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

A.  Introduction

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

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

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

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

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

B.  Growth in the Labor Force 

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

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

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

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

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

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

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

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

C.  Growth in Productivity 

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

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

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

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

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

 

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

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

Growth Rates

Employment

GDP per worker

GDP

1947-1967

2.1%

2.0%

4.1%

1967-1987

2.2%

0.9%

3.1%

1987-2007

1.6%

1.5%

3.1%

2007-2017

0.6%

0.8%

1.4%

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

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

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

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

  Alternative Growth Scenarios

 Growth Rates:

GDP 

Population

GDP per capita

Cumulative

Over 30 years

1.0%

0.8%

0.2%

6%

2.0%

0.8%

1.2%

43%

3.0%

0.8%

2.2%

91%

4.0%

0.8%

3.2%

155%

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

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

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

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

Looking forward, what pace of productivity growth might be expected?  As discussed above, while the US has been able to achieve productivity growth at a rate of about 2.0% in the 1950s and 1960s, and very briefly between 1995 and 2005, it has not been able to reach a rate higher than this for any sustained period (of 10 years or more).  And over time, there is some evidence that reaching the rates of productivity growth enjoyed in the past is becoming increasingly difficult.

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

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

Productivity Growth

Agriculture

Manufacturing

Services

Overall (calculated)

1947 to 2015:

3.3%

2.8%

0.9%

1.4%

At GDP Shares of:

   – 1947 shares

8.0%

27.7%

64.3%

1.7%

   – 1980 shares

2.2%

23.6%

74.2%

1.4%

   – 2015 shares

1.0%

13.9%

85.2%

1.2%

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

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

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

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

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

E.  Conclusion

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

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

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

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

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

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

 

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

A.  Introduction

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

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

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

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

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

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

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

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

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

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

B.  Sector Shares and Prices

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

C.  Growth in Output, Employment, and Productivity

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Growth Rates:

1947 to 2015

Employment

Productivity

Output

Total (GDP)

1.5%

1.4%

2.9%

Agriculture

-0.7%

3.3%

2.6%

Manufacturing

-0.3%

2.8%

2.5%

Services

2.1%

0.9%

3.0%

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

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

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

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

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

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

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

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

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

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

Tax Cuts Do Not Spur Growth – There Are Income as well as Substitution Effects, and Much More Besides: Econ 101

gdp-growth-and-top-marg-tax-rate-1930-to-2015

A.   Introduction, and a Brief Aside on the Macro Issues

While there is much we do not yet know on what economic policies Donald Trump will pursue (he said many things in his campaign, but they were often contradictory), one thing we can be sure of is that there will be a major tax cut.  Republicans in Congress (led by Paul Ryan) and in the Senator want the same.  And they along with Trump insist that the cuts in tax rates will spur a sharp jump in GDP growth, with the result that net tax revenues in the end will not fall by all that much.

But do tax cuts spur growth?  The chart above suggests not.  Marginal tax rates of those in the top income brackets have come down sharply since the 1950s and early 1960s, when they exceeded 90%.  They reached as low as 28% during the later Reagan years and 35% during the administration of George W. Bush.  But GDP growth did not jump to some higher rate as a result.

This Econ 101 post will discuss the economics on why this is actually what one should expect.  It will focus on the microeconomics behind this, as the case for income tax cuts is normally presented by the so-called “supply siders” as a micro story of incentives.  The macro case for tax cuts is different.  Briefly, in times of high unemployment when the economy is suffering from insufficient demand in the aggregate to purchase all that could be produced if more labor were employed, a cut in income taxes might spur demand by households, as they would then have higher post-tax incomes to spend on consumption items.  This increase in demand could then spur production and hence GDP.

Critically, this macro story depends on allowing the fiscal deficit to rise by there not being simultaneously a cut in government expenditures along with the tax cuts.  If there is such a cut in government expenditures, demand may be reduced by as much as or even more than demand would be increased by households.  But the economic plans of both Trump and Congressman (and Speaker) Paul Ryan do also call for large cuts in government expenditures.  While both Trump and Ryan have called for government expenditures to increase on certain items, such as for defense, they still want a net overall reduction.

The net impact on demand will then depend on how large the government expenditure cuts would be relative to the tax cuts, and on the design of the income tax cuts.  As was discussed in an earlier post on this blog on the size of the fiscal multiplier, If most of the income tax cuts go to those who are relatively well off, who will then save most or perhaps all of their tax windfall, there will be little or no macro stimulus from the tax cuts.  Any government expenditure cuts on top of this would then lead not to a spur in growth, but rather to output growing more slowly or contracting.  And the tax plan offered by Donald Trump in his campaign would indeed direct the bulk of the tax cuts to the extremely well off.  A careful analysis by the non-partisan Tax Policy Center found that 71% of the tax cuts (in dollar value) from the overall plan (which includes cuts in corporate and other taxes as well) would go to the richest 5% of households (those earning $299,500 or more), 51% would go to the top 1% (those earning $774,300 or more), and fully 25% would go to the richest 0.1% (those earning $4.8 million or more).

[A side note:  To give some perspective on how large these tax cuts for the rich would be, the 25% going to the richest 0.1% under Trump’s plan would total $1.5 trillion over the next ten years, under the Tax Policy Center estimates.  By comparison, the total that the Congressional Budget Office projects would be spent on the food stamp program (now officially called SNAP) for the poor over this period would come to a bit below $700 billion (see the August 2016 CBO 10-year budget projections).  That is, the tax breaks to be given under Trump’s tax plan to the top 0.1% (who have earnings of $4.8 million or more in a year) would be more than twice as large as would be spent on the entire food stamp program over the period.  Yet the Republican position is that we have to cut the food stamp program because we do not have sufficient government revenues to support it.]

The macro consequences of tax cuts that mostly go to the already well off, accompanied by government expenditure cuts to try to offset the deficit impact, are likely therefore to lead not to a spur in growth but to the opposite.

The microeconomic story is separate, and the rest of this blog post will focus on the arguments there.  Those who argue that cuts in income taxes will act as a spur to growth base their argument on what they see as the incentive effects.  Income taxes are a tax on working, they argue, and if you tax income less, people will work longer hours.  More will be produced, the economy will grow faster, and people will have higher incomes.

This micro argument is mistaken in numerous ways, however.  This Econ 101 post will discuss why.  There is the textbook economics, where it appears these “supply siders” forgot some of the basic economics they were taught in their introductory micro courses. But we should also recognize that the decision on how many hours to work each week goes beyond simply the economics.  There are important common social practices (which can vary by the nature of the job, i.e. what is a normal work day, and what do you do to get promoted) and institutional structures (the 40 hour work week) which play an important and I suspect dominant role. This blog post will review some of them.

But first, what do we know from the data, and what does standard textbook economics say?

B.  Start with the Data

It is always good first to look at what the data is telling us.  There have been many sharp cuts in income tax rates over the last several decades, and also some increases.  Did the economy grow faster after the tax cuts, and slower following the tax increases?

The chart at the top of this post indicates not.  The chart shows what GDP growth was year by year since 1930 along with the top marginal income tax rate of each year.  The top marginal income tax rate is the rate of tax that would be paid on an additional dollar of income by those in the highest income tax bracket.  The top marginal income tax rate is taken by those favoring tax cuts as the most important tax rate to focus on.  It is paid by the richest, and these individuals are seen as the “job creators” and hence play an especially important role under this point of view.  But changes in the top rates also mark the times when there were normally more general tax cuts for the rest of the population as well, as cuts (or increases) in the top marginal rates were generally accompanied by cuts (or increases) in the other rates also.  It can thus be taken as a good indicator of when tax rates changed and in what direction.  Note also that the chart combines on one scale the annual GDP percentage growth rates and the marginal tax rate as a percentage of an extra dollar of income, which are two different percentage concepts.  But the point is to compare the two.

As the chart shows, the top marginal income tax rate exceeded 90% in the 1950s and early 1960s.  The top rate then came down sharply, to generally 70% until the Reagan tax cuts of the early 1980s, when they fell to 50% and ultimately to just 28%.  They then rose under Clinton to almost 40%, fell under the Bush II tax cuts to 35%, and then returned under Obama to the rate of almost 40%.

Were GDP growth rates faster in the periods when the marginal tax rates were lower, and slower when the tax rates were higher?  One cannot see any indication of it in the chart. Indeed, even though the highest marginal tax rates are now far below what they were in the 1950s and early 1960s, GDP growth over the last decade and a half has been less than it what was when tax rates were not just a little bit, but much much higher.  If cuts in the marginal tax rates are supposed to spur growth, one would have expected to see a significant increase in growth between when the top rate exceeded 90% and where it is now at about 40%.

