An Update on the Different Employment Estimates from the Survey of Establishments and the Survey of Households, And the Resulting Job Growth Under Trump vs. Obama

A.  Revisions in the Jobs Numbers

The pace of job growth in 2019 was slower than had originally been estimated.  While such revisions to the initial job growth estimates are not unusual (there is a regular annual process that adjusts them based on more complete data), the result for 2019 was that they now estimate there were 0.5 million fewer net new jobs than had been thought before.  Along with other revisions in the estimates going further back, the result is that the pace of job growth under Trump has slowed down by even more than had been thought earlier.  While this is not surprising (unemployment is low), it does point up even more strongly that Trump is simply wrong in his assertions that the pace of job growth during his term in office is “historic”, “unthinkable” (by anyone other than himself), and far faster than before.  See, for example, Trump’s remarks in January 2020 at the Davos meetings.  It was not true before the revisions – it is even less true now.

There were earlier indications that the jobs figures would be revised downwards.  A post on this blog in May 2019 discussed an inconsistency pointing to this that had developed in two estimates of employment growth in the US.  Both come from the Bureau of Labor Statistics (BLS), with one based on the Bureau’s monthly survey of households (the CPS, for Current Population Survey) and the other based on its monthly survey of business establishments (the CES, for Current Establishment Statistics survey).  Figures from these two surveys are released each month in the BLS Employment Situation report, which provides updated estimates on the unemployment rate, net job growth, and other such closely watched numbers.

Both the CPS and the CES provide estimates on employment growth, but they arrive at those estimates from two different sources.  And while there are some small differences in how “employment” is defined in the two (as discussed in that earlier blog post, where the impact of those differing definitions was examined), the two series over time will move together.  However, in the two years leading up to April 2019 the two series drifted significantly apart.  The CPS survey (of households) indicated a slower pace of net job creation than the CES survey (of establishments) did.

With the release of the January 2020 estimates on February 7, we now have updated figures.  And they indicate that job growth has indeed been slower than what the earlier CES figures had indicated.  The chart at the top of this post shows the differences, where all the figures are defined in terms of the change in jobs relative to their level in April 2017.  The curves (with the circles or squares) ending in April 2019 reproduce the chart from the earlier post (with the labor force figures removed, for less clutter), with the estimates on jobs as known at that point.  The curves (with no circles or squares) that end in January 2020 then show the more recent, updated, estimates.

The curves in blue, of the changes in employment as estimated from the CPS survey of households, show some revisions, but generally small and with no strong trend.  While there is a much greater degree of month to month volatility in the figures from the household survey, the revised figures basically follow what had been estimated before.  As was discussed in the earlier blog post, the CPS survey of households uses an effectively far smaller sample size for its employment estimates than the CES survey of business establishments has.  The CPS surveys a sample of 60,000 households each month, and a household will normally have only one or two members employed.  The CES survey, in contrast, surveys 145,000 businesses, covering almost 700,000 different worksites, and each worksite can have dozens if not hundreds of employees.

The employment estimates from the CES survey, shown in the curves in black on the chart, therefore show far less month to month fluctuation, due to the lesser degree of statistical noise.  But the new versus old estimates began to drift apart from each other around June 2018, with the discrepancy then continuing to widen steadily over time.  And the new estimates of employment based on the CES survey (the curve in black) now follows much more closely to the trend in the estimates of employment from the CPS survey (the curve in blue).  They came especially close to each other in the figures for October 2019, but have drifted apart by some since then (although not nearly as apart as what we saw in April 2019).

The changes are significant.  For April 2019, for example, the earlier estimates from the CES were that there were 151.1 million employed in the US (employed as defined in the CES).  The new estimate is that there were only 150.5 million employed in that month, a difference of about 600,000.  When looking at job growth, i.e. changes in the number employed over time, that difference is significant.

B.  Job Growth Under Trump Compared to Under Obama 

The updated estimates provide a clearer picture of how the job market has progressed in recent years.  But it is not as Trump often boasts.

With the publication of the January 2020 estimates, we now have figures on job growth for exactly three years into Trump’s presidential term.  These figures can be compared to the growth seen in the final three years of Obama’s presidency:

This presentation of the CES monthly employment growth figures is not original with me.  A number of news sources have presented something similar (although I have constructed the chart here from the original source BLS numbers).  But it makes the point well.

