Category Archives: Econ Data

What GDP Means: A Not Terribly Surprising Lack of Understanding by Elon Musk and Trump’s Secretary of Commerce

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Update, March 28, 2025:  The BEA released on March 27 its initial estimate of GDP as measured from wage and profit income for the fourth quarter of 2024.  The BEA labels this, to avoid confusion, Gross Domestic Income – GDI.  In principle, it should be the same as GDP but will differ as data from different sources are used to estimate both; see the discussion in the post below.  The BEA also released its initial estimate for the fourth quarter of 2024 of value-added produced by sector, from which one can calculate growth in production by private industries and in production by government.  The two together sum to GDP.  Charts 1 and 3 in the post and related text have been updated to reflect these new estimates.

Economic growth was strong in the last quarter of 2024 – the last full quarter of the Biden administration.  Real GDP grew at a 2.4% annual pace when estimated from demand-side measures, and at a substantially faster 4.5% pace when estimated from income-side measures (i.e. GDI).  Growth in the average of the two measures – GDI and GDP – was 3.5%.  This is exceptionally strong.  And price inflation was modest, with growth in the Personal Consumption Expenditures (PCE) price deflator of 2.4% at an annual rate, and 2.6% in the core PCE price deflator.

Along with an unemployment rate of just 4.0% (as of January), Trump has inherited an extremely strong economy from Biden.  We will now see what develops.

 

A. Introduction

On February 28, Elon Musk posted on his social media site X the comment:

“A more accurate measure of GDP would exclude government spending.

Otherwise, you can scale GDP artificially high by spending money on things that don’t make people’s lives better.”

Two days later, on March 2, Secretary of Commerce Howard Lutnick followed up on this and said in an interview on Fox News:

“You know that governments historically have messed with GDP.  They count government spending as part of GDP.  So I’m going to separate those two and make it transparent.”

These statements reveal a lack of understanding of what GDP means by two of the most prominent officials (or in Musk’s case, a non-official official) in the Trump administration – both key players on economic issues.  While that lack of understanding is not surprising – how GDP is defined is a technical issue – what is worrying is that these prominent Trump appointees would assert this with confidence and without bothering to check first with experts whether it was in fact true.  Such arrogance is unfortunately now the norm in this administration.

Lutnick’s assertion that “governments have historically messed with GDP” (in fact they have not) is also worrisome as it looks like preparation for the Trump administration to do precisely that.  GDP estimates are prepared by the Bureau of Economic Analysis (BEA), a bureau in the Department of Commerce now headed by Lutnick.  Also, just a few days before his confused statement on GDP Lutnick disbanded the standing external technical advisory committee that advised the BEA on how GDP estimates might be improved.  Committee members were professionals at universities and in industry who were specialists on these issues.  They were not paid and only had their travel costs covered when meetings were called.  At the same time, Lutnick also disbanded several similar committees of unpaid specialists advising the Census Bureau – also part of the Department of Commerce.

The concern is that Lutnick is setting things up precisely to “mess with” how GDP is estimated.  The concern is that he could very well order the BEA to manipulate the standard methodology and estimation process to come up with figures that make it look like the economy is in less trouble than it in fact is in the coming years of Trump’s second presidential term.

Musk and Lutnick do not appear to realize that GDP – which stands for Gross Domestic Product – is a measure of the market value of all the economy produces in a given period (normally expressed in annualized terms).  Gross Domestic Product is a measure of production; production that takes place domestically (i.e. within the nation’s borders); and in gross terms (meaning without depreciation of capital taken out).  The BEA also produces estimates with depreciation subtracted – which it calls Net Domestic Product or NDP – but less attention is paid to those figures as depreciation is especially hard to estimate, both conceptually and in practice.

The aim of the GDP measure is thus to arrive at an estimate of how much the nation’s economy is producing.  How that production is used is not the purpose of the measure, and there is no assessment of whether that use should be considered “good” or “not so good”.  But it is disparaging to assert (as Musk and Lutnick have) that the services that the government provides – such as the services of the school teacher pictured above – are worthless and hence should not be counted.

Those statements of Musk and Lutnick do, however, provide a “teachable moment”.  Many others – including some in the news media – are also confused about what GDP means and signifies.  This post will seek to sort through those confusions.  The first section below will review what GDP means and how it is estimated.  Those are two separate things.  Lutnick and Musk are conflating what GDP is designed to measure (i.e. production) with one way in which GDP may be estimated (from the demand side, by estimating how that production is used).  The BEA in fact estimates GDP in three different ways, with this serving to provide also cross-checks on the estimates.  In principle, GDP as estimated by each of the three methods should be the same.  But one is dealing with real-world data, there will always be statistical noise, and hence by using three different approaches the BEA can arrive at a more robust overall estimate.

