A Carbon Tax with Redistribution Would Be a Significant Help to the Poor

A.  Introduction

Economists have long recommended taxing pollution as an effective as well as efficient way to achieve societal aims to counter that pollution.  What is commonly called a “carbon tax”, but which in fact would apply to all emissions of greenhouse gases (where carbon dioxide, CO2, is the largest contributor), would do this.  “Cap and trade” schemes, where polluters are required to acquire and pay for a limited number of permits, act similarly.  The prime example in the US of such a cap and trade scheme was the program to sharply reduce the sulfur dioxide (SO2) pollution from the burning of coal in power plants.  That program was launched in 1995 and was a major success.  Not only did the benefits exceed the costs by a factor of 14 to 1 (with some estimates even higher – as much as 100 to 1), but the cost of achieving that SO2 reduction was only one-half to one-quarter of what officials expected it would have cost had they followed the traditional regulatory approach.

Cost savings of half or three-quarters are not something to sneer at.  Reducing greenhouse gas emissions, which is quite possibly the greatest challenge of our times, will be expensive.  The benefits will be far greater, so it is certainly worthwhile to incur those expenses (and it is just silly to argue that “we cannot afford it” – the benefits far exceed the costs).  One should, however, still want to minimize those costs.

But while such cost savings are hugely important, one should also not ignore the distributional consequences of any such plan.  These are a concern of many, and rightly so.  The poor should not be harmed, both because they are poor and because their modest consumption is not the primary cause of the pollution problem we are facing.  But this is where there has been a good deal of confusion and misunderstanding.  A tax on all greenhouse gas emissions, with the revenue thus generated then distributed back to all on an equal per capita basis, would be significantly beneficial to the poor in purely financial terms.  Indeed it would be beneficial to most of the population since it is a minority of the population (mostly those who are far better off financially than most) who account for a disproportionate share of emissions.

A specific carbon tax plan that would work in this way was discussed in an earlier post on this blog.  I would refer the reader to that earlier post for the details on that plan.  But briefly, under this proposal all emissions of greenhouse gases (not simply from power plants, but from all sources) would pay a tax of $49 per metric ton of CO2 (or per ton of CO2 equivalent for other greenhouse gases, such as methane).  A fee of $49 per metric ton would be equivalent to about $44.50 per common ton (2,000 pounds, as commonly used in the US but nowhere else in the world).  The revenues thus generated would then be distributed back, in full, to the entire population in equal per capita terms, on a monthly or quarterly basis.  There would also be a border-tax adjustment on goods imported, which would create the incentive for other countries to join in such a scheme (as the US would charge the same carbon tax on such goods when the source country hadn’t, but with those revenues then distributed to Americans).

The US Treasury published a study of this scheme in January 2017, and estimated that such a tax would generate $194 billion of revenues in its initial year (which was assumed to be 2019).  This would allow for a distribution of $583 to every American (man, woman, and child – not just adults).  Furthermore, the authors estimated what the impact would be by family income decile, and concluded that the bottom 7 deciles of families (the bottom 70%, as ranked by income) would enjoy a net benefit, while only the richest 30% would pay a net cost.

That distributional impact will be the focus of this blog post.  It has not received sufficient attention in the discussion on how to address climate change.  While the Treasury study did provide estimates on what the impacts by income decile would be (although not always in an easy to understand form), views on a carbon tax often appear to assume, incorrectly, that the poor will pay the most as a share of their income, while the rich will be able to get away with avoiding the tax.  The impact would in fact be the opposite.  Indeed, while the primary aim of the program is, and should be, the reduction of greenhouse gas emissions, its redistributive benefits are such that on that basis alone the program would have much to commend it.  It would also be just.  As noted above, the poor do not account for a disproportionate share of greenhouse gas emissions – the rich do – yet the poor suffer similarly, if not greater, from the consequences.

This blog post will first review those estimated net cash benefits by family income decile, both in dollar amounts and as a share of income.  To give a sense of how important this is in magnitude, it will then examine how these net benefits compare to the most important current cash transfer program in the US – food stamp benefits.  Finally, it will briefly review the politics of such a program.  Perceptions have, unfortunately, been adverse, and many pundits believe a carbon tax program would never be approved.  Perhaps this might change if news sources paid greater attention to the distribution and economic justice benefits.

