover the cost of that test, and you will be charged the chargemaster rate.

I am personally facing a case of that right now.  While the amounts are small in absolute terms, the issue is the same.  My doctor ordered a set of routine blood tests for me earlier this year, and my insurer covered all except one.  For that one, the insurer asserted that there had not been a need (even though both my doctor, and research I found on the web, made clear that the test was in fact needed).  The lab therefore charged me the full chargemaster rate (which in this case was $213.98), even though the negotiated rate Aetna would have paid, had they agreed it should be covered, was only $16.23.  That is, the full billed rate was 13.2 times the negotiated rate.  I would have been glad to pay the negotiated rate in full, and the $16.23 the lab has negotiated with Aetna is evidently a rate sufficient to provide an adequate profit to the lab.  But find it absurd that I should have to pay over 13 times more.  I am appealing, but do not know yet the outcome.

5)  Finally, it is worth noting that the chargemaster rates can matter for other issues as well. For example, hospitals are typically required to provide a certain amount of “charity care” (care provided to the poor without health insurance for free or at discounted rates) in order to benefit from certain tax breaks.  This is especially important and valuable for private, profit-making, hospitals.  Valuing such services at the chargemaster rates, when these rates are 1000 times higher than what someone else might charge, will make it look as if the hospital is providing a good deal of charity care.

C.  Conclusion

This new study should put to rest the argument that price variation in health care services is principally due to variation in the degree of complexity of individual cases.  Common blood tests are standard, and they show price variation which is huge as well as similar in degree to that seen for standard health care procedures (see the review in the earlier post).  The prices vary not principally due to case complexity, but rather due to a grossly misfunctioning market for health care services, where there are strong forces keeping out effective competition and any pressure to converge on low prices from efficient providers.

The Economics of Health Insurance and the Health Care Market: Econ 101

A.  Introduction

The health care market and especially the health care insurance market, need to be understood if we are to come up with a viable health care reform.  Health care services are obtained from, and are paid through, such markets, but these markets have particular characteristics which set them apart from what might be considered an ordinary market. Because of these characteristics, the health care market does not lead to what economists would call an efficient outcome.  Rather, they lead to limited competition in local markets, high administrative and other costs, where the most efficient providers are not rewarded, and where there is little market pressure to move the system to those who provide the highest value to those in need of health care services.

This Econ 101 post will review these characteristics, structured around an approach based on defining some of the strange terms and language that economists use to describe such markets.  Not all terms will be covered – only those important to an understanding of what is needed in health reform.  And the focus will be on aspects relevant to the US system, not necessarily to systems elsewhere.  The first section below will be on health insurance, and the next section then on the broader market for the provision of health care services and its funding.

Most (although not all) of the discussion will be couched in terms of individuals buying health insurance directly.  It is recognized that most Americans are covered indirectly through their employer (who purchases insurance for them as part of their wage compensation package) or through government programs such as Medicare and Medicaid.  But the primary problems are in the individual health insurance market.  The Obamacare reforms are designed to address some of these, but issues will remain.  And the problems in the individual markets are important not only in themselves, but also as they illustrate issues that arise as well in the markets for insurance through employers or government backed programs.  Hence it is necessary to understand what lies behind the failures of the individual health insurance markets prior to the Obamacare reforms, which have led to the extremely high costs and limited access and coverage that Americans have faced in trying to obtain and pay for health care.

B.  Health Insurance

1)  Insurance:  Insurance is a contractual agreement between two parties:  The insurer providing the insurance, and the insured party (or insuree, or client, or customer, or patient) purchasing the insurance.  The insured party makes a regular payment (often monthly) to the insurer (the payment is called the  premium), and in return the insurer will pay part or all of the costs incurred if some event occurs (a claim, as contractually set out).  The event will be some health related event for health insurance.  The timeline is important (and will be discussed further below):  The premium payments are paid first, and the insurance claims are paid at some later point in time when an insured event occurs.

2)  Risk pool:  An insurance company is a financial institution, with sufficient capital (monitored by regulators) to allow it to pay claims that may come due, and with a high degree of statistical confidence that the capital they have on hand or have access to will indeed suffice.  One does not know for any individual whether they will incur health costs leading to an insurance claim in the next period.  However, with a large enough pool of clients being insured, the insurance company can work out with some degree of statistical confidence what the total claims will be in any given period, and from this what insurance rate (premium rate) they will need to charge to cover such costs.  The group they are insuring is called the risk pool.

