Jobs Due to Biden’s Infrastructure Plan: What is Being Discussed is Not What You Think

A.  Introduction

Politicians have always been eager to announce that a program they have proposed will “create jobs”.  The Biden administration is no exception.  Indeed, President Biden has titled his $2.2 trillion proposal to rebuild America’s infrastructure the “American Jobs Plan”.  And all this is understandable, given the politics.  You would be forgiven, however, for assuming that what is being discussed on the additional jobs that would follow from Biden’s infrastructure proposals has something to do with jobs such as those depicted in the picture above.  They don’t.  The numbers on “new jobs created” that are being bandied about are on something else entirely.

There has also been some confusion on how many jobs that might be.  In remarks made on April 2, soon after his initial announcement of the proposed $2.2 trillion infrastructure initiative, Biden said:  “Independent analysis shows that if we pass this plan, the economy will create 19 million jobs — good jobs, blue-collar jobs, jobs that pay well.”  The estimate is from an analysis made by Mark Zandi, Chief Economist of Moody’s Analytics (a subsidiary of Moody’s, the bond credit rating agency).  Zandi is a well-respected economist, who was an economic advisor to John McCain during his 2008 campaign for the presidency and who has advised both Democrats and Republicans.

The 19 million jobs figure is an estimate made by Zandi and his team at Moody’s Analytics of how many more jobs there would be in the US (or, more precisely, non-farm employees) in 2030 as compared to the average number in 2020, in a scenario where Biden’s infrastructure plan is approved as proposed and then implemented.  But it is important to note that this is an estimate of the total number of jobs that “the economy will create” over the decade if the plan is passed (which is what Biden specifically said), and not an estimate of the extra number of jobs that can be attributed to the American Jobs Plan itself.  But it would be easy to miss this distinction.  The Moody’s Analytics estimates are that the number of jobs in the economy would rise between 2020 and 2030 by 19.0 million if the plan is passed as proposed, but by 16.3 million if only the covid-relief plan (Biden’s $1.9 trillion American Rescue Plan) is passed (as it has been), and by 15.7 million in a scenario where neither plan was passed.  Thus in the Moody’s Analytics forecasts, the number of jobs in 2030 would be 2.7 million higher than otherwise if the infrastructure plan is now passed (on top of the extra 0.6 million if only the covid-relief plan were passed).

But it is easy to misstate these distinctions, and some of the administration appointees discussing the proposal with the press at first did so.  In particular, Pete Buttigieg, the Transportation Secretary, and Brian Deese, the head of the National Economic Council in the White House, at first used wording that implied that the full 19 million additional jobs would be due to the infrastructure plan itself.  They later clarified that they had misspoke, and that the Moody’s Analytics estimates were of 2.7 million additional jobs due to the infrastructure plan.  However, this did not keep various news media fact-checkers (including at CNN and at the Washington Post) from taking them to task on it (and for the Washington Post to award Biden “two Pinocchios” in their fact-checking scoring system for being, in their view, misleading).

One can question whether this is quibbling over language that was not fully clear.  But what is of far greater importance is that it misses the fundamental question of what any of these employment forecasts (whether of 19 million, or 2.7 million, or 0.6 million from the $1.9 trillion covid-relief plan) actually mean.  Keep in mind that they are all estimates of how many more people will be employed in 2030 compared to the number employed in 2020, or in a comparison of one scenario for 2030 compared to another.  They are specifically not estimates of the number of jobs of primarily construction workers who would be employed as a direct result of the new infrastructure investments being built.  Yet the wording of Biden, stating that these would be well-paying blue-collar jobs, would appear to indicate that that is what he had in mind when citing the figures.

Furthermore, if the job figures were intended to refer to the blue-collar construction workers who would be hired to build these projects, it does not make much sense to base a comparison on 2030.  By that point the infrastructure plan would be essentially over, with just a small residual amount still to be spent as the program is tailing off (of the $2.2 trillion total, just $81 billion in 2030 and a final $35 billion in 2031 would remain to be spent in the Moody’s estimates).  Few construction workers would still be employed on those projects by that point.  Rather, what may be of interest is not some relatively small change in the overall number of people employed at some end-point, but rather the number of person-years of employment of such workers during the full period of the infrastructure plan.  But the Moody’s estimates are specifically not that.

