Was Sturgis a Covid-19 Superspreader Event?: Evidence Suggests That It May Well Have Been

A.  Introduction

The Sturgis Motorcycle Rally is an annual 10-day event for motorcycle enthusiasts (in particular of Harley-Davidsons), held in the normally small town in far western South Dakota of Sturgis.  It was held again this year, from August 7 to August 16, despite the Covid-19 pandemic, and drew an estimated 460,000 participants.  Motorcyclists gather from around the country for lots of riding, lots of music, and lots of beer and partying.  And then they go home.  Cell phone data indicate that fully 61% of all the counties in the US were visited by someone who attended Sturgis this year.

Due to the pandemic, the town debated whether to host the event this year.  But after some discussion, it was decided to go ahead.  And it is not clear that town officials could have stopped it even if they wanted.  Riders would likely have shown up anyway.

Despite the on-going covid pandemic, masks were rarely seen.  Indeed, many of those attending were proud in their defiance of the standard health guidelines that masks should be worn and social distancing respected, and especially so in such crowded events.  T-shirts were sold, for example, declaring “Screw Covid-19, I Went to Sturgis”.

Did Sturgis lead to a surge in Covid-19 cases?  Unfortunately, we do not have direct data on this because the identification of the possible sources of someone’s Covid-19 infection is incredibly poor in the US.  There is little investigation of where someone might have picked up the virus, and far from adequate contact tracing.  And indeed, even those who attended the rally and later came down with Covid-19 found that their state health officials were often not terribly interested in whether they had been at Sturgis.  The systems were simply not set up to incorporate this.  And those attending who were later sick with the disease were also not always open on where they had been, given the stigma.

One is therefore left only with anecdotal cases and indirect evidence.  Recent articles in the Washington Post and the New York Times were good reports, but could only cover a number of specific, anecdotal, cases, as well as describe the party environment at Sturgis.  One can, however, examine indirect evidence.  It is reasonable to assume that those motorcycle enthusiasts who had a shorter distance to get to Sturgis from their homes would be more likely to go.  Hence near-by states would account for a higher share (adjusted for population) of those attending Sturgis and then returning home than would be the case for states farther away.  If so, then if Covid-19 was indeed spread among those attending Sturgis, one would see a greater degree of seeding of the virus that causes Covid-19 in the near-by states than would be the case among states that are farther away.  And those near-by states would then have more of a subsequent rise in Covid-19 cases as the infectious disease spread from person to person than one would see in states further away.

This post will examine this, starting with the chart at the top of this post.  As is clear in that chart, by early November states geographically closer to Sturgis had far higher cases of Covid-19 (as a share of their population) than those further away.  And the incidence fell steadily with geographic distance, in a relationship that is astonishingly tight.  Simply knowing the distance of the state from Sturgis would allow for a very good prediction (relative to the national average) of the number of daily new confirmed cases of Covid-19 (per 100,000 of population) in the 7-day period ending November 6.

A first question to ask is whether this pattern developed only after Sturgis.  If it had been there all along, including before the rally was held, then one cannot attribute it to the rally.  But we will see below that there was no such relationship in early August, before the rally, and that it then developed progressively in the months following.  This is what one would expect if the virus had been seeded by those returning from Sturgis, who then may have given this infectious disease to their friends and loved ones, to their co-workers, to the clerks at the supermarkets, and so on, and then each of these similarly spreading it on to others in an exponentially increasing number of cases.

To keep things simple in the charts, we will present them in a standard linear form.  But one may have noticed in the chart above that the line in black (the linear regression line) that provides the best fit (in a statistical sense) for a straight line to the scatter of points, does not work that well at the two extremes.  The points at the extremes (for very short distances and very long ones) are generally above the curve, while the points are often below in the middle range.  This is the pattern one would expect when what matters to the decision to ride to the rally is not some increment for a given distance (of an extra 100 miles, say), but rather for a given percentage increase (an extra 10%, say).  In such cases, a logarithmic curve rather than a straight (linear) line will fit the data better, and we will see below that indeed it does here.  And this will be useful in some statistical regression analysis that will examine possible explanations for the pattern.

It should be kept in mind, however, that what is being examined here are correlations, and being correlations one can not say with certainty that the cause was necessarily the Sturgis rally.  And we obviously cannot run this experiment over repeatedly in a lab, under varying conditions, to see whether the result would always follow.

