The Percentage Increase in the US Death Rate in 2020 Was Higher than in the Worst Year of the Spanish Flu

The Covid-19 pandemic this year has often been described as the worst public health crisis in the US since the Spanish Flu of 1918.  And there has certainly been nothing since then that compares.  But early estimates indicate that the increase in deaths this year may have even been worse than during the worst year of the Spanish Flu.

Population has of course grown, so the relevant figures to compare over time are death rates – the share of the population that died each year.  In addition, while it should not make much of a difference to the annual percentage changes, for longer-term trends one should look at the age-adjusted figures, which control for changes in the age structure of the population (as older people are more likely to die in any given year).

The National Center for Health Statistics, part of the CDC, provides such figures for the years 1900 to 2018.  The CDC also has separate figures for deaths and population for recent years, up to 2019, and hence the crude (not age-adjusted) death rate can be calculated for 2018 and 2019.  From this one can estimate what the percentage change in the death rate was for 2019, which should be close to the percentage change in the age-adjusted rate as the population age structure changes only slowly over time.

Finally, the estimate for what deaths in 2020 will be was obtained from a December 22 Associated Press article reporting on recent CDC figures.  It reports that the US “is on track to see more than 3.2 million deaths this year”.  And this may be a conservative estimate.  It would imply an increase of 345,162 over the number of deaths in 2019.  But as I write this, the number of deaths reported by Johns Hopkins from Covid-19 alone was 345,271.  While a share of those who have died due to Covid-19 this year would have died later in the year from other causes, analyses of “excess deaths” have consistently found that the increase in deaths this year has been well in excess of the number that has been attributed specifically to Covid-19.  For example, a CDC study in October estimated that for the period from late January (when Covid-19 was first recognized in the US) to October 3, there were 299,028 more who had died than one would have expected under normal conditions, while (over that period) only 198,081 deaths were directly attributed to Covid-19.  That is, the increase in the number of deaths during the period (over what would have been expected had this been a normal year) was 51% higher than the number of recorded deaths due to Covid-19.

We will not have the final figures for 2020 for some time.  But even accepting the conservative estimate of 3.2 million deaths in the US in 2020, the percentage increase in the number of deaths in the US per 100,000 of population was about the same as (and indeed very slightly higher than) in 1918, the year of the Spanish Flu.  In both years, the increase in the death rate was close to 12%.

I was surprised that the increase in the death rate this year was anywhere close to what it was during the Spanish Flu.  It is truly astounding.  But it is especially surprising given that, as the AP article cites, the number of deaths of Americans in 1918 rose by 46%.  How can that be consistent with the 12% increase in the death rates?  There are several factors that account for this.

To start, the increase (using another CDC table) was from 981,239 deaths in 1917 to 1,430,079 in 1918, or an increase of 448,840 (or 45.7%).  But note that a footnote to that CDC table indicates that these figures include military deaths in World War I.  This differs from the practice in later years, where military deaths are excluded.   World War I military deaths totalled 116,516 (according to official Department of Veterans Affairs figures), and almost all came in 2018.  While some share of these were due to soldiers dying from the Spanish Flu, I do not have figures for what those might have been.  Leaving that aside, the increase in non-military deaths in 1918 would have been close to 34%, and adjusting for US population growth in those years the increase in the death rate would have been 32%.

That is still significantly higher than the close to 12% increase in the death rate in the CDC figures.  There are two reasons for this.  First, note, as was discussed above, these CDC figures adjust for changes in the age structure of the US population over the century, and use population weights of the year 2000.  The US age structure has shifted markedly since 1918, with a far higher share of the population now in the older age groups.  Had the age structure in 1917 and 1918 been the same as it was in 2000, the base overall death rates in those years, excluding the impact of the Spanish Flu, would have been higher.  Second, the Spanish Flu was especially lethal for those in the middle age groups – those in their 20s, 30s, and 40s.  The young and the old were less affected.  But those in the middle age groups account for a smaller share of the age structure using the weights of the year 2000 than they had in 1918.  While I do not have the data to allow me to decompose the specific numbers, simple simulations with plausible parameters suggest that at the year 2000 population shares, the increase in the age-adjusted death rate of 12% can be consistent with the 32% increase observed in 1918 when military deaths are excluded, or the 46% overall increase when military deaths and population growth are included.

Few expected when Covid-19 was first detected in the US that the death rate this year would be anywhere close to what it was during the Spanish Flu.  But it has been.  Sadly, the crisis was severely exacerbated by the singularly incompetent management of the Trump administration.  The Washington Post had a particularly good summary of the many things the Trump administration got wrong in addressing Covid-19 this year in a December 26 editorial.  Or one can compare the US record to that of other countries.  The US this year had 1,040 deaths due to Covid-19 per million of population.  Canada had 412 per million, or 40% of the US rate.  If the US had simply matched what its neighbor to the north was able to do, the US would have had 137,000 deaths instead of more than 345,000, or 208,000 fewer deaths.  And others have done even far better.  New Zealand has had only 5.0 deaths per million, and Taiwan just 0.3 per million.

Better management would have been possible.  But the Trump administration failed, and hundreds of thousands of Americans have died.  As the Post editorial noted:

“It was always going to be hard. But the worst did not have to happen. It happened because Mr. Trump failed to respect science, meet the virus head-on and be honest with the American people.”

More Evidence on the Effectiveness of Masks in Limiting the Spread of Covid-19

A.  Introduction

States where a high share of the population normally wear face masks when out in public also have a significantly lower transmission of the virus that causes Covid-19.  The chart above shows the relationship between the wearing of face masks and the prevalence of Covid-19 in the community (measured in ways that will be discussed below).  It is remarkable how tight that relationship is, as well as how steep.  Wearing masks has a large effect.  States differ between each other in dozens of different ways that can significantly affect the transmission of Covid-19.  Yet the share of the population who report that they wear face masks most or all of the time when they go out in public can explain by itself most of the variation in the prevalence of Covid-19 across the states.

The data also show a remarkably strong consistency between the share of the population in a state that wear masks and whether that state voted for Clinton or Trump in 2016.  That there is such a relationship is not surprising.  Bur what is surprising is that the relationship is close to perfect.  All but one of the states that voted for Clinton in 2016 report a mask-wearing share of 88% or above.  The one exception is Colorado, with a share of 87.4%.  And every single Trump-voting state has a reported share that is below 88%.  Furthermore, several of the states where the vote margin was close (and where current polling indicates Biden would receive the most votes) are on the borderline.  Such states include Pennsylvania, Michigan, and Wisconsin, each with a share between 87 and 88%.

This post will explain where this data comes from, the statistical significance of the relationships, and how one can appropriately interpret the results – for the chart above and two more below.  And I should note that the idea for a chart similar to that above, using this data set, came from an article by the Washington Post reporter Christopher Ingraham that appeared on October 23 at the Washington Post website.  The analysis here extends what Ingraham had.

B.  A Higher Share of People Wearing Masks is Associated With A Lower Incidence of Covid-19 in the Community

The chart at the top of this post shows a remarkably tight relationship between the share of the population who say they normally or always wear a mask when out in public, and the prevalence of Covid-19 in those states (or more precisely, the share of the population who are personally aware of someone in the local community with Covid-19 like symptoms – this will be discussed below).  With a higher share wearing masks, the prevalence is lower.  There are qualifiers that need to be considered on the source of the data and how one should interpret the apparent relationship, but that there is such an association is clear.

The data underlying the analysis comes from a new set assembled as part of the COVIDcast project at Carnegie Mellon University.  With the onset of the Covid-19 crisis, this group at Carnegie Mellon designed a simple survey that participants could sign on to via Facebook, to provide data on the spread of Covid-19.  While the questionnaire has evolved over time, the most recent version (that they call Wave 4) was launched on September 8, and includes questions on mask usage.  What makes the survey particularly interesting is that they receive a huge number of responses daily (averaging over 40,000 per day from September 8 to October 7).  This allows for a statistically significant sample at not just the state level (which I focus on here), but also for most counties in the US.

