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

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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 (Lack of) Recovery in the Employment to Population Ratio: Not the Concern It Might Appear to Be

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Employment to Population Ratios, Jan 2007 to July 2014

Unemployment Rates, Ages 25 to 54, Jan 2007 to July 2014A.  Introduction

A critically important policy question is how close the US economy now is to full employment.  The unemployment rate has been falling, albeit slowly, from a peak of 10.0% in October 2009, to a current 6.2% as of mid-July (ticking up from 6.1% in June, but a 0.1% change is not statistically significant).  That is, the unemployment rate has come down by a bit less than 4% points from its peak.

However, some have noted that one does not see such a recovery if one focusses on the employment to population ratio.  Excellent analysts, such as Paul Krugman and Brad DeLong, have argued that one should.  If the unemployment rate has come down by close to 4% points, then the employment to population ratio should have gone by almost the same in percentage points unless people are dropping out of the labor force.  [It will not go up by exactly the same amount in percentage points since the base for the employment to population ratio is population while the unemployment rate is expressed as a share of the labor force.  But, all else equal, they will be close.  One could make the relationship exact by expressing the unemployment rate in terms of the share of population rather than share of the labor force, but this is not how the unemployment rate is normally reported.]

If the employment to population rate has not recovered by the same amount (in percentage points) as the unemployment rate has, then by arithmetic this is only possible if the labor force participation rate has come down.  The concern is that the pool of unemployed is coming down not because people are finding jobs (which would then be seen in a rising employment to population ratio), but rather because they are dropping out of the labor force after trying, but failing, to find a decent job (thus lowering the labor force participation rate).

There are of course demographic factors as well to take into account to explain what might be happening to the labor force participation rate, in particular the increasing share of the baby boom generation that is reaching normal retirement age.  One way to do this is to focus the analysis on the prime working age group of those aged 25 to 54 only.  All the charts in this post therefore do this.  But even with this refinement, the apparent concern remains:  The employment to population ratio does not show the same recovery that one sees in the falling unemployment rate.  What is going on?

B.  Recent Years

The chart at the top of this post shows the employment to population ratios from January 2007 to July 2014, for those aged 25 to 54, and for everyone together as well as for males and females separately.  The chart below it shows the unemployment rates for these same groups.  The data all come from the Bureau of Labor Statistics.  The peak unemployment rate was hit in October 2009, after which there was a fairly steady recovery.  [The month to month fluctuations mostly reflect statistical noise.  The employment, unemployment, and labor force participation figures are all based on surveys of households, and there will be statistical noise in any such surveys.]

For the group as a whole (male and female), the unemployment rate for those aged 25 to 54 rose by about 5% points between late 2007 / early 2008 and its peak in October 2009.  Over this period the employment to population ratio fell by a similar 5% points.

But this relationship then broke down going forward.  Over the two years between October 2009 and October 2011, for example, the unemployment rate for those aged 25 to 54 fell by 1.1 percentage points, dropping to 7.9% from 9.0% at the peak (for this age group).  But the employment to population ratio hardly moved.  And between October 2009 and the most recent figures (for July 2014), the unemployment rate came down 3.8% points, while the employment to population ratio rose by only 1.6% points.

The question for policy makers is whether the 3.8% fall in the unemployment rate is a reasonable measure of how far the economy has recovered from the 2008 collapse, or the 1.6% recovery in the employment to population ratio is.  As noted above, both the unemployment rate and the employment to population ratio deteriorated by 5% points during the 2008 collapse and follow-on into 2009.  If the 3.8% recovery in the unemployment rate is the right indicator, then we would have retraced about three-quarters of the fall (3.8/5.0 = 0.76).  But if the 1.6% recovery in the employment to population ratio is the right indicator, then we are less than one-third of the way (1.6/5.0 = .32) back.  This is a huge difference.

Since the difference between the two measures must be reflected, by arithmetic, in a declining labor force participation rate, one needs to look there to see what is going on.  For the January 2007 to July 2014 period, the picture is:

Labor Force Participation Rates, Jan 2007 to July 2014

The rates are all falling after October 2009, for males and females, and hence for the two combined.  What is interesting is that they appear to be falling at a fairly steady pace throughout the period (aside from the month to month squiggles that are mostly statistical noise).  And for males, the rate appears to be falling at a broadly similar pace before October 2009.  The trend is not so clear for females before October 2009, whose rate may have been rising until a few months before October 2009.  This then leads to little change in the overall rate for males and females combined, but the period is so short that the trends are not clear.

C.  A Longer Term Perspective

When one then takes a longer view, the trends do become clear:

Labor Force Participation Rates, Jan 1948 to July 2014

Going back to 1948 (the first year in the BLS series for all these labor market indicators), one sees a pretty steady fall in the labor force participation rate for males from around the mid-1950s (with the squiggles in the curves due to statistical noise), and a strong rise in the female labor force participation rate from the initial year with data (1948) to around 2000.  There was some acceleration in the rise for females in the 1970s, and then a deceleration from the early 1990s, leading to a leveling off around 2000.  Since then, the labor force participation rate for females has fallen, on a path that appears to parallel the similar fall in the rate for males, but at 14 to 15% points lower.