Might there be some other explanation?  Certainly there could be.   Probably the most obvious alternative is that the surge in Covid-19 cases in the upper mid-west of the US between September and early November might have been due to the onset of cold weather, where the states close to Sturgis are among the first to turn cold as winter approaches in the US.  We will examine this below.  There is, indeed, a correlation, but also a number of counter-examples (with states that also turned colder, such as Maine and Vermont, that did not see such a surge in cases).  The statistical fit is also not nearly as good.

One can also examine what happened across the border in the neighboring provinces of Canada.  The weather there also turned colder in September and October, and indeed by more than in the upper mid-west of the US.  Yet the incidence of Covid-19 cases in those provinces was far less.

What would explain this?  The answer is that it is not cold weather per se that leads to the virus being spread, but rather cold weather in situations where socially responsible behavior is not being followed – most importantly mask-wearing, but also social distancing, avoidance of indoor settings conducive to the spread of the virus, and so on.  As examined in the previous post on this blog, mask-wearing is extremely powerful in limiting the spread of the virus that causes Covid-19.  But if many do not wear masks, for whatever reason, the virus will spread.  And this will be especially so as the weather turns colder and people spend more time indoors with others.

This could lead to the results seen if states that are geographically closer to Sturgis also have populations that are less likely to wear masks when they go out in public.  And we will see that this was likely indeed a factor.  For whatever reason (likely political, as the near-by states are states with high shares of Trump supporters), states geographically close to Sturgis have a generally lower share of their populations regularly wearing masks in this pandemic.  But the combination of low mask-wearing and falling temperatures (what statisticians call an interaction effect) was supplemental to, and not a replacement of, the impact of distance from Sturgis.  The distance factor remained highly significant and strong, including when controlling for October temperatures and mask-wearing, consistent with the view that Sturgis acted as a seeding event.

This post will take up each of these topics in turn.

B.  Distance to Sturgis vs. Daily New Cases of Covid-19 in the Week Ending November 6

The chart at the top of this post plots the average daily number of confirmed new cases of Covid-19 over the 7-day period ending November 6 in a state (per 100,000 of population), against the distance to Sturgis.  The data for the number of new cases each day was obtained from USAFacts, which in turn obtained the data from state health authorities.  The data on distance to Sturgis was obtained from the directions feature on Google Maps, with Sturgis being the destination and the trip origin being each of the 48 states in the mainland US (Hawaii and Alaska were excluded), plus Washington, DC.  Each state was simply entered (rather than a particular address within a state), and Google Maps then defaulted to a central location in each state.  The distance chosen was then for the route recommended by Google, in miles and on the roads recommended.  That is, these are trip miles and not miles “as the crow flies”.

When this is done, with a regular linear scale used for the mileage on the recommended routes, one obtains the chart at the top of this post.  For the week ending November 6, those states closest to Sturgis saw the highest rates of Covid-19 new cases (130 per 100,000 of population in South Dakota itself, where Sturgis is in the far western part of the state, and 200 per 100,000 in North Dakota, where one should note that Sturgis is closer to some of the main population centers of North Dakota than it is to some of the main population centers of South Dakota).  And as one goes further away geographically, the average daily number of new cases falls substantially, to only around one-tenth as much in several of the states on the Atlantic.

The model is a simple one:  The further away a state is from Sturgis, the lower its rate (per 100,000 of population) of Covid-19 new cases in the first week of November.  But it fits extremely well even though it looks at only one possible factor (distance to Sturgis).  The straight black line in the chart is the linear regression line that best fits, statistically, the scatter of points.  A statistical measure of the fit is called the R-squared, which varies between 0% and 100% and measures what share of the variation observed in the variable shown on the vertical axis of the chart (the daily new cases of Covid-19) can be predicted simply by knowing the regression line and the variable shown on the horizontal axis (the miles to Sturgis).

The R-squared for the regression line calculated for this chart was surprisingly high, at 60%.  This is astonishing.  It says that if all we knew was this regression line, then we could have predicted 60% of the variation in Covid-19 cases across states in the week ending November 6 simply by knowing how far the states are from Sturgis.  States differ in numerous ways that will affect the incidence of Covid-19 cases in their territory.  Yet here, if we know just the distance to Sturgis, we can predict 60% of how Covid-19 incidence will vary across the states.  Regressions such as these are called cross-section regressions (the data here are across states), and such R-squares are rarely higher than 20%, or at most perhaps 30%.

