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).

The Economy Under Trump in 8 Charts – Mostly as Under Obama, Except Now With a Sharp Rise in the Government Deficit

A.  Introduction

President Trump is repeatedly asserting that the economy under his presidency (in contrast to that of his predecessor) is booming, with economic growth and jobs numbers that are unprecedented, and all a sign of his superb management skills.  The economy is indeed doing well, from a short-term perspective.  Growth has been good and unemployment is low.  But this is just a continuation of the trends that had been underway for most of Obama’s two terms in office (subsequent to his initial stabilization of an economy, that was in freefall as he entered office).

However, and importantly, the recent growth and jobs numbers are only being achieved with a high and rising fiscal deficit.  Federal government spending is now growing (in contrast to sharp cuts between 2010 and 2014, after which it was kept largely flat until mid-2017), while taxes (especially for the rich and for corporations) have been cut.  This has led to standard Keynesian stimulus, helping to keep growth up, but at precisely the wrong time.  Such stimulus was needed between 2010 and 2014, when unemployment was still high and declining only slowly.  Imagine what could have been done then to re-build our infrastructure, employing workers (and equipment) that were instead idle.

But now, with the economy at full employment, such policy instead has to be met with the Fed raising interest rates.  And with rising government expenditures and falling tax revenues, the result has been a rise in the fiscal deficit to a level that is unprecedented for the US at a time when the country is not at war and the economy is at or close to full employment.  One sees the impact especially clearly in the amounts the US Treasury has to borrow on the market to cover the deficit.  It has soared in 2018.

This blog post will look at these developments, tracing developments from 2008 (the year before Obama took office) to what the most recent data allow.  With this context, one can see what has been special, or not, under Trump.

First a note on sources:  Figures on real GDP, on foreign trade, and on government expenditures, are from the National Income and Product Accounts (NIPA) produced by the Bureau of Economic Analysis (BEA) of the Department of Commerce.  Figures on employment and unemployment are from the Bureau of Labor Statistics (BLS) of the Department of Labor.  Figures on the federal budget deficit are from the Congressional Budget Office (CBO).  And figures on government borrowing are from the US Treasury.

B.  The Growth in GDP and in the Number Employed, and the Unemployment Rate

First, what has happened to overall output, and to jobs?  The chart at the top of this post shows the growth of real GDP, presented in terms of growth over the same period one year before (in order to even out the normal quarterly fluctuations).  GDP was collapsing when Obama took office in January 2009.  He was then able to turn this around quickly, with positive quarterly growth returning in mid-2009, and by mid-2010 GDP was growing at a pace of over 3% (in terms of growth over the year-earlier period).  It then fluctuated within a range from about 1% to almost 4% for the remainder of his term in office.  It would have been higher had the Republican Congress not forced cuts in fiscal expenditures despite the continued unemployment.  But growth still averaged 2.2% per annum in real terms from mid-2009 to end-2016, despite those cuts.

GDP growth under Trump hit 3.0% (over the same period one year before) in the third quarter of 2018.  This is good.  And it is the best such growth since … 2015.  That is not really so special.

Net job growth has followed the same basic path as GDP:

 

Jobs were collapsing when Obama took office, he was quickly able to stabilize this with the stimulus package and other measures (especially by the Fed), and job growth resumed.  By late 2011, net job growth (in terms of rolling 12-month totals (which is the same as the increase over what jobs were one year before) was over 2 million per year.  It went to as high as 3 million by early 2015.  Under Trump, it hit 2 1/2 million by September 2018.  This is pretty good, especially with the economy now at or close to full employment.  And it is the best since … January 2017, the month Obama left office.

Finally, the unemployment rate:

Unemployment was rising rapidly as Obama was inaugurated, and hit 10% in late 2009.  It then fell, and at a remarkably steady pace.  It could have fallen faster had government spending not been cut back, but nonetheless it was falling.  And this has continued under Trump.  While commendable, it is not a miracle.

C.  Foreign Trade

Trump has also launched a trade war.  Starting in late 2017, high tariffs were imposed on imports of certain foreign-produced products, with such tariffs then raised and extended to other products when foreign countries responded (as one would expect) with tariffs of their own on selected US products.  Trump claims his new tariffs will reduce the US trade deficit.  As discussed in an earlier blog post, such a belief reflects a fundamental misunderstanding of how the trade balance is determined.

But what do we see in the data?:

The trade deficit has not been reduced – it has grown in 2018.  While it might appear there had been some recovery (reduction in the deficit) in the second quarter of the year, this was due to special factors.  Exports primarily of soybeans and corn to China (but also other products, and to other countries where new tariffs were anticipated) were rushed out in that quarter in order arrive before retaliatory tariffs were imposed (which they were – in July 2018 in the case of China).  But this was simply a bringing forward of products that, under normal conditions, would have been exported later.  And as one sees, the trade balance returned to its previous path in the third quarter.

The growing trade imbalance is a concern.  For 2018, it is on course for reaching 5% of GDP (when measured in constant prices of 2012).  But as was discussed in the earlier blog post on the determination of the trade balance, it is not tariffs which determine what that overall balance will be for the economy.  Rather, it is basic macro factors (the balance between domestic savings and domestic investment) that determine what the overall trade balance will be.  Tariffs may affect the pattern of trade (shifting imports and exports from one country to another), but they won’t reduce the overall deficit unless the domestic savings/investment balance is changed.  And tariffs have little effect on that balance.

And while the trend of a growing trade imbalance since Trump took office is a continuation of the trend seen in the years before, when Obama was president, there is a key difference.  Under Obama, the trade deficit did increase (become more negative), especially from its lowest point in the middle of 2009.  But this increase in the deficit was not driven by higher government spending – government spending on goods and services (both as a share of GDP and in constant dollar terms) actually fell.  That is, government savings rose (dissavings was reduced, as there was a deficit).  Private domestic savings was also largely unchanged (as a share of GDP).  Rather, what drove the higher trade deficit during Obama’s term was the recovery in private investment from the low point it had reached in the 2008/09 recession.

The situation under Trump is different.  Government spending is now growing, as is the government deficit, and this is driving the trade deficit higher.  We will discuss this next.

