The November Jobs Report Was Actually Quite Solid: One Should Not Expect More Going Forward

A.  Introduction

The Bureau of Labor Statistics (BLS) released its regular monthly “Employment Situation” report, for November 2021, on Friday, December 3.  The report is always eagerly awaited.  It provides estimates for the net number of new jobs created in the most recent month, as well as figures on the unemployment rate, certain wage measures, and much else.

The initial reaction to the report by the media was negative.  Net job growth, estimated at 210,000 in the month, was viewed as disappointing.  This was down from 546,000 net new jobs in October, and was well below Wall Street expectations (based on a survey of Wall Street firms by Dow Jones) that the figure for November would come to 573,000.  While it was noted that the unemployment rate also fell – to just 4.2% – the negative reaction contributed to a significant decline in the stock market that day, with the S&P 500 index, for example, down by over 2% at one point.

But the November jobs report was actually pretty solid.  In this post, we will look at what was reported and some factors to take into account when examining such figures.

B.  Monthly Job Gains in 2021

The chart at the top of this post shows the current BLS estimates of monthly net job growth this year, starting in February to cover the period of Biden’s presidency  The estimates are based on a survey of establishments by the BLS, that asks (along with much else) the number of employees on their payroll as of the middle week of each month.  Hence the January numbers would have been for before Biden’s January 20 inauguration.  The news reports following the release by the BLS of the November jobs report were often accompanied by charts such as this one, with the November figure showing a substantial reduction in the number of net new jobs compared to what was seen in earlier months.  The question of interest is whether this was significant.

A number of factors should be taken into account.  One is simply that there is substantial month to month variation, as seen in the chart.  This may be in part due to fluctuations in the economy, but may also be due to idiosyncratic factors (such as how the weather was in the week of the survey) and to statistical noise.  The figures are based on surveys, and surveys are never perfect.  Examined in context, the change in the November figure from the prior month is similar to the changes seen in other months this year.  Indeed, it was less than in several.

There will, however, always be limitations with any single estimate, and in part for this reason the BLS provides in its published document a few different estimates for employment growth. The measure shown in the chart at the top is rightly considered the best one.  It is based on a monthly survey (called the Current Employment Statistics, or CES, survey) of business and other establishments (including government entities as well as non-profits such as universities and hospitals) – whoever employs workers.  The sample size is huge:  144,000 different businesses and government entities, at almost 700,000 different worksites.   The BLS indicates this “sample” covers approximately one-third of all such jobs in the US.

The numbers are specifically for nonfarm payroll jobs, and hence exclude those employed on farms (which is now small in the US – about 1.4% of workers based on figures from other surveys) and more importantly the self-employed (about 6% of the labor force).  Given the large sample size, and also recognizing that those in the sample include not only small firms but also large entities employing thousands of workers, statistical noise is limited.  However, even with such a large sample size, the BLS states that the 90% confidence interval on the month to month changes in employment is +/- 110,000.  At the more commonly accepted 95% confidence interval it would be wider.

Finally, the figures for the prior two months in each report are preliminary and subject to change as more complete data comes in.  The November report, for example, indicated the estimate of net new jobs in October had been revised up by 15,000, and for September by 67,000.  And the October report last month indicated that its earlier estimate for September had been revised up by 118,000.  That is, the initial estimate for September had been 194,000 net new jobs, but this was revised up a month later to 312,000 net new jobs, and then revised again in the estimates published this month to 379,000.  Such revisions are routine, and one should expect that the initial estimate for November of 210,000 net new jobs will likely be revised in the coming months as more complete data becomes available.  While the revisions can in principle be positive or negative, in an expanding job market (as now) they are likely to be positive.  

The figures in the chart are also seasonally adjusted.  This is done via standard algorithms that estimate the normal annual pattern of employment changes in any given month based on historical data.  Employment growth is normally higher in certain months of the year (such as June, following the end of the school year) and normally lower in other months (such as January).  Analysts will therefore usually focus on the seasonally adjusted figures to see whether certain trends are developing outside of the normal seasonal fluctuations.

This is indeed appropriate.  However, it is also worth recognizing that due to Covid, with the resulting lockdowns, opening-ups, quite prudent changes in consumer behavior due to the health risks from Covid-19 even with all the protective measures taken that can be taken, and the truly historic fiscal relief measures provided through the government budget to support households in the light of all these disruptions, seasonal patterns this year (and last) are likely to be not at all similar to what they have been historically.  It is therefore of interest also to look at the underlying employment estimates, before the seasonal adjustment algorithms are run, to see what those numbers might be saying.