As one can see, there is a substantial degree of month to month volatility, even in these CES figures.  They are estimates of the month to month changes in total employment, and during Trumps’s presidential term thus far have varied from a high of over 400,000 in one month (February 2018) to a low of zero in another (February 2019).  But the average over the 36 months of Trump’s term in office thus far has been a monthly growth of 182,200.

This is well below the pace of employment growth during Obama’s last 36 months in office.  The average then was 224,400 net new jobs per month.  Trump’s repeated assertions that job creation is now faster is simply not true.

Nor was it true even with the earlier job growth estimates.  It is just even less true now:

Net Employment Growth

As Earlier Estimated

As Revised

Last 36 Months of Obama

Total

8,128,000

8,079,000

Per Month

225,800

224,400

First 36 Months of Trump

Total

6,913,000

6,559,000

Per Month

192,000

182,200

Difference in Job Growth

Total

1,215,000

1,520,000

Per Month

33,800

42,200

Under the earlier estimates, job growth had been an average of 225,800 per month over the last 36 months of Obama’s presidency, and 192,000 per month over the first 36 months of Trump’s term.  The difference was 33,800 more jobs per month under Obama compared to the period under Trump.  The difference as estimated now is 42,200 more.

And while these differences in the monthly averages may not appear to be much, over time they accumulate to a quite substantial difference.  The total growth in employment over the last 36 months of Obama’s presidency was 8,079,000.  Over Trump’s first 36 months it was slower, at a total of 6,559,000.  The difference is a not insubstantial 1.5 million jobs.  And it is higher than the 1.2 million job difference in the earlier estimates.

So Trump’s claims are simply not true.  That is important.  Trump is once again making assertions without bothering with whether or not they follow the facts.  But having said that, I would also note that this slowdown in the pace of job growth should not be at all surprising.  The unemployment rate has been low, it cannot go much if any lower, and hence an increase in the number employed can only come either from regular population growth or from an increase in the share of that population choosing to participate in the labor force.  The adult population grew by 150,600 per month during Trump’s 36 months in office, and the labor force by 137,800 per month.  This accounted for most of the 182,200 net new employment over the period.  The rest was from the reduction in the unemployment rate, from an already low rate of 4.7% when Trump took office, to the 3.6% now.  But the unemployment rate cannot go much lower.  Hence one should not be surprised that employment growth has slowed.

Still, it should not be a big request to expect honesty from a president.

The “Threat” of Job Losses is Nothing New and Not to be Feared: Issues Raised in the Democratic Debate

A.  Introduction

The televised debate held October 15 between twelve candidates for the Democratic presidential nomination covered a large number of issues.  Some were clear, but many were not.  The debate format does not allow for much explanation or nuance.  And while some of the positions taken refected sound economics, others did not.

In a series of upcoming blog posts, starting with this one, I will review several of the issues raised, focussing on the economics and sometimes the simple arithmetic (which the candidates often got wrong).  And while the debate covered a broad range of issues, I will limit my attention here to the economic ones.

This post will look at the concern that was raised (initially in a question from one of the moderators) that the US will soon be facing a massive loss of jobs due to automation.  A figure of “a quarter of American jobs” was cited.  All the candidates basically agreed, and offered various solutions.  But there is a good deal of confusion over the issue, starting with the question of whether such job “losses” are unprecedented (they are not) and then in some of the solutions proposed.

A transcript of the debate can be found at the Washington Post website, which one can refer to for the precise wording of the questions and responses.  Unfortunately it does not provide pages or line numbers to refer to, but most of the economic issues were discussed in the first hour of the three hour debate.  Alternatively, one can watch the debate at the CNN.com website.  The discussion on job losses starts at the 32:30 minute mark of the first of the four videos CNN posted at its site.

B.  Job Losses and Productivity Growth

A topic on which there was apparently broad agreement across the candidates was that an unprecedented number of jobs will be “lost” in the US in the coming years due to automation, and that this is a horrifying prospect that needs to be addressed with urgency.  Erin Burnett, one of the moderators, introduced it, citing a study that she said concluded that “about a quarter of American jobs could be lost to automation in just the next 10 years”.  While the name of the study was not explicitly cited, it appears to be one issued by the Brookings Institution in January 2019, with Mark Muro as the principal author.  It received a good deal of attention when it came out, with the focus on its purported conclusion that there would be a loss of a quarter of US jobs by 2030 (see here, here, here, here, and/or here, for examples).