Indeed, while I would not argue that higher tax rates necessarily lead to faster growth, the data do in fact show higher tax rates being positively correlated with faster growth.  That is, the economy grew faster in years when the tax rates were higher, not lower.  A simple statistical regression of the GDP growth rate on the top marginal income tax rate of the year found that if the top marginal tax rate were 10% points higher, GDP growth was 0.57% points higher.  Furthermore, the t-statistic (of 2.48) indicates that the correlation was statistically significant.

Again, I would not argue that higher tax rates lead to faster GDP growth.  Rather, much more was going on with the economy over this period which likely explains the correlation. But the data do indicate that very high top marginal income tax rates, even over 90%, were not a hindrance to growth.  And there is clearly no support in the evidence that lower tax rates lead to faster growth.

The chart above focuses on the long-term impacts, and does not find any indication that tax cuts have led to faster growth.  An earlier post on this blog looked at the more immediate impacts of such tax rates cuts or increases, focussing on the impacts over the next several years following major tax rate changes.  It compared what happened to output and employment (as well as what happened to tax revenues and to the fiscal deficit) in the immediate years following the Reagan and Bush II tax cuts, and following the Clinton and Obama tax increases.  What it found was that growth in output and employment, and in fiscal revenues, were faster following the Clinton and Obama tax increases than following the Reagan and Bush II tax cuts.  And not surprisingly given this, the fiscal deficit got worse under Reagan and Bush II following their tax cuts, and improved following the Clinton and Obama tax increases.

C.  The Economics of the Impact of Tax Rates on Work Effort

The “supply siders” who argue that cuts in income taxes will lead to faster growth base their case on what might seem (at least to them) simple common sense.  They say that if you tax something, you will produce less of it.  Tax it less, and you will produce more of it. And they say this applies to work effort.  Income taxes are a tax on work.  Lower income tax rates will then lead to greater work effort, they argue, and hence to more production and hence to more growth.  GDP growth rates will rise.

But this is wrong, at several levels.  One can start with some simple math.  The argument confuses what would be (by their argument) a one-time step-up in production, with an increase in growth rates.  Suppose that tax rates are cut and that as a result, everyone decides that at the new tax rates they will choose to work 42 hours a week rather than 40 hours a week before.  Assuming productivity is unchanged (actually it would likely fall a bit), this would lead to a 5% increase in production.  But this would be a one time increase. GDP would jump 5% in the first year, but would then grow at the same rate as it had before.  There would be no permanent increase in the rate of growth, as the supply siders assert.  This is just simple high school math.  A one time increase is not the same as a permanent increase in the rate of growth.

But even leaving this aside, the supply sider argument ignores some basic economics taught in introductory microeconomics classes.  Focussing just on the economics, what would be expected to happen if marginal income tax rates are cut?  It is true that there will be what economists call “substitution effects”, where workers may well wish to work longer hours if their after-tax income from work rises due to a cut in marginal tax rates. But the changes will also be accompanied by what economists call “income effects”.  Worker after-tax incomes will change both because of the tax rate changes and because of any differences in the hours they work.  And these income effects will lead workers to want to work fewer hours.  The income and substitution effects will work in opposite directions, and the net impact of the two is not clear.  They could cancel each other out.

What are the income effects, and why would they lead to less of an incentive to work greater hours if the tax rate falls?:

a)  First, one must keep in mind that the aim of working is to earn an income, and that hours spent working has a cost:  One will have fewer hours at home each day to enjoy with your wife and kids, or for whatever other purposes you spend your non-working time. Economists lump this all under what they call “leisure”.  Leisure is something desirable, and with all else equal, one would prefer more of it.  Economists call this a “normal good”.  With a higher income, you would want to buy more of it. And the way you buy more of it is by working fewer hours each day (at the cost of giving up the wages you would earn in those hours).

Hence, if taxes on income go down, so that your after-tax income at the original number of hours you work each day goes up, you will want to use at least some portion of this extra income to buy more time to spend at home.  This is an income effect, and will go in the opposite direction of the substitution effect of higher after-tax wages leading to an incentive to work longer hours.  We cannot say, a priori, whether the income effect or the substitution effect will dominate.  It will vary by individual, based on their individual preferences, what their incomes are, and how many hours they were already working.  It could go either way, and can only be addressed by looking at the data.

b)  One should also recognize that one works to earn income for a reason, and one reason among many is to earn and save enough so that one can enjoy a comfortable retirement. But in standard economic theory, there is no reason to work obsessively before retirement so that one will then have such a large retirement “nest egg” as to enjoy a luxurious life style when one retires.  Rather, the aim is to smooth out your consumption profile over both periods in your life.

Hence if income tax rates are cut, so that your after-tax incomes are higher, one will be able to save whatever one is aiming for for retirement, sooner.  Hence it would be rational to reduce by some amount the hours one seeks to work each day, and enjoy them with your wife and kids at home, as your savings goals for retirement can still be met with those fewer hours of work.  This is an income effect, and acts in the direction of reducing, rather than increasing, the number of hours one will choose to work if there is a general tax cut.

c)  More generally, one should recognize that incomes are earned to achieve various aims. Some of these might be to cover fixed obligations, such as to pay on a mortgage or for student debt, and some might be quasi-fixed, such as to provide for a “comfortable” living standard for one’s family.  If those aims are being met, then time spent at leisure (time spent at home with the family) may be especially attractive.  In such circumstances, the income effect from tax cuts might be especially large, and sufficient to more than offset the substitution effects resulting from the change in the after-tax wage.

Income effects are real, and it is mistake to ignore them.  They act in the opposite direction of the substitution effect, and will act to offset them.  The offset might be partial, full, or even more than full.  We cannot say simply by looking at the theory.  Rather, one needs to look at the data.  And as noted above, the data provdes no support to the suppostion that lower tax rates will lead to higher growth.  Once one recognizes that there will be income effects as well as substitution effects, one can see that this should not be a surprise.  It is fully consistent with the theory.

One can also show how the income and substitution effects work via some standard diagrams, involving indifference curves and budget constraints.  These are used in most standard economics textbooks.  However, I suspect that most readers will find such diagrams to be more confusing than enlightening.  A verbal description, such as that above, will likely be more easy to follow.  But for those who prefer such diagrams, the standard ones can be found at this web posting.  Note, however, that there is a mistake (a typo I assume) in the key Figures 2A and 2B.  The horizontal arrows (along the “leisure” axis) are pointed in the opposite direction of what they should (left instead of right in 2A and right instead of left in 2B).  These errors indeed serve to emphasize how even the experts with such diagrams can get confused and miss simple typos.

D.  But There is More to the Hours of Work Decision than Textbook Economics

The analysis above shows that the supply-siders, who stress microeconomic incentives as key, have forgotten half of the basic analysis taught in their introductory microeconomics classes.  There are substitution effects resulting from a change in income tax rates, as the supply-siders argue, but there are also income effects which act in the opposite direction. The net effect is then not clear.

However, there is more to the working hours decision than the simple economics of income and substitution effects.  There are social as well as institutional factors.  It the real world, these other factors matter.  And I suspect they matter a good deal more than the standard economics in explaining the observation that we do not see growth rates jumping upwards after the several rounds of major tax cuts of the last half century.

Such factors include the following:

a)  For most jobs, a 40 hour work week is, at least formally, standard.  For those earning hourly wages, any overtime above 40 hours is, by law, supposed to be compensated at 50% above their normal hourly wage.  For workers in such jobs, one cannot generally go to your boss and tell him, in the event of an income tax increase say, that you now want to work only 39 1/2 hours each week.  The hours are pretty much set for such workers.

b)  There are of course other workers compensated by the hour who might work a variable number of hours each week at a job.  These normally total well less than 40 hours a week.  These would include many low wage occupations such as at fast food places, coffee shops, retail outlets, and similarly.  But for many such workers, the number of hours they work each week is constrained not by the number of hours they want to work, but by the number of hours their employer will call them in for.  A lower income tax rate might lead them to want to work even more hours, but when they are constrained already by the number of hours their employer will call them in for, there will be no change.

c)  For salaried workers and professionals such as doctors, the number of hours they work each week is defined primarily by custom for their particular profession.  They work the hours that others in that profession work, with this evolving over time for the profession as a whole.  The hours worked are in general not determined by some individual negotiation between the professional and his or her supervisor, with this changing when income tax rates are changed.  And many professionals indeed already work long hours (including medical doctors, where I worry whether they suffer from sleep deprivation given their often incredibly long hours).

d)  The reason why one sees many professionals, including managers and others in office jobs, working such long hours probably has little to do with marginal income tax rates.  Rather, they try to work longer than their co-workers, or at least not less, in order to get promoted.  Promotion is a competition, where the individual seen as the best is the one who gets promoted.  And the one seen as the best is often the one who works the longest each day.  With the workers competing against each other, possibly only implicitly and not overtly recognized as such, there will be an upward spiral in the hours worked as each tries to out-do the other.  This is ultimately constrained by social norms.  Higher or lower income tax rates are not central here.

e)  Finally, and not least, most of us do take pride in our work.  We want to do it well, and this requires a certain amount of work effort.  Taxes are not the central determinant in this.