Lutnick also asserted there is a lack of transparency in the GDP figures, in that the BEA includes figures on government spending in GDP.  This is confused and it is also defamatory to assert that the BEA is anything other than transparent.  The BEA issues each month regularly updated figures not just on GDP but also on much else in what is more formally called the National Income and Product Accounts (NIPA).  In these, the BEA provides estimates on all sorts of aggregates with and without government spending.  While Lutnick and Musk are not clear about what they are seeking, one guess is that they are looking for a figure on domestic production that excludes whatever is produced directly by government (such as the services of the public school teacher pictured above).  But the BEA in fact provides this:  It is called Value-Added by Private Industries.  The BEA provides estimates of this for each calendar quarter and in both real and nominal terms.  Charts below will show (using the published BEA figures) what growth would have been whether measured by GDP as defined or by a measure of growth that excludes government-produced services.

We will also look at some simple measures of the productivity of federal government workers over time.  The calculations are of necessity rough, but if one counts only the management of federal discretionary expenditures, the growth in federal worker productivity in fact matched (over the period 1997 to 2023) the growth in overall labor productivity in the economy.  And if one includes also the management of mandatory spending (entitlement programs such as Social Security and Medicare), federal worker productivity has grown far faster than overall labor productivity in the economy.  These estimates should not be taken too seriously, as there is no way to assess how well-managed the programs are.  But in simple but crude terms, there is no evidence that growth in federal worker productivity has lagged what the rest of the economy has achieved.  Indeed, by one measure it was far better.

The post will end with some final comments.  While I am sure Musk and Lutnick do not realize it, their notion of what GDP should and should not include is in many respects similar to concepts used in the Material Balances System of national accounts (also called the Material Product System) developed in the 1930s by Gosplan in Stalin’s USSR.  It was then used (by command – there was no choice here) in the communist countries of Eastern Europe up until 1990.  Those systems excluded the concept of certain services as contributing to production, similar to what Musk and Lutnick are advocating now.  It was built on the concept from Karl Marx of productive and unproductive labor, with Musk and Lutnick asserting that certain labor (those employed in government) is unproductive.

It is ironic that senior figures in the Trump administration appear to be advocating for a system similar in nature to that developed in Stalin’s USSR.  We know that that did not end well.

B.  The Meaning of GDP versus How GDP is Estimated

GDP – Gross Domestic Product – is a measure of the market value of what the economy is producing.  That is not the same concept as what the economy is using.  While statisticians can make use of data on how output is being used in order to arrive at an estimate of what was produced, confusion on this point appears to have led to the error Musk and Lutnick made.  While what they did mean is not fully clear, they mistakenly asserted that eliminating one of the uses of output (that by government) would yield “a more accurate measure of GDP” (as Musk put it in the quote above).  That is not correct.  It would no longer be GDP.

The confusion – which others have made as well – stems from how GDP is estimated.  The BEA (and also the international standard on national account concepts) estimates GDP in three different ways.  The three should in principle yield the same figure for GDP (after some minor adjustments to reflect indirect taxes and subsidies).  But with real-world data, the three estimates will normally differ only by some – hopefully small – amount.  The three approaches will serve, however, as cross-checks on each other, and can flag that there is an issue if one of the estimates differs significantly from the others.

An earlier post on this blog discussed those three methods for estimating GDP.  Readers may wish to read that post (which covers also how recessions are identified and formally declared) for a more complete discussion.  And for a much more detailed review, they may refer to Chapter 2 of the NIPA Handbook issued by the BEA.  But briefly, GDP is estimated by:

a)  The initial, and most widely viewed, estimate of GDP comes not from estimates of what is produced, but rather from how that production is used.  As those who have studied any economics know, GDP will be equal to the sum of Personal Consumption, Private Investment, Government Consumption and Investment, and Net Exports (i.e. Exports less Imports).  This holds not because Personal Consumption and the other uses of GDP are themselves producers of GDP (although some commentators often imply that), but rather because Private Investment includes investment in inventory accumulation.  Whatever is produced and not sold will accumulate in inventories, and with this as a balancing item one can go from the sum of all the uses of output to what production itself was.

