The Unemployment Rate, the Growth in Employment, and Productivity

A.  Introduction

The January jobs report (more properly the “Employment Situation” report) released by the Bureau of Labor Statistics (BLS) on February 3, was extraordinarily – and surprisingly – strong.  The unemployment rate fell to 3.4% – the lowest it has been since May 1969 more than a half-century ago.  And despite the low unemployment rate, the number of “new jobs created” (also a misnomer – it is actually the net increase in non-farm payroll employment) was a surprising 517,000.  But it was not only this.  The regular annual revisions undertaken each January to reflect revised population controls and weights for the employment estimates led this year to significantly higher labor force and employment estimates.  With the new industry weights, the increase in the estimated number of those employed in 2022 (the number of `”new jobs”) rose to 4.8 million.  The earlier estimate had been 4.5 million.

All this is an extraordinarily strong jobs report.  However, one should not go too far.  It is important to understand what lies behind these estimates, as well as some of the implications.  For example, strong growth in the total number employed while GDP growth is more modest implies that productivity (GDP per person employed) went down.  That could be a concern, except that when viewed in the context of the last several years we will see that productivity growth has in fact been rather good.

This post will first examine the new figures on unemployment and then on employment growth.  We will then look at the change in productivity – both in the recent past and from a longer-term perspective.

B.  The Unemployment Rate and Its (Non)-Impact on Inflation

The unemployment rate in January fell to 3.4%.  This is the lowest it has been since May 1969.  And if it falls a notch further to 3.3% in some upcoming month, it will have fallen to the lowest since 1953.

A 3.4% unemployment rate is certainly low.  But what is more significant is that the unemployment rate has been almost as low for most of the past year.  It fell to just 3.6% in March 2022, and until last month varied within the narrow range of 3.5 to 3.7% – hitting the 3.5% rate several times.  It is now at 3.4%, but what is most significant is that it has been at 3.7% or less for almost a year.

One needs to recognize that the unemployment rate is derived from a survey of a sample of households (implemented by the Census Bureau) called the Current Population Survey (CPS).  The CPS sample includes approximately 60,000 households each month, in a rotating panel, and from this they derive estimates on the labor force participation rate, the unemployment rate, and much more.  It complements the Current Employment Statistics (CES) survey, which covers a much larger sample of 122,000 businesses and government agencies representing 666,000 individual worksites (with each employing many workers).  Hence employment figures are generally taken from the CES as there will be less statistical noise.  But the employers surveyed for the CES cannot know how many workers are unemployed (they will only know how many workers are employed by them), so the smaller CPS needs to be used for that.  (A brief explanation of the CPS and CES is provided by the BLS as a “Technical Note” included in each of the monthly Employment Situation reports.)

Due to the size of the sample, the estimated unemployment rate is actually only known within an error limit of +/- 0.2 percentage points, using a 90% confidence interval.  That is, simply due to the statistical noise a change in the unemployment rate of 0.1 percentage point from one month to the next should not be considered statistically significant, and 10% of the time even a 0.2 percentage point change may have just been a consequence of the statistical variation.  However, repeated observations over several months in a row of an unemployment rate at some level will be a measurement one can have much more confidence in.  That can no longer be a consequence of simply statistical noise.  Thus one should not place too much weight on the January change in the unemployment rate to 3.4% from 3.5% the month before.  But the fact that the unemployment rate has consistently been within the relatively narrow – and extremely low – range of 3.4 to 3.7% since March 2022 is highly significant.

An unemployment rate anywhere close to a range of 3.4 to 3.7% is also far below the rate at which economists used to believe would be possible without the rate of inflation accelerating – i.e. without inflation going higher and higher.  This was given the acronym name of “NAIRU” (for Non-Accelerating Inflation Rate of Unemployment).  It was held that at an unemployment rate of less than the NAIRU rate, the rate of inflation would rise from whatever pace it was at to something higher.  This was viewed as unsustainable, and hence the proper goal of economic policy was, in this view, to manage macro conditions so that the unemployment rate would never fall below the NAIRU rate.  That rate was also sometimes called the “full employment rate of unemployment”.

The question then is what the NAIRU rate might be.  While different economists came up with different estimates, estimates generally fell within the range of 5 to 6%.  An unemployment rate of less than this would then (under this theory) lead to a rise in inflation.

But that did not happen.  The unemployment rate fell to below 5% in 2016, and inflation remained low.  It fell to below 4% in 2018 and inflation remained low.  It fell to 3.5% in 2019 and into early 2020 and inflation remained low.