[Actually, the Brookings study did not say that.  Nor was its focus on the overall impact on the number of jobs due to automation.  Rather, its purpose was to look at how automation may differentially affect different geographic zones across the US (states and metropolitan areas), as well as different occupations, as jobs vary in their degree of exposure to possible automation.  Some jobs can be highly automated with technologies that already exist today, while others cannot.  And as the Brookings authors explain, they are applying geographically a methodology that had in fact been developed earlier by the McKinsey Global Institute, presented in reports issued in January 2017 and in December 2017.  The December 2017 report is most directly relevant, and found that 23% of “jobs” in the US (measured in terms of hours of work) may be automated by 2030 using technologies that have already been demonstrated as technically possible (although not necessarily financially worthwhile as yet).  And this would have been the total over a 14 year period starting from their base year of 2016.  This was for their “midpoint scenario”, and McKinsey properly stresses that there is a very high degree of uncertainty surrounding it.]

The candidates offered various answers on how to address this perceived crisis (which I will address below), but it is worth looking first at whether this is indeed a pending crisis.

The answer is no.  While the study cited said that perhaps a quarter of jobs could be “lost to automation” by 2030 (starting from their base year of 2016), such a pace of job loss is in fact not out of line with the norm.  It is not that much different from what has been happening in the US economy for the last 150 years, or longer.

Job losses “due to automation” is just another way of saying productivity has grown.  Fewer workers are needed to produce some given level of output, or equivalently, more output can be produced for a given number of workers.  As a simple example, suppose some factory produces 100 units of some product, and to start has 100 employees.  Output per employee is then 100/100, or a ratio of 1.0.  Suppose then that over a 14 year period, the number of workers needed (following automation of some of the tasks) reduces the number of employees to just 75 to produce that 100 units of output (where that figure of 75 workers includes those who will now be maintaining and operating the new machines, as well as those workers in the economy as a whole who made the machines, with those scaled to account for the lifetime of the machines).  The productivity of the workers would then have grown to 100/75, or a ratio of 1.333.  Over a 14 year period, that implies growth in productivity of 2.1% a year.  More accurately, the McKinsey estimate was that 23% of jobs might be automated, and with this the increase in productivity would be to 100/77 = 1.30.  The growth rate over 14 years would then be 1.9% per annum.

Such an increase in productivity is not outside the norm for the US.  Indeed, it matches what the US has experienced over at least the last century and a half.  The chart at the top of this post shows how GDP per capita has grown since 1870.  The chart is plotted in logarithms, and those of you who remember their high school math will recall that a straight line in such a graph depicts a constant rate of growth.  An earlier version of this chart was originally prepared for a prior post on this blog (where one can find further discussion of its implications), and it has been updated here to reflect GDP growth in recent years (using BEA data, with the earlier data taken from the Maddison Project).

What is remarkable is how steady that rate of growth in GDP per capita has been since 1870.  One straight line fits it extraordinarily well for the entire period, with a growth rate of 1.9% a year (or 1.86% to be more precise).  And while the US is now falling below that long-term trend (since around 2008, from the onset of the economic collapse in the last year of the Bush administration), the deviation of recent years is not that much different from an earlier such deviation between the late 1940s to the mid-1960s.  It remains to be seen whether there will be a similar catch-up to the long-term trend in the coming years.

One might reasonably argue that GDP per capita is not quite productivity, which would be GDP per employee.  Over very long periods of time population and the number of workers in that population will tend to grow at a similar pace, but we could also look at GDP per employee:

This chart is based on BEA data, the agency which issues the official GDP accounts for the US, for both real GDP and the number of employees (in full time equivalent terms, so part-time workers are counted in proportion to the number of hours they work).  The figures unfortunately only go back to 1929, the oldest year for which the BEA has issued estimates.  Note also that the rise in GDP during World War II looks relatively modest here, but that is because measures of “real” GDP (when carefully estimated using standard procedures) can deviate more and more as one goes back in time from the base year for prices (2012 here), coupled with major changes in the structure of production (such as during a major war).  But the BEA figures are the best available.

Once again one finds that the pace of productivity growth was remarkably stable over the period, with a growth rate here of 1.74% a year.  It was lower during the Great Depression years, but then recovered during World War II, and was then above the 1929 to 2018 trend from the early 1950s to 1980.  And the same straight line (meaning a constant growth rate) then fit extremely well from 1980 to 2010.

Since 2010 the growth in labor productivity has been more modest, averaging just 0.5% a year from 2010 to 2018.  An important question going forward is whether the path will return to the previous trend.  If it does, the implication is that there will be more job turnover for at least a temporary period.  If it does not, and productivity growth does not return to the path it has been on since 1929, the US as a whole will not be able to enjoy the growth in overall living standards the economy had made possible before.

