The Impact on the Employment Numbers of the August 21 Announcement of the Bureau of Labor Statistics

A.  Introduction

The Bureau of Labor Statistics (BLS) issued an announcement on August 21 that said it had made a preliminary estimate that its figure for total employment as of March 2024 will be revised downwards by 818,000.  Some news media articles treated the announcement as if it were something to be alarmed by, and Trump issued a blast on the social media site he owns.  Trump asserted:  “MASSIVE SCANDAL!  The Harris-Biden Administration has been caught fraudulently manipulating Job Statistics to hide the true extent of the Economic Ruin they have inflicted upon America.  New Data from the Bureau of Labor Statistics shows that the Administration PADDED THE NUMBERS with an extra 818,000 Jobs that DO NOT EXIST, AND NEVER DID.  The real Numbers are much worse …” (sic, and capitalization as in the original).

None of this is true, but we know that accuracy has never been a strong point for Trump.  And such derogatory comments about the professionals at the Bureau of Labor Statistics just doing their jobs are also appalling.  There was nothing scandalous in their work.  A few basic points:

a)  Such a “preliminary benchmark revision” is issued every August, as part of an annual process by which the monthly employment estimates of the BLS are updated and anchored to (benchmarked to) more comprehensive estimates of employment.  This is done on a regular and routine basis every year.

b)  The date of the announcement is certainly not a secret, but rather is set well beforehand.  One will find it, for example, highlighted in a box on page 4 of the July jobs report that was released on August 2.  There was no attempt at a cover-up nor a leak.

c)  The 818,000 jobs figure is not some sort of monthly job number that people normally associate with the monthly jobs reports, but rather reflects an estimate of the change in the total number of people employed in March 2024.  The monthly employment estimates are then anchored to this benchmark, which will be updated again next year to an estimate for March 2025.  Employment still grew – and grew strongly – over the period from March 2023 (the previous benchmark) to March 2024 (which, when finalized, will become the new benchmark), but not by as much as was estimated before.  The previous estimate was of job growth of 2.9 million over this March to March period.  The new estimate (if the preliminary benchmark estimate holds – but bear in mind that it is preliminary and may well change) is of job growth of about 2.1 million.  That is still strong job growth.

d)  Many of the news articles highlighted that the 818,000 revision in estimated overall employment is high.  But one should keep in mind that it is equal only to about 0.5% of total employment.  That is, the revised figure (if the preliminary benchmark figure holds) will be 99.5% of what had been estimated earlier.  The 0.5% revision is also certainly not unprecedented.  Such revisions are part of a regular annual process, and figures the BLS provides going back to 1979 show that there have been revisions of 0.7% twice (in 1994 and 2009), 0.6% twice (1991 and 2006), and 0.5% four times (1979, 1986, 1995, and now in 2024).  That is, there have been such revisions to estimated overall employment by 0.5% or more a total of 8 times in 46 years, or 17% of the time.  A 0.5% change is large compared to what the figures normally are, but it is certainly not unprecedented, and in several years the revisions have been greater.

There is no scandal here.  There is no indication of manipulation.  And if there was some kind of politically motivated manipulation possible, doesn’t Trump realize that it would have made much more sense to manipulate the employment figures to be higher rather than lower?  Did he give even a few seconds of thought to his accusations?  The BLS is just doing the professional job it always has.

With all the publicity that has surrounded the BLS announcement, some may find of interest a description of how this annual updating process of the employment estimates works.  We will review that in the next section below.  The section following will then look at the figure itself – the 818,000 change in estimated overall employment – and what it may imply.  While still preliminary, the final estimate is likely to be close.  And the main message is that the basic story on employment growth during the Biden presidency has not changed.  Employment growth under Biden has been, and continues to be, exceptionally strong.

The chart at the top of this post updates a chart I provided in an article on this blog that was posted on August 21 – the day the BLS announcement came out.  I saw that announcement and the reports on it just after I posted that article.  One focus of that post was on the employment record under Biden and how it compared to the record under Trump.  The chart above replicates one in that August 21 post, but with the addition of what the path of estimated employment may now look like once the new benchmark is taken into account for the recent employment estimates.  That revised path is shown in orange.  It is a very rough estimate as the BLS has not yet worked out and released what the monthly employment figures will be with the new benchmark.  They are working on that now, and will release it – as they always do – in early February as part of the January monthly jobs report.

The path in orange is below the original one in red, but follows the same basic course.  It is still rising at a strong pace, and the basic message remains the same.  Job growth under Biden has been far stronger than what it was under Trump.

B.  The Annual Process of the BLS to Update Its Monthly Employment Estimates

The discussion in this section is based on material the BLS provides on its website on the process it follows in updating its monthly employment estimates to tie them (anchor them) to comprehensive employment estimates arrived at once a year from census-like figures.  The summary description provided here is based primarily on the BLS posts here and here.

The monthly jobs report of the BLS (more formally: “The Employment Situation” report) is eagerly awaited by many.  It provides estimates for what happened to the number of “jobs created” during the past month (more accurately, the change in the estimated number of nonfarm employees between the current month and the month before), as well as the unemployment rate along with numerous other measures of the labor market.

The report is produced on a very tight schedule.  The employment statistics come from a sample of establishments (both public and private, and called the Current Employment Statistics, or CES, survey), where the employing entities report to the BLS the number of employees on their payroll in the week of the month which includes the 12th day of the month.  The BLS jobs report is then issued at 8:30am on the Friday three weeks later, which is usually the first Friday of the following month.

(There are also figures in the monthly Employment Situation report on unemployment, the number in the labor force, and other figures that are obtained through the much smaller Current Population Survey (CPS) of households.  Most of what we will discuss here will be for the CES survey of business establishments, but similar modeling issues arise with the CPS survey, where there is also an annual process to update the model parameters.)

The survey of establishments is a rather comprehensive one, where the reporting entities account for about one-third of all nonfarm payroll jobs.  But it is still a sample survey, and the BLS needs to estimate from this survey the overall number of employees in the country (and hence what the change was from the previous month – the growth in the number employed).

For this, what is mainly needed is a large set of weights that the BLS can use to aggregate the reports it receives from firms of various types.  That is, to estimate the overall totals the BLS will need to know what weight to give to what is found in the survey reports for a particular type of firm (such as of a given size), operating in a particular sector, and perhaps categorized in other ways as well.

For example, small firms with up to 99 employees accounted for (in March 2023) 40.0% of all private employment in the country.  But while 70.4% of the number of private firms sampled by the BLS for the CES were in this category of up to 99 employees, those in the CES survey sample accounted for only 4.6% of total private employment.  Those firms are all small.  In contrast, large firms with employment of 1,000 or more were 6.2% of the number of private firms sampled by the BLS.  But those firms accounted for 68.4% of total private employment in the sample (and 28.8% of the total private employment in the country).

The BLS thus needs to know what weights to assign to each of these categories of firms to determine the overall totals.  The annual benchmarking exercise provides this.  A comprehensive census-type of exercise is needed, and for this the BLS uses primarily the March report of the Quarterly Census of Employment and Wages (QCEW) – which the BLS is also responsible for.  The QCEW is a comprehensive accounting of essentially all workers in the US based on the filings (and unemployment insurance tax payments) all firms are required to provide for the unemployment insurance program.