This then brings up the question of what is Moody’s in fact estimating?  That will be the focus of this blog post.  It is not the number of jobs in construction that will be created as a result of the new work on infrastructure, as these will be down to a fairly minor level by 2030.  As we will see, it is rather an estimate resulting from some secondary aspects of the Moody’s model, and it is not even clear whether the differences were intended to be meaningful.

To start, this post will review how estimates of future employment are traditionally made – for example by the Bureau of Labor Statistics (BLS).  In brief, they are based on population estimates and on forecasts of what share of different population groups will seek to be part of the labor force (the labor force participation rates), with then the assumption that the economy will be at full employment at that future date.  The full employment assumption is made not because the forecaster is confident the economy will in fact be at full employment in that forecast year.  Rather, they do not really know what the short-term conditions will be in that future year, and assuming full employment is just for setting a benchmark.  Unemployment depends on how successful monetary and fiscal policies would have been in that future year to bring the economy to full employment.  Such policies are short-term, depend on the immediate situation, and we have no way of knowing now (in 2021) what shocks or surprises the economy will be facing in 2030.

With this the case, why is Moody’s forecasting any difference at all in the 2030 employment numbers?  The differences are in fact not large when compared to what overall employment will be in that year.  But there is some, and we will discuss why that is.

The post will then look at what one might say on jobs in the intervening years.  While Moody’s has produced year-by-year estimates, its approach for those years (after the next couple of years, as they forecast the economy moves to full employment) is fundamentally similar to what they assume for 2030.  What Moody’s specifically did not do in its analysis was try to estimate the direct number of jobs (or more precisely, person-years of employment) of those employed on the infrastructure projects in Biden’s plan.  Someone will likely do that at some point, but it was not done here.  The question I will then look at it is whether this should be seen as “job creation”.  I will argue that it would be more appropriate to look at it as job shifting rather than job creation, as the total number of jobs in the economy (the number employed) will likely not be all that much different.  And there is nothing wrong with that.  The primary objective, after all, is to build and maintain our badly needed infrastructure.  And on the employment that would follow, providing more attractive jobs that workers will seek to shift into is a good thing.  But the total number employed may not change, and if that is the metric one tries to use, one will likely be disappointed.  Many, including politicians, are often confused about this.

None of this should be taken to imply that the infrastructure plan is not warranted.  It desperately is, as will be discussed in the penultimate section of this post.  The US has underinvested in public infrastructure for decades, and what we have is an embarrassment compared to what is seen in Europe or East Asia.  And it has direct implications for productivity.  Truck drivers are not productive when they are sitting in traffic jams due to our poor highways.  But it is wrong to assess the value of an infrastructure investment program by some estimate of the number of jobs created.  Yes, there will be workers employed on the projects, in likely well-paid jobs.  But that should not be the objective – better public infrastructure should be the objective, achieved as efficiently as possible.  A focus on “jobs created” is instead likely to lead to confusion, as it has with the Moody’s numbers.

We will then end with a short summary and conclusions section.

Finally, note that the version of Biden’s infrastructure plan examined by Zandi and his team was estimated to cost $2.2 trillion over ten years.  However, one will see references to Biden’s plan as costing $2.0 trillion, or $2.3 trillion, or some other amount.  The final amount will depend, of course, on whatever Congress approves, but for consistency I will focus here on the plan as assessed by Zandi, at an estimated cost of $2.2 trillion.

B.  Forecasting Future Employment Levels

Yogi Berra purportedly said:  “It’s tough to make predictions, especially about the future”.  Whether he actually said that is not so clear, but it is certainly true.  And this is especially true of predictions of future employment.  But some things are more predictable than others, and the trick is to make use of factors that change only slowly over time.

In particular, population forecasts for periods of a decade or so are relatively reliable.  Those in a particular age bracket now will be ten years older a decade from now, and all one needs then to adjust for are mortality rates (which are known and change only slowly over time) and net migration rates (which are relatively small in magnitude).  Thus the Census Bureau can produce fairly reliable population forecasts for periods of a decade, and can provide these for groups broken down by age bracket as well as sex, race, and ethnicity.