The McKinsey numbers for what productivity growth might be going forward, of possibly 1.9% a year, are therefore not out of line with what the economy has actually experienced over the years.  It matches the pace as measured by GDP per capita, and while the 1.74% a year found for the last almost 90 years for the measure based on GDP per employee is a bit less, they are close.  And keep in mind that the McKinsey estimate (of 1.9% growth in productivity over 14 years) is of what might be possible, with a broad range of uncertainty over what will actually happen.

The estimate that “about” a quarter of jobs may be displaced by 2030 is therefore not out of line with what the US has experienced for perhaps a century and a half.  Such disruption is certainly still significant, and should be met with measures to assist workers to transition from jobs that have been automated away to the jobs then in need of more workers.  We have not, as a country, managed this very well in the past.  But the challenge is not new.

What will those new jobs be?  While there are needs that are clear to anyone now (as Bernie Sanders noted, which I will discuss below), most of the new jobs will likely be in fields that do not even exist right now.  A careful study by Daron Acemoglu (of MIT) and Pascual Restrepo (of Boston University), published in the American Economic Review in 2018, found that about 60% of the growth in net new jobs in the US between 1980 and 2015 (an increase of 52 million, from 90 million in 1980 to 142 million in 2015) were in occupations where the specific title of the job (as defined in surveys carried out by the Census Bureau) did not even exist in 1980.  And there was a similar share of those with new job titles over the shorter periods of 1990 to 2015 or 2000 to 2015.  There is no reason not to expect this to continue going forward.  Most new jobs are likely to be in positions that are not even defined at this point.

C.  What Would the Candidates Do?

I will not comment on all the answers provided by the candidates (some of which were indecipherable), but just a few.

Bernie Sanders provided perhaps the best response by saying there is much that needs to be done, requiring millions of workers, and if government were to proceed with the programs needed, there would be plenty of jobs.  He cited specifically the need to rebuild our infrastructure (which he rightly noted is collapsing, and where I would add is an embarrassment to anyone who has seen the infrastructure in other developed economies).  He said 15 million workers would be required for that.  He also cited the Green New Deal (requiring 20 million workers), as well as needs for childcare, for education, for medicine, and in other areas.

There certainly are such needs.  Whether we can organize and pay for such programs is of course critical and would need to be addressed.  But if they can be, there will certainly be millions of workers required.

Sanders was also asked by the moderator specifically about his federal jobs guarantee proposal (and indeed the jobs topic was introduced this way).  But such a policy proposal is more problematic, and separate from the issue of whether the economy will need so many workers.  It is not clear how such a jobs guarantee, provided by the federal government, would work.  The Sanders campaign website provides almost no detail.  But a number of questions need to be addressed.  To start, would such a program be viewed as a temporary backstop for a worker, to be used when he or she cannot find another reasonable job at a wage they would accept, or something permanent?  If permanent, one is really talking more of an expanded public sector, and that does not seem to be the intention of a jobs guarantee program.  But if a backstop, how would the wage be set?  If too high, no workers would want to leave and take a different job, and the program would not be a backstop.  And would all workers in such a program be paid the same, or different based on their skills?  Presumably one would pay an engineer working on the design of infrastructure projects more than someone with just a high school degree.  But how would these be determined?  Also, with a job guarantee, can someone be fired?  Suppose they often do not show up for work?

So there are a number of issues to address, and the answers are not clear.  But more fundamentally, if there is not a shortage of jobs but rather of workers (keep in mind that the unemployment rate is now at a 50 year low), why does one need such a guarantee?  It might be warranted (on a temporary basis) during an economic downturn, when unemployment is high, but why now, when unemployment is low?  [October 28 update:  The initial version of this post had an additional statement here saying that the federal government already had “something close to a job guarantee”, as you could always join the Army.  However, as a reader pointed out, while that once may have been true, it no longer is.  So that sentence has been deleted.]

Andrew Yang responded next, arguing for his proposal of a universal basic income that would provide every adult in the country with a grant of $1,000 per month, no questions asked.  There are many issues with such a proposal, which I will address in a subsequent blog post, but would note here that his basic argument for such a universal grant follows from his assertion that jobs will be scarce due to automation.  He repeatedly asserted in the debate that we have now entered into what has been referred to as the “Fourth Industrial Revolution”, where automation will take over most jobs and millions will be forced out of work.