With the once again very strong labor market – with unemployment hitting 3.4% – has this now changed?  The rate of inflation did rise in 2021 and into 2022.  But if one looks at this chart, one sees that the timing is wrong:  Inflation rose earlier – in 2021 – when the unemployment rate was still well over 6% early in the year.  Furthermore, nominal wages only rose later:

Inflation (measured here by the consumer price index – the CPI – for all goods and services) can be volatile, but the upward trend began already in the second half of 2020 (although in part this was initially due to a recovery in prices from depressed levels earlier in 2020 due to the Covid crisis).  The chart shows the rates in terms of 3-month rolling averages (at annual equivalent rates and in arrears, so the figure for a January, say, would be for the months of November through January).  The pace of change in nominal wages (also as 3-month rolling averages and at annual rates) did not start to rise until mid-2021.  The increase in nominal wages appears to be more in response to the prior increase in prices – as firms found it profitable to employ more workers in an economy that grew strongly in 2021 – rather than a cause of those higher prices.  This is consistent with the view that the inflation was primarily due to demand-pull, rather than cost-push, factors.

[Technical Note:  The figures on changes in the nominal wage come from data assembled by the Federal Reserve Bank of Atlanta, drawing on data that can be obtained in the underlying micro-data files of the CPS.  The rotating panel of households in the CPS are interviewed for four months, not interviewed for the next eight months, and then interviewed again for four months.  New households are added each month and then removed after month 16 for them.  This allows the researchers to match individuals with their reported wages to what they had earned 12 months before.  It also allows them to examine the wage changes broken down by individual characteristics – such as age, gender, race, education level, occupation, where they are in the income distribution, and more – as these are all recorded in the CPS.  It is all very interesting, and worth visiting their website where they make it easy to see the impact on the measured changes in wages of many of these different factors.

The matching of wage changes by individuals also provides a much more reliable index than the commonly cited changes in average wages provided in the monthly Employment Situation report.  The latter comes from what employers report in the CES survey on the average wages they are paying.  Those averages will be affected by compositional effects.  For example, the reported average wages will often jump at the start of an economic downturn – such as it did in 2020 – as the less experienced and lower-wage workers are generally laid off first.  This leaves a greater share of more highly paid workers, which will lead the reported average wage to rise even though the economy had entered into a downturn.]

Not only did the rise in inflation precede the more modest increase in the pace at which nominal wages rose, but since mid-2022 the rate of inflation has come down while the job market has, if anything, become tighter.  The unemployment rate, as noted above, has been in the 3.4 to 3.7% range since March 2022, and is now at 3.4%.  Despite this, the three-month average increase in the seasonally adjusted CPI fell from 11.0% (at an annual rate) in the three months ending in June 2022, to just 1.8% in the three months ending in December.  If a tight labor market was driving inflation, one would have expected inflation to have kept going up rather than fall – and certainly not to fall by such a degree.

Furthermore, growth in nominal wages fell slightly from a peak of over 6.7% in the three months ending in June and also July 2022 (at an annual rate), to 6.1% as of December.  One would have expected the pace of change in wages to have continued to go up, rather than start to ease.

It is still early to be definitive on any of this.  Trends could change again.  Importantly, a significant part of the sharp fall in inflation in the second half of 2022 (when measured by the full CPI) was due to a fall in the prices of oil and other energy products.  However, while more recent, there are also early indications that core inflation (where food and energy prices are left out) is also falling.  In terms of the core CPI (again the seasonally adjusted index), the pace of inflation fell from a peak of 7.9% (at an annual rate) in the three months ending in June 2022, to just 3.1% in the three months ending in December.

That measure of inflation – the core CPI, which is often taken to be a better measure of underlying inflationary trends than the overall CPI as food and energy prices are volatile and go down as well as up – is now falling despite unemployment at the lowest rate it has been in more than a half-century.  If a tight labor market was driving inflation, then one would expect the pace of inflation to be rising, not falling.

C.  Employment Growth

The January jobs report was also noteworthy for its figures on employment growth.  Nonfarm payroll employment rose by 517,000 – far higher than most expected.  It is not that an increase in employment of a half million in a month is unprecedented.  It is rather that there was such an increase even though the unemployment rate was already at an extremely low 3.5% in the prior month.  (And while nonfarm payroll employment excludes those working in agriculture, that number is now small at only 1.4% of the labor force – based on estimates from the CPS and including those in agriculture who are self-employed.  It also excludes the self-employed outside of agriculture – a more substantial 5.6% of the labor force according to the CPS – but still not that large.  In terms of changes in the numbers from one period to the next, the impact on the employment estimates will be small.)

In addition, the January report also reflected revisions – undertaken every January – where new weights are used to generalize from what is found in the sample in the CES of firms and other entities (such as government agencies) that employ workers to what is estimated for the economy as a whole.  The re-weighting is based on a comprehensive count of payroll jobs in March of the year, with this then used to revise the estimates for all of the year (2022 in this case).

