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GDP Growth in the First Half of 2025: All of It Is From the AI Investment Boom

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Chart 1

Exceptional growth in private investment in information processing equipment and in software has accounted for all of GDP growth in the first half of 2025.  The BEA released on August 28 its second estimate for GDP growth in the second quarter of this year, and most commentary focused on the upward revision in overall GDP growth in the second quarter from 3.0% (at an annual rate) in its advance estimate released last month to 3.3% now.  But what I believe is of more interest is that all of the growth so far this year has come from private investment in new information processing equipment and software:  i.e. from the AI investment boom.

As we are all taught in Econ 101, the nation’s GDP is equal to what is spent during the period for private consumption, for private investment, for government consumption and investment, and for net exports (exports less imports).  One should keep in mind that GDP is a measure of what is produced domestically during the period in gross terms (i.e. before depreciation) – hence the name Gross Domestic Product.  But since any change in inventories is included within private investment, one can measure what was produced by how it was used (with inventory accumulation as one use; see this earlier post on this blog).  Furthermore, we know from Keynes that in a modern economy, the primary driver of production (up to some capacity limit) comes from demand, i.e. from these demand-side components of overall GDP.

From this, one can calculate how much of the growth in GDP was driven by each of the demand-side components.  The change in overall GDP in dollar terms from one period to the next will, by definition, equal the sum of the change in each of the demand-side expenditure items in dollar terms.  It is of most interest to calculate these in terms of constant prices, and with this then expressed as a percentage of GDP in the prior period, to arrive at an accounting of the contribution of each of the demand components to the growth in GDP in the period.

Furthermore, the demand-side components of GDP are not simply the total levels of private consumption, private investment, and so on.  Those aggregate components of GDP can be broken down into individual types of products that add up to the aggregates. That is, the personal consumption component of GDP can be broken down into consumption of goods and consumption of services, consumption of goods can be further broken down into durable goods and nondurable goods, and durable goods further broken down into types of durable goods, etc.

The BEA provides such figures on the contribution to GDP growth from each of the demand-side components in the online Table 1.5.2 of the NIPA (National Income and Product) tables, which it updates whenever it issues a new set of GDP estimates.  The table provides figures on how much of the growth in overall GDP from the prior period is accounted for by the growth (in percentage points) in the demand-side components of GDP.  The contributions will sum to the increase in percent in overall GDP relative to the prior period.

One can also calculate the contribution figures directly, using Table 1.5.6 of the BEA’s NIPA accounts, which shows – in constant prices – GDP and its demand-side components at the same level of detail as in Table 1.5.2, for each period whether quarterly or annually.  One needs to use the figures in this table when calculating the contributions to the growth in GDP relative to a period other than the prior one (and was used here to compare the growth in GDP in the first half of 2025, i.e. between the last quarter of 2024 and the second quarter of 2025).

Of interest to us here is that two of the line items in these detailed GDP accounts are the contribution to the growth in GDP from private fixed investment in information processing equipment and from private fixed investment in software.  One can then calculate how much of the growth in GDP can be accounted for by these two components, and then what GDP growth would have been from everything else.

The chart above shows this using annual data from 2013 through 2024 (to provide context), and then for the first half of 2025.  The annual figures for 2013 through 2024 came directly from Table 1.5.6 of the NIPA accounts, and show the growth in each year relative to the prior year in overall GDP (the line in black) and then the contribution to that growth that came from private investment in information processing equipment and in software (the line in blue) and the contribution of everything else in the economy (the line in red).

Up through 2024, the contribution to the growth in GDP from private investment in information processing equipment and software was always small – averaging only 0.3% points to the growth in GDP in each period.  This is not surprising.  While a dynamic component of demand (it grew at an average rate of 7.8% per annum from 2013 to 2024, while overall GDP grew at an average rate of 2.4%), private investment in information processing equipment and software was only 4.1% of GDP in 2024 – a small component of GDP.  Thus the growth of everything on the demand side of GDP other than private investment in information processing equipment and software (the line in red) is always close to overall GDP growth up through 2024.  That is, it accounted for almost all of GDP growth over the period, as one would expect.

This then changed dramatically in the first half of 2025.  Comparing GDP in the second quarter of 2025 to what it was in the last quarter of 2024 (i.e. in the first half year of the new Trump term in office), overall GDP growth fell to just 1.4% at an annual rate.  GDP growth had been 2.8% in 2024 in the last year of the Biden presidency (and 6.1%, 2.5%, and 2.9% in 2021 to 2023 respectively), before this fall in the first half of 2025.

But more interesting is that all of the growth in GDP in the first half of 2025 (i.e. in what GDP had grown to as of the second quarter compared to what it was in the last quarter of 2024) came from growth in private investment in information processing equipment and software.  The growth of everything else in GDP was in fact slightly negative, and by itself would have led to a 0.1% fall in GDP over the period.  Growth in private investment in information processing equipment and software contributed a positive 1.5% points to GDP growth, with the two together thus leading to the 1.4% growth in GDP over the period (all at annual rates).

Private investment in information processing equipment and software by itself grew at an astounding annualized rate of 28.3% over the first half of 2025.  This is the AI boom that is underway.  Without it, GDP in the second quarter of 2025 would be below where it was in the last quarter of 2024.

Or put another way, all of the growth in GDP so far in 2025 was due to (and absorbed by) the growth in private investment in information processing equipment and software.  On a net basis, everything else was stagnant and indeed fell slightly.

Why Voters Are Upset 2: The Proximate Causes of the Underperformance of the US Economy Since the 2008 Crash

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Chart 1

A.  Introduction

The previous post on this blog described the slowdown in US growth since the 2008 crash.  GDP fell sharply in the second half of that year – the last year of the Bush administration – due to the crisis in home mortgages leading to a broad collapse in the financial markets.  It led to what has been termed the “Great Recession”.  But unlike in past recessions, GDP never recovered to its previous trend path, even though the unemployment rate fell to lows not seen since the 1960s.  GDP remains well below that previous path today.  The chart above shows how that gap opened up and has persisted since 2008.

The question is why?  The unemployment rate had averaged 4.6% in 2007 – the last full year before the 2008/09 economic and financial collapse.  While the pace of the recovery from the collapse was slowed by federal budget cuts, the economy eventually did return to full employment.  The unemployment rate was at or below 5% in Obama’s last year in office and then continued on the same downward path during the first three years of the Trump administration.  It averaged 3.9% in 2018 and 3.7% in 2019, and hit a low of 3.5% in September 2019.  After the brief but sharp 2020 Covid crisis, the unemployment rate then went even lower under Biden, reaching a low of just 3.4% in April 2023 and averaging just 3.6% in 2022 and again in 2023.  The unemployment rate has not been this low for so long since the 1960s.

In prior times, GDP would have returned to the path it had been on once the economy had recovered to full employment, with resources (in particular labor resources) being fully utilized.  But this time, despite unemployment going even lower than it had been before the downturn, GDP remained far below the path it had been on.  By 2023, real GDP would have been almost 20% above where it in fact was, had it returned to the previous path.  That is not a small difference.