It is a simple trick, but often misunderstood.  It allows the BEA to arrive at a fairly good estimate of how much production (GDP) grew in any calendar quarter just one month after the end of each quarter.  The BEA formally calls this its “Advance Estimate” of GDP, although many refer to it as the first or initial estimate of GDP.  It is then revised a month later (to produce the “Second Estimate”), and again a month after that (to produce the “Third Estimate”), as more complete data become available to the BEA.

The components of demand are also interesting and important in themselves.  They provide figures on what happened to personal consumption, fixed as well as inventory investment, and the other demand components of GDP.  To many, these figures on spending are of more interest than what happened in a particular sector of production.  And perhaps more importantly, in a modern economy production itself is largely driven by what producers can sell.  As Keynes taught us, production cannot be taken as a given at some “full employment” level, but rather will respond to what producers believe they can sell at a price that will cover their costs.

If, as Musk and Lutnick appear to be saying (again, they are not fully clear), the use of production by government (or some portion of that use) were to be subtracted from GDP, then the sum of what is used in the economy will no longer match what is produced by the economy.  That simple identity will no longer hold.

b)  GDP is also estimated by summing up the incomes (wages and profits) generated by the act of production.  This is based primarily on data obtained by the BLS from its monthly survey of business establishments (the Current Employment Statistics – or CES – survey; most commonly known for the monthly employment estimates it provides); from the Quarterly Census of Employment and Wages (QCEW) also of the BLS and with more detail on earnings; from the Quarterly Financial Report (QFR) of the Census Bureau (that obtains, for a sample of business firms, statistics on their financial positions and profits); and from a range of other sources to fill in the gaps (e.g. on farm incomes).

The value of all that is produced in the economy will accrue as someone’s income.  Thus by arriving at an estimate of aggregate incomes, the BEA will have a second approach to estimating GDP.  Those incomes include wages and other compensation paid to labor (e.g. health insurance, pension contributions, and such) plus the profits (or gross margin) accruing to the owners of the firms.  Since GDP is a gross concept (meaning before any deduction for depreciation of capital), the profit concept will be profits before any deduction for depreciation allowances.

The BEA does not try to provide an estimate of GDP by this approach in its Advance (first) Estimate of GDP released one month after the end of each quarter.  It does not yet have sufficient data to do this.  Rather, its first estimate is only (normally) provided with its Second Estimate of the GDP accounts two months after the end of each quarter.  At that time it issues a revised estimate of its demand-side estimate of GDP, and its initial estimate of GDP as arrived at from its income-side figures.  To avoid confusion, it calls this estimate Gross Domestic Income (GDI) rather than GDP, although in principle they should be the same.  But in the real world, the GDP and GDI figures will differ by some (hopefully small) amount, and to be fully transparent, the BEA shows this difference with the label of “Statistical Discrepancy”.  The BEA also provides a figure for the simple average of the GDP (demand-side) and GDI (income-side) estimates.  Many professionals take the quarterly changes in that simple average to be a better estimate of what growth has been in the economy – better than either GDP or GDI alone.

[Side note:  The initial GDI estimates are normally provided at the time of the Second Estimate of the GDP accounts, i.e. two months following the end of each quarter.  But the figures for the fourth quarter of the year are an exception, as extra time is allowed for businesses to complete their end-of-year accounting.  Thus the initial GDI estimates for the fourth quarter of each year are not released in late February but rather in late March.]

It is not clear what would happen to this GDI estimate should the BEA be required by Lutnick (its boss) to not count government spending (or some portion of government spending) in its GDP estimates.  Not only do government workers (civil servants) earn incomes, but wages are paid and profits are earned on the production of what government purchases.  While it would be straightforward (although silly) to exclude wages earned by government workers from the total incomes earned in society, it would be far more difficult to try to cancel out wages and profits earned on production sold to government.  But unless one did that, GDI would no longer match up with the concept of GDP that Musk and Lutnick appear to be calling for.

c)  The third approach the BEA uses to estimate GDP is to estimate directly what each sector in the economy produces and then add it up.  This is, however, the most difficult.  Hence the initial estimate of GDP in this way is not issued until the third month following the end of each calendar quarter (at which time the BEA issues also a second set of revised estimates of GDP from the demand side and a first set of revised estimates of GDP from the income side (i.e. GDI).  And while in principle this direct production-side estimate of GDP should match the other two estimates of GDP, the BEA does not publish what they arrived at for overall GDP from that production-side estimate.  Rather, for whatever reason (possibly limited confidence in the adequacy of the data they have at the time, or to avoid confusion by the public) the BEA scales their sector-by-sector estimates of production to match their (twice-revised) demand-side estimate of GDP.

