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!

The Simple Economics of What Determines the Foreign Trade Balance: Econ 101

“There’s no reason that we should have big trade deficits with virtually every country in the world.”

“We’re like the piggybank that everybody is robbing.”

“the United States has been taken advantage of for decades and decades”

“Last year,… [the US] lost  … $817 billion on trade.  That’s ridiculous and it’s unacceptable.”

“Well, if they retaliate, they’re making a mistake.  Because, you see, we have a tremendous trade imbalance. … we can’t lose”

Statements made by President Trump at the press conference held as he left the G-7 meetings in, Québec, Canada, June 9, 2018.

 

A.  Introduction

President Trump does not understand basic economics.  While that is not a surprise, nor something necessarily required or expected of a president, one should expect that a president would appoint advisors who do understand, and who would tell him when he is wrong.  Unfortunately, this president has been singularly unwilling to do so.  This is dangerous.

Trump is threatening a trade war.  Not only by his words at the G-7 meetings and elsewhere, but also by a number of his actions on trade and tariffs in recent months, Trump has made clear that he believes that a trade deficit is a “loss” to the nation, that countries with trade surpluses are somehow robbing those (such as the US) with a deficit, that raising tariffs can and will lead to reductions in trade deficits, and that if others then also raise their tariffs, the US will in the end necessarily “win” simply because the US has a trade deficit to start.

This is confused on many levels.  But it does raise the questions of what determines a country’s trade balance; whether a country “loses” if it has a trade deficit; and what is the role of tariffs.  This Econ 101 blog post will first look at the simple economics of what determines a nation’s trade deficit (hint:  it is not tariffs); will then discuss what tariffs do and where do they indeed matter; and will then consider the role played by foreign investment (into the US) and whether a trade deficit can be considered a “loss” for the nation (a piggybank being robbed).

B.  What Determines the Overall Trade Deficit?

Let’s start with a very simple case, where government accounts are aggregated together with the rest of the economy.  We will later then separate out government.

The goods and services available in an economy can come either from what is produced domestically (which is GDP, or Gross Domestic Product) or from what is imported.  One can call this the supply of product.  These goods and services can then be used for immediate consumption, or for investment, or for export.  One can call this the demand for product.  And since investment includes any net change in inventories, the goods and services made available will always add up to the goods and services used.  Supply equals demand.

One can put this in a simple equation:

GDP + Imports = Domestic Consumption + Domestic Investment + Exports

Re-arranging:

(GDP – Domestic Consumption) – Domestic Investment = Exports – Imports

The first component on the left is Domestic Savings (what is produced domestically less what is consumed domestically).  And Exports minus Imports is the Trade Balance.  Hence one has:

Domestic Savings – Domestic Investment = Trade Balance

As one can see from the way this was derived, this is simply an identity – it always has to hold.  And what it says is that the Trade Balance will always be equal to the difference between Domestic Savings and Domestic Investment.  If Domestic Savings is less than Domestic Investment, then the Trade Balance (Exports less Imports) will be negative, and there will be a trade deficit.  To reduce the trade deficit, one therefore has to either raise Domestic Savings or reduce Domestic Investment.  It really is as straightforward as that.

Where this becomes more interesting is in determining how the simple identity is brought about.  But here again, this is relatively straightforward in an economy which, like now, is at full employment.  Hence GDP is essentially fixed:  It cannot immediately rise by either employing more labor (as all the workers who want a job have one), nor by each of those laborers suddenly becoming more productive (as productivity changes only gradually through time by means of either better education or by investment in capital).  And GDP is equal to labor employed times the productivity of each of those workers.

In such a situation, with GDP at its full employment level, Domestic Savings can only rise if Domestic Consumption goes down, as Domestic Savings equals GDP minus Domestic Consumption.  But households want to consume, and saving more will mean less for consumption.  There is a tradeoff.

The only other way to reduce the trade deficit would then be to reduce Domestic Investment.  But one generally does not want to reduce investment.  One needs investment in order to become more productive, and it is only through higher productivity that incomes can rise.

Reducing the trade deficit, if desirable (and whether it is desirable will be discussed below), will therefore not be easy.  There will be tradeoffs.  And note that tariffs do not enter directly in anything here.  Raising tariffs can only have an impact on the trade balance if they have a significant impact for some reason on either Domestic Savings or Domestic Investment, and tariffs are not a direct factor in either.  There may be indirect impacts of tariffs, which will be discussed below, but we will see that the indirect effects actually could act in the direction of increasing, not decreasing, the trade deficit.  However, whichever direction they act in, those indirect effects are likely to be small.  Tariffs will not have a significant effect on the trade balance.

But first, it is helpful to expand the simple analysis of the above to include Government as a separate set of accounts.  In the above we simply had the Domestic sector.  We will now divide that into the Domestic Private and the Domestic Public (or Government) sectors.  Note that Government includes government spending and revenues at all levels of government (state and local as well as federal).  But the government deficit is primarily a federal government issue.  State and local government entities are constrained in how much of a deficit they can run over time, and the overall balance they run (whether deficit or surplus) is relatively minor from the perspective of the country as a whole.

It will now also be convenient to write out the equations in symbols rather than words, and we will use:

GDP = Gross Domestic Product

C = Domestic Private Consumption

I = Domestic Private Investment

G = Government Spending (whether for Consumption or for Investment)

X = Exports

M = Imports

T = Taxes net of Transfers

Note that T (Taxes net of Transfers) will be the sum total of all taxes paid by the private sector to government, minus all transfers received by the private sector from government (such as for Social Security or Medicare).  I will refer to this as simply net Taxes (T).

The basic balance of goods or services available (supplied) and goods or services used (demanded) will then be:

GDP + M = C + I + G + X

We will then add and subtract net Taxes (T) on the right-hand side:

GDP + M = (C + T) + I + (G – T) + X

Rearranging:

GDP – (C + T) – (G – T) – I = X – M

(GDP – C – T) – I + (T – G) = X – M

Or in (abbreviated) words:

Dom. Priv. Savings – Dom. Priv. Investment + Govt Budget Balance = Trade Balance

Domestic Private Savings (savings by households and private businesses) is equal to what is produced in the economy (GDP), less what is privately consumed (C), less what is paid in net Taxes (T) by the private sector to the public sector.  Domestic Private Investment is simply I, and includes investment both by private businesses and by households (primarily in homes).  And the Government Budget Balance is equal to what government receives in net Taxes (T), less what Government spends (on either consumption items or on public investment).  Note that government spending on transfers (e.g. Social Security) is already accounted for in net Taxes (T).

