How Low is Unemployment in Historical Perspective? – The Impact of the Changing Composition of the Labor Force

A.  Introduction

The unemployment rate is low, which is certainly good, and many commentators have noted it is now (at 3.7% in September and October, and an average of 3.9% so far this year) at the lowest the US has seen since the 1960s.  The rate hit 3.4% in late 1968 and early 1969, and averaged about 3.5% in each of those years.

But are those rates really comparable to what they are now?  This is important, not simply for “bragging rights” (or, more seriously, for understanding what policies led to such rates), but also for understanding how much pressure such rates are creating in the labor market.  The concern is that if the unemployment rate goes “too low”, labor will be able to demand a higher nominal wage and that this will then lead to higher price inflation.  Thus the Fed monitors closely what is happening with the unemployment rate, and will start to raise interest rates to cool down the economy if it fears the unemployment rate is falling so low that there soon will be inflationary pressures.  And indeed the Fed has, since 2016, started to raise interest rates (although only modestly so far, with the target federal funds rate up only 2.0% points from the exceptionally low rates it had been reduced to in response to the 2008/09 financial and economic collapse).

A puzzle is why the unemployment rate, at just 3.9% this year, has not in fact led to greater pressures on wages and hence inflation.  It is not because the modestly higher interest rates the Fed has set have led to a marked slowing down of the economy – real GDP grew by 3.0% in the most recent quarter over what it was a year before, in line with the pace of recent years.  Nor are wages growing markedly faster now than what they did in recent years.  Indeed, in real terms (after inflation), wages have been basically flat.

What this blog post will explore is that the unemployment rate, at 3.9% this year, is not in fact directly comparable with the levels achieved some decades ago, as the composition of the labor force has changed markedly.  The share of the labor force who have been to college is now much higher than it was in the 1960s.  Also, the share of the labor force who are young is now much less than it was in the 1960s.  And unemployment rates are now, and always have been, substantially less for those who have gone to college than for those who have not.  Similarly, unemployment rates are far higher for the young, who have just entered the labor force, than they are for those of middle age.

Because of these shifts in the shares, a given overall unemployment rate decades ago would only have happened had there been significantly lower unemployment rates for each of the groups (classified by age and education) than what we have now.  The lower unemployment rates for each of the groups, in that period decades ago, would have been necessary to produce some low overall rate of unemployment, as groups who have always had a relatively higher rate of unemployment (the young and the less educated) accounted for a higher share of the labor force then.  This is important, yet I have not seen any mention of the issue in the media.

As we will see, the impact of this changing composition of the labor force on the overall unemployment has been significant.  The chart at the top of this post shows what the overall unemployment rate would have been, had the composition of the labor force remained at what it was in 1970 (in terms of education level achieved for those aged 25 and above, plus for the share of youth in the labor force aged 16 to 24).  For 2018 (through the end of the third quarter), the unemployment rate at the 1970 composition of the labor force would then have been 5.2% – substantially higher than the 3.9% with the current composition of the labor force.  We will discuss below how these figures were derived.

At 5.2%, pressures in the labor market for higher wages will be substantially less than what one might expect at 3.9%.  This may explain the lack of such pressure seen so far in 2018 (and in recent years).  Although commonly done, it is just too simplistic to compare the current unemployment rate to what it was decades ago, without taking into account the significant changes in the composition of the labor force since then.

The rest of this blog post will first review this changing composition of the labor force – changes which have been substantial.  There are some data issues, as the Bureau of Labor Statistics (the source of all the data used here) changed its categorization of the labor force by education level in 1992.  Strictly speaking, this means that compositional shares before and after 1992 are not fully comparable.  However, we will see that in practice the changes were not such as to lead to major differences in the calculation of what the overall unemployment rate would be.

We will also look at what the unemployment rates have been for each of the groups in the labor force relative to the overall average.  They have been remarkably steady and consistent, although with some interesting, but limited, trends.  Finally, putting together the changing shares and the unemployment rates for each of the groups, one can calculate the figures for the chart at the top of this post, showing what the unemployment rates would have been over time, had the labor force composition not changed.

B.  The Changing Composition of the Labor Force

The composition of the labor force has changed markedly in the US in the decades since World War II, as indeed it has around the world.  More people have been going to college, rather than ending their formal education with high school.  Furthermore, the post-war baby boom which first led (in the 1960s and 70s) to a bulge in the share of the adult labor force who were young, later led to a reduction in this share as the baby boomers aged.

The compositional shares since 1965 (for age) and 1970 (for education) are shown in this chart (where the groups classified by education are of age 25 or higher, and thus their shares plus the share of those aged 16 to 24 will sum to 100%):

The changes in labor force composition are indeed large.  The share of the labor force who have completed college (including those with an advanced degree) has more than tripled, from 11% of the labor force in 1970 to 35% in 2018.  Those with some college have more than doubled, from 9% of the labor force to 23%.  At the other end of the education range, those who have not completed high school fell from 28% of the labor force to just 6%, while those completing high school (and no more) fell from 30% of the labor force to 22%.  And the share of youth in the labor force first rose from 19% in 1965 to a peak of  24 1/2% in 1978, and then fell by close to half to 13% in 2018.

As we will see below, each of these groups has very different unemployment rates relative to each other.  Unemployment rates are far less for those who have graduated from college than they are for those who have not completed high school, or for those 25 or older as compared to those younger.  Comparisons over time of the overall unemployment rate which do not take this changing composition of the labor force into account can therefore be quite misleading.

But first some explanatory notes on the data.  (Those not interested in data issues can skip this and go directly to the next section below.)  The figures were all calculated from data collected and published by the Bureau of Labor Statistics (BLS).  The BLS asks, as part of its regular monthly survey of households, questions on who in the household is participating in the labor force, whether they are employed or unemployed, and what their formal education has been (as well as much else).  From this one can calculate, both overall and for each group identified (such as by age or education) the figures on labor force shares and unemployment rates.

A few definitions to keep in mind:  Adults are considered to be those age 16 and above; to be employed means you worked the previous week (from when you were being surveyed) for at least one hour in a paying job; and to be unemployed means you were not employed but were actively searching for a job.  The labor force would thus be the sum of those employed or unemployed, and the unemployment rate would be the number of unemployed in whatever group as a share of all those in the labor force in that group.  Note also that full-time students, who are not also working in some part-time job, are not part of the labor force.  Nor are those, of whatever age, who are not in a job nor seeking one.

The education question in the survey asks, for each household member in the labor force, what was the “highest level of school” completed, or the “highest degree” received.  However, the question has been worded this way only since 1992.  Prior to 1992, going back to 1940 when they first started to ask about education, the question was phrased as the “highest grade or year of school” completed.  The presumption was that if the person had gone to school for 12 years, that they had completed high school.  And if 13 years that they had completed high school plus had a year at a college level.

However, this presumption was not always correct.  The respondent might only have completed high school after 13 years, having required an extra year.  Thus the BLS (together with the Census Bureau, which asks similar questions in its surveys) changed the way the question was asked in 1992, to focus on the level of schooling completed rather than the number of years of formal schooling enrolled.

For this reason, while all the data here comes from the BLS, the BLS does not make it easy to find the pre-1992 data.  The data series available online all go back only to 1992.  However, for the labor force shares by education category, as shown in the chart above, I was able to find the series under the old definitions in a BLS report on women in the labor force issued in 2015 (see Table 9, with figures that go back to 1970).  But I have not been able to find a similar set of pre-1992 figures for unemployment rates for groups classified by education.  Hence the curve in the chart at the top of this post on the unemployment rate holding constant the composition of the labor force could only start in 1992.

Did the change in education definitions in 1992 make a significant difference for what we are calculating here?  They will matter only to the extent that:  1)  the shifts from one education category to another were large; and 2) the respective unemployment rates where there was a significant shift from one group to another were very different.

As can be seen in the chart above, the only significant shifts in the trends in 1992 was a downward shift (of about 3% points) in the share of the labor force who had completed high school and nothing more, and a similar upward shift (relative to trend) in the share with some college. There are no noticeable shifts in the trends for the other groups.  And as we will see below, the unemployment rates of the two groups with a shift (completed high school, vs. some college) are closer to each other than that for any other pairing of the different groups.  Thus the impact on the calculated unemployment rate of the change in categorization in 1992 should be relatively small.  And we will see below that that in fact is the case.

There was also another, but more minor (in terms of impact), change in 1992.  The BLS always reported the educational composition of the labor force only for those labor force members who were age 25 or above.  However, prior to 1992 it reported the figures only for those up to age 64, while from 1992 onwards it reported the figure at any higher age if still in the labor force, including those who at age 65 or more but not yet retired.  This was done as an increasing share over time of those in the US of age 65 or higher have remained in the labor force rather than retiring.  However, the impact of this change will be small.  First, the share of the labor force of age 65 or more is small.  And second, this will matter only to the extent that the shares by education level differ between those still in the labor force who are age 65 or more, as compared to those in the labor force of ages 25 to 64.  Those differences in education shares are probably not that large.

C.  Differences in Unemployment Rates by Age and Education 

As noted above, unemployment rates differ between groups depending on age and education.  It should not be surprising that those who are young (ages 16 to 24) who are not in school but are seeking a job will experience a high rate of unemployment relative to those who are older (25 and above).  They are just starting out, probably do not have as high an education level (they are not still in school), and lack experience.  And that is indeed what we observe.

At the other extreme we have those who have completed college and perhaps even hold an advanced degree (masters or doctorate).  They are older, have better contacts, normally have skills that have been much in demand, and may have networks that function at a national rather than just local level.  The labor market works much better for them, and one should expect their unemployment rate to be lower.

And this is what we have seen (although unfortunately, for the reasons noted above on the data, the BLS is only making available the unemployment rates by education category for the years since 1992):

The unemployment rates of each group vary substantially over time, in tune with the business cycle, but their position relative to each other is always the same.  That is, the rates move together, where when one is high it will also be high for the others.  This is as one would expect, as movements in unemployment rates are driven primarily by the macroeconomy, with all the rates moving up when aggregate demand falls to spark a recession, and moving down in a recovery.

And there is a clear pattern to these relationships, which can be seen when these unemployment rates are all expressed as a ratio to the overall unemployment rate:

The unemployment rate for those just entering the labor force (ages 16 to 24) has always been about double what the overall unemployment rate was at the time.  And it does not appear to be subject to any major trend, either up or down.  Those in the labor force (and over age 25) with less than a high school degree (the curve in blue) also have experienced a higher rate of unemployment than the overall rate at the time – 40 to 60% higher.  There might be some downward trend, but one cannot yet say whether it is significant.  We need some more years of data.

Those in the labor force with just a high school degree (the curve in green in the chart) have had an unemployment rate very close to the average, with some movement from below the average to just above it in recent years.  Those with some college (in red) have remained below the overall average unemployment rate, although less so now than in the 1990s.  And those with a college degree or more (the curve in purple) have had an unemployment of between 60% below the average in the 1990s to about half now.

There are probably a number of factors behind these trends, and it is not the purpose of this blog post to go into them.  But I would note that these trends are consistent with what a simple supply and demand analysis would suggest.  As seen in the chart in section B of this post, the share of the labor force with a college degree, for example, has risen steadily over time, to 35% of the labor force now from 22% in 1992.  With that much greater supply and share of the labor force, the advantage (in terms of a lower rate of unemployment relative to that of others) can be expected to have diminished.  And we see that.

But what I find surprising is that that impact has been as small as it has.  These ratios have been remarkably steady over the 27 years for which we have data, and those 27 years have included multiple cycles of boom and bust.  And with those ratios markedly different for the different groups, the composition of the labor force will matter a great deal for the overall unemployment rate.

D.  The Unemployment Rate at a Fixed Composition of the Labor Force

As noted above, those in the labor force who are not young, or who have achieved a higher level of formal education, have unemployment rates which are consistently below those who are young or who have less formal education.  Their labor markets differ.  A middle-aged engineer will be considered for jobs across the nation, while someone with who is just a high school graduate likely will not.

Secondly, when we say the economy is at “full employment” there will still be some degree of unemployment.  It will never be at zero, as workers may be in transition between jobs and face varying degrees of difficulty in finding a new job.  But this degree of “frictional unemployment” (as economists call it) will vary, as just noted above, depending on age (prior experience in the labor force) and education.  Hence the “full employment rate of unemployment” (which may sound like an oxymoron, but isn’t) will vary depending on the composition of the labor force.  And more broadly and generally, the interpretation given to any level of unemployment needs to take into account that compositional structure of the labor force, as certain groups will consistently experience a higher or lower rate of unemployment than others, as seen in the chart above.

Thus it is misleading simply to compare overall unemployment rates across long periods of time, as the compositional structure of the labor force has changed greatly over time.  Such simple comparisons of the overall rate may be easy to do, but to understand critical issues (such as how close are we to such a low rate of unemployment that there will be inflationary pressure in the labor market), we should control for labor force composition.

The chart at the top of this post does that, and I repeat it here for convenience (with the addition in purple, to be explained below):

The blue line shows the unemployment rate for the labor force since 1965, as conventionally presented.  The red line shows, in contrast, what the unemployment rate would have been had the unemployment rate for each identified group been whatever it was in each year, but with the labor force composition remaining at what it was in 1970.  The red line is a simple weighted average of the unemployment rates of each group, using as weights what their shares would have been had they remained at the shares of 1970.

The labor force structure of 1970 was taken for this exercise both because it is the earliest year for which I could find the necessary data, and because 1970 is close to 1968 and 1969, when the unemployment rate was at the lowest it has been in the last 60 years.  And the red curve can only start in 1992 because that is the earliest year for which I could find unemployment rates by education category.

The difference is significant.  And while perhaps difficult to tell from just looking at the chart, the difference has grown over time.  In 1992, the overall unemployment rate (with all else equal) at the 1970 compositional shares, would have been 23% higher.  By 2018, it would have grown to 33% higher.  Note also that, had we had the data going back to 1970 for the unemployment rates by education category, the blue and red curves would have met at that point and then started to diverge as the labor force composition changed.

Also, the change in 1992 in the definitions used by the BLS for classifying the labor force by education did not have a significant effect.  For 1992, we can calculate what the unemployment rate would have been using what the compositional shares were in 1991 under the old classification system.  The 1991 shares for the labor force composition would have been very close to what they would have been in 1992, had the BLS kept the old system, as labor force shares change only gradually over time.  That unemployment rate, using the former system of compositional shares but at the 1992 unemployment rates for each of the groups as defined under the then new BLS system of education categories, was almost identical to the unemployment rate in that year:  7.6% instead of 7.5%.  It made almost no difference.  The point is shown in purple on the chart, and is almost indistinguishable from the point on the blue curve.  And both are far from what the unemployment rate would have been in that year at the 1970 compositional weights (9.2%).

E.  Conclusion

The structure of the labor force has changed markedly in the post-World War II period in the US, with a far greater share of the labor force now enjoying a higher level of formal education than we had decades ago, and also a significantly lower share who are young and just starting in the labor force.  Since unemployment rates vary systematically by such groups relative to each other, one needs to take into account the changing composition of the labor force when making comparisons over time.