3)  Unbiased sample:  To work out what to charge, the insurance company will need to know the characteristics (in terms of expected health claim costs) of those they are insuring.  If they are an unbiased sample taken from the population as a whole, then the health characteristics of the population as a whole (with the characteristics, such as age, of those in the risk pool) can be used to determine the level of claims to expect in any given period, and therefore what to charge.

4)  Biased sample:  A biased sample, in contrast, is one with a heavier share (or weighting) of some sub-group who will have a different likelihood of making a claim.  If that sub-group tends to have higher health care claims than the broader group, then the health care characteristics of that broader group will underestimate the costs that will in fact be incurred by the group being insured.

5)  Asymmetric information:  Markets do not function well when the parties on one side of a transaction have more information on what is being traded than the parties on the other side.  In health insurance, the insured individual will know more about their personal health status than a health insurance company will know.

6)  Adverse selection:   If the insurance is being priced to cover the costs of a risk pool that the insurance company assumes will be an unbiased sample from the general population, and an individual knows he or she has some illness or condition which will likely result in higher insurance claims than for an average person, then that individual will in general be eager to purchase such insurance.  And if an individual knows he or she is relatively more healthy than others, then he or she may decide to forego the purchase of such health insurance despite the risks, as on average their expected costs will be lower.  As a result, the insurer will end up with a risk pool that is biased towards those who will likely have higher insurance claims.  This is adverse selection.  The premium rate that was calculated based on an unbiased sample will not then suffice to cover the costs.

7)  Death spiral:  In a situation where there is asymmetric information and the individual can choose whether or not to purchase health insurance, a premium rate sufficient to cover costs for an unbiased risk pool will lose money for the pool actually enrolled.  The insurance company will respond by raising the premium rates in the next period.  But at the higher premium rates, some individuals who were at the borderline of deciding whether or not to enroll (as they were relatively more healthy than those in the biased risk pool), will decide not to re-enroll.  This will lead to an even more biased risk pool, leading to another round of the insurance company raising premium rates, and to another round of those then at the new borderline deciding not to re-enroll.  There might eventually be a stable equilibrium of relatively high cost enrollees and relatively high premium rates, but it is also possible and indeed likely, depending on the characteristics of the population, that there will be fewer and fewer enrollees in each round until it all collapses.  This is the death spiral.

8)  Free riders:  Individuals may choose not to enroll in a health plan because they believe they will have lower health costs than others.  But it is not that they necessarily believe their health costs will be lower than for others for the rest of their lives, but rather only for a period until they once again have the option of enrolling in a health insurance plan.  If insurance companies are required to enroll anyone who wishes to enroll at any time, then some might try to enroll literally on the day before they are scheduled to go to a hospital for a major operation.  Insurance companies try to address this by limiting open enrollment only to certain periods at some regular time each year, but this will be only partially successful.  Many medical procedures can be planned months ahead (such as whether to have a hip or knee replacement, or whether to try to become pregnant or not).  Free riders are those who try to game the system by paying in premiums for only a short period before they incur what they know will be major medical costs.  And free riders include not only those who seek to postpone coverage just to the next open enrollment period when they know they will incur some major medical expense, but also those who might be relatively young and aim to enroll only decades later when, due to their then advancing age, they know there health care costs will be high.

9)  Biased selection:  It is not only the insured parties who use the asymmetric information they have on their own health needs or who seek to exploit the system as free riders, who can play this game.  Insurance companies have become quite capable at designing health insurance plans to exclude, or at least to discourage, those who could be expected to incur higher health claim costs.  One way has been to exclude those with pre-existing medical conditions.  Those in the population who have some existing medical condition that has required treatment will generally continue to require higher than average treatment.  Insurance companies will deny them coverage if they are legally can.  Until Obamacare, they generally could.

10)  Individual mandate:  These problems of adverse and biased selection will be largely resolved if all in the population are required to secure health insurance coverage.  This is the individual mandate.  Individuals cannot then game the system as free riders, or choose to avoid cover if they expect (based on the information they know about themselves, which the insurers will not know) that their health care costs will likely be relatively low, at least until the next open enrollment period.  And with an individual mandate in place, insurers can then be required to offer coverage at non-discriminatory rates to all, including those with pre-existing conditions.  The death spiral would not then take hold.