This equation is very much like what we had before.  The overall Trade Balance will equal Domestic Private Savings less Domestic Private Investment plus the Government Budget Balance (which will be negative when a deficit, as has normally been the case except for a few years at the end of the Clinton administration).  If desired, one could break down the Government Budget Balance into Public Savings (equal to net Taxes minus government spending on consumption goods and services) less Public Investment (equal to government spending on investment goods and services), to see the parallel with Domestic Private Savings and Domestic Private Investment.  The equation would then read that the Trade Balance will equal Domestic Private Savings less Domestic Private Investment, plus Government Savings less Government Investment.  But there is no need.  The budget deficit, as commonly discussed, includes public spending not only on consumption items but also on investment items.

This is still an identity.  The balance will always hold.  And it says that to reduce the trade deficit (make it less negative) one has to either increase Domestic Private Savings, or reduce Domestic Private Investment, or increase the Government Budget Balance (i.e. reduce the budget deficit).  Raising Domestic Private Savings implies reducing consumption (when the economy is at full employment, as now).  Few want this.  And as discussed above, a reduction in investment is not desirable as investment is needed to increase productivity over time.

This leaves the budget deficit, and most agree that it really does need to be reduced in an economy that is now at full employment.  Unfortunately, Trump and the Republican Congress have moved the budget in the exact opposite direction, primarily due to the huge tax cut passed last December, and to a lesser extent due to increases in certain spending (primarily for the military).  As discussed in an earlier post on this blog, an increase in the budget deficit to a forecast 5% of GDP at a time when the economy is at full employment is unprecedented in peacetime.

What this implies for the trade balance is clear from the basic identity derived above.  An increase in the budget deficit (a reduction in the budget balance) will lead, all else being equal, to an increase in the trade deficit (a reduction in the trade balance).  And it might indeed be worse, as all else is not equal.  The stated objective of slashing corporate taxes is to spur an increase in corporate investment.  But if private investment were indeed to rise (there is in fact little evidence that it has moved beyond previous trends, at least so far), this would further worsen the trade balance (increase the trade deficit).

Would raising tariffs have an impact?  One might argue that this would raise net Taxes paid, as tariffs on imports are a tax, which (if government spending is not then also changed) would reduce the budget deficit.  While true, the extent of the impact would be trivially small.  The federal government collected $35.6 billion in all customs duties and fees (tariffs and more) in FY2017 (see the OMB Historical Tables).  This was less than 0.2% of FY2017 GDP.  Even if all tariffs (and other fees on imports) were doubled, and the level of imports remained unchanged, this would only raise 0.2% of GDP.  But the trade deficit was 2.9% of GDP in FY2017.  It would not make much of a difference, even in such an extreme case.  Furthermore, new tariffs are not being pushed by Trump on all imports, but only a limited share (and a very limited share so far).  Finally, if Trump’s tariffs in fact lead to lower imports of the items being newly taxed, as he hopes, then tariffs collected can fall.  In the extreme, if the imports of such items go to zero, then the tariffs collected will go to zero.

Thus, for several reasons, any impact on government revenues from the new Trump tariffs will be minor.

The notion that raising tariffs would be a way to eliminate the trade deficit is therefore confused.  The trade balance will equal the difference between Domestic Savings and Domestic Investment.  Adding in government, the trade balance will equal the difference between Domestic Private Savings and Domestic Private Investment, plus the equivalent for government (the Government Budget Balance, where a budget deficit will be a negative).  Tariffs have little to no effect on these balances.

C.  What Role Do Tariffs Play, Then?

Do tariffs then matter?  They do, although not in the determination of the overall trade deficit.  Rather, tariffs, which are a tax, will change the price of the particular import relative to the price of other products.  If applied only to imports from some countries and not from others, one can expect to see a shift in imports towards those countries where the tariffs have not been imposed.  And in the case when they are applied globally, on imports of the product from any country, one should expect that prices for similar products made in the US will then also rise.  To the extent there are alternatives, purchases of the now more costly products (whether imported or produced domestically) will be reduced, while purchases of alternatives will increase.  And there will be important distributional changes.  Profits of firms producing the now higher priced products will increase, while the profits of firms using such products as an input will fall.  And the real incomes of households buying any of these products will fall due to the higher prices.

Who wins and who loses can rapidly become turn into something very complicated.  Take, for example, the new 25% tariff being imposed by the Trump administration on steel (and 10% on aluminum).  The tariffs were announced on March 8, to take effect on March 23.  Steel imports from Canada and Mexico were at first exempted, but later the Trump administration said those exemptions were only temporary.  On March 22 they then expanded the list of countries with temporary exemptions to also the EU, Australia, South Korea, Brazil, and Argentina, but only to May 1.  Then, on March 28, they said imports from South Korea would receive a permanent exemption, and Australia, Brazil, and Argentina were granted permanent exemptions on May 2.  After a short extension, tariffs were then imposed on steel imports from Canada, Mexico, and the EU, on May 31.  And while this is how it stands as I write this, no one knows what further changes might be announced tomorrow.

With this uneven application of the tariffs by country, one should expect to see shifts in the imports by country.  What this achieves is not clear.  But there are also further complications.  There are hundreds if not thousands of different types of steel that are imported – both of different categories and of different grades within each category – and a company using steel in their production process in the US will need a specific type and grade of steel.  Many of these are not even available from a US producer of steel.  There is thus a system where US users of steel can apply for a waiver from the tariff.  As of June 19, there have been more than 21,000 petitions for a waiver.  But there were only 30 evaluators in the US Department of Commerce who will be deciding which petitions will be granted, and their training started only in the second week of June.  They will be swamped, and one senior Commerce Department official quoted in the Washington Post noted that “It’s going to be so unbelievably random, and some companies are going to get screwed”.  It would not be surprising to find political considerations (based on the interests of the Trump administration) playing a major role.

So far, we have only looked at the effects of one tariff (with steel as the example).  But multiple tariffs on various goods will interact, with difficult to predict consequences.  Take for example the tariff imposed on the imports of washing machines announced in late January, 2018, at a rate of 20% in the first year and at 50% should imports exceed 1.2 million units in the year.  This afforded US producers of washing machines a certain degree of protection from competition, and they then raised their prices by 17% over the next three months (February to May).

But steel is a major input used to make washing machines, and steel prices have risen with the new 25% tariff.  This will partially offset the gains the washing machine producers received from the tariff imposed on their product.  Will the Trump administration now impose an even higher tariff on washing machines to offset this?

More generally, the degree to which any given producer will gain or lose from such multiple tariffs will depend on multiple factors – the tariff rates applied (both for what they produce and for what they use as inputs), the degree to which they can find substitutes for the inputs they need, and the degree to which those using the product (the output) will be able to substitute some alternative for the product, and more.  Individual firms can end up ahead, or behind.  Economists call the net effect the degree of “net effective protection” afforded the industry, and it can be difficult to figure out.  Indeed, government officials who had thought they were providing positive protection to some industry often found out later that they were in fact doing the opposite.

Finally, imposing such tariffs on imports will lead to responses from the countries that had been providing the goods.  Under the agreed rules of international trade, those countries can then impose commensurate tariffs of their own on products they had been importing from the US.  This will harm industries that may otherwise have been totally innocent in whatever was behind the dispute.