This is not commonly done.  The unemployment rate has come down in 2018, averaging 3.9% so far and reaching 3.7% in September and October.  It is now below the 3.8% rate it hit in 2000, and is at the lowest seen since 1969, when it hit 3.4% for several months.

But it is misleading to make such simple comparisons as the composition of the labor force has changed markedly over time.  At the 1970 labor force shares, the unemployment rate in 2018 would have been 5.2%, not 3.9%.  And at a 5.2% rate, the inflationary pressures expected with an exceptionally low unemployment rate will not be as strong.  This may, at least in part, explain why we have not seen such inflationary pressures grow this past year.

The Economy Under Trump in 8 Charts – Mostly as Under Obama, Except Now With a Sharp Rise in the Government Deficit

A.  Introduction

President Trump is repeatedly asserting that the economy under his presidency (in contrast to that of his predecessor) is booming, with economic growth and jobs numbers that are unprecedented, and all a sign of his superb management skills.  The economy is indeed doing well, from a short-term perspective.  Growth has been good and unemployment is low.  But this is just a continuation of the trends that had been underway for most of Obama’s two terms in office (subsequent to his initial stabilization of an economy, that was in freefall as he entered office).

However, and importantly, the recent growth and jobs numbers are only being achieved with a high and rising fiscal deficit.  Federal government spending is now growing (in contrast to sharp cuts between 2010 and 2014, after which it was kept largely flat until mid-2017), while taxes (especially for the rich and for corporations) have been cut.  This has led to standard Keynesian stimulus, helping to keep growth up, but at precisely the wrong time.  Such stimulus was needed between 2010 and 2014, when unemployment was still high and declining only slowly.  Imagine what could have been done then to re-build our infrastructure, employing workers (and equipment) that were instead idle.

But now, with the economy at full employment, such policy instead has to be met with the Fed raising interest rates.  And with rising government expenditures and falling tax revenues, the result has been a rise in the fiscal deficit to a level that is unprecedented for the US at a time when the country is not at war and the economy is at or close to full employment.  One sees the impact especially clearly in the amounts the US Treasury has to borrow on the market to cover the deficit.  It has soared in 2018.

This blog post will look at these developments, tracing developments from 2008 (the year before Obama took office) to what the most recent data allow.  With this context, one can see what has been special, or not, under Trump.

First a note on sources:  Figures on real GDP, on foreign trade, and on government expenditures, are from the National Income and Product Accounts (NIPA) produced by the Bureau of Economic Analysis (BEA) of the Department of Commerce.  Figures on employment and unemployment are from the Bureau of Labor Statistics (BLS) of the Department of Labor.  Figures on the federal budget deficit are from the Congressional Budget Office (CBO).  And figures on government borrowing are from the US Treasury.

B.  The Growth in GDP and in the Number Employed, and the Unemployment Rate

First, what has happened to overall output, and to jobs?  The chart at the top of this post shows the growth of real GDP, presented in terms of growth over the same period one year before (in order to even out the normal quarterly fluctuations).  GDP was collapsing when Obama took office in January 2009.  He was then able to turn this around quickly, with positive quarterly growth returning in mid-2009, and by mid-2010 GDP was growing at a pace of over 3% (in terms of growth over the year-earlier period).  It then fluctuated within a range from about 1% to almost 4% for the remainder of his term in office.  It would have been higher had the Republican Congress not forced cuts in fiscal expenditures despite the continued unemployment.  But growth still averaged 2.2% per annum in real terms from mid-2009 to end-2016, despite those cuts.

GDP growth under Trump hit 3.0% (over the same period one year before) in the third quarter of 2018.  This is good.  And it is the best such growth since … 2015.  That is not really so special.

Net job growth has followed the same basic path as GDP:

 

Jobs were collapsing when Obama took office, he was quickly able to stabilize this with the stimulus package and other measures (especially by the Fed), and job growth resumed.  By late 2011, net job growth (in terms of rolling 12-month totals (which is the same as the increase over what jobs were one year before) was over 2 million per year.  It went to as high as 3 million by early 2015.  Under Trump, it hit 2 1/2 million by September 2018.  This is pretty good, especially with the economy now at or close to full employment.  And it is the best since … January 2017, the month Obama left office.

Finally, the unemployment rate:

Unemployment was rising rapidly as Obama was inaugurated, and hit 10% in late 2009.  It then fell, and at a remarkably steady pace.  It could have fallen faster had government spending not been cut back, but nonetheless it was falling.  And this has continued under Trump.  While commendable, it is not a miracle.

C.  Foreign Trade

Trump has also launched a trade war.  Starting in late 2017, high tariffs were imposed on imports of certain foreign-produced products, with such tariffs then raised and extended to other products when foreign countries responded (as one would expect) with tariffs of their own on selected US products.  Trump claims his new tariffs will reduce the US trade deficit.  As discussed in an earlier blog post, such a belief reflects a fundamental misunderstanding of how the trade balance is determined.

But what do we see in the data?:

The trade deficit has not been reduced – it has grown in 2018.  While it might appear there had been some recovery (reduction in the deficit) in the second quarter of the year, this was due to special factors.  Exports primarily of soybeans and corn to China (but also other products, and to other countries where new tariffs were anticipated) were rushed out in that quarter in order arrive before retaliatory tariffs were imposed (which they were – in July 2018 in the case of China).  But this was simply a bringing forward of products that, under normal conditions, would have been exported later.  And as one sees, the trade balance returned to its previous path in the third quarter.

The growing trade imbalance is a concern.  For 2018, it is on course for reaching 5% of GDP (when measured in constant prices of 2012).  But as was discussed in the earlier blog post on the determination of the trade balance, it is not tariffs which determine what that overall balance will be for the economy.  Rather, it is basic macro factors (the balance between domestic savings and domestic investment) that determine what the overall trade balance will be.  Tariffs may affect the pattern of trade (shifting imports and exports from one country to another), but they won’t reduce the overall deficit unless the domestic savings/investment balance is changed.  And tariffs have little effect on that balance.

And while the trend of a growing trade imbalance since Trump took office is a continuation of the trend seen in the years before, when Obama was president, there is a key difference.  Under Obama, the trade deficit did increase (become more negative), especially from its lowest point in the middle of 2009.  But this increase in the deficit was not driven by higher government spending – government spending on goods and services (both as a share of GDP and in constant dollar terms) actually fell.  That is, government savings rose (dissavings was reduced, as there was a deficit).  Private domestic savings was also largely unchanged (as a share of GDP).  Rather, what drove the higher trade deficit during Obama’s term was the recovery in private investment from the low point it had reached in the 2008/09 recession.

The situation under Trump is different.  Government spending is now growing, as is the government deficit, and this is driving the trade deficit higher.  We will discuss this next.

D.  Government Accounts

An increase in government spending is needed in an economic downturn to sustain demand so that unemployment will be reduced (or at least not rise by as much otherwise).  Thus government spending was allowed to rise in 2008, in the last year of the Bush administration, in response to the downturn that began in December 2007.  This continued, and was indeed accelerated, as part of the stimulus program passed by Congress soon after Obama took office.  But federal government spending on goods and services peaked in mid-2010, and after that fell.  The Republican Congress forced further expenditure cuts, and by late 2013 the federal government was spending less (in real terms) than it was in early 2008:

This was foolish.  Unemployment was over 9 1/2% in mid-2010, and still over 6 1/2% in late-2013 (see the chart of the unemployment rate above).  And while the unemployment rate did fall over this period, there was justified criticism that the pace of recovery was slow.  The cuts in government spending during this period acted as a major drag on the economy, holding back the pace of recovery.  Never before had a US administration done this in the period after a downturn (at least not in the last half-century where I have examined the data).  Government spending grew especially rapidly under Reagan following the 1981/82 downturn.

Federal government spending on goods and services was then essentially flat in real terms from late 2013 to the end of Obama’s term in office.  And this more or less continued through FY2017 (the last budget of Obama), i.e. through the third quarter of CY2018.  But then, in the fourth quarter of CY2017 (the first quarter of FY2018, as the fiscal year runs from October to September), in the first full budget under Trump, federal government spending started to rise sharply.  See the chart above.  And this has continued.

There are certainly high priority government spending needs.  But the sequencing has been terribly mismanaged.  Higher government spending (e.g. to repair our public infrastructure) could have been carried out when unemployment was still high.  Utilizing idle resources, one would not only have put people to work, but also would have done this at little cost to the overall economy.  The workers were unemployed otherwise.

But higher government spending now, when unemployment is low, means that workers hired for government-funded projects have to be drawn from other activities.  While the unemployment rate can be squeezed downward some, and has been, there is a limit to how far this can go.  And since we are close to that limit, the Fed is raising interest rates in order to curtail other spending.

One sees this in the numbers.  Overall private fixed investment fell at an annual rate of 0.3% in the third quarter of 2018 (based on the initial estimates released by the BEA in late October), led by a 7.9% fall in business investment in structures (offices, etc.) and by a 4.0% fall in residential investment (homes).  While these are figures only for one quarter (there was a deceleration in the second quarter, but not an absolute fall), and can be expected to eventually change (with the economy growing, investment will at some point need to rise to catch up), the direction so far is worrisome.

And note also that this fall in the pace of investment has happened despite the huge cuts in corporate taxes from the start of this year.  Trump officials and Republicans in Congress asserted that the cuts in taxes on corporate profits would lead to a surge in investment.  Many economists (including myself, in the post cited above) noted that there was little reason to believe such tax cuts would sput corporate investment.  Such investment in the US is not now constrained by a lack of available cash to the corporations, so giving them more cash is not going to make much of a difference.  Rather, that windfall would instead lead corporations to increase dividends as well as share buybacks in order to distribute the excess cash to their shareholders.  And that is indeed what has happened, with share buybacks hitting record levels this year.

Returning to government spending, for the overall impact on the economy one should also examine such spending at the state and local level, in addition to the federal.  The picture is largely similar:

This mostly follows the same pattern as seen above for federal government spending on goods and services, with the exception that there was an increase in total government spending from early 2014 to early-2016, when federal spending was largely flat.  This may explain, in part, the relatively better growth in GDP seen over that period (see the chart at the top of this post), and then the slower pace in 2016 as all spending leveled off.

But then, starting in late-2017, total government expenditures on goods and services started to rise.  It was, however, largely driven by the federal government component.  Even though federal government spending accounted only for a bit over one-third (38%) of total government spending on goods and services in the quarter when Trump took office, almost two-thirds (65%) of the increase in government spending since then was due to higher spending by the federal government.  All this is classical Keynesian stimulus, but at a time when the economy is close to full employment.

So far we have focused on government spending on goods and services, as that is the component of government spending which enters directly as a component of GDP spending.  It is also the component of the government accounts which will in general have the largest multiplier effect on GDP.  But to arrive at the overall fiscal deficit, one must also take into account government spending on transfers (such as for Social Security), as well as tax revenues.  For these, and for the overall deficit, it is best to move to fiscal year numbers, where the Congressional Budget Office (CBO) provides the most easily accessible and up-to-date figures.

Tracing the overall federal fiscal deficit, now by fiscal year and in nominal dollar terms, one finds:

The deficit is now growing (the fiscal balance is becoming more negative) and indeed has been since FY2016.  What happened in FY2016?  Primarily there was a sharp reduction in the pace of tax revenues being collected.  And this has continued through FY2018, spurred further by the major tax cut bill of December 2017.  Taxes had been rising, along with the economic recovery, increasing by an average of $217 billion per year between FY2010 and FY2015 (calculated from CBO figures), but this then decelerated to a pace of just $26 billion per year between FY2015 and FY2018, and just $13 billion in FY2018.  The rate of growth in taxes between FY2015 and FY2018 was just 0.8%, or less even than just inflation.

Federal government spending, including on transfers, also rose over this period, but by less than taxes fell.  Overall federal government spending rose by an average of just $46 billion per year between FY2010 and FY2015 (a rate of growth of 1.3% per annum, or less than inflation in those years), and then by $140 billion per year (in nominal dollar terms) between FY2015 and FY2018.  But this step up in overall spending (of $94 billion per year) was well less than the step down in the pace of tax collection (a reduction of $191 billion per year, the difference between $217 billion annual growth over FY2010-15 and the $26 billion annual growth over FY2015-18).

That is, about two-thirds (67%) of the increase in the fiscal deficit since FY2015 can be attributed to taxes being cut, and just one-third (33%) to spending going up.

Looking forward, this is expected to get far worse.  As was discussed in an earlier post on this blog, the CBO is forecasting (in their most recent forecast, from April 2018) that the fiscal deficits under Trump will reach close to $1 trillion in FY2019, and will exceed 5% of GDP for most of the 2020s.  This is unprecedented for the US economy at full employment, other than during World War II.  Furthermore, these CBO forecasts are under the optimistic scenario that there will be no economic downturn over this period.  But that has never happened before in the US.

Deficits need to be funded by borrowing.  And one sees an especially sharp jump in the net amount being borrowed in the markets in CY 2018:

 

These figures are for calendar years, and the number for 2018 includes what the US Treasury announced on October 29 it expects to borrow in the fourth quarter.  Note this borrowing is what the Treasury does in the regular, commercial, markets, and is a net figure (i.e. new borrowing less repayment of debt coming due).  It comes after whatever the net impact of public trust fund operations (such as for the Social Security Trust Fund) is on Treasury funding needs.

The turnaround in 2018 is stark.  The US Treasury now expects to borrow in the financial markets, net, a total of $1,338 billion in 2018, up from $546 billion in 2017.  And this is at time of low unemployment, in sharp contrast to 2008 to 2010, when the economy had fallen into the worst economic downturn since the Great Depression  Tax revenues were then low (incomes were low) while spending needed to be kept up.  The last time unemployment was low and similar to what it is now, in the late-1990s during the Clinton administration, the fiscal accounts were in surplus.  They are far from that now. 

E. Conclusion 

The economy has continued to grow since Trump took office, with GDP and employment rising and unemployment falling.  This has been at rates much the same as we saw under Obama.  There is, however, one big difference.  Fiscal deficits are now rising rapidly.  Such deficits are unprecedented for the US at a time when unemployment is low.  And the deficits have led to a sharp jump in Treasury borrowing needs.

These deficits are forecast to get worse in the coming years even if the economy should remain at full employment.  Yet there will eventually be a downturn.  There always has been.  And when that happens, deficits will jump even further, as taxes will fall in a downturn while spending needs will rise.

Other countries have tried such populist economic policies as Trump is now following, when despite high fiscal deficits at a time of full employment, taxes are cut while government spending is raised.  They have always, in the end, led to disasters.

The Savings from Lower Administrative Costs in a Medicare-for-All System

 

A.  Introduction

One of the most important issues facing the US is our high cost of health care.  We have a terribly inefficient system, with the highest costs in the world (reaching 18% of GDP, which is 50% more than in the second most expensive country and close to double the average of the OECD countries), yet with only mediocre results compared to other countries.  It is a market-based system, with competing health care providers (doctors, hospitals, and so on) and competing private health insurance companies.  However, the extremely wide variation in prices for the same treatments and procedures (often varying by a factor of ten or more) is a clear sign that this market is not working as it should.  And those skilled at exploiting these inefficiencies are able to profit handsomely, with CEOs and other senior staff of the major private insurance companies paid well.  Indeed, total compensation packages have occasionally even topped $100 million.