E.  Summary and Conclusion

I fully expect there to be a push to cut income tax rates early in the Trump presidency.  The tax plan Trump set out during his campaign was similar to that proposed by House Speaker Paul Ryan, and both would cut rates sharply, especially for those who are already well off. They will argue that the cuts in tax rates will spur growth in GDP, and that as a consequence, the fiscal deficit will not increase much if at all.

There is, however, no evidence in the historical data that this will be the case.  Income tax rates have been cut sharply since the Eisenhower years, when the top marginal income tax rate topped 90%, but growth rates did not jump higher following the successive rounds of cuts.

Tax cuts, if they are focused on those of lower to middle income, might serve as a macro stimulus if unemployment is significant.  Such households would be likely to spend their extra income on consumption items rather than save it, and this extra household consumption demand can serve to spur production.  But tax cuts that go primarily to the rich (as the tax cuts that have been proposed by Trump and Ryan would do), that are also accompanied by significant government expenditure cuts, will likely have a depressive rather than stimulative effect.

The supply-siders base their argument, however, for why tax cuts should lead to an increase in the growth rate of GDP, not on the macro effects but rather on what they believe will be the impact on microeconomic incentives.  They argue that income taxes are a tax on work, and a reduction in the tax on work will lead to greater work effort.

They are, however, confused.  What they describe is what economists call the substitution effect.  That may well exist.  But there are also income effects resulting from the changes in the tax rates, and these income effects will work in the opposite direction.  The net impact is not clear, even if one keeps just to standard microeconomics.  The net impact could be a wash.  Indeed, the net impact could even be negative, leading to fewer hours worked when there is a cut in income taxes.  One does not know a priori, and you need to look at the data.  And there is no indication in the data that the sharp cuts in marginal tax rates over the last half century have led to higher rates of growth.

There is also more to the working hours decision than just textbook microeconomics. There are important social and institutional factors, which I suspect will dominate.  And they do not depend on the marginal rates of income taxes.

But if you are making an economic argument, you should at least get the economics right.

The Structural Factors Behind the Steady Fall in Labor Force Participation Rates of Prime Age Workers

labor-force-participation-rate-ages-25-to-54-all-male-female-jan-1948-to-sept-2016

I would like to acknowledge and thank Mr. Steve Hipple, Economist at the Bureau of Labor Statistics, for his generous assistance in assembling data on labor force participation rates used in the blog post below.  This post would not have been possible without his help.

A.  Introduction

Increasing attention has recently been directed to the decline in labor force participation rates observed for men over the last several decades, and for women since the late 1990s.  The chart above tracks this.  It has indeed been dubbed (for men) a “quiet catastrophe” in a new book by Nicholas Eberstadt titled “Men Without Work”.

The issue has been taken up by those both on the right and on the left.  Even President Obama, in one of the rare “By invitation” pieces that The Economist occasionally publishes, has highlighted the concern in an article under his name in last week’s issue (the issue of October 8).  President Obama treats it as one of “four crucial areas of unfinished business” his successor will need to address.  A chart similar to that above is shown.  President Obama notes that in 1953, just 3% of men between the ages of 25 and 54 were not working, while the figure today is 12% (that is, the labor force participation rate fell from 97% to 88%).  The share of women of the same age group not participating in the formal labor market has similarly been falling since 1999.

While Obama is careful in his wording not to say directly that all of this increase in those not working was due to “involuntary joblessness”, he does note that involuntary joblessness takes a devastating toll on those unable to find jobs.  This is certainly correct. The fundamental question, however, is to what degree do we know whether the rise has been involuntary, and to what degree has it risen due to possibly more benign factors with rational choices being made.

Nick Eberstadt, who is perhaps the person most responsible for raising the profile of this issue, is a senior researcher at the conservative American Enterprise Institute.  He sees it as a major problem.  And a recent piece by the conservative columnist George Will, which is essentially a review of Eberstadt’s new work, appeared in the journal of conservative opinion the National Review, where it was subtitled “American men who choose not to work are choosing lives of quiet self-emasculation”.

On the left, Larry Summers praised Eberstadt’s book in a review published in the Financial Times (see this blog post of his for a non-paywalled summary), as did Justin Fox in this post at Bloomberg View.  But both emphasize factors beyond the worker’s control, in particular that “good” jobs are disappearing because of technological change (as well as, perhaps, international trade), and that America’s high prison incarceration rates have made it impossible for many men to be hired for the jobs there are.

Alan Kreuger, a Professor of Economics at Princeton and a former chair of President Obama’s Council of Economic Advisers, suggests a different factor in a recent carefully done academic analysis.  (See also this piece by Peter Coy for a non-technical summary of Krueger’s work.)  He found that poor health may well explain the high level for men, and found from independent data that nearly half of prime age men not in the labor force are taking pain medication daily.  As with the others, Krueger stresses that the issue is an important one. He concludes his paper by noting that “The decline in labor force participation in the US over the past two decades is a macroeconomic and social concern”, and that addressing it for “prime age men should be a national priority”.

While both sides have praised Eberstadt’s work, it is probably not surprising that they stress different underlying causes.  Those on the right blame individuals for becoming increasingly unwilling to work, abetted by foolish government policies that have enabled them to stay at home rather than get a job.  Those on the left emphasize instead that “good” jobs are disappearing because of technological change (as well as, perhaps, international trade), and that America’s high prison incarceration rates have made it impossible for many men to find jobs.  Professor Krueger’s conclusions are somewhere in between:  Individual factors (health) may explain what is being seen, but the health issues may be getting worse due to factors beyond the individual’s control.

It is not clear to me that any of these explanations really suffice.  But to develop an understanding of what might be going on, it is important first to examine more closely the underlying data for those who are not in the labor force and the reasons they give for this.

A first step is to separate the male and female rates, such as in the chart above.  It is an update of a chart that appeared in a post on this blog from last March (which in turn updated a similar chart from an even earlier post, from August 2014).  It tracks the monthly rates since 1948, with male and female rates shown separately as well as for everyone together.  Since demographic factors will affect labor force participation rates, particularly as a consequence of the increasing share of the baby boom generation who are now moving into their normal retirement years, the chart controls for age distribution by including only those aged between 25 and 54, the prime working years.

As the chart shows, the overall labor force participation rate (for men and women together) has been falling since the late 1990s.  The overall rate rose prior to then, but solely because the female rate was then rising strongly, as women increasingly entered into the formal, paid, job market. This peaked for women in the late 1990s, after which their rate as well as the overall rate began a slow fall.

The male rate, in contrast, started from a high level, of around 97% in the mid-1950s, after which there has been a slow but more or less steady fall.  It is now around 88%. Interestingly, since 1999 the female rate has moved almost exactly parallel to the male rate (at 83 to 84% of it, as discussed in the earlier blog post), suggesting that the underlying causes of the declines in both since 1999 might be similar.

A critical question is why.  The breakdown into separate male and female rates is a first step, but only a first step.  One wants to go beyond this.  The purpose of this blog post will be to take that next step, using BLS data I recently became aware of which reports on the survey responses of individuals on the primary reasons they are not in the labor force. This post will first review those results, and will then discuss some of the reasons that might explain the declining rates, especially for men.

B.  Non-Participation in the Labor Force by Prime-Aged Males

We will look at the rates for males first.  The chart below provides the reasons given (as a share of the male population aged 25 to 54) for why they were not participating in the labor force over the years from 1991 to 2015:

males-reasons-for-not-participating-in-the-labor-force-1991-to-2015The data was assembled by Mr. Steve Hipple, an economist on the staff of the Bureau of Labor Statistics (BLS) of the US Department of Labor.  He authored a Beyond the Numbers article of the BLS in December 2015 titled “People who are not in the labor force:  Why aren’t they working?”, which provided a first look at the reasons given by respondents for why they are not participating in the labor force, focusing on data for 2004 and for 2014.  The chart above is based on data assembled by Mr. Hipple for the full period from 1991 to 2015.