That is, while the economy recovered from the 2008 collapse – in the sense that it returned to the full utilization of the labor and other resources available to it – economic output (real GDP) with that full utilization of resources was stubbornly below (and remained stubbornly below) what it would have been had it returned to its prior growth path.  The economy had followed that path since at least the late 1960s (as seen in the chart above).  Indeed, that same growth path (in per capita terms) can be dated back to 1950 (as the previous post on this blog showed).

This post will examine the proximate factors that led to this.  The post will look first at the growth in available labor.  It has slowed since 2008.  This has not been due to a fall in the labor force participation rates of the various age groups, as some have posited.  We will see below that holding those participation rates constant at what they were in 2007 (for each of the major age groups) would not have had a significant effect on labor force totals.  Rather, labor force growth slowed in part simply because the growth in the overall population slowed, and in part due to demographic shifts:  A growing share of the adult population has been moving into their normal retirement years.  It is not a coincidence that the first of the Baby Boom generation (those born in 1946) turned 62 in 2008 and 65 in 2011.

The second proximate factor is available capital – the machinery, equipment, and everything else that labor uses to produce output.  Capital comes from investment, and we will see below that net investment as a share of GDP has fallen sharply in the decades since the 1960s.  Overall net fixed investment fell by more than half.  This led to a slowdown in capital growth, and especially so after 2008.  There was an especially sharp reduction in public investment.  Since 2008, net public investment as a share of GDP has been only one-quarter of what it was in the 1960s.  It should be no surprise why public infrastructure is so embarrassingly bad in the US.  And net residential investment (as a share of GDP) is only one-third of what it was in the 1960s.  The resulting housing shortage should not be a surprise.

The third proximate factor is productivity.  Labor working with the available capital leads to output.  How much depends on the productivity of the machinery, equipment, and other assets that make up the capital, and that productivity grows over time as technology develops and is incorporated into the machinery and equipment used.  We will see that the rate of growth in productivity fell significantly after 2008.  Given the reduction in net investment and the consequent slowdown in capital accumulation after 2008, it is not surprising that productivity growth also slowed.

For a rough estimate of the relative importance of these three factors – labor, capital, and productivity – I developed an extremely simple Cobb-Douglas production function model to simulate what could be expected.  Despite being simple, it turned out to work surprisingly well both in terms of tracking what actual GDP was (for given employment levels) and in tracking the trend path for GDP given the trend paths of labor, capital, and productivity.

As noted above, the trend level of GDP in 2023 was almost 20% above what GDP actually was in that year – a year when unemployment was at record lows.  Despite being at full employment, the economy was not producing more.  Based on the Cobb-Douglas model, roughly a quarter of the shortfall can be attributed to a slowdown in productivity growth from 2007 onwards.  Of the remaining shortfall, about 60% can be attributed to a smaller stock of capital and 40% to a smaller labor force (both relative to what they would have been had they continued on the same trend paths that they had followed before 2008).

Section B of this post will examine the labor force figures.  Section C will look at what has happened to investment and the resulting growth in available capital.  Section D will then examine the Cobb-Douglas model used to estimate the relative importance of labor and capital both growing more slowly than they had before and the impact of slower productivity growth.  Section E will conclude.

As noted above, labor growth has slowed due to demographic changes as population growth has slowed and as the population has aged.  A rising share of the population (specifically the Baby Boomers) have been moving into their normal retirement years, and this has led to a slower rate of growth in the labor force.  There is nothing wrong with this, it depends primarily on personal choices, and there is no real policy issue here.

In contrast, there are important policy issues to examine on why investment has fallen in recent decades – and especially since 2008 – with the resulting slower rate of capital accumulation as well as slower productivity growth.  But the causes of this are complex, and will not be examined here.  I hope to address them in a subsequent post on this blog.

[Note on the data:  In each chart, I used the most detailed data available for that particular data series, i.e. monthly when available (labor force statistics), quarterly (real GDP), or annual (capital accumulation). The data are current as of the date indicated for when they were downloaded, but some are subject to subsequent revision.]

B.  Growth in the Labor Force

Growth in the US labor force has slowed, but by how much, when did this start, and why?  We will examine this primarily through a series of charts.  Most of these charts will be shown with the vertical axis in logarithms.  As you may remember from your high school math, in such charts a straight line will reflect a constant rate of growth.  The slope of the lines will correspond to that rate of growth, with a steeper line indicating a faster rate of growth.

The trend lines in the charts here (including in the chart at the top of this post) have all been drawn based on what the trends appear to be (i.e. “by eyeball”) in the periods leading up to 2008.  They were not derived from some kind of statistical estimation, nor from a strict peak-to-peak connection, but rather were drawn based on what capacity appeared to be growing at over time.  They were also drawn independently for aggregate real GDP (Chart 1 above), for growth in the labor force (Chart 2 below) and for growth in net fixed assets (Chart 10 below).  Despite being independently drawn, we will see in Section D below that a very simple Cobb-Douglas model finds that they are consistent with each other to a surprising degree, in that the predicted GDP trend corresponds to and can be explained by the trends as drawn for labor and for capital.

Starting with the labor force:

Chart 2

The US labor force grew at a remarkably steady rate from the early 1980s up to 2008.  Prior to the 1980s, it grew at a faster pace (a trend line would be steeper) as women entered the labor force in large numbers and later as the Baby Boomers began to join the labor force in large numbers in the early 1970s.

But then that steady rise in the labor force (of about 1.3% per annum before 2008) decelerated sharply.  The growth rate fell to only 0.5% per year between 2007 and 2023.  Why?

We can start with overall population growth:

Chart 3

Population, too, had grown at a steady pace prior to 2008.  But population growth then slowed.  In this context, it is not surprising to see that growth in the labor force also slowed.

But there is more to it than just this.  Before 2008, the US population had been growing at a similar rate as the labor force, thus leading to a fairly constant share of the labor force in the population (generally in the range of 50 to 51%):

Chart 4

But then, in 2008, the share of the labor force in the US population fell.  Growth in the labor force slowed by more than growth in the US population.  What were the factors behind that?

One assertion that is often made is that labor force participation rates fell.  At an aggregate level this is, almost by definition, true.  As a share of the US adult population (those aged 16 and over), the labor force participation rate fell from 66.0% in 2007 to 62.6% in 2023 (using standard BLS figures).  But one can be misled by focusing on the aggregate participation rate.  The overall participation rate came down not because those in various age groups became less likely to join the labor force, but rather because an increasing share of the population was aging into their normal retirement years.

The BLS provides seasonally adjusted figures for the labor force broken into three age groups: those aged 16 to 24, those aged 25 to 54, and those aged 55 or more.  Labor force participation rates are provided for each of these three groups, and one can calculate what the labor force participation would have been for each had the participation rate always been at that of 2007:

Chart 5

The line in red shows what the labor force then would have been, with the line in blue showing the actual labor force and the line in black the trend (the same trend as in Chart 2 above).  While it would have made a significant difference before the 1980s (as women were not participating in the formal labor force to the same degree then), between 2008 and 2023 it makes very little difference.  The labor force would have still fallen by about the same figures relative to its previous trend.