Due to the new weights, the increase in the number of jobs in the economy rose from the earlier estimate of 4.5 million in 2022 (i.e. from December 2021 to December 2022) to 4.8 million.  Between January 2022 and January 2023 the increase was an estimated 5.0 million additional jobs.  That is, between January 2022 and January 2023, the number employed increased by an average of 414,000 per month.

The 4.8 million growth in the number employed in 2022 was remarkable not only because it is a big number, but also because it came after the even stronger growth in employment in 2021.  Employment grew by 7.3 million in 2021.  In absolute terms, the 4.8 million figure in 2022 is higher than that of any year (other than 2021) in the statistics going back to when they started to be collected in the present form in 1939 (using BLS data).  Such a comparison is more than a bit unfair, of course, as the US economy has been growing and there are far more people employed now than decades ago.  But taking 2021 and 2022 together, the percentage growth over the two years – at 8.5% – was exceeded since 1951 only by greater increases in 1977-78 (10.2%), in 1965-66 (9.7%), and in 1964-65 (8.7% – that is, there was strong growth in the three straight years of 1964, 1965, and 1966).  Joe Biden was right when he said job growth in the first two years of his presidency (of 12.1 million) was greater than that of any other president, but it is not really a fair comparison as the economy is now larger.  But even in percentage terms, his record is excellent.

But such growth in the number employed cannot continue much longer.  To put this in perspective, the total adult population in the US (as reflected in the CPS, and with the new population controls) rose by only 1.8 million between January 2022 and January 2023, or 150,000 per month on average.  And the labor force figure, as estimated in the CPS, grew by only 1.3 million over that period, or 111,000 per month.  One cannot keep adding 414,000 per month to the number employed (as we saw in the year to January 2022) when the labor force is only growing by 111,000 per month, when the unemployment rate is already at a historical low of 3.4%.

[Note that one cannot simply subtract the January 2022 figures reported from the new January 2023 figures, since in the CPS they do not go back and revise the previous year figures to reflect the new population controls.  But they do show what the impact would have been on the December 2022 figures, and I assumed that they would have had the same impact on the January 2023 numbers.  The impacts should be similar.  One can then do the subtractions on a consistent basis.]

An increase in the number employed of an estimated 414,000 per month when the labor force was growing by only an estimated 111,000 per month was possible in 2022 in part because the unemployment rate came down (from 4.0% in January 2022 to 3.4% in January 2023), and in part because the labor force participation rate went up slightly (from 62.2% in January 2022 to 62.4% in January 2023).

But also a factor is that these are surveys from two different sources (households for the CPS and firms and other employers for the CES), and the sample estimates will not always be fully consistent with each other.  As was discussed in an earlier post on this blog, the estimates can differ from each other sometimes for significant periods of time.  However and importantly, over the long term the two estimates will eventually have to approach each other.  The population estimates used for the CPS will yield (for a given labor force participation rate) figures on the labor force, and hence growth in the adult population will yield figures on growth in the labor force.  For a given unemployment rate, the number employed – within the bounds of the statistical estimates – cannot grow faster than this.

With the unemployment rate now at 3.4%, one should not expect much if any further fall.  Indeed, the general expectation (and the more or less openly stated hope of the Fed) is that it will start to rise.  It is possible that the labor force participation rate will rise, but changes in this are generally pretty slow, driven mostly by demographics and social factors (the share of people aging into the normal age of retirement; the share of the young entering into the labor force given their decisions on whether and for how long to enroll in colleges and universities; decisions by households on whether one or both spouses will work; and similarly).

While there will be uncertainty in what will happen to the unemployment rate and the labor force participation rate, for given levels of each of these, employment cannot grow any faster than the labor force does.  (Indeed it is slightly less:  At an unemployment rate of 3.4%, employment will only grow at 96.6% of what the labor force grows by.)  With the labor force growing by 111,000 per month in the year ending in January 2023 (with this already reflecting a small increase in the labor force participation rate from 62.2% to 62.4%), it will not be possible for the monthly increase in employment to grow by much more than this.

Looking forward, one should not, therefore, expect growth in the number employed to be sustained at a level that is anywhere close to the 517,000 we had in January.  There will be month to month fluctuations, but one should not expect an average increase over several months that would be much in excess of the 111,000 figure for the growth in the labor force seen in the year ending in January 2023.

D.  Productivity

Politicians like strong job growth.  It is indeed popular.  But the flip side of this is that while the number employed grew rapidly in 2021 (by 3.2% December to December), GDP growth was less (1.0% from the fourth quarter of 2021 to the fourth quarter of 2022, based on the most recent estimates).  With the number employed growing faster than GDP, the mathematical consequence is that GDP per person employed went down.  That is:  Productivity fell in the year.

