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

A.  Introduction

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

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

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

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

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

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

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

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

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

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

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

But it was different in this crisis:

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

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

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

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

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

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

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

D.  Conclusion

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

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

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

It is certainly desperately needed.

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

A.  Revisions in the Jobs Numbers

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

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

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

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

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

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

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

B.  Job Growth Under Trump Compared to Under Obama 

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

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

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

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

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

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

Net Employment Growth

As Earlier Estimated

As Revised

Last 36 Months of Obama

Total

8,128,000

8,079,000

Per Month

225,800

224,400

First 36 Months of Trump

Total

6,913,000

6,559,000

Per Month

192,000

182,200

Difference in Job Growth

Total

1,215,000

1,520,000

Per Month

33,800

42,200

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

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

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

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

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

A.  Introduction

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

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

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

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

B.  Job Losses and Productivity Growth

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

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

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

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

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

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

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

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

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

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

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

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

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

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

C.  What Would the Candidates Do?

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

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

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

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

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

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

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

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

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

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

D.  Summary and Conclusion

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

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

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

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

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

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