But as noted above, what we have seen in the US over the last 150 years (at least) is not that much different from what is now forecast for the next few decades.  Automation will reduce the number of workers needed to produce some given amount, and productivity per worker will rise.  And while this will be disruptive and lead to a good deal of job displacement (important issues that certainly need to be addressed), the pace of this in the coming decades is not anticipated to be much different from what the country has seen over the last 150 years.

A universal basic income is fundamentally a program of redistribution, and given the high and growing degree of inequality in the US, a program of redistribution might well be warranted.  I will discuss this is a separate blog post.  But such a program is not needed to provide income to workers who will be losing jobs to automation, as there will be jobs if we follow the right macro policies.  And $12,000 a year would not nearly compensate for a lost job anyway.

Elizabeth Warren’s response to the jobs question was different.  She argued that jobs have been lost not due to automation, but due to poor international trade policies.  She said:  “the data show that we have had a lot of problems with losing jobs, but the principal reason has been bad trade policy.”

Actually, this is simply not true, and the data do not support it.  There have been careful studies of the issue, but it is easy enough to see in the numbers.  For example, in an earlier post on this blog from 2016, I examined what the impact would have been on the motor vehicle sector if the US had moved to zero net imports in the sector (i.e. limiting car imports to what the US exports, which is not very much).  Employment in the sector would then have been flat, rather than decline by 17%, between the years 1967 and 2014.  But this impact would have been dwarfed by the impact of productivity gains.  The output of the motor vehicle (in real terms) was 4.5 times higher in 2014 than what it was in 1967.  If productivity had not grown, they would then have required 4.5 times as many workers.  But productivity did grow – by 5.4 times.  Hence the number of workers needed to produce the higher output actually went down by the 17% observed.  Banning imports would have had almost no effect relative to this.

D.  Summary and Conclusion

Automation is important, but is nothing new.  The Luddites destroyed factory machinery in the early 1800s in England due to a belief that the machines were taking away their jobs and that they would then be left with no prospects.  And data for the US that goes back to at least 1870 shows such job “destroying” processes have long been underway.  They have not accelerated now.  Indeed, over the past decade the pace has slowed (i.e. less job “destruction”).  But it is too soon to tell whether this deceleration is similar to fluctuations seen in the past, where there were occasional deviations but then always a return to the long-term path.

Looking forward, careful studies such as those carried out by McKinsey have estimated how many jobs may be exposed to automation (using technologies that we know already to be technically feasible).  While they emphasize that any such forecasts are subject to a great deal of uncertainty, McKinsey’s midpoint scenario estimates that perhaps 23% of jobs may be substituted away by automation between 2016 and 2030.  If so, such a pace (of 1.9% a year) would be similar to what productivity growth has been historically in the US.  There is nothing new here.

But while nothing new, that does not mean it should be ignored.  It will lead, just as it has in the past, to job displacement and disruption.  There is plenty of scope for government to assist workers in finding appropriate new jobs, and in obtaining training for them, but the US has historically never done this all that well.  Countries such as Germany have been far better at addressing such needs.

The candidate responses did not, however, address this (other than Andrew Yang saying government supported training programs in the US have not been effective).  While Bernie Sanders correctly noted there is no shortage of needs for which workers will be required, he has also proposed a jobs guarantee to be provided by the federal government.  Such a guarantee would be more problematic, with many questions not yet answered.  But it is also not clear why it would be needed in current circumstances anyway (with an economy at full employment).

Andrew Yang argued the opposite:  That the economy is facing a structural problem that will lead to mass unemployment due to automation, with a Fourth Industrial Revolution now underway that is unprecedented in US history.  But the figures show this not to be the case, with forecast prospects similar to what the US has faced in the past.  Thus the basis for his argument that we now need to do something fundamentally different (a universal basic income of $1,000 a month for every adult) falls away.  And I will address the $1,000 a month itself in a separate blog post.

Finally, Elizabeth Warren asserted that the problem stems primarily from poor international trade policy.  If we just had better trade policy, she said, there would be no jobs problem.  But this is also not borne out by the data.  Increased imports, even in the motor vehicle sector (which has long been viewed as one of the most exposed sectors to international trade), explains only a small fraction of why there are fewer workers needed in that sector now than was the case 50 years ago.  By far the more important reason is that workers in the sector are now far more productive.

The Survey of Establishments Say Employment is Rising, But the Survey of Households Say It Is Falling – Why?