The data is derived from responses to queries made in the Annual Social and Economic Supplement to the Current Population Survey (CPS-ASEC).  This is a joint effort by the BLS (which conducts the Current Population Survey monthly, from which the official unemployment rate, among many other measures, is derived) and the US Census Bureau. The CPS-ASEC survey is undertaken once a year, each Spring, and asks a larger national sample a broad range of questions focused on conditions (such as on employment and household incomes) in the previous calendar year.  Among the questions it asks is whether each adult member of the household was in the labor force (where the labor force is defined as all those employed and all those unemployed who were actively seeking employment at some point in the year), and if not, what the reason was.  The possible responses are those listed in the chart above.

Several points should be noted:

a)  The numbers ultimately come from a survey of individuals, and hence will have the shortcomings of any survey.  There will be statistical error simply from the size of the sample, but more importantly also non-statistical error from how people choose to respond.  The reasons why an individual may not be participating in the labor force may be interpreted differently by different individuals, and multiple factors may apply (for example, they may be somewhat ill, have had difficulty in finding a job, and are at an age where early retirement is possible).  While such inherent limitations should be recognized, they also may not so much affect the trends, and the trends are of most interest here.

b)  The CPS-ASEC survey asks the respondents on their status in the previous calendar year, and covers the status over the entire year.  Hence if they were employed for part of the year but not for the full year, they would still be counted as part of the labor force.  The CPS survey, in contrast, is monthly, and asks for the status at that point in time (or, to be more precise, in the preceding week).  For this reason, one should expect to find that the share of the population counted as in the labor force to be higher in the annual figures than in the monthly figures, since they will be included in the annual numbers if they were in the labor force at any point in the previous year and not simply at the point in time of the monthly survey.  And one does see this in the results reported.  Conversely, the share not in the labor force will then be lower in the annual figures than in the monthly estimates.

The annual and monthly figures will, however, move similarly.  But note that the chart at the top of this post is based on the monthly estimates from the CPS, while the charts here for the reasons for not participating in the labor force are drawn from the annual CPS-ASEC estimates. The totals for those not in the labor force will differ for this reason.

c)  Participation in the labor force is defined as anyone in a paid job for as little as one hour in a week, plus those unemployed (defined as those actively looking for a job but do not have one). Thus to be counted as not in the labor force but “retired” or “going to school” is quite strict.  If one is retired but working for pay a few hours a week, or in school but working in the school cafeteria for some extra income, one is counted as part of the labor force and hence will be excluded from those not in the labor force.

d)  One must similarly be careful in the interpretation of the “could not find work” category.  The CPS-ASEC questionnaire asks whether the respondent had spent “any time trying to find a job” in the previous year.  If they had, they would be included in the unemployed.  If not, they would then be asked why they were not in the labor force that year, from this list of possible reasons.  Those who responded that they “could not find work” would be saying that they could not find work during this period even though they also say they had not actively searched for a job in this period.  It is possible, however, that they had looked before, could not find anything suitable, and believed this still to be the case even though they had given up actively looking.

e)  Only data going back to 1991 could be readily assembled.  While this covers a significant period, 25 years, it would be interesting if results further back were available.  The downward trend in the male participation rate started in the mid-1950s, and it would be of interest to see whether the causes prior to 1991 were similar to those since then.  I suspect they probably were, but this is speculation and one would like to see if that is indeed true.

The CPS-ASEC goes back to 1959 I believe (although initially under a different name), the monthly CPS goes back further, and a CPS questionnaire I found from 1978 asks a similar question (although without “retired” as a separate category).  The older data is not easy to access, however, and requires special software as well as expertise that I do not have.

However, the 25 years of data from 1991 to 2015 do show some interesting trends. Specifically, for the male rates (the chart above):

a)  The share of males aged 25 to 54 not in the labor force almost doubled over this period, from 5.9% of the male population in this age group in 1991 to 11.5% in 2013 and 2014 before dipping to 11.1% in 2015.  This was a significant increase.  And as the chart at the top of this post shows, this was a continuation of a similar trend in the decades prior to 1991.

b)  The increase in the total was not due to just one or two causes, but rather to substantial increases in the shares for each of the given reasons other than, interestingly, “could not find work”.  About 0.6% of the prime working age male population responded “could not find work” in 1991, and 0.7% did in 2015.  But the share reporting “could not find work” did fluctuate over the period, bumping higher in those years when the labor market was weak and unemployment high (1992, 2002, and then especially in 2009/10), and being compressed in the mid to late 1990s (the Clinton years) when labor market conditions were strong.  It appears to be capturing that labor market participation rates of prime age males are less when labor markets are weak.  This would be hidden unemployment.  However, the extent is limited.  The difference between the peak rate (in 2010) and the low rates in the late 1990s is only about 1.0% point.

c)  In terms of shares among those prime age males not participating in the labor force, the most important reason given was “ill or disabled”.  Interestingly, this share fell from close to 60% of those not in the labor force in 1991, the first year for which we have data, to 50% in 2015.  There were larger relative increases in the other causes (other than “could not find work”).

d)  The shares that rose the most (in relative terms) were the share of prime age men going full-time to school (rising from 11.0% of those prime age males not in the labor force in 1991 to 15.5% in 2015), the share retired (rising from 5.9% to 9.6%), and the share taking on home responsibilities (rising from 4.6% to 10.8%).  The share of those who could not find work fell from 10.5% to 6.3%, and the share for “other reasons” fell from 8.5% to 7.9%.

We will discuss below some of the possible reasons for these changes.

C.  Non-Participation in the Labor Force by Prime-Aged Females

A similar chart can be drawn for the responses of women not in the formal, paid, labor force.  The huge post-World War II change was of course the entry of women into the paid labor force, almost doubling from 34% of prime working age women in 1948 to 77% in 1999.  The rate then slowly fell, in parallel with the male rate, to 74% in 2015.

What dominates in the share of women not in the paid labor force is the share with home responsibilities.  This came down sharply from 1991 to 1999, as the following chart shows, and almost certainly in the period before then as well.  Since 1999 it has fluctuated, but appears to be on an upward trend (as the male rate is as well, although starting from far lower levels):

females-home-responsibilities-as-reason-for-not-participating-in-the-labor-force-1991-to-2015

Interestingly, over the past two decades the rate fell when the labor market was strong in the mid to late 1990s, rose as the labor market weakened with the recession that began a few months after George W. Bush took office, fell once the labor market recovered (but with a lag), and then turned upward again after 2009 as the labor market weakened again. It then fell in 2015.  This pro-cyclicality may be implying that, for women, the “home responsibilities” reason is being given as the stated reason for not participating in the formal labor force, when in fact it may to some degree reflect hidden unemployment.  But we cannot know for sure.  It might also reflect what kind of jobs, and their wages, that women can get when labor market conditions are weak.  The impact of wage rates will be discussed below.

The share for “home responsibilities” remains high, however, and would dominate all else in a chart if left in.  To examine what is going on it is therefore best to first subtract out the home responsibilities cause, accounting for it separately, and then examining the break-down for all the other reasons given for not participating in the labor force.  The result then is a chart which is remarkably similar to the chart for males:

females2-reasons-for-not-participating-in-the-labor-force-excluding-home-responsibilties-1991-to-2015

One finds:

a)  The total rate for women not in the labor force, once one excludes those with home responsibilities, almost doubles between 1991 and 2015, as it did for the males.

b)  Once again, the largest share of women of ages 25 to 54 not in the labor force (and excluding also those with home responsibilities) are those recorded as ill or disabled.  But the share ill or disabled was largely flat between 1991 and 2015, accounting for 55% of the total in 1991 and 57% in 2015.

c)  The second highest share, as with the male rates, was the share going full time to school.  But it was largely flat for this group of women, at 20.2% of the total in 1991 and 20.5% in 2015.

d)  Women, similar to men, saw a relatively high increase in the share retired, rising from 5.7% in 1991 to 12.5% in 2015.  Also similar to men, the share recorded as could not find work fell sharply from 8.2% in 1991 to 4.1% in 2015.  And the “other reasons” share also fell, as it did for the males, from 10.4% to 5.9%.

There appears then to be a similar pattern in the female rates as in the male rates once one removes the effect of home responsibilities.  Furthermore, this similar pattern held prior to 1999 as it did after that; there was not a sharp break in that year.  Indeed, excluding full time home responsibilities for both men and women, one finds that the curves of the shares in the 25 to 54 age group not in the labor force basically lie on top of each other:

males-and-females-shares-not-in-labor-force-excluding-home-responsibilities-reason-1991-to-2015

 

For both men and women, the share not participating in the labor force (and excluding those with full time home responsibilities as well), both rose sharply over this period, basically doubling, with similar shares throughout (within a percentage point or less).