About 97% of the workers counted in the CES reports will be covered by regular unemployment insurance and hence included in the QCEW reports.  About 3% of workers are not, and the BLS uses various methods to arrive at a count for them.  Such “noncovered employment” (as the BLS labels it) includes, for example, certain workers at nonprofits and religious organizations, certain state and local government workers, railroad workers (where unemployment insurance is covered under the Railroad Retirement Board), paid interns and apprentices, and a range of others.

Keep in mind also that “employment” as reported in the monthly jobs report is for the nonfarm payroll, and thus excludes the self-employed as well as those working on farms (whether as self-employed owners or as employees).  But based on CPS data (the survey of households), those employed on farms (whether as employees or self-employed) only account for 1.4% of total employment.  That is so small that changes in on-farm employment do not have a significant impact on overall employment growth.  More potentially significant are the self-employed, who equal 6.1% of total employment according to the CPS data.  Unemployment insurance does not cover the self-employed, but those who are self-employed are also not employees and hence are not included in the CES definition of the nonfarm payroll.

The BLS then uses the detailed census counts from the March QCEW each year (supplemented by various sources of information for the remaining 3% of employees) to work out the weights to use to aggregate to the global estimates.  The March QCEW figures (as supplemented for the remaining 3%) then serve as an anchor on the employment totals.  It is updated on a routine basis annually on a calendar schedule that is set well ahead of time.  The monthly employment estimates are then worked out over the course of the year relative to the annual anchors of every March.

In addition to working out the weights to use to go from the monthly survey results to the overall totals, the BLS must also estimate the changes over time in the number of firms in each category.  That is, it needs to have an estimate for the number of new firms in each category that have begun operations each month (births), plus the number of firms that have ceased operations (deaths).  The QCEW census data will, by its nature, have nothing on the births and only outdated and now wrong information on the deaths.  The BLS updates its model of firm births and deaths each year as well, as part of its annual process of updating the benchmarks.

There has been speculation that the relatively large estimated reduction in estimated total employment of 818,000 in March 2024 may have been due in part to issues in the estimates of firm births and deaths.  There was an especially large jump in the number of new business establishments that opened in 2021 – a jump of 33% over what it was in 2020 or an increase of 37% over what it was in 2019 – to 1.4 million new firms in that year.  And the number of new firms was again at this record high of 1.4 million in 2022.  But small new firms typically struggle after a year or two, and many close even in the best of times.  It is possible that the BLS model for firm births and deaths did not capture well that this large jump in new business creation in 2021 and again in 2022 was followed by a relatively high number then closing in 2023 and 2024.

The BLS work begins once the March QCEW data become available, and each August it announces its preliminary benchmark revision for total employment in the prior March.  This is what the BLS announced on August 21, that Trump attacked.  The BLS will now work out the month-by-month implications of the new benchmark, adjusting the monthly employment figures that it had earlier estimated to reflect the new benchmark.  These revised monthly figures will be announced as part of the release of the January 2025 jobs report on Friday, February 7, 2025.  It does this in every January jobs report each year.

The benchmark figures on total employment are not seasonally adjusted numbers.  The anchors are the figures for each March, and hence the anchors in the upcoming revision will be for March 2023 (which is unchanged from what was determined before) and March 2024 (the new one).  The non-seasonally adjusted employment numbers will then be revised for the 21 months from April 2023 through to December 2024.  From April 2023 to the new March 2024 benchmark, the monthly employment figures will be adjusted in a simple linear fashion based on what the overall change in employment was between the March 2023 and March 2024 anchors.  If the final estimate turns out to be 818,000 (the same as the preliminary estimate), then that means the April 2023 non-seasonally adjusted employment estimate will be reduced by 68,167 (equal to one-twelfth of 818,000), the May 2023 estimate will be reduced by 136,333 (two-twelfths of 818,000), the June 2023 estimate by 204,500, and so on until the March 2024 employment estimate is reduced by 818,000.

The April 2024 to December 2024 figures for non-seasonally adjusted employment will then be re-estimated based on the models the BLS has updated based on the new March 2024 anchor estimates.  Keep in mind that by the time the January 2025 employment estimates are ready to be released (in early February 2025), the BLS will already have issued estimates for the April to December 2024 figures.  The revised estimates for all of the 2024 estimates are then provided in the Employment Situation report along with the employment figures for January.

The seasonally adjusted employment figures are then also updated.  Seasonally adjusted figures are calculated based on a statistical analysis of the regular annual patterns seen in the non-seasonally adjusted figures, using standard statistical programs.  The model parameters for this are re-estimated once the new non-seasonally adjusted employment figures are determined, and the BLS then goes back and revises the seasonally adjusted monthly employment estimates for a full five years.  Hence, once the January jobs report is released (on February 7 next year), one will find that the seasonally adjusted employment figures for the most recent five years (available online) will have also changed.

The January jobs report also has a section, in the interest of full transparency, showing what the new seasonally adjusted employment estimates are for each month of the past year, what the BLS had previously published, the difference, and the month-to-month employment changes (number of “new jobs”) as revised, as published before, and the difference.  All of this is routine.

The process is well-established and has been followed for at least 46 years (I have not looked farther back).  While the methods constantly evolve and are improved over time, there is no basis for Trump’s attack on the integrity of the BLS.

C.  How Much of an Impact?

The BLS was clear in its announcement that the new benchmark estimate for total employment in March 2024 is preliminary.  It is making this initial estimate available to the public in the interest of transparency, even though it has yet to work out the implications for the month-to-month employment figures.

But while preliminary with month-to-month specifics yet to be estimated, it is possible to get a sense of how significant a change this will likely entail to the pattern of employment growth under Biden.  And the answer is not much.  Furthermore, the change is in the direction one should have expected.  As discussed in my recent post on the economic record of Trump compared to that of Biden and Obama, employment growth during Biden’s term has been extremely fast.  This growth (whether based on the prior estimates or the preliminary revised estimates) has continued at a pace over the last year that is well in excess of separate estimates of growth in the labor force.  Over time, and at a constant unemployment rate, employment can only grow as fast as the labor force does.  In the past year the labor force participation rate rose slightly (from 62.6% of the adult population to 62.7%), which led to somewhat faster growth in the labor force than would be the case with a constant participation rate.  But the longer-term trend has been for the participation rate to drift downwards, as an aging population is leading to a higher share of adults in the usual retirement years.

The current estimate for the period of March 2023 to March 2024 – prior to any benchmark change – has been that total employment grew by 2.90 million.  This is based on the seasonally adjusted figures.  Growth over this period in the non-seasonally adjusted figures was a similar 2.96 million.  The preliminary benchmark change in total employment in March 2024 is 818,000, and formally this is the change in the non-seasonally adjusted figure for employment.  But it makes little difference whether one uses this to adjust the seasonally adjusted figures on employment or the non-seasonally adjusted figures.  With either, one ends up with a new figure for total employment in March 2024 of 2.1 million within round-off.

The month-by-month changes in the total employment estimates have yet to be worked out by the BLS, as noted before, but one can make a very rough estimate of what those might be.  The aim here is simply to give a sense of what the magnitudes are so that one can see – as in the chart at the top of this post – what the path of employment under Biden might then look like in comparison to the paths under Trump and Obama.