The Bureau of Labor Statistics starts from such Census Bureau forecasts to produce its projections of the labor force and employment.  The BLS does this annually, with the most recent such projections from September 2000 covering the period 2019 to 2029.  The BLS takes the Census Bureau forecasts for the adult population (age 16 and above), with these broken up into age groups (mostly 10-year groups, i.e. aged 25 to 34, 35 to 44, etc.) and by sex, with overriding checks based on race (white, black, other) and ethnic (Hispanic and non-Hispanic) classifications.  For each of these groups, it estimates, based on a statistical analysis of historical trends, what its labor force participation rate can be expected to be in the projection year.  The labor force participation rate is the share of the population within each group who choose to be part of the labor force (i.e. either employed or, if unemployed, seeking a job).  Labor force participation rates change only slowly over time (as was discussed in this earlier post on this blog), so this is a reasonable approach for estimating what the labor force might be in a decade’s time.

Employment will then be the labor force minus the number who are unemployed.  But there is no way to know beyond the next few years what the unemployment rate might then be.  It will depend on what shocks or surprises there might have been to the economy at that time, and these are by definition not predictable.  If they were, they would not be surprises.  While active monetary and fiscal policy would then seek to bring unemployment down to just frictional levels, how long this will take depends on many factors, including political ones.  And the problem is one that can only be addressed in the near term, as it depends on when the shock came. Thus the Fed’s Board of Governors meets as a group every six weeks throughout the year to monitor the situation, and to decide based on what they know at the time whether to tweak monetary policy through some instrument (normally short-term interest rates, which they may adjust up or, when they can, down, to affect growth).

There is thus no way to know now, in 2021, what the rate of unemployment will be in 2030.  For this reason, to set a benchmark to which comparisons under different scenarios can be made, the BLS and others following this approach assume the economy will be operating at full employment in that projection year.  That is, the benchmark sets unemployment at some specific, low, rate to reflect just frictional unemployment.  While there has been debate on what that specific rate might be (different analysts generally peg it at between 4 and 5% currently), a specific rate would be chosen for the comparisons.  Employment will then be equal to the labor force in that forecast year minus the number unemployed at this assumed rate of unemployment.

[MInor technical note:  The employment figure arrived at in this way will be employment as measured at the individual level, and will include the self-employed as well as on-farm employment.  It will also count as one person employed even if the individual holds multiple jobs.  The employment figures normally cited (and used by Moody’s) are of non-farm payroll employment, which comes from surveys of establishments, excludes the self-employed and on-farm employment, and counts each job even if one person might hold more than one job (as the establishment will only know who they employ, and will not know if some of their employees might hold second jobs).  But the differences due to these factors are small, and adjustments can be made.]

Thus, for any given set of forecast population figures (by age group, etc.), employment will follow from the labor force participation rate and the assumed rate of frictional unemployment (i.e. unemployment when the economy is assumed to be operating at full employment).  Forecast employment in any future year under different scenarios will therefore only differ if either the labor force participation rate, or the unemployment rate (or both), differ for some reason.

C.  The Moody’s Employment Scenarios for 2030

Moody’s Analytics examined three scenarios for 2030 (and the path to it):  A base case where neither the infrastructure plan of Biden nor the covid-relief plan of Biden existed, a scenario where only the covid-relief plan was in place, and a scenario where both are in place.  In the first (base case) scenario it forecasts that employment in the US would rise to 157.9 million in 2030 from an average of 142.2 million in 2020, or an increase of 15.7 million.  In the scenario with only the covid-relief plan, Moody’s forecasts that employment in 2030 would then total 158.5 million, or 0.6 million more than in the base case.  And in the scenario where the infrastructure plan is also passed and implemented, Moody’s forecasts that employment in 2030 would total 161.2 million, or 2.7 million more than in the scenario with only the covid-relief plan passed and 19.0 million more than average total employment in 2020.

But why would employment levels in 2030 differ at all between these scenarios?  As discussed above, they can only differ if labor force participation rates differ or the assumed unemployment rates in that forecast year differ.  (The basic population numbers for that year should certainly not differ.)  In the Moody’s numbers they both do, but it is not clear why.

It is in particular difficult to understand why Moody’s allowed the assumed unemployment rates in 2030 to differ across their scenarios.  The scenario with just the covid-relief plan, which will be over by 2023 at the latest, should in particular not have an impact on the unemployment rate in 2030.  But in the Moody’s figures it does, albeit by only a minor amount (with unemployment at 4.5% in 2030 in the base scenario, and 4.4% in the scenario with the covid-relief plan).