Finally, it is interesting to compare between men and women the stated underlying reasons for not participating in the labor force.  The chart above shows that the totals, once when excludes the big differences between men and women with home responsibilities, are similar, and has been over time (since at least 1991).  The chart below compares the male and female rates for the underlying reasons, for the year 2015:

share-not-participating-in-labor-force-in-2015-excl-home-responsibilties-males-and-females

The totals, excluding home responsibilities, are similar, as already seen.  But it is interesting that the male and female rates for the underlying causes, other than home responsibilities, are also mostly similar.  The shares of prime age men and women who are not in the labor force due to illness or disability are quite close, at 5.6% for men and 5.4% for women.  A conclusion that illness or disability is more of a problem for men than for women is not supported.  Also very similar are the rates in this age group who report being retired:  1.1% of men and 1.2% of women.  This is also the case for those reporting to be full time students, with rates of 1.7% for men and 1.9% for women.  The female rate reporting they could not find work is less than the male rate (0.7% for males and 0.4% for females), as is the rate reported under “other” (0.9% for males and 0.6% for females), but both of these are relatively small in absolute level.

It appears that similar factors for men and women (other than home responsibilities) might be underlying these rates, and their increase over time.  What might those be?

D.  Possible Reasons for These Changes in Shares Not in the Labor Force 

It was noted in the introduction that conservatives have interpreted the decline in the labor force participation rates, particularly of men, as reflecting an increasing unwillingness to work.  Liberals have focussed more on fewer “good jobs” or an inability to get and hold them due to conditions like previous incarceration or deteriorating health.  Can we conclude from the data reviewed above which, if any, of these interpretations might be correct?

To summarize some of the points already noted above:

a)  There appear to be multiple reasons given in the responses for each sex as to why they are not participating in the labor force.  That is, it is not just one factor that explains, at least directly, the increasing share not working.

b)  Furthermore, aside from changes in the shares taking on home responsibilities (which do differ, and differ greatly, between each sex), the multiple reasons appear to be broadly similar in the share of males and share of females not participating in the labor force.

c)  But one stated reason that is low in level and also has not grown over time is the share of the prime working age population who are not in the labor force because they say they could not find work.  The share was just 0.7% of all men of prime working age in 2015, and just 0.4% for all women of prime working age.

d)  The share saying they could not find work varies to a limited degree with the overall state of the labor market (somewhat higher when official unemployment is high, although not by a huge amount).  And while it was relatively high (in comparison to its level at other times) in 2009/2010, when official unemployment reached 10%, it is now back to levels seen previously.

e)  The “other reasons” factor is relatively low and without an obvious upward trend. This is fortunate, since we do not really know what lies behind it.  But being low, it does not appear to be important.

Can the increase be attributed then to an increased unwillingness to work, as the conservatives charge?  That is not so clear.  While this is now more speculative, one can also interpret the data as reflecting more positive developments.  Specifically:

a)  There was a substantial increase in the share of men and women aged 25 to 54 who were enrolled full time in school.  This is almost certainly a good thing.  This probably reflects an increasing share of students in their late 20s and perhaps later enrolled in post-graduate studies.  Medicine requires many years of study, for example, and business schools increasingly require students applying to their MBA programs to have worked for several years before being accepted.  One might similarly see students in post-graduate academic programs, in particular Ph.D. students, who are 25 or older.  Finally, there may now be an increasing number of students who have worked for 5 or 10 years or more who decide to go back to school to learn a new skill or profession.  This is not bad.  Finally, it is worth noting that the increasing shares of students in this 25 to 54 age group are similar for both men and women, suggesting that the underlying cause may well be due to developments in the system of education.

b)  One also sees increases, and similar increases for both men and women, in the share saying they are retired despite being age 54 or less.  Such early retirement is certainly unusual, but the shares are low (1.1% of the men in the full 25 to 54 age group in 2015, and 1.2% of the women).  But given how the retirement system has changed in recent decades, an increase over time should not be surprising. Traditional defined benefit pension systems typically required work to some age (perhaps 62 or 65) before they could be drawn.  With income in retirement now driven by individual accounts (401(k)s, IRAs, and even just normal savings), there is now more of an opportunity to retire earlier.  To the extent early retirement reflects what a person prefers, and is something that he or she can afford, this should be seen as a positive development.

c)  Home responsibilities have been the largest single reason for women not being part of the formal, paid, labor force, and came down sharply until 1999.  Since then it has risen, but with significant volatility.  As noted above, it may well reflect a degree of hidden unemployment, as it appears to rise and fall (although with a lag) with labor market conditions.  But if it is a personal preference, and something the family can afford, it is not necessarily a bad thing.

d)  The share of males reporting home responsibilities as the reason they are not participating in the formal labor force, while still well below the female rates, has trended upwards over this period.  This may reflect both changes in social acceptance of males staying home to take care of children or elderly parents, but also the increase in female participation in the labor force to the ceiling reached in 1999. With more women in the formal labor force, often in jobs that pay well, a married couple might well decide that they prefer that the husband take on home responsibilities rather than the wife.  The fact that such a choice can now be made is a good thing.

There may therefore be benign explanations for several of these developments leading to lower labor force participation rates.  From just the evidence here, we cannot be sure.  But similarly, we should not assume the development are necessarily negative.

Health issues are more complex, and also a much larger factor.  The highest single cause cited for not participating in the labor force was illness or disability, for both prime age men and for prime age women when one excludes home responsibilities.  In 2015, it accounted for 50% of the prime age men and 57% of the prime age women (excluding home responsibilities) not in the labor force.  The shares, while high, did not change that much over time.  They were 60% for men and 55% for these women in 1991.

But a roughly constant share of a total that has doubled implies a rough doubling due to illness or disability.  It is certainly possible that health conditions have deteriorated for a significant sub-set of the population in recent decades.  As earlier posts on this blog have documented, median real wages have been stagnant since around 1980.  And the distribution of income has also become remarkably worse, with all but the top 10% seeing their real incomes falling between 1980 and 2014.  Stagnant incomes do correlate with poor health status.

More directly, a study published last year by Professors Anne Case and Sir Angus Deaton (Nobel laureate) of Princeton found that mortality rates of middle-aged non-Hispanic white Americans have actually risen in the last decade and a half.  There are clearly issues with health, at least among a significant segment of the population.  This coincided with reports of increased incidence of severe pain and increased daily use of prescription strength pain killers, a factor highlighted by Professor Krueger in his paper on labor force participation rates.

It is difficult to say with certainty, however, whether such health conditions necessarily account for the rising share of prime working age Americans who report illness or disability as the reason for not participating in the labor force.  It may also be the case that the safety net that provides support for those who are ill or disabled (such as through disability insurance that is provided as part of the Social Security system in the US) has improved over time.  Workers who previously could not get such support may have remained in the official labor force, but in low wage or unproductive jobs and in great pain.  If support is now provided to such workers, which was not done before, this can be a good thing.

But it must be recognized that the level of support provided to disabled workers is extremely stingy in the US.  The average monthly benefit paid under Social Security Disability Insurance in the US is currently only $1,166 per disabled worker (as of August 2016), or $13,992 a year.  You do not take this if you can work.  The share of prime working age men not in the labor force is also higher in the US than in such countries as Canada, France, Germany, and Japan, all of which have far more generous safety nets for disabled workers than the US has.

Further work is needed to separate out these possible causes for the increase in the share reporting illness or disability.  And it is important, given the dominance of this stated reason for those not participating in the labor force.

E.  The Impact of Low and Stagnant Real Wages

Multiple factors appear, then, to underlie the rise in the share of prime working age males (for decades) and females (since 1999) who are not participating in the formal labor force. While certain analysts may emphasize one factor or another, often consistent with their particular political leanings, the truth is that the differing interpretations may well apply to different sets of individuals.  For example, those reporting that they have retired may have retired because, as noted above, they wanted to and they could afford to.  But it might well also reflect that at least some in this group could not find a good job, and hence decided (unhappily) to start to draw on their retirement savings.

More fundamentally, it is not an issue of a strict either/or.  What is missing from the various rationales given is the recognition that there are trade-offs, and a decision is made as to whether to participate or not in the formal labor force depending on a balancing of these considerations.  And this is really just basic economics.  Econ 101 teaches that decisions are made by a weighing of different factors, and that one needs to recognize that there are trade-offs.