11)  Biased selection II:  But issues may still remain.  The individual mandate requirement under Obamacare is not terribly strong, with only modest penalties for those who choose not to obtain insurance coverage (and with campaigns also underway by conservative groups to try to stop or at least discourage Americans from enrolling in any health care plan).  Insurance companies can play more subtle tricks as well.  Even though they will not now be able to block enrollment by someone with a pre-existing condition, they can design plans that will be unappealing to those who might have certain types of medical expenses, that might signal conditions associated with overall higher than average medical expenses.  Their hope is that such individuals will then choose to enroll in a health plan offered by some other insurer.  Or they can design plans that might be especially appealing to those who are more healthy.  The classic example of this is to include the price of gym membership in the insurance plan.  The premium rates will be higher than otherwise to cover the cost of gym membership, and those not interested in gym membership will then not find this to be an advantageous plan.  But it would be attractive to those who are already paying for a gym, or who wish to enroll in one.  The advantage to the health insurer is not so much that their enrollees will now start to go to the gym more often (although that will help), but more that those in the population who do use a gym are generally more healthy than the overall population for many reasons, including diet and other activities.

12)  Time inconsistency:  A further issue in health insurance is the arrow of time.  One enrolls in some health insurance plan, pays the premium for a period of time, and at some later point might have a health insurance claim.  But health insurance plans can be extremely complex (often deliberately so), with details buried in the fine print that may give the insurer an excuse to deny a claim that the patient had thought would be insured.  For a more normal product the customer would then absorb the loss and choose to switch to a different vendor, after receiving what they see as bad service or a broken promise.  But this can be difficult in health insurance.  First, the loss incurred on the medical care obtained could well be huge and not easy to absorb.  A study published in 2009 by Harvard Medical School researchers found that 62% of all personal bankruptcies filed in 2007 in the US were caused by medical problems.  Furthermore, these were not mostly bankruptcies of individuals without health insurance.  The Harvard researchers found that 78% of those filing for bankruptcy had medical insurance at the start of their illness.

But a second reason (and until the Obamacare reforms the more important one), is that a person in need for medical care cannot at that point choose to switch to a different health insurance provider.  At precisely that point when he realizes his existing health insurer is not performing, the person needs major medical care and hence has a pre-existing condition, and no new insurer will willingly take them on.  While denial of cover due to pre-existing conditions will now not be allowed under the Obamacare reforms, the individual will still not be able to switch insurance plans in the middle of the year, but only during an open enrollment period.  Depending on the treatment needed and its urgency, the patient will not be able to switch to another insurer precisely when he or she needs insurance the most.

The Obamacare reforms, with effective access for those with pre-existing conditions as well as minimum standards on other aspects of health insurance plans (such as no annual or lifetime limits, and requirements on what will be covered), will be a major step to resolving the time inconsistency problem.  But it will still not be fully resolved.

13)  Moral hazard:  Another commonly cited issue, in particular in conservative circles, is the concern that when patients do not face the full cost of the health care treatment (as insurance covers a part of the cost, and perhaps almost all of it), they will then “over-consume” health care.  They will obtain treatments that they do not really need, or choose more expensive treatments than necessary.  This is actually an issue that exists in principle for any type of insurance, whether for health or something else.  It is called “moral hazard”.

Whether this is an important issue in practice for health care is not so clear.  First, few of us want to go into surgery or be subject to some other major medical procedure unless it is really necessary, even if free.  Second, it is the doctor and not the patient who will normally decide and recommend whether some medical procedure is warranted.  And third, the recommended response by conservatives to the moral hazard issue is high deductible health insurance plans, as was discussed in a previous posting on this blog.  They argue that patients will then face the full cost of care when within the deductible.  But a high deductible plan is simply not relevant for addressing moral hazard for those who need a major medical procedure or treatment.  At that point, the deductible is no longer relevant as it would have already been paid.  Incentives and expenses will be the same.