Despite so much spending, the US is still far from providing affordable access to health care for our entire population.  While the situation improved substantially following the introduction of Obamacare (with the share of the US population without any form of health insurance falling by about 40% after Obamacare went into effect), the Trump administration is doing all it can to reverse these gains.

Faced with these issues, a number of analysts and politicians (Senator Bernie Sanders as just the most prominent) have proposed that the US move to what is termed a “single-payer” system, such as what they have in Canada, France, and a number of other countries.  In a single-payer system, doctors, hospitals, and healthcare service providers remain as they are now, as independent and typically private agents serving their patients.  The only difference is that there is only one insurer, run as a government agency.  This is what the US has in the popular Medicare system, but Medicare is restricted only to those aged 65 and above.  Hence in the US context, a single-payer system for all is often referred to as “Medicare-for-All”.

A key question is whether a Medicare-for-All system would reduce the high cost of healthcare in the US.  Those opposed to any such government managed programs have argued that costs would rise.  And they have issued reports with headline findings that can only be interpreted as being deliberately misleading.  For example, in late July, Charles Blahous (a former Bush administration official) issued an analysis through the Mercatus Center of George Mason University (a center that has received major funding from the Koch Brothers) that concluded government spending would rise by $32.6 trillion over ten years under a Medicare-for-All system.  This has received a good deal of press coverage, and is being used (as I write this) in a number of ads being televised by Republican candidates in the 2018 midterm elections.

But while worded carefully, this claim is misleading in the extreme.  First of all, that such high amounts will be spent on health care should not be a surprise, when added up over ten years.  Total US health care spending is expected to reach $3.7 trillion this year, would rise to $5.7 trillion by 2026 if nothing is done, and would total $45.0 trillion over the ten-year period of 2017 to 2026 (using National Health Expenditure data and forecasts, which will be discussed in detail below).  The portion of this covered by various forms of personal health insurance (both private and public, such as Medicare, but excluding the military and the VA) is expected to reach $2.7 trillion this year, $4.2 trillion by 2026, and would sum to $33.1 trillion over the ten years 2017 to 2026.

So high amounts will be spent on health care, unless measures are taken to improve efficiency and reduce costs.  In per capita terms, the US population will be spending in 2018 an average of $8,190 per person through the various forms of personal health insurance our system currently employs.  This is, without question, a lot.  It will be an estimated 17.9% of the median wage this year.  But if we had the far lower administrative costs that Medicare has been able to achieve for the health insurance it manages directly, instead of the significantly higher administrative costs incurred under a variety of mostly private health insurance plans (discussed below), the average per capita cost would be just $7,480 per person in 2018.  There would also be other savings (such as what health care providers will enjoy from a simplified system, which we will also discuss below), but the savings from those sources, while certainly significant, are harder to estimate.  The $7,480 figure simply reflects savings from lower administrative costs on the part of the insurers if we were able to achieve what Medicare already does.

Thus the correct question is whether we should prefer sending a check for $8,190 per person to Aetna, Cigna, United Healthcare, and the other insurers (and including what is paid through taxes for Medicare and other publicly managed insurance), or a check for $7,480 just to Medicare under a Medicare-for-All system.  The doctors we see would be the same, and the treatments and procedures would also be the same as what we have now.  The savings here is purely from more efficient administration of our health insurance.  That the check in one case goes just to the government, and in the other to a mix of private and public insurers, should not be, in itself, of consequence.  But the Blahous argument, in saying that we cannot afford the $32.6 trillion he forecasts for healthcare spending over ten years, is that for some reason a larger check (of $8,190) to our current mix of insurers is fine while we cannot afford to send instead a smaller check (of $7,480) if that check goes to a government entity.  This is silly.

For the nation as a whole, the savings from the greater efficiency of a Medicare-for-All system is substantial.  As we will see, it would add up to $204 billion in 2016, had this system been in place that year, growing to $365 billion by 2026.  For the ten year period from 2017 to 2026, the savings would sum to $2.9 trillion.  This is not a small sum.

This main point is that we should look at the data, and not presume certain outcomes based on ideology or political beliefs.  We will thus start in this blog post with an examination of what administrative costs actually are, for Medicare and for private insurance.  We will see that the cost for administering Medicare, for the portion of Medicare managed directly by government, is far less than what is spent to administer other health insurance, including in particular private health insurance.  There are many reasons for this, where the most important is the relative simplicity and scale of the Medicare system.  An annex to this blog will discuss in detail what these various factors are for the different health insurance systems that could be folded into a Medicare-for-All system.  We will also discuss in that annex why Medicare is able to achieve its far lower administrative costs, and address some of the arguments that have mistakenly asserted that this is not the case, despite the evidence.

Taking the administrative costs that Medicare has been able to achieve as a base, we will then calculate what the savings would add up to, per year for the US as a whole, under a Medicare-for-All system.  The basic result is depicted in the chart at the top of this post, and as noted above, the savings from greater administrative efficiency would rise from $204 billion in 2016 (had the system been in place then) to $365 billion in 2026.

These savings are substantial.  But there are also other savings, which are, however, more difficult to estimate.  The penultimate section of this post will discuss several.  They include savings that will be possible in the administrative and clerical costs at doctor’s offices and at hospitals and other healthcare facilities.  Doctors, hospitals, and other facilities must hire specialist staff to deal with the complex and fragmented system of insurance in the US, and the costs from this are substantial.  There will also be savings on the part of employers, who must now manage and oversee the contracts they have with private insurers.

A final, concluding, section will summarize the key issues and discuss briefly why such an obvious and large saving in costs has not been politically possible in the US (at least so far).  The short answer:  Vested interests profit substantially under the current fragmented system, and it should not be a surprise that they do not want to see it replaced.  With extra spending in the hundreds of billions of dollars each year, there is a lot to be gained by those skilled at operating in this fragmented system.

B.  The Cost of Administering Current Health Insurance Plans

It is often difficult to estimate what costs and savings might be under some major reform, as we do not yet know what will happen.  But this is not the case for estimating administrative costs for health insurance.  We already have excellent data on what those costs actually are for a variety of different health insurance providers, including Medicare.

The primary sources of the data are the National Health Expenditure Accounts (NHE), produced annually by the Centers for Medicare and Medicaid Services, and the Annual Report of the Medicare Trustees.  The current NHE (released in February 2018) provides detailed historical figures on health expenditures (broken down in numerous ways) through to 2016, plus forecasts for many of the series to 2026.  And the Annual Report of the Medicare Trustees (with the most recent released in June 2018), provides detailed financial accounts, including of government administrative costs, for the different components of Medicare and the supporting trust funds (with past as well as forecast expenditures and revenues).

Table 19 of the historical tables in the most recent NHE provides a detailed break down of health care expenditures in 2016 by payer (mostly various insurance programs, both public and private).  The expenditures shown include what is spent on administration by government entities (separately for state and federal, although I have aggregated the two in the table below), and for what they term the “net cost of health insurance”.  The net cost of private health insurance includes all elements of the difference between what the private insurer receives in premium payments, and what the insurer pays out for health services provided by doctors, hospitals, and so on.  Thus it includes such items as profits earned by the insurer.  For simplicity, I will use “administrative costs” to include all these elements, including profits, even though this is a broad use of the term.

Table 19 of the NHE shows Medicare expenditures for all components of Medicare on just one line.  While it shows separately the administrative costs incurred by government in the administration of Medicare (with all of it federal, as states are not involved), and the administrative costs (as defined above) incurred by private insurers for the Medicare programs that they manage, the NHE does not show separately which of those costs (government and private) are linked to which Medicare programs.

For those figures one must turn to the Medicare Trustees Annual Report.  Medicare Parts A and B are managed directly by Medicare officials, and provide payments for services by hospitals (Part A) and doctors (Part B).  Medicare Part C (also now called Medicare Advantage) is managed by private insurers on behalf of Medicare, and cover services that would otherwise be covered by Medicare directly in Medicare Parts A and B.  And the relatively recent Medicare Part D (for prescription drugs) is also managed by private insurers, either as a stand-alone cover or folded into Medicare Advantage plans.

Any such combination of numbers from two separate sources will often lead to somewhat different estimates for those figures that can be compared directly with each other.  There might be slight differences in definitions, or in concepts such as whether expenses are recorded as incurred or as paid, or something else.  But the figures which could be compared here were close.  In particular, the figure for total Medicare expenditures in calendar year 2016 was $678.8 billion in the Trustees report and $672.1 billion in the NHE, a difference of just 1%.  Of greater relative importance, the Trustees report has a figure for government administration (for all Medicare programs combined) of $9.3 billion, while the NHE has a figure of $10.5 billion.  However, while the difference between these two figures may appear to be large, what matters is not so much the difference between these two, but rather the difference (as a share of total costs) between either of these and the much higher cost share for privately managed insurance (as we will see below).  We will in any case run scenarios in Section C below with each of the two different estimates for government administrative costs in Medicare, and see that the overall effect of choosing one rather than the other is not large.

Based on these sources, the costs paid in 2016 under most of the major health insurance programs in the US were:

Current Expenditures for Health Care and for Administrative Costs 

   2016 data ($ billions)

Gross Cost

Gov’t Admin

Private Admin

Total Admin

Total   as %

Private Health Insurance

$1,123.4

$129.6

$129.6

11.5%

Medicare:

$678.8

    Gov’t Administered

$390.7

$9.3

$9.3

2.4%

    Privately Administered

$288.1

$36.3

$36.3

12.6%

Medicaid

$565.6

$24.2

$36.1

$60.3

10.7%

CHIP (Children’s Health Insurance Program)

$16.9

$1.5

$1.4

$2.9

17.3%

Worker’s Compensation

$50.7

$2.3

$16.4

$18.8

37.0%

Total: 

$2,435.3

$37.4

$219.8

$257.2

10.6%

* Medicare Gov’t Admin –   NHE estimate

$390.7

$10.5

$10.5

2.7%

Sources:  Medicare expenditures, other than private administrative costs, are from the 2018 Medicare Trustees Annual Report.  All other figures are from the NHE accounts, Table 19 (historical), released in February 2018.

 

The table leaves out the health care programs of the Department of Defense and the Veterans’ Administration (as they operate under special conditions, with many of the services provided directly), as well as a number of smaller government and other programs (such as for Native Americans, or worksite or school-based health programs).  Those programs have been set aside here due to their special nature.  But while significant, the $2,435.3 billion of expenditures in the programs listed in the table account for 89% of the total spent in the US in 2016 on all health care services to individuals covered through either some form of health insurance or third-party payer.  While some portion of the remaining 11% could perhaps be folded into a Medicare-for-All system (thus leading to even higher savings), we will focus in this post on the 89%.

The table shows that the administrative cost ratios vary over a wide range, from just 2.4% for the health insurance Medicare administers directly (using the Medicare Trustees figures, or 2.7% based on the NHE figures), up to 37% for the administration of the health portions of Workers’ Compensation.  The administrative cost for direct private health insurance is 11.5% on average, while the administrative cost for the privately managed portions of Medicare (Medicare Part C and Part D) is a similar, but somewhat higher, 12.6%.

This wide variation in administrative cost ratios provides clues on what is going on.  These will be discussed in the Annex to this post for those interested.  Briefly, the programs (other than government-administered Medicare) are complex, fragmented, have to make case by case assessments of whether the claim is eligible (as for Workers’ Compensation plans) or whether the individual meets the enrollment requirements (as for Medicaid and CHIP – the Children’s Health Insurance Program), and do not benefit from the scale economies that Medicare enjoys.

But while such explanations are of interest in understanding why Medicare can be provided at such a lower cost than private and other insurance, the key finding, in the end, is that it is.  The data are clear.  The next section will use this to calculate what overall savings would be at the national level if we were to move to a system with the cost efficiencies of Medicare.

C.  National Savings in Administrative Costs from a Medicare-for-All System

Medicare (for the portion managed directly by government) costs far less to administer than our current health insurance system with its complex and fragmented mix of plans (most of which are privately managed).  Only 2.4% of the cost of the portion of Medicare managed directly by government was needed for administration of the program in 2016, while the costs to administer the other identified health insurance programs range between 10.7% (for Medicaid) and 11.5% (for private health insurance) to 37% (for workers’ compensation plans).  With $2.4 trillion spent on these health insurance plans (in 2016), the savings from a more efficient approach to administration will be significant.

An estimate of what the nation-wide savings would be can then be calculated based on figures in the NHE forecasts of health expenditures (by health insurance program) for the 2017 to 2026 period (Table 17 of the forecasts), coupled with the Medicare system forecasts provided in the Medicare Trustees Annual Report.  Applying the share of administrative costs in the portion of Medicare managed directly by government (2.4% in 2016, but then using the year by year forecasts of the Medicare trustees for the full forecast period), rather than what the administrative cost ratios would have been for the other programs that would be folded into a Medicare-for-All system (private health insurance, Medicaid, CHIP, and Workers’ Compensation), using their 2016 cost ratios, yields the savings shown in the chart above.

Had a Medicare-for-All system been in effect in 2016, we would have saved $204 billion in administration, with this growing over time (with the overall growth in health expenditures over time) to an estimated $365 billion by 2026.  The savings over ten years (2017 to 2026) would be $2.9 trillion, and would by itself bring down the cost of health care (for the programs covered) from a ten year total of $33.1 trillion forecast now, to $30.2 trillion with the reform.  There would be other savings as well (discussed in the next section below), but they are more difficult to quantify.  However, a very rough estimate is that they could be double the magnitude of the savings from the more efficient administration of health insurance alone.  See the next section below for a discussion.

The calculations here required a mix of data from the NHE and from the Medicare Trustees report, and as I noted above, the estimates of the cost of government administration in these two sources were not quite the same.  The Medicare Trustees report gave a figure for government administrative costs of the overall Medicare system of $9.3 billion in 2016 (and then year by year forecasts going forward to 2026), while the NHE estimate was $10.5 billion in 2016.  As shown in the last line of the table above, the $10.5 billion figure would lead to an administrative cost share of 2.7%, compared to the 2.4% figure if the cost was at the NHE figure of $10.5 billion.  The savings in moving to a Medicare-for-All system would then not be as large.

But the impact of this would be small.  One can calculate what the cost savings would be assuming government administration would cost 2.7% rather than the 2.4% figure in the Medicare Trustees report (with also its forecasts going forward), using the same process as above.  The total national savings would have been $199 billion in 2016 rather than $204 billion, growing to savings of $345 billion in 2026 rather than $365 billion.  The ten-year total savings would be $2.7 trillion rather than $2.9 trillion.  The savings under either estimate would be large.

D.  Other Efficiency Savings in a Medicare-for-All System 

The $2.9 trillion (or $2.7 trillion) figure for savings over ten years from moving to a Medicare-for-All system comes solely from the lower administrative costs that we know can be achieved in a Medicare type system – we know because we know what Medicare in fact costs.  But there are other savings as well that will be gained by moving to this simpler system, and this section will discuss several of them.  How much would be saved is more difficult to estimate, so we have kept these savings separate.  But some rough figures are possible.