There are, of course, potential biases in such a sample that must be corrected for.  Those using Facebook, and in particular those willing to participate in such a survey seen via Facebook, will not necessarily be representative of the population.  But the Carnegie-Mellon analysts use various methods, including adjusting for the demographic characteristics of the respondents, to correct for this.  It cannot be perfect, but is likely to be reasonable.

One should also recognize that the behavior respondents record and what they actually do (such as on mask usage) may differ.  Respondents may exaggerate the consistency with which they in fact use masks.  But the Carnegie Mellon researchers have compared their results with that found from other sources, and have concluded they are consistent.  Furthermore, if there is a bias, one might expect that bias to be similar across states.  Perhaps all the responses (on, say, mask usage) are biased upwards – we may all say that we use masks more frequently than we in fact do.  But if that bias is similar (on average) across all of us, then the variation across states would remain.  They would just all be shifted upwards.  Still, one should remain cognizant that the findings are based on self-reported responses, and may be biased.

The Wave 4 questionnaire had questions on a variety of topics.  The specific question on mask usage was whether, in the past five days, the respondent had worn a mask when in public:  all of the time, most of the time, some of the time, a little of the time, or none of the time.  A mask wearer was classified as one who said that they wore a mask all or most of the time.

For whether the respondent might have Covid-19, the questionnaire asked whether they or someone in their immediate household suffer from Covid-like symptoms – specifically, whether they have a fever of 100℉ or more plus at least one of several additional possible conditions (sore throat, cough, shortness of breath, or difficulty breathing).  Thus, while they also ask later whether the person has had a formal test for Covid-19 (they may or may not have), the response reported here is for whether they have Covid-like symptoms.  Similarly, the figure for the share reporting possible cases of Covid-19 in the community (as in the chart at the top of this post), is based on whether the respondent was aware of others in their local community – who they know personally – who are suffering from Covid-19 like symptoms (with the conditions as defined for the individual).

The survey was designed this way in part as a purpose was to see whether such self-reported conditions could help local health authorities determine whether Covid-19 might be spreading in their communities, and to know this even before testing might find it.  And the results were encouraging.  The Carnegie Mellon researchers found that the daily and highly localized monitoring that was possible with the extremely large sample size of their survey generally performed well in tracking what was later found, via confirmed tests, on the spread of Covid-19 in that locality.

The resulting relationship between the respondents reporting that they wore masks when out in public all or most of the time (in the past five days), and the share reporting that they were personally aware of people in their community exhibiting Covid-19 like symptoms, is what is plotted (in terms of state averages) in the chart above.  To smooth out possible day to day statistical noise in the data (and also to be consistent with 7-day averages for reported confirmed cases of Covid-19, to be discussed below), the data shown in the chart is for the 7-day average covering October 15 to October 21 (the most recent days available when I downloaded this).

The straight line in black in the chart is the ordinary least squares regression line – the line that best fits the scatter of observations.  And from this one can calculate the statistical measure commonly referred to as the R-squared, which can vary between 0 and 1 (or 0% to 100%).  The R-squared indicates what share of the variation in the scatter of observations would be predicted by simply knowing where this straight regression line passes.  If the scatter points are all close to that line, the R-squared will be high.  In the limit, if they all lie precisely at that line, the R-squared will equal 1.  At the other extreme, if the scatter is all over and basically random, then the R-squared will be close to 0.

R-squared values are normally low for what are termed cross-section analyses (such as this, i.e. across the different states).  There are numerous reasons states differ from each other, and just knowing one factor (in this case the share who wear masks) will normally produce only a loose correlation with the result of interest (in this case the share reporting they are personally aware of people with Covid-19 like symptoms in the community).  Economists and other analysts would normally be happy to find a R-squared of 20% or so in such cross-state analyses, and elated if it is 30%.

In the chart here, the R-squared was 66%.  This is remarkable.  It indicates that if all one knows is the share of those wearing masks, we could predict 66% of the variation in the share reporting that they are aware of Covid-19 like symptoms in the community.  Despite the many reasons why states may differ in their incidence of Covid-19, this one factor (the share of those wearing masks) will by itself predict two-thirds of the variation.  Furthermore, one state (Wyoming) is an outlier.  If one runs the regression over the full sample but with this single case removed, the R-squared rises to an astonishing 76%.

There are further reasons to be surprised that such a strong statistical relationship comes through.  One is that the data come from a survey.  Poor (possibly misunderstood) responses, or lack of knowledge on whether others in the community are suffering from Covid-19 like symptoms (due, perhaps, to not knowing many in the community, or not being in touch with them) will normally add statistical noise.   But it appears that the extremely large sample sizes here have offset that.  We still see a clear and strong relationship.

One should also recognize that states in the US are not isolated from each other.  There is a substantial amount of travel from one to the other.  Thus even if mask-wearing is common in one state, with infection rates then low, there may be a continual “re-seeding” of the infection brought in by travelers from states that are not as conscientious in wearing masks.  This would weaken the relationship between local mask-wearing and local infection rates.  Yet despite this, we still see a strong and highly significant effect.

One must also always note that what is being examined is a correlation between two variables, and that correlation does not necessarily indicate causation.  One must examine whether it may in each individual analysis.  In the case here, however, one can readily see a mechanism where a higher share of the population wearing masks will lead to a lower share of the population in the community being infected with the virus that causes Covid-19.  But what would be the mechanism where a higher incidence of Covid-19 in the community would affect the share wearing masks?  There might well be such a causal relationship, but one would then expect it to act in precisely the opposite way to the relationship found in the data:  When a high share of the local community is infected with Covid-19, one would expect a high share of the population then to wear masks.  It would be rational to be extra careful.  But the relationship seen in the data is the opposite:  The data show that a high share of the community being infected is associated with a low share of the population wearing masks.  The line slopes downwards.  It is reasonable to conclude that the causation goes from the wearing of masks to the share infected, not the reverse.

There is, however, a factor in the statistical analysis which may well be quite important.  The data here show a high degree of correlation (negative correlation, as the line slopes downwards) between the wearing of masks and the incidence of Covid-19 in the locality.  But the data on the wearing of masks may itself be, and indeed likely will be, highly correlated with other actions that may be taken to limit the spread of Covid-19.  Responsible individuals who wear masks likely also are careful to practice social distancing, to wear gloves when shopping, to avoid crowded bars and nightclubs, and to avoid crowded events where many of the attendees do not wear masks (such as Trump rallies).  Thus it may not simply be the wearing of masks that explains why a high share of the local population wearing masks in an area is correlated with a more limited spread of Covid-19:  It is may well be the whole set of socially responsible behaviors that matter.

This is true and should be recognized.  While the direct measure here is the share of the population that mostly or always wear masks, such behavior likely goes together with a full set of socially responsible behaviors that together lead to a lower spread of Covid-19.  While we will often refer to the wearing of masks as the factor that is associated with a limited spread of Covid-19, we should recognize that the wearing of masks likely goes together with a broader set of behaviors that together are important.

C.  A Higher Share of People Wearing Masks is Associated With A Lower Incidence of Self-Reported Cases of Covid-19, and a Lower Official Count of Confirmed Cases of Covid-19 

Two other charts are of interest.  The first examines the association between the share reporting they mostly or always wear masks, and whether they (or someone in their household) is exhibiting the symptoms of Covid-19:

One again sees a strong (negative) association between the wearing of masks and cases of those with symptoms consistent with Covid-19 (in this case of the survey respondents themselves).  And the R-squared measures of the degree of correlation are even higher:  70% for the full sample, and 78% if the single case of Wyoming is removed.  This again suggests that the wearing of masks (along with other responsible behaviors such as social distancing, etc.) is associated with a more limited spread of Covid-19.  Furthermore, the impact is not simply statistically significant, but also large.  Based just on the values on the regression line, a state with a reported 69% who wear masks (such as South Dakota) compared to a state (or locale) with a reported 97% who wear masks (such as Washington, DC) would be expected to have more than 6.1 times the share of cases.  (The actual South Dakota vs. DC ratio is even higher, at over 7, as South Dakota is above the regression line and DC a bit below).

The findings are also consistent with the official counts of new confirmed cases of Covid-19 per 100,000 of population:

The data on the official counts were downloaded from the COVIDcast site, but they in turn were obtained from compilations at USAFacts.  And USAFacts obtained the figures from state public health agencies.

