Trump’s Attack on Social Security

Trump famously promised in his 2016 campaign for the presidency that he would never cut Social Security.  He just did.  How much is not yet clear.  It could be minor or it could be major, depending on how he follows up (or is allowed to follow up) on the executive order he signed on Saturday, August 8 while spending a weekend at his luxury golf course in New Jersey.  The executive order (one of four signed at that time) would defer collection of the 6.2% payroll tax paid by employees earning up to $104,000 a year for the pay periods between September 1 and December 31 (usefully straddling election day, as many immediately noted).

What would then happen on December 31?  That is not clear.  On signing the executive order, Trump said that “If I’m victorious on November 3rd, I plan to forgive these taxes and make permanent cuts to the payroll tax.  I’m going to make them all permanent.”  He later added:  “In other words, I’ll extend beyond the end of the year and terminate the tax.”

The impact on Social Security and the trust fund that supports it will depend on how far this goes.  If Trump is re-elected and he then, as promised, defers beyond December 31 collection of the payroll tax that workers pay for their Social Security, the constitutional question arises of what authority he has to do this.  While temporary deferrals of collections are allowed during a time of crisis, what happens when the president says he will bar the IRS from collecting them ever?  The president swore in his oath of office that he would uphold the law, the law clearly calls for these taxes to be collected, and a permanent deferral would clearly violate that.  But would repeated “temporary” deferrals become a violation of the statutory obligations of a president?  And he has clearly already said that he wants to make the suspension permanent and to “terminate the tax”.

There is much, therefore, which is not yet clear.  But one can examine what the impact would be under several scenarios.  They are all adverse, undermining the system of retirement benefits that has served the country well since Franklin Roosevelt signed the program into law.

Some of the implications:

a)  Deferring the collection of the Social Security payroll taxes will lead to a huge balloon payment coming due on December 31:

The executive order that Trump signed directs that firms need not (and he wants that they should not) withhold from employee paychecks the 6.2% that goes to fund the employee share of the Social Security tax.  But under current law the taxes are still due, and would need to be paid in full by December 31.

Suppose firms did decide not to withhold the 6.2% tax, and instead allow take-home pay to rise by that amount over this four-month period straddling election day.  Unless deferred further, the total of what would have been withheld will now come due on December 31, in one large balloon payment.  For those on a two-week paycheck cycle, that balloon payment would have grown to 54% of their end of the year paycheck.  It is doubtful that many employees would be very happy to see that cut in end-year pay.  Plus how would firms collect on the taxes due on workers who had been with the firm but had left for any reason before December 31?  By tax law, the firms are still obliged to pay to the IRS the payroll taxes that were due when the workers were employed with them.

Hence most expect that firms will continue to withhold for the payroll taxes due, as they always have.  The firms would likely hold off on forwarding these payments to the IRS until December 31 and instead place the funds in an escrow account to earn a bit of interest, but they would still withhold the taxes due in each paycheck just as they always have (and as their payroll systems are set up to do).  This also then defeats the whole purpose of Trump’s re-election gambit.  Workers would not see a pre-election bump up in their take-home pay.

b)  But even in this limited impact scenario, there will still be a loss to the Social Security Trust Fund:

Thus there is good reason to believe that Trump’s executive order will likely be basically just ignored.  There would, however, still be a loss to the Social Security Trust Fund, although that loss would be relatively small.

Payroll taxes paid for Social Security go directly into the Social Security Trust Fund, where they immediately begin to earn interest (at the long-term US Treasury rate).  Based on what was paid in payroll taxes in FY2019 ($1,243 billion according to the Congressional Budget Office), and adjusting for the fewer jobs now due to the sharp downturn this year, the 6.2% component of payroll taxes due would generate approximately $40 billion in revenue each month.  Assuming the $160 billion total (over four months) were then all paid in one big balloon payment on December 31 rather than monthly, the Social Security Trust Fund would lose what it would have earned in interest on the amounts deferred.  At current (low) interest rates, the total loss to Social Security would come to approximately $250 million.  Not huge, but still a loss.

c)  If collection of the 6.2% payroll tax is deferred further, beyond December 31, the losses to the Social Security Trust Fund would then grow further, and exponentially, and become disastrous if terminated:

Trump promised that “if re-elected” he would defer collection by the IRS of the taxes due further, beyond December 31.  How much further was not said, but Trump did say he would want the tax to be “terminated” altogether.  This would of course be disastrous for Social Security.  Even if the employer share of the payroll tax for Social Security (an additional 6.2%) continued to be paid in (where what would happen to it is not clear), the loss to Social Security of the employee share would lead the Trust Fund to run out in less than six years.  At that point, under current law the amounts paid to Social Security beneficiaries (retirees and dependents) would be sharply scaled back, by 50% or more (assuming the employer share of 6.2% continued to be paid).

d)  Even if the Social Security Trust Fund were kept alive by Congress acting to replenish it from other sources of tax revenues, under current law individual benefits would be reduced on those who saw their payroll tax contributions diminished:

There is also an issue at the level of individual benefits, which I have not seen mentioned but which would be significant.  The extent of this impact would depend on the particular scenario assumed, but suppose that the payroll taxes that would have come due and collected from September 1 to December 31 were permanently suspended.  For each individual, this would affect how much they had paid in to the Social Security system, where benefits are calculated by a formula based on an individual’s top 35 years of earnings (with earnings from prior years adjusted to current prices as of the year of retirement eligibility based on an average wage inflation index).

The impact on the benefits any individual will receive will then depend on the individual’s wage profile over their lifetime.  Workers may typically have 20 or 25 or maybe even 30 years of solid earnings, but then also a number of years within the 35 where they may have been not working, e.g. to raise a baby, or were unemployed, or employed only part-time, or employed in a low wage job (perhaps when a student, or when just starting out), and so on.