Rather, the labor force has been aging, with a growing share of the population now in the normal retirement years when labor force participation rates are low.  From the BLS numbers, one can work out the share of the population that are age 55 or older:

Chart 6

The share in the population of those aged 55 or older started to turn sharply upward around 1998.  They thus would have been 65 or older starting around 2008.  And as noted before, this is also when the first of the Baby Boomers (those born in 1946) would have started to reach their normal retirement age.

[Side note:  The discontinuities that one sees at various points in this chart are there because of adjustments made by the BLS in their control totals.  They adjust these control totals once new results are available from the decennial US population censuses.  They need such control totals for the shares of the various demographic groups since the labor force estimates come from its Current Population Survey (CPS), and as with any survey, control totals are needed to generalize from the sample survey results.  But the BLS does not then revise prior CPS estimates once the control totals are updated with each decennial census.  That then leads to these discontinuities.  For our purposes here, those discontinuities are not important.]

Labor force growth thus slowed from 2008 onwards.  This can be explained by basic demographics with an aging population.  This was not due to less willingness to participate in the labor force – an assertion one often sees.  Holding participation rates constant at what they were in 2007 for just three broad age groups led to no significant difference in what the labor force would have been.  Rather, people are just aging into their normal retirement years.

C.  Growth in Capital

Labor works with machinery, equipment, structures, and other fixed assets – which together will be referred to as simply capital – to produce output.  Those assets also reflect the technology that was available and economic (in terms of cost) when they were installed.  Those assets are acquired by investment, and it is important to recognize that net investment has fallen sharply over the last several decades.

This is not often recognized, as most analysts and news reports focus not on net investment but rather on gross investment.  Gross investment figures are provided in the GDP accounts that are released each month, and gross investment as a share of GDP has not varied all that much.  The decade-long averages for gross private fixed investment have varied only between 16 and 18 1/2% of GDP since the 1960s.

But the accumulated stock of capital does not arise simply out of gross investment but rather out of investment net of depreciation – i.e. net investment.  Less attention is paid to net investment figures, and estimating depreciation is not easy.  It is certainly not depreciation as defined by tax law, as tax law as written reflects a deliberate attempt to encourage investment by allowing firms to declare depreciation to be greater than it actually is (e.g. through accelerated depreciation).  Assigning a higher cost to depreciation will reduce reported profit levels and hence reduce what needs to be paid in taxes on that profit income.

For the GDP accounts (NIPA accounts) the BEA needs to record what actual depreciation was, not what depreciation as allowed under the tax code may have been.  The BEA estimates of this are carefully done and are the best available.  However, one still needs to recognize that these are estimates and that there are both conceptual and data issues when estimates of depreciation are made.

Based on the BEA estimates in the NIPA accounts, both public and private net fixed investment levels – as shares of GDP – have fallen sharply since the 1960s:

Chart 7

There are significant year-to-year fluctuations in the shares – especially in the private investment figures – as investment varies significantly over the course of the business cycle.  It falls in recessions and increases when the economy recovers.  The trends may thus be more clearly seen using decade averages of the net investment shares:

Chart 8

Total public and private net fixed investment fell from over 10% of GDP in the 1960s (and almost as much in the 1950s) to just 4.2% of GDP in the period from 2009 to 2023 – a fall of close to 60%.  Total private net fixed investment fell from about 7% of GDP in the 1950s, 60s, and 70s, to just 3.4% since 2009 – a fall by half.  Public net fixed investment fell even more sharply:  from over 3% of GDP in the 1960s to just 0.8% of GDP in recent years – a reduction of three-quarters (in the figures before rounding).  It should be no surprise why public infrastructure is so embarrassingly poor in the US.

The chart also shows private net fixed investment broken down into the share for investment in residential assets (housing) and non-residential assets.  Much of the decline in private net fixed investment was driven by an especially sharp reduction in investment in housing. Still, private investment in assets other than housing has also been cut back substantially, with a reduction of over 40% compared to where it was in the 1980s.

Based on their net fixed investment estimates and other data, the BEA also provides estimates of how the accumulated stock of real fixed capital has changed over time, with those levels shown in terms of quantity indices.  The resulting rates of growth in accumulated capital (which the BEA refers to, more precisely, as the net stock of fixed assets) have declined sharply with the reductions in the net investment shares:

Chart 9

In the 1960s, the annual growth rates varied between 3.5% (for residential fixed assets) and 4.4% (for public fixed assets).  But in the period from 2009 to 2023 those growth rates had fallen to just 1.9% for private non-residential fixed assets, 1.1% for public fixed assets, 0.8% for residential fixed assets, and 1.3% for all fixed assets.  Such a slow rate of capital accumulation will not be supportive of robust growth.

The reductions in the growth rates were especially sharp following the 2008 crisis.  This led capital accumulation to fall well below the trend path that it had previously been on:

Chart 10

As was the case for growth in the labor force, there is again a substantial fall after 2008 in the growth of an important factor in production relative to its previous trend.  This time it is accumulated capital.  It should not be surprising that this slowdown in the growth of both available labor and capital would then be accompanied by a slowdown in the growth of GDP – all relative to their previous trends.  But an open question is how much of the close to 20% shortfall in GDP as of 2023 was due to labor, how much to capital, and how much to the productivity of labor working with the available capital?  This will be examined in the next section.

D.  Modeling GDP:  The Relative Importance of Labor, Capital, and Productivity to the Shortfall

Output (GDP) has fallen relative to the path it was on before – and a 20% shortfall is a lot – as have both the size of the labor force and of accumulated capital.  To estimate how much of the shortfall in GDP can be attributed to the shortfall of labor, how much to the shortfall of capital, and how much to a slowdown in the growth in productivity of that labor and capital, one needs a model.

For this analysis, I used the extremely simple but standard model of production called the Cobb-Douglas.  Its formulation is credited to Paul Douglas (an economist) and Charles Cobb (a mathematician) in 1927, although Douglas recognized and acknowledged that a number of economists before them had worked with a similar relationship.  While extremely simple, it allows us to arrive at an estimate of how much of the shortfall in GDP can be attributed to labor, how much to capital, and how much to a change in productivity growth.  Despite being simple, there was a good fit when the model was tested for its predictions of GDP against what GDP actually was historically.  There was also a very surprisingly good fit against whether the trend growth in GDP was close to what the model predicted based on the trend growth observed for labor and for capital.

The Cobb-Douglas production function is an equation that relates what output (real GDP) would be for given levels of labor and capital as inputs.  The following subsection will provide a brief overview of that equation and of the parameters used.  Those who prefer to avoid equations can skip over this section and go directly to subsection (b) below, where the model was tested via a comparison of the model’s predicted values for GDP to what GDP actually was, both year-by-year and in its trend.

a)  The Cobb-Douglas Equation and Parameters 

The Cobb-Douglas production function can be written as:

Y = A(1+r)tLβK1−β

where Y is real GDP, L is labor, K is capital as measured, r is a rate of growth for the increase in productivity over time (t), A is a scaling factor, and β is an exponent indicating how much output (Y) will increase for a given percentage increase in L as an input.  With constant returns to scale (which is generally assumed), the exponent for K will then be 1- β.  They will also match (under the assumptions of this model) the shares in national income of labor and capital, respectively.  In the NIPA accounts for 2023, the compensation of employees was 62% of national income.  All other income (e.g. basically various forms of profit) was 38% of national income.  I rounded these to just a 60 / 40 split, so β = 0.60 and 1-β = 0.40.