D.  Government Accounts

An increase in government spending is needed in an economic downturn to sustain demand so that unemployment will be reduced (or at least not rise by as much otherwise).  Thus government spending was allowed to rise in 2008, in the last year of the Bush administration, in response to the downturn that began in December 2007.  This continued, and was indeed accelerated, as part of the stimulus program passed by Congress soon after Obama took office.  But federal government spending on goods and services peaked in mid-2010, and after that fell.  The Republican Congress forced further expenditure cuts, and by late 2013 the federal government was spending less (in real terms) than it was in early 2008:

This was foolish.  Unemployment was over 9 1/2% in mid-2010, and still over 6 1/2% in late-2013 (see the chart of the unemployment rate above).  And while the unemployment rate did fall over this period, there was justified criticism that the pace of recovery was slow.  The cuts in government spending during this period acted as a major drag on the economy, holding back the pace of recovery.  Never before had a US administration done this in the period after a downturn (at least not in the last half-century where I have examined the data).  Government spending grew especially rapidly under Reagan following the 1981/82 downturn.

Federal government spending on goods and services was then essentially flat in real terms from late 2013 to the end of Obama’s term in office.  And this more or less continued through FY2017 (the last budget of Obama), i.e. through the third quarter of CY2018.  But then, in the fourth quarter of CY2017 (the first quarter of FY2018, as the fiscal year runs from October to September), in the first full budget under Trump, federal government spending started to rise sharply.  See the chart above.  And this has continued.

There are certainly high priority government spending needs.  But the sequencing has been terribly mismanaged.  Higher government spending (e.g. to repair our public infrastructure) could have been carried out when unemployment was still high.  Utilizing idle resources, one would not only have put people to work, but also would have done this at little cost to the overall economy.  The workers were unemployed otherwise.

But higher government spending now, when unemployment is low, means that workers hired for government-funded projects have to be drawn from other activities.  While the unemployment rate can be squeezed downward some, and has been, there is a limit to how far this can go.  And since we are close to that limit, the Fed is raising interest rates in order to curtail other spending.

One sees this in the numbers.  Overall private fixed investment fell at an annual rate of 0.3% in the third quarter of 2018 (based on the initial estimates released by the BEA in late October), led by a 7.9% fall in business investment in structures (offices, etc.) and by a 4.0% fall in residential investment (homes).  While these are figures only for one quarter (there was a deceleration in the second quarter, but not an absolute fall), and can be expected to eventually change (with the economy growing, investment will at some point need to rise to catch up), the direction so far is worrisome.

And note also that this fall in the pace of investment has happened despite the huge cuts in corporate taxes from the start of this year.  Trump officials and Republicans in Congress asserted that the cuts in taxes on corporate profits would lead to a surge in investment.  Many economists (including myself, in the post cited above) noted that there was little reason to believe such tax cuts would sput corporate investment.  Such investment in the US is not now constrained by a lack of available cash to the corporations, so giving them more cash is not going to make much of a difference.  Rather, that windfall would instead lead corporations to increase dividends as well as share buybacks in order to distribute the excess cash to their shareholders.  And that is indeed what has happened, with share buybacks hitting record levels this year.

Returning to government spending, for the overall impact on the economy one should also examine such spending at the state and local level, in addition to the federal.  The picture is largely similar:

This mostly follows the same pattern as seen above for federal government spending on goods and services, with the exception that there was an increase in total government spending from early 2014 to early-2016, when federal spending was largely flat.  This may explain, in part, the relatively better growth in GDP seen over that period (see the chart at the top of this post), and then the slower pace in 2016 as all spending leveled off.

But then, starting in late-2017, total government expenditures on goods and services started to rise.  It was, however, largely driven by the federal government component.  Even though federal government spending accounted only for a bit over one-third (38%) of total government spending on goods and services in the quarter when Trump took office, almost two-thirds (65%) of the increase in government spending since then was due to higher spending by the federal government.  All this is classical Keynesian stimulus, but at a time when the economy is close to full employment.

So far we have focused on government spending on goods and services, as that is the component of government spending which enters directly as a component of GDP spending.  It is also the component of the government accounts which will in general have the largest multiplier effect on GDP.  But to arrive at the overall fiscal deficit, one must also take into account government spending on transfers (such as for Social Security), as well as tax revenues.  For these, and for the overall deficit, it is best to move to fiscal year numbers, where the Congressional Budget Office (CBO) provides the most easily accessible and up-to-date figures.

Tracing the overall federal fiscal deficit, now by fiscal year and in nominal dollar terms, one finds:

The deficit is now growing (the fiscal balance is becoming more negative) and indeed has been since FY2016.  What happened in FY2016?  Primarily there was a sharp reduction in the pace of tax revenues being collected.  And this has continued through FY2018, spurred further by the major tax cut bill of December 2017.  Taxes had been rising, along with the economic recovery, increasing by an average of $217 billion per year between FY2010 and FY2015 (calculated from CBO figures), but this then decelerated to a pace of just $26 billion per year between FY2015 and FY2018, and just $13 billion in FY2018.  The rate of growth in taxes between FY2015 and FY2018 was just 0.8%, or less even than just inflation.

Federal government spending, including on transfers, also rose over this period, but by less than taxes fell.  Overall federal government spending rose by an average of just $46 billion per year between FY2010 and FY2015 (a rate of growth of 1.3% per annum, or less than inflation in those years), and then by $140 billion per year (in nominal dollar terms) between FY2015 and FY2018.  But this step up in overall spending (of $94 billion per year) was well less than the step down in the pace of tax collection (a reduction of $191 billion per year, the difference between $217 billion annual growth over FY2010-15 and the $26 billion annual growth over FY2015-18).

That is, about two-thirds (67%) of the increase in the fiscal deficit since FY2015 can be attributed to taxes being cut, and just one-third (33%) to spending going up.

Looking forward, this is expected to get far worse.  As was discussed in an earlier post on this blog, the CBO is forecasting (in their most recent forecast, from April 2018) that the fiscal deficits under Trump will reach close to $1 trillion in FY2019, and will exceed 5% of GDP for most of the 2020s.  This is unprecedented for the US economy at full employment, other than during World War II.  Furthermore, these CBO forecasts are under the optimistic scenario that there will be no economic downturn over this period.  But that has never happened before in the US.

Deficits need to be funded by borrowing.  And one sees an especially sharp jump in the net amount being borrowed in the markets in CY 2018:

 

These figures are for calendar years, and the number for 2018 includes what the US Treasury announced on October 29 it expects to borrow in the fourth quarter.  Note this borrowing is what the Treasury does in the regular, commercial, markets, and is a net figure (i.e. new borrowing less repayment of debt coming due).  It comes after whatever the net impact of public trust fund operations (such as for the Social Security Trust Fund) is on Treasury funding needs.

The turnaround in 2018 is stark.  The US Treasury now expects to borrow in the financial markets, net, a total of $1,338 billion in 2018, up from $546 billion in 2017.  And this is at time of low unemployment, in sharp contrast to 2008 to 2010, when the economy had fallen into the worst economic downturn since the Great Depression  Tax revenues were then low (incomes were low) while spending needed to be kept up.  The last time unemployment was low and similar to what it is now, in the late-1990s during the Clinton administration, the fiscal accounts were in surplus.  They are far from that now. 