Higher productivity is ultimately what allows for higher living standards.  Falling productivity would thus be a problem.  However, in the context of the last several years, productivity growth has in fact been pretty good:

We are once again seeing the consequences of the highly unusual circumstances surrounding the Covid crisis.  With the onset of a downturn, firms will lay off workers.  But they may often lay off more workers than their output falls.  This might be because of uncertainty on how much the demand for whatever they make will fall in the downturn (and they will wish to be careful and if anything to overcompensate, given the difficulty of obtaining finance in a downturn and the very real possibility of bankruptcy); or because special government programs during the downturn reduce the cost to them and their workers of these layoffs (for example through the common response of extending unemployment benefits and making them more generous); or because the first workers being laid off are the least productive ones (possibly because they are relatively new and do not yet have as much experience as others working there) so that they end up with a workforce which is on average more productive.  Or, and very likely, it could be a combination of all three factors.  It looks very much like Schumpeter’s “creative destruction”.

The consequence is that productivity can in fact jump up in a downturn.  One sees such a clear jump in the chart in 2020, at the time of the sharp collapse due to the Covid crisis.  One also sees it in 2008-09, with the financial and economic collapse in the last year of the Bush administration and then the turnaround that began in mid-2009.  In terms of the numbers:  Real GDP fell by 1.3% between the first quarter of 2020 and the third quarter of 2020 (in absolute terms – not annualized).  But employment over this period fell by 7.4%.  As a result, productivity (real GDP per person employed) jumped by 6.6% in this half year.  In 2008/2009, real GDP was basically flat between the last quarter of 2008 and the last quarter of 2009 – rising by just 0.1%  But employment over this period fell by 4.1%, leading to an increase in productivity of 4.4%.

Following these brief periods where businesses are scrambling to survive the downturn by producing more (or perhaps not too much less) with many fewer workers, firms then enter into a more normal period where, as the economy recovers, they are able to sell more of their product.  They hire additional workers who are, by definition, less experienced in the work of that firm than their existing workforce.  The new workers might also be less capable or have a less applicable skill mix.  Productivity may then level off or even go down.  The latter situation is in particular likely when the economy recovers quickly and firms scramble to keep up with the increased demand for their product.

The latter fits well with what we saw in 2021.  GDP in 2021 rose by 5.9%, the highest of any year since 1984.  And the Personal Consumption component of GDP rose by 8.3% in 2021, the highest of any year since 1946.  This was spurred by the series of Covid relief packages passed in 2020 (under Trump) and in 2021 (under Biden), which totaled $5.7 trillion in the two years, or 12.8% of GDP of 2020 and 2021 together.  Personal savings rose to an unprecedented level as a share of GDP (other than during World War II, with data that go back to 1929), which then supported the strong growth in personal consumption in 2021.  This is consistent with a demand-led inflation that got underway in late 2020 or early 2021 (discussed above) – a risk of inflation that Larry Summers had warned of in early February 2021 when Biden’s $1.9 trillion Covid package was first proposed (and eventually passed, largely as proposed).

But what matters to long-term living standards is not so much the changes in average productivity in the periods surrounding economic downturns, but rather the trends in productivity growth over time.  A ten-year moving average is a useful metric:

The chart shows rolling ten-year averages starting from 1947/57 through to 2012/22 of the growth in GDP, in employment, and in productivity (GDP per person employed).  Productivity growth was relatively high at about 2% per annum in the 1950s and through most of the 1960s.  But it then started to fall in the 1970s to less than 1% a year before recovering and returning to about 2% a year in the ten-year period ending in 2004.  It then fell to roughly 0.8% a year since about 2017 (in terms of the ten-year averages), with some sharp fluctuations around that rate associated with the 2020 Covid crisis.  As of the end of 2022, the most recent ten-year average growth rate for productivity was 0.80%.

This has important implications for GDP growth might be going forward.  The labor force grew by 0.8% in 2022 (the adult population grew by 0.7%).  With unemployment close to a record low, employment will not be able to grow faster than the labor force – as discussed above.  And the labor force cannot grow faster than the adult population unless labor force participation rates increase.  But while there major disruptions in labor force participation in 2020 and 2021 surrounding the Covid crisis – with its lockdowns, economic collapse and then recovery, as well as health concerns affecting many – labor force participation largely returned to previous patterns in 2022.  Labor force participation rates have been slowly trending downwards since the late 1990s, and while it is possible this pattern might be reversed, it is difficult to see why it would.  There might well be short-term fluctuations for a period of a few years, but longer-term patterns are driven mostly by demographics (the age structure of the population) and social customs (e.g. whether women decide to enter into the paid labor force).

