The Rapid Growth in Deaths from Covid-19: The Role of Politics

Deaths from Covid-19 have been growing at an extremely rapid rate.  The chart above shows what those rates have been in the month of March, averaged over seven day periods to smooth out day-to-day fluctuations.  The figures are for the daily rate of growth over the seven day period ending on the date indicated.  The curves start in the first period when there were at least 10 cases, which was on March 3 for the US as a whole.  Hence the first growth rate shown is for the one week period of March 3 to 10.  As I will discuss below, the chart has not only the growth rates for the US as a whole but also for the set of states that Trump won in 2016 and for the set that Clinton won.  They show an obvious pattern.

The data come from the set assembled by The New York Times, based on a compilation of state and local reports.  The Times updates these figures daily, and has made them available through the GitHub site.  And it provides a summary report on these figures, with a map, at least daily.

I emphasize that the figures are of daily growth rates, even though they are calculated over one week periods.  And they are huge.  For the US as a whole, that rate was just over 28% a day for the seven day period ending March 30.  It is difficult to get one’s head around such a rapid rate of growth, but a few figures can be illustrative.  In the New York Times database, 3,066 Americans had died of Covid-19 as of March 30.  If the 28% rate of growth were maintained, then the entire population of the US (330 million) would be dead by May 16.  For many reasons, that will not happen.  The entire population would have been infected well before (if there was nothing to limit the spread) and it is fatal for perhaps 1% of those infected.  And the 99% infected who do not die develop an immunity, where once they recover they cannot spread the virus to others.  For this reason as well, 100% of those not previously exposed will not catch the virus.  Rather, it will be some lower share, as the spread becomes less and less likely as an increasing share of the population develops an immunity.  This is also the reason why mass vaccination programs are effective in stopping the spread of a virus (including to those not able to receive a vaccination, such as very young children or those with compromised immune systems).

So that 28% daily rate of growth has to come down, preferably by policy rather than by running out of people to infect.  And there has been a small reduction in the last two days (the seven day periods ending March 29 and March 30), with the rate falling modestly to 28% from a 30% rate that had ruled since the seven day period ending March 22.  But it has much farther to go to get to zero.

The recent modest dip might be an initial sign that the social distancing measures that began to be put in place around parts of the nation by March 16 are having a positive effect (and where many individuals, including myself, started social distancing some time before).  It is believed that it takes about 4 to 7 days after being infected before one shows any symptoms, and then, in those cases where the symptoms are severe and require hospitalization (about 20% of the total), another several days to two weeks before it becomes critical for those where it will prove fatal.  Hence one might be starting to see the impacts of the policies about now.

But the social distancing measures implemented varied widely across the US.  They were strict and early in some locales, and advisory only and relatively late in other locales.  Sadly, Trump injected a political element into this.  Trump belittled the seriousness of Covid-19 until well into March, even calling Covid-19 a “hoax” conjured up by the Democrats while insisting the virus soon would go away.  And even since mid-March Trump has been inconsistent, saying on some days that it needs to be taken seriously and on others that it was not a big deal.  Fox News and radio hosts of the extreme right such as Rush Limbaugh also belittled the seriousness of the virus.

It is therefore understandable that Trump supporters and those who follow such outlets for what they consider the news, have not shown as much of a willingness to implement the social distancing measures that are at this point the only way to reduce the spread of the virus.  And it shows in the death figures.  The red curve in the chart at the top of this post shows the daily growth rates of fatalities from this virus in those states that voted for Trump in the 2016 election.  While the spread of the virus in these states, many of which are relatively rural, started later than in the states that voted for Clinton, their fatalities from the virus have since grown at a substantially faster pace.

The pace of growth in the states that voted for Clinton has also been heavily influenced by the rapid spread of the virus in New York.  As of March 30, more than half (57%) of the fatalities in the Clinton states was due to the fatalities in New York alone.  And New York is a special case.  With its dense population in New York City, where a high proportion use a crowded subway system or buses to commute to work, with the work then often in tall office buildings requiring long rides in what are often crowded elevators, it should not be surprising that a virus that goes person to person could spread rapidly.

Excluding New York, the rate of increase in the other states that voted for Clinton (the curve in green in the chart above) is more modest.  The rates are also then even more substantially lower than those in the Trump-voting states.

But any of these growth rates are still incredibly high, and must be brought down to zero quickly.  That will require clear, sustained, and scientifically sound policy, from the top.  But Trump has not been providing this.

The Ineffectiveness of Travel Bans for Addressing the COVID-19 Pandemic

A)  Introduction

The US is sinking into what looks likely to be its biggest public health crisis in over a century (i.e. since the Spanish Flu pandemic of 1918/19).  But President Trump continues to insist that he is not to be blamed for its mismanagement.  Rather, he insists that he should be commended for instituting the travel ban on China in early February, that “everyone” was opposed to him doing it but he decided to impose anyway, and that it turned out to be a “great success”.

None of this is true.