The difference is larger in the scenario with both the covid-relief plan and the infrastructure plan.  Moody’s forecasts that unemployment in 2030 would then be just 3.8%, or well less than the 4.5% rate in the base scenario.  Why would that be?  While there would still be a small amount of spending under the infrastructure plan in 2030 (Moody’s uses a figure of $81 billion in its scenario), the impact of such spending in that year would be small (just 0.2% of forecast GDP in that year) and would in any case have been diminishing over time as the infrastructure plan was being phased down.  That is, the reductions in spending under the infrastructure plan in the outer years, relative to what they would have been a few years before, would (if not offset by other actions) be deflationary at that point, not expansionary.  But regardless of whether Biden’s infrastructure plan had been passed in 2021 or not, one would assume that fiscal and monetary policy would have sought in that future year (2030) to bring the economy to full employment, at whatever the assumed rate of (frictional) unemployment that it then is. There is no rationale for assuming the rate of unemployment in 2030 will differ across the scenarios.

The other difference in the Moody’s forecasts for 2030 under the different scenarios is in the labor force participation rates.  One can work out from the numbers Moody’s provided in its document (coupled with the BLS numbers for the adult population) that the labor force participation rate would be 58.5% in the base scenario, 58.7% in the scenario where only the Biden covid-relief package was passed, and 59.3% if the Biden infrastructure plan is also passed.  (More precisely, these are the Moody’s figures for non-farm payroll employment as a share of the population, not the overall labor force, with the small differences noted above between those two concepts).  Compared to the scenario of the covid-relief plan only, two-thirds (66%) of the extra 2.7 million in employment in 2030 is due to the higher labor force participation rates Moody’s forecasts for that year, and one-third (34%) is due to its forecast of a lower unemployment rate in that year.

Why should the labor force participation rate be higher in 2030 if Biden’s infrastructure plan is passed?  One could postulate a connection, but it would be tenuous and it is not clear if this was in fact intended by Moody’s or was just an outcome following from other relationships in its model.  I do not know enough about the structure of its model to say.  But one can speculate that the model may have linked the labor force participation rate in a forecast year to real wages in that year, with a higher real wage leading to a higher labor force participation rate.  Furthermore, the model might link greater infrastructure investment (or greater investment generally) to higher productivity, and higher productivity to higher wages.  In that case, the higher investment might lead, by such a route, to a higher labor force participation rate.  But this would require estimation of the responses in a series of steps, each of which might be tenuous.  It is difficult to forecast how much economy-wide productivity might rise as a result of such investment; difficult to forecast how much real wages would rise if productivity rises (real wages have been flat since around 1980, even though overall productivity rose by almost 80%); and difficult to forecast how much a rise in real wages might then raise the labor force participation rate.

But this is conceivable.  Whether it was an intended relationship in the Moody’s model is not so clear.  Such models are large and complicated, with a focus on particular issues.  Certain results might then follow, but those constructing the model might not have paid much attention to such outcomes when constructing the model, as the focus was on something else.

In any case, one has to be careful in interpreting the results as implying there would be 2.7 million additional jobs “created” in 2030 as a consequence of the Biden infrastructure plan.  There would, in the model, be 2.7 million more people employed, but this would mostly be due to a higher proportion of the population seeking employment in that year (a higher labor force participation rate).  And assuming an economy at full employment in that year, the additional number seeking employment would translate into that additional number being employed.  But it would be a stretch to interpret this as the infrastructure plan “creating” those additional jobs.  Rather, a higher share of the population are looking for work (a higher labor force participation rate), and are assumed to be able to find it.

D.  The Jobs Directly Created by the Infrastructure Plan

The Biden infrastructure plan would certainly create a huge number of jobs while the infrastructure is being built.  There would be jobs such as depicted in the photo at the top of this post, and with $2.2 trillion being spent there would be a large number of them (even with a share of the $2.2 trillion being spent in high priority areas outside of what is traditionally considered “hard” infrastructure, such as for labor training and health infrastructure).

These would, however, be jobs for a fixed period.  Once the particular projects are finished, those jobs would end.  Thus one should think of these as being so many person-years of employment (employment of one person for one year).  These are not permanent jobs being “created”, but rather workers being employed for a period of time to build a project or to complete a specific maintenance or repair task (e.g. repaving a road).