But before going to these other sources of efficiency gains, we should mention one possible source of lower costs which has often been discussed by others, but which I would not include here.  It has often been asserted that Medicare pays doctors, hospitals, and other health service providers, less than what other insurance plans pay.  But first, it is not clear whether this is in fact true.  It might be, but I have not seen reliable data to back it up.  The problem is that most of what is paid to doctors, hospitals, and others by private health insurance plans is now at network negotiated rates, and these rates are kept as trade secrets.  It is not in the interest of the doctors and other health care providers to reveal them (as it would undermine their bargaining power with other insurers), nor in the interest of the insurance companies to reveal them (as other insurers would gain a competitive advantage in their negotiations with the providers).  Indeed, secrecy clauses are common in the negotiated agreements.

In the absence of such publicly available data, one is limited to citing either anecdotal cases, or statements by various health care providers who have a vested interest in trying to persuade Medicare to pay them more.  Neither will be reliable.

But second, and aside from this difficulty in knowing what the truth really is, the focus in this blog post is solely on the gains that could be achieved by moving to a more efficient system.  If doctors and hospitals are indeed paid less under Medicare, costs would go down, but this would be in the nature of what economists call a transfer payment, not an efficiency gain.  Efficiency gains come from being able to do more with less (e.g. administer more at a lower cost).  Transfers are a payment from one party to another, with no net gain – the gain to one party is offset by a loss of the same amount to the other.

Excluding such transfers (if they in fact exist), what are other efficiency gains that one would obtain with a Medicare-for-All system (other than the gains from lower administrative costs for the health insurance itself, which we estimated above)?  There are several:

a)  Doctors offices now need to employ specialists in handling billing, who are able to handle the numerous (and often changing) health insurance plans their patients are enrolled in.  These specialists are critical, and good ones are paid well, as they are needed if the doctors want to be paid in full for the services they provided.  Based on personal experience, I am often amazed that the staff good at this are indeed able to stay on top of the numerous health insurance plans they must deal with (I find it difficult enough to stay on top of just my own).  While essential to ensuring the doctors can survive financially, such staff are a significant cost.  While one will still need to ensure proper billing under any Medicare-for-All system, it would be far simpler.

b)  Similarly, hospitals and other medical facilities need to employ such specialist staff to handle billing.  The same issues arise.  They must contend with numerous health insurance plans, each with its own set of requirements, and ensure the bills they file with the insurers will compensate the facilities properly (and from their perspective most advantageously) for the services provided.  This is not easy to do under the present highly complex system, and would be far simpler under Medicare-for-All.

c)  There are also costs that must be borne by employers in managing the primarily employer-based health insurance system used in the US.  The employer must work out which health insurance provider would work best for them, negotiate a complex but critical and expensive contract, and then oversee the insurer to ensure they are providing services in accordance with that contract.  Firms must often hire specialist (and expensive) consultants to advise them on how best to do this.  With the cost of healthcare so high in the US, these health insurance contracts are costly.  It is important to get them right.  But all this necessary oversight is also a major cost for the firm.

How much might then be saved from such sources by moving to a more efficient Medicare-for-All system?  This is not so easy to estimate, but one study looked at the costs in the US from such expenses and compared them to similarly measured expenses in Canada, which has a single-payer system.  As noted above, a Medicare-for-All system is a single-payer system, and thus (along with the other similarities between the US and Canadian economies, such as the similar levels of income) the difference between what the costs are in the US and the costs in Canada for the same services can provide an estimate of how much might be saved by moving to a single-payer, Medicare-for-All system.

The study was prepared by Steffie Woolhander (lead author – Harvard Medical School), along with Terry Campbell, and David Himmelstein, and was published in the New England Journal of Medicine, August 2003.  They drew from a variety of sources to arrive at their estimates, and some had to be approximate.  The data is also from 1999 – almost 20 years ago.  Things may have changed, but with the upward trend in costs over time in the US, the cost shares now are likely even worse.  The authors presented the basic figures in per capita terms (and all in US dollars), and I have scaled them up to what they would be in 2016 (assuming the shares are unchanged) in accordance with the overall growth in US personal health care spending (from the NHE accounts).

The results are:

Admin costs 1999/2016

Per capita in $

Per capita in $

Per capita     in $

Total in $ billion

US –    1999

Canada – 1999

US excess – 1999

US excess – 2016

Insurance overheads

$259

$47

$212

$156.9

Doctors, hospitals, other

$743

$252

$491

$363.3

    Doctors only

$324

$107

$217

$160.6

    Hospitals & other facilities

$419

$145

$274

$202.8

Employers’ admin costs

$57

$8

$49

$36.3

Total:

$1,059

$307

$752

$556.5

Total excluding Insurance overheads

$399.6

Source:  Calculated from Woolhander, Campbell, and Himmelstein, “Costs of Health Care Administration in the United States and Canada”, New England Journal of Medicine, 349: 768-775, August 21, 2003.

Note:  “Insurance overheads” exclude health insurer profits as well as certain expenses (such as for advertising and marketing).

 

The first three columns show the estimated spending in per capita terms (and in US dollars) for each category of costs, for the US, for Canada, and then for the difference between the two.  US spending is always higher.  Thus, for example, for the line labeled “doctors”, the authors estimate that doctor’s offices have to spend an average of $324 per every US resident for expenses related to billing and other dealings with health insurance companies in 1999.  The cost in Canada with its single-payer system, in contrast, is on average just $107 per resident (in US dollar terms).  The difference is $217 per person, in 1999.  Grossing this up to the US population, and rescaled to total health care expenditures in the US in 2016 relative to 1999, the excess cost in the US in 2016 is an estimated $160.6 billion.  This is what would be saved in the US in 2016 if doctor’s offices were able to manage their health insurance billings with the same efficiency as they can in Canada.

The other lines show the estimated savings from other sources.  The top line is for insurance overheads.  The estimate here is that the US would have been able to save $156.9 billion in 2016 if health insurance administration were as efficient as what is found in Canada with its single-payer system.  While on the surface this appears to be less than the $204 billion savings estimated (for 2016) if the US moved to a Medicare-for-All system, they are in fact consistent.  The estimate in Woolhander, et. al., of the excessive cost of health insurance administration excludes what is paid out in insurance company profits and certain other expenses (such as advertising and marketing).  As discussed in the Annex below, insurance company profits can add one-third to administrative costs, so a $150 billion cost would become $200 billion when one uses the same definitions for what is encompassed.  The two estimates are in fact surprisingly consistent, even though very different approaches were used for the estimation of each.

Overall, the US would have saved about $400 billion (excluding the savings from lower expenses at the insurance companies) had a single-payer system been in effect in 2016, according to these estimates.  That is double the estimated $204 billion in savings from lower administration costs at the health insurers alone, estimated in the section above.  These additional cost savings from moving to a Medicare-for-All system are clearly significant, but are often ignored in the debate on how much would be saved from efficiency gains in a Medicare-for-All system.  They are (I would acknowledge) rough estimates.  They cannot be estimated with the same precision as one can for the savings from the more efficient administration of health insurance alone under a Medicare-for-All system.  But neither should they be forgotten.

E.  Summary and Conclusion

Medicare is a well-managed and popular program.  It is a single-payer system, but currently restricted to those aged 65 and above.  And administrative costs, on that portion of Medicare managed directly by government, are only 2.4%.  This 2.4% is far below the 11.5% administrative cost share for regular private health insurance, or 12.6% for that portion of Medicare that is managed through private health insurance companies.

And even with such low costs, Medicare is a popular program, where numerous surveys have found Medicare to be more highly rated (including in terms of user experiences with the program) than private health insurance plans (see, for example, here, here, here, and here).

Creating a Medicare-like system to cover also those Americans below the age of 65 would not be difficult.  We already have the model of Medicare itself to see what could be done and how such a system can be managed.  And we also have the examples of other countries, such as Canada, that show that such systems are not only feasible but can work well.  It is also not, as conservative critics often assert, a government “takeover” of healthcare (a criticism also often used in attacks on Obamacare):  Under a single-payer system, the providers of health care services (doctors, hospitals, and so on) remain as they are now, as private or non-profit entities, competing with each other in the services they offer.

Nor would an extension of health insurance under a Medicare-like system to those below age 65 lead to issues for the current Medicare system.  This has now become an attack line being asserted in numerous Republican political campaigns this fall, including in a signed piece by President Trump published on October 10 by USA Today.  This was in essence a campaign ad (but published for free), which fact checkers immediately saw contained numerous false statements.  As Glenn Kessler noted in the Washington Post, “almost every sentence contained a misleading statement or a falsehood”.

There is no reason why extending a Medicare-like system to those below age 65 should somehow harm Medicare.  The cost for the health insurance for those below age 65 would be paid for by sending the checks we currently must send to private insurers (such as Aetna or United Healthcare), instead to the new single-payer insurer.  As noted above, with such an entity copying the Medicare management system and achieving its low administrative costs, we would have been able to reduce the average per person cost of healthcare in 2018 from the $8,190 we are paying now, to $7,480 instead, a savings of $710 for each of us.  That $7,480 would still need to be paid in, but it is far better to send in $7,480 to the single-payer (for the same health care services as we now receive) than to send in $8,190 to the mix of insurers we now have.

Furthermore, these savings are solely from the more efficient administration of health insurance that we see can be done in Medicare.  There will also be very substantial savings from other sources in a Medicare-for-All system, including in what doctors and hospitals must now spend to deal with our currently highly fragmented and complex health insurance system, and savings by employers in what they must spend to manage their employer-based private health insurance plans.  The magnitude of such additional savings are more difficult to estimate, but they might be on the order of double the size of savings from the more efficient administration of the health insurance itself.  That is, total savings in 2016 might have been on the order of $600 billion, or three times the $200 billion in savings from more efficient administration of health insurance alone.

And such savings (or rather the lack of it under our current complex and fragmented system) can account for a significant share of the far higher cost of health care in the US than elsewhere.  As noted before, health care costs about 18% of GDP in the US, or 50% more than in the second most expensive country where it is just 12%.  Had the US been able to save $600 billion in health care expenditures in 2016 by moving to a Medicare-for-All system, US healthcare spending would have been reduced from 18% of GDP to below 15% (more precisely, from 17.9% in 2016 to 14.7%).  This, by itself, would have gotten us over halfway to what other countries spend.  More should be done, to be sure, but such a reform would be a major step.

So why has it not been done?  While the lower costs under a Medicare-for-All system would be attractive to most of us, one needs also to recognize that those higher costs are a windfall to those who are skilled at operating within our complex and fragmented system.  That is, there are vested interests who benefit under the current system, and the dollar amounts involved are massive.  Private health insurers, and their key staff (CEOs and others), profit handsomely under this system, and it should not be surprising that they lobby aggressively to keep it.  Under a Medicare-for-All system, there would be no need (or a greatly reduced need, if some niches remain) for such private health insurance.

This is not to deny that there will be issues in any such transition.  Just the paperwork involved to ensure everyone is enrolled properly will be a massive undertaking (although for all those currently enrolled in some health insurance plan, mostly via employer-based plans, the paperwork could presumably be transferred automatically to the new program).  Nor can one guarantee that while on average health care consumers will save, that each and every one will.  But the same is true in any tax reform, where even if taxes on average are being cut, there are some who end up paying more.

One should also acknowledge that many doctors and hospitals are concerned that in a Medicare-for-All system they will have little choice but to agree to the Medicare-approved rates for their services.  However, it is not clear this is much different from the current system for the doctors, where they must either agree to accept the in-network rates negotiated with the private health insurers, or expect few patients.  And surveys of doctors on their support for a Medicare-for-All system show a turnaround from earlier opposition to strong support.  A survey published in August 2017 found 56% of physicians in support (and 41% opposed), a flip from the results of a similar survey in 2008 (when only 42% were in support, and 58% opposed).  A key reason appears to be the costs and difficulties (discussed above) doctors face in dealing with the multiple, fragmented, insurance plans they must contend with now.  Even the American Medical Association, a staunch opponent of Medicare when it was approved in the 1960s, and an opponent ever since, may now be changing its views.

Finally, 70% of Americans now support a Medicare-for-All system, according to a recent Reuters survey.  It is time for such a system.

 

 


Annex:  The Causes of the Wide Variation in Administrative Cost Shares

a.  The Wide Range of Administrative Cost Shares

Administrative cost shares vary enormously across different health insurance programs, from just 2.4% for government-managed Medicare to 37% for health insurance provided through Workers’ Compensation plans.  The figures are shown above in the top table in the post.  Some might say that this cannot be – that they are all providing health insurance so why should the differ by so much.  But they can and they do, and this annex will discuss why.

Take the case of Workers’ Compensation first.  Workers’ Compensation insurance was established by states in the US starting in 1902 (Maryland was the first).  Most states passed laws between 1910 and 1920 requiring businesses to arrange for such insurance, and by 1920 all but five states (all in the South) had such coverage (and by 1948 all states had it).  And in most (but not all) states, health care benefits are provided through the purchase of privately managed insurance.

But these programs are expensive to administer.  Each individual claim must be scrutinized to determine that it was in fact due to a covered workplace injury.  This leads to the extremely high (37%) administrative cost share.  If the injury is indeed covered, the workers’ compensation insurance arranged by the business will pay for the associated health care costs.  But if it is not, the injury will now normally be covered by the individual’s regular health care insurance.  The treatment is still needed, and is provided.  The issue is only who pays for it.

Hence the time and effort spent to ascertain whether the injury was in fact due to a covered workplace injury is a pure social cost, and would not be needed (at least for the health care treatments) in a Medicare-for-All system.  The injuries would still be treated, but funds would not need to be spent to see whether the costs can be shifted from one insurer to a different one.  And when each individual claim must be assessed (with many then rejected), the administrative costs for Workers’ Compensation plans can be a high share of what is in the end paid for healthcare treatments.

When workers’ compensation programs were first set up, in the early 20th century, individual health insurance was not common.  Such health insurance (set up through employers) only began to be widespread during World War II, when the Roosevelt administration approved favorable tax treatment of such insurance by businesses (who were trying to attract workers, but were subject to general wage and price controls).  But workers’ compensation programs continue to exist, despite their high administrative costs.  And from the point of view of the private insurer providing the workers’ compensation cover, spending such money to assess liability for some injury makes sense, as (from the private perspective of the individual insurer) they would gain if the health treatment costs can be shifted to a different insurer.  But such expenditures do not make sense from the perspective of society as a whole.  They are just a cost.  And under a Medicare-for-All system the injury would simply be treated, with no need to ascertain if one insurer or a different one was responsible for making the payment.  Overall costs would be less, with the same health care treatments provided.

There are similar socially wasteful expenditures in other health insurance programs, which drive up their administrative costs.  CHIP (Children’s Health Insurance Program) has a relatively high administrative cost share (17.3% in 2016) in part because it is relatively small ($16.9 billion in expenditures in 2016, which can be compared to the $678.8 billion for Medicare), so it does not enjoy the economies of scale of other programs, but also because eligibility for the program must be assessed for each individual participating.  While rules vary by state, children and teens are generally eligible for CHIP coverage up to age 18, for families whose incomes are below some limit, but who are not receiving Medicaid (or in coordination with Medicaid in certain cases).  The CHIP insurance for the children and teens is then either free or low-cost, depending on family income.