Productivity will grow over time.  That is, the output that can be generated for a given amount of labor and of capital will grow over time.  As technology changes and is reflected in the accumulated stock of capital, labor working with the available machinery and equipment will be able to produce more.  While the contribution of the growth in productivity can be incorporated into the Cobb-Douglas in various different ways, the simplest is to assume that it augments the combination of labor and capital together.  This growth in productivity can then also be referred to as the growth in Total Factor Productivity (TFP).

For the simulations here, I took the year 2007 (the last full year before the 2008 collapse) as the base period, and hence scaled the labor and capital inputs in proportion to what they were in 2007.  Thus they were both set to the value of 1.00 in 2007, and if they were then, say, 10% higher in some future year they would have a value of 1.10 in that year.  The scaling coefficient A would then be equal to real GDP in 2007 ($16,762.4 billion in terms of 2017 constant $).

Finally, the rate of TFP growth was set so that GDP as modeled would roughly track what the actual values for GDP were historically.  It turned out that an annual rate of growth in TFP of 1.20% worked well for the years leading up to 2007, with this then falling to 0.90% per year in the years following 2007 up to and including 2023.  I did not try to fine-tune this to any greater precision (i.e. I looked at annual TFP growth to the nearest 0.1% and not more finely, i.e. to 1.20% or 1.30% but not to 1.21%).  I also constrained the TFP growth to be at just one given rate for all of the years before 2007 (1.20%) and one rate after 2007 (0.90%), even though it is certainly conceivable that it could fluctuate over time.

b)  Comparison of GDP as Modeled by the Cobb-Douglas versus Actual and Trend GDP

The Cobb-Douglas just provides a model, and the first question to address is whether that model appears to track what we know about the economy.  There were two tests to look at:  1)  how well it tracked actual GDP as a function of actual labor employed and capital (net fixed assets), and 2)  how well the model tracked the trend line for GDP growth (as drawn in Chart 1 at the top of this post) as a function of the trend line as drawn for the labor force (Chart 2) and the trend line as drawn for capital (Chart 10).  Keep in mind that these trend lines were drawn independently and “by eyeball” based on what appeared to fit best in the decades leading up to 2008.

This chart shows how well the modeled GDP tracked actual historical GDP:

Chart 11

The line in black shows what actual real GDP was in each year from 1959 to 2023.  The line in red shows what the simple Cobb-Douglas model predicted real GDP would be in each year with the parameters as discussed above and with the labor input based on actual employment in that year rather than the available labor force.  The capital input is always available net fixed assets (as an index, which is all we need for the relative changes), as estimated by the BEA for the NIPA accounts (shown in Chart 10 above).

The line in red for the modeled GDP tracks well the line in black of actual GDP, especially from about the early 1980s onwards.  A reduction in the growth rate for TFP in the years prior to 1980 would have led it to track the earlier years better, but I did not want to try to “fine-tune” the TFP rate.  My main interest is in how well predicted GDP tracks actual GDP over the last several decades.  Over this period, a simple Cobb-Douglas with fixed parameters and with TFP growth of 1.20% for the years before 2007 and 0.90% in the years since, tracked quite well.  And this was over a period when GDP grew from just $7.3 trillion in 1980 (in 2017 constant $) to $22.7 trillion in 2023 – more than tripling.

A second test is whether something close to the GDP trend line (as drawn in Chart 1 at the top of this post) will be generated by the Cobb-Douglas model when the labor force grows on its trend line (as drawn in Chart 2) and capital grows on its trend line (as drawn in Chart 10).  Each of these trend lines were drawn independently and “by eyeball”.

The answer is that it does, and to an astonishing degree.  This may have been the case in part by luck or coincidence, but regardless, was extremely close.  The line for GDP as predicted from the Cobb-Douglas model using labor and capital inputs that each followed their own trend lines, was so close to the GDP trend line that they were on top of each other in the chart and could not be distinguished.

One should keep in mind that, by construction, the predicted GDP in 2007 from the Cobb-Douglas model will be equal to actual GDP in that year.  The scaling factor was set that way.  But the question being examined is whether the predicted GDP (based on the labor and capital trend lines) would drift away from the trend line for GDP (as drawn) over time.  It did not.  Calculating it back over a 60-year period (i.e. equivalent to going back to 1947 from the 2007 base), the predicted GDP was only 0.7% greater than what GDP on the drawn trend line would have been 60 years before.

This is tiny, and indeed so tiny that I at first thought it might be a mistake.  But after simulating what would have been generated by various alternative parameters for the Cobb-Douglas, as well as alternative trend paths for labor and capital, the calculations were confirmed.  The implication is that the trend lines for GDP, labor, and capital – while independently drawn – are consistent with each other and with this simple Cobb-Douglas framework.

The rate of productivity growth – TFP growth – for the years leading up to 2007 was 1.20%.  It was derived, as noted above, by trying various alternatives and seeing which appeared to fit best with the figures for actual GDP in those years.  Going forward from 2007, however, it would have over-predicted what GDP would have been.  What fit well with the data on actual GDP (and based on actual employment and available net fixed assets) was a reduction in the TFP rate from the 1.20% used for the years up to 2007 to a rate of 0.90% for the years after.

The resulting path for actual GDP versus the path as modeled by the Cobb-Douglas can be more clearly seen in the following chart.  It is the same as Chart 11, but now only for the period from 2000 to 2023:

Chart 12

The red line shows the path for the simulated GDP, where from 2007 onwards the assumed TFP growth rate was 0.90%.  The fit is very good, and especially in 2022 and 2023 – the years of most interest to us – when the simulated GDP (from the Cobb-Douglas) is almost identical to actual GDP.  These are both well below the path (the green line) that would have been followed based on the previous trend growth in labor and capital, as well as the continuation of productivity growth at a 1.20% rate rather than falling to 0.90%.

c)  The Causes of the Below Trend Growth of GDP Since 2008

From this simple Cobb-Douglas model, we can try various simulations of what growth in GDP might have been had the labor force continued to grow at the rate it had before 2008, had capital continued to grow at the rate it had before 2008, and had productivity (TFP) continued to grow at the rate it had before 2008.

The results are shown in the following chart:

Chart 13

The resulting paths for GDP are shown as a ratio to what actual GDP was in each year, with the differences expressed in percentage points.  By definition, there will be no difference for actual GDP, so it is a flat line (in black) with a zero difference in each year.  The line in red then shows what the modeled GDP was in each year in terms of the percentage point difference with actual GDP, using actual labor employed in each year and available capital.  The red line shows at most a 2 percentage point difference with actual GDP – and no difference at all in 2022 and 2023.  The model tracks actual GDP well when the labor input is equal to observed employment.

The line in blue then shows what GDP would have been (according to the model) had capital growth continued after 2007 along its pre-2008 trend path (the path drawn in Chart 10 above) while labor grew at the actual rate of employment.  It shows how much the shortfall in GDP was as a consequence of capital accumulation slowing down from 2008 onwards.  As seen in the chart, the impact of this slowdown has grown over time.