The data are consistent with the broader socio-economic story we have of the labor market in the post-World War II period.  Male labor force participation rates are quite high, but have fallen some over time.  Female rates started very low but then grew, and grew at an especially rapid rate starting in the 1970s.  Female labor market participation rates then reached maturity and leveled off around 2000, after which the female rates paralleled the downward path of the male rates, but at a certain distance below.

In this longer term perspective, the decline in the labor force participation rates since 2009 therefore does not appear to be unusual, but rather a continuation of the longer term trend.  There have been some small fluctuations around the long term trends in recent years that appear to coincide with the business cycle (in particular for the female rates), but they are small and dominated over time by the long term trends.  There have also been similar fluctuations in the participation rates in the past (such as in the mid-1990s) that did not coincide in the same way with the business cycle, as well as large business cycle changes in the past that did not show such fluctuations (such as during the big downturn in the early 1980s at the start of the Reagan presidency, that did not lead to such fluctuations in the labor force participation rates).

The implication of this analysis is that the reported unemployment rates are a better indicator of the state of the labor market than the employment to population ratio is.  The fall in the labor market participation rates in recent years has not been something new, driven by the 2008 economic downturn, but rather a continuation of the trend seen in these rates over the longer term.

Looking at unemployment rates for this age group going back to 1948 provides a useful perspective on what to expect for it:

Unemployment Rates, Jan 1948 to July 2014

Unemployment rates continue to be high in mid-2014.  Even though they have retraced about three-quarters of the deterioration in 2008/2009 (more for males, less for females), they are, at 5.2% currently (for males and females together) still well above the unemployment rates for this group of about 4% in late 2007 /early 2008, and of only 3 1/2% in late 2006 / early 2007.  And the unemployment rate for this group was only 3.0% in late 2000, at the end of the Clinton years.

There is therefore still a significant distance to go before the economy will have returned to full employment.  But the improvement since October 2009 is substantial, and is real.

D.  Implications of the Long Term Trends for Aggregate GDP

Finally, while the employment to population ratio might not be a good indicator of how much slack there is in the labor market in the short run, there are long term implications of the trends noted above.  Specifically, while the overall labor force participation rate rose steadily from 1948 (the earliest year for which we have this data) to about 2000, this was entirely due to the strong rise in the female rate over this period.  The male rate was falling, steadily but slowly.  Once the female rate peaked in the year 2000 and then began to fall at a rate similar to that for males, the overall rate began to fall.  There is no indication this will be reversed any time soon.  Indeed, the degree to which the female rate is now paralleling the male rate suggests that this really is a “new normal”.

A falling labor force participation rate is not necessarily an indication of something bad in itself.  It might reflect increased prosperity, which is being enjoyed by choosing not to work but to retire early, or to attend university or post-graduate education programs in your 20s, or to stay at home and raise a family.  But to the extent it reflects lack of free choice, such as being fired in your 40s or 50s and then not being able to find a job, or to remain a perpetual student due to lack of job opportunities, or to stay at home due to the unavailability of affordable child care, the implications are different.  But it is well beyond the scope of this blog post to dig into this deeper.

But there will be important long term implications of declining labor force participation rates on long term GDP growth.  With fewer in the labor force, aggregate GDP growth will be less.  Note that this does not imply growth in GDP per capita (or more precisely, GDP per worker) will be less.  GDP per worker is a function of productivity growth.  But with fewer workers than otherwise, aggregate GDP growth will be less.

Two final charts, then, to close this blog post.  The first shows the absolute number of people in the ages 25 to 54 population cohort, who are not in the labor force:

Population Not in Labor Force, Jan 1948 to July 2014

The number of males in this age group not in the labor force has been growing steadily since the late 1960s.  The number of females not in the labor force fell until around 1990, was then flat for a decade, and then began to grow.  Overall, the number aged 25 to 54 not in the labor force started to grow around 1990, and has continued to grow since.

Looking at the numbers of those in the 25 to 54 age group in the labor force:

Labor Force Number, Jan 1948 to July 2014

Due to a growing population in this age group (baby boomers, for example, but others as well), and the growing labor force participation rates of females until 2000, the total labor force in this group rose from the starting year (1948) until 2008.  It grew especially fast in the 1970s, 80s, and 90s.  But the absolute size of the labor force (in the 25 to 54 age group) then started to fall from 2008.  This is a historic change for the US, and based on the fall in labor force participation rates discussed above, as well as slowing population growth, should be expected to continue.  While GDP growth per capita (or per worker) might continue to grow as it has in the past (and it has grown at a remarkably consistent 1.9% a year since 1870 in the US, as discussed in this earlier blog post), one should expect aggregate GDP growth to slow.