Confirming that children to be enrolled under CHIP meet the eligibility requirements is costly.  Hence it is not surprising that this (along with the lack of the economies of scale that larger programs can take advantage of) leads to the relatively high share for administrative costs.  But this eligibility question would not be an issue that would need to be individually assessed in a Medicare-for-All system.  It is a socially wasteful expenditure that is required only because the program needs to confirm those enrolled meet the specific eligibility requirements of this narrow program.  And a Medicare-for-All system would of course enjoy huge economy of scale advantages.

Medicaid also has to bear the cost of assessing whether eligibility requirements have been met, and certain states are indeed now making those eligibility requirements even more burdensome and complex (in the apparent hope of reducing enrollment).  Most recently, the Trump administration in early 2018 issued new rules allowing states to impose work requirements on those enrolled in Medicaid, and several states have now started to impose such restrictions.  But such requirements are themselves costly to assess.  While enrollment in Medicaid may then fall (leading to the health care costs of those individuals being shifted on to someone else), administrative costs as a share of what is spent will rise.  But from the point of view of society as a whole, shifting the cost of health treatment for those individuals who would otherwise be enrolled in Medicare on to someone else does not save on the overall cost of health care.  And indeed, if it shifts such treatment from doctor’s offices to treatment in emergency rooms, the cost will go up, and probably by a lot.

This would no longer be an issue in a Medicare-for-All system.  There would be no need to waste funds on assessing whether the individual meets the eligibility requirements of some specific health insurance program or another.

Despite such special costs. the overall costs of administration for Medicaid were 10.7% in 2016.  This is a bit below the cost for regular private insurance of 11.5%, and probably reflects the significant economies of scale Medicaid is able to benefit from.  And while a significant share of the Medicaid administrative costs are incurred by private insurers contracted to manage the Medicaid programs in many of the states ($36 billion of the $60 billion total for administration according to the NHE figures), government itself takes on a significant share of the administration.  And the overall administrative cost combined is still less than what private health insurance requires (as a share).

b)  The Cost of Administering Private Health Insurance

Which brings us to the question of why private health insurance costs so much to administer, at 11.5% of the total paid for such insurance.  Medicare, when administered directly by government, has a cost of just 2.4%.  Why does private insurance cost so much more?

First, a note on terminology.  Up to this point, as we have discussed various government health insurance plans (such as Medicaid or CHIP), we have not had to distinguish the total cost of the health insurance plans (the total of what is paid out in benefits to health care providers, plus what is paid for administration) from the total paid for the insurance cover.  We need to be more precise for private insurance cover.  One has the total paid in any period (a year in these figures) in insurance premia by the subscribers, and the total in what is paid by the private insurer in each such period to cover benefits.  The NHE has estimates for each of these, and then calculates the difference between the two as the “net cost of health insurance”.  We have referred to this as a broad concept of administrative costs, as it includes any profits earned by the insurers as part of their current operations.  But private insurers have an additional source of earnings, and that is from revenues on invested capital.  Premia are paid upfront and benefits paid out later (in overall probabilistic terms), and an important source of income to insurers comes from what they earn on those funds as they are invested in various asset markets, such as stocks and bonds, real estate, commodities, and so on.

For private insurance we should therefore be clear that what we have so far referred to as the “total cost” of the health insurance is synonymous with the total premia paid (which some sources refer to as “underwriting revenue”).  Subtracting the total paid to health care providers under the insurance policies from the total paid in premia will then lead to the broad concept of administrative costs, including profits earned from the current period insurance operations.  On top of this, private insurers will generate earnings from investments on their accumulated capital (obtained, in part, from premia being paid in before benefits are paid out).  For the figures here we are excluding these latter earnings.  Such earnings will be on top of those obtained from their current insurance operations.

Why then, do private insurers incur administrative costs (as defined here) of 11.5% when government-administered Medicare has a far lower cost of just 2.4%?

There are a number of reasons.  First, private health insurance is a tremendously fragmented system, where health plans are mostly organized at the individual firm level.  This is costly, and the cost share varies systematically by firm size.  Administrative costs (including insurer profits) will typically range between 5 and 15% of the total paid for the insurance in firms with greater than 50 employees, between 15 and 25% in firms with fewer than 50 employees, and (in the period before the Obamacare market exchanges were set up) between 25 and 40% of the total for individuals seeking health insurance (see, for example, this report from the Commonwealth Fund).

These high and rising costs (in inverse direction to firm size) arise as there are significant fixed costs in setting up any such system at some firm, which leads to a high cost-share when there are fewer workers to spread it over.  Commissions paid to insurance brokers also play a role, as the use of brokers is typical and especially significant for the small-group market.  The Commonwealth Fund report cites figures indicating these commissions can account for 4 to 11% of the total in premia paid for insurance in such markets.  And in those cases where the insurers themselves take on the risk (as opposed to simply managing the claims process while the firm itself pays the claims – this is called “self-insurance”, and is typical in large firms with 1,000 employees or more, as it ends up cheaper for such firms), the insurers must then invest significant resources in assessing the risk of the pool of workers covered in order to price the policy appropriately.  The costs the insurance company will need to pay out will depend not only on the local cost of health care services (which can vary tremendously across different parts of the country), but also by the industry of the firm (as the health risks of the typical workers employed will vary by industry) and specifics of the firm being covered (such as the average age of the workers employed, the male/female ratio, and other such factors).

There are also high fixed costs of the insurers themselves under their business model.  They typically offer dozens of insurance plans, each with different features on what is covered and by how much.  And most of the plans are built around networks of care providers (doctors, hospitals, and so on) with whom they have individually negotiated “in-network” prices for subscribers of the particular health insurance plan.  These in-network prices can still vary tremendously (even by a factor of ten or more, for those I have been able to check with my own insurer, and all for the same metropolitan area), and are set through some negotiation process.  The price eventually agreed to reflects some balance in negotiating strength.  If you are a hospital chain that dominates in some metro area, you will be able to negotiate a price close to what you wish to charge as the insurer has to include hospital services.  Similarly, if you are an insurance company that dominates in some metro area, then the hospitals have to agree to charge something close to what you are willing to pay, as otherwise they will not have many patients.  And individual doctors operating in private practices will generally have very little negotiating power.

But such negotiations (for each and every health care provider, and then for each possible service) are expensive to carry out, regardless of the outcome.  And while some argue that such negotiations hold down the cost of health care, it is not at all clear that such is the case.  The US, after all and as noted before, has by far the most expensive health care services in the world (close to double the average in OECD countries, as a share of GDP), and yet achieves only mediocre results.  Furthermore, the actual volume of health care services provided in the US (as measured, for example, by doctor consultations per capita per year, or hospital beds per 1,000 of population, and so on) has the US at close to the bottom among OECD countries.  The problem is not excessive health care services utilized, but rather their high cost in the US.  Negotiated in-network pricing has not helped, and quite possibly (due to the resulting fragmentation into non-competing markets) has hurt.

This complex and fragmented system does lead, however, to high rewards to those who are good at operating in it.  Hence CEOs (and other senior staff) of insurance companies skilled at this are rewarded handsomely, with such CEOs typically receiving compensation of more than $10 million a year, and in some cases far more.  Indeed, as recounted in an earlier blog post, the CEO of UnitedHealth Care personally received total compensation of more than $1.3 billion over his 15-year tenure of 1991 to 2006 (even after the SEC forced him to forfeit stock options worth a further $620 million due to illegalities in how they were priced).  Such salaries are reflected in the administrative costs of the health insurance plans offered, and account for a substantial share of it.

Finally, this complex and fragmented market has also led to high profits for the private health insurance companies.  If this were due to the exceptional efficiency of certain of the health insurance firms as compared to others, all in a competitive market, then such high profits of such firms might be explained.  But there is no indication that health insurance markets operate anywhere close to what economists would call “perfect competition”.  The extremely wide variation in prices for the same health care services (often by a factor of ten or more) is a clear sign of markets that are nowhere close to perfectly competitive.

And the amount paid to cover such profits is high.  For example, an examination of health insurance markets in New York State found (in data for 2006) that profits from underwriting (i.e. excluding profits from capital invested) accounted for 4.9% of total underwriting revenue (the total premia paid) before taxes, or roughly one-third of the total 14.9% in administrative costs (including underwriting profits).  After taxes, it would be roughly one-quarter of the total.  Applying that ratio to the 11.5% administrative cost share found in the NHE accounts for the nation as a whole in 2016, the charge to cover profits would be close to 3% points.  That, by itself, would be greater than the 2.4% cost share for government-managed Medicare.

c)  The Cost of Administering the Portion of Medicare Managed Directly by Government

Why, then, does the portion of Medicare (Parts A and B) managed directly by government cost so little?  It is fundamentally because Medicare does not bear many of the costs discussed above for the other insurance plans, and can spread the costs that remain over a far larger enrollment base.  Specifically:

1)  Medicare enjoys huge economy of scale advantages:  The portion of Medicare managed directly by government is huge, at $390.7 billion spent in 2016 ($381.4 billion of which went to health care providers, and only $9.3 billion to administration).  And this is for a single plan.  Private health insurers instead each manage dozens of plans covering millions of firms (at rates which vary firm to firm, depending on the risk pool).

2)  Medicare does not have to make a determination for each individual claim as to whether it will be covered (as Workers’ Compensation plans must), nor whether the individual is eligible (other than whether they are of age 65 or more, and have paid the relevant premia and taxes).  That is, Medicare does not need to contend with the complex (and now being made increasingly complex) eligibility requirements for participants in Medicaid, CHIP, and other such programs.

3)  Medicare has one set of compensation rates, which doctors and hospitals accept or not.  The compensation rates vary by region and other such factors, but they are not individually negotiated each year with each of the possible providers.

4)  And Medicare does not have the costs private insurers need to pay to retain the CEOs and other senior staff who are skilled at operating within the fragmented US healthcare system, nor do they pay large amounts for marketing and such.  Nor does Medicare pay profits, and profits, as noted above, are high for private health insurers in the US.

It is this “business model” of Medicare which keeps its costs down.  It is a relatively simple model (relative to that of private insurers – no health care payment system is simple in an absolute sense), and enjoys great economies of scale.  Thus Medicare can keep its costs down, and needs to spend on administration only a fraction of what private health insurers spend.

d)  The Conservative Critics of Medicare Costs

There are critics who contend that Medicare costs are not in fact low.  These critics have issued analyses through such groups as the Heritage Foundation (conservative, with major funding from the Koch brothers), the Cato Institute (conservative – libertarian), lobby groups with a vested interest, and publications that link back to these analyses.  But these arguments are flawed.  Indeed, some of the responses to the assertions are so obvious that one must assume that ideology (a view that it is impossible for government to be more efficient) was the primary driver.

These critics make three primary arguments:

1)  First, several contend that Medicare does not pay for, nor include in its recorded administrative costs, the costs incurred by Social Security and other government agencies that provide services that are essential to Medicare’s operations.  For example, initial enrollment in Medicare at age 65 is handled through the Social Security Administration, and Medicare premia payments (for Parts B and D) are normally collected out of Social Security checks.

However, while it is true that Social Security provides such services to Medicare, it is not true that Medicare does not pay for this.  A simple look at the Medicare income and expenses tables in the Medicare Trustees Annual Report will show what those payments are.  For example, for fiscal year 2017, Tables V.H1 and V.H.2 (on pages 217 and 218 of the 2018 report) indicate that $980,805,000 was paid to the Social Security Administration under the Medicare HI Trust Fund (“Hospital Insurance”, for Part A) and $1,247,226,000 under the Medicare SMI Trust Fund (“Supplementary Medical Insurance”, for Parts B and D).  These are substantial amounts, and they are not hidden.

And the tables similarly show the amounts paid by Medicare (as components in its administrative costs) to other government agencies for services they provide to Medicare.  These include payments made to the FBI and the Department of Justice (for fraud and abuse control), to the Office of the Secretary of Health and Human Services (HHS, for oversight) as well as to other HHS offices (such as the Inspector General), to the US Treasury, and to a number of others.  They are all shown.  The conservative critics who assert Medicare expenses do not include payments for such services simply never looked.

2)  Second, the critics argue that while private insurers must raise the capital they need to fund their operations, and that that capital has a cost, the costs of funding Medicare’s “capital” are not counted but rather are hidden away in the overall government budget.

But this reflects a fundamental confusion on the capital requirements of established insurers, whether private or public.  Insurers are not banks.  Banks raise funds (at a cost) and then lends them out.  Insurers take in premia payments from those insured, and at some later time make payments out under the insurance policies for covered costs.  On average, the payments they make come later than the payments they receive in premia, and hence they have capital to invest.  That capital is invested in stocks and bonds, real estate, commodities, or whatever, they make a return on those investments, and that return is factored into, and can reduce (not raise), the premia they need to charge to cover their overall costs.

Private insurers hence generate earnings from their capital, as it is invested as an asset.  It is not a cost.  Furthermore, Medicare operates in fundamentally the same way as other insurers.  The Medicare Trust Funds (HI and SMI) reflect funds that have been paid in and not yet expended in covered claims or other expenses, and they earn interest on the balances in those trust funds (at the long-term US Treasury bond rate).  The accounting is all there to be seen, for those interested, in the Medicare Trustees Annual Report.

3)  Probably most importantly, the conservative critics of Medicare assert that it is incorrect to calculate administrative costs as a share of the total costs paid.  Rather, they say those costs should be calculated per person enrolled.  Since older people have far higher medical costs each year than younger people do (which is certainly true), they argue that the low administrative cost share seen in Medicare (when taken as a share of total costs) is actually a reflection of the high health care costs of the elderly.

But there are two problems with this.  First, when elderly people see doctors at a pace of say 10 times a year rather than perhaps once a year when younger, they will be generating 10 times as many bills that need to be recorded and properly paid.  Each bill must go through the system, checked for possible fraud, and then paid in the correct amount.  That will cost more, indeed one should expect it will cost 10 times as much.  And if anything, medical procedures are more complicated for the elderly (as they have more complicated medical conditions), so it should be expected that the costs to process the more complex bills will indeed go up more than in proportion to the amount spent.  The conservative critics assert the costs of administering this do not go up with the more frequent billing, but rather are the same, flat, rate per person regardless of how many, how complex, and how costly the medical interventions are that they have in any given year.

Second, one has data.  The Medicare Parts C (Medicare Advantage) and D (for drugs) are managed via private health insurers.  And this Medicare is for the same elderly population that government-managed Medicare covers.  If what the conservative critics assert is correct, then the cost of administering these privately-managed Medicare programs should be similar to the cost of administering the portion of Medicare that government manages directly.  But this is not the case.  Government-managed Medicare spent only 2.4% on administration in 2016, while privately-managed Medicare spent 12.6%.  These are far from the same.

Indeed, the 12.6% administrative cost share for the privately-managed portion of Medicare is similar to, but a bit more than, the 11.5% share seen with regular private health insurance.  This is what one would expect, where the somewhat higher cost share might well be because of the greater complexity of the medical interventions required for the elderly population.