While not permanent jobs, it would still be important to have good estimates of how many there would be.  Moody’s did not do that, nor was it their intention, but one needs to be clear about that.  It will be important, however, that there be a serious effort at some point to work out such estimates, and I would guess that someone in government is working on this now.  They are needed precisely because there will be a large number who will be employed on these infrastructure projects, and workers with the necessary skills for such work are limited, in part because the US has so woefully underinvested in its infrastructure in recent decades (as will be discussed in the next section below).  It will thus be important to pay attention to the phasing of the individual projects, both over time and geographically, to ensure there will be sufficient capacity (both in terms of the workers needed and the firms that manage such projects) to build the projects at a given place and at a particular time.  It does not help much that there might be workers with the requisite skill in New York, say, when the need is for a project in California.

This will therefore need to be worked out, and I suspect it will be.  This will also guide what workforce development and training needs there will need to be, and the BLS routinely provides such estimates (at least at a broad, economy-wide, level).  But while it is correct to term jobs (or more precisely person-years of jobs) as being “created” under such an infrastructure plan, this does not necessarily mean that the total number of jobs in the economy will be higher.  If the economy is at full employment (and the labor force participation rate otherwise unchanged), the total number employed in the economy will be unchanged.  It is just that some share of those employed will be working on these infrastructure projects.  And that means fewer will be working in other jobs.

That is not a bad thing.  While the overall number employed will be the same, there will be jobs in the infrastructure projects which will have been attractive enough (either due to higher wages that they pay or for some other reason) to draw workers to those jobs.  Those who shift to those new jobs will then be better off, which is good.  Furthermore, the workers shifting to those new jobs would then have left positions that others may find attractive enough to move into (due to a higher wage, or whatever).  Thus there would be shifts across the economy.  Some less attractive jobs would cease to be filled, with employers forced to learn how to make do with less, but that is how competition works.

It is thus not correct to assert the total number employed in the economy will be higher as a consequence of the infrastructure investment plan (aside from during an initial few years as the economy moves to full employment – and Moody’s forecasts that this will be complete by 2022 with the covid-recovery and infrastructure plans enacted and even by 2024 without them).  The total number employed in such forecasts will be largely the same with or without the plans.  But that does not mean they are not without value to workers.  There will be new jobs to be filled, which will need to be attractive enough to draw workers to them.  And that helps workers.

E.  Public Infrastructure Investment in the US

Public infrastructure in the US is an embarrassment.  And it has a direct impact on productivity.  As was noted before, a truck driver sitting in a traffic jam is not terribly productive.  Similarly, exporters of soybeans who have to wait weeks to ship their product due to inadequate capacity at the ports cannot be terribly competitive in global markets (and will have to accept a price cut in order to sell their product).  And so on.

The major reason public infrastructure in the US is so poor is that the US has simply underinvested in it.  Using a broad definition of all government investment excluding that for the military, as a share of GDP, one has (calculated from BEA NIPA statistics):

Government investment peaked in the mid-1960s (as a share of GDP) and has declined ever since.  In gross terms it has been lower in recent years than in any time since the early 1950s.  Net of depreciation, it has been a good deal lower over the last half-decade (to 2019 – the 2020 figure is not yet available) than it has ever been in the last 70 years at least.  (And note that the blip up in the GDP share in 2020 was not because public investment rose.  The rate of growth of gross government investment in 2020 was in fact less than in 2019 and about the same as in 2018.  Rather it was because GDP collapsed in 2020, in the last year of the Trump administration, which pushed the share higher.)

What is of most interest for the state of public infrastructure is such investment net of depreciation.  That is shown as the curve in red in the chart, and it has fallen from a peak of 3.0% of GDP in 1966 to just 0.7% of GDP in recent years (up to 2019), a fall of 77%.  And at such a pace of adding to the net stock of public capital (infrastructure), the stock of such capital as a share of GDP will be falling.  By simple arithmetic, the ratio will be falling if the stock of that capital as a share of GDP is greater than the net investment share of GDP (0.7% here) divided by the rate of growth of nominal GDP.  Taking a nominal growth rate for GDP of, say, 4% (i.e. a real growth rate of 2% and a growth in prices of 2%), then the stock of public capital as a share of GDP will fall if the current stock of that capital is 17.5% of GDP or more (where 17.5% is equal to 0.7% / 4%).  The stock of public capital will certainly be well more than that in any modern economy, including the US.  And that underinvestment is why our highways are becoming increasingly subject to traffic jams, for example.  Our infrastructure is simply not keeping up.