What was in fact done?  On January 31, the Trump administration announced that he would impose a ban on travelers from China entering the US, with this going into effect the evening of February 2.  It would not apply to returning US citizens. But there were other countries doing the same at that point, or even earlier (not many, but some).  Australia, for example, set a ban on travelers from China which went into effect on February 1, and New Zealand set a ban effective February 2.  Furthermore, numerous airlines were already suspending their flights from China.  American Airlines had implemented a suspension on all its flights to the US from China effective on January 31.  Delta and United Airlines had already announced that they would also be suspending their flights, and Delta did so on February 2 and United on February 5.  Air Canada had already suspended its flights on January 30, and numerous European airlines suspended theirs starting January 29 (Lufthansa, Swiss International, Austrian, British Airways), January 30 (KLM, Air France), and January 31 (SAS, Iberia).

And it is not correct for Trump to claim that “everyone” was opposed to such a travel ban.  I read the news closely, and I cannot recall any politician, nor any widely expressed public sentiment, arguing against the ban (although I acknowledge that there may well have been some – just not enough to be significant).  Infectious disease experts did say that such a ban would not do much good at that point, as the disease was certainly already in the US and would spread.  Keep in mind that any such disease starts with only one case, of a newly mutated virus that some animal carries (scientists believe it originated in bats, and then passed to some other animal species before jumping to some person).  It then expands person to person from that one case.  A travel ban, by itself, will not stop a spread if there are cases already here.

What a travel ban can do is buy some time.  It can postpone a major spread of the disease by a few weeks.  That can be of value if the ban is implemented very early and if those weeks are then spent to address aggressively the spread of the disease.  This includes rapid testing of all those individuals that may have been exposed to the virus, the isolation of all the cases thus found, and the quarantining of all those who may have been exposed but have not shown symptoms at that point.

But none of this was done in the US.  And as the experts noted, such travel bans will be harmful if they lull policymakers into a false sense of security, with an excuse then to delay taking urgent measures in the false belief that the country is now protected.  It is clear that Trump himself believed this, or at least acted (or rather did not act) consistent with such a belief.

If such a travel ban might buy time, how much time might that be?  This blog post will present some calculations of scenarios of what to expect.  I should stress that I am not an epidemiologist, and the scenarios discussed here are in no way a forecast of what specifically might have happened.  Epidemiologists are looking at that now, using far more sophisticated models (and with far greater knowledge than I have), but are still in an early stage as many of the characteristics of the disease are not yet known with any degree of certainty.

But what matters most is the basic mathematics of pandemics or epidemics (I will use the terms interchangeably here – a pandemic is simply an epidemic of greater range or coverage).  An infectious disease will expand at an exponential rate early on and is subject to a ceiling on those it can infect (no more than 100% of the population, and normally less).  And it is that basic mathematics of the process which shows why travel bans will be futile, and at best will simply delay by only a very short time the spread of a virus such as that which causes COVID-19.

The basic result is summarized in the chart at the top of this post and will be discussed in the next section below.  With plausible parameters, a complete and total travel ban applied to all travelers (including US citizens) might have delayed the spread of the disease by perhaps 2 1/2 weeks.  That is not much.

Far more effective would be policies to reduce the pace at which the disease spreads.  Such policies include “social distancing”, where activities involving crowds are canceled or avoided, and one encourages everyone to wash their hands frequently, stay away from others to the extent they can, and so on.  The chart shows (in the curve in orange) what that might achieve for a plausible parameter.  Its impact is far greater than that of a travel ban.

Slowing down the pace at which the virus spreads is also supremely important, as otherwise our health system could easily become swamped with an overwhelming number of cases requiring care all at one time.  As will be discussed and illustrated in section C below, a travel ban does not help with this at all.  But social distancing will, and quite remarkably so.  It could reduce the peak load on our health system (for the parameters examined here) by 75%.  That could literally mean that thousands of lives could be saved.

What was done during February, after the travel ban had been put into effect?  Sadly, not much.  There was no significant effort to identify and then isolate cases, and quarantine those exposed to those cases.  The development of a rapid COVID-19 specific test was also delayed as the initial version of the test turned out to be flawed.  While other nations around the world developed and quickly applied tests of their own, the US only tested (through other means) a small number of possible cases of individuals meeting highly restrictive criteria (such as recent travel in China).  And with only highly limited testing being done, the reported number of confirmed cases in the US was low.  But you can only confirm cases if you test, and if you do not test you will have no confirmations.

President Trump, and his administration, has yet to acknowledge its responsibility in this fiasco.  Trump has instead insisted that cases in the US are exceptionally low because, and only because, of the travel ban on China.  The numbers suggest otherwise.

B)  The Simple Mathematics of an Epidemic, and the Impact of a Travel Ban

One can model what an epidemic might look like (in terms of how fast it will spread) with some simple mathematics.  While this is far from the sophisticated models epidemiologists have for such processes, a simple model will suffice for an examination of the issue of what a travel ban might do.