E.  Summary and Conclusion

The unemployment rate has fallen substantially since hitting its peak in October 2009, but one does not see a similar recovery in the employment to population ratio.  The labor force participation rate therefore has to have fallen.  However, it does not appear that this fall in the labor force participation rate has been driven by the economic downturn, where high unemployment and poor job prospects led workers to drop out of the labor force on a widespread basis.  Rather it appears largely to be a continuation of longer term trends, that become clear when one separates out the paths for male and female labor force participation rates.

The implication is that the unemployment rate is probably a good indicator of how much slack there is in the labor force.  The unemployment rate has retraced about three-quarters of the rise during the 2008/2009 downturn, but is still high.  And it is substantially higher than what was seen as possible in late 2006 / early 2007, and especially the rate achieved in late 2000.

But there are longer term implications.  The analysis suggests that we should not expect much of a recovery in the labor force participation rate when the economy finally returns to full employment.  Rather, the labor force participation rate is on a downward slope, and has been since the year 2000 (when the female rates reached maturity).  This is likely to continue.  The result is that the absolute size of the labor force in the prime working age years of 25 to 54 should be expected to continue to fall for the foreseeable future.  Japan and most of the European economies have already been facing this.  While GDP per worker, which is driven by productivity change, need not necessarily slow, one should expect growth in aggregate GDP to be less than what one saw in the past.  The ability to adapt to, and manage in, this new economic environment remains to be seen.

The Pace of Job Growth by Presidential Term

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Monthly Job Gains by Presidential Term - Total

Paul Krugman in a post today on his blog notes that the continued claim by Reaganites that job growth during Reagan’s presidential term was especially strong, is a myth.  With a chart such as the one above (which copies his), Krugman notes that monthly net job gains were in fact higher during the presidential terms of Carter and Clinton.  (The data comes from the Bureau of Labor Statistics (BLS).)

This is true.  He also could have gone further.  The record during recent presidential terms differs from the myths pushed by conservatives not only in terms of total job growth, but also in terms of how the net job growth breaks down between private and public sector jobs.  Obama is far from a socialist.

Looking first at private sector jobs:

Monthly Job Gains by Presidential Term - Private

Monthly net private sector job gains are again highest under Clinton and Carter; private jobs in fact fell under Bush II; and growth was quite modest under Bush I.  Reagan comes in after Clinton and Carter.  They have averaged a growth of a bit over 86,000 per month so far under Obama, but more on this below.

Private jobs fell under Bush II even though total jobs rose by a small amount during his term because public sector job growth added to his totals, and were sufficient to make overall job growth under Bush II slightly positive.  Looking at the figures for all of the presidential terms:

Monthly Job Gains by Presidential Term - Public

Public sector jobs include jobs at all government levels (federal, state, and local).  State and local jobs dominate – they currently account for 88% of total public sector jobs.  The story on federal government jobs only can differ, and has been discussed in an earlier post on this blog.  Note also the difference in the scales in the charts for the public sector jobs vs. the charts for private (and overall) jobs.  There are far fewer public sector jobs than private ones in the US economy.

What is striking in this chart is the absolute fall in public sector jobs during Obama’s term.  They increased for everyone else, but have fallen at a rate of about 10,000 per month under Obama.  And has been discussed in earlier posts on this blog, this fall in government jobs during Obama’s term (along with cut-backs in government spending more broadly, which is of course related) can fully account for the slow pace of the recovery from the 2008 economic collapse.

Paul Krugman also notes that one could well argue that it may not be fair to count job growth (or fall) in the first year of a presidential term, as the president inherited the economic situation from his predecessor.  It takes some time for new presidential policies to have an impact.  Defenders of Reagan like to point this out.  But as Krugman notes, one should then do the same for the others as well.  The figures for private job growth are then:

Monthly Job Gains from 12 Months In by Presidential Term - Private

Obama now turns out to have presided over the second highest pace of private job growth (after Clinton), and indeed comes out ahead (even if modestly) of the pace during Reagan.  Reagan is lauded as the “job creator” and Obama as the “job destroyer”.  The facts do not support this, at least if one is focused on private sector (rather than public sector) jobs.

In terms of public sector jobs:

Monthly Job Gains from 12 Months In by Presidential Term - Public

What is striking here is how consistent the pace of public sector job growth now is under Carter, Reagan, Bush I, and Clinton – two Republicans and two Democrats.  The differences are tiny.  The pace of growth is slower under Bush II, but still substantially positive.  But public sector jobs have fallen sharply under Obama, and only under Obama.

If Obama is a “job destroyer”, it is as a destroyer of public sector jobs.  One would not expect that from a “socialist”.  And private jobs (counting from 12 months after inauguration) have grown faster under this “socialist” than under the hero of the right wing – Ronald Reagan.