But as was discussed above in the introduction, trip decisions involving distances often work better (fit the data better) when the scale used is logarithmic.  On a logarithmic scale, what enters into the decision to make the trip of not is not some fixed increment of distance (e.g. an extra 100 miles) but rather some proportional change (e.g. an extra 10%).  A statistical regression can then be estimated using the logarithms of the distances, and when this estimated line is re-calculated back on to the standard linear scale, one will have the curve shown in blue in the chart:

The logarithmic (or log) regression line (in blue) fits the data even better than the simple linear regression line (in black), including at the two extremes (very short and very long distances).  And the R-squared rises to 71% from the already quite high 60% of the linear regression line.  The only significant outlier is North Dakota.  If one excludes North Dakota, the R-squared rises to 77%.  These are remarkably high for a cross-section analysis.

This simple model therefore fits the data well, indeed extremely well.  But there are still several issues to consider, starting with whether there was a similar pattern across the states before the Sturgis rally.

C.  Distance to Sturgis vs. Daily New Cases of Covid-19 in the Week Ending August 6, and the Progression in Subsequent Months

The Sturgis rally began on August 7.  Was there possibly a similar pattern as that found above in Covid-19 cases before the rally?  The answer is a clear no:

In the week ending August 6, the relationship of Covid-19 cases to distance from Sturgis was about as close to random as one can ever find.  If anything, the incidences of Covid-19 cases in the 10 or so states closest to Sturgis were relatively low.  And for all 48 states of the Continental US (plus Washington, DC), the simple linear regression line is close to flat, with an R-squared of just 0.4%.  This is basically nothing, and is in sharp contrast to the R-squared for the week ending November 6 of 60% (and 71% in logarithmic terms).

One should also note the magnitudes on the vertical scale here.  They range from 0 to 40 cases (per 100,000 of population) per day in the 7-day period.  In the chart for cases in the 7-day period ending on November 6 (as at the top of this post), the scale goes from 0 to 200.  That is, the incidence of Covid-19 cases was relatively low across US states in August (relative to what it was later in parts of the US).  That then changed in the subsequent months.  Furthermore, one can see in the charts above for the week ending November 6 that the states further than around 1,400 miles from Sturgis still had Covid new case rates of 40 per day or less.  That is, the case incidence rates remained in that 0 to 40 range between August and early November for the states far from Sturgis.  The states where the rates rose above this were all closer to Sturgis.

There was also a steady progression in the case rates in the months from August to November, focused on the states closer to Sturgis, as can be seen in the following chart:

Each line is the linear regression line found by regressing the number of Covid-19 cases in each state (per 100,000 of population) for the week ending August 6, the week ending September 6, the week ending October 6, and the week ending November 6, against the geographic distance to Sturgis.  The regression lines for the week ending August 6 and the week ending November 6 are the same as discussed already in the respective charts above.  The September and October ones are new.

As noted before, the August 6 line is essentially flat.  That is, the distance to Sturgis made no difference to the number of cases, and they are also all relatively low.  But then the line starts to twist upwards, with the right end (for the states furthest from Sturgis) more or less fixed and staying low, while the left end rotated upwards.  The rotation is relatively modest for the week ending September 6, is more substantial in the month later for the week ending October 6, and then the largest in the month after that for the week ending November 6.  This is precisely the path one would expect to find with an exponential spread of an infectious disease that has been seeded but then not brought under effective control.

D.  Might Falling Temperatures Account for the Pattern?

The charts above are consistent with Sturgis acting as a seeding event that later then led to increases in Covid-19 cases that were especially high in near-by states.  But one needs to recognize that these are just correlations, and by themselves cannot prove that Sturgis was the cause.  There might be some alternative explanation.

One obvious alternative would be that the sharp increase in cases in the upper mid-west of the US in this period was due to falling temperatures, as the northern hemisphere winter approached.  These areas generally grow colder earlier than in other parts of the US.  And if one plots the state-wide average temperatures in October (as reported by NOAA) against the average number of Covid-19 cases per day in the week ending November 6 one indeed finds:

There is a clear downward trend:  States with lower average temperatures in October had more cases (per 100,000 of population) in the week ending November 6.  The relationship is not nearly as tight as that found for the one based on geographic distance from Sturgis (the R-squared is 35% here, versus 60% for the linear relationship based on distance), but 35% is still respectable for a cross-state regression such as this.