The basic characteristic of an epidemic is that it will grow at an exponential rate to start with, but since it is subject to a ceiling (it cannot infect more than 100% of a population, and normally will tail off well before this point), the expansion will eventually have to level off.  A simple model with such characteristics is a logistic model, which was first proposed by a Belgian mathematician, Pierre Verhulst, in 1838.

The key parameter, called the “basic reproduction number” (and often designated as R0) is the number of new people who will, on average, be infected by a person who has been infected.  If that number is 2.0, then (to start) two new people will be infected by each person that has been infected, and the number of people who are infected at any given time will double in each period (to start).  If that number is 1.0, then (again, to start) one person will on average be infected by each person that has been infected, and the number of people who are infected in any given period will be constant (and the number who have cumulatively been infected will grow linearly over time).  And if the number is less than 1.0, then the number of new cases of infection will decline in each period, eventually going to zero (with the cumulative total climbing week to week as long as there are any new cases, but at a diminishing rate and eventually leveling off).

The basic reproduction number depends both on the characteristics of the disease, and on the degree of interpersonal contact in the society.  For the disease itself, some are more easily transmissible than others.  Measles, for example, spreads extremely easily.  Ebola (fortunately, as it has a high fatality rate) spread only if you had direct contact with bodily fluids, and hence did not spread easily.

But the spread also depends on what society is doing.  When people are in close direct contact, for example in crowds at concerts or in church or in a crowded subway car, more will be infected than if people are well separated.  Hence policy matters, and we will examine below the impact of measures that would reduce the degree of such close contact.

A key question for the virus that causes COVID-19 is how transmissible it is.  A number of scholars have hurriedly examined this, mostly using data from the initial spread in Wuhan, China, but have come up with a fairly wide range of possible figures.  The parameter is inherently hard to measure as data on the total number of people coming down with the virus week to week are simply not available, with the published figures possibly underestimates.  But a careful study published in The Lancet on March 11 estimated a figure of 2.35 in Wuhan before travel restrictions were imposed, falling to 1.05 after the rather draconian travel and quarantine measures went into effect.  An early study by a group of Chinese researchers published in the New England Journal of Medicine on January 29 (and summarized in an editorial co-authored by Dr. Anthony Fauci and others in the New England Journal of Medicine on February 28) arrived at an estimate of 2.2.  An estimate in a study published on February 22 and based on the spread of the virus in the cruise ship Diamond Princess came to a figure of 2.28.  And a review published on February 13 that examined as many other published studies as they could find up to that point (a total of 12, some of which might not have been of high quality) found a median estimate of 2.79, a mean of 3.28, and a range of 1.4 to 6.49.

I used an R0 of 2.3 for the calculations here.  It might be a bit on the low side, and if it were higher then the impact of a full travel ban (the main issue I am examining here) would be even less.  I am erring on the conservative side.  I am also, for these scenarios, looking at what the impact would be if that number remains unchanged over time.  That is, the scenarios examine what the impact would be if nothing is done to reduce the R0 by social distancing measures, either from policy (i.e. school closures) or simply by individuals being more careful and avoiding crowds or places where they could pick up an infection.  I stress again that these are scenarios of what would happen under specific circumstances, not forecasts of what will happen.

Assumptions are required for several other factors as well.  For simplicity, I am taking a discrete form of the logistic model, with calculations of week to week changes.  It is assumed that there will be a one week incubation period of a person who has been infected, and that that person can then infect others in their second week of infection.  After that, they can no longer infect others.  These assumptions are broadly consistent with what appear to be some of the basic parameters of the disease (based on material from a good summary article published in The Lancet on March 9), where the authors state that the mean time it takes for a newly infected person to pass the disease on to others is estimated to be 4.4 to 7.5 days.  So roughly one week after someone catches the virus they, on average, pass it on to others.

To examine the impact of a travel ban, I included as part of the model that a certain number of infected people would arrive from abroad each week, and that they would then add to those who could infect others domestically in the next week.  That is, those who would (domestically) be infected each week depends on the number who had been infected domestically in the prior week plus those infected who had arrived from abroad in the prior week.  To start, in period zero, I assumed there were 100 cases already active in the country domestically, and that 100 cases arrived from abroad.  I also assumed that the cases arriving from abroad, if nothing were done, would increase exponentially week to week (reflecting that the number of cases abroad are also growing) until they reached 10,000 per week (given that there are only so many who fly back and forth, even in normal times), after which the number was kept at 10,000 per week.

Finally, I set the ceiling on the population that might be infected by the virus at roughly one-third of the US population.  This model is too simple to forecast what that ceiling might be, so I used estimates made by others of the share of the US population that might in the end be infected if nothing is done.  But this ceiling is primarily just a scaling variable.  The results would not be impacted much by a different ceiling, within a reasonable range.  What matters is that, for the scaling used here, one starts with 100, caps those coming from abroad at 10,000, and has an overall domestic ceiling of over 100 million.