Critical to this is the need to recognize that median real wages have been stagnant for decades, as discussed in the earlier post on this blog cited above.  The issue is not whether or not “good” jobs exist, but rather how much is being paid in wages for those jobs.  Real wages have been flat, and the minimum wage is now more than one-third less in real terms than what it was in 1968.

At such rates of pay, prime working age men or women may well find it better, for example, to go to school for a few more years in the hope of getting a better paying job later, rather than work now at relatively low wages on a job.  As standard economics teaches, an important cost of schooling (and typically far more important than the cost of tuition) is the cost of not working while one is in school.  But if wages are low, what is lost from not working is not so high, and at the margin it makes more sense to go to school. And this factor has become increasingly important over the past several decades, as real wages have stagnated.

Similarly, at the margin one might well decide to retire early, if one can afford it, than to work longer in a low paying job.  As wages have stagnated in recent decades, standard economics teaches that more workers, at the margin, will choose to switch over to early retirement.  This will also hold for those who are ill or disabled.  What matters is not just what might be available to the worker in disability payments (which are low), but how much this is relative to the wages they might earn.  As the real minimum wage has fallen over recent decades, taking disability payments (if one is eligible) becomes relatively more attractive than taking a minimum wage job.

And this of course very much holds for those taking on home responsibilities, in particular for those taking care of children or elderly parents.  If the wage of the job one can get is stagnant and low, while the cost of child care and elder care has been going up, the rational choice will increasingly be to stay at home to provide such care rather than work in a formal paid job.

The stagnation in real wages since around 1980 might then help explain, at least in part, the increase in the share in recent decades of those prime working age men and women choosing not to participate in the formal labor market.  And it is interesting to note that the pace at which prime age men have chosen to stay out of the formal labor force accelerated in the period after 1980 compared to the period before.  Over the 27 years between 1953, when the prime working age male labor force participation rate peaked at an average over the year of 97 1/2%, and 1980, when the average over the year was 94 1/2%, the average pace of change in the rate was 0.111% points a year.  Over the 36 years from 1980 to 2016, the pace picked up to an average of 0.167% points a year.  This is 50% faster.  It accelerated in the period that real wages have stagnated.

F.  Conclusion

The labor force participation rate for the prime working age population has been declining for men since the mid-1950s and for women since 1999.  This is significant.  Growth depends on the working population, and if fewer work, there will be less growth.  And not being able to find a job when one wants one, whether you are counted among the openly unemployed or the hidden unemployed, can be devastating to an individual.  It is not just the income that is lost, although this is of course hugely important, but also the impact on the psyche and sense of self-worth.

One has to be careful, however, in any attribution of the cause of this increasing share of the population not in the formal labor force.  Many factors are involved, and one should not jump to a conclusion such as that people are lazier now than they were before, or that jobs are simply not available now and were before.  One should rather recognize that choices are being made and that there are tradeoffs.  People may rationally and happily be choosing to enroll as a full time student, or to stay at home to take care of children or elderly parents, or to retire early.

But one should similarly not jump to the conclusion that these are necessarily happy choices.  This is especially clear for those who are not working due to illness or disability, who may obtain minimal or even no support from various disability insurance programs. Indeed, I would suspect that most of those who are not working due to illness or disability are depending on a working spouse for support.

Recognizing that these are choices that are being made is simple, basic, Economics 101. The choices may be happy ones or not, but they are all choices.  Basic to such choices is what one would obtain by working at a job, which is the opportunity cost of what one is giving up by deciding not to participate in the formal, paying, job market.  Central to this is the fact that wages have been stagnant for decades in the US, since around 1980.  At the margin, it might make more sense now than it had before not to seek a job but rather to enroll as a student, take care of home responsibilities, or retire early.  This stagnation in real wages may in part explain the acceleration of the pace of working age men dropping out of the formal labor force since around 1980.

This then suggests a further reason for why we need to be concerned with real wages that have remained stagnant, despite the significant productivity growth of recent decades. Real GDP per worker (i.e. productivity) is now 60% higher than what it was in 1980, but wages have been flat.  Prior to 1980, real wages and GDP per worker both rose at a similar rate. This then broke down after around 1980.

Returning to a more equitable growth process is not, however, a trivial task nor one which can be accomplished by fiat.  But this analysis suggests that should progress be made towards this, one would then expect to see higher labor force participation rates.  And there are indeed actions the government can take.  A number were discussed in this earlier blog post.  Ensuring workers are in a position to bargain for good wages by keeping the economy at close to full employment is probably the most basic.  And raising the minimum wage, which is now more than one-third below where it was in real terms in 1968, and indeed lower in real terms than what it was when Harry Truman was president, would be important for all low wage workers.

Low and stagnant real wages have had a number of adverse effects on the economy, including on productivity.  A lower rate of labor force participation is likely also one of them. If you want more people in paying jobs, pay them better.

Productivity: Do Low Real Wages Explain the Slowdown?

GDP per Worker, 1947Q1 to 2016Q2,rev

A.  Introduction, and the Record on Productivity Growth

There is nothing more important to long term economic growth than the growth in productivity.  And as shown in the chart above, productivity (measured here by real GDP in 2009 dollars per worker employed) is now over $115,000.  This is 2.6 times what it was in 1947 (when it was $44,400 per worker), and largely explains why living standards are higher now than then.  But productivity growth in recent decades has not matched what was achieved between 1947 and the mid-1960s, and there has been an especially sharp slowdown since late 2010.  The question is why?

Productivity is not the whole story; distribution also matters.  And as this blog has discussed before, while all income groups enjoyed similar improvements in their incomes between 1947 and 1980 (with those improvements also similar to the growth in productivity over that period), since then the fruits of economic growth have gone only to the higher income groups, while the real incomes of the bottom 90% have stagnated.  The importance of this will be discussed further below.  But for the moment, we will concentrate on overall productivity, and what has happened to it especially in recent years.

As noted, the overall growth in productivity since 1947 has been huge.  The chart above is calculated from data reported by the BEA (for GDP) and the BLS (for employment).  It is productivity at its most basic:  Output per person employed.  Note that there are other, more elaborate, measures of productivity one might often see, which seek to control, for example, for the level of capital or for the education structure of the labor force.  But for this post, we will focus simply on output per person employed.

(Technical Note on the Data: The most reliable data on employment comes from the CES survey of employers of the BLS, but this survey excludes farm employment.  However, this exclusion is small and will not have a significant impact on the growth rates.  Total employment in agriculture, forestry, fishing, and hunting, which is broader than farm employment only, accounts for only 1.4% of total employment, and this sector is 1.2% of GDP.)

While the overall rise in productivity since 1947 has been huge, the pace of productivity growth was not always the same.  There have been year-to-year fluctuations, not surprisingly, but these even out over time and are not significant. There are also somewhat longer term fluctuations tied to the business cycle, and these can be significant on time scales of a decade or so.  Productivity growth slows in the later phases of a business expansion, and may well fall as an economic downturn starts to develop.  But once well into a downturn, with businesses laying off workers rapidly (with the least productive workers the most likely to be laid off first), one will often see productivity (of those still employed) rise.  And it will then rise further in the early stages of an expansion as output grows while new hiring lags.

Setting aside these shorter-term patterns, one can break down productivity growth over the close to 70 year period here into three major sub-periods.  Between the first quarter of 1947 and the first quarter of 1966, productivity rose at a 2.2% annual pace.  There was then a slowdown, for reasons that are not fully clear and which economists still debate, to just a 0.4% pace between the first quarter of 1966 and the first quarter of 1982.  The pace of productivity growth then rose again, to 1.4% a year between the first quarter of 1982 and the second quarter of 2016.  But this was well less than the 2.2% pace the US enjoyed before.

An important question is why did productivity growth slow from a 2.2% pace between the late 1940s and mid-1960s, to a 1.4% pace since 1982.  Such a slowdown, if sustained, might not appear like much, but the impact would in fact be significant.  Over a 50 year period, for example, real output per worker would be 50% higher with growth at a 2.2% than it would be with growth at a 1.4% pace.

There is also an important question of whether productivity growth has slowed even further in recent years.  This might well still be a business cycle effect, as the economy has recovered from the 2008/09 downturn but only slowly (due to the fiscal drag from cuts in government spending).  The pace of productivity growth has been especially slow since late 2010, as is clear by blowing up the chart from above to focus on the period since 2000:

GDP per Worker, 2000Q1 to 2016Q2,rev

Productivity has increased at a rate of just 0.13% a year since late 2010.  This is slow, and a real problem if it continues.  I would hasten to add that the period here (5 1/2 years) is still too short to say with any certainty whether this will remain an issue.  There have been similar multi-year periods since 1947 when the pace of productivity growth appeared to slow, and then bounced back.  Indeed, as seen in the chart above, one would have found a similar pattern had one looked back in early 2009, with a slow pace of productivity growth observed from about 2005.