An example of what can then happen has been the impact on Harley-Davidson, the American manufacturer of heavy motorcycles (affectionately referred to as “hogs”).  Harley-Davidson is facing what has been described as a “triple whammy” from Trump’s trade decisions.  First, they are facing higher steel (and aluminum) prices for their production in the US, due to the Trump steel and aluminum tariffs.  Harley estimates this will add $20 million to their costs in their US plants.  For a medium-sized company, this is significant.  As of the end of 2017, Harley-Davidson had 5,200 employees in the US (see page 7 of this SEC filing).  With $20 million, they could pay each of their workers $3,850 more.  This is not a small amount.  Instead, the funds will go to bolster the profits of steel and aluminum firms.

Second, the EU has responded to the Trump tariffs on their steel and aluminum by imposing tariffs of their own on US motorcycle imports.  This would add $45 million in costs (or $2,200 per motorcycle) should Harley-Davidson continue to export motorcycles from the US to the EU.  Quite rationally, Harley-Davidson responded that they will now need to shift what had been US production to one of their plants located abroad, to avoid both the higher costs resulting from the new steel and aluminum tariffs, and from the EU tariffs imposed in response.

And one can add thirdly and from earlier, that Trump pulled the US out of the already negotiated (but still to be signed) Trans-Pacific Partnership agreement.  This agreement would have allowed Harley-Davidson to export their US built motorcycles to much of Asia duty-free.  They will now instead be facing high tariffs to sell to those markets.  As a result, Harley-Davidson has had to set up a new plant in Asia (in Thailand), shifting there what had been US jobs.

Trump reacted angrily to Harley-Davidson’s response to his trade policies.  He threatened that “they will be taxed like never before!”.  Yet what Harley-Davidson is doing should not have been a surprise, had any thought been given to what would happen once Trump started imposing tariffs on essential inputs needed in the manufacture of motorcycles (steel and aluminum), coming from our major trade partners (and often closest allies).  And it is positively scary that a president should even think that he should use the powers of the state to threaten an individual private company in this way.  Today it is Harley-Davidson.  Who will it be tomorrow?

There are many other examples of the problems that have already been created by Trump’s new tariffs.  To cite a few, and just briefly:

a)  The National Association of Home Builders estimated that the 20% tariff imposed in 2017 on imports of softwood lumber from Canada added nearly $3,600 to the cost of building an average single-family home in the US and would, over the course of a year, reduce wages of US workers by $500 million and cost 8,200 full-time US jobs.

b)  The largest nail manufacturer in the US said in late June that it has already had to lay off 12% of its workforce due to the new steel tariffs, and that unless it is granted a waiver, it would either have to relocate to Mexico or shut down by September.

c)  As of early June, Reuters estimated that at least $2.5 billion worth of investments in new utility-scale solar installation projects had been canceled or frozen due to the tariffs Trump imposed on the import of solar panel assemblies.  This is far greater than new investments planned for the assembly of such panels in the US.  Furthermore, the jobs involved in such assembly work are generally low-skill and repetitive, and can be automated should wages rise.

So there are consequences from such tariffs.  They might be unintended, and possibly not foreseen, but they are real.

But would the imposition of tariffs necessarily reduce the trade deficit, as Trump evidently believes?  No.  As noted above, the trade deficit would only fall if the tariffs would, for some reason, increase domestic savings or reduce domestic investment.  But tariffs do not enter directly into those factors.  Indirectly, one could map out some chains of possible causation, but these changes in some set of tariffs (even if broadly applied to a wide range of imports) would not have a major effect on overall domestic savings or investment.  They could indeed even act in the opposite direction.

Households, to start, will face higher prices from the new tariffs.  To try to maintain their previous standard of living (in real terms) they would then need to spend more on what they consume and hence would save less.  This, by itself, would reduce domestic savings and hence would increase the trade deficit to the extent there was any impact.

The impacts on firms are more various, and depend on whether the firm will be a net winner or loser from the government actions and how they might then respond.  If a net winner, they have been able to raise their prices and hence increase their profits.  If they then save the extra profits (retained earnings), domestic savings would rise and the trade deficit would fall.  But if they increase their investments in what has now become a more profitable activity (and that is indeed the stated intention behind imposing the tariffs), that response would lead to an increase in the trade deficit.  The net effect will depend on whether their savings or their investment increases by more, and one does not know what that net change might be.  Different firms will likely respond differently.

One also has to examine the responses of the firms who will be the net losers from the newly imposed tariffs.  They will be paying more on their inputs and will see a reduction in their profits.  They will then save less and will likely invest less.  Again, the net impact on the trade deficit is not clear.

The overall impact on the trade deficit from these indirect effects is therefore uncertain, as one has effects that will act in opposing directions.  In part for this reason, but also because the tariffs will affect only certain industries and with responses that are likely to be limited (as a tariff increase today can be just as easily reversed tomorrow), the overall impact on the trade balance from such indirect effects are likely to be minor.

Increases in individual tariffs, such as those being imposed now by Trump, will not then have a significant impact on the overall trade balance.  But tariffs still do matter.  They change the mix of what is produced, from where items will be imported, and from where items will be produced for export (as the Harley-Davidson case shows).  They will create individual winners and losers, and hence it is not surprising to see the political lobbying as has grown in Washington under Trump.  Far from “draining the swamp”, Trump’s trade policy has made it critical for firms to step up their lobbying activities.

But such tariffs do not determine what the overall trade balance will be.

D.  What Role Does Foreign Investment Play in the Determination of the Trade Balance?

While tariffs will not have a significant effect on the overall trade balance, foreign investment (into the US) will.  To see this, we need to return to the basic macro balance derived in Section B above, but generalize it a bit to include all foreign financial flows.

The trade balance is the balance between exports and imports.  It is useful to generalize this to take into account two other sources of current flows in the national income and product accounts which add to (or reduce) the net demand for foreign exchange.  Specifically, there will be foreign exchange earned by US nationals working abroad plus that earned by US nationals on investments they have made abroad.  Economists call this “factor services income”, or simply factor income, as labor and capital are referred to as factors of production.  This is then netted against such income earned in the US by foreign nationals either working here or on their investments here.  Second, there will be unrequited transfers of funds, such as by households to their relatives abroad, or by charities, or under government aid programs.  Again, this will be netted against the similar transfers to the US.

Adding the net flows from these to the trade balance will yield what economists call the “current account balance”.  It is a measure of the net demand for dollars (if positive) or for foreign exchange (if a deficit) from current flows.  To put some numbers on this, the US had a foreign trade deficit of $571.6 billion in 2017.  This was the balance between the exports and imports of goods and services (what economists call non-factor services to be more precise, now that we are distinguishing factor services from non-factor services).  It was negative – a deficit.  But the US also had a surplus in 2017 from net factor services income flows of $216.8 billion, and a deficit of $130.2 billion on net transfers (mostly from households sending funds abroad).  The balance on current account is the sum of these (with deficits as negatives and surpluses as positives) and came to a deficit of $485.0 billion in 2017, or 2.5% of GDP.  As a share of GDP, this deficit is significant but not huge.  The UK had a current account deficit of 4.1% of GDP in 2017 for example, while Canada had a deficit of 3.0%.