Major public investment will be needed to reverse this, and the Biden infrastructure plan will be a start.  To put things in perspective, I have taken what would be spent annually under the Biden Plan (as estimated by Moody’s), as a share of GDP, and added this to a base amount where I simply assume other government investment in gross terms will remain at the average share it was between 2013 and 2019 (when it was quite steady at about 2.65% of GDP).  The figures for real GDP used for these calculations were those forecast by Moody’s under the scenario that the Biden infrastructure plan goes ahead, with these converted to nominal GDP (for the shares) using the forecast GDP deflators of the Congressional Budget Office.  Spending under the Biden Plan alone would start at 0.5% of GDP in 2023, rise to a peak of 1.3% of GDP in 2025, and then fall to 0.2% of GDP in 2030 and 0.1% in 2031.  Adding these figures to a base level of 2.65%, one would have:

A $2.2 trillion infrastructure investment plan is certainly large.  But the chart puts this in perspective.  Even with such an investment program, public investment would still not rise to as high as it was in the mid-1960s, nor would it last nearly as long.  Public investment had been relatively high (compared to later periods) from the mid-1950s to around 1980 – almost a quarter-century.  The $2.2 trillion Biden plan would raise public investment, but only for about eight years.  A question that will need to be addressed later is what happens after that.  Reverting to the recent, low, levels of infrastructure investment, would eventually lead back to the problems we have now.

F.  Summary and Conclusions

Politicians will always tout the jobs that will be “created” if their programs are approved.  If they didn’t, they likely would not hold office for long.  President Biden is no exception.  And the administration has cited independent estimates made by Mark Zandi’s team at Moody’s Analytics to say that Biden’s “American Jobs Plan” would indeed create a large number of jobs.  They cite Moody’s estimates that the number of jobs in 2030 would be 19 million higher than in 2020 if the infrastructure plan (as well as the covid-relief plan) are approved, and 2.7 million higher in 2030 if that infrastructure plan is approved as compared to a scenario where it is not.

These are, indeed, the Moody’s numbers.  But one should be careful in the interpretation of what they in fact mean, and Moody’s can be criticized for not being fully clear on this.  These are not jobs, generally in construction, that would follow directly from the infrastructure investment program (which should be counted as person-years of employment in any case, as such jobs are not permanent).  Rather, what Moody’s has done has been to use its model of the US economy to examine what overall employment levels would be in 2030 under the various scenarios.  It found that the number employed would be 2.7 million higher in 2030 (1.7% of forecast employment in that year) in the scenario with the infrastructure plan as compared to a scenario without it.  One can calculate that roughly two-thirds of this would be due to a higher labor force participation rate, and one-third due to a lower unemployment rate in that year.

It is not clear, however, why forecasts of either of those two variables – participation rates and the unemployment rate – should differ at all across the scenarios.  I would not be surprised if these were simply unintended consequences in a complex model.  In any case the differences in employment in that forecast year of 2030 are small, as one would expect.  Furthermore, by 2030 the infrastructure plan would be winding down, with only small residual amounts remaining to be spent.

During the course of the 2020s, however, a very significant number of people will be employed on these infrastructure investments.  They will be employed for limited periods until the projects are completed (and hence should be counted in person-years of employment), but this would still be significant.  It will be important to estimate not just how many will be employed and for what periods, but also what skills will be required and where and when they will be required.  This is probably now being done somewhere in government.  But Moody’s did not attempt to do that.

And while such jobs, mostly in construction, can be correctly termed as “created” under the infrastructure investment plan, this does not necessarily mean the overall number of people employed in the economy will be higher.  Unless labor force participation rates would then be higher for some reason (and it is difficult to see why that would be the case) or the unemployment rate is lower (which it cannot be if the economy is already at full employment), the overall number employed in the economy will be unchanged.  What would happen, rather, would be shifts in the job structure, not in the number of jobs overall.  Some workers would shift into the construction jobs needed to build the infrastructure, and others would shift into the jobs these workers had occupied before.  That is all good – the new jobs will need to be more attractive in terms of pay and/or for other reasons for workers to shift to them – but the total number employed (the total number of “jobs”) would largely be the same.