The relationship between those reporting that they wear masks most or all of the time, and the number of confirmed new cases by state (per 100,000 of population, and a seven-day average covering the October 15 to October 21 week), remains significant, negative, and strong.  The states where mask-wearing is a higher share of the population routinely wear masks (as reported in the surveys) see a significantly lower incidence of confirmed new cases of Covid-19.  The statistical relationship is not as strong as before (the R-squared is 47%), but this is not surprising.  The average number of daily new confirmed cases over the 7-day period (October 15 to 21) counts only those with a test result, for a new case, reported over those seven days.  The number of people who are sick with Covid-19 will include not just those newly-tested individuals, but also others who have been sick for some time plus individuals with Covid-19 like symptoms who may have the disease but have not (or not yet) been tested.  It is not surprising that the correlation of mask-wearing with just a slice of the population who are sick with Covid-19 will be weaker.  But the R-squared of 47% is still quite high.

D.  Conclusion:  The Effectiveness of Wearing Masks

Masks work by reducing the transmission of an infectious disease to and from others.  They are not perfect.  But neither do they need to be perfect, as one can see from the simple arithmetic of the spread of an infectious disease.

Infectious diseases are viruses, which cannot survive on their own but can only survive by spreading from person to person.  Any individual will have a disease such as Covid-19 for a finite period of time (a few weeks, normally, in the case of Covid-19) beyond which they would either have recovered or (in a small percentage of the cases) have died.  And they will normally only be able to infect others for about a week (starting one week after they themselves had become infected), although possibly for up to two weeks.

Any such infectious disease will therefore spread when, on average, each individual with the disease spreads the disease on to more than one other person.  And given the arithmetic of compounding, that number can grow to be very large very quickly.  If each individual on average infects 2 other individuals in each cycle, then after just 10 cycles the one individual with the disease would have led to the infections of over 1,000.  It doubles in each cycle.  If each cycle is, on average, a week and a half (one week for the virus to multiply in the individual, and then one week during which the person can be infectious, so on average will infect others at the mid-point of the second week), those 10 cycles will require only 15 weeks.

But if the wearing of masks (along with other socially responsible behaviors, such as social distancing) reduces the average number of people that an individual with the disease will infect to less than one, then the disease will die out.  And again, with the arithmetic of compounding, this can be quite quick.  Suppose one starts out with 100 individuals with the disease in some locality.  If, on average, each infected individual spreads the disease to another person only half the time, then 100 individuals will spread it to 50 during the first cycle, to 25 in the next, and so on.  One can calculate that if this continues at such a rate, then less than one new person would become infected after just 7 cycles (or 10 1/2 weeks if each cycle is on average a week and a half).  And the disease would have been stopped.

Masks work because they can bring down that reproduction rate (what epidemiologists call Rt) from something above 1.0 to something below.  The example here is that masks (along with other socially responsible behaviors) reduced the Rt to 0.5.  This would be a 75% reduction if the Rt is 2.0 when nothing is done to stop the spread of the disease.  That is not perfect, but it does not need to be perfect to stop the spread.  And 70 to 80% is a reasonable estimate of how effective masks are.  If the US were to reduce the Rt to 0.5 going forward, then the daily number of new cases (currently, as I write this, about 80,000 each day) would fall to less than 100 in just 10 cycles (15 weeks).

This is of course just arithmetic, but the power of compounding is extremely important to recognize when addressing how to bring an infectious disease under control.  Masks do not need to be 100% effective – they merely need to bring the Rt down to less than 1.0.  And in this they are similar to vaccines.  No vaccine is 100% effective.  For the virus that causes Covid-19, the FDA has issued guidelines stating that a vaccine that is safe and has a minimum effectiveness of just 50% would be approved.  It is hoped that the vaccines currently being tested will have a greater degree of effectiveness, but the expectation is that they might at most be perhaps 80% effective, and probably 70% or less is more likely.

That does not mean such vaccines would not be valuable.  As just noted, a vaccine that brought the Rt down to 0.5 would lead to the disease dying out in a relatively short time.  But as Dr. Robert Redfield, the head of the CDC, noted in testimony before Congress on September 16, the effectiveness of masks is similar if not greater than what is expected for a vaccine.  In that testimony he stated, as he has in other fora in recent months (see here and here, for example), that if Americans wore these simple masks, that in “six, eight, 10, 12 weeks we’d bring this pandemic under control.”  And further in that testimony: “I might even go so far as to say this face mask is more guaranteed to protect me against COVID than when I take a COVID vaccine, because the immunogenicity might be 70%, and if I don’t get an immune response the vaccine’s not going to protect me. This face mask will.”

But there is an important proviso.  These effectiveness percentages, whether for masks or for vaccines, reflect how likely they will protect an individual who is exposed to the virus.  But their effectiveness in reducing Rt will then depend on what share of the population wears a mask or is vaccinated.  Usage of masks or vaccinations will never cover 100% of the population, and the reduction in Rt will then be less.  If not enough people follow responsible social behaviors – most importantly wearing masks – or choose not to be vaccinated once a vaccine becomes available, the virus will continue to spread.

Political leadership is therefore critical, but Trump has been unwilling.  Despite the uniform advice of medical professionals in the field, Trump has been unwilling to call on all Americans, and in particular all of his supporters, to wear masks.  He rarely wears masks himself, makes a big show of pulling it off when he has had to wear one (such as when he returned to the White House from Walter Reed Hospital, where he had been treated for Covid-19), and continues to organize large political rallies where few wear masks (but with participants required to sign legal waivers saying that should they become infected as a result, they cannot sue the Trump campaign).  And Trump continues to mock Joe Biden and others who are conscientious in wearing masks when in public.

Why?  Wearing a mask makes it obvious that an infectious disease is circulating.  It makes it obvious that Trump and his administration have failed to bring this terrible disease under control.  Trump continues to assert instead, as he has from the start as well as more recently (during, for example, the second, October 22, debate with Joe Biden), that all is under control and that while there have been “spikes” they are all either “gone” or “will soon be gone”.  From the start in January, Trump has repeatedly asserted that it was “totally under control”, that “It’s going to be just fine”, that it was just a hoax (indeed, a “new hoax” of the Democrats), and that it would soon (Trump asserted in February) just disappear (“like a miracle”).  And Trump’s repeated assertion that “it’s going away” is well-documented in this Washington Post video compilation.

But cases are in fact rising as I write this, and rising rapidly.  Confirmed cases hit over 83,000 on October 23 and then over 83,000 again on October 24 – they had never before exceeded 77,300 in a single day in the US.  Hospitalizations are rising as well, and the surge in hospitalizations is starting again to overwhelm hospitals in parts of the country.  It is absurd to say, as Trump repeatedly insists, that cases are rising only because more testing is being done.  (As one wag put it:  “I stopped gaining weight as soon as I stopped weighing myself.”)

The number of dead in the US from this disease now exceeds (as I write this) over 228,000.  That exceeds the number of soldiers who died in battle in the US Civil War (Union plus Confederate together) of 214,938.  It is 70% greater than the 134,575 Americans who died in battle in World War I plus the Korean War plus the Vietnam War, combined.  This has been the worst public health crisis in the US in more than a century.  Yet Trump claims he has been a great success.

The widespread wearing of masks would be an obvious signal of Trump’s failure.  It is understandable (but not defensible) that he would want to hide such overt signs of his failure before the upcoming election.  But to put short-term politics above public health concerns is deplorable.

The US Has Hit Record High Fiscal and Trade Deficits

A.  Introduction

The final figures to be issued before the election for the federal government fiscal accounts and for the US trade accounts have now been published.  The US Treasury published earlier today the Final Monthly Treasury Statement for the FY2020 fiscal year (fiscal years end September 30), and earlier this month the BEA and the Census Bureau issued their joint monthly report on US International Trade in Goods and Services, with trade data through August.  The chart above shows the resulting fiscal deficit figures (as a share of GDP) for all fiscal years since FY1948, while a chart for the trade deficit will be presented and discussed below.  The figures here update material that had been presented in a post from last month on Trump’s economic record.