There would thus be a good deal of variation.  In an extreme case, the loss of four months of contributions to the Social Security Trust Fund from their employment history might have almost no impact.  This would be the case where a worker’s income in their 36th year of employment history was very similar to what it was in their 35th, and the loss in 2020 of four months of employment history would lead to 2020 dropping out of their employment top 35 altogether.  But this situation is likely to be rare.

More likely is that 2020 would remain in the top 35 years for the individual, but now with four months less of payroll contributions being recorded.  One can then calculate how much their Social Security retirement benefits would be reduced as a result.

The formulae used can be found at the Social Security website (see here, here, and here).  Using the parameters for 2020, and assuming a person had earned each year the median wages for the year (see table 4.B.3 of the 2019 Annual Statistical Supplement of Social Security), one can calculate what the benefits would be with a full year of earnings recorded for 2020 and what they would be with four months excluded, and hence the difference.

In this scenario of median earnings throughout 35 years, annual benefits to the retiree would be reduced by $105 (from $17,411 without the four months of non-payment, to $17,306 with the four months of the payroll tax not being paid).  Not huge, but not trivial either when benefits are tied to a full 35 years of earnings.  That $105 annual reduction in benefits would have been in return for the one-time reduction of $669 in payroll taxes being paid (6.2% for four months where median annual earnings of $32,378 in 2019 were assumed to apply also in 2020 despite the economic downturn).  That is, the $669 not paid in now would lead to a $105 reduction in benefits (15.8%) each and every year of retirement (assuming retirement at the Social Security normal retirement age).

The loss in retirement benefits would be greater in dollar amount if the period of non-payment of the payroll tax were extended.  Assuming, for example, a scenario where it was extended for a full year (and one then had just 34 years of contributions being paid in, with the rest at zero), with wages at the median level throughout those now 34 years, the reduction in retirement benefits would be $316 each year (three times as much as for the four-month reduction).  Payroll taxes paid would have been reduced by $2,007 in this scenario, and the $316 annual reduction is again (given how the arithmetic works) 15.8% of the $2,007 one-time reduction in payroll taxes paid.

All this assumes Social Security would continue to pay out retiree benefits in accordance with current law and assumes the Trust Fund remained adequate.  The suspension of these payroll taxes would make this difficult, as noted above, unless there was then some general bailout enacted by Congress.  But any such bailout would raise further issues.

e)  If Congress were to appropriate funds to ensure the Social Security Trust Fund remained adequately funded, the resulting gains would be far greater for those who are well off than for those who are poor:

Suppose Congress allowed these payroll taxes to be “terminated”, as Trump has called for, but then appropriated funds to ensure benefits continued to be paid as per the current formulae.  Who would gain?

For at least this part of the transaction (the origin of the funds is not clear), it would be the rich.  The savings in the payroll taxes that would be paid in order to keep one’s benefits would be five times as high for someone earning $100,000 a year as for someone earning $20,000.  The tax is a fixed 6.2% for all earnings up to the ceiling (of $137,700 in 2020, after which the tax is zero).  The difference in terms of the benefits paid would be less, since the formulae for benefits have a degree of progressivity built-in, but one can calculate with the formulae that the change in benefits from such a Congressional bailout would still be 2.3 times higher for those earning $100,000 than for those earning $20,000.

One might question whether this is the best use of such funds.  Normally one would want that the benefits accrue more to the poor than to those who are relatively well off.  The opposite would be the case here.

f)  Importantly, none of this helps those who are unemployed:

Unemployment has shot up this year due to the mismanagement of the Covid-19 crisis, with the unemployment rate rising to a level not seen in the US since the Great Depression.  Unemployment insurance, expanded in this crisis, has proven to be a critical lifeline not only to the unemployed but also to the economy as a whole, which would have collapsed by even more without the expanded programs.

Yet cutting payroll taxes for those who have a job and are on a payroll will not help with this.  If you are on a payroll you are still earning a wage, and that wage is, except in rare conditions, the same as what you had been earning before.  You have not suffered, as the newly unemployed have, due to this crisis.  Why, then, should you then be granted, in the middle of this crisis where government deficits have rocketed to unprecedented levels, a tax cut?

It makes no sense.  Some other motive must be in play.

g)  This does make sense, however, if your intention is to undermine Social Security:

Trump pushed for a cut in the payroll taxes supporting Social Security when discussions began in July in the Senate on the new Covid-19 relief bill (the House had already passed such a bill in May).  But even the Republicans in the Senate said this made no sense (as did business groups who are normally heavily in favor of tax cuts, such as the US Chamber of Commerce), and they kept it out of the bill they were drafting.

The primary advisor pushing this appears to have been Stephen Moore, an informal (unpaid) White House advisor close to Trump.  He co-authored an opinion column in The Wall Street Journal just a week before Trump’s announcement advocating the precise policy of deferring collection of the Social Security payroll tax.  Joining Moore were Arthur Laffer (author of the repeatedly disproven Laffer Curve, whom Trump had awarded the Presidential Medal of Freedom in 2019), and Larry Kudlow (Trump’s primary economic advisor and a strong advocate of tax cuts).

Moore has long been advocating for an end to Social Security, arguing that individual retirement accounts (such as 401(k)s for all) would be preferable.  As discussed above, the indefinite deferral of collection of the payroll taxes that support Social Security would, indeed, lead to a collapse of the system.  Thus this policy makes sense if you want to end Social Security.  It does not otherwise.

Yet Social Security is popular, and critically important.  Fully one-third of Americans aged 65 or older depend on Social Security for 90% or more of their income in retirement.  And 20% depend on Social Security for 100% of their income in retirement.  Cuts have serious implications, and Social Security is a highly popular program.