To avoid double-counting, only the value that is added in each sector is counted (“value-added”).  The value-added in a sector of production is the gross revenues from the sale of what is produced minus what that sector purchases from other sectors – which are called intermediate inputs.  Thus, for example, the value-added of a bakery will equal the revenues of the bread that it sells less the cost of the inputs it had purchased – the flour, energy, water, and other ingredients it used.  A portion of that value-added is paid to the workers in wages and other compensation, and what is left is the gross margin or profits (the BEA uses the term “gross operating surplus”) of the bakery.  And for Gross Domestic Product (as opposed to Net National Product), it will be the profits before any deduction for depreciation.

Adding up value-added across all sectors of the economy should then match GDP as estimated from the demand side and GDP (GDI) as estimated from the income side.  But a question that then arises is how do they estimate value-added by the part of government that does not sell its output on the market?  While government enterprises (such as the Post Office, government-owned public utilities such as water companies, certain toll roads, and similarly) are an exception, they are a relatively small share of what government does.  A similar issue arises for non-profits who do not sell what they provide.

For what is called “general government” (government excluding government enterprises), as well as non-profits who do not sell what they provide on the market, the BEA follows standard international guidelines and sets the value-added of such entities to equal what it pays in wages and other compensation to government workers plus an estimate of what depreciation was on the capital assets of the government (or the non-profits).  The estimated depreciation is added in order to make these figures comparable to the figures for value-added in the sectors where goods and services are sold in the market.

Adding value-added estimated in this way for general government (and non-profits) to the figures for value-added in the sectors of the economy that sell in the markets (including by government enterprises), will then yield an estimate for GDP that in principle will match both GDP as estimated from the demand side (how the production was used, including for any inventory accumulation) and from the income side (wages and profits).

A generous interpretation of the comments of Musk and Lutnick is that they would exclude the counting of any value from the services that government itself provides when measured in the way described above.  That does not appear to be the case, but it is a possible interpretation and would indeed make more sense than excluding government spending (or some portion of government spending) from the demand side estimate of GDP.  That is, under this interpretation of Musk and Lutnick, the BEA would be instructed to provide an estimate of all that is produced in the economy excluding that provided by government.

But the BEA already publishes precisely such a figure.  It is the sum of value-added across all of the private sectors of the economy (which the BEA labels “Private Industries”).  Quarterly estimates on this are provided in the table titled “Gross Domestic Product by Industry Group” which is part of their publication of the Third Estimates of GDP, released three months following the end of each quarter.  The contribution to GDP from Private Industries plus the contribution from Government sums to their overall estimate of GDP.  The BEA is fully transparent on this.

What difference would it make if we assume Musk and Lutnick are referring to this concept of production as a measure of how the economy is performing?  The next section will examine this.

C.  Growth in GDP Compared to Growth in Private Production

How much would growth differ if it were measured based on the figures the BEA provides (and has been providing for many years) on private production as opposed to GDP?  There would be some difference, but not all that much.  What may be interesting is that, at least during recent presidential terms, growth in private production has been consistently higher than growth in overall GDP – although not by a substantial amount.  That is, growth in government production has been kept constrained through tight budgets, which has kept overall GDP growth below the growth in private production.  This is the opposite of the assertions of Musk and Lutnick that government spending has been some kind of artificial boost to the recorded figures on GDP growth.

It is interesting to compare the figures on growth by presidential term.  Comparing that under Biden and in Trump’s first term:

Chart 1

The plain lines show the levels of real GDP compared to what it was in the first quarter of each presidential term, while the lines of the same color but with symbols show what real growth was in private production (what the BEA labels Private Industries).  Private production was consistently somewhat higher, although not by much.  In terms of growth rates, real GDP grew at a 1.7% rate during Trump’s first term and at a rate of 2.8% during Biden’s.  Growth in 2020 was hurt, of course, due to the Covid crisis, and Trump’s mismanagement of the crisis made it worse than it would have been.  Private production grew at a faster rate for both presidents:  at a 1.9% rate under Trump and a substantially higher 3.0% rate under Biden.

One can draw a similar chart for Obama’s two terms in office:

Chart 2

The same pattern holds, with growth in private production faster than the growth in GDP as a whole.  The four-year growth rates were 1.8% and 2.25% for GDP in Obama’s two terms, and 2.1% and 2.5% for private production.