The public infrastructure is certainly needed.  The US has been underinvesting in its public infrastructure for decades, and when account is taken for depreciation it is clear that the net stock of public capital has not kept up with the overall growth of the economy.  That is why roads, for example, are now so often jammed.  The Biden Plan would bring public investment up to levels not seen for decades, although still not matching (even at $2.2 trillion) the public investment levels of the 1960s as a share of GDP.  It is also a time-limited program, which would phase down in the second half of the 2020s.  At some point, this will need to be addressed.  Bringing public investment levels back down to the far from adequate levels of recent decades will lead to the same problems again.  But that will likely be an issue that will not be seriously considered until the next presidential term.

Lower Life Expectancy in a State is Correlated with a Higher Share Voting for Trump

A lower life expectancy in a state is associated with a higher share in the state voting for Trump.  The chart above shows the simple correlation, using state-wide averages, between the life expectancy in a state and Trump’s share of the vote in that state in the 2020 presidential election.  States where life expectancy is relatively low saw, on average, a higher share of their population voting for Trump.  Life expectancy was especially low in a set of mostly Southern states that also had a high share voting for Trump (the bottom right corner of the chart).

The figures on life expectancy come from a recently issued set of estimates produced by the CDC.  The CDC estimates are geographically highly detailed, providing estimates down to the census tract level, but I have only used here the overall state-wide averages.  Due to their fine level of geographic detail, the CDC estimates are averaged over several years (2010 to 2015) to smooth out year-to-year statistical noise.  But life expectancy figures generally change only slowly over time (2020 was an exception, due to Covid-19), so figures for 2010-15 will provide a good estimate of what should be considered normal for life expectancy currently (i.e. with the exception of the Covid-19 impact).  The presidential election results are from Wikipedia, where the Trump share is his share in the overall vote in each state (including third party and other minor candidates).

The correlation is a strong one.  The regression equation (shown in the chart) for the relationship has an R-squared of 0.45.  This means that if one simply knew the life expectancy in a state, one could predict 45% of the variation in the share across the states that would vote for Trump.  This is high for such a simple cross-section relationship.  The negative slope of the equation (-0.11) means that every percentage point increase in the share of the vote for Trump is associated with a 0.11 year lower life expectancy.  Or put another way, a state with a life expectancy that is one year less than in another is associated with an expected 9 percentage point higher share of those voting for Trump (where 9 is roughly equal to 1 / 0.11).

Why this correlation?  Note that it is not saying that a high or low life expectancy in itself would necessarily be driving a tendency to vote for Trump or not.  Rather, a number of factors that enter into the determination of life expectancy are quite possibly also factors in common with the views of Trump supporters.  Life expectancy depends on personal factors and decisions (smoking, diet and exercise, obesity, vaccinations, whether to wear a mask to protect oneself and others to reduce the spread of a deadly disease), as well as on decisions made by state and local governments chosen by that electorate   (such as on access to health care, e.g. whether Medicaid should be available for the poor).  Life expectancy also depends on income levels and for any given average income level on income inequality.

And it will depend on the social norms of the region, such as car driving habits (speeding) and access to guns.  Of the factors reducing life expectancy in the US between 2014 and 2017 (mostly offsetting factors that would have, by themselves, led to a higher life expectancy) unintentional injuries accounted for just over half (50.6%) while suicides and homicides accounted for a further 15% (suicide 7.8% and homicide 7.5%).  That is, these non-medical factors accounted for two-thirds of the factors that had a negative impact on life expectancy in this period.

Few would question that better health is better than poorer health.  The high correlation seen here between life expectancy and the degree of Trump support suggests that there are significant commonalities in the various states between behaviors (both personal and social) that lead to poorer health outcomes and support for Trump.

Older Americans Account for an Overwhelming Share of the Deaths from Covid-19

It is well known that older individuals are more likely than younger individuals to die from Covid-19 should they become infected with the virus.  What some might not be aware of is how much this higher vulnerability by the older population has translated into older individuals accounting for the overwhelming share of those who have died from this terrible disease.  More than 95% of all those who have died from the disease in the US were age 50 or older.

This was not due to older individuals being more likely to get the disease in the first place.  As we will see below, the distribution of those coming down with Covid-19 is broadly similar to the distribution of population shares, and for those 50 and older, the share of this age group of all those who came down with the disease is almost identical to their population share.  Rather, the cause is that when an older individual comes down with the disease they are far more likely to die from it.