Thus advocates for ending Social Security cannot expect that their proposals would go far, particularly just before an election.  But suspending the payroll taxes that support the program, with a promise to terminate those taxes if re-elected, might appear to be more attractive to those who do not see the implications.

The issue then becomes whether enough see what those implications are, and vote accordingly in the election.

The Spread of Covid-19: Trump States vs. Clinton States – An Update

An earlier post on this blog compared the spread of Covid-19 in the states that Trump had won in 2016 to that in the states won by Clinton, with data through June 24.  This post will update those figures to July 16.  The trends have become even clearer.

As seen in the chart above, new cases in the states won by Trump have continued to shoot upwards, at an alarming pace.  They had reached 22,000 new cases per day as of June 24 (based on a seven-day rolling average ending on that date), but have now (as of July 16, just three weeks later) more than doubled to 48,500.  The decisions to rapidly reopen by the governors of such Trump-won states as Florida, Georgia, Texas, and Arizona, as well as others, have clearly been a disaster.  The virus is now spreading rapidly in those states, and some of these governors are now putting back in place (albeit only partially) the social distancing measures that had earlier worked.

Daily new cases are also now clearly increasing in the states won by Clinton.  This trend was still too recent to be clear in the data through June 24.  But the pace of spread in the Clinton states is far below that of the Trump states, and the number of new daily cases in the Clinton states (16,500 as of July 16) is only one-third the number in the Trump states.

The trends in the figures for the number of deaths from Covid-19 have also now become clear:

In the previous data through June 24, the daily number of deaths (again based on seven-day rolling averages) had come down from their mid-April peaks to a relatively flat level as of mid-June.  This had marked a sharp decline of over 80% in the daily number of deaths in the Clinton states (where peaks early in the crisis in New York had overwhelmed the hospital system, at a time when still little was known on how best to treat the extremely sick), and by a lesser but still significant decline (about 50%) in the Trump states.

Since mid-June, the daily number of deaths in the Clinton states has been relatively flat (hovering between about 200 and 300).  But there has now been a significant increase in deaths in the Trump states, rising from a trough of about 280 per day to now almost 500, an increase of about 75%.  And the path points to a continued rise, as one would expect given the even sharper rise in daily new cases (as there is a lag – deaths occur several weeks after when a case is first confirmed).

These trends should be worrisome in the extreme.  They are not the consequence of increased testing in the US, as Trump has repeatedly asserted.  While testing was slow to start in the US (the administration had bungled the roll-out in February and into much of March), there has not been a significant change in test availability since mid or late April, and certainly since May.  The increases in cases started in June.  More people are now being tested because more people are getting sick, and seek a test as they come down with the symptoms.  And the increase in the number of people dying from the disease is certainly not a consequence of testing, but rather of more people becoming sick.

More could be done, but sadly this presidential administration isn’t.  And it would not be all that difficult.  As I had noted in my June 25 post, a relatively easy measure would be for everyone to wear masks.  Since that post, Robert Redfield (the head of the CDC) noted in an interview on July 14 that “if we could get everyone to wear a mask right now, I really do think that over the next four-six-eight weeks we could bring this epidemic under control” (see this YouTube video of the interview, starting at about the 4-minute mark).  He noted that this is not difficult – the problem is just that not enough people do it.

For many of those refusing to wear a mask – some adamantly so – the issue is seen as political.  The problem started with Trump, where at the April 3 press conference announcing CDC guidelines calling on people to wear face masks, Trump simultaneously emphasized that he would not himself abide by those guidelines.  With any other president, this would be unbelievable.  Since then, supporters of Trump have increasingly seen the issue as one of making a political statement rather than as the public health matter that it is.  A recent academic study found that political partisanship is the most important factor in explaining whether or not people will wear masks and exercise other social distancing recommendations, and that this partisan difference has grown over time.

This has even become violent.  In early May, for example, a security guard at a Famlly Dollar store told a customer she would need to wear a face mask to enter, as per the state orders of the time.  She returned with members of her family about 20 minutes later who shot the guard, who died.  More recently, a 43-year old man entering a convenience store without a mask was asked by another customer to put on a mask.  He responded by stabbing the 77-year old customer.  The man then fled, was later spotted by police, and started to attack the policewoman who then shot him.  He died.  And there have been, sadly, a number of such incidents.

Those refusing to wear face masks when in public insist that such a requirement infringes on their “freedom”.  Thus, as a matter of principle, they refuse to do it.  If it was indeed the case that the only one suffering harm from not wearing a mask was that individual only, I would not be so concerned.  But that is not the case – others exposed may then become infected, and possibly even die.  It is similar to speed limits on highways.  If the only one who might be harmed by speeding is the speeder only, I would not be so concerned.  But speeders may harm, and possibly kill, others as well.  Hence we have speed limits and those limits are enforced.

Refusing to wear a face mask under a belief that it is an infringement on freedom, and responding with threats or even violence when asked to do so, is madness.  With true leadership in Washington we would have a president who would act on this.  Not only would that president model responsible behavior by wearing a face mask himself when in public or when meeting with others, he would also call on all his supporters to do so as well.  They might listen to him.  But his refusal to do so speaks volumes itself.

The Failure of the US to Limit the Spread of Covid-19: A Comparison to What Other Countries Have Been Able to Achieve

A.  Introduction

The virus that causes Covid-19 has struck countries around the world, and it is the same virus everywhere.  But countries have responded differently.  Many countries have responded effectively, and some highly effectively.  The US is not among them.  The experience in other countries shows what would have been possible, had the US responded as they did.  Unfortunately, the US, with Trump leading as president, did not.