E. Conclusion 

The economy has continued to grow since Trump took office, with GDP and employment rising and unemployment falling.  This has been at rates much the same as we saw under Obama.  There is, however, one big difference.  Fiscal deficits are now rising rapidly.  Such deficits are unprecedented for the US at a time when unemployment is low.  And the deficits have led to a sharp jump in Treasury borrowing needs.

These deficits are forecast to get worse in the coming years even if the economy should remain at full employment.  Yet there will eventually be a downturn.  There always has been.  And when that happens, deficits will jump even further, as taxes will fall in a downturn while spending needs will rise.

Other countries have tried such populist economic policies as Trump is now following, when despite high fiscal deficits at a time of full employment, taxes are cut while government spending is raised.  They have always, in the end, led to disasters.

How Fast Can GDP Grow?: Not as Fast as Trump Says

A.  Introduction

A debate now underway between the Trump Administration and others is on the question of how fast the economy can and will grow.  Trump claimed during the presidential campaign that if elected, he would get the economy to grow at a sustained rate of 5% or even 6%.  Since then the claim has been scaled back, to a 4% rate over the next decade according to the White House website (at least claimed on that website as I am writing this).  And an even more modest rate of growth of 3% for GDP (to be reached in 2020, and sustained thereafter) was forecast in the budget OMB submitted to Congress in May of this year.

But many economists question whether even a 3% growth rate for a sustained period is realistic, as would I.  One needs to look at this systematically, and this post will describe one way economists would address this critically important question.  It is not simply a matter of pulling some number out of the air (where the various figures presented by Trump and his administration, varying between 6% growth and 3%, suggests that that may not be far removed from what they did).

One way to approach this is to recognize the simple identity:  GDP will equal GDP per worker employed times the number of workers employed.  Over time, growth in the number of workers who can be employed will be equal to the growth in the labor force, and we have a pretty good forecast for that will be from demographic projections.  The other element will then depend on growth in how much GDP is produced per worker employed.  This is the growth in productivity, and while more difficult to forecast, we have historical numbers which can provide a sense for what its growth might be, at best, going forward.  The chart at the top of this post shows what it has been since 1947, and will be discussed in detail below.  Forecasts that productivity will now start to grow at rates that are historically unprecedented need to be viewed with suspicion.  Miracles rarely happen.

I should also be clear that the question being examined is the maximum rate at which one can expect GDP to grow.  That is, we are looking at growth in what economists call capacity GDP.  Capacity GDP is what could be produced in the economy with all resources, in particular labor, being fully utilized.  This is the full employment level of GDP, and the economy has been at or close to full employment since around 2015.  Actual GDP can be less than capacity GDP when the economy is operating at less than full employment.  But it cannot be more.  Thus the question being examined is how fast the economy could grow, at most, for a sustained period going forward, not how fast it actually will grow.  With mismanagement, such as what was seen in the government oversight of the financial markets (or, more accurately, the lack of such oversight) prior to the financial and economic collapse that began in 2008 in the final year of the Bush administration, the economy could go into a recession and actual GDP will fall below capacity GDP.  But we will give Trump the benefit of the doubt and look at how fast capacity GDP could grow at, assuming the economy can and will remain at full employment.

We will start with a look at what is expected for growth in the labor force and hence in the number of workers who can be employed.  That is relatively straightforward, and the answer is not to expect much possible growth in GDP from this source.  We will then look at productivity growth:  what it has been in the past and whether it could grow at anything close to what is implicit in the Trump administration forecasts.  Predicting what that actual rate of productivity growth might be is beyond the scope of this blog post.  Rather, we will be looking at it whether it can grow as fast as is implied by the Trump forecasts.  The answer is no.

B.  Growth in the Labor Force 

Every two years, the Bureau of Labor Statistics provides a detailed ten-year forecast of what it estimates the US labor force will be.  The most recent such forecast was published in December 2015 and provided its forecast for 2024 (along with historical figures up to 2014).  The basic story is that while the labor force is continuing to grow in the US, it is growing at an ever decreasing rate as the population is aging, the baby boom generation is entering into retirement, and decades ago birth rates fell.  The total labor force grew at a 1.2% annual rate between 1994 and 2004, at a 0.6% rate between 2004 and 2014, and is forecast by the BLS to grow at a 0.5% rate between 2014 and 2024.

But it is now 2017.  With a decelerating rate of growth, a growth rate in the latter part of a period will be less than in the early part of a period.  Taking account of where the labor force is now, growth going forward to 2024 will only be 0.3% (with these figures calculated based on the full numbers before round-off).  This is not much.

A plot of the US civilian labor force going back to 1948 puts this in perspective:

The labor force will be higher in 2024 than it is now, but not by much.  The labor force grew at a relatively high rate from the 1950s to the 1970s (of a bit over 2% a year), but then started to level off.  As it did, it continued to grow but at an ever slower rate.  There was also a dip after the economic collapse of 2008/09, but then recovered to its previous path.  When unemployment is high, some workers drop out of the labor force for a period. But we are now back to what the path before would have predicted.  If the BLS forecasts are correct, growth in the labor force will continue, but at a rate of just 0.3% from where it is now to 2024, to the point shown in red on the chart.  And this is basically a continuation of the path followed over the last few decades.

One should in particular not expect the labor force to get back to the rapid growth rate (of over 2% a year) the US had from the 1950s to the 1970s.  This would require measures such as that immigration be allowed to increase dramatically (which does not appear to enjoy much support in the Trump administration), or that grandma and grandpa be forced back into the labor force in their 70s and 80s rather than enjoy their retirement years (where it is not at all clear how this would “make America great again”).

I have spoken so far on the figures for the labor force, since that is what the BLS and others can forecast based largely on demographics.  Civilian employment will then be some share of this, with the difference equal to the number of unemployed.  That curve is also shown, in blue, in the chart.  There will always be some unemployment, and in an economic downturn the rate will shoot up.  But even in conditions considered to be “full employment” there will be some number of workers unemployed for various reasons. While economists cannot say exactly what the “full employment rate of unemployment” will be (it will vary over time, and will also depend on various factors depending on the make-up of the labor force), it is now generally taken to be in the range of a 4 to 5% unemployment rate.

The current rate of unemployment is 4.4%.  It is doubtful it will be much lower than this in the future (at least not for any sustained period).  Hence if the economy is at full employment in 2024, with unemployment at a similar rate to what it is now, the rate of growth of total employment from now to 2024 will be the same as the rate of growth of the labor from now to then.  That is, if unemployment is a similar share of the total labor force in 2024 as it is now, the rates of growth of the labor force and of total employment will match.  And that rate of growth is 0.3% a year.