A number of assumptions are needed.  First, while the 818,000 adjustment in the benchmark employment total is formally a non-seasonally adjusted figure, I will assume the seasonally adjusted estimate will be similar.  The chart at the top of this post uses seasonally adjusted figures throughout, and the adjusted path for employment growth under Biden will be as well.

Second, for the period from April 2023 to March 2024 I adjusted the month-by-month employment estimates linearly, as the BLS does (although the BLS does this with the non-seasonally adjusted figures for the monthly employment estimates; I am assuming the changes in the seasonally adjusted figures will be similar).  That is, the April 2023 employment total was reduced by 68,167 (one-twelfth of 818,000), the May 2023 total by 136,333 (two-twelfths), and so on to March 2024.

Third, adjusting the figures going forward from March 2024 is more difficult as the BLS will use its updated models to make the revisions to the estimates from April.  Note that while the revised BLS estimates – when they are released as part of the January Employment Situation report – will cover the months through to December, all that we need now are estimates for the months of April, May, June, and July.

While very rough, for this I assumed the revisions for these four months will follow a pattern similar to what was found in the 2019 revision.  This was relatively recent but also pre-Covid (with all of the disruptions of patterns associated with that), and in that year the benchmark employment estimate was reduced by 0.3%.  While less than the 0.5% preliminary revision in the 2024 benchmark estimate, it was a still major revision downward (and during the Trump administration, although I do not recall ever seeing a reference by Trump to that reduction in the job totals).  I then used the month-by-month revisions in the seasonally adjusted employment estimates in 2019 for April through July, rescaled the percentage changes of each by the ratio of 0.5%/0.3% (in fact using the more precise figures of 0.517%/0.341%) and then applied those adjusted percentage changes to the current estimates of total employment in those four months.

The new path for total employment for the period of March 2023 to July 2024 is then shown as the orange line in the chart at the top of this post.  While below the current employment estimates (the line in red), the difference is not large.

The basic story remains the same.  Employment growth has been exceptionally strong under Biden, and has continued.  A downward revision in the benchmark total for March 2024 of 818,000 does not change this.

Trump’s Claims on the Economy and the Reality: A Comparison of Trump to Biden and Obama

“We had the greatest economy in the history of the world.  We had never done anything like it. … Nobody had seen anything like it.”

Donald Trump, Republican National Convention, Milwaukee, July 18, 2024

A.  Introduction

Donald Trump is fond of asserting that the US “had the greatest economy in the history of the world” while he was president.  He claimed this when he accepted the nomination at the Republican National Convention (as quoted above); he claimed it when he debated President Biden in June; and it is a standard line repeated at his campaign rallies.  He also asserts that this is all in sharp contrast to the economy he inherited from Obama and to where it is now under Biden.  In a June 22 speech, for example, Trump said “Under Biden, the economy is in ruins.”

These assertions of Trump are not new.  He was already repeatedly making this claim in 2018 – in the second year of his administration – asserting that the US was then enjoying “the greatest economy that we’ve had in our history” (or with similar wording).  And he repeated it.  The Washington Post Fact Checker recorded in their database that Trump made this claim in public fora at least 493 different times (from what they were able to find and verify) by the end of his term in January 2021.

Repetition does not make something true.  And numerous fact-checkers have shown that the assertion is certainly not true (see, for example, here, here, and here, and for the 2018 statements here).  But readers of this blog may nonetheless find a review of the actual data to be of interest, and in charts so that the extent to which Trump is simply making this up is clear.

The post will focus on Trump’s record compared to that of Obama’s second presidential term (immediately before Trump) and Biden’s presidential term (immediately after).  The post will also show that even if you just focus on the first three years of his presidential term – thus excluding the economic collapse in his fourth year during the Covid crisis – Trump’s record is nothing special.  The collapse in that fourth year was certainly severe, and with that included Trump’s record would have been one of the worst in US history.  But Covid would have been difficult to manage even by the most capable of administrations.  Trump’s was far from that, and that mismanagement had economic consequences, but Trump’s record is not exceptional even if you leave that fourth year out.

This post complements and basically updates a longer post on this blog from September 2020.  That post compared Trump’s economic record not only to that of Obama but also to that of American presidents going back to Nixon/Ford.  I will not repeat those comparisons here as they would not have changed.  I will focus this post on just a few of the key comparisons, adding in the record of Biden.

B.  The Record on Growth

The two charts at the top of this post show how Trump’s record compares to that of Obama and Biden in the two measures most commonly taken as indicators of economic performance – growth in national output (real GDP) and growth in total employment (jobs).  This section will focus on Trump’s not-so-special record on growth, while the section following will focus on employment.

Trump has repeatedly asserted that economic growth while he was president surpassed that of any in history.  This is not remotely true in comparison to growth under a number of post-World War II presidents.  (Quarterly GDP statistics only began in 1947 so older comparisons are more difficult, but there were certainly many other cases further back as well.)  Giving Trump the benefit of excluding the economic collapse in 2020 during the Covid crisis, real GDP grew at an annual rate of 2.8% over the first three years of Trump’s presidential term.  But real GDP grew at an annual rate of 5.3% during the eight years of the Kennedy/Johnson presidency; at a rate of 3.7% during the Clinton presidency; 3.4% during Reagan; and 3.4% as well during the Carter presidency.  The 2.8% during the first three years of Trump is not so historic.  Carter’s economic record is often disparaged (inappropriately), but Carter’s record on GDP growth is significantly better than that of Trump – even when one leaves out the collapse in the fourth year of Trump’s presidency.

Nor is the Trump record on growth anything special compared to that of Biden or Obama.  As seen in the chart at the top of this post, growth under Biden over the first three years of his presidency matched what Trump bragged about for that period (it was in fact very slightly higher for Biden).  GDP growth then remained strong in the fourth year of Biden’s presidency instead of collapsing.  Growth in the Obama presidential term immediately preceding Trump was also similar:  sometimes a bit above and sometimes a bit below, and with no collapse in the fourth year.  It was also similar in Obama’s first term once he had turned around the economy from the economic and financial collapse he inherited from the last year of the Bush presidency.

Trump’s repeated assertion that “we had the greatest economy in the history of the world” was a result – he claimed – of the tax cuts that Republicans rammed through Congress (with debate blocked) in December 2017.  While the law did cut individual income tax rates to an extent (heavily weighted to benefit higher income groups), the centerpiece was a cut in the tax rate on corporate profits from 35% to just 21%.  The argument made was that this dramatic slashing of taxes on corporate profits would lead the companies to invest more, and that this spur to investment would lead to faster growth in GDP benefiting everyone.

That did not happen.  As we have already seen, real GDP did not grow faster under Trump than it had before (nor since under Biden).  Nor, as one can see in the chart at the top of this post, was there any acceleration in the pace of GDP growth starting in 2018 when the new tax law went into effect in the second year of his presidential term (i.e. starting in Quarter 5 in the charts).

The promised acceleration in growth was supposed to be a consequence of a sustained spur to greater private investment from the far lower taxes on corporate profits.  There is no evidence of that either:

The measure here is of fixed investment (i.e. excluding inventories), by the private sector (not government), in real terms (not nominal), and nonresidential (not in housing but rather in factories, machinery and equipment, office structures, and similar investments in support of production by private firms).