The current account for foreign transactions, basically a generalization of the trade balance, is significant as it will be the mirror image of the capital account for foreign transactions.  That is, when the US had a current account deficit of $485.0 billion (as in 2017), there had to be a capital account surplus of $485.0 billion to match this, as the overall purchases and sales of dollars in foreign exchange transactions will have to balance out, i.e. sum to zero.  The capital account incorporates all transactions for the purchase or sale of capital assets (investments) by foreign entities into the US, net of the similar purchase or sale of capital assets by US entities abroad.  When the capital account is a net positive (as has been the case for the US in recent decades), there is more such investment going into the US than is going out.  The investments can be into any capital assets, including equity shares in companies, or real estate, or US Treasury or other bonds, and so on.

But while the two (the current account and the capital account) have to balance out, there is an open question of what drives what.  Look at this from the perspective of a foreigner, wishing to invest in some US asset.  They need to get the dollars for this from somewhere.  While this would be done by means of the foreign exchange markets, which are extremely active (with trillions of dollars worth of currencies being exchanged daily), a capital account surplus of $485 billion (as in 2017) means that foreign entities had to obtain, over the course of the year, a net of $485 billion in dollars for their investments into the US.  The only way this could be done is by the US importing that much more than it exported over the course of the year.  That is, the US would need to run a current account deficit of that amount for the US to have received such investment.

If there is an imbalance between the two (the current account and the capital account), one should expect that the excess supply or demand for dollars will lead to changes in a number of prices, most directly foreign exchange rates, but also interest rates and other asset prices.  These will be complex and we will not go into here all the interactions one might then have.  Rather, the point to note is that a current account deficit, even if seemingly large, is not a sign of disequilibrium when there is a desire on the part of foreign investors to invest a similar amount in US markets.  And US markets have traditionally been a good place to invest.  The US is a large economy, with markets for assets that are deep and active, and these markets have normally been (with a few exceptions) relatively well regulated.

Foreign nationals and firms thus have good reason to invest a share of their assets in the US markets.  And the US has welcomed this, as all countries do.  But the only way they can obtain the dollars to make these investments is for the US to run a current account deficit.  Thus a current account deficit should not necessarily be taken as a sign of weakness, as Trump evidently does.  Depending on what governments are doing in their market interventions, a current account deficit might rather be a sign of foreign entities being eager to invest in the country.  And that is a good sign, not a bad one.

E.  An “Exorbitant Privilege”

The dollar (and hence the US) has a further, and important, advantage.  It is the world’s dominant currency, with most trade contracts (between all countries, not simply between some country and the US) denominated in dollars, as are contracts for most internationally traded commodities (such as oil).  And as noted above, investments in the US are particularly advantageous due to the depth and liquidity of our asset markets.  For these reasons, foreign countries hold most of their international reserves in dollar assets.  And most of these are held in what have been safe, but low yielding, short-term US Treasury bills.

As noted in Section D above, those seeking to make investments in dollar assets can obtain the dollars required only if the US runs a current account deficit.  This is as true for assets held in dollars as part of a country’s international reserves as for any other investments in US dollar assets.  Valéry Giscard d’Estaing in the 1960s, then the Minister of Finance of France, described this as an “exorbitant privilege” for the US (although this is often mistakenly attributed Charles de Gaulle, then his boss as president of France).

And it certainly is a privilege.  With the role of the dollar as the preferred reserve currency for countries around the world, the US is able to run current account deficits indefinitely, obtaining real goods and services from those countries while providing pieces of paper generating only a low yield in return.  Indeed, in recent years the rate of return on short-term US Treasury bills has generally been negative in real terms (i.e. after inflation).  The foreign governments buying these US Treasury bills are helping to cover part of our budget deficits, and are receiving little to nothing in return.

So is the US a “piggybank that everybody is robbing”, as Trump asserted to necessarily be the case when the US is has a current account deficit?  Not at all.  Indeed, it is the precise opposite.  The current account deficit is the mirror image of the foreign investment inflows coming into the US.  To obtain the dollars needed to do this those countries must export more real goods to the US than they import from the US.  The US gains real resources (the net exports), while the foreign entities then invest in US markets.  And for governments obtaining dollars to hold as their international reserves, those investments are primarily in the highly liquid and safe, short-term US Treasury bills, despite those assets earning low or even negative returns.  This truly is an “exorbitant privilege”, not a piggybank being robbed.

Indeed, the real concern is that with the mismanagement of our budget (tax cuts increasing deficits at a time when deficits should be reduced) plus the return to an ideologically driven belief in deregulating banks and other financial markets (such as what led to the financial and then economic collapse of 2008), the dollar may lose its position as the place to hold international reserves.  The British pound had this position in the 1800s and then lost it to the dollar due to the financial stresses of World War I.  The dollar has had the lead position since.  But others would like it, most openly by China and more quietly Europeans hoping for such a role for the euro.  They would very much like to enjoy this “exorbitant privilege”, along with the current account deficits that privilege conveys.

F.  Summary and Conclusion

Trump’s beliefs on the foreign trade deficit, on the impact of hiking tariffs, and on who will “win” in a trade war, are terribly confused.  While one should not necessarily expect a president to understand basic economics, one should expect that a president would appoint and listen to advisors who do.  But Trump has not.

To sum up some of the key points:

a)  The foreign trade balance will always equal the difference between domestic savings and domestic investment.  Or with government accounts split out, the trade balance will equal the difference between domestic private savings and domestic private investment, plus the government budget balance.  The foreign trade balance will only move up or down when there is a change in the balance between domestic savings and domestic investment.

b)  One way to change that balance would be for the government budget balance to increase (i.e. for the government deficit to be reduced).  Yet Trump and the Republican Congress have done the precise opposite.  The massive tax cuts of last December, plus (to a lesser extent) the increase in government spending now budgeted (primarily for the military), will increase the budget deficit to record levels for an economy in peacetime at full employment.  This will lead to a bigger trade deficit, not a smaller one.

c)  One could also reduce the trade deficit by making the US a terrible place to invest in.  This would reduce foreign investment into the US, and hence the current account deficit.  In terms of the basic savings/investment balance, it would reduce domestic investment (whether driven by foreign investors or domestic ones).  If domestic savings was not then also reduced (a big if, and dependant on what was done to make the US a terrible place to invest in), this would lead to a similar reduction in the trade deficit.  This is of course not to be taken seriously, but rather illustrates that there are tradeoffs.  One should not simplistically assume that a lower trade deficit achieved by any means possible is good.