The scenario then looked at what would happen with a complete and total ban on anyone coming from abroad.  This would be far more extreme than any actual travel ban would be, as it would exclude returning American citizens and not just foreigners, plus it would cover travel from all countries in the world.  This was far more comprehensive than simply a ban on non-citizen arrivals from China.  But the aim was to be as generous as possible in calculating what the impact of a travel ban would be.

The chart at the top of this post shows what that impact might be.  It would not be much.  Even under such an extreme ban on travel, the path of the epidemic would be delayed by only about 2 1/2 weeks.  With other values assumed for the basic reproduction number R0 within a reasonable range, that time delay might be as short as 2 weeks or as long as 3 1/2 weeks.  None of these are large.  A travel ban would, at best, buy some time, but not much time.

But that extra time was not used in any case.  A travel ban in the very early stages of an epidemic can play a role if it is early enough (and February 2 was not early enough), and with then a major effort mounted to test all possible cases for the virus, with those testing positive isolated and those who had come into contact with such cases (or possible cases) quarantined.  None of this was done.

More modestly, what could have been done would be immediately to have increased social distancing, so that the infection rate (the R0) would be reduced.  The chart at the top of this post shows (in the curve in orange) what the impact would be had such measures been undertaken instead of the travel ban, and were sufficient to reduce the R0 to 1.5 from the 2.3 assumed in the other scenarios.  That is, the curve shows the impact where, on average, each infected person then infects a further 1.5 people instead of 2.3 people.  And again, to be clear, the curve assumes no foreign travel restrictions were imposed.

The spread of the disease is then slowed significantly.  Furthermore, the total number infected rises just to 75 million, or one-third less than come down with the disease in the base scenarios (with or without a travel ban).  The lower total number infected following from a lower R0 is an outcome of the random processes assumed in the logistic function, where as you approach the ceiling on the number who might be infected (the population), there is an increased likelihood that one will encounter only people who have already been infected and hence are now immune.  When one encounters fewer people (an R0 of 1.5 rather than 2.3), the likelihood goes up that all of the people encountered will be immune, and hence the number who will be infected in those later periods falls below 1.0.  The further spread of the disease then dies out.  It is for this reason as well that the curves for the case where R0 equals 2.3 level off at the odd number of 113.5 million.  I assumed a potential population of 120 million, and the logistic curve will level off below this.

Another scenario examined was one where the total travel ban was not implemented in week zero but rather in week six.  This would be similar to a delayed travel ban, such as that Trump recently imposed for travelers from Europe.  In the simple model, by week six the number of infected travelers coming in from abroad has reached its assumed peak of 10,000 per week.  I assumed that this was instead brought to zero in week six and then remained at zero.  The impact was trivial.  A plot of the new curve sits basically on top of the old (no travel ban) curve.  I therefore did not include it here as it simply looks almost exactly the same as the curve with no travel ban imposed.

C)  Impact on Cases to be Treated

As many have stressed, what matters is not only the total number of people being infected but also the number of new cases of infection each week.  Since about 20% of those coming down with the disease will likely need hospital treatment (based on current estimates), the burden on our hospital system will depend on how rapidly the number of new cases increase.  There are only a limited number of hospital beds, a far more limited number of the ventilators (about 160,000) that many of those who come down with this respiratory illness will require, and an even more limited number of beds in intensive care units (only 46,500, with perhaps a similar number that could be added in a crisis).  Furthermore, the patients that will need these ventilators and ICU beds may need to use them for two or three weeks.  This is far longer than would be the typical use of such hospital facilities for other disease treatments where they are required.

Hence, as numerous news reports have flagged in recent days, we need to “flatten the curve”.  That is, there is a critical need to reduce the peak load on such hospital facilities, with the need instead spread out over time.  A travel ban does not do this:

The peak loads on our hospital facilities would be almost exactly the same, with or without a total travel ban.  The peak is just shifted by 2 1/2 weeks.  In contrast, policies that by social distancing and other such measures reduces the basic reproduction number to 1.5 would have quite a marked effect, reducing the peak load by almost 75%.  That could directly translate into possibly thousands of lives that might be saved.  A travel ban does not help.

D)  Conclusion

The US is facing a major public health crisis.  Yet the response has been terribly mismanaged by the Trump administration.  Direction starts at the top, but Trump has repeatedly asserted that there is no major problem and that the disease will soon go away.  Even as late as March 10 (less than one week from when I am writing this), Trump said in remarks to the press at the White House that “And it will go away.  Just stay clam.  It will go away.”  He also continued to assert in those remarks that the ban on travel from China that he put in place, which he insisted others would not have done, had “made a big difference”.

But as shown above, imposing a travel ban, and one far more sweeping than the one Trump imposed on non-American travelers from China, will not have a major effect on the path of an epidemic such as the one we are facing.  This follows from the mathematics of compound growth as a disease spreads person to person.  At best it will buy some time, but plausible estimates are that it would amount to only a few weeks at best.  And those extra few weeks will only help if one makes use of that time to aggressively attack the disease.  That was not done.