What follows from this is that if the labor force continues to grow at 0.8% a year (as it did in 2022 – and it grew only at a lower rate of 0.6% a year in the ten-year period ending in 2022), and productivity grows at 0.8% a year (as it did in the ten-year period ending in 2022), then GDP can at most grow at 1.6% a year on average.  This would be disappointing to many.  While there certainly can be and will be significant year to year variation around such a trend, faster growth would require either higher productivity growth or more entering into the labor force.

E.  Summary and Conclusion

The January jobs report was strong.  The unemployment rate is now at the lowest it has been in more than a half-century, and the number employed grew by more than a half million – a very high figure when the unemployment rate is so low.  While these are still preliminary figures and are subject to change as additional data become available, they present a picture of an extremely strong labor market.

The fall in the unemployment rate by one notch to 3.4% from the previous 3.5% should not, in itself, be taken too seriously.  That is well within the normal statistical error for this figure.  But what is indeed significant is that the unemployment rate has been within the narrow range of just 3.4 to 3.7% since March 2022.  That is low.  And it was in this low range during a period (in the second half of 2022) when inflation was coming down.  While changes in the price of oil have been a major factor in driving the inflation rate in 2022, the core rate of inflation (which excludes energy prices as well as those for food) has also started to come down.  The rate of change in nominal wages did start to grow in mid-2021, but this appears more to be a consequence of the rising prices rather than a cause of them.  And there has been a slight reduction in the pace of change in wages in recent months.

One does not see in this any evidence that a tight labor market with extremely low unemployment (the lowest in more than a half-century), has led to higher inflation.  The opposite has happened.  Inflation has come down at precisely the time the labor market has been the tightest.

GDP grew rapidly in 2021, but then slowed to a more modest 1.0% rate in 2022 (from fourth quarter to fourth quarter).  Coupled with rapid employment growth in the year, productivity (as measured by GDP per employed person) fell.  However, this appears more to be a continued reaction to changes surrounding the disruptions resulting from the 2020 Covid crisis.  During that crisis, GDP fell but employment fell by much more, leading to a jump in productivity despite the downturn.  As the economy recovered and the situation normalized, workers were hired to bring workforces back to desired levels.  Viewed in a longer timeframe, productivity growth has been similar to what it has now been since the mid-2010s.

That productivity growth is not especially high.  It was 0.8% at an annual rate in the most recent ten-year average.  Coupled with a labor force that grew at 0.8% in 2022, and going forward might grow by even less (it grew at 0.6% a year in the ten-year period ending in 2022), the ceiling on GDP growth would be 1.6% a year, or less.  That is not high, but expectations need to adjust.

That is also a ceiling on what GDP growth might be.  Many expect that there very well could be a recession either later in 2023 or in 2024.  Much will depend on whether the government will be able to respond appropriately if the economy appears to be heading into a downturn.  But with Republicans now in control of the House of Representatives, and threatening to force the US Treasury into default on the nation’s public debt if their demands for drastic spending cuts are not met, one cannot be optimistic that the government will be allowed to respond appropriately.

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.

How Low is Unemployment in Historical Perspective? – The Impact of the Changing Composition of the Labor Force

A.  Introduction

The unemployment rate is low, which is certainly good, and many commentators have noted it is now (at 3.7% in September and October, and an average of 3.9% so far this year) at the lowest the US has seen since the 1960s.  The rate hit 3.4% in late 1968 and early 1969, and averaged about 3.5% in each of those years.

But are those rates really comparable to what they are now?  This is important, not simply for “bragging rights” (or, more seriously, for understanding what policies led to such rates), but also for understanding how much pressure such rates are creating in the labor market.  The concern is that if the unemployment rate goes “too low”, labor will be able to demand a higher nominal wage and that this will then lead to higher price inflation.  Thus the Fed monitors closely what is happening with the unemployment rate, and will start to raise interest rates to cool down the economy if it fears the unemployment rate is falling so low that there soon will be inflationary pressures.  And indeed the Fed has, since 2016, started to raise interest rates (although only modestly so far, with the target federal funds rate up only 2.0% points from the exceptionally low rates it had been reduced to in response to the 2008/09 financial and economic collapse).

A puzzle is why the unemployment rate, at just 3.9% this year, has not in fact led to greater pressures on wages and hence inflation.  It is not because the modestly higher interest rates the Fed has set have led to a marked slowing down of the economy – real GDP grew by 3.0% in the most recent quarter over what it was a year before, in line with the pace of recent years.  Nor are wages growing markedly faster now than what they did in recent years.  Indeed, in real terms (after inflation), wages have been basically flat.

What this blog post will explore is that the unemployment rate, at 3.9% this year, is not in fact directly comparable with the levels achieved some decades ago, as the composition of the labor force has changed markedly.  The share of the labor force who have been to college is now much higher than it was in the 1960s.  Also, the share of the labor force who are young is now much less than it was in the 1960s.  And unemployment rates are now, and always have been, substantially less for those who have gone to college than for those who have not.  Similarly, unemployment rates are far higher for the young, who have just entered the labor force, than they are for those of middle age.