d)  It is also not at all clear that one should be overly concerned about the size of the trade and current account deficits, at where they are today.  The US had a trade deficit of 2.9% of GDP in 2017 and a current account deficit of 2.5% of GDP.  While significant, these are not huge.  Should they become much larger (due, for example, to the forecast increases in government budget deficits to record levels), they might rise to problematic levels.  But at the current levels for the current account deficit, we have seen the markets for foreign exchange and for interest rates functioning pretty well and without overt signs of concern.  The dollars being made available through the current account deficit have been bought up and used for investments in US markets.

e)  Part of the demand for dollars to be invested and held in the US markets comes from the need for international reserves by governments around the world.  The dollar is the dominant currency in the world, and with the depth and liquidity of the US markets (in particular for short-term US Treasury bills) most of these international reserves are held in dollars.  This has given the US what has been called an “exorbitant privilege”, and permits the US to run substantial current account deficits while providing in return what are in essence paper assets yielding just low (or even negative) returns.

f)  The real concern should not be with the consequences of the dollar playing such a role in the system of international trade, but rather with whether the dollar will lose this privileged status.  Other countries have certainly sought this, most openly by China but also more quietly for the euro, but so far the dollar has remained dominant.  But there are increasing concerns that with the mismanagement of the government budget (the recent tax cuts) plus ideologically driven deregulation of banks and the financial markets (as led to the 2008 financial collapse), countries will decide to shift their international reserves out of the dollar towards some alternative.

g)  What will not reduce the overall trade deficit, however, is selective increases in tariff rates, as Trump has started to do.  Such tariff increases will shift around the mix of countries from where the imports will come, and/or the mix of products being imported, but can only reduce the overall trade deficit to the extent such tariffs would lead somehow to either higher domestic savings and/or lower domestic investment.  Tariffs will not have a direct effect on such balances, and indirect effects are going to be small and indeed possibly in the wrong direction (if the aim is to reduce the deficits).

h)  What such tariff policies will do, however, is create a mess.  And they already have, as the Harley-Davidson case illustrates.  Tariffs increase costs for US producers, and they will respond as best they can.  While the higher costs will possibly benefit certain companies, they will harm those using the products unless some government bureaucrat grants them a special exemption.

But what this does lead to is officials in government picking winners and losers.  That is a concern.  And it is positively scary to have a president lashing out and threatening individual firms, such as Harley-Davidson, when the firms respond to the mess created as one should have expected.

Tax Cuts Do Not Spur Growth – There Are Income as well as Substitution Effects, and Much More Besides: Econ 101

gdp-growth-and-top-marg-tax-rate-1930-to-2015

A.   Introduction, and a Brief Aside on the Macro Issues

While there is much we do not yet know on what economic policies Donald Trump will pursue (he said many things in his campaign, but they were often contradictory), one thing we can be sure of is that there will be a major tax cut.  Republicans in Congress (led by Paul Ryan) and in the Senator want the same.  And they along with Trump insist that the cuts in tax rates will spur a sharp jump in GDP growth, with the result that net tax revenues in the end will not fall by all that much.

But do tax cuts spur growth?  The chart above suggests not.  Marginal tax rates of those in the top income brackets have come down sharply since the 1950s and early 1960s, when they exceeded 90%.  They reached as low as 28% during the later Reagan years and 35% during the administration of George W. Bush.  But GDP growth did not jump to some higher rate as a result.

This Econ 101 post will discuss the economics on why this is actually what one should expect.  It will focus on the microeconomics behind this, as the case for income tax cuts is normally presented by the so-called “supply siders” as a micro story of incentives.  The macro case for tax cuts is different.  Briefly, in times of high unemployment when the economy is suffering from insufficient demand in the aggregate to purchase all that could be produced if more labor were employed, a cut in income taxes might spur demand by households, as they would then have higher post-tax incomes to spend on consumption items.  This increase in demand could then spur production and hence GDP.

Critically, this macro story depends on allowing the fiscal deficit to rise by there not being simultaneously a cut in government expenditures along with the tax cuts.  If there is such a cut in government expenditures, demand may be reduced by as much as or even more than demand would be increased by households.  But the economic plans of both Trump and Congressman (and Speaker) Paul Ryan do also call for large cuts in government expenditures.  While both Trump and Ryan have called for government expenditures to increase on certain items, such as for defense, they still want a net overall reduction.

The net impact on demand will then depend on how large the government expenditure cuts would be relative to the tax cuts, and on the design of the income tax cuts.  As was discussed in an earlier post on this blog on the size of the fiscal multiplier, If most of the income tax cuts go to those who are relatively well off, who will then save most or perhaps all of their tax windfall, there will be little or no macro stimulus from the tax cuts.  Any government expenditure cuts on top of this would then lead not to a spur in growth, but rather to output growing more slowly or contracting.  And the tax plan offered by Donald Trump in his campaign would indeed direct the bulk of the tax cuts to the extremely well off.  A careful analysis by the non-partisan Tax Policy Center found that 71% of the tax cuts (in dollar value) from the overall plan (which includes cuts in corporate and other taxes as well) would go to the richest 5% of households (those earning $299,500 or more), 51% would go to the top 1% (those earning $774,300 or more), and fully 25% would go to the richest 0.1% (those earning $4.8 million or more).

[A side note:  To give some perspective on how large these tax cuts for the rich would be, the 25% going to the richest 0.1% under Trump’s plan would total $1.5 trillion over the next ten years, under the Tax Policy Center estimates.  By comparison, the total that the Congressional Budget Office projects would be spent on the food stamp program (now officially called SNAP) for the poor over this period would come to a bit below $700 billion (see the August 2016 CBO 10-year budget projections).  That is, the tax breaks to be given under Trump’s tax plan to the top 0.1% (who have earnings of $4.8 million or more in a year) would be more than twice as large as would be spent on the entire food stamp program over the period.  Yet the Republican position is that we have to cut the food stamp program because we do not have sufficient government revenues to support it.]

The macro consequences of tax cuts that mostly go to the already well off, accompanied by government expenditure cuts to try to offset the deficit impact, are likely therefore to lead not to a spur in growth but to the opposite.

The microeconomic story is separate, and the rest of this blog post will focus on the arguments there.  Those who argue that cuts in income taxes will act as a spur to growth base their argument on what they see as the incentive effects.  Income taxes are a tax on working, they argue, and if you tax income less, people will work longer hours.  More will be produced, the economy will grow faster, and people will have higher incomes.

This micro argument is mistaken in numerous ways, however.  This Econ 101 post will discuss why.  There is the textbook economics, where it appears these “supply siders” forgot some of the basic economics they were taught in their introductory micro courses. But we should also recognize that the decision on how many hours to work each week goes beyond simply the economics.  There are important common social practices (which can vary by the nature of the job, i.e. what is a normal work day, and what do you do to get promoted) and institutional structures (the 40 hour work week) which play an important and I suspect dominant role. This blog post will review some of them.