Furthermore, a travel ban will not change the basic pattern of the epidemic.  It will merely shift it.  The peak loads on a stretched hospital system will remain the same.  Far more effective would be an early and sustained effort to promote social distancing.  This will not only reduce the total number getting the infection, but will also spread the infections out significantly over time.  Even a relatively modest reduction in the pace at which the disease spreads will have a major impact on those peak loads.  And reducing those peak loads on the hospital system can make a major difference in the number of deaths, reducing them by quite possibly thousands.

Why, then, the travel bans?  Probably because it may lead some to believe you are being serious and decisive, even macho, with such a clear-cut (albeit ineffective) measure.  Plus this makes it look like foreigners are to blame.  All this is appealing to someone like Trump.  And as he has repeatedly done throughout his term in office, he discounts the evidence-based advice of scientists with expertise in a field.  He thinks he knows better.

Sadly, Trump is accepting no responsibility for this fiasco.  On March 13, when asked specifically whether he accepts any responsibility for the delay of more than a month in rolling out the extensive testing that is critical early in an epidemic to identify and quickly isolate those infected, Trump replied “No.  I don’t take responsibility at all.”

The Performance of the Stock Market During Trump’s Term in Office: Not So Special

A.  Introduction

Stock market performance is often taken to be a good measure of how the economy as a whole is performing.  But it is not.  For most Americans it is simply irrelevant, as the overwhelming share of investments in the stock markets are held by only a small segment of the population (the wealthy).  And its track record as a broader indicator of how the economy is performing is imperfect at best.

Still, many do focus on stock market returns, and Trump brags that the performance of the market during his term in office has been spectacular.

That is not the case.  This post will look at how the stock market has performed during Trump’s term in office thus far, and compare it to what that performance was under presidents going back to Reagan up to the same point in their terms.

First, however, we will briefly discuss to what extent one should expect stock market prices to reflect actions a president might be taking.  And the answer is some, but there is much more going on.

B.  Presidential Policies and the Stock Market

Owning shares of a firm entitles the owner to a share of the profits generated by that firm, both now and into the future.  And while there are many complications, a simple metric commonly used to assess the price of a share in a firm, is the price/earnings ratio.  If earnings (profits) go up, now and into the future, then for a given price/earnings ratio the price of the stock would go up in proportion.

Economic policies affect profits.  And in a thriving economy, profits will also be rising.  The policies of a presidential administration will affect this, and although the link is far from a tight one (with important lags as well), policies that are good for the economy as a whole will generally also lead to a rising stock market.

But there is also a more specific link to policy.  What accrues to the shareholders are not overall profits, but profits after taxes.  And this changed significantly as a result of the new tax law pushed through Congress by Trump and the Republicans in December 2017.  It resulted in the effective corporate profits (income) tax being cut by more than half:

This chart is an update of one prepared for an earlier post on this blog (where one can see a further discussion of what lies behind it).  It shows corporate profit taxes at the federal level as a share of corporate profits (calculated from figures in the national income accounts issued by the BEA).  While Trump and the Republicans in Congress asserted the 2017 tax bill would not lead to lower corporate profit taxes being paid (as loopholes would be closed, they asserted), in fact they did.  And dramatically so, with the effective corporate tax rate being slashed by more than half –  from around 15 to 16% prior to 2017, to just 7% or so since the beginning of 2018 (and to just 6.3% most recently).

This cut therefore led to a significant increase in after-tax profits for any given level of before-tax profits, which has accrued to the shareholders.  Note that this would not be due to the corporations becoming more productive or efficient, but rather simply from taxing profits less and shifting the tax burden then on to others (i.e. a redistributive effect).  And based on a reduction in the taxes from 16% of corporate profits to 7%, after-tax profits would have gone from 84% of profits to 93%, an increase of about 11%.  For any given price/earnings ratio, one would then expect stock prices, for this reason alone, to have gone up by about 11%.

[Side note:  Technically one should include in this calculation also the impact of taxes on profits by other government entities – primarily those of state and local governments.  These have been flat at around 3 1/2% of profits, on average.  With these taxes included, after-tax profits rose from 80 1/2% of before-tax profits to 89 1/2%, an increase that is still 11% within round-off.]

One should therefore expect that stock prices following this tax cut (or in anticipation of it) would have been bumped up by an additional 11% above what they otherwise would have been.  Other things equal, the performance of the stock market under Trump should have looked especially good as a result of the shift in taxes away from corporations onto others.  But what has in fact happened?

C.  Trump vs. Obama

The chart at the top of this post compares the performance of the stock market during Trump’s term in office thus far (through December 31, 2019) to that under Obama to the same point in his first term in office.  The difference is clear.  Other than during Obama’s first few months in office, when he inherited from George W. Bush an economy in freefall, stock market performance under Obama was always better than it has been under Trump.  Even after slashing corporate profit taxes by more than half, the stock market under Trump did not do exceptionally well.