However, there are some counterexamples.  The average October temperatures in Maine and Vermont were colder than all but 7 or 10 states (for Maine and Vermont, respectively), yet their Covid-19 case rates were the two lowest in the country.

More telling, one can compare the rates in North and South Dakota (with the two highest Covid-19 rates in the country in the week ending November 6) plus Montana (adjacent and also high) with the rates seen in the Canadian provinces immediately to their north:

The rates are not even close.  The Canadian rates were all far below those in the US states to their south.  The rate in North Dakota was fully 30 times higher than the rate in Saskatchewan, the Canadian province just to its north.  There is clearly something more than just temperature involved.

E.  The Impact of Wearing Masks, and Its Interaction With Temperature

That something is the actions followed by the state or provincial populations to limit the spread of the virus.  The most important is the wearing of masks, which has proven to be highly effective in limiting the spread of this infectious disease, in particular when complemented with other socially responsible behaviors such as social distancing, avoiding large crowds (especially where many do not wear masks), washing hands, and so on.  Canadians have been far more serious in following such practices than many Americans.  The result has been far fewer cases of Covid-19 (as a share of the population) in Canada than in the US, and far fewer deaths.

Mask wearing matters, and could be an alternative explanation for why states closer to Sturgis saw higher rates of Covid-19 cases.  If a relatively low share of the populations in the states closer to Sturgis wear masks, then this may account for the higher incidence of Covid-19 cases in those near-by states.  That is, perhaps the states that are geographically closer to Sturgis just happen also to be states where a relatively low share of their populations wear masks, with this then possibly accounting for the higher incidence of cases in those states.

However, mask-wearing (or the lack of it), by itself, would be unlikely to fully account for the pattern seen here.  Two things should be noted.  First, while states that are geographically closer to Sturgis do indeed see a lower share of their population generally wearing masks when out in public, the relationship to this geography is not as strong as the other relationships we have examined:

The data in the chart for the share who wear masks by state come from the COVIDCast project at Carnegie Mellon University, and was discussed in the previous post on this blog.  The relationship found is indeed a positive one (states geographically further from Sturgis generally have a higher share of their populations wearing masks), but there is a good deal of dispersion in the figures and the R-squared is only 27.5%.  This, by itself, is unlikely to explain the Covid-19 rates across states in early November.

Second, and more importantly:  While the states closer to Sturgis generally have a lower share of mask-wearing, this would not explain why one did not see similarly higher rates of Covid-19 incidence in those states in August.  Mask-wearing was likely similar.  The question is why did Covid-19 incidence rise in those states between August (following the Sturgis rally) and November, and not simply why they were high in those states in November.

However, mask-wearing may well have been a factor.  But rather than accounting for the pattern all by itself, it may have had an indirect effect.  With the onset of colder weather, more time would be spent with others indoors, and wearing a mask when in public is particularly important in such settings.  That is, it is the combination of both a low share of the population wearing masks and the onset of colder weather which is important, not just one or the other.

These are called interaction effects, and investigating them requires more than can be depicted in simple charts.  Multiple regression analysis (regression analysis with several variables – not just one as in the charts above) can allow for this.  Since it is a bit technical, I have relegated a more detailed discussion of these results to a Technical Annex at the conclusion of this post for those who are interested.

Briefly, a regression was estimated that includes miles from Sturgis, average October temperatures, the share who wear masks when out in public, plus an interaction effect between the share wearing masks and October temperatures, all as independent variables affecting the observed Covid-19 case rates of the week ending November 6.  And this regression works quite well.  The R-squared is 75.4%, and each of the variables (including the interaction term) are either highly significant (miles from Sturgis) or marginally so (a confidence level of between 6 and 8% for the variables, which is slightly worse than the 5% confidence level commonly used, but not by much).

Note in particular that the interaction term matters, and matters even while each of the other variables (miles to Sturgis, October temperatures, and mask-wearing) are taken into account individually as well.  In the interaction term, it is not simply the October temperatures or the share wearing masks that matter, but the two acting together.  That is, the impact of relatively low temperatures in October will matter more in those states where mask-wearing is low than they would in states where mask-wearing is high.  If people generally wore masks when out in public (and followed also the other socially responsible behaviors that go along with it), the falling temperatures would not matter as much.  But when they don’t, the falling temperatures matter more.