Rather, high deductible plans will, at best, lead to lower expenditures for initial doctor visits to determine if there is a problem, as the consumer will face 100% of those costs (when still within the deductible for the year).  But as noted in the blog cited above, such expenditures are not where our medical costs primarily lie.  The bottom 50% of the population only accounts for 3% of all medical expenditures, so even cutting these in half, say, would have an insignificant impact on overall costs.  Indeed it might well lead to higher costs in the end, as visits to doctors are postponed and what would have been minor problems develop into something major.

14)  Race to the bottom:  Most working age Americans obtain their health insurance coverage through either their employer or the employer of their spouse (or parent, if a child).  Most employers, and especially employers with 50 or more employees, offer health insurance coverage to their employees as part of their wage compensation package.  Due to substantial tax advantages (as health insurance payments are not subject to income tax, while regular wages are), it is a good deal less expensive for the employer to offer health insurance coverage instead of not doing so and then paying the worker higher wages sufficient to allow them then to purchase on their own equivalent insurance.  Those higher wages would be subject to income tax.

This system can provide health insurance at reasonable cost for firms with a high number of employees (say a few hundred employees or more).  Such a large number of workers will provide a relatively unbiased sample of workers for the risk pool.  If all of the workers and their families (both young and old, sick and not so sick) are enrolled, then a death spiral will not take hold.  There would be no problem of free riders.  While there are coverage issues for those not employed and for those working in small firms (too small to provide a reasonably diversified risk pool), the system worked well enough in the 1950s and 1960s for those employed at larger firms.

However, issues developed as more and more spouses entered into the work force.  If both spouses worked for employers offering health insurance coverage, then the spouses could choose from which firm they would obtain their health insurance.  Family plans are normally cheaper than two individual plans.  The spouses would of course normally choose that plan which was most advantageous to them.  That would be the plan of the employer offering the best benefits.

The result was that those employers offering the plans with the best benefits, which would also be the more expensive plans, would see families choose them rather than a less generous plan offered by the employer of the other spouse.  The costs of the firms offering the more generous plans would then rise, as spouses switched to the better plans.  The incentive, then, was for employers to offer less and less generous plans, in the hope that employees would choose to enroll in the health plan of the employer of the other spouse.  This was a race to the bottom.  The consequence is that employer sponsored health insurance plans have become less and less adequate in recent decades, compared to what they covered before.

The Obamacare reforms will address this partially by setting minimum standards for what a health insurance plan must cover, for it to be considered an acceptable health insurance plan.  This will set a floor.  However, the standards are not high, and there will remain pressures on firms to go down to that floor.

C.  The Health Care Market

1)  Bilateral Oligopoly:  There are tens of thousands of health care providers in the US, and dozens of significant insurers.  However, medical care markets are overwhelmingly local, so what matters is not the number of providers at the national level but rather at the local level.  And medical care providers are of course divided into specialties.  There may also only be a few hospitals which one can effectively reach, and possibly only one or two.  As a result, when treatment in needed for some medical condition, one may effectively have only limited choices.

Similarly, there may be only a few insurers who offer insurance policies in any locality.  This is in part due to regulatory reasons, as insurance companies are regulated in the US at the state level.  As noted above, regulation of insurance is important to ensure that the companies maintain adequate capital to allow them to pay claims with a high degree of statistical confidence.  But even without regulation at the state level, insurance companies will pick and choose which localities to focus their activities in, depending on their knowledge of that local market and the activities of their competitors.

The primary model of health insurance coverage now in the US is for the insurance company to establish a network of “preferred providers” of health care services in each local market, with strong financial incentives for their insurance customers to choose services from members of that network.  The insurance company will negotiate payment rates with each member of that preferred provider network for the services they provide, with these payment rates well below the list prices (or “chargemaster” rates, when referring to hospitals) of those providers.  Indeed, as noted in an earlier blog in this series on health care, the rate negotiated with the preferred provider can be sometimes be ten times (or even more) lower than the rate that same provider would charge for someone with a different insurer or with no insurer.  There are therefore strong incentives to seek out services from members of the preferred provider network of your insurer.

(Health Maintenance Organizations, or HMOs, are also a common model of health care coverage in the US.  There is an even more restrictive network of health care providers in an HMO, and the HMO will generally not cover any of the costs incurred when an out-of-network provider is used.  In contrast, in a preferred provider network the health insurance will still cover some portion of the costs incurred when on out-of-network provider is used, but what is covered is much less than for an in-network provider.  For the discussion below the distinction is not important, so for brevity it will be couched in terms of preferred provider networks.)