This short post will review the figures, basically through a series of charts.

The chart at the top of this post shows the shares of those who died from Covid-19 in the US by each age group (where the sum across age groups will be 100%).  These are based on totals since the start of the pandemic in February 2020.  The data comes from the CDC, which now reports on this each day.  Deaths and the causes of those deaths are regularly reported to the CDC by the US health system, and while reports with details on age and similar information will lag (the totals in these CDC figures come only to 71% of the total number of deaths from Covid-19 that the CDC also reports), for these share estimates the partial figures will be fine.

The CDC numbers on confirmed cases of Covid-19 by the same age groups are:

Note the scale, where those aged 50 and older made up just 35% of all those who had a confirmed case of Covid-19.  This is far from the 95% of deaths from the disease.

The shares of individual age groups varied, but this is not surprising as the share of the population of the US by each such age group also varies widely.  The interesting question is whether the shares of those coming down with the disease were different from the population shares.

And in broad terms they weren’t:

This chart shows the ratio of confirmed cases of Covid-19 of each age group to the share of the US population of that age group (using Census Bureau numbers).  By definition, that ratio will be 1.0 for the population as a whole.  Interestingly, for those age 50 and older, the ratio is very close to 1.0.  That is, those 50 and older accounted for 35% of cases in the US of Covid-19, and they similarly account for about 36% of the US population.  In practice, at least, they were only as likely to pick up the disease as their overall population share.

For the other individual age groups the ratio varies by about +/- 20 to 30% around equal shares (a ratio of 1.0), and there might be some simple explanations for that variation.  The ratio is about 0.8 for those between the ages of 65 and 84, where this might be because most of those in this age bracket are retired and can isolate themselves from much exposure to others.  In contrast, a significant share of those aged 85 and older might reside in nursing homes or may otherwise need assistance, thus exposing themselves to others.  The ratio for that age group is 1.2.

The ratios are also above 1.0 for those of working age below age 50, as many in these age groups will work and this may necessitate exposure to others.  Finally, the far lower ratios for children through to age 17 might well be anomalies due to limited testing.  Those in these age groups are far less likely to die (as we will discuss immediately below), and will often also show only limited symptoms should they get the disease (and may indeed often be totally asymptomatic).  As a result, many in those age groups may have not been tested even if they had the disease, as they did not exhibit symptoms.  The young may still get the disease, and indeed often do, and it may be a devastating disease for a significant number of them.  But the shares appear to be well less than for other age groups.

Broadly, therefore, the distribution of confirmed cases of Covid-19 by age group is similar to the population share of that age group (with the possible exception of those age 17 and below, although this may be a testing issue).  The data suggest that if one is exposed to the virus then similar shares of the population, regardless of age, get the disease.  Certain age groups, and in particular the elderly, do not appear to be more susceptible than others.

Why then do the elderly account for the overwhelming share (95% by those age 50 and above) of those who have died from Covid-19?  It is because it is far more deadly for them should they get it:

More than one in five (22%) of those aged 85 and older die from Covid-19 should they come down with the disease.  This is incredibly high for a communicable disease.  But the mortality rates then fall steadily for younger age groups.  It is still high at more than one in ten (11%) of those between ages 75 and 84, and about 5% for those between 65 and 74.  But it then falls to just 0.01% for those between 5 and 17, and 0.02% for those 0 to 4 (and with wider testing, leading to a higher number of confirmed cases, it might in fact be less than this).  Put another way, the mortality rate from Covid-19 is 2,000 times higher for those aged 85 and older than it is for the young.

These figures provide the rationale for prioritizing the elderly in the distribution of vaccinations.  Once one is able to vaccinate the 36% of the population aged 50 and above, one would have vaccinated the age groups accounting for 95% of the deaths from Covid-19.  The similar figures for those aged 65 and above are that they make up 16.5% of the US population (54 million people), but account for 81% of Covid-19 deaths.  And as I write this (on February 16), the CDC reports that 55 million doses of the vaccine have so far been administered in the US, with almost 40 million having received at least the first dose (and 15 million the second dose as well).

The prioritization of the elderly makes sense.  Covid-19 appears to spread similarly across age groups, but mortality from it is concentrated to a shockingly high degree among those who are older.