B.  The US Compared to Italy, Spain, Germany, and the UK

The chart above shows the daily number of new confirmed cases (on a 7-day moving average basis) since the start of the pandemic through to July 6, for the US plus several of the larger countries of Western Europe:  Italy, Spain, Germany, and the UK.  These countries were chosen in part as they were all hit with the virus that causes Covid-19 earlier than most (including earlier than in the US).  They thus faced a crisis when much was still not known about the virus, including how quickly it could spread and under what conditions, and uncertainty on what should be done to bring it under control.  The underlying data on Covid-19 case totals, from which the figures for the chart were derived, comes from the widely-used data set maintained by Johns Hopkins University.  Population numbers from the UN were used to put the number of cases on comparable terms:  of daily new cases per million residents.

Italy was the first major country in Europe to have been hit by the virus, for reasons still not fully known.  Cases rose quickly, reaching a peak at the end of March.  Spain came next, roughly a week later than Italy at first, but then rose especially quickly to a peak in early April of almost double the peak in Italy.  Germany also had a high number of cases early, but was then more successful through aggressive testing and quarantining to keep the peak from rising as high.  Finally, the UK saw a similar peak to that of Germany, but with that peak then lasting for close to a month.

Each of these European countries was then able to bring their daily new case numbers down sharply, to less than 10 new cases a day per million residents by early July (and indeed by early June for all other than the UK).  Each country had its own policies, and I will not go into the nuances of the country-specific differences here, but they succeeded through a combination of social distancing (including lockdowns), wide use of masks, extensive testing, contact tracing, and then isolation or quarantining of those infected or exposed to someone infected.  And with their success in bringing down the number of Covid-19 cases, these countries are now opening up for business, schools, and travel, and are doing so safely.

The US followed a different path.  Cases rose similarly at first as in these European countries, although with a lag (or about two weeks compared to Italy).  One should be cautious with these early numbers as testing, particularly in the US, was not as complete as was being done later, but the early trends appear to be broadly similar.

But what is important is what happened next.  In contrast to the European countries, who were all able to bring down their case numbers by 90% or more, new daily cases in the US fell much more modestly.  Despite official policies (in much, although not all, of the country) to lock down the economy to limit person-to-person spread of the virus, plus guidelines encouraging (and in some cases mandating, but with lax to no enforcement) the wearing of masks and social distancing, the daily case numbers in the US were reduced only from about 95 per million in early/mid April to a trough in early June that never fell below 60.

US cases then started to shoot up.  This followed the easing of social distancing and other measures to limit the spread of the virus during the month of May.  While there were important differences by state and indeed often by locality, most states started to lift the measures cautiously in early May and much more comprehensively by the end of May (and sometimes completely so by that point).  And as was examined in an earlier post on this blog, the increases in daily cases have been particularly sharp in the states won by Trump in 2016 – states often with governments and a population that have been particularly aggressive in lifting (or increasingly ignoring) those measures.

As a further example of the impact of this politicization of what should be seen as basic public health measures, the number of Covid-19 cases in Tulsa, Oklahoma, have now spiked two weeks after Trump held a large campaign rally in an indoor arena there.  Local health officials have said it is “more than likely” that the two are linked.  Few at the Trump rally wore masks, they were grouped closely together for the cameras, and loud cheering was of course encouraged.  The two week lag from the rally to the spike in Covid-19 cases is about what health experts say one should expect, between when there is exposure to the virus at an event such as this to when confirmed case numbers will rise as results are obtained for people seeking tests following an onset of symptoms.

C.  The US Compared to Europe, Canada, and Sweden

The chart at the top of this post highlights only a few countries.  But the same results hold for Western Europe as a whole as well as for Canada:

Cases in Western Europe as a whole rose early, reached a peak, and then fell.  Since early June cases have remained below 10 per day per million.  As of July 6, they were at 8.3, or less than 6% of the US rate of 149 per day.  The path for the countries of Eastern Europe (the countries from Estonia on the north to Bulgaria on the south, who are now mostly members of the EU) is interesting as they were able to contain the virus throughout, with a peak of less than 14 in early to mid-April.  But a modest increase in recent weeks (to almost 15 currently) warrants watching.

Canada is also interesting as the economy and the population are broadly similar to that of the US, but with very different politics.  Cases rose in Canada to a peak of about 50 in mid-April.  But they were then brought down, to levels now very similar to that of Western Europe.  Again, this is in sharp contrast to the US.

Sweden is an exception to others in Europe.  It is also the one country of the rich Western democracies that explicitly followed a different policy path.  Instead of mandating a lockdown of the economy, the wearing of masks, social distancing, and other such measures, it only issued general guidance.  And even this guidance was eased later.  Daily cases per million then reached about 60 in late April, fell only modestly to about 50 in late May, before increasing significantly to as much as 120 at points in June (although with erratic numbers that probably reflect reporting practices).  Sweden is now taken as a good example of what not to do.  Furthermore, while “protecting the economy” was presented as a rationale for Sweden’s decision to issue only general guidelines, with no requirement for businesses such as restaurants to close, early evidence indicates that the Swedish economy has suffered similarly to those of its neighbors.  There was no economic gain, but a profound human loss in sickness as well as lives.  As I write this (July 9), the accumulated number of deaths per million of population has come to 545 in Sweden, or roughly ten times the totals of 46 in neighboring Norway and 59 in Finland.

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

Europe (with the exception of Sweden), as well as Canada, have therefore been far more successful than the US in limiting the spread of the virus that causes Covid-19.  But the countries that have been by far the most successful in containing the virus have been those of East Asia, as well as Australia and New Zealand:

Drawn on the same scale as the other charts, one can barely distinguish their case levels, other than during a few, and still always low, periods (in early March in South Korea and in late March and early April for most of the others).  And the daily case rates in Taiwan were never over 1 per million of population, so one cannot distinguish its curve from the horizontal axis of the chart.  Yet Taiwan has probably closer contact with China, from business relationships as well as personal travel, than any other country in the world other than Hong Kong.

