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

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

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.

The Long-Term Downward Trend in Federal Government Employment Has Continued Under Obama

Fed Govt Employment as % of US Population, Jan 1953 - June 2014


A.  The Long-Term Downward Trend in Federal Government Employment

Previous posts on this blog have reported on the fall in total government employment (federal, state, and local) during Obama’s presidential term, in stark contrast to the increases seen during the term of George W. Bush as well as during the terms of other recent presidents.  The same pattern, of government jobs falling under Obama and rising under other presidents, is seen when one takes as the starting point the beginning of an economic downturn rather than the date of inauguration.  Government jobs have been cut compared to what they were at the start of the most recent downturn, in contrast to the increases approved in previous downturns.  These cuts in government jobs during Obama’s tenure, as well as the cuts in government spending, are of course exactly the opposite of what one should do during an economic downturn.  An earlier post estimated that if government spending been allowed to grow at the pace it had under Reagan, the economy would likely have reached full employment output already in 2011.

In terms of overall fiscal impact on the economy, a focus on total government employment and spending makes sense.  The total impact depends on what is being done at all levels of government – federal, state, and local.  But a president has only limited influence on what is happening at the state and local government levels.  It is federal government employment and spending that a president, together with the congress, determine.  Hence in terms of assessing the direction of policy during different presidential terms, limiting the analysis to the federal level makes sense.  The question to be addressed is whether, as many conservatives charge, the number of federal employees has grown sharply under Obama.

The chart at the top of this post shows federal government employment as a share of the US population going back to January 1953, the month Eisenhower was inaugurated.  The government employment figures come from the Bureau of Labor Statistics.  Note that federal government employment excludes active duty military personnel (but includes civilian employees of the Department of Defense).  The population numbers come from the Census Bureau (most conveniently accessed via FRED).

The chart shows that federal government employment as a share of the US population is now well below what it has been historically, and that it has fallen further during the presidential term of Obama.  Federal government employment is now only 0.85% of the US population, or over a third less than the 1.3% share it averaged from the second half of the 1950s through to the end of the 1980s (more than a third of a century).  There were sharp cuts at the start of the Eisenhower administration as the Korean War came to an end, and significant increases in the mid-1960s due to the Vietnam War and Great Society programs, but the averages, over any of the decades, were each still within round-off of 1.3%.  [Note:  While the government employment figures exclude active duty military, there are still major increases in government civilian employment during times of war in the Department of Defense and elsewhere.]

But since the mid-1960s (with the major exception of most of the Reagan term), federal government employment has been on a downward trend, most sharply during the presidential terms of Clinton and now Obama.  The recent fall during Obama’s term might be less of a concern if it was a reversal from a recent sharp increase, or as a reversal of an upward trend.  But government employment is already a third below (as a share of population) where it was through the 1980s, and is being cut further.  A fall by a third is huge.

B.  Federal Government Employment in Specific Presidential Terms

While government employment as a share of population is most appropriate when examining long term trends, some might want also to see the figures in terms of absolute numbers of employees over the course of presidential terms.  Here are the recent ones:

Number of Federal Government Jobs
       (numbers in thousands)
Reagan:  Jan 1981 to Jan 1989 2,961 3,158  197
Bush I:  Jan 1989 to Jan 1993 3,158 3,092 -66
Clinton:  Jan 1993 to Jan 2001 3,092 2,753 -339
Bush II:  Jan 2001 to Jan 2009 2,753 2,786  33
Obama:  Jan 2009 to June 2014 2,786 2,713 -73

As has been noted before, federal government employment rose sharply during Reagan’s presidential term.  It then fell during the term of Bush I, although not by enough to offset the increase under Reagan (when Bush was the Vice President).  There was then a sharp fall under Clinton, which was reversed to an increase under Bush II.  But federal government employment is now falling again under Obama.

Reagan and Bush II have been celebrated by Republicans as small government conservatives.  But federal government employment rose during their terms.  Clinton and Obama have been denounced by conservatives as big government liberals.  But federal employment fell sharply during their presidencies.  And federal employment also fell during the term of Bush I, the most moderate of recent Republican presidents.  The facts are simply not consistent with the stories told of these presidents.