This rate of growth in what employment can be going forward (at 0.3%) is well below what it was before.  Total employment grew at an annual rate of 2.1% over the 20 years between 1947 and 1967, and a slightly higher 2.2% between 1967 and 1987.  With total employment able to grow only at 1.8 or 1.9% points per annum less than what was seen between 1947 and 1987, total GDP growth (for any given rate of productivity growth) will be 1.8 or 1.9% points less.  This is not a small difference.

C.  Growth in Productivity 

Growth in productivity (how much GDP is produced per worker employed) is then the other half of the equation.  What it will be going forward is hard to predict; economists have never been very good at this.  But one can get a sense of what is plausible based on the historical record.

The chart below is the same as the one at the top of this post, but with the growth rates over 20 year periods from 1947 (10 years from 2007) also shown:

These 20 year periods broadly coincide with the pattern often noted for the post-World War II period for the US:  Relatively high growth (2.0% per year) from the late 1940s to the late 1960s; a slowdown from then to the mid 1980s (to 0.9%); a return to more rapid growth in productivity in the 1990s / early 2000s, although not to as high as in the 1950s and 60s (1.5% for 1987 to 2007); and then, after the economic collapse of 2008/2009, only a very modest growth (0.8% for 2007 to 2017, but much less from 2010 onwards).

Note also that these break points all coincide, with one exception (1987), with years where the economy was operating at full employment.  In the one exception (1987, near the end of the Reagan administration) unemployment was still relatively high at 6.6%.  While one might expect productivity levels to reach a local peak when the economy is at or close to full employment, that is not always true (the relationship is complex), and is in any case controlled for here by the fact the break points coincide (with the one exception) with full employment years.

Another way to look at this is productivity growth as a rolling average, for example over continuous 10 year periods:

 

Productivity, averaged over 10 year periods, grew at around 2% a year from the late 1940s up to the late 1960s.  It then started to fall, bottoming out at roughly 0.5% in the 1970s, before reverting to a higher pace.  It reached 2% again in the 10 year period of 1995 to 2005, but only for a short period before starting to fall again.  And as noted before, it fell to 0.8% for the 2007 to 2017 period.

What productivity growth going forward could at most be will be discussed below, but first it is useful to summarize what we have seen so far, putting employment growth and productivity growth together:

Growth Rates

Employment

GDP per worker

GDP

1947-1967

2.1%

2.0%

4.1%

1967-1987

2.2%

0.9%

3.1%

1987-2007

1.6%

1.5%

3.1%

2007-2017

0.6%

0.8%

1.4%

Employment grew at over 2% a year between the late 1940s and 1987.  This was the period of the post-war recovery and baby boom generation coming of working age.  With GDP per worker growing at 2.0% a year between 1947 and 1967, total GDP grew at a 4.1% rate.  It still grew at a 3.1% rate between 1967 and 1987 despite productivity growth slowing to just 0.9%, as the labor force continued to grow rapidly over this period.  And total GDP continued to grow at a 3.1% rate between 1987 and 2007 despite slower employment (and labor force) growth, as a recovery in productivity growth (to a 1.5% pace) offset the slower availability of labor.

It might, at first glance, appear from this that a return to 3% GDP growth (or even 4%) is quite doable.  But it is not.  Employment growth fell to a pace of just 0.6% between 2007 and 2017 (and the unemployment rates were almost exactly the same in early 2007, at 4.5%, and now, at 4.4%, so this matched labor force growth).  Going forward, as discussed above, the labor force is forecast to grow at a 0.3% pace between now and 2024.  To get to a 3% GDP growth rate now at such a pace of labor growth, one would need productivity to grow at a 2.7% pace.  To get a 4% GDP growth, productivity would have to grow at a 3.7% pace.  But productivity growth in the US since 1947 has never been able to get much above a 2% pace for any sustained period.  To go well beyond this would be unprecedented.

D.  Why Does This Matter?  And What Can Be Achieved?

Some readers might wonder why all this matters.  On the surface, the difference between growth at a 2% rate or 3% rate may not seem like much.  But it is, as some simple arithmetic illustrates:

  Alternative Growth Scenarios

 Growth Rates:

GDP 

Population

GDP per capita

Cumulative

Over 30 years

1.0%

0.8%

0.2%

6%

2.0%

0.8%

1.2%

43%

3.0%

0.8%

2.2%

91%

4.0%

0.8%

3.2%

155%

This table works out the implications of varying rates of hypothetical GDP growth, between 1.0% and 4.0%.  Population growth in the US is forecast by the Census Bureau at 0.8% a year (for the period to the 2020s).  It is higher than the forecast pace of labor force growth (of 0.3% in the BLS figures) primarily because of the aging of the population, so a higher and higher share of the adult population is entering their retirement years.

The result is that GDP growth at 1.0% a year will be just 0.2% a year in per capita terms with a 0.8% population growth rate.  After 30 years (roughly one generation) this will cumulate to a total growth in per capita income of just 6%.  But GDP growth at 2% a year will, by the same calculation, cumulate to total per capita income growth of 43%, to 91% with GDP growth of 3%, and to 155% with GDP growth of 4%.  These differences are huge.  What might appear to be small differences in GDP growth rates add up over time to a lot.  It does matter.

[Note that this does not address the distribution issue.  Overall GDP per capita may grow, as it has over the last several decades, but all or almost all may go only to a few.  As a post on this blog from 2015 showed, only the top 10% of the income distribution saw any real income growth at all between 1980 and 2014 – real incomes per household fell for the bottom 90%.  And the top 1%, or richer, did very well.

But total GDP growth is still critically important, as it provides the resources which can be distributed to people to provide higher standards of living.  The problem in the US is that policies followed since 1980, when Ronald Reagan was first elected, have led to the overwhelming share of the growth the US has achieved to go to the already well off. Measures to address this critically important, but separate, issue have been discussed in several earlier posts on this blog, including here and here.]

Looking forward, what pace of productivity growth might be expected?  As discussed above, while the US was able to achieve productivity growth at a rate of about 2.0% in the 1950s and 1960s, since then it was able to achieve a rate as high as this over a ten year period only once (between 1995 and 2005), and only very briefly.  And over time, there is some evidence that reaching the rates of productivity growth enjoyed in the past is becoming increasingly difficult.