There has been a good deal of work done by excellent economists on why productivity growth has been what it was, and what it might be in the future.  But there is no consensus.  Robert J. Gordon of Northwestern University, considered by many to be the “dean in the field”, takes a pessimistic view on the prospects in his recently published magnum opus “The Rise and Fall of American Growth”.  Erik Brynjolfsson and Andrew McAfee of MIT, in contrast, argue for a more optimistic view in their recent work “The Second Machine Age” (although “optimistic” might not be the right word because of their concern for the implication of this for jobs).  They see productivity growth progressing rapidly, if not accelerating.

But such explanations are focused on possible productivity growth as dictated by what is possible technologically.  A separate factor, I would argue, is whether investment in fact takes place that makes use of the technology that is available.  And this may well be a dominant consideration when examining the change in productivity over the short and medium terms.  A technology is irrelevant if it is not incorporated into the actual production process.  And it is only incorporated into the production process via investment.

To understand productivity growth, and why it has fallen in recent decades and perhaps especially so in recent years, one must therefore also look at the investment taking place, and why it is what it is.  The rest of this blog post will do that.

B.  The Slowdown in the Pace of Investment

The first point to note is that net investment (i.e. after depreciation) has been falling in recent decades when expressed as a share of GDP, with this true for both private and public investment:

Domestic Fixed Investment, Total, Public, and Private, Net, percentage of GDP, 1951 to 2015, updated Aug 16, 2016

Total net investment has been on a clear downward trend since the mid-1960s.  Private net investment has been volatile, falling sharply with the onset of an economic downturn and then recovering.  But since the late 1970s its trend has also clearly been downward. Net private investment has been less than 3 1/2% of GDP in recent years, or less than half what it averaged between 1951 and 1980 (of over 7% of GDP).  And net public investment, while less volatile, has plummeted over time.  It averaged 3.1% of GDP between 1951 and 1968, but is only 0.5% of GDP now (as of 2015), or less than one-sixth of what it was before.

With falling net investment, the rates of growth of public and private capital stocks (fixed assets) have fallen (where 2014 is the most recent year for which the BEA has released such data):

Rate of Growth In Per Capita Net Stock of Private and Government Fixed Assets, edited, 1951 to 2014

Indeed, expressed in per capita terms, the stock of public capital is now falling.  The decrepit state of our highways, bridges, and other public infrastructure should not be a surprise.  And the stock of private capital fell each year between 2009 and 2011, with some recovery since but still at almost record low growth.

Even setting aside the recent low (or even negative) figures, the trend in the pace of growth for both public and private capital has declined since the mid-1960s.  Why might this be?

C.  Why Has Investment Slowed?

The answer is simple and clear for pubic capital.  Conservative politicians, in both the US Congress and in many states, have forced cuts in public investment over the years to the current low levels.  For whatever reasons, whether ideological or something else, conservative politicians have insisted on cutting or even blocking much of what the United States used to invest in publicly.

Yet public, like private, investment is important to productivity.  It is not only commuters trying to get to work who spend time in traffic jams from inadequate roads, and hence face work days of not 8 1/2 hours, but rather 10 or 11 or even 12 hours (with consequent adverse impacts on their productivity).  It affects also truck drivers and repairmen, who can accomplish less on their jobs due to time spent in jams.  Or, as a consequence of inadequate public investment in computer technology, a greater number of public sector workers are required than otherwise, in jobs ranging from issuing driver’s licenses to enrolling people in Medicare.  Inadequate public investment can hold back economic productivity in many ways.

The reasons behind the fall in private investment are less obvious, but more interesting. An obvious possible cause to check is whether private profitability has fallen.  If it has, then a reduction in private investment relative to output would not be a surprise.  But this has in fact not been the case:

Rate of Return on Produced Assets, 1951 to 2015, updated

The nominal rate of return on private investment has not only been high, but also surprisingly steady over the years.  Profits are defined here as the net operating surplus of all private entities, and is taken from the national account figures of the BEA.  They are then taken as a ratio to the stock of private produced assets (fixed assets plus inventories) as of the beginning of the year.  This rate of return has varied only between 8 and 13% over the period since at least 1951, and over the last several years has been around 11%.

Many might be surprised by both this high level of profitability and its lack of volatility.  I was.  But it should be noted that the measure of profitability here, net operating surplus, is a broad measure of all the returns to capital.  It includes not only corporate profitability, but also profits of unincorporated businesses, payments of interest (on borrowed capital), and payments of rents (as on buildings). That is, this is the return on all forms of private productive capital in the economy.

The real rates of return have been more volatile, and were especially low between 1974 and 1983, when inflation was high.  They are measured here by adjusting the nominal returns for inflation, using the GDP deflator as the measure for inflation.  But this real rate of return was a good 9.6% in 2015.  That is high for a real rate of return.  It was higher than that only for one year late in the Clinton administration, and for several years between the early 1950s and the mid-1960s.  But it was never higher than 11%.  The current real rate of return on private capital is far from low.

Why then has private investment slowed, in relation to output, if profitability is as high now as it has ever been since the 1950s?  One could conceive of several possible reasons. They include:

a)  Along the lines of what Robert Gordon has argued, perhaps the underlying pace of technological progress has slowed, and thus there is less of an incentive to undertake new investments (since the returns to replacing old capital with new capital will be less).  The rate of growth of capital then slows, and this keeps up profitability (as the capital becomes more scarce relative to output) even as the attractiveness of new investment diminishes.

b)  Conservatives might argue that the reduced pace of investment could be due to increased governmental regulations, which makes investment more difficult and raises its cost.  This might be difficult to reconcile with the rate of return on capital nonetheless remaining high, but in principle could be if one argues that the slower pace of new investment keeps up profitability as capital then becomes more scarce relative to output. But note that this argument would require that the increased burden of regulation began during the Reagan years in the early 1980s (when the share of private investment in GDP first started to slow – see the chart above), and built up steadily since then through both Republican and Democratic administrations.  It would not be something that started only recently under Obama.

c)  One could also argue that the reduced investment might be a consequence of “Baumol’s Cost Disease”.  This was discussed in earlier posts on this blog, both for overall government spending and for government investment in infrastructure specifically.  As discussed in those posts, Baumol’s Cost Disease explains why activities where productivity growth may be relatively more difficult to achieve than in other activities, will see their relative costs increase over time.  Construction is an example, where productivity growth has been historically more difficult to achieve than has been the case in manufacturing.  Thus the cost of investing, both public and private, relative to the cost of other items will increase over time.  This can then also be a possible explanation of slowing new investment, with that slower investment then keeping profitability up due to increasing scarcity of capital.

One problem with each of the possible explanations described above is that they all depend on capital investments becoming less attractive than before, either due to higher costs or due to reduced prospective return.  If such factors were indeed critical, one would need to take into account also the effect of taxes on investment returns.  And such taxes have been cut sharply over this same period.  As discussed in an earlier blog post, taxes on corporate profits, for example, are taxed now at an effective rate of less than 20%, based on what is actually paid after all the legal deductions and credits are included.  And this tax rate has fallen steadily over time.  The current 20% rate is less than half the effective rate that applied in the 1950s and 1960s, when the effective rate averaged almost 45%.  And the tax rate on long-term capital gains, as would apply to returns on capital to individuals, fell from a peak of just below 40% in the mid-1970s to just 15% following the Bush II tax cuts and to 20% since 2013.

Such sharp cuts in taxes on profits implies that the after-tax rate of return on assets has risen sharply (the before-tax rate of return, shown on the chart above, has been flat).  Yet despite this, private investment has fallen steadily since the early 1980s as a share of GDP.

Such explanations for the reason behind the fall in private investment since the early 1980s are therefore questionable.  However, the purpose of this blog post is not to debate this. Economists are good at coming up with models, possibly convoluted, which can explain things ex post.  Several could apply here.

Rather, I would suggest that there might be an alternative explanation for why private investment has been declining.  While consistent with basic economics, I have not seen it before.  This explanation focuses on the stagnant real wages seen since the early 1980s, and the impact this would have on whether or not to invest.