All of these countries reacted quickly as soon as it became clear that an infectious disease had spread in China.  While travel limits were imposed, these limits were complemented by extensive testing and contact tracing, quarantining of all travelers (whether citizens or not), and wide use of masks and other social distancing measures.  None of this was secret.  Nor did it require special expertise.  Others could have responded similarly, but did not.

E.  Countries with a Similar Result as the US

Which, then, are the country cases that are broadly comparable to that of the US?  The closest are Brazil and South Africa, with similarities also in the cases of Russia and Mexico:

These are not countries that the US would normally compare itself to.  One should certainly be cautious and note that the quality of the case number data may not be all that good in some of these countries (and indeed, it is not all that good in the US itself).  But the patterns are probably broadly accurate.

Brazil is the one major country in the world with more confirmed cases (per million of population) than the US.  Its right-wing president, Jair Bolsonaro, has responded to the virus in many ways similar to Trump.  He has consistently downplayed the virus (like Trump), has refused to wear a mask (like Trump), has encouraged rallies to oppose rules on social distancing that some Brazilian states and localities had issued (also like Trump), and has insisted that the disease is not serious but rather “It’s just a little flu or the sniffles”.  And like Trump, he accuses the media of stoking hysteria.

The result is that the number of cases in Brazil per million of population is now the highest of any large country in the world, and indeed second only to the US in absolute total number.  And on July 7, Bolsonaro himself tested positive for the virus.  Again like Trump (who took the drug when he was possibly exposed to the virus), Bolsonaro is now taking doses of hydroxychloroquine as a treatment, even though there is clear evidence that this drug does not help with Covid-19 and may in fact do harm.

Other countries with rising numbers of new cases include South Africa and Mexico.  The daily cases for South Africa now match the US number, with a path since mid-June broadly similar to the US path.  Russia saw an increase in April to mid-May, after which there has been some decrease.  But the daily numbers in Russia remain high.

F.  Conclusion

There is not much here for the US to be proud of.  While countries in Western Europe, as well as Canada, saw sharp increases in cases in much of March and early April, they were then all (with the notable exception of Sweden) able to bring the rates for new cases down to modest levels.  With that success, they are now reopening their economies, are permitting travel (other than, notably, to and from the US), and will be reopening schools.  They are all still cautious, and maintain aggressive efforts at testing, contact tracing, and then quarantining when warranted, but their success in bringing down the daily case numbers means they can, albeit carefully, resume a degree of normalcy.  It is possible that things will take a turn for the worse in the weeks and months ahead.  Until there is an effective vaccine that is broadly available, there will remain conditions in which the virus could pop up and cause major disruptions again.  But the situation in these countries has remained stable there for more than a month now.

Countries in East Asia, as well as Australia and New Zealand, have done far better.  They kept rates low from the start and have thus been able to reopen safely and more quickly.  Indeed, schools in Taiwan never even closed (other than for a two-week extension of the traditional Chinese New Year holiday in February).  But Taiwan then opened schools safely, with students required to wear masks, temperature checks carried out daily of all students, and with plastic shields installed to separate desks from each other.  [Not everyone liked this.  I know from direct personal information that at least a few elementary school age children thought it horribly unfair that they have had to go to school while children around the world were able to stay home.]

So who resembles the US in effectiveness in limiting the spread of the virus that causes Covid-19?:  Among the larger countries of the world, only Brazil and South Africa, and to some extent Mexico and Russia.  In the past, they were not the countries the US would see as comparables.  But they are now.

The Increase in Covid-19 Cases is Real: Hospitalization Has Gone Up in Trump States

Cases of Covid-19 infection are going up in the US.  Indeed, the daily number of new confirmed cases have been hitting record levels, with almost all of the recent increase recorded in states that Trump won in 2016.  But Trump has continued to insist the record highs are only because his administration has done such a great job in making tests finally available.  Health professionals who actually have expertise in such issues dispute this.  And many more people are seeking tests, even waiting in lines in their cars that are miles (and many hours) long.  You don’t do this if it does not look serious.

But while it is true that there would be fewer cases confirmed if we did not know about them due to fewer tests, one statistic this would not affect would be the number of those being sent to a hospital having contracted a severe case of the infection.  Numbers on those hospitalized due to the virus are available for most US states (with Florida an important exception – this will be discussed below).  One then gets the chart above when the hospitalization numbers for those states won by Trump in 2016 are compared to those won by Clinton (as a proxy for the more conservative, mostly Republican, states compared to the more liberal, mostly Democratic, ones).

The chart shows that there has been a marked increase in hospitalizations in the Trump states since about June 15.  Excluding Florida, hospitalizations in the Trump states have grown to almost 20,000 as of June 29 from only about 12,000 in early June, an increase of two-thirds.  In contrast, hospitalizations in the states won by Clinton rose fast early, but then fell.  Little was known early on about the virus and how fast it was spreading in the US, particularly in dense urban locations, in part because of the early blunders of the Trump administration that severely limited testing in February and into most of March.  But from a peak in hospitalizations in mid-April in the states won by Clinton, the numbers have come down steadily, although with some leveling off since mid-June.  They are now well below the number hospitalized in the Trump states.