The accounts show that the federal fiscal deficit as a share of GDP has reached a record level (other than during World War II), while the trade deficit in goods (in dollar amount, although not as a share of GDP) has also never been so high.  Trump campaigned in 2016 arguing that these deficits were too high, that he would bring them down sharply, and indeed would pay off the entire federal government debt (then at over $19 trillion) within eight years.  Paying off the debt in full in such a time frame was always nonsense.  But with the right policies he could have at least had them go in the directions he advocated.  However, they both have moved in the exact opposite direction.  Furthermore, this was not only a consequence of the economic collapse this year.  They were both already increasing before this year.  The economic collapse this year has simply accelerated those trends – especially so in the case of the fiscal deficit.

B.  The Record High Fiscal Deficit

The federal deficit hit 15.2% of GDP in FY2020 (using the recently issued September 2020 estimate by the CBO of what GDP will be in FY2020).  The highest it had been before (other than during World War II) was 9.8% of GDP in FY2009, in the final year of Bush / first year of Obama, due to the economic collapse in that final year of Bush.  In dollar terms, the deficit this fiscal year hit $3.1 trillion, which was not far below the entire amount collected in tax and other revenues of $3.4 trillion.

This deficit is incredibly high, which does not mean, however, that an increase this year was not warranted.  The US economy collapsed due to Covid-19, but with a downturn sharper than it otherwise would have been had the administration not mismanaged the disease so badly (i.e. had it not neglected testing and follow-up measures, plus had it encouraged the use of masks and social distancing rather than treat such measures as a political statement).  By neglecting such positive actions to limit the spread of Covid-19, the only alternative was to limit economic activity, whether by government policy or by personal decision (i.e. to avoid being exposed to this infectious disease by those unwilling to wear masks).

The sharp increase in government spending this year was therefore necessary.  The real mistake was the neglect by this administration of measures to reduce the fiscal deficit during the period when the economy was at full employment, as it has been since 2015.  Instead of the 2017 tax cut, prudent fiscal policy to manage the debt and to prepare the economy for the risk of a downturn at some point would have been to call for a tax increase under such conditions.  The tax cut, coupled also with an acceleration in government spending, led fiscal deficits to grow under Trump well before Covid-19 appeared.  Indeed, they grew to record high levels for periods of full employment (they have been higher during downturns).  As the old saying goes:  “The time to fix the roof is when the sun is shining.”  Trump received from Obama an economy where jobs and GDP had been growing steadily and unemployment was just 4.7%.  But instead of taking this opportunity to reduce the fiscal deficit and prepare for a possible downturn, the fiscal deficit was increased.

The result is that federal government debt (held by the public) has jumped to 102% of GDP (using the CBO estimate of GDP in FY2020):

The last time the public debt to GDP ratio had been so high was at the end of World War II.  But the public debt ratio will soon certainly surpass that due to momentum, as fiscal deficits cannot be cut to zero overnight.  The economy is weak, and fiscal deficits will be required for some time to restore the economy to health.

C.  The US Trade Deficit is Also Hitting Record Highs in Dollar Terms

In the 2016 campaign, Trump lambasted what he considered to be an excessively high US trade deficit (specifically the deficit in goods, as the US has a surplus in the trade in services), which he asserted was destroying the economy.  He asserted these were due to the various trade agreements reached over the years (by several different administrations).  He would counter this by raising tariffs, on specific goods or against specific countries, and through this force countries to renegotiate the trade deals to the advantage of the US.  Deficits would then, he asserted, rapidly fall.  They have not.  Rather, they have grown:

Trump has, indeed, launched a series of trade wars, unilaterally imposing high tariffs and threatening to make them even higher (proudly proclaiming himself “Tariff Man”).  And his administration has reached a series of trade agreements, including most prominently with South Korea, Canada, Mexico, Japan, the EU, and China.  But the trade deficit in goods reached $83.9 billion in August.  It has never been so high. The deficit in goods and services together is not quite yet at a record high level, although it too has grown during the Trump period in office.  In August that broader deficit hit $67.1 billion, a good deal higher than it ever was under Obama but still a bit less than the all-time record of a $68.3 billion deficit reached in 2006 during the Bush administration, at the height of the housing bubble.

The fundamental reason the deficits have grown despite the trade wars Trump has launched is that the size of the overall trade deficit is determined not by whatever tariffs are imposed on specific goods or on specific countries, nor even by what trade agreements have been reached, but rather by underlying macro factors.  As discussed in an earlier post on this blog, the balance in foreign trade will be equal to the difference between aggregate domestic savings and aggregate domestic investment.  Tariffs and trade agreements will not have a significant direct impact on those macro aggregates.  Rather, tariffs applied to certain goods or to certain countries, or trade agreements reached, may lead producers and consumers to switch from whom they might import items or to whom they might export, but not the overall balance.  Trade with China, for example, might be reduced by such trade wars (and indeed it was), but this then just led to shifts in imports away from China and towards such countries as Viet Nam, Cambodia, Bangladesh, and Mexico.  Unless aggregate savings in the US increases or aggregate investment falls, the overall trade deficit will remain where it was.

Tariffs and trade agreements can thus lead to switches in what is traded and with whom.  Tariffs are a tax, and are ultimately paid largely by American households.  Purchasers may choose either to pay the higher price due to the tariff, or switch to a less desirable similar product from someone else (which had been either more expensive, pre-tariff, or less desirable due to quality or some similar issue), but unless the overall savings / investment balance in the economy is changed, the overall trade deficit will remain as it was.  The only difference resulting from the trade wars is that American households will then need to pay either a higher price or buy a less desirable product.

It is understandable that Trump might not understand this.  He is not an economist, and his views on trade are fundamentally mercantilist, which economists had already moved beyond over 250 years ago.  But Trump’s economic advisors should have explained this to him.  They have either been unwilling, or unable, to do so.

Are the growing trade deficits nevertheless a concern, as Trump asserted in 2016 (when the deficits were lower)?  Actually, in themselves probably not.  In the second quarter of 2020 (the most recent period where we have actual GDP figures), the trade deficit in goods reached 4.5% of GDP.  While somewhat high (generally a level of 3 to 4% of GDP would be considered sustainable), the trade balance hit a substantially higher 6.4% of GDP in the last quarter of 2005 during the Bush administration.  The housing bubble was then in full swing, households were borrowing against their rising home prices with refinancings or home equity loans and spending the proceeds, and aggregate household savings was low.  With savings low and domestic investment moderate (not as high as a share of GDP as it had been in 2000, in the last year of Clinton, but close), the trade deficit was high.  And when that housing bubble burst, the economy plunged into the then largest economic downturn since the Great Depression (largest until this year).

Thus while the trade deficit is at a record level in dollar terms (the measure Trump refers to), it is at a still high but more moderate level as a share of GDP.  It is certainly not the priority right now.  Recovering from the record economic slump (where GDP collapsed at an annualized rate of 31% in the second quarter of 2020) is of far greater concern.  And while expectations are that GDP bounced back substantially (but only partially) in the third quarter (the initial estimate of GDP for the third quarter will be issued by the BEA on October 29, just before the election), the structural damage done to the economy from the mismanagement of the Covid-19 crisis will take substantial time to heal.  Numerous firms have gone bankrupt.  They and others who may survive but who have been under severe stress will not be paying back their creditors (banks and others), so financial sector balance sheets have also been severely weakened.  It will take some time before the economic structure will be able to return to normal, even if a full cure for Covid-19 magically appeared tomorrow.

D.  Conclusion

Trump promised he would set records.  He has.  But the records set are the opposite of what he promised.

Death Rates due to Covid-19: An International Comparison

A.  Introduction

In an interview in early August, when over 1,000 Americans were dying each day due to Covid-19, President Trump was asked how he could consider the disease to be then under control.  He responded “They are dying, that’s true”, and then went on to say “it is what it is.  But that doesn’t mean we aren’t doing everything we can.  It’s under control as much as you can control it.”

If it were true that the disease was “under control as much as you can control it”, then deaths in the US would be similar (as a share of population) to what they are in other countries around the world.  It is the same disease everywhere.  And it would especially be true now, more than nine months into this pandemic.  While much was still not known in the early months on how best to bring this terrible disease under control, we now know what has worked in other countries plus we have results from numerous scientific studies.