The next section will look at this, along with other measures of the change in employment.

C.  Alternative Measures, and Long-Term Limits on What Employment Growth Could Be

As noted, the BLS makes available in its monthly Employment Situation report several measures of how employment is estimated to have changed in the month, in addition to the one discussed above.  These additional measures should not be seen as better measures (at least in normal circumstances) than the seasonally adjusted measure based on the findings from the huge CES survey of establishments.  Rather, it is best to see them as supplementary measures, or alternative measures, that together help us understand what may be going on in terms of employment. There is always uncertainty in any individual measure, as they are all estimates.  It is better to look at several, to see what the overall story might be.

The estimated change in employment in November (or, more precisely, the change in nonfarm payroll), based on figures from the CES survey of establishments, was 210,000 after seasonal adjustment.  But three alternative estimates for employment growth in November were far higher, as depicted in this chart:

In the CES estimate before the normal seasonal adjustment, the growth in net new jobs in November was 778,000.  This difference between the seasonally adjusted and non-seasonally adjusted figures is substantially greater than what one has normally seen for November.  Seasonal adjustment is complicated, but a simple average of the difference between the seasonally adjusted figures for November and the non-seasonally adjusted figures over the 20 years from 2000 to 2019, is 205,000.  But in November 2021 it was 568,000, suggesting something unusual.  If the November 2021 increase in the number of jobs was adjusted by 205,000 rather than the 568,000 estimated by the algorithms, then the “seasonally adjusted” change in the number of jobs would have been 573,000 (= 778,000 – 205,000).  This is exactly what the pre-release expectation was on Wall Street (as noted at the start of this post).  That it was exactly the same as the Wall Street forecast is just a coincidence, but the fact it was close at all might be significant.  It may be suggesting that the standard seasonal adjustment calculations, built from patterns historically seen for the month, might not have captured well the circumstances in this highly unusual year.

Quite separately, the BLS also has an employment measure from the monthly survey of households conducted by the US Census Bureau (with BLS input on what is asked), called the Current Population Survey (CPS).  This survey of a sample of 60,000 households is used by the BLS to determine how many are in the labor force (i.e. are working or are looking for work), whether they are employed (including self-employed and on farms), and thus the number unemployed (those in the labor force but not employed).  The BLS uses this to determine the unemployment rate, but to get to that they have to first estimate, based on this survey, how many are employed.

The November estimates based on the CPS of net new employment were 1,136,000 for the seasonally adjusted figure and 831,000 for the figure before seasonal adjustment.  Why the seasonal adjustment led to a reduction in the job growth estimate from the CPS while it led to an increase in the job growth estimate from the CES is not clear (seasonal adjustment is complicated), but in any case, both figures are relatively close to the 778,000 estimate from the CES estimate before seasonal adjustment.  And all three are all well above the 210,000 seasonally adjusted estimate from the CES that we normally focus on.  Together they suggest that the 210,000 estimate, while usually the most reliable one, might in this case be on the low side.

I have also included in the chart four figures for what I have termed the “long-term limits” on what monthly job growth might be for an economy at full employment.  I included them on the same chart so that one can easily recognize the relative scale.

For an economy at full employment (with unemployment at frictional levels), employment growth cannot exceed the growth of the adult population.  And indeed it will be less, as not all adults (defined by the BLS as all those in the population at age 18 and above) will be in the labor force – some will be retired, some will be students in college, some will have voluntarily left the labor force to raise children or provide care for others, and for other reasons.  Examining what these limits are for the US will provide a sense of what monthly employment growth might be, on average, in the coming years.

First, on population:  Population growth is relatively steady and predictable.  For the ten-year period from November 2011 to November 2021, it averaged 180,000 per month in the US.  It will be similar to this in the coming years, and it sets a (very) crude upper limit on what job growth could be in a steady state.  But one can see even from this figure that it will not be possible to sustain forever monthly net new job growth of even 200,000.  There will not be that many new adults available each month.

But 100% of the adult population are not in the labor force.  As noted, some will be retired, some students, and so on.  The labor force participation rate (LFPR) is the ratio of those who choose to be in the labor force (employed or looking for employment) to the adult population.  In the November CPS figures, that LFPR was 61.8%.  If one assumes that it will remain at that rate, then the monthly growth in the labor force will not be 180,000 (the growth in the adult population) but 61.8% of this, or 111,000.  And if one assumes that unemployment will be something steady, at say 4% at full employment, then potential employment growth would be even less, at 107,000.