Since GDP is the sum of output of private industries and of government (both in value-added terms, as discussed in Section B above), the implication is that the growth in government value-added was slower than the growth in private value-added in each of these four presidential terms.  This is not surprising, as government growth has been kept constrained by tight budgets limiting what government was allowed to do.

Presidents – together with Congress – are responsible for government spending at the federal level.  Examining this, growth in federal government production (in value-added terms) was indeed flat or modest in each of the terms of Biden and Obama.  But it grew quickly under Trump:

Chart 3

Note that this federal production under Trump was already growing rapidly well before the start of the special programs passed by Congress in response to the Covid crisis.  Those special programs began only in the second quarter of 2020 – quarter #13 of his term in office.  They were also largely transfer programs, and hence separate from what would be counted in government value-added.  They do not explain the rapid growth in government during Trump’s first term in office, in contrast to the only modest growth, or even zero growth (in Obama’s second term), in federal programs when the Democrats were in office.

With this growth in federal production under Trump, why do we still see a substantially greater growth in private production than in overall GDP during Trump’s term (Chart 1 above)?  The reason is that government value-added as a component of GDP will include not only production by the federal government, but also production by state and local governments.  Furthermore, state and local level government production accounts for about two-thirds (68% in 2023) of overall government production (in value-added terms) in the US.  Federal programs account only for one-third.  And value-added in the state and local government sector fell at a 0.5% annual rate during Trump’s term in office, thus holding down overall government value-added despite the sharp rise in federal spending under Trump.

D.  Growth in Federal Worker Productivity Over Time

Musk and Lutnick also assert that government civil servants are unproductive whose contribution should not be counted in GDP.  It is, however, difficult to come up with a good measure of their contribution.  What metric would one use?

While it is difficult to come up with some absolute measure, one can conceive of a measure of the efficiency with which civil servants have, over time, managed their basic work.  Most of the work of federal civil servants is in the management and oversight of government-funded contracts or of federal transfer programs.  Examples of the first (discretionary government spending) include the management of contracts for medical research, or to buy tanks and planes for the military, or via state and local governments for the building of public infrastructure.  Examples of the second (mandatory government spending) include Social Security and Medicare.

There will be a dollar value associated with all such programs.  A crude measure of growth in federal government worker productivity would be how the dollar value of the contracts they have managed has changed over time – in real terms – per federal worker.  Relative to 1997 (the earliest year possible with a consistent series for all the data required), federal spending (in real terms) managed per federal worker has increased substantially:

Chart 4

Two curves are shown for federal government spending per federal worker (where only federal civilian workers are included; active duty military is excluded).  One counts discretionary government spending only, and excludes mandatory spending (for programs such as Social Security and Medicare) as well as interest on government debt and wages of the federal workers themselves.  This curve is shown here in blue.  While there have been substantial fluctuations, as of 2023 it was almost 50% higher than what it was in 1997.  And as of 2023, the increase was almost exactly the same as the increase in productivity for the economy as a whole, i.e. of real GDP per worker.

An alternative measure would include federal spending on mandatory programs in addition to spending on discretionary programs.  These are highly efficient programs, with administrative spending by government entities far below what is seen at private entities managing similar programs.  As was shown in an earlier post on this blog, the administrative cost of private health insurance is on average five times more expensive than the administrative cost for Medicare health insurance.  And administrative costs paid on an average 401(k) retirement accounts are more than an order of magnitude higher than the administrative costs of the Social Security system.  As discussed in another earlier post on this blog, the administrative costs of managing the Social Security system are only 0.5% of the benefits paid out each year.  In contrast, the annual cost of the fees paid out to private accounting and financial institutions on a 401(k) is generally between 2 and 3% of the outstanding balance in the 401(k) account.  The government designed programs are large, simple, and far more efficiently managed than private programs for similar matters.

Including these (and other) mandatory federal programs along with federal discretionary spending, the dollar values of the spending managed per federal worker doubled between 1997 and 2023 (see Chart 4).  This was far greater than the almost 50% increase in real GDP per worker in the economy as a whole.  It spiked even higher in 2020 and 2021 due to the massive Covid relief programs of those years.

This metric cannot assess how well federal workers are managing these programs in absolute terms.  But in terms of the effectiveness (in dollar costs per federal worker) with which they are being managed, federal worker productivity has grown substantially over the past quarter century.