B.  Net Benefits or Costs by Family Income Decile from a Carbon Tax with Redistribution

The chart at the top of this post shows what the average net impact would be in dollars per person, by family cash income decile, if a carbon tax of $49 per metric ton were charged with the revenues then distributed on an equal per capita basis.  While prices of energy and other goods whose production or use leads to greenhouse gas emissions would rise, the revenues from the tax thus generated would go back in full to the population.  Those groups who account for a less than proportionate share of greenhouse gas emissions (the poor and much of the middle class) would come out ahead, while those with the income and lifestyle that lead to a greater than average share of greenhouse gas emissions (the rich) will end up paying in more.

The figures are derived from estimates made by the staff of the US Treasury – staff that regularly undertake assessments of the incidence across income groups of various tax proposals.  The study was published in January 2017, and the estimates are of what the impacts would have been had the tax been in place for 2019.  The results were presented in tables following a standard format for such tax incidence studies, with the dollars per person impact of the chart above derived from those tables.

To arrive at these estimates, the Treasury staff first calculated what the impact of such a $49 per metric ton carbon tax would be on the prices of goods.  Such a tax would, for example, raise the price of gasoline by $0.44 per gallon based on the CO2 emitted in its production and when it is burned.  Using standard input-output tables they could then estimate what the price changes would be on a comprehensive set of goods, and based on historic consumption patterns work out what the impacts would be on households by income decile.  The net impact would then follow from distributing back on an equal per capita basis the revenues collected by the tax.  For 2019, the Treasury staff estimated $194 billion would be collected (a bit less than 1% of GDP), which would allow for a transfer back of $583 per person.

Those in the poorest 10% of households would receive an estimated $535 net benefit per person from such a scheme.  The cost of the goods they consume would go up by $48 per person over the course of a year, but they would receive back $583.  They do not account for a major share of greenhouse gas emissions because they cannot afford to consume much.  They are poor, and a family earning, say, $20,000 a year consumes far less of everything than a family earning $200,000 a year.  In terms of greenhouse gas emissions implicit in the Treasury numbers, the poorest 10% of Americans account only for a bit less than 1.0 metric tons of CO2 emissions per person per year (including the CO2 equivalent in other greenhouse gases).  The richest 10% account for close to 36 tons CO2 equivalent per person per year.

As one goes from the lower income deciles to the higher, consumption rises and CO2 emissions from the goods consumed rises.  But it is not a linear trend by decile.  Rather, higher-income households account for a more than proportionate share of greenhouse gas emissions.  As a consequence, the break-even point is not at the 50th percentile of households (as it would be if the trend were linear), but rather something higher.  In the Treasury estimates, households up through the 70th percentile (the 7th decile) would on average still come out ahead.  Only the top three deciles (the richest 30%) would end up paying more for the carbon tax than what they would receive back.  But this is simply because they account for a disproportionately high share of greenhouse gas emissions.  It is fully warranted and just that they should pay more for the pollution they cause.

But it is also worth noting that while the richer household would pay more in dollar terms than they receive back, those higher dollar amounts are modest when taken as a share of their high incomes:

In dollar terms the richest 10% would pay in a net $1,166 per person in this scheme, as per the chart at the top of this post.  But this would be just 1.0% of their per-person incomes.  The 9th decile (families in the 80 to 90th percentile) would pay in a net of 0.7% of their incomes, and the 8th decile would pay in a net of 0.3%. At the other end of the distribution, the poorest 10% (the 1st decile) would receive a net benefit equal to 8.9% of their incomes.  This is not minor.  The relatively modest (as a share of incomes) net transfers from the higher-income households permit a quite substantial rise (in percentage terms) in the incomes of poorer households.

C.  A Comparison to Transfers in the Food Stamps Program

The food stamps program (formally now called SNAP, for Supplemental Nutrition Assistance Program) is the largest cash income transfer program in the US designed specifically to assist the poor.  (While the cost of Medicaid is higher, those payments are made directly to health care providers for their medical services to the poor.)  How would the net transfers under a carbon tax with redistribution compare to SNAP?  Are they in the same ballpark?