In particular, it has become clear that a highly effective measure to contain the virus is also the simplest:  Everyone should just wear a mask when out in public.  The experience of East Asian countries, which will be examined below and where mask-wearing was common even before Covid-19, is consistent with this.  There are also now scientific studies backing this up, as discussed in an editorial published on July 14 in JAMA – the Journal of the American Medical Association.  Dr. Robert Redfield, the head of the CDC, was a co-author of that editiorial, and in interviews and press conferences since he has made clear that if everyone simply wore a mask when in public, the disease would be brought under control in as little as four to eight weeks.

Dr. Redfield said the same in testimony to Congress on September 16 (although with a more cautious time scale, allowing between 6 and 12 weeks for the pandemic to be brought under control).  Indeed, Dr. Redfield noted in that testimony that wearing of masks could be more effective than even a vaccine, as any vaccine that is developed will likely have an effectiveness of 70% or less.  A mask, if worn, can do better.

But getting most of the population to wear a mask requires political leadership, and that has been sorely lacking under President Trump.  Indeed, under Trump the wearing of masks has been turned into an issue of political identity, and he has even mocked Joe Biden and Democrats generally for wearing them.  Trump also asserted, on the same day as Dr. Redfield’s congressional testimony, that the doctor was wrong in his medical advice on masks.

The sad result is that death rates from Covid-19 in the US are now not simply higher than in many other countries around the world, but higher by a large multiple.  There is no basis for asserting that this disease is “under control as much as you can control it”.

We will examine here what other countries have been able to achieve in comparison to what the US has, basically through a series of charts.  A word on the data:  The figures were all calculated from the reported deaths by country from Covid-19 downloaded from the site maintained by the Center for Systems Science and Engineering at Johns Hopkins University.  The data were downloaded on the afternoon of September 15, with the country data current through September 14.

B.  US Compared to Canada and Europe

The chart at the top of this post shows the number of deaths from Covid-19 per day per million of population (based on a rolling seven-day average ending on the date shown), from January 29 through to September 14, in the US, Canada, and Western and Eastern Europe (with Eastern Europe covering the Baltics through to Albania).

Starting with the US, deaths rose rapidly in late March and early April, peaked in mid-April, and then fell.  This continued until early July.  But then, as a number of states rushed to re-open their economies in May and especially June (with the strong encouragement of Trump), death rates rose again, doubling from their not-so-low early-July lows.  They then came down modestly in August and the first half of September, but remain far higher than elsewhere.

The profiles in Europe and Canada are different in an important way.  While death rates rose early in Western Europe (and to rates higher than what came later for the US), when much was still not known about the virus and how it was spread, they were then brought down to very low rates – well below those of the US.  And they have remained low (at least so far).  This is in contrast to the US, where death rates rose in July as lessons on how to manage the virus were ignored.

Canada followed a similar profile to that of Western Europe, although with an initial peak that came later (and with a substantially lower peak – only half that of Western Europe), with then a decline to low levels that have remained low.  In Eastern Europe, early rates in the spring never rose that high, but then still came down by June.  Since then they have risen some, but to rates that remain well below those of the US (at less than a third of the US rate, as of mid-September).

Breaking this down for some of the major countries of Western Europe:

Rates peaked early and at high levels in Italy, France, and the UK, but then all came down and remained down.  The peak in Germany came at roughly the same time as that of the US (but at well less than half the US rate), and then came down to an extremely low level.  As of mid-September, the death rate in Germany is only 2% of the US rate.  If it’s “under control as much as you can control it” in the US, as Trump asserted, why is it that the death rate, per million of population, can be 98% less in Germany?

There are two special cases in Western Europe that are worth examining – Spain and Sweden:

Rates rose rapidly and to quite high levels in Spain early in the crisis.  Its hospital system was overwhelmed and many died.  But then Spain brought down the rates to very low levels by June and July.  They have, however, trended up since mid-August, as it appears Spain opened up its seasonal tourism industry too rapidly (tourism as a share of GDP is far higher in Spain than in any other OECD member country).  But even with the recent increase, the number of deaths per million in Spain remains less than half (45%) of what the rate is in the US as of mid-September.

(One might also note the negative numbers recorded for the number of deaths in Spain due to Covid-19 for a period in late May, as well as an odd spike up in late June.  The reason for this is that Spain revised its counts of the number who had died from Covid-19 as they later reviewed what had been submitted during the peak of the crisis.  A focus on the statistics was not the highest priority earlier – saving lives was.  It is of course impossible for there to be a negative number of deaths.  But figures are recorded each day for the cumulative number of deaths due to Covid-19, and when that total was revised down on May 25, the daily change in the total (which is the basis for the daily death count) will be negative (and will be negative for a week, as the numbers are seven-day averages).  And a later upward revision in late June will look like a spike up.)

Sweden is also an interesting case as, early in the crisis, it deliberately decided not to mandate closures of restaurants, offices, and other non-essential work locations, but rather left this to be decided by each entity.  But the policy failed:  Deaths from Covid-19 rose to rates well above US levels (and was especially far above the rates of its Nordic neighbors of Norway, Finland, and Denmark, although below the peak levels seen in Italy, Spain, France, and the UK).  The rates then fell relatively slowly in Sweden.  They eventually moved to policies more in line with the rest of Europe, and eventually saw similarly low rates.

D.  US Compared to East Asia, Australia, and New Zealand

As an earlier post on this blog on the number of Covid-19 cases discussed, the countries of East Asia, as well as Australia and New Zealand, show what is possible if serious measures are taken to control the spread of the virus (and possible in a region with more travel and business exposure to China than any other region).  The measures required are not exotic.  Nor did they require resources that others did not have.  All that was required were the standard public health measures used to control the spread of any infectious disease – extensive testing with follow-up tracing of contacts and quarantining of those exposed, plus the normal and widespread use of simple masks.  With such measures, Taiwan was able, for example, to keep open its schools basically throughout (in February it extended its regular Chinese New Year holiday by an extra two weeks, but has since followed its regular schedule).

The result was few cases of Covid-19, and few deaths:

 

The rates for all the countries listed on the chart were plotted.  But they were all so close to zero that, other than for the few names shown, one could not distinguish one from the other.

There was an increase in the rates since mid-July in Australia, and to a lesser extent in Hong Kong (and a far lesser extent in Japan), as some of the earlier controls were eased.  But these have all now been brought back under control.  And even with these outbreaks, the rates never approached the US rates.

E.  Who are the Comparables for the US?

Who, then, might have a record comparable to that of the US?  Among the larger countries:

Donald Trump can be proud to say that death rates in the US have, since June, been lower than the rates in Mexico and Brazil.  The US has not performed as poorly as they have.  The pattern in South Africa is somewhat odd in that its rates were higher than those of the US between mid-July and mid-August, but are now substantially less.  And Russia as well as India have had lower rates throughout.

All this assumes the tracking statistics on deaths from Covid-19 are accurate, and one might question this for some of these countries.  As was discussed above for the case of Spain, such numbers can be difficult to assemble even with resources that the countries here do not have.  But for the ranges in the numbers seen here, the conclusions would still hold even if the rates were substantially higher.  As of mid-September, the South African rate would have needed to have been twice as high, and the Indian and Russian rates three times as high, to reach the US rate.

Note that I have not included China.  If it were added, it would show extremely low death rates per million throughout, with a peak of just 0.1 in mid-February.  But while the deaths from Covid-19 may well have been low compared to others (particularly when expressed per million, given its population), I am not confident they were in fact that low.  Restrictions on the news media and what they can report do not engender confidence.

But overall, to find countries with records on management of Covid-19 comparable to what they have been in the US, one needs to look at countries with per capita incomes that are far below that of the US.  The US has thought of itself as belonging in the top rank of countries.  But for this, the only countries with comparable death rates from Covid-19 are countries that, before Trump, the US had not normally been grouped with.

F.  What Deaths in the US Would Have Been at the Rates Other Countries Have Been Able to Achieve

As noted at the top of this post, President Trump claimed that the disease is “under control as much as you can control it.”  But as we have seen, it is not.  Other countries, facing the same disease, have been able to manage it with far lower death rates than the US has had.  How much of a difference would this have made?