C.  An Example of The Adverse Impact on Government Capacity

Federal government employment is thus now at a historic low (as a share of population) over a period of at least six decades.  It should not then be a surprise that government programs may be inadequately administered.  One often simply needs more personnel to do it.  A clear recent example of this is the crisis in the Veterans Administration.  The number of veterans has increased sharply in recent years due to the legacy of the Iraq and Afghanistan wars, and veterans from the Vietnam War are now of the age when they most need health care.  A significant number of aged World War II and Korean War veterans also remain and need care.  But the number of doctors, nurses, and other staff at the VA hospitals have been held back from the number needed to provide good care.  And it cannot be said that the existing staff are lazy.  The doctors are fully booked.

Rather than face up to this, lower level staff have for many years now been falsifying records to make it appear that veterans can see a doctor within a reasonable time, when that is not the case.  This of course has made the problem worse, as no one then faced up to the problem and did something about it.  Senior officials in the VA have stated that they were unaware of what was going on.  Having worked in a large bureaucracy myself for most of my career (the World Bank), I can readily believe this to be true.  Senior officials are often unaware of what is common knowledge among front-line staff.  But covering it up by falsifying records only served to make the problem worse.

A bipartisan deal appears to be close to congressional approval to provide additional funding to the Veterans Administration, to go at least some way to resolving the problem.  An additional $5 billion in funds will be provided to hire more doctors, nurses, and other staff needed to provide the care veterans have a right to, while $10 billion will be provided to allow veterans to go to more expensive private health care providers in those cases where they cannot receive a VA doctor’s appointment within 30 days, or when they live more than 40 miles from a VA hospital or clinic.  Whether this funding will suffice is not clear, and has been questioned.  Earlier estimates on the funding needed were far higher.  But Republicans in Congress have refused to approve more, and as of this writing, a final vote on the proposed measure still awaits.  However, any extra funding will at least help.

The veterans issue illustrates well the fundamental problem.  While it is certainly true in any large organization, private or public, that employees exist who are not providing effective service, there are in practice limits to how much one can cut before service suffers.

America’s Underinvestment in Public Infrastructure

Real per Capita Public Investment vs. GDP, 1950-2013

Public infrastructure in the United States is an embarrassment.  This is clear even to ordinary travelers.  Countries in Europe and in much of East Asia enjoy far better roads, highways, public transit, and other forms of public infrastructure than the US does, even though the real per capita incomes of these countries are lower than that of the US.  And this is backed up by more systematic global comparisons, such as in the Global Competitiveness Report of the World Economic Forum.  The most recent report ranked the US as only number 15 in the world in terms of its infrastructure (transport, power, and telecom).  This put the US behind Canada, the major countries of Western Europe, and such countries as Japan, Korea, Hong Kong, Singapore, and Taiwan in East Asia.

The poor quality of public infrastructure in the US should not, however, be a surprise.  As the chart at the top of this post shows, the US is spending no more now, in real per capita terms, than it did over a half century ago in 1960, in the last year of the Eisenhower administration.  The chart draws on data issued in the standard GDP (NIPA) accounts of the BEA of the US Department of Commerce.  Infrastructure investment is taken to be total government investment (at all levels of government – Federal, State, and Local) in structures, excluding such spending by the military.  Most government infrastructure spending in the US is for transport (primarily roads and associated bridges, but also including investment in mass transit, ports, and airports), with a significant amount also for water and wastewater treatment.

Public infrastructure spending in real per capita terms rose during the Eisenhower administration in the 1950s (when the Interstate Highway system was started) and continued rising during the Kennedy and Johnson administrations in the 1960s.  Indeed, during this period, such spending rose at a somewhat faster pace than real per capita GDP, the blue line in the chart.  But starting in 1969, the year Nixon took office, public infrastructure spending was cut.  By the mid-1970s it was down close to the level seen at the end of the Eisenhower administration (in real per capita terms), and then was cut even further at the start of the Reagan administration.  It then began to increase from 1984 with this continuing to a peak in 2002, after which it fell again.  By 2013 it was 2% lower than it was in 1960.  Over this same period, real per capita GDP almost tripled.

In dollar terms, real per capita spending on public infrastructure (in terms of 2009 prices, the base now used in the GDP accounts) was $793 in 1960 and was 2% lower, at $776, in 2013 (about 1.6% of GDP).  Over this same period, per capita real GDP rose from $17,159 in 1960 to $49,852 in 2013.  The increment in real per capita GDP was $32,693 over this period.  None of this growth went to increased investment in public infrastructure.