But first, what do we know from the data, and what does standard textbook economics say?

B.  Start with the Data

It is always good first to look at what the data is telling us.  There have been many sharp cuts in income tax rates over the last several decades, and also some increases.  Did the economy grow faster after the tax cuts, and slower following the tax increases?

The chart at the top of this post indicates not.  The chart shows what GDP growth was year by year since 1930 along with the top marginal income tax rate of each year.  The top marginal income tax rate is the rate of tax that would be paid on an additional dollar of income by those in the highest income tax bracket.  The top marginal income tax rate is taken by those favoring tax cuts as the most important tax rate to focus on.  It is paid by the richest, and these individuals are seen as the “job creators” and hence play an especially important role under this point of view.  But changes in the top rates also mark the times when there were normally more general tax cuts for the rest of the population as well, as cuts (or increases) in the top marginal rates were generally accompanied by cuts (or increases) in the other rates also.  It can thus be taken as a good indicator of when tax rates changed and in what direction.  Note also that the chart combines on one scale the annual GDP percentage growth rates and the marginal tax rate as a percentage of an extra dollar of income, which are two different percentage concepts.  But the point is to compare the two.

As the chart shows, the top marginal income tax rate exceeded 90% in the 1950s and early 1960s.  The top rate then came down sharply, to generally 70% until the Reagan tax cuts of the early 1980s, when they fell to 50% and ultimately to just 28%.  They then rose under Clinton to almost 40%, fell under the Bush II tax cuts to 35%, and then returned under Obama to the rate of almost 40%.

Were GDP growth rates faster in the periods when the marginal tax rates were lower, and slower when the tax rates were higher?  One cannot see any indication of it in the chart. Indeed, even though the highest marginal tax rates are now far below what they were in the 1950s and early 1960s, GDP growth over the last decade and a half has been less than it what was when tax rates were not just a little bit, but much much higher.  If cuts in the marginal tax rates are supposed to spur growth, one would have expected to see a significant increase in growth between when the top rate exceeded 90% and where it is now at about 40%.

Indeed, while I would not argue that higher tax rates necessarily lead to faster growth, the data do in fact show higher tax rates being positively correlated with faster growth.  That is, the economy grew faster in years when the tax rates were higher, not lower.  A simple statistical regression of the GDP growth rate on the top marginal income tax rate of the year found that if the top marginal tax rate were 10% points higher, GDP growth was 0.57% points higher.  Furthermore, the t-statistic (of 2.48) indicates that the correlation was statistically significant.

Again, I would not argue that higher tax rates lead to faster GDP growth.  Rather, much more was going on with the economy over this period which likely explains the correlation. But the data do indicate that very high top marginal income tax rates, even over 90%, were not a hindrance to growth.  And there is clearly no support in the evidence that lower tax rates lead to faster growth.

The chart above focuses on the long-term impacts, and does not find any indication that tax cuts have led to faster growth.  An earlier post on this blog looked at the more immediate impacts of such tax rates cuts or increases, focussing on the impacts over the next several years following major tax rate changes.  It compared what happened to output and employment (as well as what happened to tax revenues and to the fiscal deficit) in the immediate years following the Reagan and Bush II tax cuts, and following the Clinton and Obama tax increases.  What it found was that growth in output and employment, and in fiscal revenues, were faster following the Clinton and Obama tax increases than following the Reagan and Bush II tax cuts.  And not surprisingly given this, the fiscal deficit got worse under Reagan and Bush II following their tax cuts, and improved following the Clinton and Obama tax increases.

C.  The Economics of the Impact of Tax Rates on Work Effort

The “supply siders” who argue that cuts in income taxes will lead to faster growth base their case on what might seem (at least to them) simple common sense.  They say that if you tax something, you will produce less of it.  Tax it less, and you will produce more of it. And they say this applies to work effort.  Income taxes are a tax on work.  Lower income tax rates will then lead to greater work effort, they argue, and hence to more production and hence to more growth.  GDP growth rates will rise.

But this is wrong, at several levels.  One can start with some simple math.  The argument confuses what would be (by their argument) a one-time step-up in production, with an increase in growth rates.  Suppose that tax rates are cut and that as a result, everyone decides that at the new tax rates they will choose to work 42 hours a week rather than 40 hours a week before.  Assuming productivity is unchanged (actually it would likely fall a bit), this would lead to a 5% increase in production.  But this would be a one time increase. GDP would jump 5% in the first year, but would then grow at the same rate as it had before.  There would be no permanent increase in the rate of growth, as the supply siders assert.  This is just simple high school math.  A one time increase is not the same as a permanent increase in the rate of growth.

But even leaving this aside, the supply sider argument ignores some basic economics taught in introductory microeconomics classes.  Focussing just on the economics, what would be expected to happen if marginal income tax rates are cut?  It is true that there will be what economists call “substitution effects”, where workers may well wish to work longer hours if their after-tax income from work rises due to a cut in marginal tax rates. But the changes will also be accompanied by what economists call “income effects”.  Worker after-tax incomes will change both because of the tax rate changes and because of any differences in the hours they work.  And these income effects will lead workers to want to work fewer hours.  The income and substitution effects will work in opposite directions, and the net impact of the two is not clear.  They could cancel each other out.

What are the income effects, and why would they lead to less of an incentive to work greater hours if the tax rate falls?:

a)  First, one must keep in mind that the aim of working is to earn an income, and that hours spent working has a cost:  One will have fewer hours at home each day to enjoy with your wife and kids, or for whatever other purposes you spend your non-working time. Economists lump this all under what they call “leisure”.  Leisure is something desirable, and with all else equal, one would prefer more of it.  Economists call this a “normal good”.  With a higher income, you would want to buy more of it. And the way you buy more of it is by working fewer hours each day (at the cost of giving up the wages you would earn in those hours).

Hence, if taxes on income go down, so that your after-tax income at the original number of hours you work each day goes up, you will want to use at least some portion of this extra income to buy more time to spend at home.  This is an income effect, and will go in the opposite direction of the substitution effect of higher after-tax wages leading to an incentive to work longer hours.  We cannot say, a priori, whether the income effect or the substitution effect will dominate.  It will vary by individual, based on their individual preferences, what their incomes are, and how many hours they were already working.  It could go either way, and can only be addressed by looking at the data.

b)  One should also recognize that one works to earn income for a reason, and one reason among many is to earn and save enough so that one can enjoy a comfortable retirement. But in standard economic theory, there is no reason to work obsessively before retirement so that one will then have such a large retirement “nest egg” as to enjoy a luxurious life style when one retires.  Rather, the aim is to smooth out your consumption profile over both periods in your life.