E.  Concluding Comments – Similarities with the Material Product System of National Accounts of the USSR

While I am pretty sure Musk and Lutnick did not have it in mind when they asserted that “GDP” would be better measured by excluding the value of what government workers provide (or government spending in general), their proposal has parallels with the national accounts system developed by Gosplan in the 1930s in Stalin’s USSR.  That system – called the Material Product System (or also Material Balance Planning) – placed a value only on the production of material goods and not of certain services.  The concept for overall output in this system was not GDP but rather what the system defined as Net Material Product (NMP).  In contrast to GDP, NMP excluded the value of what it called “non-productive” services, which in that system included government services as well as health care, education, housing, passenger (but not freight) transport, financial services, and more.

Gosplan developed their system of national accounts based on the concept from Marx of productive and unproductive labor.  Only the production of material goods was viewed as productive, while labor used in the production of non-material goods was unproductive.  Thus whatever was provided by the latter should be excluded from their concept of overall output – i.e. excluded from their NMP.

I doubt that Musk and Lutnick were trying to follow some version of these Marxist concepts in their assertion that “GDP” should exclude the value of what government provides.  But the parallels are interesting.  The Material Product System of national accounts was used in the USSR and then in the post-war period in the Communist countries of Eastern Europe until 1990.  As we know, that did not end well.  One of the problems was that with their system of national accounts, they did not have a good view of what was in fact happening in their economies as conditions deteriorated over the decades.

There will be basic inconsistencies as well in trying to exclude one component of GDP in the integrated NIPA accounts, claiming that government does not contribute to GDP.  The three-way equality of GDP based on how production is used, the wage and profit income generated in production, and the value-added produced in all sectors of the economy, will then no longer hold.

A generous interpretation of Musk and Lutnick would be that they are calling for national income accounts that would count in GDP only the value of what is produced in private activities, i.e. excluding government.  One could do this, but it would no longer be GDP.  Rather, one would then have simply the sum of value-added across all private industries.  But if that is what they want, the BEA already provides it.

Not understanding national income accounting is, of course, minor in comparison to what Trump and his administration have done since taking office in January.  The disregard of basic laws and indeed the Constitution, the firing for no cause of thousands of civil servants and the closure of agencies established by Congress, and the vindictiveness of Trump’s attacks – and his willingness to use raw government power – on American media, universities, law firms, and individuals who have criticized him, are all of far greater consequence.

But the confident assertions of Musk and Lutnick on what should count in GDP is a further example of the self-confident arrogance of this administration.

The Economic Record of Biden and Trump Compared to That of Presidents Since Truman

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Chart 1

A.  Introduction

The BEA released on January 30 its first estimate (what it calls its “Advance Estimate” ) of GDP in the fourth quarter of 2024.  This provides the first good estimate of GDP growth during Biden’s full term in office.  We can now see how that growth compares to growth during the terms of other US presidents, and in particular Trump.  Trump has repeatedly claimed that “we had the greatest economy in the history of the world” when he was president, while “Under Biden, the economy is in ruins”.

We now have concrete statistics on this.  Like much of Trump’s bombast, there is no truth to his claims.  This post will examine what the record has been for growth in per capita real GDP, for unemployment, and for inflation, comparing the record we now have for Trump and Biden to that of the other post-World War II presidents.

B.  Growth in Per Capita Real GDP

The chart at the top of this post shows what most take as the broadest measure of economic performance:  the growth in real GDP per capita.  The figures were calculated from BEA National Income and Product Account (NIPA) data (as updated on January 30), accessed via FRED.  They show the average rate of growth in per capita real GDP for each four-year presidential term going back to Truman’s second term (as quarterly GDP data only began to be estimated in 1947).  The presidential terms are defined from the first quarter of their inaugural year to the last full quarter of the final year of their term.

Per capita real GDP grew at an average annual rate of 2.5% under Biden, which is the highest in any presidential term since Clinton’s second term.  And it is almost twice as high as the average rate of growth of just 1.3% per year during Trump’s first term.  Compared to the 19 presidential terms since Truman, Biden would have been sixth (i.e. in the top third) while Trump would have been thirteenth (on the border of the bottom third).

Trump claims that this poor growth performance should be blamed not on him but on the onset of the Covid pandemic in 2020.  Covid would, indeed, have been difficult to manage by even the most capable of administrations.  The Trump administration was, however, certainly far from the most capable.  His mismanagement of the pandemic (including, for example, his repeated claim that it would simply “go away” on its own, his lack of leadership in not calling on his supporters to wear masks, his promotion of wacky “cures” that had no basis in actual evidence, and much more) made things far worse than they would have been.