I had expected they would not be close.  However, it turns out that they are not that far apart.  While food stamps would still provide a greater transfer for the very poorest households, the supplement to income that those households would receive by such a carbon tax scheme would be significant.  Furthermore, the carbon tax scheme would be of greater benefit than food stamps are, on average, for lower middle-class households (those in the 3rd decile and above).

The Congressional Budget Office (CBO) has estimated how food stamp (SNAP) benefits are distributed by household income decile.  While the forecast year is different (2016 for SNAP vs. 2019 for the carbon tax), for the purposes here the comparison is close enough.  From the CBO figures one can work out the annual net benefits per person under SNAP for households in the 1st to 4th deciles (with the 5th through the 10th deciles then aggregated by the CBO, as they were all small):

The average annual benefits from SNAP were estimated to be about $1,500 per person for households in the poorest decile and $690 per person in the 2nd decile.  These are larger than the estimated net benefits of these two groups under a carbon tax program (of $535 and $464 per person, respectively), but it was surprising, at least to me, that they are as close as they are.  The food stamp program is specifically targeted to assist the poor to purchase the food that they need.  A carbon tax with redistribution program is aimed at cutting back greenhouse gas emissions, with the funds generated then distributed back to households on an equal per capita basis.  They have very different aims, but the redistribution under each is significant.

D.  But the Current Politics of Such a Program Are Not Favorable

A carbon tax with redistribution program would therefore not only reduce greenhouse gas emissions at a lower cost than traditional approaches, but would also provide for an equitable redistribution from those who account for a disproportionate share of greenhouse gas emissions (the rich) to those who do not (the poor).  But news reporters and political pundits, including those who are personally in favor of such a program, consider it politically impossible.  And in what was supposed to be a personal email, but which was part of those obtained by Russian government hackers and then released via WikiLeaks in order to assist the Trump presidential campaign, John Podesta, the senior campaign manager for Hillary Clinton, wrote:  “We have done extensive polling on a carbon tax.  It all sucks.”

Published polls indicate that the degree of support or not for a carbon tax program depends critically on how the question is worded.  If the question is stated as something such as “Would you be in favor of taxing corporations based on their carbon emissions”, polls have found two-thirds or more of Americans in support.  But if the question is worded as something such as “Would you be in favor of paying a carbon tax on the goods you purchase”, the support is less (often still more than a majority, depending on the specific poll, but less than two-thirds).  But they really amount to the same thing.

There are various reasons for this, starting with that the issue is a complex one, is not well understood, and hence opinions can be easily influenced based on how the issue is framed.  This opens the field to well-funded vested interests (such as the fossil fuel companies) being able to influence votes by sophisticated advertising.  Opponents were able to outspend proponents by 2 to 1 in Washington State in 2018, when a referendum on a proposed carbon tax was defeated (as it had been also in 2016).  Political scientists who have studied the two Washington State referenda believe they would be similarly defeated elsewhere.

There appear to be two main concerns:  The first is that “a carbon tax will hurt the poor”.  But as examined above, the opposite would be the case.  The poor would very much benefit, as their low consumption only accounts for a small share of carbon emissions (they are poor, and do not consume much of anything), but they would receive an equal per capita share of the revenues raised.

In distinct contrast, but often not recognized, a program to reduce greenhouse gas emissions based on traditional regulation would still see an increase in costs (and indeed likely by much more, as noted above), but with no compensation for the poor.  The poor would then definitely lose.  There may then be calls to add on a layer of special subsidies to compensate the poor, but these rarely work well.

The second concern often heard is that “a carbon tax is just a nudge” and in the end will not get greenhouse gas emissions down.  There may also be the view (internally inconsistent, but still held) that the rich are so rich that they will not cut back on their consumption of high carbon-emission goods despite the tax, while at the same time the rich can switch their consumption (by buying an electric car, for example, to replace their gasoline one) while the poor cannot.