The implication is that if the labor force participation rate remains where it is now, one should not be surprised to see monthly figures on job growth of no more than roughly 100,000.  This follows by simple arithmetic.  It could be higher for some period (but not forever) if the labor force participation rate rises from the current 61.8%.  This is possible, and perhaps even likely in the very near term, but probably not for long.  The LFPR in fact rose in the November BLS report to 61.8% from 61.6% in the prior month.  It normally changes only slowly over time.  The disruption that followed from Covid-19 led to relatively wide swings at first, with the LFPR falling from 63.4% in January 2020 to 60.2% in April 2020 with the lockdowns.  But by June 2020 it was back to 61.4% and since has fluctuated in a relatively narrow range before rising the 61.8% of November 2021.

What no one knows is what will happen to the LFPR now.  It might rise a bit more, but the long term trend has been downward.  It peaked in the year 2000, with a steady increase up until then following from a rising participation rate of women in the labor force.  But since 2000 the participation rate for women has moved down, paralleling (but about 20% below) the slow downward trend seen for men since the mid-1950s.  (The factors behind this are discussed in some detail in this earlier blog post.)  It is due to this downward trend over the period of 2011 to 2021 that actual labor force growth over this period was just 67,000 per month (as depicted in the chart above) even though adult population growth was 180,000 per month over this same period.

The current 61.8% LFPR is in fact close to what a simple extrapolation of the trend since 2000 suggests it would be in November 2021.  While the LFPR has behaved unusually since 2016 (when it flattened out for several years and indeed then rose a bit until the start of 2020, before collapsing and then partially recovering in the spring of 2020 due to the Covid-19 crisis), it is now back roughly to what one would find by a simple extrapolation of the trend since the year 2000.

There may well be surprises in what now happens to labor force participation.  After the disruptions of the Covid-19 crisis, it may never revert to where it was just before the crisis.  Those who retired early may mostly choose to stay retired.  And many of those in low-paying jobs, particularly in cases of one spouse in a couple with young children, may have discovered during the Covid-19 crisis that one spouse dropping out of the labor force is not all that costly, and in a two-earner household they may be able to manage financially.

There is therefore a substantial degree of uncertainty on what will now happen to the LFPR.  If it goes up, with a substantial number of adults re-entering the labor force, there will be a transition period when the labor force (and hence the number employed) could rise by significantly more than the 107,000 per month that one would see at a constant LFPR.  Monthly changes in employment during this transition period could be substantial.  For example (and again, this is simple arithmetic), if the LFPR were to increase from the current 61.8% by one percentage point to 62.8% (which would put it back to where it was in much of 2016 through 2018), then the number in the labor force would increase by 2.5 million over what would follow from regular population growth.  Possible employment growth would be about the same 2.5 million if unemployment stays where it is now.  Thus there could be a transition period of five months during which employment could potentially grow by 600,000 per month (a fifth of the extra 2.5 million in the labor force under this scenario, on top of about 100,000 per month from natural population growth).  Or the transition period could be shorter or longer depending on the number of new jobs each month.

But the point is that even if the LFPR should rise, the impact would be a transitory one, after which one should expect employment growth each month of no more than 100,000 or so.  And as noted before, the trend over the last 20 years has been that the LFPR has been moving downward, not upward.

D.  Conclusion

The November jobs report was interpreted by many as disappointing, as the estimated number of net new jobs (based on the estimate normally used – and rightly so) was 210,000.  This was seen as low, and the stock market fell.  However, the report was in fact a pretty strong one, and analysts may have recognized this once they started to look at it more closely.  While one never knows with any certainty why the stock market moves as it does (and there will always be other factors as well), the S&P 500, after falling by over 2% at one point on December 3, started to recover partially by the end of the day.  And it then rose strongly on the next two trading days.

There are reasons to believe the estimate of 210,000 net new jobs in November may have been low.  Seasonal adjustment factors mattered more than normally, and other measures of job growth were significantly higher.  But even at 210,000, analysts need to recognize that as the economy returns to more normal conditions, monthly job growth will likely be a good deal less than that.  While monthly job growth during Biden’s presidency from February to November has so far averaged over a half-million per month (588,000 per month to be more precise), this was only possible because the unemployment rate could come down.  But unemployment is now low – it reached 4.2% in November – and cannot go much lower.  If the labor force participation rate stays where it is now, possible employment growth will only be around 107,000 per month.  If the LFPR rises, then this could go up for some transition period, but that transition period is limited in time and when it is over employment growth will then have to revert to something close to 100,000.

What is more likely is that the LFPR will now return to the longer-term trend seen since it reached its peak in the year 2000, and will fall slowly over time.  Monthly employment growth would then be less, at something less than 100,000 per month (where how much less depends on the pace at which the LFPR falls).