A reason for this is the changing structure of the economy.  Productivity growth has been, and continues to be, relatively high in manufacturing and especially in agriculture. Mechanization and new technologies (including biological technologies) can raise productivity in manufacturing and in agriculture.  It is more difficult to do this in services, which are often labor intensive and personal.  And with agriculture and manufacturing a higher share of the economy in the past than they are now (precisely because their higher rates of productivity growth allowed more to be produced with fewer workers), the overall pace of productivity growth in the economy will move, over time, towards the slower rate found in services.

The following table illustrates this.  The figures are taken from an earlier blog post, which looked at the changing shares of the economy resulting from differential rates of productivity growth.

Productivity Growth

Agriculture

Manufacturing

Services

Overall (calculated)

1947 to 2015:

3.3%

2.8%

0.9%

1.4%

At GDP Shares of:

   – 1947 shares

8.0%

27.7%

64.3%

1.7%

   – 1980 shares

2.2%

23.6%

74.2%

1.4%

   – 2015 shares

1.0%

13.9%

85.2%

1.2%

The top line (with the figures in bold) shows the overall rates of productivity growth between 1947 and 2015 in agriculture (3.3%), manufacturing (2.8%), services (0.9%), and overall (1.4%).  The overall is for GDP, and matches the average for growth in GDP per employed worker between 1947 and 2017 in the chart shown at the top of this post.

The remaining lines on the table show what the pace of overall productivity growth would then have been, hypothetically, at these same rates of productivity growth by sector but with the sector shares in GDP what they were in 1947, or in 1980, or in 2015.  In 1947, with the sector shares of agriculture and manufacturing higher than what they were later, and services correspondingly lower, the pace of productivity growth overall (i.e. for GDP) would have been 1.7%.  But at the sector shares of 2015, with services now accounting for 85% of the economy, the overall rate of productivity growth would have been just 1.2%, or 0.5% lower.

This is just an illustrative calculation, and shows the effects of solely the shifts in sector shares with the rates of productivity growth in the individual sectors left unchanged.  But those individual sector rates could also change over time, and did.  Briefly (see the earlier blog post for a discussion), the rate of productivity growth in services decelerated sharply after the mid-1960s; the pace in agriculture was remarkably steady; while the pace in manufacturing accelerated after the early 1980s (explaining, to a large extent, the sharp fall in the manufacturing share of the economy from 24% in 1980 to just 14% in 2015).  But with services dominating the economy (74% in 1980, rising to 85% in 2015), it was the pace of productivity growth in services, and its pattern over time, which dominated.

What can be expected going forward?  The issue is a huge one, and goes far beyond what is intended for this post.  But especially given the headwinds created by the structural transformation in the economy of the past 70 years towards a dominance by the services sector, it is unlikely that the economy will soon again reach a pace of 2% productivity growth a year for a sustained period of a decade or more.  Indeed, a 1.5% rate would be exceptionally good.

And with labor force growth of 0.3%, a 1.5% pace for productivity would imply a 1.8% rate for overall GDP.  This is well below the 3% rate that the Trump administration claims it will achieve, and of course even further below the 4% (and 5% and 6%) rates that Trump has claimed he would get.

E.  Conclusion

As a simple identity, GDP will equal GDP per worker employed (productivity) times the number of workers employed.  Growth in GDP will thus equal the sum of the growth rates of these two components.  With a higher share of our adult population aging into the normal retirement years, the labor force going forward (to 2024) is forecast to grow at just 0.3% a year.  That is not much.  Overall GDP growth will then be this 0.3% plus the growth in productivity.  That growth in the post World War II period has never much exceeded 2% a year for any 10-year period.  If we are able to get to such a 2% rate of productivity growth again, total GDP would then be able to grow at a 2.3% rate.  But this is below the 3% figure the Trump administration has assumed for its budget, and far below the 4% (or 5% or 6%) rates Trump has asserted he would achieve.  Trump’s forecasts (whether 3% or 4% or 5% or 6%) are unrealistic.

But a 2% rate for productivity growth is itself unlikely.  It was achieved in the 1950s and 1960s when agriculture and manufacturing were greater shares of the economy, and it has been in those sectors where productivity growth has been most rapid.  It is harder to raise productivity quickly in services, and services now dominate the economy.

Finally, it is important to note that we are speaking of growth rates in labor, productivity, and GDP over multi-year, sustained, periods.  That is what matters to what living standards can be achieved over time, and to issues like the long-term government budget projections.  There will be quarter to quarter volatility in the numbers for many reasons, including that all such figures are estimates, derived from surveys and other such sources of information.  It is also the case that an exceptionally high figure in one quarter will normally soon be followed by an exceptionally low figure in some following quarter, as the economy, as well as the statistical measure of it, balances out over time.

Thus, for example, the initial estimate (formally labeled the “advance estimate”) for GDP growth in the second quarter of 2017, released on July 28, was 2.6% (at an annual rate). Trump claimed this figure to be “an unbelievable number” showing that the economy is doing “incredibly well”, and claimed credit for what he considered to be a great performance.  But it is a figure for just one quarter, and will be revised in coming months as more data become available.  It also follows an estimate of GDP growth in the first quarter of 2017 of just 1.2%.  Thus growth over the first half of the year averaged 1.9%. Furthermore, productivity (GDP per worker) grew at just a 0.5% rate over the first half of 2017.  While a half year is too short a period for any such figure on productivity to be taken seriously, such a performance is clearly nothing special.

The 1.9% rate of growth of GDP in the first half of 2017 is also nothing special.  It is similar to the rate achieved over the last several years, and is in fact slightly below the 2.1% annual rate seen since 2010.  More aptly, in the 28 calendar quarters between the second quarter of 2010 and the first quarter of 2017, GDP grew at a faster pace than that 2.6% estimated rate a total of 13 times, or almost half. The quarter to quarter figures simply bounce around, and any figure for a single quarter is not terribly meaningful by itself.

It therefore might well be the case that a figure for GDP growth of 3%, or even 4% or higher, is seen for some quarter or even for several quarters.  But there is no reason to expect that the economy will see such rates on a sustained basis, as the Trump administration has predicted.

 

Long-Term Structural Change in the US Economy: Manufacturing is Simply Following the Path of Agriculture

A.  Introduction

A major theme of Trump, both during his campaign and now as president, has been that jobs in manufacturing have been decimated as a direct consequence of the free trade agreements that started with NAFTA.  He repeated the assertion in his speech to Congress of February 28, where he complained that “we’ve lost more than one-fourth of our manufacturing jobs since NAFTA was approved”, but that because of him “Dying industries will come roaring back to life”.  He is confused.  But to be fair, there are those on the political left as well who are similarly confused.