From this overall regression equation, one can also use the coefficients found to estimate what the impact would be of small changes in each of the variables.  These are called elasticities, and based on the estimated equation (and computing the changes around the sample means for each of the variables):  a 1% reduction in the number of miles from Sturgis would lead to a 1.0% rise in the incidence of Covid-19 cases; a 1% reduction (not a 1 percentage point increase, but rather a 1% reduction from the sample mean) in the share of the population wearing masks when out in public would lead to a 1.7% rise in the incidence of Covid-19 cases; and a 1% reduction in the average October temperature across the different states would lead to a 1.2% rise in the incidence of Covid-19 cases.  All of these elasticity estimates look quite plausible.

These results are consistent with an explanation where the Sturgis rally acted as a significant superspreader event that led to increased seeding of the virus in the locales, in near-by states especially. This then led to significant increases in the incidence of Covid-19 cases in the different states as this infectious disease spread to friends and family and others in the subsequent months, and again especially in the states closest to Sturgis.  Those increases were highest in the states that grew colder earlier than others when the populations wearing masks regularly in those states was relatively low.  That is, the interaction of the two mattered.  But even with this effect controlled for, along with controlling also for the impact of colder temperatures and for the impact of mask-wearing, the impact of miles to Sturgis remained and was highly significant statistically.

F.  Conclusion

As noted above, the analysis here cannot and does not prove that the Sturgis rally acted as a superspreader event.  There was only one Sturgis rally this year, one cannot run repeated experiments of such a rally under various alternative conditions, and the evidence we have are simply correlations of various kinds.  It is possible that there may be some alternative explanation for why Covid-19 cases started to rise sharply in the weeks after the rally in the states closest to Sturgis.  It is also possible it is all just a coincidence.

But the evidence is consistent with what researchers have already found on how the virus that causes Covid-19 is spread.  Studies have found that as few as 10% of those infected may account for 80% of those subsequently infected with the virus.  And it is not just the biology of the disease and how a person reacts to it, but also whether the individual is then in situations with the right conditions to spread it on to others.  These might be as small as family gatherings, or as large as big rallies.  When large numbers of participants are involved, such events have been labeled superspreader events.

Among the most important of conditions that matter is whether most or all of those attending are wearing masks.  It also matters how close people are to each other, whether they are cheering, shouting, or singing, and whether the event is indoors or outdoors.  And the likelihood that an attendee who is infectious might be there increases exponentially with the number of attendees, so the size of the gathering very much matters.

A number of recent White House events matched these conditions, and a significant number of attendees soon after tested positive for Covid-19.  In particular, about 150 attended the celebration on September 26 announcing that Amy Coney Barrett would be nominated to the Supreme Court to take the seat of the recently deceased Ruth Bader Ginsburg.  Few wore masks, and at least 18 attendees later tested positive for the virus.  And about 200 attended an election night gathering at the White House.  At least 6 of those attending later tested positive.  While one can never say for sure where someone may have contracted the virus, such clusters among those attending such events are very unlikely unless the event was where they got the virus.  It is also likely that these figures are undercounts, as White House staff have been told not to let it become publicly known if they come down with the virus.  Finally, as of November 13 at least 30 uniformed Secret Service officers, responsible for security at the White House, have tested positive for the coronavirus in the preceding few weeks.

There is also increasing evidence that the Trump campaign rallies of recent months led to subsequent increases in Covid-19 cases in the local areas where they were held.  These ranged from studies of individual rallies (such as 23 specific cases traced to three Trump rallies in Minnesota in September), to a relatively simple analysis that looked at the correlation between where Trump campaign rallies were held and subsequent increases in Covid-19 cases in that locale, to a rigorous academic study that examined the impact of 18 Trump campaign rallies on the local spread of Covid-19.  This academic study was prepared by four members of the Department of Economics at Stanford (including the current department chair, Professor B. Douglas Bernheim).  They concluded that the 18 Trump rallies led to an estimated extra 30,000 Covid-19 cases in the US, and 700 additional deaths.

One should expect that the Sturgis rally would act as even more of a superspreader event than those campaign rallies.  An estimated 460,000 motorcyclists attended the Sturgis rally, while the campaign rallies involved at most a few thousand at each.  Those at the Sturgis rally could also attend for up to ten days; the campaign rallies lasted only a few hours.  Finally, there would be a good deal of mixing of attendees at the multiple parties and other events at Sturgis.  At a campaign rally, in contrast, people would sit or stand at one location only, and hence only be exposed to those in their immediate vicinity.