Expectations have to be reset.  Other than during a transition period should the labor force participation rate rise above where it is now, monthly net new jobs growth of 100,000 per month or so is likely to be the limit of what one will see.  But that would be a good performance in an economy that remains at full employment.  Only if unemployment shoots up due to some future downturn could one then see – during a recovery from that downturn – something more.

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.

The Pattern of Unemployment: Fewer on Temporary Layoff, but More of the Rest

A.  Introduction

The economic downturn this year has been unprecedented in many ways.  Millions were laid off in March and April as the country desperately went into lockdowns to limit the spread of the virus that causes Covid-19, following the failure of the Trump administration to recognize the extent of the crisis.  But it was always known that those lockdowns would be temporary (albeit with differing views on how long they would be needed), and hence those laid off in March and April were generally put on temporary layoff.

The number on temporary layoff then started to decline in May, with this continuing (although at a diminishing rate) through November.  This has brought down the headline figure on total unemployment – the figure most people focus on – from 14.7% in April to 6.7% as of November.  But while that focus on the overall rate of unemployment is normally appropriate (as the number on temporary layoff has usually been steady and low, while the labor force has fluctuated little), the unusual conditions of the downturn this year have masked important aspects of the story.  Unemployment is a good deal worse than the traditional measures appear to suggest.

One key issue is what happened to those who were unemployed but not on temporary layoff.  The Bureau of Labor Statistics (the source of the data used here) defines those on temporary layoff to be those who are unemployed but who either have been given a date for when they will be able to return to their job, or expect to return to it within six months.  All other unemployed (defined by the BLS as being in the labor force but not employed, not on temporary layoff, and have taken concrete actions within the previous four weeks to look for a job), include those who were permanently laid off, who completed some temporary job, who left a job by choice (quit), or have newly entered (or re-entered) the labor force actively seeking a job but do not yet have a job.

That distinction – treating separately the unemployed on temporary layoff and the rest – will be examined in this post.  Also important to the story is how many are counted in the official statistics to be in the labor force at all, as that has also changed in this unprecedented downturn.  That will be examined as well.

B.  The Unemployed on Temporary Layoff Spiked Up and Then Came Back Down, but Other Unemployed Rose Steadily

The chart at the top of this post shows the unemployment rates (as a percent of the labor force) for all who were unemployed (in black), for those on temporary layoff (in blue), and for all others who were unemployed (in red).  Unemployment surged, at an unprecedented rate, in March and April of this year.  The increase in those on temporary layoff accounted for this – indeed for all of this in those months in the estimated figures.  The total increase in unemployment in March and April compared to February was 17.25 million; the increase in those on temporary layoff was almost exactly the same at 17.26 million.  (But keep in mind that these figures are estimates based on household surveys, and thus that there will be statistical noise.  That the numbers were almost exactly the same was certainly in part a coincidence.  Still, they were definitely close.)

The total unemployment rate then came down sharply from its April peak of 14.7% to 6.7% as of November.  It was led, once again. by changes in those on temporary layoff, but this time the number unemployed for reasons other than temporary layoff rose.  Their rate was 3.0% in February, which then rose to 5.0% by September.  It has kept at roughly this rate since (although so far with data for only two more months).

That increase – of 2.0% points – is significant but modest.  With all the disruption this year, one might have expected to see more.  Certainly important and effective in partially alleviating the crisis was the $3.1 trillion in several packages approved by Congress in March and April (of new government spending, tax cuts, and new loan facilities).  While adding to the public debt, such spending is needed when confronted with a crisis such as this.  The time to reduce the fiscal deficit would have been when the economy was at full employment.  But Trump added to the fiscal deficit in those years (with both higher spending and massive tax cuts) instead of using that opportunity to prepare for when a crisis would necessitate higher spending.

C.  But the Number in the Labor Force Also Fell, Which Had a Significant Impact on the Reported Unemployment Rates

There is, however, another factor important to the understanding of why the unemployment rate (for those other than on temporary layoff) rose only by this modest amount.  And that is that the number in the labor force abruptly changed.  This was another unusual development in this unprecedented crisis.

The labor force (formally the civilian labor force, as those on active military duty are excluded) changes only slowly.  It is driven primarily by demographic factors, coupled with long-term decisions such as when to retire, whether to attend college rather than seek a job, whether both spouses in a married couple will seek to work or whether one (usually in this society the wife) will choose to remain at home with the children, and so on.