The government-managed portion of Medicare has a far low administrative cost share than private health insurance.  The conservative critics have not looked at the data.

Taxes on Corporate Profits Have Collapsed

A.  Introduction:  The Plunge in Corporate Profit Tax Revenues

Corporate profit tax revenues have collapsed following the passage by Congress last December of the Trump-endorsed Republican tax plan.  And this is not because corporate profits have decreased:  They have kept going up.  The initial figures, for the first half of 2018, show federal corporate profit taxes (also referred to as corporate income taxes) collected have fallen to an annual rate of roughly just $150 billion.  This is only half, or less, of the $300 to $350 billion collected (at annual rates) over the past several years.  See the chart above.

The estimates on corporate profit taxes actually being paid through the first half of 2018 come from the National Income and Product Accounts (NIPA, and commonly also referred to as the GDP accounts) produced by the Bureau of Economic Analysis.  The figures are collected as part of the process of producing the GDP accounts, but for various reasons the figures on corporate profit taxes are not released with the initial GDP estimates (which come out at the end of the month that follows the end of each quarter), but rather one month later (i.e. on August 29 this time, for the estimates for the April to June quarter).  The quarterly estimates are seasonally adjusted (which is important, as tax payments have a strong seasonality to them), and are then shown at annual rates.  While we already saw such a collapse in corporate tax revenues in the figures for the first quarter of 2018 (first published in May), it is always best with the estimates of GDP and its components to wait until a second quarter’s figures are available to see whether any change is confirmed.  And it was.

This initial data on what is actually now being collected in taxes following the passage of the Republican tax plan last December suggests that the revenue losses will be substantially higher than the $1.5 trillion over ten years that the staff at the Joint Committee on Taxation (the official arbiters for Congress on such matters) forecast.  Indeed, the plunge in corporate profit tax collections alone looks likely to well exceed this.  On top of this, there were also sharp cuts in non-corporate business taxes and in income taxes for those in higher income groups.

This blog post will look at what the initial figures are revealing on the tax revenues being collected, as estimated in the GDP accounts.  The focus will be on corporate income taxes, although in looking at the total tax revenue losses we will also look briefly at what the initial data is indicating on reductions in individual income taxes being paid.

The chart above shows what the reduction has been in corporate profit taxes in dollar terms.  In the next section below we will look at this in terms of the taxes as a share of corporate profits.  That implicit average actual tax rate is more meaningful for comparisons over time, and it has also plunged.  And the implicit actual rate now being paid, of only about 7% for the taxes at the federal government level, shows how misleading it is to focus on the headline rate of tax on corporate profits of 21% (down from 35% before the new law).  The actual rate being paid is only one-third of this, as a consequence of the numerous loopholes built into the law.  The Republican proponents of the bill had argued that while they were cutting the headline rate from 35% to 21%, they were also (they asserted) ending many of the loopholes which allowed corporations to pay less.  But in fact numerous loopholes were added or expanded.

The next section of the post will then look at this in the longer term context, with figures on the implicit corporate profit tax rate going back to 1950.  The implicit rate has fallen steadily over time, from a rate that reached over 50% in the early 1950s, to just 7% now.  While Trump and his Republican colleagues argued the cut in corporate taxes was necessary in order for the economy to grow, the economy in fact grew at a faster pace in the 1950s and 1960s, when the rate paid varied between 30 and 50%, than it has in recent decades despite the now far lower rates corporations face.

But this is for the federal tax on corporate profits alone.  There are also taxes on corporate profits imposed at the state and local level, as well as by foreign governments (although such foreign taxes are then generally deductible from the taxes due domestically).  This overall tax burden is more meaningful for understanding whether the overall burden is too high.  But, as we shall see below, that rate has also fallen steadily over time.  There is again no evidence that lower rates lead to higher growth.

The final substantive section of the post will then look more closely at the magnitude of the revenue losses from the December bill.  They are massive, and based on the initial evidence could very well total over $2 trillion over ten years for the losses on the corporate profit tax alone.  The losses from the other tax cuts in the new law, primarily for the wealthy and for non-corporate business, will add to this.  A very rough estimate is that the losses in individual income tax revenues may total an additional $1 trillion, bringing the total to over $3 trillion.  This is double the $1.5 trillion loss in revenues originally forecast.

But first, an analysis of what we see from the initial evidence on what is being paid.

B.  Profit Taxes as a Share of Corporate Profits

The chart at the top of this post shows what has been collected, by quarter (but shown at an annual rate), by the federal tax on corporate profits over the last several years.  Those figures are in dollars, and show a fall in the first half of 2018 of a half or more compared to what was collected in recent years.  But for comparisons over time, it is more meaningful to look at the implicit corporate tax rate, as corporate profits change over time (and generally grow over time).  And this can be done as the National Income and Product Accounts include an estimate of what corporate profits have been, as part of its assessment of how national income is distributed among the major functional groups.

That share since 2013 has been:

Between 2013 and 2016, the implicit rate (quarter by quarter) varied between about 15 and 17%.  It came down to about 14% for most of 2017 for some reason (possibly tied to the change in administration in Washington, with its new interpretation of regulatory and tax rules), but one cannot know from the aggregate figures alone.  But the rate then fell sharply, by half, to just 7% after the new tax law entered into effect.

A point to note is that the corporate profit figures provided here are corporate profits as estimated in the National Income and Product Accounts.  They are a measure of what corporate profits actually are, in an economic sense, and will in general differ from what corporate profits are as defined for tax purposes.  Thus, for example, accelerated depreciation allowed for tax purposes will reduce taxable corporate profits.  But the BEA estimates for the NIPA accounts will reflect not the accelerated depreciation allowed for tax purposes, but rather an estimate of what depreciation actually was.  Thus the figures as shown in the chart above will be a measure of what the true average corporate tax rate actually was, before the adjustments made (as permitted under tax law) to arrive at taxable corporate profits.

That average rate is now just 7%.  That is only one-third of the headline rate under the new law of 21%.  Provisions in the tax code allow corporations to pay far less in tax than what the headline rate would suggest.  This is not new (the headline rate previously was 35%, but the actual average rate paid was just 15 to 17% between 2013 and 2016, and 14% in most of 2017).  But Trump administration officials had asserted that many of the loopholes allowing for lower taxes would be ended under the new tax law, so that the actual rate paid would be closer to the headline rate.  But this clearly did not happen.  As many independent analysts pointed out before the bill was passed, the new tax law had numerous provisions which would allow the system to be gamed.  And we now see the result of that in the figures.

C.  Corporate Taxes in a Longer Term Context

The cuts in corporate profit taxes are not new.  Taxes on corporate profits in the US used to be far higher:

In the early 1950s, the federal tax on corporate profits (actually paid, not the headline rate) reached over 50%.  While it then fell, it kept to a rate of between about 30% and 50% through the 1950s and 60s.  And this was a period of good economic growth in the US – substantially faster than it has been since.  A high tax rate on corporate profits did not block growth.  Indeed, if one looked just at the simple correlation, one might conclude that a higher tax on corporate profits acts as a spur to growth.  But this would be too simplistic, and I would not argue that.  But what one can safely conclude is that a high rate of tax on corporate profits does not act as a block to more rapid growth.

There have also been important distributional consequences, however.  Corporate wealth is primarily owned by the wealthy (duh), and the sharp decline in taxes paid on corporate profits means that a larger share of the overall tax burden has been shifted to taxes on individual incomes, which are primarily borne by the middle classes.  Based on figures in the NIPA accounts, in 1950 taxes on individual incomes (including Social Security taxes) accounted for 47% of total federal taxes, while taxes on corporate profits accounted for 35% (with the rest primarily various excise taxes such as on fuels, liquor, tobacco, etc., plus import duties).  By 2017, however, the share of taxes on individual incomes had grown to 87.4%, while the share on corporate profits had declined to just 8.6%.  There was a gigantic shift away from taxes on wealth to taxes on individual incomes – taxes that are borne primarily by the middle class.  And that share will now fall further in 2018, by about half.

The chart above is for federal corporate profit taxes alone.  It could be argued that what matters to growth is not just the corporate profit taxes paid at the federal level, but all such taxes, including those paid at the state and local level, as well as to foreign governments (although the taxes paid abroad are generally deductible on their domestic taxes, so that will be a wash).

That chart looks like:

This follows the same path as the chart for federal corporate profit taxes alone, with a similar decline.  With the federal share of such taxes averaging 84% over the period (up to 2017), this is not surprising.  The federal share will now fall sharply in 2018, due to the new tax law.  But over the 1950 to 2017 period, the chart covering all taxes on corporate profits is basically a close to proportionate increase over what the tax has been at the federal level alone.

So the same pattern holds, and the total of the taxes on corporate profits varied between 33% to over 50% in the 1950s and 60s, to between 15 and 20% in recent years before the plunge in the first half of 2018 to just 10%.  But the relatively high taxes in the 1950s and 60s did not lead to slow growth in those years, nor did the low taxes in recent decades lead to more rapid growth.  Rather, one had the reverse.

D.  An Estimate of the Revenue Losses Due to the Tax Bill

These initial figures on the taxes actually being paid following the passage of the Republican tax bill allow us to make an estimate of what the revenue losses will turn out to be.  These will be very rough estimates, as we only have data for half a year, and one should be cautious in extrapolating this to what the losses will be over a decade.  But they can give us a sense of the magnitude.  And it is large.  As we will see below, based on the evidence so far the revenue losses (from the cuts in both corporate taxes and in personal income taxes) might be over $3 trillion over ten years, or about double the $1.5 trillion loss estimate originally forecast.

First, for the federal taxes on corporate profits, as the largest changes are there:  As was discussed before (and seen in the charts above), corporate profit taxes paid as a share of corporate profits were relatively flat between 2013 and 2016, varying between 15 and 17% each quarter, before falling to 14% for most of 2017.  For the full 2013 to 2017 period, the simple average was 15.3%.  The implicit rate then fell to just 7.0% in the first half of 2018.  Had the rate instead remained at 15.3%, corporate profit taxes collected in 2018 would have been $184 billion higher (on an annual basis).

This is not small, and is twice as high as the estimate of the staff of the Joint Committee on Taxation of revenue losses of $91 billion in FY2019 (the first full year under the new tax regime) from all the tax measures affecting businesses (including non-corporate businesses, and covering both domestic business and overseas business).  It is three times as high as the estimated loss of $60 billion in FY18, but the new tax law did not affect the first quarter of FY2018 (October to December).

One should be cautious with any extrapolation of this loss estimate going forward, as not only is the time period of actual experience under the new tax regime short (only a half year), but the law is also a complicated one, with certain provisions changing over time.  But a simple extrapolation over ten years, based on the assumption that corporate profits grow at just a modest 3% a year in nominal terms (meaning 1% a year in real terms if inflation is 2% a year), and that the tax rate on corporate profits will be 7.0% a year (as seen so far in 2018) rather than the 15.3% of recent years, implies that the reduction in corporate profit tax revenues will sum to about $2.1 trillion.

Note that the losses would be greater (everything else equal) if the assumed growth rate of corporate profits is higher.  But the results are not very sensitive to this.  The total losses over ten years would be $2.2 trillion, for example, at an assumed nominal growth rate of 4% (i.e. with inflation still at 2%, then with corporate profits growing at 2% a year in real terms, or double the 1% rate of the base scenario).  Note this also counters the argument of some that such tax cuts will lead to such a large spurt in growth that total tax collections will rise despite the cut in the rates.  As will be discussed below, there is no evidence that this has ever been the case in the US.  But even assuming there were, the argument is undermined by the basic arithmetic.  In the example here, a doubling of the assumed growth rate of profits (from 1% in real terms to 2%) would imply taxes on corporate profits would still fall by $2.0 trillion over the next ten years.  This is not far from the $2.1 trillion loss if there is no rise at all in the growth of corporate profits.  And a doubling of the real growth rate is far above what anyone would reasonably assume could follow from such a cut in the tax rate.

Second, there were also substantial cuts in individual income taxes, although primarily for the wealthy.  While far less in proportional terms, the substantially higher taxes that are now paid by individuals than by corporations means that this is also significant for the totals.

Specifically, individual (federal) income taxes as a share of GDP in the NIPA accounts were quite steady in the quarterly GDP accounts for the period from 2015Q1 to 2017Q4, varying only between 8.22% and 8.44%.  The average was 8.31%.  But then this fell to an average of 7.89% in the first half of 2018 (7.90% in the first quarter, and 7.87% in the second quarter).  Had the rate remained at 8.31%, then $86 billion more in revenues (at an annual rate) would have been collected.

Extrapolating this out for ten years, assuming again just a modest rate of growth for GDP of 3% a year in nominal terms (i.e. just 1% a year in real terms if inflation is 2% a year), the total loss would be $1.0 trillion.  With a higher rate of growth, and everything else the same, the losses would again be larger.  This extrapolation is, however, particularly fraught, as the Republicans wrote into their bill that the cuts in individual taxes would be reversed in 2026.  They did this to keep the forecast cost of the tax bill to the $1.5 trillion envelope they had set, and an effort is already underway to make this permanent (Speaker Paul Ryan has said he will schedule a vote on this in September).  But even if we left out the tax revenue losses in the final two years of the period, the losses in individual taxes would still reach about $0.8 trillion.

Adding the lower revenues from the taxes on corporate profits and the taxes on individual incomes, the total revenue losses would come, over the ten years, to about $3 trillion.  This is double the $1.5 trillion loss that had been forecast.  It is not a small difference.

To give a sense of the magnitude, the loss in 2018 alone (a total of $270 billion) would allow a doubling of the entire budgets (based on FY2017 actual outlays) of the Departments of Education, Housing and Urban Development, and Labor; the Environmental Protection Agency; all international assistance programs (foreign aid); NASA; the National Science Foundation; the Army Corps of Engineers (civil works); and the Small Business Administration.  Note I am not arguing that all of their budgets should necessarily be doubled (although many should, indeed, be significantly increased).  Rather, the point is simply to give readers a sense of the size of the revenues lost as a consequence of the tax cut bill.

As another comparison to give a sense of the magnitude, just half of the lost revenue (now and into the future) would suffice to fund fully the Social Security Trust Fund for the foreseeable future.  If nothing is done, the Social Security Trust Fund will run out at some point around 2034.  Republicans have asserted that nothing can be done for Social Security except to scale back (already low) Social Security pensions.  This is not true.  Just half of the revenues that will be lost under the tax cut bill would suffice to ensure the pensions can be paid in full for at least 75 years (the forecast period used by the Social Security trustees).

But as noted above, proponents of the tax cuts argue that the lower taxes will spur growth.  This has been discussed in earlier posts on this blog, where we have seen that there is no evidence that this will follow.  There are not only basic conceptual problems with this argument (a misreading of basic economics), but also no indication in what we have in fact observed for the economy that this has ever been the case (whether in the years immediately following the major tax cuts of Reagan or Bush, nor if one focuses on the longer term).