But the prices do matter.  As noted at the start of this post, the experience with the cap and trade program for SO2 from the burning of coal (where a price is put on the SO2 emissions) found it to be highly effective in bringing SO2 emissions down quickly.  Or as was discussed in an earlier post on this blog, charging polluters for their emissions would be key to getting utilities to switch use to clean energy sources.  The cost of both solar and wind new generation power capacity has come down sharply over the past decade, to the point where, for new capacity, they are the cheapest sources available.  But this is for new generation.  When there is no charge for the greenhouse gases emitted, it is still cheaper to keep burning gas and often coal in existing plants, as the up-front capital costs have already been incurred and do not affect the decision of what to use for current generation.  But as estimated in that earlier post, if those plants were charged $40 per ton for their CO2 emissions, it would be cheaper for the power utilities to build new solar or wind plants and use these to replace existing fossil fuel plants.

There are many other substitution possibilities as well, but many may not be well known when the focus is on a particular sector.  For example, livestock account for about 30% of methane emissions resulting from human activity.  This is roughly the same share as methane emissions from the production and distribution of fossil fuels.  And methane is a particularly potent greenhouse gas, with 86 times the global warming potential over a 20-year horizon of an equal weight of CO2.  Yet a simple modification of the diets of cows will reduce their methane emissions (due to their digestive system – methane comes out as burps and farts) by 33%.  One simply needs to add to their feed just 100 grams of lemongrass per day and the digestive chemistry changes to produce far less methane.  Burger King will now start to purchase its beef from such sources.

This is a simple and inexpensive change, yet one that is being done only by Burger King and a few others in order to gain favorable publicity.  But a tax on such greenhouse gas emissions would induce such an adjustment to the diets of livestock more broadly (as well as research on other dietary changes, that might lead to an even greater reduction in methane emissions).  A regulatory focus on emissions from power plants alone would not see this.  One might argue that a broader regulatory system would cover emissions from such agricultural practices, and in principle it should.  But there has been little discussion of extending the regulation of greenhouse gas emissions to the agricultural sector.

More fundamentally, regulations are set and then kept fixed over time in order to permit those who are regulated to work out and then implement plans to comply.  Such systems are not good, by their nature, at handling innovations, as by definition innovations are not foreseen.  Yet innovations are precisely what one should want to encourage, and indeed the ex-post assessment of the SO2 emissions trading program found that it was innovations that led to costs being far lower than had been anticipated.  A carbon tax program would similarly encourage innovations, while regulatory schemes can not handle them well.

There may well be other concerns, including ones left unstated.  Individuals may feel, for example, that while climate change is indeed a major issue and needs to be addressed, and that redistribution under a carbon tax program might well be equitable overall, that they will nonetheless lose.  And some will.  Those who account for a disproportionately high share of greenhouse gas emissions through the goods they purchase will end up paying more.  But costs will also rise under the alternative of a regulatory approach (and indeed rise by a good deal more), which will affect them as well.  If they do indeed account for a disproportionately high share of greenhouse gas emissions, they should be especially in favor of an approach that would bring these emissions down at the lowest possible cost.  A scheme that puts a price on carbon emissions, such as in a carbon tax scheme, would do this at a lower cost than traditional approaches.

So while many have concerns with a carbon tax with redistribution scheme, much of this is due to a misunderstanding of what the impacts would be, as well as of what the impacts would be of alternatives.  One sees this in the range of responses to polling questions on such schemes, where the degree of support depends very much on how the questions are worded or framed.  There is a need to explain better how a carbon tax with redistribution program would work, and we have collectively (analysts, media, and politicians) failed to do this.

There are also some simple steps one can take which would likely increase the attractiveness of such a program.  For example, perceptions would likely be far better if the initial rebate checks were sent up-front, before the carbon taxes were first to go into effect, rather than later, at the end of whatever period is chosen.  Instead of households being asked to finance the higher costs over the period until they received their first rebate checks, one would have the government do this.  This would not only make sense financially (government can fund itself more cheaply than households can), but more important, politically.  Households would see up-front that they are, indeed, receiving a rebate check before the prices go up to reflect the carbon tax.

And one should not be too pessimistic.  While polling responses depend on the precise wording used, as noted above, the polling results still usually show a majority in support.  But the issue needs to be explained better.  There are problems, clearly, when issues such as the impact on the poor from such a scheme are so fundamentally misunderstood.

E.  Conclusion 

Charging for greenhouse gases emitted (a carbon tax), with the revenues collected then distributed back to the population on an equal per capita basis, would be both efficient (lower cost) and equitable.  Indeed, the transfers from those who account for an especially high share of greenhouse gas emissions (the rich) to those who account for very little of them (the poor), would provide a significant supplement to the incomes of the poor.  While the redistributive effect is not the primary aim of the program (reducing greenhouse gases is), that redistributive effect would be both beneficial and significant.  It should not be ignored.