The line in orange shows what GDP would have been had labor growth continued after 2007 on its pre-2008 trend path (the path drawn in Chart 2 above), while capital grew not along its trend but rather as measured.  Here one needs to take into account that the growth rate of actual employment and the growth rate of the labor force will only match between periods when the unemployment rate was the same.  Thus comparisons should be limited to periods when the economy was close to full employment, such as between 2007 (when unemployment averaged 4.6%), 2016 to 2019 (annual unemployment rates of 4.9% to 3.7%), and 2022/23 (annual unemployment rates of 3.6%).  That is, the “peaks” seen in the orange line in 2009 and again in 2020 are not significant, as they reflect labor not being fully used.  This was not because the labor force was not available but rather due to the disruptions of the downturns in those years.

The line in burgundy then shows what GDP would have been (in terms of its percentage point difference with actual GDP) had both labor and capital inputs continued to grow (and been used) on their pre-2008 trend paths.  Note that the values here will not be the simple addition of the percentage point contributions of the slower than trend growth of the labor force and the slower than trend growth of capital.  The Cobb-Douglas relationship is a multiplicative one, not a linear one.  But if one does multiply out the two (the blue and orange lines, but as ratios rather than percentage points), and adjust for the model’s tracking error (the red line), one will get the impact of the two together (the burgundy line).

Finally, there is the impact of the slowdown in TFP growth from 1.20% per year before 2007 to 0.90% after.  That will appear as the difference between what GDP would have been had it followed the previous trend path (the green line in the chart) and the impact of labor and capital both slowing down from their respective trends (the burgundy line).  Its impact grows steadily larger over time.

Based on these simulations, as of 2023 approximately 25% of the shortfall in GDP relative to what it would have been had it continued on its pre-2008 trend can be attributed to a fall in the rate of productivity growth (TFP) from 1.20% to 0.90%.  Of the remaining shortfall, approximately 60% was due to the slowdown in investment and hence capital accumulation, and approximately 40% was due to the slowdown in the growth of the labor force.  Or put another way (and keeping in mind that the impacts are not linearly additive, but only approximately so), of the total shortfall in 2023, about 70% was due to the slowdown in productivity growth together with the related slowdown in capital growth, and about 30% was due to the slowdown in labor force growth.

But these figures are for 2023 and will shift over time.  Going forward, and unless something is done to change things, the shortfall in GDP (its deviation from the pre-2008 trend) will be widening, and the shortfall in capital accumulation (due to the fall in investment as a share of GDP) plus the related reduction in productivity growth, can be expected to account for an increasing share of this increasing shortfall in GDP.  These already accounted for about 70% of the shortfall in 2023, and on current patterns that share will grow in the coming years.

E.  Conclusion

GDP fell sharply in the economic and financial collapse that began in the second half of 2008.  But while there was a recovery, with employment eventually returning to full employment levels, GDP never returned to the path it had previously been on.  This was new.  In prior recessions (as seen in Chart 1 at the top of this post), GDP was back close to its earlier path once employment had recovered to full employment levels.  As a consequence, by 2023 GDP would have been close to 20% higher than what it was had GDP returned to its previous path.  And 20% higher GDP is huge.  In terms of current GDP in current prices, that is close to $6 trillion of increased output and incomes each year.  Total federal government spending on everything is about $7 trillion currently.

The proximate causes of this can be broken down into three.  First, the labor force began to grow at a slower rate in the years following 2008.  This was not due to labor force participation rates falling for individual age groups.  Rather, this in part reflected a slowdown in the growth of the overall US population (and to this extent, will then be offset when GDP is looked at in per capita terms).  But in addition, there was the impact of an aging population, with the Baby Boom generation entering into their normal retirement years.

In policy terms, there is not much one can or should want to do about labor force growth.  Population growth is what it is, and an aging population will see an increasing share of the population moving into their retirement years.  These all reflect personal choices.

In contrast, the slowdown in investment and the resulting slowdown in capital accumulation and productivity growth is a policy question that merits a careful review.  Why are firms investing less now than they did before?  Profits (especially after-tax profits) are at record highs and the stock market is booming.  In a market economy where firms are avidly competing with each other, this should have led to an increase – not a decrease – in net investment.

A future post in this series will examine the factors behind this.  But first, a post will examine the specific case of residential investment.  Net residential investment fell especially sharply after 2008 (see Charts 8 and 9 above), while home prices have shot up.  Housing is important, and its rising cost has been the source of much displeasure in recent years by those who do not own a home and must rent.  The rising cost of housing is the primary (indeed, the only) reason why the CPI inflation index remains above the Fed’s target of 2%.  It merits its own review.

Trump’s Claims on the Economy and the Reality: A Comparison of Trump to Biden and Obama

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“We had the greatest economy in the history of the world.  We had never done anything like it. … Nobody had seen anything like it.”

Donald Trump, Republican National Convention, Milwaukee, July 18, 2024

A.  Introduction

Donald Trump is fond of asserting that the US “had the greatest economy in the history of the world” while he was president.  He claimed this when he accepted the nomination at the Republican National Convention (as quoted above); he claimed it when he debated President Biden in June; and it is a standard line repeated at his campaign rallies.  He also asserts that this is all in sharp contrast to the economy he inherited from Obama and to where it is now under Biden.  In a June 22 speech, for example, Trump said “Under Biden, the economy is in ruins.”

These assertions of Trump are not new.  He was already repeatedly making this claim in 2018 – in the second year of his administration – asserting that the US was then enjoying “the greatest economy that we’ve had in our history” (or with similar wording).  And he repeated it.  The Washington Post Fact Checker recorded in their database that Trump made this claim in public fora at least 493 different times (from what they were able to find and verify) by the end of his term in January 2021.

Repetition does not make something true.  And numerous fact-checkers have shown that the assertion is certainly not true (see, for example, here, here, and here, and for the 2018 statements here).  But readers of this blog may nonetheless find a review of the actual data to be of interest, and in charts so that the extent to which Trump is simply making this up is clear.

The post will focus on Trump’s record compared to that of Obama’s second presidential term (immediately before Trump) and Biden’s presidential term (immediately after).  The post will also show that even if you just focus on the first three years of his presidential term – thus excluding the economic collapse in his fourth year during the Covid crisis – Trump’s record is nothing special.  The collapse in that fourth year was certainly severe, and with that included Trump’s record would have been one of the worst in US history.  But Covid would have been difficult to manage even by the most capable of administrations.  Trump’s was far from that, and that mismanagement had economic consequences, but Trump’s record is not exceptional even if you leave that fourth year out.

This post complements and basically updates a longer post on this blog from September 2020.  That post compared Trump’s economic record not only to that of Obama but also to that of American presidents going back to Nixon/Ford.  I will not repeat those comparisons here as they would not have changed.  I will focus this post on just a few of the key comparisons, adding in the record of Biden.

B.  The Record on Growth

The two charts at the top of this post show how Trump’s record compares to that of Obama and Biden in the two measures most commonly taken as indicators of economic performance – growth in national output (real GDP) and growth in total employment (jobs).  This section will focus on Trump’s not-so-special record on growth, while the section following will focus on employment.