Little was known about the disease early in the crisis, and one can argue that countries were searching then for what best to do.  And after the high early peaks, the rates did come down in the US as well as in Europe and Canada.  But then the US reversed course while rates continued to fall elsewhere.  It is thus this more recent period that most clearly shows the consequences of the choices the US made compared to others.  For the purposes of this exercise, we will therefore look at the period since August 1.

From August 1 to September 14, a period of 45 days, US deaths totaled 40,459.  This is a bit over a fifth (21%) of the total US deaths as of September 14 of 194,493.  It is still a substantial figure:   The number of US soldiers who died in battle in the Korean War totaled 33,739, and the number who died in the Vietnam War totaled 47,434.  But based on the numbers of deaths per million in other countries and regions, how many would have died for a population equal to that of the US?:

If the US had had the number of deaths per million that Romania had over this same period, then 31,700 would have died, or about three-quarters of the number of Americans who died.  If the US had the rate of Albania, about 20,800 would have died, or about half the number of Americans who died.  One might ask that if “it is what it is”, and that “It’s under control as much as you can control it”, why is it that Romania could control it so that there would only be three-quarters as many deaths, and Albania could control it so that there would only be half as many deaths?  Neither Romania nor Albania has the resources the US has, plus they are small and open.

Other cases are more extreme.  If the US had the rate over this period of the EU as a whole, there would have been 5,465 deaths.  Instead, it was 7.4 times higher.  At the rate of Canada, there would have been 2,184 deaths.  Instead, it was 18.5 times higher.  And Singapore and Taiwan both had zero deaths over this period.  The most recent death (as of this writing) was on July 14 in Singapore and on May 11 in Taiwan.  If the US had their rates, there would have been no deaths.

There is of course a wide range here.  Plus things may change.  Infection rates have been rising in Europe in recent days, and increases in death rates may soon follow.  The US has also today (on September 22, as I write this) passed a significant milestone:  More than 200,000 have now died in the US from this disease.  And there are widespread concerns that rates will increase this fall and winter across the Northern Hemisphere in a “second wave”, as more people remain inside and as they become less vigilant as time goes on. One has seen this with prior infectious diseases, particularly those that spread through the air.  There is also increasing pressure to reopen schools for in-class teaching and to fully reopen businesses.

So there is uncertainty on how this will progress.  But based on what we know for the last month and a half, a question to address is why the Trump administration has not been able to do as good a job of reducing deaths from this virus as have the governments of Romania, Albania, Bulgaria, Russia, Spain, Australia, Croatia, Serbia, Luxembourg, Portugal, Poland, France, Greece, Hong Kong, Italy, Sweden, Czechia, Slovenia, the Netherlands, Belgium, the United Kingdom, Canada, Switzerland, Hungary, Austria, Ireland, Japan, Denmark, Lithuania, Germany, Norway, Slovakia, Latvia, Finland, South Korea, Estonia, New Zealand, Singapore, and Taiwan.

Trump’s Economic Record in Charts

A.  Introduction

Donald Trump has repeatedly asserted that he built “the greatest economy in history”.  A recent example is in his acceptance speech for the Republican nomination to run for a second term.  And it is not a surprise that Trump would want to claim this.  It would be nice, if true.  But what is surprising is that a number of election surveys have found that Trump polls well on economic issues, with voters rating Trump substantially above Biden on who would manage the economy better.

Yet any examination of Trump’s actual record, not just now following the unprecedented economic collapse this year resulting from the Covid-19 crisis, but also before, shows Trump’s repeated assertion to be plainly false.

The best that can be said is that Trump did not derail, in his first three years in office, the economic expansion that began with the turnaround Obama engineered within a half year of his taking office in 2009 (when Obama had inherited an economy that was, indeed, collapsing).  But the expansion that began under Obama has now been fully and spectacularly undone in Trump’s fourth year in office, with real GDP in the second quarter of 2020 plummeting at an annualized rate of 32% – to a level that is now even well below what it was when Trump took office.  The 32% rate of decline is by far the fastest decline recorded for the US since quarterly data on GDP began to be recorded in 1947 (the previous record was 10%, under Eisenhower, and the next worst was an 8.4% rate of decline in the last quarter of 2008 at the very end of the Bush administration.

This post will look at Trump’s record in comparison to that not just of Obama but also of all US presidents of the last almost 48 years (since the Nixon/Ford term).  For his first three years in office, that Trump record is nothing special.  It is certainly and obviously not the best in history.  And now in his fourth year in office, it is spectacularly bad.

The examination will be via a series of charts.  The discussion of each will be kept limited, but the interested reader may wish to study them more closely – there is a lot to the story of how the economy developed during each presidential administration.  But the primary objective of these “spaghetti” charts is to show how Trump’s record in his first three years in office fits squarely in the middle of what the presidents of the last half-century have achieved.  It was not the best nor the worst over those first three years – Trump inherited from Obama an expanding and stable economy.  But then in Trump’s fourth year, it has turned catastrophic.

Also, while there is a lot more that could be covered, the post will be limited to examination of the outcomes for growth in overall output (GDP), for the fiscal accounts (government spending, the fiscal deficit, and the resulting public debt), the labor market (employment, unemployment, productivity, and real wages), and the basic trade accounts (imports, exports, and the trade balance).

The figures for the charts were calculated based on data from a number of official US government sources.  Summarizing them all here for convenience (with their links):

a)  BEA:  Bureau of Economic Analysis of the US Department of Commerce, and in particular the National Income and Product Accounts (NIPA, also commonly referred to as the GDP accounts).

b)  BLS:  Bureau of Labor Statistics of the US Department of Labor.

c)  OMB Historical Tables:  Office of Management and Budget, of the White House.

d)  Census Bureau – Foreign Trade Data:  Of the US Department of Commerce.

It was generally most convenient to access the data via FRED, the Federal Reserve Economic Database of the St. Louis Fed.

B.  Real GDP

Trump likes to assert that he inherited an economy that was in terrible shape.  Larry Kudlow, the director of the National Economic Council and Trump’s principal economic advisor recently asserted, for example in his speech to the Republican National Convention, that the Trump administration inherited from Obama “a stagnant economy that was on the front end of a recession”.  While it is not fully clear what a “front end” of a recession is (it is not an economic term), the economy certainly was not stagnant and there was no indication whatsoever of a recession on the horizon.

The chart at the top of this post shows the path followed by real GDP during the course of Obama’s first and second terms in office, along with that of Trump’s term in office thus far.  Both are indexed to 100 in the first calendar quarter of their presidential terms.  Obama inherited from Bush an economy that was rapidly collapsing (with a banking system in ruin) and succeeded in turning it around within a half year of taking office.  Subsequent growth during the remainder of Obama’s first term was then similar to what it was in his second term (with the curve parallel but shifted down in the first term due to the initial downturn).

Growth in the first three years of Trump’s presidency was then almost exactly the same as during Obama’s second term.  There is a bit of a dip at the start of the second year in Obama’s second term (linked to cuts in government spending in the first year of Obama’s second term – see below), but then a full recovery back to the previous path.  At the three-year mark (the 12th quarter) they are almost exactly the same.  To term this stagnation under Obama and then a boom under Trump, as Kudlow asserted, is nonsensical – they are the same to that point.  But the economy has now clearly collapsed under Trump, while it continued on the same path as before under Obama.

Does Trump look better when examined in a broader context, using the record of presidents going back to the Nixon/Ford term that began almost 48 years ago?  No:

The best that can be said is that the growth of real GDP under Trump in his first three years in office is roughly in the middle of the pack.  Growth was worse in a few administrations – primarily those where the economy went into a recession not long after they took office (such as in the first Reagan term, the first Bush Jr. term, and the Nixon/Ford term).  But growth in most of the presidential terms was either similar or distinctly better than what we had under Trump in his first three years.

And now real GDP has collapsed in Trump’s fourth year to the absolute worst, and by a very significant margin.