The results are also consistent with a rigorous academic study of the more immediate impact of the Sturgis rally on the spread of Covid-19, by Professor Joseph Sabia of San Diego State University and three co-authors.  Using anonymous cell phone tracking data, they found that counties across the US that received the highest inflows of returning participants from the Sturgis rally saw, in the immediate weeks following the rally (up to September 2), an increase of 7.0 to 12.5% in the number of Covid-19 cases relative to the counties that did not contribute inflows.  But their study (issued as a working paper in September) looked only at the impact in the immediate few weeks following Sturgis.  They did not consider what such seeding might then have led to.  The results examined in the analysis here, which is longer-term (up to November 6), are consistent with their findings.

It is therefore fully plausible that the Sturgis rally acted as a superspreader event.  And the evidence examined in this post supports such a conclusion.  While one cannot prove this in a scientific sense, as noted above, the likelihood looks high.

Finally, as I finish writing this, the number of deaths in the US from this terrible virus has just surpassed 250,000.  The number of confirmed cases has reached 11.6 million, with this figure rising by 1 million in just the past week.  A tremendous surge is underway, far surpassing the initial wave in March and April (when the country was slow to discover how serious the spread was, due in part to the botched development in the US of testing for the virus), and far surpassing also the second, and larger, wave in June and July (when a number of states, in particular in the South and Southwest, re-opened too early and without adequate measures, such as mask mandates, to keep the disease under control).  Daily new Covid-19 cases are now close to 2 1/2 times what they were at their peak in July.

This map, published by the New York Times (and updated several times a day) shows how bad this has become.  It is also revealing that the worst parts of the country (the states with the highest number of cases per 100,000 of population) are precisely the states geographically closest to Sturgis.  There is certainly more behind this than just the Sturgis rally.  But it is highly likely the Sturgis rally was a significant contributor.  And it is extremely important if more cases are to be averted to understand and recognize the possible role of events such as the rally at Sturgis.

Average Daily Cases of Covid-19 per 100,000 Population

7-Day Average for Week Ending November 18, 2020

Source:  The New York Times, “Covid in the US:  Latest Map and Case Count”.  Image from November 19, with data as of 8:14 am.

 


Technical Annex:  Regression Results

As discussed in the text, a series of regressions were estimated to explore the relationship between the Sturgis rally and the incidence of Covid-19 cases (the 7-day average of confirmed new cases in the week ending November 6) across the states of the mainland US plus Washington, DC.  Five will be reported here, with regressions on the incidence of Covid-19 cases (as the dependent variable) as a function of various combinations of three independent variables: miles from Sturgis (in terms of their natural logarithms), the average state-wide temperature in October (also in terms of their natural logarithms), and the share of the population in the respective states who reported they always or most of the time wore masks when out in public.  Three of the five regressions are on each of the three independent variables individually, one on the three together, and one on the three together along with an interaction effect measured by multiplying the October temperature variable (in logs) with the share wearing masks.  The sources for each variable were discussed above in the main text.

The basic results, with each regression by column, are summarized in the following table:

Regressions on State Covid-9 Cases – November 6

     Miles to Sturgis and Temperatures are in natural logs

Miles only

Temp only

Masks only

Miles, Temp, &Masks

All with Interaction

Miles to Sturgis

Slope

-54.9

-41.9

-36.6

t-statistic

-10.7

-5.2

-4.3

Avg Temperature

Slope

-133.3

-45.5

-516.8

t-statistic

-5.5

-2.0

-1.9

Share Wear Masks

Slope

-3.1

-0.8

-22.4

t-statistic

-3.9

-1.3

-1.8

Interaction Temp & Masks

Slope

5.44

t-statistic

1.8

Intercept

425.5

572.5

309.4

582.5

2,422.5

t-statistic

11.9

6.0

4.5

7.1

2.3

R-squared

71.0%

39.4%

24.2%

73.7%

75.4%

In the regressions with each independent variable taken individually, all the coefficients (slopes) found are highly significant.  The general rule of thumb is that a confidence level of 5% is adequate to call the relationship statistically “significant” (i.e. that the estimated coefficient would not differ from zero just due to random variation in the data).  A t-statistic of 2.0 or higher, in a large sample, would signal significance at least at a 5% confidence level (that is, that the estimated coefficient differs from zero at least 95% of the time), and the t-statistics are each well in excess of 2.0 in each of the single-variable regressions.  The R-squared is quite high, at 71.0%, for the regression on miles from Sturgis, but more modest in the other two (39.4% and 24.2% for October temperature and mask-wearing, respectively).