Administration officials have not surprisingly argued that the relatively rapid pace of growth in the second quarter of 2018 (of 4.2% at an annual rate in the end-August BEA estimates) is evidence of the tax cut working as intended.  But it is not.  Not only should one not place much weight on one quarter’s figures (the quarterly figures bounce around), but this followed first-quarter figures which were modest at best (with GDP growth of an estimated 2.2% at an annual rate).

But more fundamentally, one should dig into the GDP figures to see what is going on.  The argument that tax cuts (especially cuts in corporate profit taxes) will spur growth is based on the presumption that such cuts will spur business investment.  More such investment, especially in equipment, could lead to higher productivity and hence higher growth.  But growth in business investment in equipment has slowed in the first half of 2018.  Such investment grew at the rates of 9.1%, 9.7%, 9.8%, and 9.9% through the four quarters of 2017 (all at annual rates).  It then decelerated to a pace of 8.5% in the first quarter of 2018 and to a pace of 4.4% in the second quarter.  While still early (these figures too bounce around a good deal), the evidence so far is the exact opposite of what proponents have argued the tax cut bill would do.

So what might be going on?  As noted before, there is first of all a good deal of volatility in the quarterly figures for GDP growth.  But to the extent growth has accelerated this year, a more likely explanation is simple Keynesian stimulus.  Taxes were cut, and while most of the cuts went to the rich, some did go to the lower and middle classes.  In addition, government spending is now rising, while it been kept flat or falling for most of the Obama years (since 2010).  It is not surprising that such stimulus would spur growth in the short run.

The problem is that with the economy now running at or close to full capacity, such stimulus will not last for long.  And when it was needed, in the years from 2010 until 2016, as the economy recovered from the 2008/09 downturn (but slowly), such stimulus measures were repeatedly blocked by a Republican-controlled Congress.  This sequence for fiscal policy is the exact opposite of the path that should have been followed.  Contractionary policies were followed after 2010 when unemployment was still high, while expansionary fiscal policies are being followed now, when unemployment is low.  The result is that the fiscal deficit is rising soon to exceed $1 trillion in a year (5% of GDP), which is unprecedented for a period with the economy at full employment.

E.  Conclusion

We now have initial figures on what is being collected in taxes following the tax cut bill of last December.  While still early, the figures for the first two quarters of 2018 are nonetheless clear for corporate profit taxes:  They have fallen by half.  Corporate profit taxes paid would be an estimated $184 billion higher in 2018 had the tax rate remained at the level it had been over the last several years.

While this post has not focused on personal income taxes, they too were cut.  The reduction here was more modest – only by about 5% overall (although certain groups got far more, while others less).  But with their greater importance in overall federal tax collections, this 5% reduction is leading to an estimated $86 billion reduction in revenues (in 2018) from this source.

Based on what has been observed in the first two quarters of 2018, the two taxes together (corporate and individual) will see a combined reduction in taxes paid of about $270 billion in 2018.  Extrapolating over ten years, the combined losses may be on the order of $3 trillion.

These losses are huge.  And they are double what had been earlier forecast for the tax bill.  Just half of what is being lost would suffice to ensure Social Security would be fully funded for the foreseeable future.  And the rest could fund programs to rebuild and strengthen the physical infrastructure and human capital on which growth ultimately depends.  Or some could be used to reduce the deficit and pay down the public debt.  But instead, massive tax cuts are going to the rich.

Why Do the Quarterly GDP Figures Bounce Around So Much?: Econ 101

A.  Introduction

The Bureau of Economic Analysis (BEA) released on July 27 its initial estimate of GDP growth in the second quarter of 2018 (what it technically calls its “advance estimate”).  It was a good report:  Its initial estimate is that GDP grew at an annualized rate of 4.1% in real terms in the quarter.  Such growth, if sustained, would be excellent.

But as many analysts noted, there are good reasons to believe that such a growth rate will not be sustained.  There were special, one-time, factors, such as that the second quarter growth (at a 4.1% annual rate) had followed a relatively modest rate of growth in the first quarter of 2.2%.  Taking the two together, the growth was a good, but not outstanding, rate of 3.1%.

More fundamentally, with the economy now at full employment, few (other than Trump) expect growth at a sustained rate of 4% or more.  Federal Reserve Board members, for example, on average expect GDP growth of 2.8% in 2018 as a whole, with this coming down to a rate of 1.8% in the longer run.  And the Congression Budget Office (in forecasts published in April) is forecasting GDP growth of 3.0% in 2018, coming down to an average rate of 1.8% over 2018 to 2028.  The fundamental issue is that the population is aging, so the growth rate of the labor force is slowing.  As discussed in an earlier post on this blog, unless the productivity of those workers started to grow at an unprecedented rate (faster than has ever been achieved in the post-World War II period), we cannot expect GDP to grow for a sustained period going forward at a rate of 3%, much less 4%.

But there will be quarter to quarter fluctuations.  As seen in the chart at the top of this post covering the period just since 2006, there have been a number of quarters in recent years where GDP grew at an annualized rate of 4% or more.  Indeed, growth reached 5.1% in the second quarter of 2012, with this followed by an also high 4.9% rate in the next quarter.  But it then came back down.  And there were also two quarters (setting aside the period of the 2008/09 recession) which had growth of a negative 1.0%.  On average, GDP growth was around 2% (at an annual rate) during Obama’s two terms in office (2.2% annually from the end of the recession in mid-2009).

Seen in this context, the 4.1% rate in the initial estimate for the second quarter of 2018 was not special.  There have been a number of such cases (and with even substantially higher growth rates for a quarter or even two) in the recent past, even though average growth was just half that.  The quarterly rates bounce around.  But it is of interest to examine why they bounce around so much, and that is the purpose of this blog post.

B.  Reasons for this Volatility

There are a number of reasons why one should not be surprised that these quarter to quarter growth rates in GDP vary as they do.  I will present several here.  And note that these reasons are not mutually exclusive.  Several of them could be acting together and be significant factors in any given quarter.

a)  There may have been actual changes in growth:

To start, and to be complete, one should not exclude the possibility that the growth in the quarter (or the lack of it) was genuine.  Perhaps output did speed up (or slow down) as estimated.  Car plants might go on extra shifts (or close for a period) due to consumers wanting to buy more cars (or fewer cars) in the period for some reason.  There might also be some policy change that might temporarily spur production (or the opposite).  For example, Trump’s recent trade measures, and the response to them from our trading partners, may have brought forward production and trade that would have been undertaken later in the year, in order to avoid tariffs threatened to be imposed later.  This could change quarterly GDP even though GDP for the year as a whole will not be affected positively (indeed the overall impact would likely be negative).

[Side note:  But one special factor in this past quarter, cited in numerous news reports (see, for example, here, here, here, here, and here), was that a jump in exports of soybeans was a key reason for the higher-than-recently-achieved rate of GDP growth.  This was not correct.  Soybean exports did indeed rise sharply, with this attributed to the response threatened by China and others to the new tariffs Trump has imposed.  China and others said they would respond with higher tariffs of their own, targeted on products such as soybeans coming from the US.  There was thus a rush to export soybeans in the period between when China first announced they would impose such retaliatory tariffs (in late March) and when they were then imposed (ultimately on July 6).

But while soybean exports did indeed increase sharply in the April to June quarter, soybeans are a crop that takes many months to grow.  Whatever increase in shipments there was had to come out of inventories.  An increase in exports would have to be matched by a similar decrease in inventories, with this true also for corn and other such crops.  There would be a similar issue for any increase in exports of Kentucky bourbon, also a target of retaliatory tariffs.  Any decent bourbon is aged for at least three years.

One must keep in mind that GDP (Gross Domestic Product) is a measure of production, and the only production that might have followed from the increased exports of soybeans or similar products would have been of packing and shipping services.  But packing and shipping costs are only a relatively small share of the total value of the products being exported.

Having said that, one should not then go to the opposite extreme and assume that the threatened trade war had no impact on production and hence GDP in the quarter.  It probably did.  With tariffs and then retaliatory tariffs being threatened (but to be imposed two or three months in the future), there were probably increased factory orders to make and ship various goods before such new tariffs would enter into effect.  Thus there likely was some impact on GDP, but to an extent that cannot be quantified in what we see in the national level accounts.  And with such factory orders simply bringing forward orders that likely would have been made later in the year, one may well see a fallback in the pace of GDP growth in the remainder of the year.  But there are many other factors as well affecting GDP growth, and we will need to wait and see what the net impact will be.]

So one should not exclude the possibility that the fluctuation in the quarterly growth rate is real.  But it could be due to many other factors as well, as we will discuss below.

b)  Change at an Annualized Rate is Not the Change in a Quarter:

While easily confused, keep in mind also that in the accounts as normally published and presented in the US, the rates of growth of GDP (and of the other economic variables) are shown as annual equivalent rates.  The actual change in the quarter is only about one-fourth of this (a bit less due to compounding).  That is, in the second quarter of 2018, the BEA estimated that GDP (on a seasonally adjusted basis, which I will discuss below as a separate factor) grew by 1.00% (and yes, exactly 1.00% within two significant digits).  But at an annualized rate (some say “annual rate”, and either term can be used), this would imply a rate of growth of 4.06% (which rounded becomes 4.1%).  It is equal to slightly more than 4.0% due to compounding.  [Technically, 1% growth in the quarter means 1.00 will grow to 1.01, and taking 1.01 to the fourth power yields 1.0406, or an increase of 4.06%.]

Thus it is not correct to say that “GDP grew by 4.1% in the second quarter”.  It did not – it grew by 1.0%.  What is correct is to say that “GDP grew at an annualized rate of 4.1% in the second quarter”.

Not all national statistical agencies present such figures in annualized terms.  European agencies, for example, generally present the quarterly growth figures as simply the growth in the quarter.  Thus, for example, Eurostat on June 7 announced that GDP in the eurozone rose by an estimated 0.4% in the first quarter of 2018.  This 0.4% was the growth in the quarter.  But that 0.4% growth figure would be equivalent to growth of 1.6% on an annualized basis (actually 1.61%, if the growth had been precisely 0.400%).  Furthermore, the European agencies will generally also focus on the growth in GDP over what it had been a year earlier in that same quarter.  In the first quarter of 2018, this growth over the year-earlier period was an estimated 2.5% according to the Eurostat release.  But the growth since the year-earlier period is not the same as the growth in the current quarter at an annualized rate.  They can easily be confused if one is not aware of the conventions used by the different agencies.

c)  Don’t confuse the level of GDP with the change in GDP:

Also along the lines of how we might misleadingly interpret figures, one needs to keep in mind that while the quarterly growth rates can, and do, bounce around a lot, the underlying levels of GDP are really not changing much.  While a 4% annual growth rate is four times as high as a 1% growth rate, for example, the underlying level of GDP in one calendar quarter is only increasing to a level of about 101 (starting from a base of 100 in this example) with growth at a 4% annual rate, versus to a level of 100.25 when  growth is at an annual rate of 1%.  While such a difference in growth rates matters a great deal (indeed a huge deal) if sustained over time, the difference in any one quarter is not that much.

Indeed, I personally find the estimated quarter to quarter levels of GDP in the US (after seasonal adjustment, which will be discussed below) to be surprisingly stable.  Keep in mind that GDP is a flow, not a stock.  It is like the flow of water in a river, not a stock such as the body of water in a reservoir.  Flows can go sharply up and down, while stocks do not, and some may mistakenly treat the GDP figures in their mind as a stock rather than a flow.  GDP measures the flow of goods and services produced over some period of time (a calendar quarter in the quarterly figures).  A flow of GDP to 101 in some quarter (from a base of 100 in the preceding quarter) is not really that different to an increase to 100.25.  While this would matter (and matter a good deal) if the different quarterly increases are sustained over time, this is not that significant when just for one quarter.

d)  Statistical noise matters:

Moving now to more substantive reasons why one should expect a significant amount of quarter to quarter volatility, one needs to recognize that GDP is estimated based on surveys and other such sources of statistical information.  The Bureau of Economic Analysis (BEA) of the US Department of Commerce, which is responsible for the estimates of the GDP accounts in the US (which are formally called the National Income and Product Accounts, or NIPA), bases its estimates on a wide variety of surveys, samples of tax returns, and other such partial figures.  The estimates are not based on a full and complete census of all production each quarter.  Indeed, such an economic census is only undertaken once every five years, and is carried out by the US Census Bureau.

One should also recognize that an estimate of real GDP depends on two measures, each of which is subject to sampling and other error.  One does not, and cannot, measure “real GDP” directly.  Rather, one estimates what nominal GDP has been (based on estimates in current dollars of the value of all economic transactions that enter into GDP), and then how much prices have changed.  Price indices are estimated based on the prices of surveyed samples, and the components of real GDP are then estimated from the nominal GDP of the component divided by the relevant price index.  Real GDP is only obtained indirectly.

There will then be two sets of errors in the measurements:  One for the nominal GDP flows and one for the price indices.  And surveys, whether of income flows or of prices, are necessarily partial.  Even if totally accurate for the firms and other entities sampled, one cannot say with certainty whether those sampled in that quarter are fully representative of everyone in the economy.  This is in particular a problem (which the BEA recognizes) in capturing what is happening to newly established firms.  Such firms will not be included in the samples used (as they did not exist when the samples were set up) and the experiences of such newly established firms can be quite different from those of established firms.

And what I am calling here statistical “noise” encompasses more than simply sampling error.  Indeed, sampling error (the fact that two samples will come up with different results simply due to the randomness of who is chosen) is probably the least concern.  Rather, systemic issues arise whenever one is trying to infer measures at the national level from the results found in some survey.  The results will depend, for example, on whether all the components were captured well, and even on how the questions are phrased.  We will discuss below (in Section C, where we look at a comparison of estimates of GDP to estimates of Gross Domestic Income, or GDI, which in principle should be the same) that the statistical discrepancy between the estimates of GDP and GDI does not vary randomly from one quarter to the next but rather fairly smoothly (what economists and statisticians call “autocorrelation” – see Section C).  This is an indication that there are systemic issues, and not simply something arising from sample randomness.

Finally, even if that statistical error was small enough to allow one to be confident that we measured real GDP within an accuracy of just, say, +/- 1%, one would not then be able to say whether GDP in that quarter had increased at an annualized rate of about 4%, or decreased by about 4%.  A small quarterly difference looms large when looked at in terms of annualized rates.

I do not know what the actual statistical error might be in the GDP estimates, and it appears they are well less than +/- 1% (based on the volatility actually observed in the quarter to quarter figures).  But a relatively small error in the estimates of real GDP in any quarter could still lead to quite substantial volatility in the estimates of the quarter to quarter growth.

e)  Seasonal adjustment is necessary, but not easy to do:

Economic activity varies over the course of the year, with predictable patterns.  There is a seasonality to holidays, to when crops are grown, to when students graduate from school and enter the job market, and much much more.  Thus the GDP data we normally focus on has been adjusted by various statistical methods to remove the seasonality factor, making use of past data to estimate what the patterns are.