This private investment grew as fast or often faster under Obama (when the tax rate on corporate profits was 35%) as under Trump (when the tax rate was cut to just 21%).  Growth under Biden has also been similar, even though the tax rate on corporate profits remains at 21%.  This similar growth is, in fact, somewhat of a surprise, as the Fed raised interest rates sharply starting in March 2022 with the aim of slowing private investment and hence the economy in order to bring down inflation.

With the far lower corporate profit tax rates going into effect in the first quarter of 2018 and the Fed raising interest rates starting in the first quarter of 2022 – both cases in the fifth quarter of the Trump and Biden presidential terms respectively – a natural question is what happened to private investment in the periods following those changes?  Rebasing real private non-residential fixed investment to 100 in the fourth quarter of the presidential terms, one has:

The paths followed by private investment under Biden (facing the higher interest rates of the Fed) and under Trump (following corporate profit taxes being slashed) were largely the same – with the path under Biden often a bit higher.  They diverged only in the 12th quarter of each administration (the fourth quarter of 2019 for Trump, and the fourth quarter of 2023 for Biden).  Under Trump, private investment fell in that quarter – well before Covid appeared – and then collapsed once Covid did appear.  Under Biden, in contrast, it kept rising up until the most recent period for which we have data.

It is also worth noting that private investment during the similar period in Obama’s second term rose by even more than under Trump (and for a period faster than under Biden, although later it rose by more under Biden).  This was despite a tax rate on corporate profits that was still at 35% when Obama was in office.  There is no evidence the tax rate mattered.  And although not shown in the chart here, private investment rose by far more in the similar period during Obama’s first term (although from a low base following the 2008 economic collapse).

With similar growth in such investment in all three presidential terms (leaving out the collapse in 2020), the conclusion one can draw is that taxes at such rates on corporate profits simply do not have a meaningful impact on investment decisions.  Decisions on how much to invest and on what depend on other factors, with a tax rate on profits of 21% or of 35% not being central.  Nor did the Fed’s higher interest rates matter all that much to investment during Biden’s term.  With a strong economy under Biden, firms recognized that there were investment opportunities to exploit, and they did.

The far lower tax rate of 21% on corporate profits did, however, lead to a windfall gain for those who owned these companies.  Far less was paid in such taxes.  That is, the tax cuts did have distributional consequences.  But they did not spur private investment nor overall growth.  They did not lead to “the greatest economy in the history of the world”.

C.  The Record on Employment

As seen in the chart at the top of this post, growth in total employment was higher under Obama than it was under Trump, and has been far higher under Biden – even if you restrict the comparison to the first three years of the respective presidential terms.  In the face of this clear evidence in favor of Biden’s record, Trump has now started to assert that the growth in jobs under Biden was due to a “bounce back” in jobs following the collapse in the last year of his administration, or that they all went to new immigrants.  But neither is true.

First, as one can see in the chart there has been strong growth in the number employed not only early in Biden’s administration but on a sustained basis throughout.  And second, nor was the growth only in the employment of immigrants.  The Bureau of Labor Statistics provides figures from its Current Population Survey (CPS) of households on the employment of those who were born in the US (the native-born) and those born abroad (the foreign-born).  Leaving out the collapse in 2020, employment growth over the first three years of Trump’s presidential term of the native-born averaged 1.3% per year.  During the first three years of the Biden presidential term, employment growth of the native-born averaged 1.8% per year.  The growth in employment of the native-born was not zero under Biden – as Trump claims – but rather was faster under Biden than under Trump.  While there is a good deal of noise in the CPS figures (which will be discussed below), these numbers do not provide support for Trump’s assertion.

There has also been concern expressed in the media with what was interpreted as a “disappointing” growth in employment in July.  The BLS “Employment Situation” report for July, released on August 2, indicated that employment rose by an estimated 114,000 in the month.  This is a good deal below the average in the 12 months leading up to July of 209,300 per month.  But an increase of 114,000 net new jobs in the month is substantial.  While there will often be large month-to-month fluctuations, one should not expect more on average going forward.

With the economy basically at full employment (the recent uptick in the unemployment rate – to a still low 4.3% – will be discussed below), the number employed cannot grow on a sustained basis faster than the labor force does.  And the labor force will grow at a monthly pace dictated by growth in the adult civilian population (i.e. age 16 and over) and what share of that adult population chooses to participate in the labor force.  The labor force participation rate in July was 62.7% and has been trending downward over the past several decades.  While a number of factors are behind this, the primary one has been the aging of the population structure with the Baby Boom generation moving into their normal retirement years.

The BLS report (using figures obtained from the Census Bureau) indicates that the adult civilian population rose by an average of 136,800 per month in the 12 months leading up to July.  At a labor force participation rate of 62.7%, the labor force would thus have increased by 85,800 per month.  Without an increase in the labor force participation rate, employment cannot grow faster than this on a sustained basis going forward.

In the past 12 months, however, the BLS report for July indicates that the labor force in fact grew at an average pace of 109,700 per month.  How was this possible?  The reason is that although the labor force participation rate is on a long-term downward trend due to the aging population, there can be and have been fluctuations around this trend.  And a small fluctuation can have a significant effect.  The labor force participation rate one year ago in July 2023 was 62.6%, and thus the rate in fact rose by 0.1% from July 2023 to July 2024.  If the labor force participation rate in July 2023 had in fact been 62.7%, then the labor force in July 2023 would have been 167,410,000 rather than the actual 167,113,000, and the increase over the 12 months leading to July 2024 would have averaged 84,900.  Within round-off, this is the same as the 85,800 figure calculated in the preceding paragraph for a constant 62.7% labor force participation rate,  (With more significant digits, the labor force participation rates were 62.589% and 62.696% respectively, and a constant 62.696% participation rate would have yielded the 85,800 figure for labor force growth.)

We should therefore not expect, going forward, that monthly employment will increase on a sustained basis by more than about 90,000 or so, or even less.  It could be higher if the labor force participation rate increases (and a small change can have a major effect), but the trend over the past couple of decades has been downward – as noted already – due to the aging of the population.  How then, was it possible for employment to have gone up by an average of 209,300 per month over the past year?  And this was also a period where the estimated unemployment rate rose from 3.5% in July 2023 to 4.3% in July 2024, which “absorbed” a share of the increase in the labor force as well.

The reason for these not fully consistent numbers is that employment estimates come from the Current Employment Statistics (CES) survey of establishments where people are employed, while the labor force and unemployment estimates come from the different Current Population Survey (CPS) – a survey of households.  The CES is a survey of nonfarm employers in both the private and public sectors, and covers 119,000 different establishments at 629,000 different worksites each month.  The “sample” (if it can be called that) covers an estimated one-third of all employees.

The CPS, in contrast, is a survey of about 60,000 households each month.  There will only generally be one or two members of the labor force in each household, so the share of the labor force covered will be far less than in the CES.  If each household had two members in the labor force, for example, the total of 120,000 would be only 0.07% of the labor force –  a sharp contrast to the one-third covered in the CES.  There is therefore much more statistical noise in the CPS data.  There are also definitional differences:  The CPS will include not only those employed on farms but also the self-employed and those employed in households.  Also, a person with two or more jobs will be counted as one person “employed” in the CPS.  The CES, in contrast, counts the employees of a firm, and the employers will not know if the individual may be working at a second job as well.  Thus a person working two jobs at two different firms will be counted as two “employees” in the CES.