The rates paid for health care treatments are therefore largely determined in the negotiations between health insurers and the local health care providers in their preferred provider network.  If there is only one insurer active in some region, that insurer will then have a good deal of leverage over providers to force them to accept low compensation rates.  If the health care provider does not accept those rates, they will see few patients as the patients will instead seek out those providers who joined the preferred provider network at the compensation rate agreed to with the insurance company.

At the other extreme, if there is effectively only one health care provider in some locality for some medical specialty or service (say one large hospital), but a number of insurers, then those medical providers will have a great deal of leverage over the insurers to force them to accept the compensation rates they demand.  The insurance company cannot offer health care coverage if the local hospitals or medical specialists refuse to work with them.  The insurance company must then agree to compensate those health care providers at the rates they demand.

The result has been an arms race:  Both health insurers on one side, and health care providers on the other side, will seek to merge and consolidate with others offering similar services in each local market across the US, in order to strengthen their bargaining position in these key negotiations.  And that is what one has seen over the last two decades.  Health insurers have merged at the national level or have bought up what were previously local or regional insurers, while doctor groups and especially hospitals have merged into chains.

This has led to what are now highly concentrated local markets.  The American Medical Association (representing doctors) has been publishing a report each year for the last 12 years on concentration in health insurers in US states as well as in each of the metropolitan statistical areas of the US (metropolitan areas as defined by the US Bureau of the Census).  The 2013 edition of the report (released in November 2013, and based on data for 2011) reported that health insurance markets would be deemed “highly concentrated” (based on the 2010 guidelines issued by the US Department of Justice and Federal Trade Commission) in 71% of the 388 metropolitan statistical areas of the US.  They also noted that just two insurers accounted for over 50% (together) of the health insurance market in 45 of the 50 US states, and that just one insurer accounted for over 50% of the market in 15 states.  The 2012 edition of the report noted that at least one insurer accounted for over 30% alone of the health insurance market in 89% of US metropolitan areas, and that just one insurer accounted for over 50% of the market in 38% of the US metropolitan areas.  And concentration has increased further since these reports were prepared.

Health insurers have not surprisingly strongly criticized the AMA reports, and have responded with a commissioned report of their own, criticizing health care providers for high and increasing concentration among hospitals.  This report concluded that hospital ownership is “highly concentrated” (by the guidelines of the US Department of Justice and Federal Trade Commission) in 80% of the US metropolitan statistical areas, and is “moderately concentrated” in a further 13% metro areas.  And there was only one hospital in 11% of the metro areas.

So who is right in this debate?  Actually, both are.  US health insurance markets are highly concentrated by local area, as are the local markets for hospital services.  And each side is racing to consolidate further.  Monopolies are still rare in the local markets, but with only a few players on each side, the markets have developed into what economists term “bilateral oligopolies”, where a small number of suppliers (health care providers) must sell their services to a small number of buyers (health care insurers, acting on behalf of their insured clients).

Without further information, one cannot predict whether health insurers or health care providers will be more profitable in a situation of bilateral oligopoly.  It will depend on their relative strength in each of the local markets, and this will vary from one market to the next depending on the local conditions.  However, the party that will face high prices regardless will be the ultimate consumers.  Suppose one is in a market where there are only a few local health care providers but many insurers.  The few health care providers will have a great deal of negotiating leverage with the insurers, and can demand high prices for their services.  The insurers, all of whom face these high prices, will then have to pass along these high prices to their insured customers in high premium rates.

Alternatively, suppose one is in a market where there are many health care providers (note this would be for each medical specialty as well as for hospital services), but only a few insurers (and maybe even effectively only one).  The health insurers would then have a good deal of leverage to drive down the doctor and hospital rates.  However, since there will then not be much (if any) competition among the health insurers (as there will be only a few and maybe effectively only one), there will be little or no competitive pressure to pass along these low prices to their insured customers.  The insured customers will again face high prices.