Because of these shifts in the shares, a given overall unemployment rate decades ago would only have happened had there been significantly lower unemployment rates for each of the groups (classified by age and education) than what we have now.  The lower unemployment rates for each of the groups, in that period decades ago, would have been necessary to produce some low overall rate of unemployment, as groups who have always had a relatively higher rate of unemployment (the young and the less educated) accounted for a higher share of the labor force then.  This is important, yet I have not seen any mention of the issue in the media.

As we will see, the impact of this changing composition of the labor force on the overall unemployment has been significant.  The chart at the top of this post shows what the overall unemployment rate would have been, had the composition of the labor force remained at what it was in 1970 (in terms of education level achieved for those aged 25 and above, plus for the share of youth in the labor force aged 16 to 24).  For 2018 (through the end of the third quarter), the unemployment rate at the 1970 composition of the labor force would then have been 5.2% – substantially higher than the 3.9% with the current composition of the labor force.  We will discuss below how these figures were derived.

At 5.2%, pressures in the labor market for higher wages will be substantially less than what one might expect at 3.9%.  This may explain the lack of such pressure seen so far in 2018 (and in recent years).  Although commonly done, it is just too simplistic to compare the current unemployment rate to what it was decades ago, without taking into account the significant changes in the composition of the labor force since then.

The rest of this blog post will first review this changing composition of the labor force – changes which have been substantial.  There are some data issues, as the Bureau of Labor Statistics (the source of all the data used here) changed its categorization of the labor force by education level in 1992.  Strictly speaking, this means that compositional shares before and after 1992 are not fully comparable.  However, we will see that in practice the changes were not such as to lead to major differences in the calculation of what the overall unemployment rate would be.

We will also look at what the unemployment rates have been for each of the groups in the labor force relative to the overall average.  They have been remarkably steady and consistent, although with some interesting, but limited, trends.  Finally, putting together the changing shares and the unemployment rates for each of the groups, one can calculate the figures for the chart at the top of this post, showing what the unemployment rates would have been over time, had the labor force composition not changed.

B.  The Changing Composition of the Labor Force

The composition of the labor force has changed markedly in the US in the decades since World War II, as indeed it has around the world.  More people have been going to college, rather than ending their formal education with high school.  Furthermore, the post-war baby boom which first led (in the 1960s and 70s) to a bulge in the share of the adult labor force who were young, later led to a reduction in this share as the baby boomers aged.

The compositional shares since 1965 (for age) and 1970 (for education) are shown in this chart (where the groups classified by education are of age 25 or higher, and thus their shares plus the share of those aged 16 to 24 will sum to 100%):

The changes in labor force composition are indeed large.  The share of the labor force who have completed college (including those with an advanced degree) has more than tripled, from 11% of the labor force in 1970 to 35% in 2018.  Those with some college have more than doubled, from 9% of the labor force to 23%.  At the other end of the education range, those who have not completed high school fell from 28% of the labor force to just 6%, while those completing high school (and no more) fell from 30% of the labor force to 22%.  And the share of youth in the labor force first rose from 19% in 1965 to a peak of  24 1/2% in 1978, and then fell by close to half to 13% in 2018.

As we will see below, each of these groups has very different unemployment rates relative to each other.  Unemployment rates are far less for those who have graduated from college than they are for those who have not completed high school, or for those 25 or older as compared to those younger.  Comparisons over time of the overall unemployment rate which do not take this changing composition of the labor force into account can therefore be quite misleading.

But first some explanatory notes on the data.  (Those not interested in data issues can skip this and go directly to the next section below.)  The figures were all calculated from data collected and published by the Bureau of Labor Statistics (BLS).  The BLS asks, as part of its regular monthly survey of households, questions on who in the household is participating in the labor force, whether they are employed or unemployed, and what their formal education has been (as well as much else).  From this one can calculate, both overall and for each group identified (such as by age or education) the figures on labor force shares and unemployment rates.

A few definitions to keep in mind:  Adults are considered to be those age 16 and above; to be employed means you worked the previous week (from when you were being surveyed) for at least one hour in a paying job; and to be unemployed means you were not employed but were actively searching for a job.  The labor force would thus be the sum of those employed or unemployed, and the unemployment rate would be the number of unemployed in whatever group as a share of all those in the labor force in that group.  Note also that full-time students, who are not also working in some part-time job, are not part of the labor force.  Nor are those, of whatever age, who are not in a job nor seeking one.