Trump has repeatedly asserted that economic growth while he was president surpassed that of any in history.  This is not remotely true in comparison to growth under a number of post-World War II presidents.  (Quarterly GDP statistics only began in 1947 so older comparisons are more difficult, but there were certainly many other cases further back as well.)  Giving Trump the benefit of excluding the economic collapse in 2020 during the Covid crisis, real GDP grew at an annual rate of 2.8% over the first three years of Trump’s presidential term.  But real GDP grew at an annual rate of 5.3% during the eight years of the Kennedy/Johnson presidency; at a rate of 3.7% during the Clinton presidency; 3.4% during Reagan; and 3.4% as well during the Carter presidency.  The 2.8% during the first three years of Trump is not so historic.  Carter’s economic record is often disparaged (inappropriately), but Carter’s record on GDP growth is significantly better than that of Trump – even when one leaves out the collapse in the fourth year of Trump’s presidency.

Nor is the Trump record on growth anything special compared to that of Biden or Obama.  As seen in the chart at the top of this post, growth under Biden over the first three years of his presidency matched what Trump bragged about for that period (it was in fact very slightly higher for Biden).  GDP growth then remained strong in the fourth year of Biden’s presidency instead of collapsing.  Growth in the Obama presidential term immediately preceding Trump was also similar:  sometimes a bit above and sometimes a bit below, and with no collapse in the fourth year.  It was also similar in Obama’s first term once he had turned around the economy from the economic and financial collapse he inherited from the last year of the Bush presidency.

Trump’s repeated assertion that “we had the greatest economy in the history of the world” was a result – he claimed – of the tax cuts that Republicans rammed through Congress (with debate blocked) in December 2017.  While the law did cut individual income tax rates to an extent (heavily weighted to benefit higher income groups), the centerpiece was a cut in the tax rate on corporate profits from 35% to just 21%.  The argument made was that this dramatic slashing of taxes on corporate profits would lead the companies to invest more, and that this spur to investment would lead to faster growth in GDP benefiting everyone.

That did not happen.  As we have already seen, real GDP did not grow faster under Trump than it had before (nor since under Biden).  Nor, as one can see in the chart at the top of this post, was there any acceleration in the pace of GDP growth starting in 2018 when the new tax law went into effect in the second year of his presidential term (i.e. starting in Quarter 5 in the charts).

The promised acceleration in growth was supposed to be a consequence of a sustained spur to greater private investment from the far lower taxes on corporate profits.  There is no evidence of that either:

The measure here is of fixed investment (i.e. excluding inventories), by the private sector (not government), in real terms (not nominal), and nonresidential (not in housing but rather in factories, machinery and equipment, office structures, and similar investments in support of production by private firms).

This private investment grew as fast or often faster under Obama (when the tax rate on corporate profits was 35%) as under Trump (when the tax rate was cut to just 21%).  Growth under Biden has also been similar, even though the tax rate on corporate profits remains at 21%.  This similar growth is, in fact, somewhat of a surprise, as the Fed raised interest rates sharply starting in March 2022 with the aim of slowing private investment and hence the economy in order to bring down inflation.

With the far lower corporate profit tax rates going into effect in the first quarter of 2018 and the Fed raising interest rates starting in the first quarter of 2022 – both cases in the fifth quarter of the Trump and Biden presidential terms respectively – a natural question is what happened to private investment in the periods following those changes?  Rebasing real private non-residential fixed investment to 100 in the fourth quarter of the presidential terms, one has:

The paths followed by private investment under Biden (facing the higher interest rates of the Fed) and under Trump (following corporate profit taxes being slashed) were largely the same – with the path under Biden often a bit higher.  They diverged only in the 12th quarter of each administration (the fourth quarter of 2019 for Trump, and the fourth quarter of 2023 for Biden).  Under Trump, private investment fell in that quarter – well before Covid appeared – and then collapsed once Covid did appear.  Under Biden, in contrast, it kept rising up until the most recent period for which we have data.

It is also worth noting that private investment during the similar period in Obama’s second term rose by even more than under Trump (and for a period faster than under Biden, although later it rose by more under Biden).  This was despite a tax rate on corporate profits that was still at 35% when Obama was in office.  There is no evidence the tax rate mattered.  And although not shown in the chart here, private investment rose by far more in the similar period during Obama’s first term (although from a low base following the 2008 economic collapse).

With similar growth in such investment in all three presidential terms (leaving out the collapse in 2020), the conclusion one can draw is that taxes at such rates on corporate profits simply do not have a meaningful impact on investment decisions.  Decisions on how much to invest and on what depend on other factors, with a tax rate on profits of 21% or of 35% not being central.  Nor did the Fed’s higher interest rates matter all that much to investment during Biden’s term.  With a strong economy under Biden, firms recognized that there were investment opportunities to exploit, and they did.

The far lower tax rate of 21% on corporate profits did, however, lead to a windfall gain for those who owned these companies.  Far less was paid in such taxes.  That is, the tax cuts did have distributional consequences.  But they did not spur private investment nor overall growth.  They did not lead to “the greatest economy in the history of the world”.

C.  The Record on Employment

As seen in the chart at the top of this post, growth in total employment was higher under Obama than it was under Trump, and has been far higher under Biden – even if you restrict the comparison to the first three years of the respective presidential terms.  In the face of this clear evidence in favor of Biden’s record, Trump has now started to assert that the growth in jobs under Biden was due to a “bounce back” in jobs following the collapse in the last year of his administration, or that they all went to new immigrants.  But neither is true.

First, as one can see in the chart there has been strong growth in the number employed not only early in Biden’s administration but on a sustained basis throughout.  And second, nor was the growth only in the employment of immigrants.  The Bureau of Labor Statistics provides figures from its Current Population Survey (CPS) of households on the employment of those who were born in the US (the native-born) and those born abroad (the foreign-born).  Leaving out the collapse in 2020, employment growth over the first three years of Trump’s presidential term of the native-born averaged 1.3% per year.  During the first three years of the Biden presidential term, employment growth of the native-born averaged 1.8% per year.  The growth in employment of the native-born was not zero under Biden – as Trump claims – but rather was faster under Biden than under Trump.  While there is a good deal of noise in the CPS figures (which will be discussed below), these numbers do not provide support for Trump’s assertion.

There has also been concern expressed in the media with what was interpreted as a “disappointing” growth in employment in July.  The BLS “Employment Situation” report for July, released on August 2, indicated that employment rose by an estimated 114,000 in the month.  This is a good deal below the average in the 12 months leading up to July of 209,300 per month.  But an increase of 114,000 net new jobs in the month is substantial.  While there will often be large month-to-month fluctuations, one should not expect more on average going forward.

With the economy basically at full employment (the recent uptick in the unemployment rate – to a still low 4.3% – will be discussed below), the number employed cannot grow on a sustained basis faster than the labor force does.  And the labor force will grow at a monthly pace dictated by growth in the adult civilian population (i.e. age 16 and over) and what share of that adult population chooses to participate in the labor force.  The labor force participation rate in July was 62.7% and has been trending downward over the past several decades.  While a number of factors are behind this, the primary one has been the aging of the population structure with the Baby Boom generation moving into their normal retirement years.