Hence if income tax rates are cut, so that your after-tax incomes are higher, one will be able to save whatever one is aiming for for retirement, sooner.  Hence it would be rational to reduce by some amount the hours one seeks to work each day, and enjoy them with your wife and kids at home, as your savings goals for retirement can still be met with those fewer hours of work.  This is an income effect, and acts in the direction of reducing, rather than increasing, the number of hours one will choose to work if there is a general tax cut.

c)  More generally, one should recognize that incomes are earned to achieve various aims. Some of these might be to cover fixed obligations, such as to pay on a mortgage or for student debt, and some might be quasi-fixed, such as to provide for a “comfortable” living standard for one’s family.  If those aims are being met, then time spent at leisure (time spent at home with the family) may be especially attractive.  In such circumstances, the income effect from tax cuts might be especially large, and sufficient to more than offset the substitution effects resulting from the change in the after-tax wage.

Income effects are real, and it is mistake to ignore them.  They act in the opposite direction of the substitution effect, and will act to offset them.  The offset might be partial, full, or even more than full.  We cannot say simply by looking at the theory.  Rather, one needs to look at the data.  And as noted above, the data provdes no support to the suppostion that lower tax rates will lead to higher growth.  Once one recognizes that there will be income effects as well as substitution effects, one can see that this should not be a surprise.  It is fully consistent with the theory.

One can also show how the income and substitution effects work via some standard diagrams, involving indifference curves and budget constraints.  These are used in most standard economics textbooks.  However, I suspect that most readers will find such diagrams to be more confusing than enlightening.  A verbal description, such as that above, will likely be more easy to follow.  But for those who prefer such diagrams, the standard ones can be found at this web posting.  Note, however, that there is a mistake (a typo I assume) in the key Figures 2A and 2B.  The horizontal arrows (along the “leisure” axis) are pointed in the opposite direction of what they should (left instead of right in 2A and right instead of left in 2B).  These errors indeed serve to emphasize how even the experts with such diagrams can get confused and miss simple typos.

D.  But There is More to the Hours of Work Decision than Textbook Economics

The analysis above shows that the supply-siders, who stress microeconomic incentives as key, have forgotten half of the basic analysis taught in their introductory microeconomics classes.  There are substitution effects resulting from a change in income tax rates, as the supply-siders argue, but there are also income effects which act in the opposite direction. The net effect is then not clear.

However, there is more to the working hours decision than the simple economics of income and substitution effects.  There are social as well as institutional factors.  It the real world, these other factors matter.  And I suspect they matter a good deal more than the standard economics in explaining the observation that we do not see growth rates jumping upwards after the several rounds of major tax cuts of the last half century.

Such factors include the following:

a)  For most jobs, a 40 hour work week is, at least formally, standard.  For those earning hourly wages, any overtime above 40 hours is, by law, supposed to be compensated at 50% above their normal hourly wage.  For workers in such jobs, one cannot generally go to your boss and tell him, in the event of an income tax increase say, that you now want to work only 39 1/2 hours each week.  The hours are pretty much set for such workers.

b)  There are of course other workers compensated by the hour who might work a variable number of hours each week at a job.  These normally total well less than 40 hours a week.  These would include many low wage occupations such as at fast food places, coffee shops, retail outlets, and similarly.  But for many such workers, the number of hours they work each week is constrained not by the number of hours they want to work, but by the number of hours their employer will call them in for.  A lower income tax rate might lead them to want to work even more hours, but when they are constrained already by the number of hours their employer will call them in for, there will be no change.

c)  For salaried workers and professionals such as doctors, the number of hours they work each week is defined primarily by custom for their particular profession.  They work the hours that others in that profession work, with this evolving over time for the profession as a whole.  The hours worked are in general not determined by some individual negotiation between the professional and his or her supervisor, with this changing when income tax rates are changed.  And many professionals indeed already work long hours (including medical doctors, where I worry whether they suffer from sleep deprivation given their often incredibly long hours).

d)  The reason why one sees many professionals, including managers and others in office jobs, working such long hours probably has little to do with marginal income tax rates.  Rather, they try to work longer than their co-workers, or at least not less, in order to get promoted.  Promotion is a competition, where the individual seen as the best is the one who gets promoted.  And the one seen as the best is often the one who works the longest each day.  With the workers competing against each other, possibly only implicitly and not overtly recognized as such, there will be an upward spiral in the hours worked as each tries to out-do the other.  This is ultimately constrained by social norms.  Higher or lower income tax rates are not central here.

e)  Finally, and not least, most of us do take pride in our work.  We want to do it well, and this requires a certain amount of work effort.  Taxes are not the central determinant in this.

E.  Summary and Conclusion

I fully expect there to be a push to cut income tax rates early in the Trump presidency.  The tax plan Trump set out during his campaign was similar to that proposed by House Speaker Paul Ryan, and both would cut rates sharply, especially for those who are already well off. They will argue that the cuts in tax rates will spur growth in GDP, and that as a consequence, the fiscal deficit will not increase much if at all.

There is, however, no evidence in the historical data that this will be the case.  Income tax rates have been cut sharply since the Eisenhower years, when the top marginal income tax rate topped 90%, but growth rates did not jump higher following the successive rounds of cuts.

Tax cuts, if they are focused on those of lower to middle income, might serve as a macro stimulus if unemployment is significant.  Such households would be likely to spend their extra income on consumption items rather than save it, and this extra household consumption demand can serve to spur production.  But tax cuts that go primarily to the rich (as the tax cuts that have been proposed by Trump and Ryan would do), that are also accompanied by significant government expenditure cuts, will likely have a depressive rather than stimulative effect.

The supply-siders base their argument, however, for why tax cuts should lead to an increase in the growth rate of GDP, not on the macro effects but rather on what they believe will be the impact on microeconomic incentives.  They argue that income taxes are a tax on work, and a reduction in the tax on work will lead to greater work effort.

They are, however, confused.  What they describe is what economists call the substitution effect.  That may well exist.  But there are also income effects resulting from the changes in the tax rates, and these income effects will work in the opposite direction.  The net impact is not clear, even if one keeps just to standard microeconomics.  The net impact could be a wash.  Indeed, the net impact could even be negative, leading to fewer hours worked when there is a cut in income taxes.  One does not know a priori, and you need to look at the data.  And there is no indication in the data that the sharp cuts in marginal tax rates over the last half century have led to higher rates of growth.

There is also more to the working hours decision than just textbook microeconomics. There are important social and institutional factors, which I suspect will dominate.  And they do not depend on the marginal rates of income taxes.

But if you are making an economic argument, you should at least get the economics right.

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

A)  Introduction

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

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

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

B)  Health Insurance

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

C.  The Health Care Market

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

D.  Conclusion

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

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

An Increase in Government Spending Can Reduce the Debt to GDP Ratio: Econ 101

Most people realize that it is not the absolute value of the government debt that matters, but rather the ratio of that debt to GDP.  A larger economy can support a larger debt.  But most people will also think instinctively that an increase in government spending will necessarily lead to an increase in the government debt to GDP ratio.  It is not surprising that they should think so.  But it is wrong.