The education question in the survey asks, for each household member in the labor force, what was the “highest level of school” completed, or the “highest degree” received.  However, the question has been worded this way only since 1992.  Prior to 1992, going back to 1940 when they first started to ask about education, the question was phrased as the “highest grade or year of school” completed.  The presumption was that if the person had gone to school for 12 years, that they had completed high school.  And if 13 years that they had completed high school plus had a year at a college level.

However, this presumption was not always correct.  The respondent might only have completed high school after 13 years, having required an extra year.  Thus the BLS (together with the Census Bureau, which asks similar questions in its surveys) changed the way the question was asked in 1992, to focus on the level of schooling completed rather than the number of years of formal schooling enrolled.

For this reason, while all the data here comes from the BLS, the BLS does not make it easy to find the pre-1992 data.  The data series available online all go back only to 1992.  However, for the labor force shares by education category, as shown in the chart above, I was able to find the series under the old definitions in a BLS report on women in the labor force issued in 2015 (see Table 9, with figures that go back to 1970).  But I have not been able to find a similar set of pre-1992 figures for unemployment rates for groups classified by education.  Hence the curve in the chart at the top of this post on the unemployment rate holding constant the composition of the labor force could only start in 1992.

Did the change in education definitions in 1992 make a significant difference for what we are calculating here?  They will matter only to the extent that:  1)  the shifts from one education category to another were large; and 2) the respective unemployment rates where there was a significant shift from one group to another were very different.

As can be seen in the chart above, the only significant shifts in the trends in 1992 was a downward shift (of about 3% points) in the share of the labor force who had completed high school and nothing more, and a similar upward shift (relative to trend) in the share with some college. There are no noticeable shifts in the trends for the other groups.  And as we will see below, the unemployment rates of the two groups with a shift (completed high school, vs. some college) are closer to each other than that for any other pairing of the different groups.  Thus the impact on the calculated unemployment rate of the change in categorization in 1992 should be relatively small.  And we will see below that that in fact is the case.

There was also another, but more minor (in terms of impact), change in 1992.  The BLS always reported the educational composition of the labor force only for those labor force members who were age 25 or above.  However, prior to 1992 it reported the figures only for those up to age 64, while from 1992 onwards it reported the figure at any higher age if still in the labor force, including those who at age 65 or more but not yet retired.  This was done as an increasing share over time of those in the US of age 65 or higher have remained in the labor force rather than retiring.  However, the impact of this change will be small.  First, the share of the labor force of age 65 or more is small.  And second, this will matter only to the extent that the shares by education level differ between those still in the labor force who are age 65 or more, as compared to those in the labor force of ages 25 to 64.  Those differences in education shares are probably not that large.

C.  Differences in Unemployment Rates by Age and Education 

As noted above, unemployment rates differ between groups depending on age and education.  It should not be surprising that those who are young (ages 16 to 24) who are not in school but are seeking a job will experience a high rate of unemployment relative to those who are older (25 and above).  They are just starting out, probably do not have as high an education level (they are not still in school), and lack experience.  And that is indeed what we observe.

At the other extreme we have those who have completed college and perhaps even hold an advanced degree (masters or doctorate).  They are older, have better contacts, normally have skills that have been much in demand, and may have networks that function at a national rather than just local level.  The labor market works much better for them, and one should expect their unemployment rate to be lower.

And this is what we have seen (although unfortunately, for the reasons noted above on the data, the BLS is only making available the unemployment rates by education category for the years since 1992):

The unemployment rates of each group vary substantially over time, in tune with the business cycle, but their position relative to each other is always the same.  That is, the rates move together, where when one is high it will also be high for the others.  This is as one would expect, as movements in unemployment rates are driven primarily by the macroeconomy, with all the rates moving up when aggregate demand falls to spark a recession, and moving down in a recovery.

And there is a clear pattern to these relationships, which can be seen when these unemployment rates are all expressed as a ratio to the overall unemployment rate:

The unemployment rate for those just entering the labor force (ages 16 to 24) has always been about double what the overall unemployment rate was at the time.  And it does not appear to be subject to any major trend, either up or down.  Those in the labor force (and over age 25) with less than a high school degree (the curve in blue) also have experienced a higher rate of unemployment than the overall rate at the time – 40 to 60% higher.  There might be some downward trend, but one cannot yet say whether it is significant.  We need some more years of data.

Those in the labor force with just a high school degree (the curve in green in the chart) have had an unemployment rate very close to the average, with some movement from below the average to just above it in recent years.  Those with some college (in red) have remained below the overall average unemployment rate, although less so now than in the 1990s.  And those with a college degree or more (the curve in purple) have had an unemployment of between 60% below the average in the 1990s to about half now.