The BLS report (using figures obtained from the Census Bureau) indicates that the adult civilian population rose by an average of 136,800 per month in the 12 months leading up to July.  At a labor force participation rate of 62.7%, the labor force would thus have increased by 85,800 per month.  Without an increase in the labor force participation rate, employment cannot grow faster than this on a sustained basis going forward.

In the past 12 months, however, the BLS report for July indicates that the labor force in fact grew at an average pace of 109,700 per month.  How was this possible?  The reason is that although the labor force participation rate is on a long-term downward trend due to the aging population, there can be and have been fluctuations around this trend.  And a small fluctuation can have a significant effect.  The labor force participation rate one year ago in July 2023 was 62.6%, and thus the rate in fact rose by 0.1% from July 2023 to July 2024.  If the labor force participation rate in July 2023 had in fact been 62.7%, then the labor force in July 2023 would have been 167,410,000 rather than the actual 167,113,000, and the increase over the 12 months leading to July 2024 would have averaged 84,900.  Within round-off, this is the same as the 85,800 figure calculated in the preceding paragraph for a constant 62.7% labor force participation rate,  (With more significant digits, the labor force participation rates were 62.589% and 62.696% respectively, and a constant 62.696% participation rate would have yielded the 85,800 figure for labor force growth.)

We should therefore not expect, going forward, that monthly employment will increase on a sustained basis by more than about 90,000 or so, or even less.  It could be higher if the labor force participation rate increases (and a small change can have a major effect), but the trend over the past couple of decades has been downward – as noted already – due to the aging of the population.  How then, was it possible for employment to have gone up by an average of 209,300 per month over the past year?  And this was also a period where the estimated unemployment rate rose from 3.5% in July 2023 to 4.3% in July 2024, which “absorbed” a share of the increase in the labor force as well.

The reason for these not fully consistent numbers is that employment estimates come from the Current Employment Statistics (CES) survey of establishments where people are employed, while the labor force and unemployment estimates come from the different Current Population Survey (CPS) – a survey of households.  The CES is a survey of nonfarm employers in both the private and public sectors, and covers 119,000 different establishments at 629,000 different worksites each month.  The “sample” (if it can be called that) covers an estimated one-third of all employees.

The CPS, in contrast, is a survey of about 60,000 households each month.  There will only generally be one or two members of the labor force in each household, so the share of the labor force covered will be far less than in the CES.  If each household had two members in the labor force, for example, the total of 120,000 would be only 0.07% of the labor force –  a sharp contrast to the one-third covered in the CES.  There is therefore much more statistical noise in the CPS data.  There are also definitional differences:  The CPS will include not only those employed on farms but also the self-employed and those employed in households.  Also, a person with two or more jobs will be counted as one person “employed” in the CPS.  The CES, in contrast, counts the employees of a firm, and the employers will not know if the individual may be working at a second job as well.  Thus a person working two jobs at two different firms will be counted as two “employees” in the CES.

These definitional differences are not major, however, and in part offset each other.  An earlier post on this blog looked at these differences in detail, and how, in an earlier period (2018/2019) there was a substantial deviation in the employment growth figures between the estimates in the CES and the CPS.  This was the case even with the figures adjusted (to the extent possible) to the same definition of “employment” in each.  There is a similar deviation between the employment estimates in the CES and in the CPS currently, with this accounting for a strong growth in employment as estimated by the CES (of 209,300 net new jobs each month over the past year) even though the labor force has grown -according to the CPS – by a more modest 109,700 per month over this period.

The labor market remains tight, however, even with the rise in the estimated unemployment rate to 4.3% in July:

The unemployment rate fell rapidly under Biden, following the chaos of 2020.  It was at a rate of 3.9% or less for over two years (27 months), despite the efforts by the Fed to slow the economy by raising interest rates.  The unemployment rate was also 3.9% or less for a period under Trump (for 20 months).  But as one sees in the chart, during the first three years of Trump’s term it basically followed the same downward path as it had under Obama.  It then shot up in March 2020 when the nation was caught unprepared for Covid.  As with the other key economic indicators (the growth in GDP, in employment, and in private investment), the paths followed by the economy during the first three years of Trump’s term were basically the same as – although usually not quite as good as – the paths set during Obama’s presidency.  They all then collapsed in Trump’s fourth year.

Any unemployment rate near 4%, and indeed near 5%, is traditionally seen as low.  Economists have defined the concept of the “Non-Accelerating Inflation Rate of Unemployment” (NAIRU) as the rate of unemployment that can be sustained without being so low that inflation will start to rise.  While one can question how robust this concept is (as will be discussed below), the NAIRU rate of unemployment has generally been estimated (for example by the staff at the Federal Reserve Board) to be between 5 and 6%.  An unemployment rate of 4.3% is well below this.  While the unemployment rate has gone up some in recent months, it is still extremely low.

D.  The Record on Real Living Standards

Ultimately, what matters is not the growth in overall output (GDP) or in employment, but rather in real living standards.  Many have asserted that because of recent inflation, living standards have gone down during Biden’s presidential term.  This is not true, as we will see below.  But first we will look at inflation.

Inflation rose significantly early in Biden’s presidential term.  The pace moderated in mid-2022, but until recently prices continued to rise:

Inflation was less during Trump’s term in office but was even lower under Obama.  Indeed, consumer price inflation has been low since around 1997, during Clinton’s presidency, until the jump in 2021.  Why did that happen?

The rise in 2021 can be attributed to both demand and supply factors.  On the demand side, both Trump and Biden supported and signed into law a series of genuinely huge fiscal packages to provide relief and support during the Covid crisis.  The packages were popular – especially the checks sent to most Americans (up to a relatively high income ceiling) that between the various packages totaled $3,200 per person.  But the overall cost for all the various programs supported was $5.7 trillion.  That is huge.  The funds were spent mostly over the two years of 2020 (under Trump) and 2021 (under Biden), and $5.7 trillion was the equivalent of 12.8% of GDP over those two years.  Or, as another comparison, the total paid in individual income taxes in the US in the single year of FY2023 was “only” $2.2 trillion.

While there was this very substantial income support provided through the series of Covid relief packages, households were limited in how much they could spend – out of both these income transfers and their regular incomes – in 2020 due to the Covid pandemic.  One went out only when necessary, and kept only to shopping that was necessary.  This carried over into early 2021.  But people could become more active as the Biden administration rolled out the massive vaccination campaign in the first half of 2021.  People then had a backlog of items to buy as well as the means to do so from what had been saved in 2020 and early 2021.  Demand rose sharply, and indeed Personal Consumption Expenditures in the GDP accounts rose by more in 2021 (by 8.4%) than in any year since 1946 (when it rose by 12.4%, and for similar reasons).

But at the same time, supply was constrained.  Supply chains had been sharply disrupted in 2020 worldwide due to Covid, and took some time to return to normal.  There was then the additional shock from the Russian invasion of Ukraine in February 2022, leading oil and many other commodity prices to spike.