The data comes from figures assembled by the CovidTracking project, a private initiative launched by The Atlantic Monthly.  The project has assembled, on a daily basis, figures officially reported by US states and territories on Covid-19 tests being conducted (and the positive or negative results), the number of deaths, the numbers hospitalized, those in an ICU and those on ventilators, and more.  The data available, and its quality, are only as good, however, as what the states and territories report.  While the figures on confirmed positive tests and on deaths appear to be of fairly good quality and completeness, what the states report on the other variables is uneven and often incomplete.  One then has to be careful in interpreting the numbers, as figures not reported by certain states (or on certain dates) are left blank and then treated as a zero when the national numbers are aggregated.  The figures on numbers in ICU beds or on ventilators are notably incomplete.  And one should be especially careful with the earlier numbers, as they are often quite partial.  The later numbers are more complete and generally more reliable.

The figures on those hospitalized due to Covid-19 are complete (as I write this) except for four states:  Kansas, Idaho, Hawaii, and notably Florida.  The number of cases in Kansas, Idaho, and especially Hawaii are all relatively small, in part as all three are relatively small states.  Based on a 7-day moving average to smooth out day to day fluctuations, the daily number of new confirmed cases in the three states totaled only 482 as of June 29 (with only 12 in the case of Hawaii, which has done a superb job of containing the virus that causes Covid-19).  In contrast, Florida alone averaged 6,589 cases daily in the 7-day period ending on June 29, or almost 14 times the other three states combined.  Florida matters – the other three states not so much.

But data reporting on the spread of Covid-19 by Florida has been especially poor, and politicized.  Rebekah Jones, the state employee who developed the Florida “dashboard” that presented the Covid-19 results by county was fired in May when she refused to manipulate the data in a way to make it appear that much of the state was meeting the criteria for reopening when in fact they were not.  She has since developed and made available over the internet a dashboard similar to the one she had developed for the State of Florida, but with data that has not been so manipulated.

The underlying problem was that Florida Governor Ron DeSantis (a close ally of Trump) had been declaring victory over the virus that causes Covid-19 already in early May, as he proceeded to reopen the state early and aggressively.  He held news conferences, including at the White House, claiming he had succeeded where others had failed, and that Florida should serve as a model for the country.  Trump lavished praise on the governor, saying he was doing a “spectacular job”.

It is therefore more than a bit embarrassing for DeSantis that cases in Florida have been rising so fast since his May 1 reopening.  For the US as a whole, the average number of daily new cases for the 7-day period ending June 29 was 37% higher than what it was for the period ending on May 1.  But in Florida, the number of daily new cases for the 7-day period ending June 29 was 11.0 times higher than what it was for the 7-day period ending May 1.

With the high number of cases in Florida, it is worthwhile to try to estimate, even if only roughly, what the hospitalization figures would look like if Florida reported its results.  They do have such data – they have reported on the number of new hospitalizations each day.  But this is incompatible with what most other states report.  And knowing the number of those infected with the virus who are currently hospitalized is closely monitored everywhere as it is important to know how close one is to current hospital limits on the ability to handle more cases.  But Florida has not made these figures available.

One can, however, make a rough estimate of what the impact would be if figures for Florida were available.  Other states with a similarly sharp rise in new cases since mid-June include Texas, Arizona, and Georgia.  Hospitalization figures are available for each.  In those states, the ratio of the number currently hospitalized (where one should keep in mind that those hospitalized for Covid-19 are always there for at least several days, and sometimes several weeks), to the 7-day average daily number of new cases, averages across the three states and on two dates to 1.015 (with not much variation around this average).  Using that ratio, one can estimate what the hospitalization figures in Florida might be, given the number of new cases found in Florida.

The result is shown in the curve in orange in the chart above.  The number of patients hospitalized due to the coronavirus in the Trump states would, with this estimate for Florida, have risen to over 26,000 as of June 29.  This is a third higher than the 19,600 hospitalized in the Trump states as of that date excluding Florida.  Or in another comparison, the increase in hospitalizations in the Trump states between June 15 and June 29 was 51% excluding Florida.  But with these estimates for Florida included, the increase over that period was an even higher 78%.

Trump’s reaction to this sharp increase in cases, concentrated in states that supported him in 2016?  It appears that he simply does not know what to do.  So while it has become clear that the increase in cases is real, with the increase in hospitalizations now also confirming this, Trump appears to have retreated into a fantasy world where the virus that causes Covid-19 simply disappears.  In an interview on June 29 on the Trump-friendly Fox Business Network, Trump said:

“I think we’re going to be very good with the coronavirus. I think that at some point that’s going to sort of just disappear”

He then added, “I hope”.  During the worst health crisis the nation has been through since the Spanish Flu pandemic of 1918/19, the US has a president who is lost, does not know what to do, and is reduced to hoping it will just go away.

Covid-19 by State: The Impact of Urbanization on the Spread

A.  Urban Concentration and Covid-19 Cumulative Deaths as of May 3

The virus that causes Covid-19, like other such viruses, spreads person to person.  Thus one should expect that there will be a more rapid pace of spread in urban areas, where people are in closer day-to-day contact.  This is not an indication of what the ultimate spread might be, as catching an infectious disease is a one-time event and contacts with others still add up over time.  It is just that instead of encountering a certain number of people in one day, it might instead take several days or even weeks.  But greater person-to-person contact increases the likelihood that one will catch the disease earlier.

Thus one should expect that at this point in the middle of the spread of Covid-19, those states that are more highly urbanized will have seen a greater number of deaths from the disease (per unit of population) than states that are more rural.  And that is indeed what one finds, although with some interesting exceptions.

The chart above shows the number of deaths in each US state per million of population, plotted against the percentage share of the urban population in the state.  The share of the state’s population that is defined as residing in an “urban” area comes from the US Census Bureau, which applies a very specific (and uniform) definition of what it labels as urban.  The calculations are based on what the Census Bureau defines as “urbanized areas”.  Under this definition, the urban population is the total population in the state living in an area with a dense urban core, including in the surrounding (suburban) areas meeting certain population density requirements, and with a total population within that area of 50,000 or more.  (Note that the Census Bureau also has a broader concept of what it considers “urban” that includes communities down to a population of 2,500.  Statements on urban populations in states are often based on this broader definition.)