The S&P500 Index is being used as the measure of the US stock market.  Most professionals use this index as the best indicator of overall stock market performance, as it is comprehensive and broad (covering the 500 largest US companies as measured by stock market value, with the companies weighted in the index based on their market valuations).  The data were downloaded from Yahoo Finance, where it is conveniently available (with daily values for the index going back to 1927), but can be obtained from a number of sources.  The chart shows end-of-month figures, starting from December 31 of the month before inauguration, and going through to December 31 of their third year in office.  The index is scaled to 100.0 on exactly January 20 (with this presented as “month” 0.65).

So if one wants to claim “bragging rights” for which president saw a better stock market performance, Obama wins over Trump, at least so far in their respective terms.

D.  Trump vs. All Presidents Since Reagan

A comparison to just one president is limited.  How does the performance under Trump compare to that under other US presidents up to the same points in their terms in office?  Trump is roughly in the middle:

This chart tracks the performance under each president since Reagan up through the third year of their first terms in office.  I have adjusted here for inflation (using the CPI), as inflation was substantially higher during the Reagan and Bush Sr. terms in office than it has been since.  (I left the chart at the top of this post of just Obama vs. Trump in nominal terms as inflation in recent years has been steady and low.  But for those interested in the impact of this, one can see the Obama and Trump numbers in real terms in the current chart.)  I have included in this chart only the first terms of each president (with one exception) as the chart is already cluttered and was even more so when I had all the presidential terms.

The exception is that I included for perspective the stock market performance during Clinton’s second term in office.  The stock market rose over that period by close to 80% in real terms, which was substantially higher than under any other president since at least before Reagan in either their first or second terms.  The performance in Obama’s first term (of 146% in real terms) was the second-highest.  There was then a set of cases which, at the three-year mark, showed surprising uniformity in performance, with increases of between 32% and 34% in the second Reagan term, the first Clinton term, the second Obama term, and Trump’s term so far.  Bush Sr. was not far behind this set with an increase of 28%.

The worst performances were under Bush Jr. ( a fall of 22% to the third-year point in his first term), and Reagan (an increase of just 8% to that point in his first term).

So the performance of the market under Trump is in the middle – not the worst, but well below the best.

E.  Single Year Increases in the S&P500 from 1946 to 2019

Finally, was the increase under Trump in his best single year so far (2019) a record?  No, it was not.  Looking at the single year performances (in real terms) since 1946, the top 15 were:

The increase in 2019, of 25.9%, was good, but only the sixth-highest of the 74 years between 1946 and 2019 (inclusive).  The stock market rose by more in 2013 during Obama’s term in office (by 27.7%), and in 1997 (28.8%) and 1995 (30.8%) which were both Clinton years.  And the highest increases were in 1958 (35.7%) and 1954 (45.6%) when Eisenhower was president.

The market also rose substantially in 2017, in Trump’s first year in office, by 16.9%.  But it then fell by 8.0% in 2018, in Trump’s second year in office.  Overall, the average rank (out of the 74 years from 1946 to 2019) of the individual year performances over the three years Trump has been in office so far, would place Trump in the middle third.  Not the worst, but also far from the best.  And comparing the three-year average while Trump has been president to rolling three-year averages since 1946, Trump’s average (of 11.6%) is well below the best.  The highest was an average return of 25.3% in 1995-97 during Clinton’s term in office.  And the three-year average return was also higher at 16.7% in 2012-14 during Obama’s term.

F.  Summary and Conclusion

Trump likes to brag that the performance of the stock market during his term in office has been exceptional.  But despite a slashing of corporate profit taxes (which, other things being equal would be expected to increase stock prices by 11%), the performance of the market during Trump’s term in office would put him in the middle.  Specifically:

a)  The market rose by more during the first three years of Obama’s term in office than it has under Trump;

b)  Compared to the first three years in office of all presidents since Reagan (whether first terms only, or first and second terms) would place Trump in the middle.  Indeed, the increase under Trump so far was almost exactly the same as the increases seen (at the three-year point) in Obama’s second term, in Reagan’s second term, and in Clinton’s first term.  And the return under Trump was well below that seen in Obama’s first term, and especially far below that in Clinton’s second term.

c)  The individual year performances during Trump’s three years have also not been exceptional.  While the performance in 2019 was good, it was below that of a number of other years since World War II, and below that of individual years during Obama’s and Clinton’s terms in office.

But as noted at the start of this post, stock market returns should not be over-emphasized.  An increase in the stock market does little for those who do not have the wealth to have substantial holdings in the stock market, and as a broader indicator of how the overall economy is performing, stock market returns are imperfect at best.

Still, one should be accurate in one’s claims.  And as on many things, Trump has not been.