But even if we allow Trump to claim a mulligan (as he reportedly often demands in his golf game) and leave out 2020, growth during Trump’s first three years in office would have averaged 2.2% per year – still less than what it has been under Biden.  Growth in Trump’s first three years would also have been well less than in Clinton’s second term (when per capita real GDP grew at a 3.1% rate), as well as under Reagan (both terms), Kennedy and Johnson (both terms), and Truman.  It would have been about the same as growth during Clinton’s first term, Carter’s term, and that of Nixon.  Still nothing special.

In other words, even when we leave out the chaos of 2020 during the Covid pandemic, Trump’s economic record was middling at best – with substantially slower growth than under a number of post-World War II presidents.  It was certainly far from the “greatest” in history.  And for his full term, Trump’s growth record ranks at about the bottom-third mark.

C.  Unemployment Rate

Another common measure of economic performance is the unemployment rate:

Chart 2

These figures were calculated from BLS data (via FRED), and are the simple averages of the unemployment rate over presidential terms from January of their inauguration year through to December of their final year.  (One could reasonably argue that the period should start only in February of the inauguration year and end in January, but that one month shift would not lead to a significant difference in the four-year average.  Plus, as I write this I do not yet have the unemployment statistic for January 2025.)

Unemployment under Biden has been exceptionally low, at an average of just 4.2% over his full term.  It was well below the average rate under Trump (5.0%).  Indeed, the average unemployment rate under Biden was lower than under any president since Johnson’s full term in office (1965 through 1968, when it averaged 3.9%), although the average rate in Clinton’s second term (4.4%) was not too much higher.  Over the full post-World War II period, Biden’s record on unemployment was the second-best out of the 19 presidential terms.  Trump’s record was tied with two others for the sixth through eighth ranking.

Again, if we give Trump a mulligan and count only the first three years of his term, the average unemployment rate would have been 4.0%.  Quite good, although the unemployment rate averaged an even lower 3.8% during Biden’s final three years in office.  Also, and as noted in an earlier post on this blog, the unemployment rate during Trump’s first three years in office simply reflects a continuation of the same downward trend it had been on during Obama’s presidency.  What can be said is that in his first three years in office, Trump did not wreck the path the economy was following during Obama’s second term in office (with GDP growth also similar).  The wreck then came in Trump’s fourth year.

D.  Inflation

The main criticism directed at Biden’s economic record was the increase in the rate of inflation (with data from the BLS via FRED):

Chart 3

As measured by the CPI, inflation during Biden’s term averaged 4.9% at an annual rate, the highest since Reagan.  Inflation rose sharply in much of the world following the supply and other disruptions arising from the 2020/21 Covid pandemic, with supply chain issues continuing to mid-2022.  The Russian invasion of Ukraine in February 2022 also added to price pressures as the prices of oil, natural gas, wheat, and other commodities soared for a period.  Overall consumer prices rose in the US, as they did in other developed OECD economies (and by more than in the US in most of them).

Inflation came down sharply (as well as suddenly) in the US in mid-2022 as the supply chain issues were resolved.  Since then inflation has remained above the Fed target of 2% solely because of (not just largely because of, but entirely because of) the rising cost of housing.  Rising housing prices are certainly important – and I plan to address the issue in an upcoming post on this blog – but to address inflation effectively one should be clear on the cause.  One should also recognize that the mirror image of the rising cost of housing is that homeowners are enjoying rising home values, and that two-thirds of US households own their homes.  Those two-thirds are benefiting from the rising values.

Inflation as measured by the CPI was 1.9% over Trump’s first term in office.  This was basically similar to the average rate of inflation since Clinton’s second term, although a bit below where it had been from Clinton through Obama’s first term.  And it was higher than inflation in Obama’s second term (which was arguably too low).

The Trump record on inflation was helped by especially low inflation in 2020 due to the Covid crisis.  With much of the economy shut down, the overall price index in fact fell.  This is highly unusual for the seasonally adjusted rates.  The seasonally adjusted overall CPI fell in each month from March to May, and it was not until August that it returned to where it had been in February.