Whether the government debt to GDP ratio will rise or will fall when government spending increases will depend on economic conditions and other structural factors.  In conditions of high unemployment and where the Central Bank has driven the interest rates it can control essentially to zero, such as exist now in the US and Europe, an increase in government spending will increase the demand for goods and services, and hence will increase the demand for labor to produce those goods and services.

Employment and output will then rise. How much they will rise will depend on the multiplier, but as was discussed in a previous Econ 101 post on this site, in conditions of high unemployment and close to zero Central Bank controlled interest rates such as currently exist, the multiplier will be relatively high.  The higher incomes that then follow from the higher employment and output will also then lead to higher tax revenues, as a share of the higher incomes will be paid in taxes.

Hence the addition to the deficit and thus the public debt will be less than simply the increase in government spending, due to the higher tax revenues.  With GDP higher due to the greater demand and with the debt also possibly higher but not by as much, the debt to GDP ratio could fall.  And indeed, under conditions such as currently exist in the US and Europe, the debt ratio will almost certainly fall.

To see this, one can start with a simple numerical example.  Suppose one starts with a GDP equal to 100 units (it could be $100 billion), a public debt of 50 (or 50% of GDP, roughly where it was in the US in 2009), a multiplier equal to 2.0 (a reasonable estimate for the US in recent years), and a marginal tax rate on additional income of 30% (also a reasonable estimate for what it is for US federal government level revenues; it would be higher if one included state and local government revenues).

In these conditions, suppose government spending rises by 1 unit.  With a multiplier of two, GDP will then rise by 2 units.  Tax revenues will then rise by 0.6 units, when the marginal tax rate is 30% on the additional 2 units of GDP.  The government deficit, and hence the public debt, will rise by 0.4 units, equal to the extra 1 unit of government spending less the 0.6 units of additional tax revenue.  The resulting public debt will be 50.4, while GDP will then be 102, and the ratio of 50.4/102 is equal to 0.494.  Hence the debt ratio fell from 50% to 49.4% when government spending rose by 1.  Higher government spending led to a reduction in the debt to GDP ratio.  While the total debt rose, GDP rose by proportionately more, leading to a fall in the debt to GDP ratio.

Further numerical examples will help give a feel to what is going on:

Impact on Debt/GDP Ratio from a One Unit Increase in Government Spending
        Scenario: (a) (b) (c) (d) (e)
GDP: Y 100 100 100 100 100
Public Debt: D 50 70 30 50 50
multiplier: m 2.0 2.0 2.0 0.5 3.5
marginal tax rate: t 0.3 0.3 0.3 0.3 0.3
pre-change D/Y 0.500 0.700 0.300 0.500 0.500
Change in G 1 1 1 1 1
Change in Y 2 2 2 0.5 3.5
Change in D 0.4 0.4 0.4 0.85 -0.05
Resulting D/Y 0.494 0.690 0.298 0.506 0.483

Scenario (a) is the case just discussed.  With an initial public debt ratio of 50%, a multiplier of 2, and a marginal tax rate of 30%, a unit increase in government spending will lead the debt to GDP ratio to fall to 49.4%.  This is robust to different initial debt to GDP ratios:  The debt to GDP ratio will fall with higher government spending with an initial debt ratio of 70% (scenario (b), with the debt ratio where it was in FY2012) or at 30% (scenario (c), almost what the debt ratio had fallen to at the end of the Clinton administration, before the Bush tax cuts).

Under conditions where the economy is close to full employment, so that the multiplier will be relatively small, the debt ratio could rise with the higher government spending.  GDP will not rise by much, if at all, if the economy is already producing at or close to full employment levels.  The denominator in the ratio hence will not rise by much, if at all, while the numerator (the level of debt) will rise by the level of extra government spending, with only limited or no extra tax revenues to offset this since GDP has not increased by much.   Scenario (d) provides an example, with a multiplier of 0.5.  The debt ratio will rise from 50% to 50.6% in this example, when government spending rises by 1.

At the other extreme, a very high multiplier may lead to such a large increase in GDP that the extra tax revenues thus generated are greater than the increase in government spending, leading to an actual decrease in the deficit and hence the debt.  Scenario (e) presents an example, with a multiplier of 3.5.  Debt actually falls from 50 units to 49.95 units, despite the increase in government spending by 1 unit, and the debt to GDP ratio falls from 50% to 48.3%.

One will also get this result if the extra tax revenues generated for a given increase in GDP is sufficiently high.  The above examples assume a marginal tax rate of 30%.  More generally, if the marginal tax rate times the multiplier is greater than one (e.g. 30% times 3.5 = 1.05 in the example above), then the absolute value of the debt will fall with the higher government spending.

It may well be unlikely, however, that the multiplier will be as high as 3.5, even with the current high unemployment in the US and Europe.  Thus it is unlikely that the absolute value of the debt will fall with higher government spending, even in conditions of high unemployment.  But as was discussed above, with a reasonable estimate of the multiplier at around 2, one will see the debt to GDP ratio fall, under conditions such as now exist in the US and Europe.

For those with some mathematical expertise, it is straightforward to derive the specific conditions which will determine whether the debt to GDP ratio will rise or fall with an increase in government spending.  This requires some elementary differential calculus, and I will not go through the derivation here.  But the final result is that the debt to GDP ratio will fall if:

(t + D/Y) – (1/m) > 0

and the debt ratio will fall if the sum on the left is less than zero.  That is, the debt to GDP ratio will fall if the marginal tax rate (t), plus the initial debt to GDP ratio (D/Y), minus the inverse of the multiplier (m), is greater than zero (and will rise if the sum is less than zero). Thus if t=30%, D/Y=50%, and m=2 (so 1/m=0.5), with a sum then of 0.3 + 0.5 – 0.5 = 0.3, which is greater than zero, the debt to GDP ratio will fall.

The material above is straightforward.  There is nothing deep or complex.  It also just examines the immediate impact on the public debt to GDP ratio from an increase in government spending.  For a more elaborate look at the long-term impact, see the paper of Brad DeLong and Larry Summers published in 2012.  They show there that higher government spending will not only spur GDP in the short run under conditions such as exist now, but also that such spending will likely pay for itself in the long run through its long term positive impact on growth.

But this post simply focuses on the short term, and shows that counter to what many people might at first believe, higher government spending can lead to a fall in the public debt to GDP ratio.  All this result requires is the recognition that under conditions such as exist now, when unemployment is high and Central Bank controlled interest rates are close to zero, there will be a significant multiplier effect from an increase in government spending.  The resulting increase in GDP along with the extra tax revenues thus generated could very well then lead to a fall in the debt to GDP ratio.  Indeed, with the conditions and parameters such as now exist in the US and Europe, one should expect this result.