While this is the best one can do in defining what it means to be living in an urban area, note that it is still highly imperfect for the purposes here.  Urban areas differ greatly.  The day-to-day contact one would experience in New York is quite different from what would normally find in a city of 50,000.  Even comparing similarly large cities, it will be quite different between New York and, say, Los Angeles.  Still, it is of interest to see whether states with a higher share of their population living in urbanized areas, as defined by the Census Bureau, have at this point in the spread of Covid-19 experienced a higher fatality rate from the disease.

The chart indicates that in general they have.  The data on the number of deaths from Covid-19 comes from the data set maintained by the New York Times, with the figures as of May 3, 2020 (and downloaded in the afternoon of May 4).  The Census Bureau figures on state total populations and on those living within urbanized areas (of 50,000 or more) are all from the 2010 census.  While these are now ten years old and will be updated once the 2020 census is completed, for the purposes of this exercise they more than suffice.  The relative populations across states will not have changed all that much.

At this point in the pandemic, states with urban population shares of up to almost 60% have uniformly relatively low (as compared to other states) death rates from Covid-19 per million of population, with all at about 100 or less (Mississippi is at 102).  Half the states (25 of the 51 including Washington, DC, as a 51st) fall into this category, with their names on the chart crowded and overlapping.  For those interested, the figures for individual states can be found in a table at the bottom of this post.

The states with urban population shares of just below 60% (Indiana) up to 80% then show more variety.  The fatality rates are very low for some (e.g. Hawaii, at 12.5 per million with an urban share of 71.5%) and substantially higher for others (e.g. Louisiana, at 434 per million and an urban share of 61%).

But the most substantial variation is seen in those states with an urban share of 80% or more.  The fatality rate at this point in the pandemic is just 18 per million in Utah despite an urban share of 81%, while it is close to 1,000 per million in the state of New York with an urban share of 83%.  Several other states in this group also have relatively low fatality rates, including California, Arizona, Nevada, and Florida.  Thus while there is a clear association seen between a higher share of a state’s population living in an urbanized area and the deaths per million from Covid-19, that relationship is not fate.  There are important exceptions.

The broad range in cumulative death rates among the states with the higher urban population shares is a consequence of several factors.  While it is not surprising that a higher urban share appears to make a location more vulnerable to a rapid spread of the virus, it is also clear that it is not inevitable.  There are a number of exceptions.  California, while vulnerable, imposed state-wide lockdown orders relatively early, for example.  The Utah public health system has also functioned particularly well.  And the state totals may be consistent with some very limited evidence (but disputed, and far from certain) that the virus that causes Covid-19 might spread less in warmer and moderately humid environments.  This might in part explain the low rates seen, despite high urbanized shares, in Arizona, California, Florida, and Nevada, as well as in Texas and Hawaii.

At the other end, the areas around New York City (in the states of New York, New Jersey, and Connecticut) saw an early and rapid spread of the virus before many were aware of it.  Based on analysis of the genome, researchers have found that the virus found there had in most cases arrived from Europe rather than directly from China.  Furthermore, they found that it was introduced to the New York area from multiple independent sources (i.e. not from just one traveler) and that it may well have arrived already in January.  There has also been a recent report that the virus had already been introduced into Europe as early as late December.  A recent analysis of a sample of bodily fluids taken from a French man living in the Paris region, who went to a local hospital on December 27 with a case of suspected pneumonia, indicated that he in fact had the virus that causes Covid-19.  He had not traveled abroad.

Thus bad luck can also play a role.  A region with a high degree of urban concentration (such as New York), with frequent travelers to and from a region where the disease had spread but where this was not known at the time (Europe), would be particularly susceptible to a highly infectious viral disease such as Covid-19.

Florida may be a surprising case.  It is a state with a relatively high share (87%) of its population residing in urbanized areas (as defined by the Census Bureau measure).  But its cumulative death rate (as of May 3) is also relatively low.  Florida has been criticized for not shutting down the spring break holidays of mid-March when numerous college students from around the country fly to Florida for parties and more.  But while the impact on cases leading to deaths in Florida itself appears to have been limited, outbreaks of the virus in other parts of the US have been traced to the spring break vacationers in Florida then returning to their homes across the US.

B.  Urban Concentration and the Recent Daily Path of Covid-19 Deaths

The picture outlined above is a static one, as it focused on the rate of fatalities from the disease at a particular point in time (May 3).  It is also of interest to review what the path has been in daily deaths from the disease, particularly over the past several weeks.  The social distancing measures that the states imposed in mid to late-March (with a good deal of variation in both when they were imposed and how strong the measures were) would be expected to have an impact on reducing the pace of the spread, with a lag of a few weeks.  They would then hopefully reduce the number of deaths from the disease a further week or so later.

In this, it is clear that the social distancing measures did succeed in flattening and then bringing down the curve, but with an important difference between the more highly urbanized states and the less urbanized ones:

The fatality rate for the US as a whole has come down since reaching a peak of about 2,000 deaths per day in mid-April (using 7-day moving averages to smooth out day-to-day fluctuations, where the dates shown are for the end of each 7-day period).  The number of deaths then fell to just below 1,800 by May 4, a reduction of 10%.  Based in part on this, the Trump administration is now encouraging states to lift their social distancing measures so that economic activity would, they hope, then recover.