A.  Introduction

Those who follow the monthly release of the Employment Situation report of the Bureau of Labor Statistics (with the most recent issue, for April, released on May 3) may have noticed something curious.  While the figures on total employment derived from the BLS survey of establishments reported strong growth, of an estimated 263,000 in April, the BLS survey of households (from which the rate of unemployment is estimated) reported that estimated employment fell by 103,000.  And while there is month-to-month volatility in the figures (they are survey estimates, after all), this has now been happening for several months in a row:  The establishment survey has been reporting strong growth in employment while the household survey has been reporting a fall.  The one exception was for February, where the current estimate from the establishment survey is that employment grew that month by a relatively modest 56,000 (higher than the initial estimate), while the household survey reported strong growth in employment that month of 255,000.

The chart above shows this graphically, with the figures presented in terms of their change relative to where they were in April 2017, two years ago.  For reasons we will discuss below, there is substantially greater volatility in the employment estimates derived from the household survey than one finds in the employment estimates derived from the establishment survey.  But even accounting for this, a significant gap appears to have opened up between the estimated growth in employment derived from the two sources.  Note also that the estimated labor force (derived from the household survey) has also been going down recently.  The unemployment rate came down to just 3.6% in the most recent month not because estimated employment rose – it in fact fell by 103,000 workers.  Rather, the measured unemployment rate came down because the labor force fell by even more (by 490,000 workers).

There are a number of reasons why the estimates from the two surveys differ, and this blog post will discuss what these are.  To start, and as the BLS tries to make clear, the concept of “employment” as estimated in the establishment survey is different from that as measured in the household survey.  They are measuring different, albeit close, things.  But there are other factors as well.

One can, however, work out estimates where the employment concepts are defined almost, but not quite, the same.  What is needed can be found in figures provided as part of the household survey.  We will look at those below and present the results in a chart similar to that above, but with employment figures from the household survey data adjusted (to the extent possible) to match the employment concept of the establishment survey.  But one finds that the gap that has opened up between the employment estimates of the two surveys remains, similar to that in the chart above.

There are residual differences in the two employment estimates.  And they follow a systematic pattern that appear to be correlated with the unemployment rate.  The final section below will look at this, and discuss what might be the cause.

The issues here are fairly technical ones, and this blog post may be of most interest to those interested in digging into the numbers and seeing what lies behind the headline figures that are the normal focus of news reports.  And while a consistent discrepancy appears to have opened up between the two estimates of employment growth, the underlying cause is not clear.  Nor are the implications for policy yet fully clear.  But the numbers may imply that we should be paying more attention to the much slower growth in the estimates of total employment derived from the household survey, than the figures from the establishment survey that we normally focus on.  We will find in coming months whether the inconsistency that has developed signals a change in the employment picture, or simply reflects unusual volatility in the underlying data.

B.  The BLS Surveys of Establishments, and of Households

The monthly BLS employment report is based on findings from two monthly surveys the BLS conducts, one of establishments and a second of households.  As described by the BLS in the Techincal Note that is released as part of each month’s report (and which we will draw upon here), they need both.  And while the surveys cover a good deal of material other than employment and related issues, we will focus here just on the elements relevant to the employment estimates.

The establishment survey covers primarily business establishments, but also includes government agencies, non-profits, and most other entities that employ workers for a wage.  However, the establishment survey does not include those employed in agriculture (for some reason, possibly some historical bureaucratic issue between agencies), as well as certain employment that can not be covered by a survey of establishments.  Thus they do not cover the self-employed (if they work in an unincorporated business), nor unpaid family workers.  Nor do they cover those employed directly by households (e.g. for childcare).

But for the business establishments, government agencies, and other entities that they do cover, they are thorough.  They survey more than 142,000 establishments each month, covering 689,000 individual worksites, and in all cover in this “sample” approximately one-third of all nonfarm employees.  This means they obtain direct figures each month on the employment of about 50 million workers (out of the approximately 150 million employed in the US), with this closer to a census than a normal sample survey.  But the extensive coverage is necessary in order to be able to arrive at statistically valid sample sizes at the detailed individual industries for which they provide figures.  And because of this giant sample size, the monthly employment figures cited publicly are normally taken from the establishment survey.

To arrive at unemployment rates and other figures, one must however survey households.  Businesses will know who they employ, but not who is unemployed.  And while the current sample size used of households is 60,000, this is far smaller relative to the sample size used for establishments (142,000) than it might appear.  A household will in general have just one or two workers, while a business establishment (or a government agency) could employ thousands.