One can speculate on what will happen to real GDP in the final two quarters of Trump’s presidency.  Far quicker than in earlier economic downturns, Congress responded in March and April with a series of relief bills to address the costs of the Covid-19 crisis, that in total amount to be spent far surpass anything that has ever been done before.  The Congressional Budget Office (CBO) estimates that the resulting spending increases, tax cuts, and new loan facilities of measures already approved will cost a total of $3.1 trillion.  This total approved would, by itself, come to 15% of GDP (where one should note that not all will be spent or used in tax cuts in the current fiscal year – some will carry over into future years).  Such spending can be compared to the $1.2 trillion, or 8.5% of the then GDP, approved in 2008/09 in response to that downturn (with most of the spending and tax cuts spread over three years).  Of this $1.2 trillion, $444 billion was spent under the TARP program approved under Bush and $787 billion for the Recovery Act under Obama).

And debate is currently underway on additional relief measures, where the Democratic-controlled Congress approved in May a further $3 trillion for relief, while leaders in the Republican-controlled Senate have discussed a possible $1 trillion measure.  What will happen now is not clear.  Some compromise in the middle may be possible, or nothing may be passed.

But the spending already approved will have a major stimulative effect.  With such a massive program supporting demand, plus the peculiar nature of the downturn (where many businesses and other centers of employment had to be temporarily closed as the measures taken by the Trump administration to limit the spread of the coronavirus proved to be far from adequate), the current expectation is that there will be a significant bounceback in GDP in the third quarter.  As I write this, the GDPNow model of the Atlanta Fed forecasts that real GDP in the quarter may grow at an annualized rate of 29.6%.  Keep in mind, however, that to make up for a fall of 32% one needs, by simple arithmetic, an increase of 47% from the now lower base.  (Remember that to make up for a fall of 50%, output would need to double – grow by 100% – to return to where one was before.)

Taking into account where the economy is now (where there was already a 5% annualized rate of decline in real GDP in the first quarter of this year), what would growth need to be to keep Trump’s record from being the worst of any president of at least the last half-century?  Assuming that growth in the third quarter does come to 29.6%, one can calculate that GDP would then need to grow by 5.0% (annualized) in the fourth quarter to match the currently worst record – of Bush Jr. in his second term.  And it would need to grow by 19% to get it back to where GDP was at the end of 2019.

C.  The Fiscal Accounts

Growth depends on many factors, only some of which are controlled by a president together with congress.  One such factor is government spending.  Cuts in government spending, particularly when unemployment is significant and businesses cannot sell all that they could and would produce due to a lack of overall demand, can lead to slower growth.  Do cuts in government spending perhaps explain the middling rate of growth observed in the first three years of Trump’s term in office?  Or did big increases in government spending spur growth under Obama?

Actually, quite the opposite:

Federal government spending on goods and services did rise in the first year and a half of Obama’s first term in office, with this critical in reversing the collapsing economy that Obama inherited.  But the Republican Congress elected in 2010 then forced through cuts in spending, with further cuts continuing until well into Obama’s second term (after which spending remained largely flat).  While the economy continued to expand at a modest pace, the cuts slowed the economy during a period when unemployment was still high.  (There is also government spending on transfers, where the two largest such programs are Social Security and Medicare, but spending on such programs depends on eligibility, not on annual appropriations.)

Under Trump, in contrast, government spending has grown, and consistently so.  And indeed government spending grew under Trump at a faster pace than it had almost any other president of the last half-century (with even faster growth only under Reagan and Bush, Jr., two presidents that spoke of themselves, as Trump has, as “small government conservatives”):

The acceleration in government spending growth under Trump did succeed, in his first three years in office, in applying additional pressure on the economy in a standard Keynesian fashion, which brought down unemployment (see below).  But this extra government spending did not lead to an acceleration in growth – it just kept it growing (in the first three years of Trump’s term) at the same pace as it had before, as was seen above.  That is, the economy required additional demand pressure to offset measures the Trump administration was taking which themselves would have reduced growth (such as his trade wars, or favoritism for industries such as steel and aluminum, which harmed the purchasers of steel and aluminum such as car companies and appliance makers).

Trump has also claimed credit for a major tax cut bill (as have Reagan and Bush, Jr.).  They all claimed this would spur growth (none did – see above and a more detailed analysis in this blog post), and indeed such sufficiently faster growth, they predicted, that tax revenue would increase despite the reductions in the tax rates.  Hence fiscal deficits would be reduced.  They weren’t:

Fiscal deficits were large and sustained throughout the Reagan/Bush Sr. years.  They then moved to a fiscal surplus under Clinton, following the major tax increase passed in 1993 and the subsequent years of steady and strong growth.  The surplus was then turned back again into a deficit under Bush Jr., with his major tax cuts of 2001 and 2003 coupled with his poor record for economic growth.  Obama then inherited a high fiscal deficit, which grew higher due to the economic downturn he faced on taking office and the measures that were necessary to address it.  But with the economic recovery, the deficit under Obama was then reduced (although at too fast a pace –  this held back the economy, especially in the early years of the recovery when unemployment was still high).

Under Trump, in contrast, the fiscal deficit rose in his first three years in office, at a time when unemployment was low.  This was the time when the US should have been strengthening rather than weakening the fiscal accounts.  As President Kennedy said in his 1962 State of the Union Address: “The time to repair the roof is when the sun is shining.”  Under Trump, in contrast, the fiscal deficit was reaching 5% of GDP even before the Covid-19 crisis.  The US has never before had such a high fiscal deficit when unemployment was low, with the sole exception of during World War II.

This left the fiscal accounts in a weak condition when government spending needed to increase with the onset of the Covid-19 crisis.  The result is that the fiscal deficit is expected to reach an unprecedented 16% of GDP this fiscal year, the highest it has ever been (other than during World War II) since at least 1930, when such records began to be kept.

The consequence is a public debt that is now shooting upwards:

As a share of GDP, federal government debt (held by the public) is expected to reach 100% of GDP by September 30 (the end of the fiscal year), based on a simple extrapolation of fiscal account and debt data currently available through July (see the US Treasury Monthly Statement for July, released August 12, 2020).  And with its momentum (as such fiscal deficits do not turn into surpluses in any short period of time), Trump will have left for coming generations a government debt that is the highest (as a share of GDP) it has ever been in US history, exceeding even what it was at the end of World War II.

When Trump campaigned for the presidency in 2016, he asserted he would balance the federal government fiscal accounts “fairly quickly”.  Instead the US will face this year, in the fourth year of his term in office, a fiscal deficit that is higher as a share of GDP than it ever was other than during World War II.  Trump also claimed that he would have the entire federal debt repaid within eight years.  This was always nonsense and reflected a basic lack of understanding.  But at least the federal debt to GDP ratio might have been put on a downward trajectory during years when unemployment was relatively low.  Instead, federal debt is on a trajectory that will soon bring it to the highest it has ever been.

D.  The Labor Market

Trump also likes to assert that he can be credited with the strongest growth in jobs in history.  That is simply not true:

Employment growth was higher in Obama’s second term than it ever was during Trump’s term in office.  The paths were broadly similar over the first three years of Trump’s term, but Trump was simply – and consistently – slower.  In Obama’s first term, employment was falling rapidly (by 800,000 jobs a month) when Obama took his oath of office, but once this was turned around the path showed a similar steady rise.

Employment then plummeted in Trump’s fourth year, and by a level that was unprecedented (at least since such statistics began to be gathered in 1947).  In part due to the truly gigantic relief bills passed by Congress in March and April (described above), there has now been a substantial bounceback.  But employment is still (as of August 2020) well below what it was when Trump took office in January 2017.

Even setting aside the collapse in employment this year, Trump’s record in his first three years does not compare favorably to that of other presidents:

A few presidents have done worse, primarily those who faced an economy going into a downturn as they took office (Obama) or where the economy was pushed into a downturn soon after they took office (Bush Jr., Reagan) or later in their term (Bush Sr., Nixon/Ford).  But the record of other presidents was significantly better, with the best (which some might find surprising) that of Carter.

Trump also claims credit for pushing unemployment down to record low levels.  The unemployment rate did, indeed, come down (although not to record low rates – the unemployment rate was lower in the early 1950s under Truman and then Eisenhower, and again in the late 1960s).  But one cannot see any significant change in the path on the day Trump was inaugurated compared to what it had been under Obama since 2010:

And of course now in 2020, unemployment has shot upwards to a record level (since at least 1948, when these records began to be kept systematically).  It has now come down with the bounceback of the economy, but remains high (8.4% as of August).