The estimated coefficients (slopes) are also all negative.  That is, the incidence of Covid-19 goes down with additional miles from Sturgis, with higher October temperatures, and with higher mask-wearing.  The actual coefficients themselves should not be compared to each other for their relative magnitudes.  Their size will depend on the units used for the individual measures (e.g. miles for distance, rather than feet or kilometers; or temperature measured on the Fahrenheit scale rather than Centigrade; or shares expressed as, say, 80 for 80% instead of 0.80).  The units chosen will not matter.  Rather, what is of interest is how the predicted incidence of Covid-19 changes when there is, say, a 1% change in any of the independent variables.  These are elasticities and will be discussed below.

In the fourth regression equation (the fourth column), where the three independent variables are all included, the statistical significance of the mask-wearing variable drops to a t-statistic of just 1.3.  The significance of the temperature variable also falls to 2.0, which is at the borderline for the general rule of thumb of 5% confidence level for statistical significance.  The miles from Sturgis variable remains highly significant (its t-statistic also fell, but remains extremely high).  If one stopped here, it would appear that what matters is distance from Sturgis (consistent with Sturgis acting as a seeding event), coupled with October temperatures falling (so that the thus seeded virus spread fastest where temperatures had fallen the most).

But as was discussed above in the main text, there is good reason to view the temperature variable acting not solely by itself, but in an interaction with whether masks are generally worn or not.  This is tested in the fifth regression, where the three individual variables are included along with an interaction term between temperatures and mask-wearing.  The temperature, mask-wearing, and interaction variables now all have a similar level of significance, although at just less than 5% (at 6% to 8% for each).  While not quite 5%, keep in mind that the 5% is just a rule of thumb.  Note also that the positive sign on the interaction term (the 5.44) is an indication of curvature.  The positive sign, coupled with the negative signs for the temperature and mask-wearing variables taken alone, indicates that the curves are concave facing upwards (the effects of temperature and mask-wearing diminish at the margin at higher values for the variables).  Finally, the miles to Sturgis variable remains highly significant.

Based on this fifth regression equation, with the interaction term allowed for, what would be the estimated response of Covid-19 cases to changes in any of the independent variables (miles to Sturgis, October temperatures, and mask-wearing)?  These are normally presented as elasticities, with the predicted percentage change in Covid-19 cases when one assumes a small (1%) change in any of the independent variables.  In a mixed equation such as this, where some terms are linear and some logarithmic (plus an interaction term), the resulting percentage change can vary depending on the starting point is chosen.  The conventional starting point taken is normally the sample means, and that will be done here.

Also, I have expressed the elasticities here in terms of a 1% decrease in each of the independent variables (since our interest is in what might lead to higher rates of Covid-19 incidence):

Elasticities from Full Equation with Interaction Term

      Percent Increase in Number of Covid-19 Cases from a 1% Decrease Around Sample Means

Elasticity

Miles to Sturgis

1.02%

October Temperature

1.16%

Share Wearing Masks

1.69%

All these estimated elasticities are quite plausible.  If one is 1% closer in geographic distance to Sturgis (starting at the sample mean, and with the other two variables of October temperature and mask-wearing also at their respective sample means), the incidence of Covid-19 cases (per 100,000 of population) as of the week ending November 6 would increase by an estimated 1.02%.  A 1% lower October temperature (from the sample mean) would lead to an estimated 1.16% increase in Covid-19 cases.  And the impact of the share wearing masks is important and stronger, where a 1% reduction in the share wearing masks would lead to an estimated 1.69% increase in cases, with all the other factors here taken into account and controlled for.

These results are consistent with a conclusion that the Sturgis rally led to a significant seeding of cases, especially in near-by states, with the number of infections then growing over time as the disease spread.  The cases grew faster in those states where mask-wearing was relatively low, and in states with lower temperatures in October (leading people to spend more time indoors).  When the falling temperatures were coupled with a lower share (than elsewhere) of the population wearing masks, the rate of Covid-19 cases rose especially fast.

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”.