Taxes on Corporate Profits Have Continued to Collapse

 

The Bureau of Economic Analysis (BEA) released earlier today its second estimate of GDP growth in the fourth quarter ot 2018.  (Confusingly, it was officially called the “third” estimate, but was only the second as what would have been the first, due in January, was never done due to Trump shutting down most agencies of the federal government in December and January due to his border wall dispute.)  Most public attention was rightly focussed on the downward revision in the estimate of real GDP growth in the fourth quarter, from a 2.6% annual rate estimated last month, to 2.2% now.  And current estimates are that growth in the first quarter of 2019 will be substantially less than that.

But there is much more in the BEA figures than just GDP growth.  The second report of the BEA also includes initial estimates of corporate profits and the taxes they pay (as well as much else).  The purpose of this note is to update an earlier post on this blog that examined what happened to corporate profit tax revenues following the Trump / GOP tax cuts of late 2017.  That earlier post was based on figures for just the first half of 2018.

We now have figures for the full year, and they confirm what had earlier been found – corporate profit tax revenues have indeed plummeted.  As seen in the chart at the top of this post, corporate profit taxes were in the range of only $150 to $160 billion (at annual rates) in the four quarters of 2018.  This was less than half the $300 to $350 billion range in the years before 2018.  And there is no sign that this collapse in revenues was due to special circumstances of one quarter or another.  We see it in all four quarters.

The collapse shows through even more clearly when one examines what they were as a share of corporate profits:

 

The rate fell from a range of generally 15 to 16%, and sometimes 17%, in the earlier years, to just 7.0% in 2018.  And it was an unusually steady rate of 7.0% throughout the year.  Note that under the Trump / GOP tax bill, the standard rate for corporate profit tax was cut from 35% previously to a new headline rate of 21%.  But the actual rate paid turned out (on average over all firms) to come to just 7.0%, or only one-third as much.  The tax bill proponents claimed that while the headline rate was being cut, they would close loopholes so the amount collected would not go down.  But instead loopholes were not only kept, but expanded, and revenues collected fell by more than half.

If the average corporate profit tax rate paid in 2018 had been not 7.0%, but rather at the rate it was on average over the three prior fiscal years (FY2015 to 2017) of 15.5%, an extra $192.2 billion in revenues would have been collected.

There was also a reduction in personal income taxes collected.  While the proportional fall was less, a much higher share of federal income taxes are now borne by individuals than by corporations.  (They were more evenly balanced decades ago, when the corporate profit tax rates were much higher – they reached over 50% in terms of the amount actually collected in the early 1950s.)  Federal personal income tax as a share of personal income was 9.2% in 2018, and again quite steady at that rate over each of the four quarters.  Over the three prior fiscal years of FY2015 to 2017, this rate averaged 9.6%.  Had it remained at that 9.6%, an extra $77.3 billion would have been collected in 2018.

The total reduction in tax revenues from these two sources in 2018 was therefore $270 billion.  While it is admittedly simplistic to extrapolate this out over ten years, if one nevertheless does (assuming, conservatively, real growth of 1% a year and price growth of 2%, for a total growth of about 3% a year), the total revenue loss would sum to $3.1 trillion.  And if one adds to this, as one should, the extra interest expense on what would now be a higher public debt (and assuming an average interest rate for government borrowing of 2.6%), the total loss grows to $3.5 trillion.

This is huge.  To give a sense of the magnitude, an earlier post on this blog found that revenues equal to the original forecast loss under the Trump / GOP tax plan (summing to $1.5 trillion over the next decade, and then continuing) would suffice to ensure the Social Security Trust Fund would be fully funded forever.  As things are now, if nothing is done the Trust Fund will run out in about 2034.  And Republicans insist that the gap is so large that nothing can be done, and that the system will have to crash unless retired seniors accept a sharp reduction in what are already low benefits.

But with losses under the Trump / GOP tax bill of $3.1 trillion over ten years, less than half of those losses would suffice to ensure Social Security could survive at contracted benefit levels.  One cannot argue that we can afford such a huge tax cut, but cannot afford what is needed to ensure Social Security remains solvent.

In the nearer term, the tax cuts have led to a large growth in the fiscal deficit.  Even the US Treasury itself is currently forecasting that the federal budget deficit will reach $1.1 trillion in FY2019 (5.2% of GDP), up from $779 billion in FY2018.  It is unprecedented to have such high fiscal deficits at a time of full employment, other than during World War II.  Proper fiscal management would call for something closer to a balanced budget, or even a surplus, in those periods when the economy is at full employment, while deficits should be expected (and indeed called for) during times of economic downturns, when unemployment is high.  But instead we are doing the opposite.  This will put the economy in a precarious position when the next economic downturn comes.  And eventually it will, as it always has.

The Fed is Not to Blame for the Falling Stock Market

Just a quick note on this Christmas Eve.  The US stock markets are falling.  The bull market that had started in March 2009, two months after Obama took office, and which then continued through to the end of Obama’s two terms, may be close to an end.  A bear market is commonly defined as one where the S&P500 index (a broad stock market index that most professionals use) has fallen by 20% or more from its previous peak.  As of the close of the markets this December 24, the S&P500 index is 19.8% below the peak it had reached on September 20.  The NASDAQ index is already in bear market territory, as it is 23.6% lower than its previous peak.  And the Dow Jones Industrial average is also close, at a fall of 18.8% from its previous peak.