All this reflects a sad lack of understanding of history.  Manufacturing jobs have indeed been declining in recent decades, and as the chart above shows, they have been declining as a share of total jobs in the economy since the 1940s.  Of all those employed, the share employed in manufacturing (including mining) fell by 7.6% points between 1994 (when NAFTA entered into effect) and 2015 (the most recent year in the sector data of the Bureau of Economic Analysis, used for consistency throughout this post), a period of 21 years. But the share employed in manufacturing fell by an even steeper 9.2% points in the 21 years before 1994.  The decline in manufacturing jobs (both as a share and in absolute number) is nothing new, and it is wrong to blame it on NAFTA.

It is also the case that manufacturing production has been growing steadily over this period.  Total manufacturing production (measured in real value-added terms) rose by 64% over the 21 years since NAFTA went into effect in 1994.  And this is also substantially higher than the 42% real growth in the 21 years prior to 1994.  Blaming NAFTA (and the other free trade agreements of recent decades) for a decline in manufacturing is absurd.  Manufacturing production has grown.

For those only interested in the assertion by Trump that NAFTA and the other free trade agreements have killed manufacturing in the US and with it the manufacturing jobs, one could stop here.  Manufacturing has actually grown strongly since NAFTA went into effect, and there are fewer manufacturing jobs now than before not because manufacturing has declined, but because workers in manufacturing are now more productive than ever before (with this a continuation of the pattern underway over at least the entire post-World War II period, and not something new).  But the full story is a bit more complex, as one also needs to examine why manufacturing production is at the level that it is.  For this, one needs to bring in the rest of the economy, in particular services. The rest of this blog post will address this broader issue,

Manufacturing jobs have nonetheless indeed declined.  To understand why, one needs to look at what has happened to productivity, not only in manufacturing but also in the other sectors of the economy (in particular in services).  And I would suggest that one could learn much by an examination of the similar factors behind the even steeper decline over the years in the share of jobs in agriculture.  It is not because of adverse effects of free trade.  The US is in fact the largest exporter of food products in the world.  Yet the share of workers employed in the agricultural sectors (including forestry and fishing) is now just 0.9% of the total.  It used to be higher:  4.3% in 1947 and 8.4% in 1929 (using the BEA data).  If one wants to go really far back, academics have estimated that agricultural employment accounted for 74% of all US employment in 1800, with this still at 56% in 1860.

Employment in agriculture has declined so much, from 74% of total employment in 1800 to 8.4% in 1929 to less than 1% today, because those employed in agriculture are far more productive today than they were before.  And while it leads to less employment in the sector, whether as a share of total employment or in absolute numbers, higher productivity is a good thing.  The US could hardly enjoy a modern standard of living if 74% of those employed still had to be working in agriculture in order to provide us food to eat. And while stretching the analysis back to 1800 is extreme, one can learn much by examining and understanding the factors behind the long-term trends in agricultural employment.  Manufacturing is following the same basic path.  And there is nothing wrong with that.  Indeed, that is exactly what one would hope for in order for the economy to grow and develop.

Furthermore, the effects of foreign trade on employment in the sectors, positive or negative, are minor compared to the long-term impacts of higher productivity.  In the post below we will look at what would have happened to employment if net trade would somehow be forced to zero by Trumpian policies.  The impact relative to the long term trends would be trivial.

This post will focus on the period since 1947, the earliest date for which the BEA has issued data on both sector outputs and employment.  The shares of agriculture as well as of manufacturing in both total employment and in output (with output measured in current prices) have both declined sharply over this period, but not because those sectors are producing less than before.  Indeed, their production in real terms are both far higher. Employment in those sectors has nevertheless declined in absolute numbers.  The reason is their high rates of productivity growth.  Importantly, productivity in those two sectors has grown at a faster pace than in the services sector (the rest of the economy).  As we will discuss, it is this differential rate of productivity growth (faster in agriculture and in manufacturing than in services) which explains the decline in the share employed in agriculture and manufacturing.

These structural changes, resulting ultimately from the differing rates of productivity growth in the sectors, can nonetheless be disruptive.  With fewer workers needed in a sector because of a high rate of productivity growth, while more workers are needed in those sectors where productivity is growing more slowly (although still positively and possibly strongly, just relatively less strongly), there is a need for workers to transfer from one sector to another.  This can be difficult, in particular for individuals who are older or who have fewer general skills.  But this was achieved before in the US as well as in other now-rich countries, as workers shifted out of agriculture and into manufacturing a century to two centuries ago.  Critically important was the development of the modern public school educational system, leading to almost universal education up through high school. The question the country faces now is whether the educational system can be similarly extended today to educate the workers needed for jobs in the modern services economy.

First, however, is the need to understand how the economy has reached the position it is now in, and the role of productivity growth in this.

B.  Sector Shares and Prices

As Chart 1 at the top of this post shows, employment in agriculture and in manufacturing have been falling steadily as a share of total employment since the 1940s, while jobs in services have risen.

[A note on the data:  The data here comes from the Bureau of Economic Analysis (BEA), which, as part of its National Income and Product Accounts (NIPA), estimates sector outputs as well as employment.  Employment is measured in full-time equivalent terms (so that two half-time workers, say, count as the equivalent of one full-time worker), which is important for measuring productivity growth.

And while the BEA provides figures on its web site for employment going all the way back to 1929, the figures for sector output on its web site only go back to 1947.  Thus while the chart at the top of this post goes back to 1929, all the analysis shown below will cover the period from 1947 only.  Note also that there is a break in the employment series in 1998, when the BEA redefined slightly how some of the detailed sectors would be categorized. They unfortunately did not then go back to re-do the categorizations in a consistent way in the years prior to that, but the changes are small enough not to matter greatly to this analysis.  And there were indeed similar breaks in the employment series in 1948 and again in 1987, but the changes there were so small (at the level of aggregation of the sectors used here) as not to be noticeable at all.

Also, for the purposes here the sector components of GDP have been aggregated to just three, with forestry and fishing included with agriculture, mining included with manufacturing, and construction included with services.  As a short hand, these sectors will at times be referred to simply as agriculture, manufacturing, and services.

Finally, the figures on sector outputs in real terms provided by the BEA data are calculated based on what are called “chain-weighted” indices of prices.  Chain-weighted indices are calculated based on moving shares of sector outputs (whatever the share is in any given period) rather than on fixed shares (i.e. the shares at the beginning or the end of the time period examined).  Chain-weighted indices are the best to use over extended periods, but are unfortunately not additive, where a sum (such as real GDP) will not necessarily equal exactly the sum of the estimates of the underlying sector figures (in real terms).  The issue is however not an important one for the questions being examined in this post.  While we will show the estimates in the charts for real GDP (based on a sum of the figures for the three sectors), there is no need to focus on it in the analysis.  Now back to the main text.]