The conventional wisdom, however, is that such a scheme could not command a majority in a referendum.  The issue is complex, and well-funded vested interests (the fossil fuel companies) have been able to use that complexity to propagate a sufficient level of concern to defeat such referenda.  The impact on the poor has in particular been misportrayed.

But climate change really does need to be addressed.  One should want to do this at the lowest possible cost while also in an equitable manner.  Hopefully, as more learn what carbon tax schemes can achieve, politicians will obtain the support they need to move forward with such a program.

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.

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

Update:  A more recent post, with data for these charts through July 16, is now available.

It has been much noted in the news in recent days that confirmed cases of Covid-19 have been soaring in a number of states in the US, primarily in the south and southwest.  But it is of interest to examine how widespread this is, and how it correlates with the politics of the different states.  With the politicization by Trump of what should be a matter of public health, states (and their residents) are responding differently in their management of this public health crisis.

One way to look at this is to group the states according to who won there in 2016:  Trump or Clinton.  This divides the country roughly in half, between more liberal and more conservative areas.  The chart above shows what then results for daily new confirmed cases (on a 7-day moving average).

The division is stark.  The states won by Clinton (which included New York, New Jersey, the Northeast, Illinois, California, and Washington) were exposed early to Covid-19.  These states are generally more highly urbanized and there is more international travel by both residents and visitors.  This left them especially vulnerable as the virus that causes Covid-19 started to spread (first with little knowledge of how fast it was spreading, due to blunders in rolling out the necessary testing program in February and into much of March).  But after peaking in April, these states brought down the daily number of new cases by over 60%, although with a partial and still limited reversal in the past week.

The pattern in the Trump states is quite different.  Confirmed cases rose in the period leading up to April (in part as testing only became broadly available then), but then leveled off in these states through essentially all of April and May.  Furthermore, the numbers leveled off at roughly 10,000 cases a day, or less than half the 21,000 cases per day seen in the Clinton states at their peak.  The Trump states are often more rural, and there is less international travel (by both residents and visitors), so the lower numbers there were taken by some as indicating they were less vulnerable to this infectious disease.

But this then changed markedly at the end of May.  As the states that voted for Trump relaxed their lockdown and social distancing measures, often rapidly, the case numbers began to rise.  And over the last ten days they have accelerated markedly.  The number of confirmed new cases is now significantly higher in the Trump states than they ever were in the Clinton states.  And there is no sign yet of this leveling off.  Quite the contrary – it is accelerating rapidly.

The similar figure for the number of deaths per day from Covid-19:

The number of daily deaths (again using 7-day moving averages) peaked in the Clinton states in mid-April at about 1,670, and has since come down to about 300 (or by 82%).  In the Trump states the peak was only around 600, but it stayed there longer and then came down more slowly, to also around 300 now (so by half).

That the death rates have come down in each is encouraging, but it is still too early to know precisely why.  It may be a combination of factors, including that doctors and hospitals know better now how to treat the most severe cases (with some therapeutics, such as dexamethasone and remdesivir, showing promise, while the doctors also now know that the therapeutic promoted strongly by Trump, hydroxychloroquine, may in fact increase death rates – the FDA has warned against its use).  Also, hospitals have become less crowded at centers of the outbreak, at least until recently, which has allowed them to provide better care.  Finally (and I would guess likely the most important reason, although I have seen no data on this), lower death rates would result if the age distribution of those infected has shifted to those who are younger.  Death rates for the elderly are an order of magnitude higher than for the middle-aged (and two orders of magnitude higher than for the young), so even a relatively modest shift in the age distribution of those infected could lead to a marked change in mortality rates.

Finally, deaths from Covid-19 come only with an average lag that may be a month or more from the day of initial exposure (with this also differing by age).  The number of daily confirmed cases began to rise less than a month ago in the Trump states (around May 30), and more sharply about ten days ago.  We will see in the days and weeks ahead whether this will now lead to a rise in the death figures.  So far, it appears that the daily death numbers have leveled off (since June 17 in the Trump states, and June 21 in the Clinton states), while they had been declining before.