D.  The Impact of Low Real Wages

Real wages have stagnated in the US since the early 1980s, as has been discussed in earlier posts on this blog (see in particular this post).  The chart below, updated to the most recent figures available, compares the real median wage since 1979 (the earliest year available for this data series) to real GDP per worker employed:

Real GDP per Worker versus Real Median Wage, 1979Q1 to 2016Q2, rev

Real median wages have been flat overall:  Just 3% higher in 2016 than what they were 37 years before.  But real GDP per worker is almost 60% higher over this same period.  This has critically important implications for both private investment and for productivity growth. To sum up in one line the discussion that will follow below, there is less and less reason to invest in new, productivity enhancing, capital, if labor is available at a stagnant real wage that has changed little in 37 years.

Traditional economics, as commonly taught, would find it difficult to explain the observed stagnation in real wages while productivity has risen (even if at a slower pace than before). A core result taught in microeconomics is that in “perfectly competitive” markets, labor will be paid the value of its marginal product.  One would not then see a divergence such as that seen in this chart between growth in productivity and a lack of growth in the real wage.

(The more careful observers among the readers of this post might note that the productivity curve shown here is for average productivity, and not the marginal productivity of an extra worker.  This is true.  Marginal productivity for the economy as a whole cannot be easily observed, nor indeed even be well defined.  However, one should note that the average productivity curve, as shown here, is rising over time.  This can only happen if marginal productivity on new investments are above average productivity at any point in time.  For other reasons, the real average wage would not rise permanently above average productivity (there would be an “adding-up” problem otherwise), but the theory would still predict a rise in the real wage with the increase in observed productivity.)

There are, however, clear reasons why workers might not be paid the value of their marginal product in the real world.  As noted, the theory applies in markets that are assumed to be perfectly competitive, and there are many reasons why this is not the case in the world we live in.  Perfect competition assumes that both parties to the transaction (the workers and employers) have complete information on not only the opportunities available in the market and on the abilities of the individual worker, but also that there are no costs to switching to an alternative worker or employer.  If there is a job on the other side of the country that would pay the individual worker a bit more, then the theory assumes the worker will switch to it.  But there are, of course, significant costs to moving to the other side of the country.  Furthermore, there will be uncertainty on what the abilities of any individual worker will be, so employers will normally seek to keep the workers they already have to fill their needs (as they know what these workers can do), than take a risk on a largely unknown new worker who might be willing to work for a lower wage.

For these and other reasons, labor markets are not perfectly competitive, and one should not then be surprised to find workers are not being paid the value of their marginal product.  But there is also an important factor coming from the macroeconomy. Microeconomics assumes that all resources, including labor resources, are being fully employed.  But unemployment exists and is often substantial.  Additional workers can then be hired at the current wage, without a need for the firm to raise that wage.  And that will hold whether or not the productivity of those workers has risen.

In such an environment, when unemployment is substantial one should not be surprised to find a divergence between growth in productivity and growth in the real wage.  And while there have of course been sharp fluctuations arising from the business cycle in the rate of unemployment from year to year, the simple average in the rate since 1979 has been 6.4%.  This is well in excess of what is normally considered the full employment rate of unemployment (of 5% or less).  Macro policy (both fiscal and monetary) has not done a very good job in most of the years since 1979 in ensuring there is sufficient demand in the aggregate in the economy to allow all workers who want to be employed in fact to be employed.

In such an environment, of workers being available for hire at a stagnant real wage which over time diverges more and more from their productivity, consider the investment decision a private firm faces.  Suppose they see a market opportunity and can sell more. To produce more, they have two options.  They can hire more labor to work with their existing plant and equipment to produce more, or they can invest in new plant and equipment.  If they choose the latter, they can produce more with fewer workers than they would otherwise need at the new level of production.  There will be more output per unit of labor input, or put another way, productivity will rise if the latter option is chosen.

But in an economy where labor is available at a flat real wage that has not changed in decades, the best choice will often simply be to hire more labor.  The labor is cheap.  New investment has a cost, and if the cost of the alternative (hire more labor) is low enough, then it is more profitable for the firm simply to hire more labor.  Productivity in such a case will then not go up, and may indeed even go down.  But this could be the economically wise choice, if labor is cheap enough.

Viewed in this way, one can see that the interpretation of many conservatives on the relationship between productivity growth and the real wage has it backwards.  Real wages have not been stagnant because productivity growth has been slow.  Labor productivity since 1979 has grown by a cumulative 60%, while real median wages have been basically flat.

Rather, the causation may well be going the other way.  Stagnant and low real wages have led to less and less of an incentive for private firms to invest.  And such a cut-back is precisely what we saw in the chart above on private (as well as public) investment as a share of GDP.  With less investment, the pace of productivity growth has then slowed.

As a reflection of this confusion, conservatives have denounced any effort to raise wages, asserting that if this is done, jobs will be lost as firms choose instead to invest and automate.  They assert that raising the minimum wage, which is currently lower in real terms than what it was when Harry Truman was president, would lead to minimum wage workers losing their jobs.  As a former CEO of McDonalds put it in a widely cited news report from last May, a $15 minimum wage would lead to “a job loss like you can’t believe.”   Fast food outlets like McDonalds would then find it better to invest in robotic arms to bag the french fries, he said, rather than hire workers to do this.

This is true.  The confusion comes from the widespread presumption that this is necessarily bad.  Outlets like McDonalds would then require fewer workers, but they would still need workers (including to operate the robotic arms), and those workers would be more productive.  They could be paid more, and would be if the minimum wage is raised.

The error in the argument comes from the presumption that the workers being employed at the current minimum wage of $7.25 an hour do not and can not possess the skills needed to be employed in some other job.  There is no reason to believe this to be the case.  There was no problem with ensuring workers could be fully employed at a minimum wage which in real terms was higher in 1950, when Harry Truman was president, than what it is now.  And average worker productivity is 2.4 times higher now than what it was then.

Ensuring full employment in the economy as a whole is not a responsibility of private business.  Rather, it is a government responsibility.  Fiscal and monetary policy need to be managed so that labor markets are tight enough to ensure all workers who want a job can get a job, while not so tight at to lead to inflation.

Following the economic collapse at the end of the Bush administration in 2008, monetary policy did all it could to try to ensure sufficient aggregate demand in the economy (interest rates were held at or close to zero).  But monetary policy alone will not be enough when the economy collapsed as far as it did in 2008.  It needs to be complemented by supportive fiscal policy.  While there was the initial stimulus package of Obama which was critical to stabilizing the economy, it did not go far enough and was allowed to run out. And government spending from 2010 was then cut, acting as a drag which kept the pace of recovery slow.  The economy has only in the past year returned to close to full employment.  It is not a coincidence that real wages are finally starting to rise (as seen in the chart above).

E.  Conclusion

Productivity growth is key in any economy.  Over the long run, living standards can only improve if productivity does.  Hence there is reason to be concerned with the slower pace of productivity growth seen since the early 1980s, and especially in recent years.

Investment, both public and private, is what leads to productivity growth, but the pace of investment has slowed since the levels seen in the 1950s and 60s.  The cause of the decline in public investment is clear:  Conservative politicians have slowed or even blocked public investment.  The result is obvious in our public infrastructure:  It is overused, under-maintained, and often an embarrassment.

The cause of the slowdown in private investment is less obvious, but equally important. First, one cannot blame a decline in private investment on a fall in profitability:  Profitability is higher now than it has been in all but one year since the mid-1960s.

Rather, one needs to recognize that the incentive to invest in productivity enhancing tools will not be there (or not there to the same extent) if labor can be hired at a wage that has stagnated for decades, and which over time became lower and lower relative to existing productivity.  It then makes more sense for firms to hire more workers with their existing stock of capital and other equipment, rather than invest in new, productivity enhancing, capital.  And this is what we have observed:  Workers are being hired, but productivity is not growing.

An argument is often made that if firms did indeed invest in capital and equipment that would raise productivity, that workers would then lose their jobs.  This is actually true by definition:  If productivity is higher, then the firm needs fewer workers per unit of output than they would otherwise.  But whether more workers would be employed in the economy as a whole does not depend on the actions of any individual firm, but rather on whether fiscal and monetary policy is managed to ensure full employment.

That is, it is the investment decisions of private firms which determine whether productivity will grow or not.  It is the macro management decisions of government which determine whether workers will be fully employed or not.

To put this bluntly, and in simplistic “bumper sticker” type terms, one could say that private businesses are not job creators, but rather job destroyers.  And that is fine.  Higher productivity means that a firm needs fewer workers to produce what they make than would otherwise have been needed, and this is important for ensuring efficiency.  As a necessary complement to this, however, it is the actions of government, through its fiscal and monetary policies, which “creates” jobs by managing aggregate demand to ensure all workers who want to be employed, are employed.