Trump is blaming the Fed for this.  The Fed has indeed been raising interest rates, since 2015.  The Fed had kept interest rates at close to zero since the financial collapse in 2008 at the end of the Bush administration in order to spur a recovery.  And it had to keep interest rates low for an especially long time as fiscal policy turned from expansionary, in 2009/10, to contractionary, as the Republican Congress elected in 2010 forced through cuts in government spending even though employment had not yet then fully recovered.

Employment did eventually recover, so the Fed could start to bring interest rates back to more normal levels.  This began in late 2015 with an increase in the Fed’s target for the federal funds rate from the previous range of 0% to 0.25%, to a target range of 0.25% to 0.50%.  The federal funds rate is the rate at which banks borrow or lend federal funds (funds on deposit at the Fed) to each other, so that the banks can meet their deposit reserve requirements.  And the funds are borrowed and lent for literally just one night (even though the rates are quoted on an annualized basis).  The Fed manages this by buying and selling US Treasury bills on the open market (thus loosening or tightening liquidity), to keep the federal funds rate within the targeted range.

Since the 2015 increase, the Fed has steadily raised its target for the federal funds rate to the current range of 2.25% to 2.50%.  It raised the target range once in 2016, three times in 2017, and four times in 2018, always in increments of 0.25% points.  The market has never been surprised.  With unemployment having fallen to 5.0% in late 2015, and to just 3.7% now, this is exactly one would expect the Fed to do.

The path is shown in blue in the chart at the top of this post.  The path is for the top end of the target range for the rate, which is the figure most analysts focus on.  And the bottom end will always be 0.25% points below it.  The chart then shows in red the path for the S&P500 index.  For ease of comparison to the path for the federal funds rate, I have rescaled the S&P500 index to 1.0 for March 16, 2017 (the day the Fed raised the target federal funds rate to a ceiling of 1.0%), and then rescaled around that March 16, 2017, value to roughly follow the path of the federal funds rate.  (The underlying data were all drawn from FRED, the economic database maintained by the Federal Reserve Bank of St. Louis.  The data points are daily, for each day the markets were open, and the S&P 500 is as of the daily market close.)

Those paths were roughly similar up to September 2018, and only then did they diverge.  That is, the Fed has been raising interest rates for several years now, and the stock market was also steadily rising.  Increases in the federal funds rate by the Fed in those years did not cause the stock market to fall.  It is disingenuous to claim that it has now.

Why is the stock market now falling then?  While only fools claim to know with certainty what the stock market will do, or why it has moved as it has, Trump’s claim that it is all the Fed’s fault has no basis.  The Fed has been raising interest rates since 2015.  Rather, Trump should be looking at his own administration, capped over the last few days with the stunning incompetence of his Treasury Secretary, Steven Mnuchin.  With a perceived need to “do something” (probably at Trump’s instigation), Mnuchin made a big show of calling on Sunday the heads of the six largest US banks asking if they were fine (they were, at least until they got such calls, and might then have been left wondering whether the Treasury Secretary knew something that they didn’t), and then organizing a meeting of the “Plunge Protection Team” on Monday, Christmas Eve. This all created the sense of an administration in panic.

This comes on top of the reports over the weekend that Trump wants to fire the Chairman of the Fed, Jerome Powell.  Trump had appointed Powell just last year.  Nor would it be legal to fire him (and no president ever has), although some may dispute that.  Finally, and adding to the sense of chaos, a major part of the federal government is on shutdown starting from last Friday night, as Trump refused to approve a budget extension unless he could also get funding to build a border wall.  As of today, it does not appear this will end until some time after January 1.

But it is not just these recent events which may have affected the markets.  After all, the S&P500 index peaked on September 20.  Rather, one must look at the overall mismanagement of economic policy under Trump, perhaps most importantly with the massive tax cut to corporations and the wealthy of last December.  While a corporate tax cut will lead to higher after-tax corporate profits, all else being equal, all else will not be equal.  The cuts have also contributed to a large and growing fiscal deficit, to a size that is unprecedented (even as a share of GDP) during a time of full employment (other than during World War II).  A federal deficit which is already high when times are good will be massive when the next downturn comes.  This will then constrain our ability to address that downturn.

Plus there are other issues, such as the trade wars that Trump appears to take personal pride in, and the reversal of the regulatory reforms put in place after the 2008 economic and financial collapse in order not to repeat the mistakes that led to that crisis.

What will happen to the stock market now?  I really do not know.  Perhaps it will recover from these levels.  But with the mismanagement of economic policy seen in this administration, and a president who acts on whim and is unwilling to listen, it would not be a surprise to see a further fall.  Just don’t try to shift the blame to the Fed.