Thus the much greater volatility seen in the employment estimates from the household survey should not be a surprise.  But they need the household survey to determine who is in the labor force.  They define this to be those adults of age 16 or older, who are either employed (even for just one hour, if paid) in the preceding week, or who, if not employed, were available for a job and were actively searching for one at some point in the four week period before the week of the survey.  Only in this way can the BLS determine the share of the labor force that is employed, and the share unemployed.  The survey of establishments by its nature cannot provide such information no matter what its sample size.

For this and other reasons, the definition of what is covered in “employment” between these two surveys will differ.  In particular:

a)  As discussed above, the establishment survey does not cover employment in the agricultural sector.  While they could, in principle, include agriculture, for some reason they do not.  The household survey does include those in agriculture.

b)  The establishment survey also does not include the self-employed (unless they are running an incorporated business).  They only survey businesses (or government agencies and non-profits), and hence cannot capture those who are self-employed.

c)  The establishment survey also does not capture unpaid family workers.  The household survey counts them as part of the labor force and employed if they worked in the family business 15 hours or more in the week preceding the survey.

d)  The establishment survey, since it does not cover households, cannot include private household workers (such as those providing childcare services).  The household survey does.

e)  Each of the above will lead to the count in the household survey of those employed being higher than what is counted in the establishment survey.  Working in the opposite direction, someone holding two or more jobs will be counted in the establishment survey two or more times (once for each job they hold).  The establishment being surveyed will only know who is working for them, and not whether they are also working elsewhere.  The household survey, however, will count such a worker as just one employed person.

f) The household survey also counts as employed those who are on unpaid leave (such as maternity leave).  The establishment survey does not (although it is not clear to me why they couldn’t – it would improve comparability if they would).

g)  The household survey also only includes those aged 16 or older as possibly in the labor force and employed.  The establishment survey covers all its workers, whatever their age.

There are therefore important differences between the two surveys as to who is covered in the figures provided for “total employment”.  And while the BLS tries to make this clear, the differences are often ignored in references by, for example, the news media.  One can, however, adjust for most, but not all, of these differences.  The data required are provided in the BLS monthly report (for recent months), or online (for the complete series).  But how to do so is not made obvious, as the data series required are scattered across several different tables in the report.

I will discuss in more detail in the next section below what I did to adjust the household survey figures to the employment concept as used in the establishment survey.  Adjustments could be made for each of the categories (a) through (e) in the list above, but was not possible for (f) and (g).  However, the latter are relatively small, with the residual difference following an interesting pattern that we will examine.

When those adjustments are made, the number of employed as estimated from the household survey, but reflecting (almost) the concept as estimated in the establishment survey, looks as follows:

 

While there are some differences between the estimates here and those in the chart at the top of this post of employment made using the household survey (as adjusted), the basic pattern remains.  While employment as estimated from the household survey (and excluding those in agriculture, the self-employed, unpaid family workers, household employees, and adjusted for multiple jobholders) is now growing, it was growing over the last half year at a much slower pace than what the establishment survey suggests.

C.  Adjustments Made to the Employment Estimates So They Will Reflect Similar Concepts

As noted above, adjustments were made to the employment figures to bring the two concepts of the different surveys into line with each other, to the extent possible.  While in principle one could have adjusted either, I chose to adjust the employment concept of the household survey to reflect the more narrow employment concept of the establishment survey.  This was because the underlying data needed to make the adjustments all came from the household survey, and it was better to keep the figures for the adjustments to be made all from the same source.

Adjustments could be made to reflect each of the issues listed above in (a) through (e), but not for (f) or (g).  But there were still some issues among the (a) through (e) adjustments.  Specifically:

1)  I sought to work out the series going back to January 1980, in order to capture several business cycles, but not all of the data required went back that far.  Specifically, the series on those holding multiple jobs started only in January 1994, and the series on household employees only started in January 2000.

2)  I also worked, to the extent possible, with the seasonally adjusted figures (for the establishment survey figures as well as those from the household survey).  However, the figures on unpaid family workers and of household employees were only available without seasonal adjustment.  I was therefore forced to use these.  But since the numbers in these categories are quite small relative to the overall number employed, one does not see a noticeable difference in the graphs.

One can then compare, as a ratio, the figures for total employment as adjusted from the household survey to those from the establishment survey.  The ratio will equal 1.0 when the figures are the same.  This was done in steps (depending on how far back one could go with the data), with the result:

 

The curve in black, which can go back all the way to 1980, shows the ratio when the employment figure in the household survey is adjusted by taking out those who are self-employed (in unincorporated businesses) and those employed in agriculture.  The curve in blue, from 1994 onwards, then adds in one job for each of those holding multiple jobs.  The assumption being made is that those with multiple jobs almost always have two jobs.  The establishment survey would count these as two employees (at two different establishments), while the household survey will only count these as one person (holding more than one job).  Therefore adding a count of one for each person holding multiple jobs will bring the employment concepts used in the two surveys into alignment (and on the basis used in the establishment survey).