It is this stagnation in real per capita spending, and huge lag behind income growth, that has led to bridges and highways that are both congested and in poor condition.  People drive more, fly more, and import and export more goods, as their real incomes grow.  Public infrastructure has not kept up.  A 2009 report issued by the American Association of State Highway and Transportation Officials (AASTO) notes that vehicle miles driven between 1990 and 2007 rose by 41%, about double the increase in the US population over this 17-year period (of 20.6%).  Based on the figures in the chart above (which however covers all public infrastructure, not just highways), spending to build or maintain such infrastructure per mile driven fell by over 20% over that period.

The AASTO report also found (based on an analysis of US Federal Highway Administration data) that one-third (33%) of the nation’s major highways was classified as being only in poor or mediocre condition (as of 2007).   Thirteen percent was classified to be in poor condition, with this rising to over 60% poor in some major urban areas.   And roads in poor or mediocre condition deteriorate quickly, leading to much higher costs when the road eventually has to be repaired.  The AASTO report notes that the cost per mile over 25 years is three times higher if roads are left to be reconstructed, instead of maintained on the regular recommended schedule.

This stagnation in real per capita spending on public infrastructure over more than a half century may be surprising to some.  While many might be aware that infrastructure spending has not kept up with real per capita GDP (which has almost tripled), most people would assume that there has been at least some increase in per capita infrastructure spending.  But that is not the case.

Part of the reason for this mis-conception is that when measured as a share of GDP, it might not appear that public infrastructure spending has fallen so far behind.  As a share of GDP, public infrastructure spending (using the figures cited above for public investment in non-Defense structures, from the BEA accounts) was 39% less in 2013 than it was in 1960.  Put another way, public infrastructure spending would have had to increase by 64% (=1/(1-.39)) between 1960 and 2013, to match the GDP share it had in 1960.  But the figures shown above in the chart indicate that public infrastructure spending would have had to triple over this period to match the increase in GDP.

Why this big difference?  The reason is Baumol’s Cost Disease, which was discussed in an earlier post on this blog.  If the price index for public infrastructure spending over this period had matched the price index for overall GDP, then an increase in infrastructure spending of 64% would suffice to bring it into line with the increase in real GDP over the period.  But the cost of building infrastructure has risen at a faster pace than the cost of making goods generally.  This is not because of increased waste, but rather because building infrastructure is by nature labor intensive and hard to automate.  The relative cost of infrastructure will therefore increase over time relative to the cost of goods whose production can be increasingly automated.

The importance of this is huge, but is often ignored in the debates.  As the chart above shows, investment in public infrastructure has stagnated in real per capita terms over more than a half century, and would need to almost triple at this point to catch up with how much real per capita GDP has grown.  This is far greater than the 64% increase (which is itself not small) that one might assume would be necessary by simply focussing on GDP shares.

The fundamental cause of this stagnation in real spending on public infrastructure has been an unwillingness in Congress to pay for it.  The most important source of funding for highway expenditures has been the gasoline tax, which supports the Highway Trust Fund. But as was discussed in an earlier post on this blog, gasoline taxes have been set as so many cents per gallon and are not adjusted regularly for inflation.  The last time the tax was raised (in nominal terms) was in 1993, over 20 years ago.  Since then, even general inflation has eroded this by over 50%.  If one took into account that prices for infrastructure investments rise at a substantially faster pace than general prices (due to Baumol’s Cost Disease, discussed above), the real erosion has been much greater.  As a result, funds in the Highway Trust Fund are far from adequate.

The result has been repeated crises as the Congress passes one short term patch after another to allow even the overly low on-going highway investments to continue.  One such crisis is underway now, where expenditures would need to be slashed on August 1 if nothing is done.  The Senate is currently expected to vote this week on an extension, although it would only be for a few months at best.  If passed and can then be reconciled with a similar House passed measure (passed two weeks ago), spending on highway investment will be able to continue for a few more months.

To provide the needed funds, given that the Highway Trust Fund is far from sufficient (due to the failure to adjust the tax to reflect inflation), Congress has included again an especially stupid provision in the draft bills.  As it did in an earlier authorization in 2012 (see the blog post cited above), Congress would allow corporations to make assumptions on their pension obligations which will in effect allow them to underfund their pension obligations by even more than currently.  The corporations will then show (on their balance sheets) higher profits, which will generate somewhat higher corporate income tax obligations.  These higher tax obligations will be counted as government revenues.  But those reliant on corporate pensions will be at greater risk of not receiving the pensions they are owed.  Ultimately the government may be obliged to cover these pension obligations (through the Pension Benefit Guarantee Corporation).  But these costs latter costs are being ignored.