But the sharp increase in the number of new cases over the last month, in particular in the Trump states as social distancing measures were lifted, suggests a number of lessons.  One is that social distancing measures worked.  When they were in place they brought down the number of new cases and deaths from the disease, while lifting them (or increasingly, ignoring them even while formally still in place) has led to a sharp rebound in the number of cases.  Trump has now made this into a political issue, with his flagrant refusal to wear a mask or to keep a distance from others.  In other times this would be considered bizarre behavior in a public health crisis, but is seen here by his supporters as a signal of freedom and independence rather than as a behavior that will lead many of them, as well as others, to become sick (and some to die).

The problem starts at the top.  Rather than model responsible behavior, Trump has insisted he will never wear a mask in public – he believes it hurts his image.  Trump also orchestrated his daily press briefings on the crisis so that through most of March the other officials present were crowded around him, shoulder to shoulder, with no masks.  This only changed (and changed only partially) later.  And now Trump has restarted his political rallies in large indoor arenas, with people crowded tightly together but with few wearing masks, while loud cheering is strongly encouraged.

Most importantly, the Trump administration has failed to address the real and important challenges of this pandemic.  Rather, he has said recently (such as on an interview on June 17 on Fox News) that the coronavirus is “fading away, it’s going to fade away” even if no vaccine is ever developed.  Similarly, at a rally at a megachurch in Phoenix, Arizona, on June 23, to an estimated 3,000 (mostly young) cheering supporters (with few, if any, wearing protective masks), Trump asserted that “It’s going away” while claiming his administration had done a wonderful job.  And over the last week he has repeatedly said that he has asked for less testing to be done, since with less testing there will be fewer cases confirmed.  See, for example, this June 23 tweet, where he says “With smaller [sic] testing we would show fewer cases!”.  Certainly true, but why he would think this wise is worrisome.

Over 124,000 Americans are now dead from the virus (as of today).  This is well more than in any other country in the world (Brazil is second at 55,000).  The US has had 377 deaths per million of population.  In contrast, Japan has had 8 deaths per million, South Korea 6, Australia 4, New Zealand 4, Singapore 4, Hong Kong 0.9, and Taiwan 0.3.  As noted in an earlier post on this blog, the US could learn a lot by simply examining why those countries, all with far closer interactions with China through travel and trade than is the case for the US, have been able to contain the virus while the US has not.

While there are a number of elements to a successful program, one simple but key component is the wearing of masks.  This is common in East Asia, and no one there treats the wearing or not of a mask as a political statement (nor did anyone in the US, until this crisis).  It is simply something easy to do that will protect the health of you, the ones you love, and others.

Yet even now, a full half-year since the start of this crisis, it remains difficult to find in the US the N-95 masks that are the most protective against a viral infection.  Supplies are short, and the masks that are available are provided (as they should be under the circumstances) only to health professionals (although even here there are shortages).  The regular population cannot find such masks other than on a black market (with those available of uncertain pedigree and reliability).  Yet N-95 masks are not hard to make.  3M is the major manufacturer, it is based here in the US, and it would be straightforward to open up additional production lines.  Why hasn’t the Trump administration done something to ensure an adequate supply?

Consider, for example, what a more capable administration might have done.  After ensuring an adequate supply, a box of say a dozen masks per person could be mailed to every household in the US.  With 120 million households (an average of 2.6 people per household), and assuming a production and mailing cost of $20 per household, the total cost would be $2.4 billion.  This is less than one / one-thousandth of the $2.8 trillion that Congress has already approved to be spent to provide partial relief to the effects of the economic crisis brought on by the pandemic.  If everyone then wore such a mask every time they left their home, within a few weeks there would likely be a major knock-back of the infection chain to where focused efforts on the hotspots that might then still remain, or hotspots that later spring up, could be very effective.

This might well be unrealistic.  But even if feasible it would not go far in the current political environment.  Even if an adequate supply of such masks were made available, the politicization by Trump of this public health crisis means that many of his supporters would refuse to wear a mask.  They now see it as a statement of their political, and indeed cultural, beliefs to openly and flagrantly refuse.

As others have noted, it would be hard to find a time when the US was more poorly served by its president than now.