These definitional differences are not major, however, and in part offset each other.  An earlier post on this blog looked at these differences in detail, and how, in an earlier period (2018/2019) there was a substantial deviation in the employment growth figures between the estimates in the CES and the CPS.  This was the case even with the figures adjusted (to the extent possible) to the same definition of “employment” in each.  There is a similar deviation between the employment estimates in the CES and in the CPS currently, with this accounting for a strong growth in employment as estimated by the CES (of 209,300 net new jobs each month over the past year) even though the labor force has grown -according to the CPS – by a more modest 109,700 per month over this period.

The labor market remains tight, however, even with the rise in the estimated unemployment rate to 4.3% in July:

The unemployment rate fell rapidly under Biden, following the chaos of 2020.  It was at a rate of 3.9% or less for over two years (27 months), despite the efforts by the Fed to slow the economy by raising interest rates.  The unemployment rate was also 3.9% or less for a period under Trump (for 20 months).  But as one sees in the chart, during the first three years of Trump’s term it basically followed the same downward path as it had under Obama.  It then shot up in March 2020 when the nation was caught unprepared for Covid.  As with the other key economic indicators (the growth in GDP, in employment, and in private investment), the paths followed by the economy during the first three years of Trump’s term were basically the same as – although usually not quite as good as – the paths set during Obama’s presidency.  They all then collapsed in Trump’s fourth year.

Any unemployment rate near 4%, and indeed near 5%, is traditionally seen as low.  Economists have defined the concept of the “Non-Accelerating Inflation Rate of Unemployment” (NAIRU) as the rate of unemployment that can be sustained without being so low that inflation will start to rise.  While one can question how robust this concept is (as will be discussed below), the NAIRU rate of unemployment has generally been estimated (for example by the staff at the Federal Reserve Board) to be between 5 and 6%.  An unemployment rate of 4.3% is well below this.  While the unemployment rate has gone up some in recent months, it is still extremely low.

D.  The Record on Real Living Standards

Ultimately, what matters is not the growth in overall output (GDP) or in employment, but rather in real living standards.  Many have asserted that because of recent inflation, living standards have gone down during Biden’s presidential term.  This is not true, as we will see below.  But first we will look at inflation.

Inflation rose significantly early in Biden’s presidential term.  The pace moderated in mid-2022, but until recently prices continued to rise:

Inflation was less during Trump’s term in office but was even lower under Obama.  Indeed, consumer price inflation has been low since around 1997, during Clinton’s presidency, until the jump in 2021.  Why did that happen?

The rise in 2021 can be attributed to both demand and supply factors.  On the demand side, both Trump and Biden supported and signed into law a series of genuinely huge fiscal packages to provide relief and support during the Covid crisis.  The packages were popular – especially the checks sent to most Americans (up to a relatively high income ceiling) that between the various packages totaled $3,200 per person.  But the overall cost for all the various programs supported was $5.7 trillion.  That is huge.  The funds were spent mostly over the two years of 2020 (under Trump) and 2021 (under Biden), and $5.7 trillion was the equivalent of 12.8% of GDP over those two years.  Or, as another comparison, the total paid in individual income taxes in the US in the single year of FY2023 was “only” $2.2 trillion.

While there was this very substantial income support provided through the series of Covid relief packages, households were limited in how much they could spend – out of both these income transfers and their regular incomes – in 2020 due to the Covid pandemic.  One went out only when necessary, and kept only to shopping that was necessary.  This carried over into early 2021.  But people could become more active as the Biden administration rolled out the massive vaccination campaign in the first half of 2021.  People then had a backlog of items to buy as well as the means to do so from what had been saved in 2020 and early 2021.  Demand rose sharply, and indeed Personal Consumption Expenditures in the GDP accounts rose by more in 2021 (by 8.4%) than in any year since 1946 (when it rose by 12.4%, and for similar reasons).

But at the same time, supply was constrained.  Supply chains had been sharply disrupted in 2020 worldwide due to Covid, and took some time to return to normal.  There was then the additional shock from the Russian invasion of Ukraine in February 2022, leading oil and many other commodity prices to spike.

Supply chains did, however, return more or less to normal early in the summer of 2022.  And as they did, one saw a sudden and sharp reduction in pressures on prices, in particular on the prices of goods that can be traded:

This chart shows the annualized inflation rates for 6-month rolling periods (ending on the dates shown) for the overall CPI, for the shelter component of the CPI, and for the CPI excluding shelter.  The overall inflation rate rose from an annualized rate of 3.2% in the six months ending in January 2021 (the end of Trump’s term) to a peak of 10.4% in the six months ending in June 2022.  It then fell remarkably fast, to an annualized rate of just 2.6% in the six months ending in December 2022.

This sudden drop in the inflation rate is seen even more clearly in the CPI index of prices for everything but shelter:  The annualized rate fell from 12.4% in the first half of 2022 (the six months ending in June) to a negative 0.2% rate in the second half of 2022 (the six months ending in December).  Why?  There was not a sudden collapse in consumer or other demand.  Rather, supply chains finally normalized in the summer of 2022, and this shifted pricing behavior.  When markets are supply constrained (as they were with the supply chain problems), firms can and will raise prices as competitors cannot step in and supply what the purchaser wants – they are all supply constrained.  But as the supply chains normalized, pricing returned to its normal condition where higher demand can be met by higher production – whether by the firm itself or, if it is unwilling, by its competitors.  It is similar to a phase change in conditions.

Shelter is different.  It covers all living accommodations (whether owned or rented), and as has been discussed in earlier posts on this blog (see here and here), the cost of shelter is special in the way it is estimated for the CPI.  It is also important, with a weight of 36% in the overall CPI index (and 45% in the core CPI index, where the core index excludes food and energy).  The data for the shelter component of the CPI comes from changes observed in the rents paid by those who rent their accommodation, and rental contracts are normally set for a year.  Hence, rental rates (and therefore the prices of the shelter component of the CPI) respond only with a lag.  One can see that in the chart above, with the peak in the inflation rate for shelter well after the peak in the inflation rate for the rest of the CPI.

Since mid-2022, the rate of inflation as measured by the overall CPI has generally been in the range of 3 to 4% annualized.  Increases in the cost of shelter have kept it relatively high and above the Fed’s target of about 2% per annum.  But as seen in the chart, it has recently come down – falling to an annualized rate of 2.5% in the six months ending in July.  For everything but shelter, the rate in the six months ending in July was only 1.4%.

One question that some might raise is whether the very tight labor markets – with an unemployment rate that was 4% or less until two months ago – might have led to the inflation observed.  The answer is no.  As noted above, inflation in all but shelter fell suddenly in mid-2022, falling from a rate of 12.4% in the first half of the year to a negative 0.2% in the second half, even though the unemployment rate was extremely low at 4% or less throughout (and only 3.5 or 3.6% in all of the second half of 2022).  Unemployment has remained low since while inflation has come down.  If the cause was tight labor markets, then the rate of inflation would have gone up rather than down.