There are probably a number of factors behind these trends, and it is not the purpose of this blog post to go into them.  But I would note that these trends are consistent with what a simple supply and demand analysis would suggest.  As seen in the chart in section B of this post, the share of the labor force with a college degree, for example, has risen steadily over time, to 35% of the labor force now from 22% in 1992.  With that much greater supply and share of the labor force, the advantage (in terms of a lower rate of unemployment relative to that of others) can be expected to have diminished.  And we see that.

But what I find surprising is that that impact has been as small as it has.  These ratios have been remarkably steady over the 27 years for which we have data, and those 27 years have included multiple cycles of boom and bust.  And with those ratios markedly different for the different groups, the composition of the labor force will matter a great deal for the overall unemployment rate.

D.  The Unemployment Rate at a Fixed Composition of the Labor Force

As noted above, those in the labor force who are not young, or who have achieved a higher level of formal education, have unemployment rates which are consistently below those who are young or who have less formal education.  Their labor markets differ.  A middle-aged engineer will be considered for jobs across the nation, while someone with who is just a high school graduate likely will not.

Secondly, when we say the economy is at “full employment” there will still be some degree of unemployment.  It will never be at zero, as workers may be in transition between jobs and face varying degrees of difficulty in finding a new job.  But this degree of “frictional unemployment” (as economists call it) will vary, as just noted above, depending on age (prior experience in the labor force) and education.  Hence the “full employment rate of unemployment” (which may sound like an oxymoron, but isn’t) will vary depending on the composition of the labor force.  And more broadly and generally, the interpretation given to any level of unemployment needs to take into account that compositional structure of the labor force, as certain groups will consistently experience a higher or lower rate of unemployment than others, as seen in the chart above.

Thus it is misleading simply to compare overall unemployment rates across long periods of time, as the compositional structure of the labor force has changed greatly over time.  Such simple comparisons of the overall rate may be easy to do, but to understand critical issues (such as how close are we to such a low rate of unemployment that there will be inflationary pressure in the labor market), we should control for labor force composition.

The chart at the top of this post does that, and I repeat it here for convenience (with the addition in purple, to be explained below):

The blue line shows the unemployment rate for the labor force since 1965, as conventionally presented.  The red line shows, in contrast, what the unemployment rate would have been had the unemployment rate for each identified group been whatever it was in each year, but with the labor force composition remaining at what it was in 1970.  The red line is a simple weighted average of the unemployment rates of each group, using as weights what their shares would have been had they remained at the shares of 1970.

The labor force structure of 1970 was taken for this exercise both because it is the earliest year for which I could find the necessary data, and because 1970 is close to 1968 and 1969, when the unemployment rate was at the lowest it has been in the last 60 years.  And the red curve can only start in 1992 because that is the earliest year for which I could find unemployment rates by education category.

The difference is significant.  And while perhaps difficult to tell from just looking at the chart, the difference has grown over time.  In 1992, the overall unemployment rate (with all else equal) at the 1970 compositional shares, would have been 23% higher.  By 2018, it would have grown to 33% higher.  Note also that, had we had the data going back to 1970 for the unemployment rates by education category, the blue and red curves would have met at that point and then started to diverge as the labor force composition changed.

Also, the change in 1992 in the definitions used by the BLS for classifying the labor force by education did not have a significant effect.  For 1992, we can calculate what the unemployment rate would have been using what the compositional shares were in 1991 under the old classification system.  The 1991 shares for the labor force composition would have been very close to what they would have been in 1992, had the BLS kept the old system, as labor force shares change only gradually over time.  That unemployment rate, using the former system of compositional shares but at the 1992 unemployment rates for each of the groups as defined under the then new BLS system of education categories, was almost identical to the unemployment rate in that year:  7.6% instead of 7.5%.  It made almost no difference.  The point is shown in purple on the chart, and is almost indistinguishable from the point on the blue curve.  And both are far from what the unemployment rate would have been in that year at the 1970 compositional weights (9.2%).

E.  Conclusion

The structure of the labor force has changed markedly in the post-World War II period in the US, with a far greater share of the labor force now enjoying a higher level of formal education than we had decades ago, and also a significantly lower share who are young and just starting in the labor force.  Since unemployment rates vary systematically by such groups relative to each other, one needs to take into account the changing composition of the labor force when making comparisons over time.

This is not commonly done.  The unemployment rate has come down in 2018, averaging 3.9% so far and reaching 3.7% in September and October.  It is now below the 3.8% rate it hit in 2000, and is at the lowest seen since 1969, when it hit 3.4% for several months.

But it is misleading to make such simple comparisons as the composition of the labor force has changed markedly over time.  At the 1970 labor force shares, the unemployment rate in 2018 would have been 5.2%, not 3.9%.  And at a 5.2% rate, the inflationary pressures expected with an exceptionally low unemployment rate will not be as strong.  This may, at least in part, explain why we have not seen such inflationary pressures grow this past year.