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.