Similarly, inflation as measured by the CPI was not high in 2018 nor in 2019 when labor markets were almost as tight during Trump’s presidency – with overall inflation then between 2 and 3% on an annual basis.  Nor did inflation go up during the similarly tight labor market of 1999 and 2000 during the Clinton presidency:  CPI inflation was generally in the 1 1/2 to 3 1/2 % range during that period.  All this calls into question the NAIRU concept, with its estimate that an unemployment rate below somewhere in the 5 to 6% range will lead to pressures that will raise the rate of inflation.

Managing inflation coming out of the chaos of 2020 was certainly difficult.  Inflation spiked in most countries of the world following the Covid crisis, reaching a peak in 2022.  But the rate of inflation has since come down as supply conditions normalized.  That does not mean that the absolute level of prices came down, only that they were no longer increasing at some high rate.  Wages and other sources of income will then adjust to the new price levels, and what matters in the end is whether real levels of consumption improve or not.  And they have:

The chart shows the paths followed for per capita real levels of personal consumption expenditures, as measured in the GDP accounts, during the presidential terms of Trump, Biden, and the second term of Obama.  The path followed under Trump was basically the same as that followed under Obama – until the collapse in the last year of Trump’s term.  The path followed under Biden has been substantially higher than either.  It was boosted in his first year as the successful vaccination campaign allowed people to return to their normal lives.  They could then purchase items with not only their then current incomes, but also with the savings they had built up in 2020.  But even if one excludes that first year, the growth under Biden has been similar to that under Obama and under Trump up to the collapse in Trump’s fourth year.

Once again, there is no basis for Trump’s claim of the “greatest economy”.

E.  Summary and Conclusion

The economy during Trump’s presidency was certainly not “the greatest in the history of the world”.  Nor was it even if you leave out the disastrous fourth year of his presidency.  Covid would have been difficult to manage even by the most capable of administrations, and Trump’s was far from that.  Instead of preparing for the shock this highly contagious disease would bring, Trump’s response was to insist – repeatedly – “it’s going to go away”.

Trump’s economic record was certainly nothing special.  Real GDP grew as fast or faster under Obama and Biden as it had under Trump.  Trump insisted that growth would be – and was – spurred by the tax cuts that he signed into law in late 2017 that slashed the tax on corporate profits.  But there is no indication of this in the data.  Nor is there even any indication that private investment rose as a result of the lower taxes.

Employment has grown far faster under Biden than it had under Trump, and also grew faster in Obama’s second term – even leaving out Trump’s disastrous fourth year.  Unemployment fell during the first three years of Trump’s term in office (before sky-rocketing in his fourth year), but here it just followed a very similar path to that under Obama.  For this, as with GDP and employment growth, perhaps the biggest accomplishment of Trump’s first three years in office was that he did not mess up the path that had been set under Obama.  And unemployment has been even lower under Biden.

Inflation was certainly higher in 2021 as the US came out of the Covid crisis.  Supply chains were still snarled, but there was pent-up demand from consumers who had had to avoid shopping in 2020 due to Covid and who also benefited from a truly huge set of Covid relief packages passed under both Trump and Biden.  Supply chains then normalized in mid-2022, sharply reducing pricing pressures for goods other than shelter.  Due in part to lags in how rental rates for housing are set (as they are normally fixed for a year) and then estimated by the BLS, the cost of the shelter component of the CPI came down more slowly than the cost of the rest of the CPI.  This kept inflation as measured higher than what the Fed aims for, although recently (in the last half year) it has come down again.  Most anticipate that the Fed will soon start to cut interest rates from their current high levels.  The inflationary episode resulting from the Covid crisis appears to be coming to an end.

There is thus no justification for the claim by Trump that “we had the greatest economy in the history of the world”.  Yet he has repeatedly asserted it, both now and when he was president.  Why?  Stephanie Grisham, who served in the Trump administration as press secretary and in other senior positions, and who had been – by her own description – personally close to Trump, explained it well in a speech she made on August 20 to the Democratic National Convention.  She noted that Trump used to tell her:  “It doesn’t matter what you say, Stephanie.  Say it enough, and people will believe you.”

Many do appear to believe that the economy was exceptionally strong when Trump was president:  that it was “the greatest in history”.  But that is certainly not true.  Facts matter; reality matters; and a president needs to know that they matter.

There Have Been Real Consequences From Not Taking Covid Seriously

A.  Introduction

Earlier posts on this blog have documented that vaccination rates against Covid-19 have been systematically lower in accordance with the share of a state’s vote for Trump in the 2020 election, and that mask-wearing to protect the individuals and those around them have also been systematically lower.  The higher the share voting for Trump in a state, the lower the share vaccinated and the lower the share wearing masks.

Those choices have had consequences.  As shown in the charts above, it should not then be surprising that states with a higher share of their vote for Trump have seen, on average, a higher number of cases of Covid-19 (per 10,000 of population) as well as a higher number of deaths.  The relationship is statistically a very strong one.  While many factors affect the likelihood of being infected with Covid-19 and of dying from it (including factors such as urban density, extent of travel, health status of the population, adequacy of the health care system, and more), political identification by itself appears to be a strong and independent factor.

In what is literally a life and death issue, one would have thought that rational self-interest would have dominated.  It has not.  Following a review of the data, this post will discuss some possible reasons why.

B.  The Relationship Between the Incidence of Covid-19 Cases and Deaths and the Share Voting for Trump

The figures at the top of this post plot the relationship between the number of cases of Covid-19 in a state (per 10,000 of population), or the number of deaths (also per 10,000), and the share in the state who voted for Trump in 2020.  The Covid data come from the CDC.  It was downloaded October 26, but since case and death counts from the states may not be fully reported to the CDC for up to a week to ten days, I used October 15 as the end date for the analysis here.  “Cases” are confirmed cases, and “deaths” are deaths as a consequence of Covid-19, both as defined in the CDC guidance for how these should be recorded.

For the start date I used July 1, 2020.  This came at the end of the first wave of Covid-19 cases and deaths.  Cases and deaths in this first wave were excluded for two reasons.  One is that the first wave arrived suddenly in mid-March and with an intensity that surprised many.  The nation was unprepared, with little done to prepare for the disease that was spreading around the world as Trump was claiming it was all under control, that it was “going to disappear”, and that it would soon “go away”.  Also, the CDC had bungled the initial testing (where testing was more readily accessible in parts of Africa than in the US in the key initial months), so the full extent of the developing problem was not clear until it hit.  The response, and the then only possible response, was to quickly institute lockdowns, and this was soon done in all 50 states.  The lockdowns were effective, albeit costly, and by late April the approach had succeeded in starting to bring down the daily number of new cases.  Case numbers continued to fall in May and into June.

But starting in early May, disparate decisions were taken across the different states on how fast to lift the lockdown measures.  Some opened up early and with little guidance on or advocacy for the wearing of masks, while others opened up more cautiously.  But with the opening up, and the refusal by a significant share of the population to wear masks and to follow social distancing recommendations, the daily number of new cases stopped falling and by around mid-June began to rise again.  The daily number of deaths followed a similar pattern but with a lag of about two weeks, and so began to rise around the end of June. Thus July 1 can be taken as a turning point – the end of the first wave and the start of the second.  While differences across the states had already started to develop from early May (when decisions were taken on how rapidly to open up), the consequences of the varying approaches only became clear as the second wave started to build.  On average across the nation, this was around July 1.