There have therefore been strong incentives for the US health care market to evolve over recent decades into a system of local bilateral oligopolies, with health care providers on one side and health insurers on the other.  There has been strong pressure on each to consolidate, and both have done so in an “arms race” like fashion.  The result is now highly concentrated local markets, where your profitability depends on your ability to negotiate favorable prices.  But whether it is the health insurers or the health care providers who win in these negotiations (and this will vary by locality), the consumer loses and ends up paying high prices.  This is the major reason for the extremely high US health care costs, where the high prices in the US (compared to other countries) was discussed in earlier posts in this series on health care (see here and here).

2)  Competing on Risk Pool Selection, and Other Sources of High Insurance Costs:  In addition to high health care costs as a consequence of the largely unregulated bilateral oligopolies in most local markets in the US, health costs are high also due to the high administrative costs of private health insurers.  Administrative costs are high since health insurers compete primarily on their ability to assemble networks of preferred providers of health care services in each locality (with prices negotiated with each provider for each possible service), as discussed immediately above, but also based on their ability to assemble a pool of insurees which excludes those who are of higher risk.  The open individual health insurance exchanges will limit this under the Obamacare reforms (or at least shift it to more subtle games in how health insurance plans are structured, as discussed above), but at least until now, the focus on risk pool selection has led to high administrative expenses, since individual applicants had to be vetted.

Health insurance costs are high also because of the high salaries and other compensation paid to the CEOs and other senior management of the insurance companies, as documented in a previous post in this series on health care, as well as their high profitability.  The result is administrative cost margins (which includes the net profits of the insurers in the data as assembled) of the private health insurers.  As was discussed in the blog post just cited, in 2011 the administrative cost margin (including profits) of private health insurance came to 14.0% of the cost of benefits paid.  The admin costs of private insurance companies were even higher for the programs they managed on behalf of government (such as the Medicare Advantage program of Medicare).  Those costs came to 18.6% of benefits paid.

Since the government does not incur the high costs that private health insurers do as a consequence of seeking to bias the risk pool to those of lower risk and other such actions, nor pay out profits or high salaries to CEOs and other senior managers, the administrative cost margin for direct government administered health insurance programs are far below that of private insurers.  As discussed in the blog post cited above, administrative costs for the Medicare programs the government administers directly was only 2.1% of benefits in 2011, far below the costs private insurers incur.

Total private administrative costs (including profits) of private health insurers came to $157.6 billion in 2011, based on the recently released new estimates of the National Health Expenditures data set of the Center for Medicare and Medicaid Services (CMS).  Of this, $109.9 billion was spent on the administrative costs (including profits) of the private health insurers for their privately provided health insurance plans, and $47.7 billion was spent on the administrative costs (including profits) of the private health insurers for the government health insurance plans (primarily Medicare and Medicaid, but also others) that the private health insurers administer on behalf of the government.

If the costs of administering health insurance plans were at the low cost Medicare incurs (of 2.1%) rather than the 14.0% and 18.6% that the private insurers incur, the nation would have saved $135.7 billion in 2011.  This is proportionately a huge savings in administrative costs, of 86%.  Still, a savings of $135.7 billion should also be compared to the roughly $900 billion in savings one would have needed in 2011 for US health care costs in that year (out of total health care costs of $2.7 trillion in 2011) to fall, as a share of GDP, to what the second most expensive OECD country spends on health care (as discussed in an earlier blog post; note that total health care costs of $3.0 trillion are expected in 2014, so a one-third reduction would now be $1.0 trillion).  The $135.7 billion in savings in 2011 would have been significant, but still only 15% of the overall savings needed.

D.  Conclusion

US health care costs are high and excessive, compared to what any other country in the world spends on health care.  These high costs are a consequence of the structure of the health care market in the US, with its focus on private health insurance plans.  As discussed above, there are a number of reasons (including asymmetric information, adverse selection, free riders, and biased selection, as well as non-competitive local markets of bilateral oligopolies), for why private health insurance markets will act quite differently than what economists would call a “normal” market.  They will not be efficient and low-cost.  Rather, a reliance on a private health insurance focussed system has led to inefficiency and high costs, but also high profits for the insurers.

There therefore needs to be a fundamental change in the structure of these health care markets, and the incentives for how they operate, if one is to reduce US health care costs to what other countries in the world have been able to achieve.  Future blog posts in this series on health reform will discuss what such a system might be.