The pattern in a chart of sector outputs as shares of GDP (measured in current prices by the value-added of each sector), is similar to that seen in Chart 1 above for the employment shares:

Agriculture is falling, and falling to an extremely small share of GDP (to less than 1% of GDP in 2015).  Manufacturing and mining is similarly falling from the mid-1950s, while services and construction is rising more or less steadily.  On the surface, all this appears to be similar to what was seen in Chart 1 for employment shares.  It also might look like the employment shares are simply following the shifts in output shares.

But there is a critical difference.  The shares of workers employed is a measure of numbers of workers (in full-time equivalent terms) as a share of the total.  That is, it is a measure in real terms.  But the shares of sector outputs in Chart 2 above is a measure of the shares in terms of current prices.  They do not tell us what is happening to sector outputs in real terms.

For sector outputs in real terms (based on the prices in the initial year, or 1947 here), one finds a very different chart:

Here, the output shares are not changing all that much.  There is only a small decline in agriculture (from 8% of the total in 1947 to 7% in 2015), some in manufacturing (from 28% to 22%), and then the mirror image of this in services (from 64% to 72%).  The changes in the shares were much greater in Chart 2 above for sector output shares in current prices.

Many might find the relatively modest shifts in the shares of sector outputs when measured in constant price terms to be surprising.  We were all taught in our introductory Economics 101 class of Engel Curve effects.  Ernst Engel was a German statistician who, in 1857, found that at the level of households, the share of expenditures on basic nourishment (food) fell the richer the household.  Poorer households spent a relatively higher share of their income on food, while better off households spent less.  One might then postulate that as a nation becomes richer, it will see a lower share of expenditures on food items, and hence that the share of agriculture will decline.

But there are several problems with this theory.  First, for various reasons it may not apply to changes over time as general income levels rise (including that consumption patterns might be driven mostly by what one observes other households to be consuming at the time; i.e. “keeping up with the Joneses” dominates).  Second, agricultural production spans a wide range of goods, from basic foodstuffs to luxury items such as steak.  The Engel Curve effects might mostly be appearing in the mix of food items purchased.

Third, and perhaps most importantly, the Engel Curve effects, if they exist, would affect production only in a closed economy where it was not possible to export or import agricultural items.  But one can in fact trade such agricultural goods internationally. Hence, even if domestic demand fell over time (due perhaps to Engel Curve effects, or for whatever reason), domestic producers could shift to exporting a higher share of their production.  There is therefore no basis for a presumption that the share of agricultural production in total output, in real terms, should be expected to fall over time due to demand effects.

The same holds true for manufacturing and mining.  Their production can be traded internationally as well.

If the shares of agriculture and manufacturing fell sharply over time in terms of current prices, but not in terms of constant prices (with services then the mirror image), the implication is that the relative prices of agriculture as well as manufacturing fell relative to the price of services.  This is indeed precisely what one sees:

These are the changes in the price indices published by the BEA, with all set to 1947 = 1.0.  Compared to the others, the change in agricultural prices over this 68 year period is relatively small.  The price of manufacturing and mining production rose by far more.  And while a significant part of this was due to the rise in the 1970s of the prices of mined products (in particular oil, with the two oil crises of the period, but also in the prices of coal and other mined commodities), it still holds true for manufacturing alone.  Even if one excludes the mining component, the price index rose by far more than that of agriculture.

But far greater was the change in the price of services.  It rose to an index value of 12.5 in 2015, versus an index value of just 1.6 for agriculture in that year.  And the price of services rose by double what the price of manufacturing and mining rose by (and even more for manufacturing alone).

With the price of services rising relative to the others, the share of services in GDP (in current prices) will then rise, and substantially so given the extent of the increase in its relative price, despite the modest change in its share in constant price terms.  Similarly, the fall in the shares of agriculture and of manufacturing (in current price terms) will follow directly from the fall in their prices (relative to the price of services), despite just a modest reduction in their shares in real terms.

The question then is why have we seen such a change in relative prices.  And this is where productivity enters.

C.  Growth in Output, Employment, and Productivity

First, it is useful to look at what happened to the growth in real sector outputs relative to 1947:

All sector outputs rose, and by substantial amounts.  While Trump has asserted that manufacturing is dying (due to free trade treaties), this is not the case at all.  Manufacturing (including mining) is now producing 5.3 times (in real terms) what it was producing in 1947.  Furthermore, manufacturing production was 64% higher in real terms in 2015 than it was in 1994, the year NAFTA went into effect.  This is far from a collapse.  The 64% increase over the 21 years between 1994 and 2015 was also higher than the 42% increase in manufacturing production of the preceding 21 year period of 1973 to 1994. There was of course much more going on than any free trade treaties, but to blame free trade treaties on a collapse in manufacturing is absurd.  There was no collapse.

Production in agriculture also rose, and while there was greater volatility (as one would expect due to the importance of weather), the increase in real output over the full period was in fact very similar to the increase seen for manufacturing.

But the biggest increase was for services.  Production of services was 7.6 times higher in 2015 than in 1947.

The second step is to look at employment, with workers measured here in full-time equivalent terms:

Despite the large increases in sector production over this period, employment in agriculture fell as did employment in manufacturing.  One unfortunately cannot say with precision by how much, given the break in the employment series in 1998.  However, there were drops in the absolute numbers employed in manufacturing both before and after the 1998 break in the series, while in agriculture there was a fall before 1998 (relative to 1947) and a fairly flat series after.  The change in the agriculture employment numbers in 1998 was relatively large for the sector, but since agricultural employment was such a small share of the total (only 1%), this does not make a big difference overall.

In contrast to the falls seen for agriculture and manufacturing, employment in the services sector grew substantially.  This is where the new jobs are arising, and this has been true for decades.  Indeed, services accounted for more than 100% of the new jobs over the period.

But one cannot attribute the decline in employment in agriculture and in manufacturing to the effects of international trade.  The points marked with a “+” in Chart 6 show what employment in the sectors would have been in 2015 (relative to 1947) if one had somehow forced net imports in the sectors to zero in 2015, with productivity remaining the same. There would have been an essentially zero change for agriculture (while the US is the world’s largest food exporter, it also imports a lot, including items like bananas which would be pretty stupid to try to produce here).  There would have been somewhat more of an impact on manufacturing, although employment in the sector would still have been well below what it had been decades ago.  And employment in services would have been a bit less. While most production in the services sector cannot be traded internationally, the sector includes businesses such as banking and other finance, movie making, professional services, and other areas where the US is in fact a strong exporter.  Overall, the US is a net exporter of services, and an abandonment of trade that forced all net imports (and hence net exports) to zero would lead to less employment in the sector.  But the impact would be relatively minor.