Over the long term, nothing is more important in raising living standards than higher productivity.  And this was the argument Trump and the Republicans in Congress made to rationalize their sharp cuts in corporate tax rates in the December 2017 tax bill.  The argument was that companies would then invest more in the capital assets that raise productivity (basically structures and equipment).  But this did not happen.  Even before the collapse this year, private non-residential investment in structures and equipment was no higher, and indeed a bit lower, as a share of GDP than what it was before the 2017 tax bill passed.

And it certainly has not led to a jump in productivity:

Productivity growth during Trump’s term in office has been substantially lower (by 3%) than what it was during Obama’s first term, although somewhat better than during Obama’s second term (by a cumulative 1% point at the same calendar quarter in their respective terms).

And compared to that of other presidents, Trump’s record on productivity gains is nothing special:

Finally, what happened to real wages?  While higher productivity growth is necessary in the long term for higher wages (workers cannot ultimately be paid more than what is produced), in the short term a number of other factors (such as relative bargaining strength) will dominate.  When unemployment is high, wage gains will typically be low as firms can hire others if a worker demands a higher wage.  And when unemployment is low, workers will typically be in a better bargaining position to demand higher wages.

How, then, does Trump’s record compare to that of Obama?:

During the first three years of Trump’s tenure in office, real wage gains were basically right in the middle of what they were over the similar periods in Obama’s two terms.  But then it looks like real wages shot upwards at precisely the time when the Covid-19 crisis hit.  How could this be?

One needs to look at what lies behind the numbers.  With the onset of the Covid-19 crisis, unemployment shot up to the highest it has been since the Great Depression.  But two issues were then important.  One is that when workers are laid off, it is usually the least senior, least experienced, workers who are laid off first.  And such workers will in general have a lower wage.  If a high share of lower-wage workers become unemployed, then the average wage of the workers who remain employed will go up.  This is a compositional effect.  No individual worker may have seen an increase in his or her wage, but the overall average will go up if fewer lower-wage workers remain employed.

Second, this downturn was different from others in that a high share of the jobs lost were precisely in low-wage jobs – workers in restaurants, cafeterias, and hotels, or in retail shops, or janitors for office buildings, and so on.  As the economy shut down, these particular businesses had to close.  Many, if not most, office workers could work from home, but not these, commonly low-wage, workers.  They were laid off.

The sharp jump in average real wages in the second quarter of 2020 (Trump’s 14th quarter in office) is therefore not something to be pleased about.  As the lower-wage workers who have lost their jobs return to being employed, one should expect this overall average wage to fall back towards where it was before.

But the path of real wages in the first three years of Trump’s presidency, when the economy continued to expand as it had under Obama, does provide a record that can be compared.  How does it look relative to that of other presidents of the last half-century?:

Again, Trump’s record over this period is in the middle of the range found for other presidents.  It was fairly good (unemployment was low, which as noted above would be expected to help), but real wages in the second terms of Clinton and Obama rose by more, and performance was similar in Reagan’s second term.

E.  International Trade Accounts

Finally, how does Trump’s record on international trade compare to that of other presidents?  Trump claimed he would slash the US trade deficit, seeing it in a mercantilistic way as if a trade deficit is a “loss” to the country.  At a 2018 press conference (following a G-7 summit in Canada), he said, for example, “Last year,… [the US] lost  … $817 billion on trade.  That’s ridiculous and it’s unacceptable.”  And “We’re like the piggybank that everybody is robbing.”

This view on the trade balance reflects a fundamental lack of understanding of basic economics.  Equally worrisome is Trump’s view that launching trade wars targeting specific goods (such as steel and aluminum) or specific countries (such as China) will lead to a reduction in the trade deficit.  As was discussed in an earlier post on this blog, the trade balance ultimately depends on the overall balance between domestic savings and domestic investment in an economy.  Trade wars may lead to reductions in imports, but then there will also be a reduction in exports.  If the trade wars do not lead to higher savings or lower investment, such trade interventions (with tariffs or quotas imposed by fiat) will simply shift the trade to other goods or other nations, leaving the overall balance where it would have been based on the savings/investment balance.

But we now have three and a half years of the Trump administration, and can see what his trade wars have led to.  In terms of imports and exports:

Imports did not go down under Trump – they rose until collapsing in the worldwide downturn of 2020.  Exports also at first rose, but more slowly than imports, and then leveled off before imports did.  They then also collapsed in 2020.  Going back a bit, both imports and exports had gone up sharply during the Bush administration.  Then, after the disruption surrounding the economic collapse of 2008/9 (with a fall then a recovery), they roughly stabilized at high levels during the last five years of the Obama administration.

In terms of the overall trade balance:

The trade deficit more than doubled during Bush’s term in office.  While both imports and exports rose (as was seen above), imports rose by more.  The cause of this was the housing credit bubble of the period, which allowed households to borrow against home equity (which in turn drove house prices even higher) and spend that borrowing (leading to higher consumption as a share of current income, which means lower savings).  This ended, and ended abruptly, with the 2008/9 collapse, and the trade deficit was cut in half.  After some fluctuation, it then stabilized in Obama’s second term.

Under Trump, in contrast, the trade deficit grew compared to where it was under Obama.  It did not diminish, as Trump insisted his trade wars would achieve, but the opposite.  And with the growing fiscal deficit (as discussed above) due to the December 2017 tax cuts and the more rapid growth in government spending (where a government deficit is dis-saving that has to be funded by borrowing), this deterioration in the trade balance should not be a surprise.  And I also suspect that Trump does not have a clue as to why this has happened (nor an economic advisor willing to explain it to him).

F.  Conclusion

There is much more to Trump’s economic policies that could have been covered.  It is also not yet clear how much damage has been done to the economic structure from the crisis following the mismanagement of Covid-19 (with the early testing failures, the lack of serious contact tracing and isolation of those who may be sick, and importantly, Trump’s politicizing the wearing of simple masks).  Unemployment rose to record levels, and this can have a negative impact (both immediate and longer-term) on the productivity of those workers and on their subsequent earnings.  There has also been a jump in bankruptcies, which reduces competition.  And bankrupt firms, as well as stressed firms more generally, will not be able to repay their loans in full.  The consequent weakening of bank balance sheets will constrain how much banks will be able to lend to others, which will slow the pace of any recovery.

But these impacts are still uncertain.  The focus of this post has been on what we already know of Trump’s economic record.  It is not a good one. The best that can be said is that during his first three years in office he did not derail the expansion that had begun under Obama.  Growth continued (in GDP, employment, productivity, wages), at rates similar to what they were before.  Compared to paths followed in other presidencies of the last half-century, they were not special.

But this growth during Trump’s tenure in office was only achieved with rapid growth in federal government spending.  Together with the December 2017 tax cuts, this led to a growing, not a diminishing, fiscal deficit.  The deficit grew to close to 5% of GDP, which was indeed special:  Never before in US history has the fiscal deficit been so high in an economy at or close to full employment, with the sole exception of during World War II.

The result was a growing public debt as a share of GDP, when prudent fiscal policy would have been the reverse.  Times of low unemployment are when the country should be reducing its fiscal deficit so that the public debt to GDP ratio will fall.  Reducing public dis-saving would also lead to a reduction in the trade deficit (other things being equal).  But instead the trade deficit has grown.

As a consequence, when a crisis hits (as it did in 2020) and government needs to spend substantial sums for relief (as it had to this year), the public debt to GDP ratio will shoot upwards from already high levels.  Republicans in Congress asserted in 2011 that a public debt of 70% of GDP was excessive and needed to be brought down rapidly.  Thus they forced through spending cuts, which slowed the recovery at a time when unemployment was still high.

But now public debt under Trump will soon be over 100% of GDP.  Part of the legacy of Trump’s term in office, for whoever takes office this coming January 20, will therefore be a public debt that will soon be at a record high level, exceeding even that at the end of World War II.

This has certainly not been “the greatest economy in history”.