Finally, the curve in red subtracts out unpaid family workers in non-agricultural sectors (as those in the agricultural sector will have already been taken out when total employees in agriculture were subtracted), plus subtracts out household employees.  Neither of these series are available in seasonally adjusted form, but they are small relative to total employment, so this makes little difference.

What is interesting is that even with all these adjustments, the ratio of the adjusted figures for employment from the household survey to those from the establishment survey follows a regular pattern.  The ratio is low when unemployment was low (as it was in 2000, at the end of the Clinton administration, and to a lesser extent now).  And it is high when unemployment was high, such as in mid-1980s during the Reagan administration (with a downturn that started in 1982) and again during the downturn of 2008/09 that began at the end of the Bush administration, with unemployment then peaking in 2010 before it started its steady recovery.

Keep in mind that the relative difference in the employment figures between the household survey (as adjusted) and the establishment survey are not large:  about 1% now and a peak of about 3% in 2009/10.  But there is a consistent difference.

Why?  In part there are still two categories of workers where we had no estimates available to adjust the figures from the household survey to align them with the employment concept of the establishment survey:  for those on unpaid leave (who are included as “employed” in the household survey but not in the establishment survey), and for those under age 16 who are working (who are not counted in the household survey but are counted as employees in the establishment survey).

These two categories of workers might account for the difference, but we do not know whether they will fully account for the difference as we have no estimates.  A more interesting question is whether these two categories might account for the correlation observed with unemployment.  We could speculate that during periods of high unemployment (such as 2009/10), those taking unpaid leave might be relatively high (thus bumping up the ratio), and that those under age 16 may find it particularly hard, relative to others, to find jobs when unemployment is high (as employers can easily higher older workers then, with this then also bumping up the ratio relative to times when overall unemployment is low).  But this would just be speculation, and indeed more like an ex-post rationalization of what is observed than an explanation.

Still, despite the statistical noise seen in the chart, the basic pattern is clear.  And that is of a ratio that goes up and down with unemployment.  But it is not large.  Based on the change in the ratio observed from May 2010 to April 2011 (using a 12 month average to smooth out the monthly fluctuations), to the average over May 2018 to April 2019, the monthly divergence in the employment growth figures would only be 23,000 workers.  That is, the unexplained residual difference in recent years between the growth in employment (as estimated by the household survey and as estimated by the establishment survey) would be about 23,000 jobs per month.

But the differences in the estimates for the monthly change in employment between the (adjusted) series from the household survey and that from the establishment survey are much more.  Between October 2018 and April 2019, employment in the adjusted household survey series grew by 65,000 per month on average.  In the establishment survey series the growth was 207,000 per month.  The difference (142,000) is much greater than the 23,000 that can be explained by whatever has been driving down the ratio between the two series since 2010 as unemployment has come down.  Or put another way, the 65,000 figure can be increased by 23,000 per month to 88,000 per month, from adding in the unexplained residual change we observe in the ratio between the two series in recent years.  That 88,000 increase in employment per month from the (adjusted) household survey figures is substantially less than the 207,000 per month figure found in the establishment survey.

D.  Conclusion

Due to the statistical noise in the employment estimates of the household series, one has to be extremely cautious in drawing any conclusions.  While a gap has opened up in the last half year between the growth in the employment estimates of the household survey and those of the establishment survey, it is still early to say whether that gap reflects something significant or not.

The gap is especially large if one just looks at the “employment” figures as published.  Employment as recorded in the household survey has fallen between December 2018 and now, and has been essentially flat since October.  But the total employment concepts between the two surveys differ, so such a direct comparison is not terribly meaningful.  However, if the figures from the household survey are adjusted (to the extent possible) to match the employment concept of the business survey, there is still a large difference.  Employment (under this concept) grew by 207,000 per month in the establishment survey, but by just 88,000 per month in the adjusted household survey figures.

Whether this difference is significant is not yet clear, due to the statistical noise in the household survey figures.  But it might be a sign that employment growth has been less than the headline figures from the establishment survey suggest.  We will see in coming months whether this pattern continues, or whether one series starts tracking the other more closely (and if so, which to which).