But while the number of fatalities from this disease have begun to fall in the US as a whole, this has been entirely in the more urbanized states.  Between the 7-day periods ending on April 17 and on May 4, the number of fatalities in the highly urbanized states fell by 25%.  During that same period, they rose by 15% in the less urbanized states.

While the daily number of deaths remains at this point higher in the more urbanized states than in the less urbanized ones, this might soon change:

The daily number of new confirmed cases of Covid-19 is now higher in the less urbanized states.  While the measurement of confirmed cases has been suspect (it depends on how broadly one is testing), it is better now than it was in March and even early April, when testing supplies were still limited and constrained the availability of testing.  And the chart suggests that with the number of new confirmed cases now higher in the less urbanized states than in the more urbanized ones, and still heading upwards, the number of deaths from the disease in the less urbanized states may soon be higher in absolute number.

C.  What is the Plan? 

The Trump administration, and especially Trump himself, are now encouraging states to lift their social distancing measures.  The stated aim is for the economy then to recover.  However, with all the disruption that has resulted from the failure of the Trump administration to take this pandemic seriously early on, it is far from clear that this will suffice.  The economy has been severely affected, where an astounding 30 million Americans (18% of the labor force) have already applied for unemployment insurance as of the week of April 25.  Such a sharp and rapid collapse is unprecedented.  It did not happen even in the Great Depression.

The Trump administration has argued that with the daily number of deaths from Covid-19 now falling in the US, the time has come to reopen businesses.  And a number of governors, primarily Republicans in the more rural states, have started to do this, arguing that with their more rural spaces there is no longer a need for such social distancing.  But as seen in the charts above, while the accumulated number of deaths per million from Covid-19 has often (but not always) been less in the less urbanized states, the absolute number of deaths in these states has continued to grow over the last several weeks even while they have gone down significantly in the more urbanized states.  And the number of deaths each day may indeed soon be higher in the less urbanized states than in the more urbanized ones.

But what is the plan to address this?  From all I can see, there is no plan.  The Trump administration has not set out any coherent plan to safely reopen the economy.  Rather, it has simply called for the lifting of social distancing measures while hoping for the best.

Could there be a plan?  Certainly.  As public health experts have called for from the start, and as the developed market economies of East Asia and the Pacific have demonstrated is possible, management of a pandemic requires wide testing of those who appear they may have the disease, isolation if the test proves that they do, tracing the contacts of all those found to have the disease, and then testing and quarantining for about two weeks those contacts who might have been exposed to the virus.

This can be most easily done early in the course of a pandemic, when the number of cases is relatively small.  However, in January (and still through February) Trump insisted that all was fine and under control, and little was done.  Now, with over 27,000 new confirmed cases each day (as of the week ending May 4), this will be far more difficult.  The social distancing measures were implemented to stabilize the situation and then bring this number down to more manageable levels.  But while they succeeded in bringing the total number down from its peak (the daily number of new cases had been over 31,000), it is still far too high.

In addition to bringing down the daily number of new cases to more manageable levels, the social distancing measures were also put in place to give the government time to develop the capacity then to carry out the standard public health measures of testing, isolating, contact tracing, and quarantining.  But while some states appear to be building up that capacity to the extent they can, the evidence for others is scant, and for few, if any, does the capacity appear to be anywhere close to adequate.

And what is certainly missing is any leadership at the top – from Trump and his administration.  States have rather been left largely on their own, with some assistance perhaps at the working levels but without a clear nationally-led program to build the necessary capacity.

The economy of course certainly needs to be reopened, with the social distancing measures loosened and eventually lifted.  The issue is not whether this should be done but instead under what conditions.  Rather than lead a national effort to bring down the number of daily new cases through a coherent and consistent program of social distancing measures (which may well differ between urban and rural areas, but not based on political boundaries), and using the time thus gained to ramp up the public health capacity that is required, the Trump administration has floundered, with a response that has been limited, ineffective, and rudderless.

 

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The data underlying the chart at the top of this post:

Urban population %

Deaths per million

Vermont

17.4%

84.7

Wyoming

24.5%

12.4

Maine

26.2%

42.9

Montana

26.5%

16.2

Mississippi

27.6%

102.1

South Dakota

29.9%

25.8

West Virginia

33.2%

27.0

Arkansas

39.5%

26.1

North Dakota

40.0%

37.2

Kentucky

41.0%

58.8

Iowa

41.7%

60.4

Alaska

44.5%

9.9

Oklahoma

45.8%

63.4

New Hampshire

47.3%

65.3

Alabama

48.7%

60.7

Kansas

50.2%

49.8

Idaho

50.5%

40.8

New Mexico

53.8%

73.3

Nebraska

53.8%

42.7

Tennessee

54.4%

34.7

North Carolina

54.9%

45.7

South Carolina

55.8%

59.5

Wisconsin

55.8%

59.6

Missouri

56.6%

63.1

Minnesota

58.0%

79.0

Indiana

59.2%

174.6

Louisiana

61.3%

434.3

Oregon

62.5%

28.5

Ohio

65.3%

90.0

Georgia

65.4%

120.6

Michigan

66.4%

409.7

Delaware

68.7%

197.1

Virginia

69.8%

82.5

Pennsylvania

70.7%

223.8

Hawaii

71.5%

12.5

Washington

75.0%

124.9

Texas

75.4%

35.4

Colorado

76.9%

167.0

Illinois

80.0%

205.1

Arizona

80.1%

56.6

Utah

81.2%

18.1

New York

82.7%

990.2

Maryland

83.5%

204.7

Connecticut

84.8%

681.6

Nevada

86.5%

97.0

Florida

87.4%

73.3

California

89.7%

60.0

Massachusetts

90.3%

611.5

Rhode Island

90.5%

304.0

New Jersey

92.2%

895.3

District of Columbia

100.0%

417.1