Labor productivity is then simply production per unit of labor.  Dividing one by the other leads to the following chart:

Productivity in agriculture grew at a strong pace, and by more than in either of the other two sectors over the period.  With higher productivity per worker, fewer workers will be needed to produce a given level of output.  Hence one can find that employment in agriculture declined over the decades, even though agricultural production rose strongly. Productivity in manufacturing similarly grew strongly, although not as strongly as in agriculture.

In contrast, productivity in the services sector grew at only a modest pace.  Most of the activities in services (including construction) are relatively labor intensive, and it is difficult to substitute machinery and new technology for the core work that they do.  Hence it is not surprising to find a slower pace of productivity growth in services.  But productivity in services still grew, at a positive 0.9% annual pace over the 1947 to 2015 period, as compared to a 2.8% annual pace for manufacturing and a 3.3% annual pace in agriculture.

Finally, and for those readers more technically inclined, one can convert this chart of productivity growth onto a logarithmic scale.  As some may recall from their high school math, a straight line path on a logarithmic scale implies a constant rate of growth.  One finds:

While one should not claim too much due to the break in the series in 1998, the path for productivity in agriculture on a logarithmic scale is remarkably flat over the full period (once one abstracts from the substantial year to year variation – short term fluctuations that one would expect from dependence on weather conditions).  That is, the chart indicates that productivity in agriculture grew at a similar pace in the early decades of the period, in the middle decades, and in the later decades.

In contrast, it appears that productivity in manufacturing grew at a certain pace in the early decades up to the early 1970s, that it then leveled off for about a decade until the early 1980s, and that it then moved to a rate of growth that was faster than it had been in the first few decades.  Furthermore, the pace of productivity growth in manufacturing following this turn in the early 1980s was then broadly similar to the pace seen in agriculture in this period (the paths are then parallel so the slope is the same).  The causes of the acceleration in the 1980s would require an analysis beyond the scope of this blog post. But it is likely that the corporate restructuring that became widespread in the 1980s would be a factor.  Some would also attribute the acceleration in productivity growth to the policies of the Reagan administration in those years.  However, one would also then need to note that the pace of productivity growth was similar in the 1990s, during the years of the Clinton administration, when conservatives complained that Clinton introduced regulations that undid many of the changes launched under Reagan.

Finally, and as noted before, the pace of productivity growth in services was substantially less than in the other sectors.  From the chart in logarithms, it appears the pace of productivity growth was relatively robust in the initial years, up to the mid-1960s.  While slower than the pace in manufacturing or in agriculture, it was not that much slower.  But from the mid-1960s, the pace of growth of productivity in services fell to a slower, albeit still positive, pace.  Furthermore, that pace appears to have been relatively steady since then.

One can summarize the results of this section with the following table:

Growth Rates:

1947 to 2015

Employment

Productivity

Output

Total (GDP)

1.5%

1.4%

2.9%

Agriculture

-0.7%

3.3%

2.6%

Manufacturing

-0.3%

2.8%

2.5%

Services

2.1%

0.9%

3.0%

The growth rate of output will be the simple sum of the growth rate of employment in a sector and the growth rate of its productivity (output per worker).  The figures here do indeed add up as they should.  They do not tell us what causes what, however, and that will be addressed next.

D.  Pulling It Together:  The Impact on Employment, Prices, and Sector Shares

Productivity is driven primarily by technological change.  While management skills and a willingness to invest to take advantage of what new technologies permit will matter over shorter periods, over the long term the primary driver will be technology.

And as seen in the chart above, technological progress, and the resulting growth in productivity, has proceeded at a different pace in the different sectors.  Productivity (real output per worker) has grown fastest over the last 68 years in agriculture (a pace of 3.3% a year), and fast as well in manufacturing (2.8% a year).  In contrast, the rate of growth of productivity in services, while positive, has been relatively modest (0.9% a year).

But as average incomes have grown, there has been an increased domestic demand in what the services sector produces, not only in absolute level but also as a share of rising incomes.  Since services largely cannot be traded internationally (with a few exceptions), the increased demand for services will need to be met by domestic production.  With overall production (GDP) matching overall incomes, and with demand for services growing faster than overall incomes, the growth of services (in real terms) will be greater than the growth of real GDP, and therefore also greater than growth in the rest of the economy (agriculture and manufacturing; see Chart 5).  The share of services in real GDP will then rise (Chart 3).

To produce this, the services sector needed more labor.  With productivity in the services sector growing at a slower pace (in relative terms) than that seen in agriculture and in manufacturing, the only way to obtain the labor input needed was to increase the share of workers in the economy employed in services (Chart 1).  And depending on the overall rate of labor growth as well as the size of the differences in the rates of productivity growth between the sectors, one could indeed find that the shift in workers out of agriculture and out of manufacturing would not only lead to a lower relative share of workers in those sectors, but also even to a lower absolute number of workers in those sectors.  And this is indeed precisely what happened, with the absolute number of workers in agriculture falling throughout the period, and falling in manufacturing since the late 1970s (Chart 6).

Finally, the differential rates of productivity growth account for the relative price changes seen between the sectors.  To be able to hire additional workers into services and out of agriculture and out of manufacturing, despite a lower rate of productivity growth in services, the price of services had to rise relative to agriculture as well as manufacturing. Services became more expensive to produce relative to the costs of agriculture or manufacturing production.  And this is precisely what is seen in Chart 4 above on prices.

To summarize, productivity growth allowed all sectors to grow.  With the higher incomes, there was a shift in demand towards services, which led it to grow at a faster pace than overall incomes (GDP).  But for this to be possible, particularly as its pace of productivity growth was slower than the pace in agriculture and in manufacturing, workers had to shift to services from the other sectors.  The effect was so great (due to the differing rates of growth of productivity) that employment in services rose to the point where services now employs close to 90% of all workers.

To be able to hire those workers, the price of services had to grow relative to the prices of the other sectors.  As a consequence, while there was only a modest shift in sector shares over time when measured in real terms (constant prices of 1947), there was a much larger shift in sector shares when measured in current prices.

The decline in the number of workers in manufacturing should not then be seen as surprising nor as a reflection of some defective policy.  Nor was it a consequence of free trade agreements.  Rather, it was the outcome one should expect from the relatively rapid pace of productivity growth in manufacturing, coupled with an economy that has grown over the decades with this leading to a shift in domestic demand towards services.  The resulting path for manufacturing was then the same basic path as had been followed by agriculture, although it has been underway longer in agriculture.  As a result, fewer than 1% of American workers are now employed in agriculture, with this possible because American agriculture is so highly productive.  One should expect, and indeed hope, that the same eventually becomes true for manufacturing as well.