But it was different in this crisis:

The number in the labor force fell abruptly in March and April – by 8.1 million compared to February, or 4.9% of the labor force.  There has never before been such an abrupt fall, at least since 1948 when such data first began to be collected.  The largest previous two-month fall was just 1.0 million, in 1953 when this was 1.6% of the labor force.  (And the month to month “squiggles” seen in the chart above should not be taken too seriously.  They likely reflect statistical noise in the household surveys.)

Those who drop out of the labor force are not counted as unemployed, as formally defined by the BLS, as they are not actively seeking a job.  And the sharp collapse in available jobs in March and April probably contributed to some dropping out of the labor force, as that scarcity of jobs would, by itself, induce some not even to try to find a job if they lost one.  But probably more important in this unprecedented crisis is a parent (and usually the wife) dropping out of the labor force in order to take care of their children when the schools and/or daycare centers closed.  This has never happened before.

Since April, the number in the labor force has recovered some but only partially.  Compared to what the labor force likely would have been by November 2020, based on a simple extrapolation of the January 2015 to January 2020 trend (growth at an annual rate of 0.95%), the labor force in November was 5.4 million less than what it otherwise would have been.

This will have a significant impact on the unemployment figures.  Since the number unemployed are, by definition, equal to the difference between the number in the labor force less the number employed, the number unemployed will be substantially higher if one counts those who abruptly dropped out of the labor force to take care of their children.  These, including others who dropped out of the labor force but would prefer to be employed if labor market conditions were more hospitable, should be counted when assessing how much slack there may be in the economy.  And they can be considered as part of those who are unemployed for reasons other than temporary layoff (as they are similar in nature to those who had, or in this case would have, re-entered the labor force but do not have a job).

Counting such individuals as among those who are in fact unemployed, the labor market does not look to be nearly as strong as the headline figures would suggest.  Assuming that the labor force in 2020 would have continued to grow at the trend rate of the previous several years, that the number employed would have been the same as was recorded, and that the number on temporary layoff would have also been as recorded, the chart on unemployment rates then becomes:

Superficially, this chart may appear similar to that at the top of this post.  But there are two important differences.  First, note the scale is different.  Instead of peaking in April at an overall unemployment rate of 14.7%, the unemployment rate would instead have reached over 19%.  Furthermore, it would still be at 9.7% as of November, which is high.  It is not far from the peak 10.0% rate reached in 2009 following the 2008 economic collapse.

Second, both the path and the levels of the unemployment rate for those other than on temporary layoff are now quite different.  That rate jumps abruptly in March and April to 8.2% of the labor force, from 3.1% before, and then remains at around 7 1/2 to 8% since then.  This a much more worrisome level than was seen above when no correction was made for what has happened to the labor force this year.  There is also no downward trend.  All the gains in the reduction of overall employment since April would have been due to the reduction in those on temporary layoff.

D.  Conclusion

The economy remains weak.  And president-elect Joe Biden is certainly correct that a necessary (although not sufficient) condition for the economy to recover fully will be that Covid-19 be addressed.  Australia, New Zealand, and the countries of East Asia have shown that this can be done, and how it could have been done.  Simply wearing masks would have been central.  Dr. Robert Redfield, the head of the CDC, has noted that wearing a mask could very well be more effective in stopping the spread of the virus that causes Covid-19 than some of the vaccines now under development, if everyone wore them.  But Trump has been unwilling to call on all Americans, including in particular his supporters, to wear a mask.  Indeed, he has even repeatedly mocked those who choose to wear a mask.

As a longer-term solution, however, vaccinations will be key.  But this also depends on most Americans (probably a minimum of 70 to 80%, but at this point still uncertain) being vaccinated.  Even under the most optimistic of circumstances, constraints on vaccine availability alone means this will not be possible before the summer.  But this also assumes that, once available, 70 to 80% of the population (or whatever the minimum share required will be) will choose to be vaccinated.  Given how the simple wearing of face masks was politicized by Trump (and turned into a signal of whether one supports him or not), plus controversies among some on both the left and the right on vaccinations that pre-dates Trump’s presidency, it is hard to be optimistic that such a vaccination share will soon be reached.

Hopefully a sufficiently large share of the population will at some point have chosen to be vaccinated to end the spread of the virus.  But until that happens, further support to the economy, and not least relief to those most affected by the crisis, needs to be passed by Congress and signed by the president.  The House passed such a measure already last May, but Mitch McConnell, the Republican Majority Leader in the Senate, has so far blocked consideration of anything similar.  As I write this, there appears to be a possibility of some compromise being considered in the Senate, but it remains to be seen if that will happen (and if Trump then will sign it).

It is certainly desperately needed.