The importance of this can be seen if one compares what the seasonally adjusted levels of GDP look like compared to the levels before seasonal adjustment.  Note the level of GDP here is for one calendar quarter – it will be four times this at an annual rate:

There is a regular pattern to GDP:  It is relatively high in the last quarter of each year, relatively low in the first quarter, and somewhere in between in the second and third quarters.  The seasonally adjusted series takes account of this, and is far smoother.  Calculating quarterly growth rates from a series which has not been adjusted for seasonality would be misleading in the extreme, and not of much use.

But adjusting for seasonality is not easy to do.  While the best statisticians around have tried to come up with good statistical routines to do this, it is inherently difficult.  A fundamental problem is that one can only look for patterns based on what they have been in the past, but the number of observations one has will necessarily be limited.  If one went back to use 20 years of data, say, one would only have 20 observations to ascertain the statistical pattern.  This is not much.  One could go back further, but then one has the problem that the economy as it existed 30 or 40 years ago (and indeed even 20 years ago) was quite different from what it is now, and the seasonal patterns could also now be significantly different.  While there are sophisticated statistical routines that have been developed to try to make best use of the available data (and the changes observed in the economy over time), this can only be imperfect.

Indeed, the GDP estimates released by the BEA on July 27 incorporated a number of methodological changes (which we will discuss below), one of which was a major update to the statistical routines used for the seasonal adjustment calculations.  Many observers (including at the BEA) had noted in recent years that (seasonally adjusted) GDP growth in the first quarter of each year was unusually and consistently low.  It then recovered in the second quarter.  This did not look right.

One aim of the update to the seasonal adjustment statistical routines was to address this issue.  Whether it was fully successful is not fully clear.  As seen in the chart at the top of this post (which reflects estimates that have been seasonally adjusted based on the new statistical routines), there still appear to be significant dips in the seasonally adjusted first quarter figures in many of the years (comparing the first quarter GDP figures to those just before and just after – i.e. in 2007, 2008, 2010, 2011, 2014, and perhaps 2017 and 2018.  This would be more frequent than one would expect if the residual changes were now random over the period).  However, this is an observation based just on a simple look at a limited sample.  The BEA has looked at this far more carefully, and rigorously, and believes that the new seasonal adjustment routines it has developed have removed any residual seasonality in the series as estimated.

f)  The timing of weekends and holidays may also enter, and could be important:

The production of the goods and services that make up the flow of GDP will also differ on Saturdays, Sundays, and holidays.  But the number of Saturdays, Sundays, and certain holidays may differ from one year to the next.  While there are normally 13 Saturdays and 13 Sundays in each calendar quarter, and most holidays will be in the same quarter each year, this will not always be the case.

For example, there were just 12 Sundays in the first quarter of 2018, rather than the normal 13.  And there will be 14 Sundays in the third quarter of 2018, rather than the normal 13.  In 2019, we will see a reversion to the “normal” 13 Sundays in each of the quarters.  This could have an impact.

Assume, just for the sake of illustration, that production of what goes into GDP is only one-half as much on a Saturday, Sunday, or holiday, than it is on a regular Monday through Friday workday.  It will not be zero, as many stores, as well as certain industrial plants, are still open, and I am just using the one-half for illustration.  Using this, and based on a simple check of the calendars for 2018 and 2019, one will find there were 62 regular, Monday through Friday, non-holiday workdays in the first quarter of 2018, while there will be 61 such regular workdays in the first quarter of 2019.  The number of Saturdays, Sundays, and holidays were 28 in the first quarter of 2018 (equivalent to 14 regular workdays in terms of GDP produced, assuming the one-half figure), while the number of Saturdays, Sundays, and holidays will be 29 in the first quarter of 2019 (equivalent to 14.5 regular workdays).  Thus the total regular work-day equivalents will be 76 in 2018 (equal to 62 plus 14), falling to 75.5 in 2019 (equal to 61 plus 14.5).  This will be a reduction of 0.7% between the periods in 2018 and 2019 (75.5/76), or a fall of 2.6% at an annualized rate.  This is not small.

The changes due to the timing of holidays could matter even more, especially for certain countries around the world.  Easter, for example, was celebrated in March (the first quarter) in 2013 and 2016, but came in April (the second quarter) in 2014, 2015, 2017, and 2018.  In Europe and Latin America, it is customary to take up to a week of vacation around the Easter holidays.  The change in economic activity from year to year, with Easter celebrated in one quarter in one year but a different one in the next, will make a significant difference to economic activity as measured in the quarter.

And in Muslim countries, Ramadan (a month of fasting from sunrise to sunset), followed by the three-day celebration of Eid al-Fitr, will rotate through the full year (in terms of the Western calendar) as it is linked to the lunar cycle.

Hence it would make sense to adjust the quarterly figures not only for the normal seasonal adjustment, but also for any changes in the number of weekends and holidays in some particular calendar quarter.  Eurostat and most (but not all) European countries make such an adjustment for the number of working days in a quarter before they apply the seasonal adjustment factors.  But I have not been able to find how the US handles this.  The adjustment might be buried somehow in the seasonal adjustment routines, but I have not seen a document saying this.  If no adjustment is made, then this might explain part of the quarterly fluctuations seen in the figures.

g)  There have been, and always will be, updates to the methodology used:

As noted above, the GDP figures released on July 27 reflected a major update in the methodology followed by the BEA to arrive at its GDP estimates.  Not only was there extensive work on the seasonal adjustment routines, but there were definitional and other changes.  The accounts were also updated to reflect the findings from the 2012 Economic Census, and prices were changed from a previous base of 2009 to now 2012.  The July 27 release summarized the changes, and more detail on what was done is available from a BEA report issued in April.  And with the revisions in definitions and certain other methodological changes, the BEA revised its NIPA figures going all the way back to 1929, the first year with official GDP estimates.

The BEA makes such changes on a regularly scheduled basis.  There is normally an annual change released each year with the July report on GDP in the second quarter of the year.  This annual change incorporates new weights (from recent annual surveys) and normally some limited methodological changes, and the published estimates are normally then revised going back three and a half years.  See, for example, this description of what was done in July 2017.

On top of this, there is then a much larger change once every five years.  The findings from the most recent Economic Census (which is carried out every five years) are incorporated, seasonal adjustment factors are re-estimated, and major definitional or methodological changes may be incorporated.  The July 2018 release reflected one of those five-year changes.  It was the 15th such comprehensive revision to the NIPA accounts undertaken by the BEA.

I stress this to make the point that the GDP figures are estimates, and as estimates are always subject to change.  The professionals at the BEA are widely admired around the world for the quality of their work, and do an excellent job in my opinion.  But no estimates will ever be perfect.  One has to recognize that there will be a degree of uncertainty surrounding any such estimates, and that the quarter to quarter volatility observed will derive at least in part from the inherent uncertainty in any such estimates.

C.  Estimates of GDP versus Estimates of GDI

One way to develop a feel for how much the changes in quarterly GDP may be due to the inherent uncertainty in the estimates is to compare it to the estimated quarterly changes in Gross Domestic Income (GDI).  GDP (Gross Domestic Product) measures the value of everything produced.  GDI measures the value of all incomes (wages, profits, rents, etc.) generated.  In principle, the totals should be the same, as the value of whatever is produced accrues to someone as income.  They should add up to the same thing.

But the BEA arrives at its estimates of GDP and of GDI by different routes.  As a consequence, the estimates of the totals will then differ.  The differences are not huge in absolute amount, nor have they grown over time (as a share of GDP or of GDI).  That is, on average the estimates match each other over time, with the same central tendency.  But they differ by some amount in any individual quarter, and hence the quarter to quarter growth rates will differ.  And for the reasons reviewed above, those slight changes in the levels in any individual quarter can translate into often major differences in the growth rates from one quarter to the next.  And these differences may appear to be particularly large when the growth rates are then presented in annualized terms.

For the period since 2006, the two sets of growth rates were (where the initial estimate for the second quarter of 2018 will not be available until the end-August figures come out):

As is seen, the alternative estimates of growth in any individual quarter can be quite different.  There was an especially large difference in the first quarter of 2012, when the estimated growth in GDP was 3.2% at an annual rate, while the estimated growth in GDI was a giant 8.7%.

Which is correct?  Was the growth rate in the first quarter of 2012 3.2% (as found with the GDP estimate) or 8.7% (as found with the GDI estimate)?  The answer is we do not know, and indeed that probably neither is correct.  What is most likely is that the true figure is probably somewhere in between.

Furthermore, and also moderating what the impact on the differences in the respective estimated growth rates will be, it is not the case that the estimates of GDP and GDI are statistically independent of each other, with the two bouncing around randomly with respect to each other.  Rather, if one looks at what the BEA calls the “statistical discrepancy” (the difference between GDP and GDI), one finds that if, say, the estimate of GDP were above the estimate of GDI in one quarter, then it likely would also be above in the next quarter.  Not by the same amount, and the differences would evolve over time, but moving more like waves than as balls ricocheting around.  Economists and statisticians refer to this as “autocorrelation”, and it indicates that there is some systemic error in the estimates of GDP and of GDI, which carries over from one quarter to the next.  What the source of that is, we do not know.  If we did know, then it would be eliminated.  But the fact such autocorrelation exists tells us that there is some source of systemic error in the measures of GDP and GDI, and we have not been able to discover the source.

Estimates are estimates.  We need to recognize that there will be statistical uncertainty in any such figures.  Even if they even out over time, the estimated growth from one quarter to the next will reflect such statistical volatility.  The differences seen in the estimated rates of growth in any one quarter for total output (estimated by way of GDP versus by way of GDI) provides a useful benchmark for how to judge the reported changes seen in growth for GDP in any individual quarter.  The true volatility (for purely statistical reasons) is likely to be at least as much, if not more.

D.  Conclusion

There are many reasons, then, to expect the quarterly growth figures to bounce around.  One should not place too much weight on the estimates from any individual quarter.  It is the longer term trends that matter.  The estimated figure for growth in GDP of 4.1% in the second quarter was not out of line with what has been seen in a number of quarters in recent years.  But growth since mid-2009 has only been about one half as much on average, despite several quarters when estimated growth was well in excess of 4.1%.

To conclude, some may find of interest three country cases I am personally familiar with which illustrate why one needs to exercise care, and with an understanding of the country context, when considering what is meaningful or not for reported figures on GDP growth.  The countries are Japan, China, and an unidentified, but newly independent, former colony in the 1960s.

a)  Japan:  In the late 1990s / early 2000s, while holding a position within the World Bank Group, I was responsible for assessments of the prospects and risks of the countries of East Asia where the World Bank was active.  This was not long after the East Asia crisis of 1997, and the countries were just beginning to recover.  Japan was important, both as a trading partner to the others and because Japan itself had gone through a somewhat similar crisis following 1990, when the Japanese financial bubble burst.

As part of this, I followed closely the quarterly GDP growth figures for Japan.  But as many analysts at the time noted, the quarter to quarter figures behaved in ways that were difficult to understand.  Components went up when one would have thought they would go down (and vice versa), the quarterly changes were far more extreme than seen elsewhere, and in general the quarter to quarter fluctuations were difficult to make sense of.  The volatility in the figures was far greater than one would have expected for an economy such as Japan’s.

This view among analysts was such a common one that the government agency responsible for the estimates felt it necessary to issue a news release in June 2000 defending its work and addressing a number of the concerns that had been raised.

I have no doubt that the Japanese government officials responsible for the estimates were well-qualified and serious professionals.  But it is not easy to estimate GDP and its components, the underlying data on which the statisticians relied might have had problems (including sample sizes that were possibly too small), and there may have been segments of the economy (in the less formal sectors) which might not have been captured well.

I have not followed closely in recent years, and do not know if the issues continue.  But Japan’s case illustrates that even a sophisticated agency, with good professionals, can have difficulty in arriving at GDP estimates that behave as one would expect.

b)  China:  The case of China illustrates the mirror image problem of what was found in Japan.  While the Japanese GDP estimates bounced around far too sharply from one quarter to the next, the GDP estimates for China showed remarkable, and not believable, stability.

Chinese growth rates have normally been presented as growth of GDP in the current period over what it was in the same period one year ago.  Seasonal adjustment is then not needed, and indeed China only started to present seasonally adjusted figures in 2011.  However, these estimates are still not fully accepted by many analysts.  Comparing GDP in the current quarter to what it was in the same quarter a year before overcomes this, but at the cost that it does not present information on growth just in the quarter, as opposed to total growth over the preceding year.

And the growth rates reported over the same quarter in the preceding year have been shockingly smooth.  Indeed, in recent years (from the first quarter of 2015 through to the recently released figures for the second quarter of 2018), China’s reported growth of its GDP over the year-earlier period has not been more than 7.0% nor less than 6.7% in each and every quarter.  Specifically, the year on year GDP growth rates from the first quarter of 2015 through to the second quarter of 2018 were (in sequence):  7.0%, 7.0%, 6.9%, 6.8%, 6.7%, 6.7%, 6.7%, 6.8%, 6.9%, 6.9%, 6.8%, 6.8%, 6.8%, and 6.7% (one can find the figures in, for example, the OECD database).  Many find this less than credible.

There are other problems as well in the Chinese numbers.  For example, it has often been the case that the reported growth in provincial GDP of the 31 provincial level entities in China was higher in almost all of the 31 provinces, and sometimes even in all of the provinces, than GDP growth was in China as a whole.  This is of course mathematically impossible, but not surprising when political rewards accrue to those with fast reported growth.

With such weak credibility, analysts have resorted to coming up with proxies to serve as indicators of what quarter to quarter might have been.  These might include electricity consumption, or railway tonnage carried, or similar indicators of economic production.  Indeed, there is what has been labeled the “Li index”, named after Li Keqiang (who was vice premier when he formulated it, and later China’s premier).  Li said he did not pay much attention to the official GDP statistics, but rather focused on a combination of electricity production, rail cargo shipments, and loan disbursements.  Researchers at the Federal Reserve Bank of San Francisco who reproduced this and fitted it through some regression analysis found that it worked quite well.

And the index I found most amusing is calculated using nighttime satellite images of China, with an estimation of how much more night-time illumination one finds over time.  This “luminosity” index tracks well what might be going on with China’s GDP.

c)  An unidentified, newly independent, former colony:  Finally, this is a story which I must admit I received third hand, but which sounds fully believable.  In the mid-1970s I was working for a period in Kuala Lumpur, for the Government of Malaysia.  As part of an economic modeling project I worked closely with the group in the national statistical office responsible for estimating GDP.  The group was led by a very capable, and talkative, official (of Tamil origin), who related a story he had heard from a UN consultant who had worked closely with his group in the early 1970s to develop their system of national accounts.

The story is of a newly independent country in the mid-1960s (whose name I was either not told or cannot remember), and its estimation of GDP.  An IMF mission had visited it soon after independence, and as is standard, the IMF made forecasts of what GDP growth might be over the next several years.  Such forecasts are necessary in order to come up with estimates for what the government accounts might be (as tax revenues will depend on GDP), for the trade accounts, for the respective deficits, and hence for what the financing needs might be.

Such forecasts are rarely very good, especially for a newly independent country where much is changing.  But something is needed.

As time passed, the IMF received regular reports from the country on what estimated GDP growth actually was.  What they found was that reported GDP growth was exactly what had been forecast.  And when asked, the national statisticians responded that who were they to question what the IMF officials had said would happen!