Supply chains did, however, return more or less to normal early in the summer of 2022.  And as they did, one saw a sudden and sharp reduction in pressures on prices, in particular on the prices of goods that can be traded:

This chart shows the annualized inflation rates for 6-month rolling periods (ending on the dates shown) for the overall CPI, for the shelter component of the CPI, and for the CPI excluding shelter.  The overall inflation rate rose from an annualized rate of 3.2% in the six months ending in January 2021 (the end of Trump’s term) to a peak of 10.4% in the six months ending in June 2022.  It then fell remarkably fast, to an annualized rate of just 2.6% in the six months ending in December 2022.

This sudden drop in the inflation rate is seen even more clearly in the CPI index of prices for everything but shelter:  The annualized rate fell from 12.4% in the first half of 2022 (the six months ending in June) to a negative 0.2% rate in the second half of 2022 (the six months ending in December).  Why?  There was not a sudden collapse in consumer or other demand.  Rather, supply chains finally normalized in the summer of 2022, and this shifted pricing behavior.  When markets are supply constrained (as they were with the supply chain problems), firms can and will raise prices as competitors cannot step in and supply what the purchaser wants – they are all supply constrained.  But as the supply chains normalized, pricing returned to its normal condition where higher demand can be met by higher production – whether by the firm itself or, if it is unwilling, by its competitors.  It is similar to a phase change in conditions.

Shelter is different.  It covers all living accommodations (whether owned or rented), and as has been discussed in earlier posts on this blog (see here and here), the cost of shelter is special in the way it is estimated for the CPI.  It is also important, with a weight of 36% in the overall CPI index (and 45% in the core CPI index, where the core index excludes food and energy).  The data for the shelter component of the CPI comes from changes observed in the rents paid by those who rent their accommodation, and rental contracts are normally set for a year.  Hence, rental rates (and therefore the prices of the shelter component of the CPI) respond only with a lag.  One can see that in the chart above, with the peak in the inflation rate for shelter well after the peak in the inflation rate for the rest of the CPI.

Since mid-2022, the rate of inflation as measured by the overall CPI has generally been in the range of 3 to 4% annualized.  Increases in the cost of shelter have kept it relatively high and above the Fed’s target of about 2% per annum.  But as seen in the chart, it has recently come down – falling to an annualized rate of 2.5% in the six months ending in July.  For everything but shelter, the rate in the six months ending in July was only 1.4%.

One question that some might raise is whether the very tight labor markets – with an unemployment rate that was 4% or less until two months ago – might have led to the inflation observed.  The answer is no.  As noted above, inflation in all but shelter fell suddenly in mid-2022, falling from a rate of 12.4% in the first half of the year to a negative 0.2% in the second half, even though the unemployment rate was extremely low at 4% or less throughout (and only 3.5 or 3.6% in all of the second half of 2022).  Unemployment has remained low since while inflation has come down.  If the cause was tight labor markets, then the rate of inflation would have gone up rather than down.

Similarly, inflation as measured by the CPI was not high in 2018 nor in 2019 when labor markets were almost as tight during Trump’s presidency – with overall inflation then between 2 and 3% on an annual basis.  Nor did inflation go up during the similarly tight labor market of 1999 and 2000 during the Clinton presidency:  CPI inflation was generally in the 1 1/2 to 3 1/2 % range during that period.  All this calls into question the NAIRU concept, with its estimate that an unemployment rate below somewhere in the 5 to 6% range will lead to pressures that will raise the rate of inflation.

Managing inflation coming out of the chaos of 2020 was certainly difficult.  Inflation spiked in most countries of the world following the Covid crisis, reaching a peak in 2022.  But the rate of inflation has since come down as supply conditions normalized.  That does not mean that the absolute level of prices came down, only that they were no longer increasing at some high rate.  Wages and other sources of income will then adjust to the new price levels, and what matters in the end is whether real levels of consumption improve or not.  And they have:

The chart shows the paths followed for per capita real levels of personal consumption expenditures, as measured in the GDP accounts, during the presidential terms of Trump, Biden, and the second term of Obama.  The path followed under Trump was basically the same as that followed under Obama – until the collapse in the last year of Trump’s term.  The path followed under Biden has been substantially higher than either.  It was boosted in his first year as the successful vaccination campaign allowed people to return to their normal lives.  They could then purchase items with not only their then current incomes, but also with the savings they had built up in 2020.  But even if one excludes that first year, the growth under Biden has been similar to that under Obama and under Trump up to the collapse in Trump’s fourth year.

Once again, there is no basis for Trump’s claim of the “greatest economy”.

E.  Summary and Conclusion

The economy during Trump’s presidency was certainly not “the greatest in the history of the world”.  Nor was it even if you leave out the disastrous fourth year of his presidency.  Covid would have been difficult to manage even by the most capable of administrations, and Trump’s was far from that.  Instead of preparing for the shock this highly contagious disease would bring, Trump’s response was to insist – repeatedly – “it’s going to go away”.

Trump’s economic record was certainly nothing special.  Real GDP grew as fast or faster under Obama and Biden as it had under Trump.  Trump insisted that growth would be – and was – spurred by the tax cuts that he signed into law in late 2017 that slashed the tax on corporate profits.  But there is no indication of this in the data.  Nor is there even any indication that private investment rose as a result of the lower taxes.

Employment has grown far faster under Biden than it had under Trump, and also grew faster in Obama’s second term – even leaving out Trump’s disastrous fourth year.  Unemployment fell during the first three years of Trump’s term in office (before sky-rocketing in his fourth year), but here it just followed a very similar path to that under Obama.  For this, as with GDP and employment growth, perhaps the biggest accomplishment of Trump’s first three years in office was that he did not mess up the path that had been set under Obama.  And unemployment has been even lower under Biden.

Inflation was certainly higher in 2021 as the US came out of the Covid crisis.  Supply chains were still snarled, but there was pent-up demand from consumers who had had to avoid shopping in 2020 due to Covid and who also benefited from a truly huge set of Covid relief packages passed under both Trump and Biden.  Supply chains then normalized in mid-2022, sharply reducing pricing pressures for goods other than shelter.  Due in part to lags in how rental rates for housing are set (as they are normally fixed for a year) and then estimated by the BLS, the cost of the shelter component of the CPI came down more slowly than the cost of the rest of the CPI.  This kept inflation as measured higher than what the Fed aims for, although recently (in the last half year) it has come down again.  Most anticipate that the Fed will soon start to cut interest rates from their current high levels.  The inflationary episode resulting from the Covid crisis appears to be coming to an end.

There is thus no justification for the claim by Trump that “we had the greatest economy in the history of the world”.  Yet he has repeatedly asserted it, both now and when he was president.  Why?  Stephanie Grisham, who served in the Trump administration as press secretary and in other senior positions, and who had been – by her own description – personally close to Trump, explained it well in a speech she made on August 20 to the Democratic National Convention.  She noted that Trump used to tell her:  “It doesn’t matter what you say, Stephanie.  Say it enough, and people will believe you.”

Many do appear to believe that the economy was exceptionally strong when Trump was president:  that it was “the greatest in history”.  But that is certainly not true.  Facts matter; reality matters; and a president needs to know that they matter.