The second reason to exclude this first wave is that the quality of the data for that initial period was poor.  The Trump administration was slow in launching and then ramping up testing, with testing limited even well into April to those who showed obvious symptoms or who had been in close contact with someone with a confirmed case.  Thus many cases were missed.  While testing has been far from perfect throughout this pandemic, it was much worse in the earlier months than it was later.  For this reason as well, excluding the estimates from the earlier months will provide a better measure of how successful or not the different states were as they responded to the pandemic in their different ways after the initial lockdowns.

Excluding the first wave leads to the exclusion of 6% of confirmed cases and 18% of deaths from the overall totals as of October 15, 2021.  Most thus remained.  Note also the disparity in these figures.  That the official figures recorded that just 6% of the confirmed cases in the US (as of October 15) were in this initial, first wave, period, while this same period recorded 18% of deaths, strongly suggests that cases were significantly undercounted in that first wave.

The charts then show the incidence of total confirmed cases of Covid-19, or deaths from it, per 10,000 of population, over the period from July 1, 2020, to October 15, 2021, with this plotted against the share of the vote that Trump received in that state in 2020.  The relationship is a strong one:  The higher the share of the state vote for Trump, the higher the incidence of Covid-19 cases and of deaths.  Taking averages, the average number of confirmed cases over this period per 10,000 in the states won by Trump was 1,461 (i.e. 14.6% of their population) vs. 1,113 in the states won by Biden.  That is, there were on average 31% more cases in the states won by Trump.  The number of deaths from Covid-19 came to 21.2 per 10,000 in the states won by Trump vs. 15.3 in the states won by Biden, or 38% more in the states won by Trump.

But averaging across all the states won by Trump or by Biden is not terribly meaningful as there will be a mix of voters in every state.  Furthermore, there were a number of states where the vote was close to 50/50.

It is thus more meaningful to examine the trend across the different states, as a function of the share voting for Trump.  This trend is provided in the regression line shown in each chart, where simple, linear, ordinary least squares regression was used.  The statistical relationship found was very strong, and especially so for the regression for the number of cases of Covid-19.  The R-squared (a measure of how much of the variation in the values is accounted for by the regression line alone) was extremely high for such a cross-state sample as here – at 0.63 for the number of Covid-19 cases and a still high 0.36 for the number of Covid-19 deaths.  (R-squared values can vary between 1.0, in which case the regression line explains 100% of the variation across states, and 0.0, in which case the regression line explains none of the variation.)

The higher correlation (the higher R-squared) observed in the relationship for the number of cases than in the relationship for the number of deaths is what one would expect.  To die from the disease, one must first have caught it.  Hence this will depend on the number of cases in the state.  But deaths from it will then depend on additional factors such as the age structure of the population, general health conditions (obesity rates, for example), as well as the availability and quality of health care services (hospitals, for example).  These factors will vary by state, and hence add additional variation to that found for the number of confirmed cases.

The slope of the regression line is an estimate of how many additional cases of (or deaths from) Covid-19 to expect (per 10,000) for each 1% point higher share of the vote for Trump.  For each additional 1% point in the share of the vote for Trump in a state, there were on average 23.8 more cases (per 10,000 of population) of Covid-19 during the period examined, and on average 0.36 more deaths (per 10,000).  The t-statistics for these slope coefficients were both extremely high, at 9.1 for the number of cases and 5.2 for the number of deaths.  A t-statistic of 2.0 or higher is generally taken to be an indicator that the relationship found is statistically significant (as it implies that in 95% of the cases, the slope is something different from zero – a slope of zero would imply no relationship).  A t-statistic of 3.5 would raise that significance to 99.9%.  The t-statistics here of 9.1 and 5.2 are both far above even that mark.

One can also use the regression lines to address the question of what the impact would have been on Covid-19 cases and deaths if everyone behaved as Biden voters did (or as Trump voters did).  The regression lines look at how the incidence of cases or deaths change based on each additional percentage point in the vote for Trump.  If one extrapolates this to the extreme case of zero votes for Trump (and hence a “pure” Biden vote), one can estimate what cases and deaths would have been if all behaved as Biden voters did.

This is a straight line, i.e. linear, extrapolation of the effects, and the limitations from this assumption will be addressed in a moment.  But using linearity, the effects are easily calculated by simply inserting zeroes for the Trump share of the vote into the regression equations, so that one is left with the constants of +96.94 for the number of cases (per 10,000 of population) and -0.69 for the number of deaths.  That is, there would have been a predicted 97 (per 10,000) cases of Covid-19 over this period in the US rather than the actual figure of 1,261 (per 10,000).  This is 92% lower.  And the number of deaths would have been essentially zero (and indeed would have reached zero with still some share voting for Trump – based on the regression equation coefficients it would have been at the 2% point share for the Trump vote).

Are these results plausible?  Would cases and deaths have fallen by so much if all of the population had behaved (in terms of wearing masks, social distancing, getting vaccinated once vaccines became available, and other such behaviors) as the Biden voters did?  The answer is yes.  Indeed, the linear extrapolation is conservative, as infectious diseases such as Covid-19 spread exponentially.

If in some state each infected person infects, on average, two further people, the number infected will double in each time period for the disease.  This is exponential growth, with a reproduction rate of two in this example – a doubling in each period.  For Covid-19, the time period from when a person is infected to when that person may, on average, spread it to another, is a week and a half.  A person becomes infectious (can spread it to others) about one week after they became infected with the disease, and then can infect others for about a week (with the average then at the half-way point of that week).  Thus 100 cases of active infections in some region would double to 200 in that time period of a week and a half, then to 400 in the next time period, and so on.  If, in contrast, responsible behavior (such as vaccinations and mask-wearing) reduces the reproduction rate to one-half rather than two, then 100 cases will lead to 50 in the next time period, to 25 in the next, and so on down to zero.

In any given state there is a mix of Biden voters and Trump voters.  While there are many factors that matter, if these two identities reflect, on average, differing shares of people that do or do not choose to be vaccinated, wear masks, and so on, then the average reproduction rate will vary depending on the relative shares of such voters.  That average reproduction rate will be lower in states with a higher share of Biden voters, and for a sufficiently high share of Biden voters (a sufficiently low share of Trump voters), there will be an exponential decline in new infections from Covid-19.  The linear extrapolation based on the regression equations would thus be a conservative estimate of the number of cases to expect when most of the population behaves as the Biden voters have.

There are, of course, many factors that enter into whether a person is infected by someone with Covid-19, and whether they then die from the infection they got from someone.  But the charts and the regression results suggest that the share of the population in a state voting for Biden or for Trump is, by itself, strongly correlated with how likely that was.  Why?

C.  Personal Behavior and Political Identity

The fact of, and then the consequences from, this political divide for infection by Covid should not be a surprise to anyone.  As noted before, Trump voters are far less likely to be vaccinated or to wear masks to protect themselves and others from this highly infectious, and deadly, disease.  This then translates into higher infection rates, and the higher infection rates then to higher deaths.