Particularly noteworthy was the drop in the price of crude oil.  In terms of today’s prices (i.e. the CPI of December 2024), the price of the benchmark West Texas Intermediate crude oil fell to just $23.19 per barrel in April 2020.  This was below its inflation-adjusted price of $25.36 in July 1973 – just before the first OPEC oil price increase – and the lowest in real terms of any month since then except for the single month of November 1998 (when oil prices fell to $21.59 in a brief but intense international financial crisis following from Russia’s financial collapse that year).

A crisis – such as the one brought on by Covid in 2020 – that leads to a sharp and sudden drop in demand can certainly lead to low inflation for a period.  Prices will often then bounce back as the economy recovers.  But one should certainly not want to cause a crisis – where in 2020 the unemployment rate shot up to the highest it had been since the Great Depression of the 1930s – to keep inflation low.

E.  Conclusion

Assessing the economic performance of a presidential term by just three measures is simplistic, of course.  There is much more going on.  These are also aggregate measures, and the measures of relevance to any individual can be quite different.  Distribution matters when looking at growth in GDP per capita; the unemployment rate matters most to those who are at risk of losing their jobs; and what matters in terms of price increases will vary by person (where, for example, increases in home prices are a benefit – not a cost – to the two-thirds of US households who own their own homes).

Still, the three measures of growth in real output, of the unemployment rate, and of consumer price inflation are important and are a common focus in assessments of the economic record of a period.  They receive a good deal of attention in the press and by the public.

One can also question whether the record of any president should be measured by periods that begin and end with the inauguration date.  It can reasonably be argued that the record should begin only three months later, or six months later, or even twelve months later, as new presidential policies and management will only begin to have an influence with a lag.  While there is certainly some lag, what that lag might be is not at all clear.  Also, that lag might be different at different times and for different conditions, depending on the state of the economy when the president takes office.

Given the impossibility of determining what the appropriate lag might be, it is probably fairest to begin the measurement from the date the president takes office.  But one could argue that it should be later.

More fundamentally, some might argue that the influence a president has on economic developments is limited.  No doubt there are limitations, with many underlying economic forces that a president alone cannot affect – at least in the near term.  But while recognizing such limitations, it is too extreme then to say that a president has no influence.  Policy matters, and it is reasonable to assess a president’s success in determining the right policies, getting them passed and implemented, and then seeing the outcome.

Recognizing these limitations, it is nonetheless clear that the economic record of Biden was relatively good.  Growth was strong, unemployment was low (the lowest of any presidential term since Lyndon Johnson), and consumer price inflation – while relatively high – was largely a consequence of the post-Covid disruptions.  And inflation came down from mid-2022 to target levels or below, with the significant exception of housing.

Trump’s economic record in his first term, in contrast, was relatively poor.  It was not the worst among the post-World War II presidential terms, but in terms of growth it was around the bottom third mark.  It was around the middle if one is generous and leaves out the collapse in 2020 due to Covid.  Unemployment and inflation during his first three years in office were relatively good (although not the best compared to others).  But on each, Trump can basically be commended for not wrecking the path they were on that he had inherited from Obama.  And then 2020 came.  It was certainly not “the greatest economy in the history of the world”.

We will now see what Trump’s record will be in his second term.  Trump is now inheriting from Biden (as he had from Obama) an economy where GDP is growing at a strong rate, unemployment is extremely low, and inflation is low.  But while in his first term Trump did not – during his first three years – upset too much the strong path the economy was on, Trump is now moving much more aggressively in his second term.  As I write this, he has just issued orders that from February 4, the federal government will charge US importers additional tariffs of 25% on all imports from Canada (other than 10% on imports of oil), an additional 25% on all imports from Mexico, and an additional 10% on all imports from China (all additional to whatever the tariffs were before, which varied by item)

Such new tariffs are not just costly for the US firms and ultimately consumers who will pay them, the ones on Canada and Mexico are also in clear violation of the USMCA free trade treaty (better known as NAFTA 2.0, as it was largely the same as the original NAFTA treaty).  The first Trump administration negotiated the USMCA treaty, and Trump himself signed it together with Canada and Mexico in 2018.  While Trump is now claiming the 25% tariffs are being imposed due to some new “emergency”, that justification strains credulity.  What is happening at the border now is not fundamentally different from what was happening in 2018 when the treaty was signed.

If the tariffs are not soon lifted, they will cause significant damage to the US economy.  But this is just the start, and it looks like Trump is imposing such tariffs (including on Canada – probably the closest ally of the US – at least until now) to show no country is exempt from his attempt at bullying.

We will see what results.

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.