One sees this unwillingness to be vaccinated also in surveys.  The most recent of the regular surveys by the Kaiser Family Foundation (published on October 28) found that 90% of Democrats had received at least the first dose of the Covid vaccine, while only 61% of Republicans had.  Furthermore, 31% of Republicans declared they would “definitely not” be vaccinated, while just 2% of Democrats held that view.  Gallup surveys have found similar results, with a survey from mid-September finding that 92% of Democrats had received at least the first dose of the Covid vaccine, but that only 56% of Republicans had.  And 40% of Republicans in that survey said they are not planning on being vaccinated ever, while only 3% of Democrats said that.

Not surprisingly, one then sees this reflected in state politics.  Republican governors (such as Abbott of Texas and DeSantis of Florida) have gone so far as to issue executive orders to block private companies from protecting their staff and their customers from this disease, and even to prohibit local school boards from taking measures to protect schoolchildren.

The direct result is that the virus that causes Covid-19 has continued to spread.  An infectious disease such as Covid-19 will only persist as long as it is being spread on to others.  It cannot survive on its own.  The issue, then, is not just that someone refusing to wear a mask or to be vaccinated is highly likely to catch the disease, but that that person is likely to spread it to others.  While Republican governors such as Abbott and DeSantis have said this is a matter of “personal freedom”, it is not that at all.  No one is free to do harm to others.  It is the same reason why there are laws against drunk driving.  Drunk drivers are more likely to cause crashes (not all of the time, but often), and those crashes will harm others, up to and including killing others.  Spreading Covid-19 is similar, up to and including that those who become infected may die from it.

For whatever causal reason, the facts themselves are clear.  But why has a significant share of the population chosen to behave this way?  This is now more speculative, and goes into an area that I openly acknowledge is not my area of expertise.  With that proviso, some speculation.

It is clear that political identity has played a central role, where Trump from the start treated the then developing pandemic as an issue where you were either with him – and his assertion that he had it all under control – or against him.  This started with Trump’s assertion in an interview on January 22, 2020 (from Davos, Switzerland) that he had no worries, that “we have it totally under control”, and that “It’s going to be just fine”.  This claim continued through February (as cases were growing in the US), where on February 27 he said “It’s going to disappear.  One day it’s like a miracle.  It will disappear.”  And in campaign rallies in February, he claimed to his cheering supporters that he had been doing a superb job in stopping the virus and that any charge to the contrary was simply a “hoax” coming from the Democrats.

Thus, from the start, Trump made the issue a political one.  If you were a true supporter of Trump you could not treat the disease as something of concern – Trump had taken care of it.  Any assertion that the developing pandemic was in fact serious, and needed to be addressed, was a “hoax” perpetrated by the Democrats.

Trump then continued to assert all would soon be well, saying on March 10 that “it will go away”, on April 29 that “This is going away.  It’s gonna go.  It’s gonna leave.  It’s gonna be gone.”, on May 11 that “we have prevailed”, on June 17 that “It’s fading away.”, and on July 19 that “It’s going to disappear”.  But more than 600,000 Americans have died since July 19, 2020, not far short of the 651,000 Americans who have died in battle in all of America’s wars since 1775.  From the start of the pandemic, more than 750,000 Americans have now died.

Trump’s politicization of Covid-19 was then amplified when, at the April 3 press conference in which he announced the CDC recommendation that everyone should wear face masks when going out, he immediately then added that he would not himself wear a face mask.  Face masks are highly effective in hindering the spread from person to person of the virus that causes Covid-19, and until vaccines became available, were the best way to hinder that spread.  But wearing a face mask is also highly visible.  For those who saw themselves as supporters of Trump, and believed what he said (that the virus was going away, that he had it under control, and that any concerns over this were merely a hoax promoted by the Democrats), then it was not surprising that many would follow Trump’s highly public example and not wear a mask either.  Some even went so far as to shoot, and kill, store personnel when told they should wear a face mask inside some store.

It is not surprising that such views would then carry over to vaccination.  Having rationalized not wearing a mask, it is easy to rationalize a refusal to be vaccinated.  And rationalizations could easily be found just by watching Fox News.  In the six months from April through September this year, for example, Fox News chose to air a claim undermining vaccination on all but two of those more than 180 broadcast days.  Many were also exposed to claims that can only be described as truly bizarre, such as that the vaccination will be secretly inserting a microchip into your body for the government to track you, with Bill Gates behind it all; or that it will make you magnetic with this managed through 5G telecom towers; or that it will re-write your body’s DNA; and more.

One can therefore easily come up with rationalizations not to be vaccinated, of varying degrees of plausibility, if you are predisposed against it.  But many of those providing such rationalizations must have realized that their rationalizations often did not make much sense.  Rather, their decisions appear to have been driven more by a visceral or emotional reaction (vaccinations just “feel” wrong) than as an outcome of a rational process.  That is, the decision not to be vaccinated was made first, based on emotions or feelings, with the rationalizations then arrived at later to justify a decision that had already been made.  (Such a process is in accord with the “social intuitionist” model of Jonathan Haidt, where decisions are made first, in a visceral reaction based on emotion, while rationalizations then come later to justify that decision.)

In the case of Covid-19, those decisions on vaccination (and earlier on wearing masks) were made in accordance with political identity – a perceived loyalty to Trump – rather than in recognition of the very real risks that would follow if one contracted Covid-19.  Wearing a mask or accepting a vaccination would simply be “wrong” and disloyal.

I have found it astonishing how strong this emotional reaction has apparently become.  Covid-19 is new (it did not even exist just two years ago), it is deadly (where on average about 1.5% of those infected have died – with a much higher fatality rate than this average for those who are older or who have other health issues), and may have serious long-term ill effects even for those who do not die from it.  Yet this visceral reaction appears to have been so powerful that many supporters of Trump still refuse to be vaccinated, despite the risk of genuine life and death consequences.

I should hasten to add that not all voters for Trump have refused to be vaccinated.  Indeed, according to the surveys, about 60% (a majority) have as of October.  There are also highly vocal partisans on the left who have refused to be vaccinated.  Their reasons are likely very different from that of the typical Trump voter, but the underlying cause appears still to be intuitive – the feeling that such vaccinations are simply “wrong”.  But the issue is that the relative shares of the two groups have been very different:  A far higher share of those who voted for Trump have refused vaccination than is the case for those who voted for Biden.  The consequences are as shown in the charts at the top of this post.

As noted before, the cause for this relationship cannot be known with certainty, and what I have presented here should be viewed as speculative on my part.  There may well be other explanations.  For example, a related but somewhat different explanation would be that a common third factor explains both the tendency of some to vote for Trump and also to be resistant to vaccinations.  Those in this group may put faith in conspiracy theories (including, but not limited to, terrible consequences from being vaccinated), distrust authority, proudly but stubbornly insist on doing the opposite of whatever is recommended, and for such reasons not only refuse to be vaccinated but also vote for Trump.

Whatever the explanation, the results have been tragic.  This has also been a lesson in how strongly some will keep to a held position, even as they have seen prominent figures, and sometimes friends or even family members, come down with this disease.  When an issue becomes one of identity, it appears that even with such tragic consequences there will be many who steadfastly refuse to change.