The Direct Impact of the Government Shutdown on Measured GDP: Econ 101

Chart 1

A.  Introduction

The Bureau of Economic Analysis of the US Department of Commerce (BEA) released on April 30 its initial estimate (what it calls its “Advance Estimate”) of the GDP accounts for the first quarter of 2026.  The headline rate was of growth in real GDP in the quarter of 2.0% (at an annual rate).  While it noted that this was due in part to a rise in government spending in the first quarter relative to the fourth of 2025 (as government spending recovered from a temporarily depressed level due to the federal government shutdown in October/November), the way this impacted GDP may not be clear to many.  The impact being referred to was not due to a demand effect, as some might assume and as may seem to be implied by the use of the word “spending”.  Rather, it was a supply-side effect – a consequence of how the government’s direct contribution to the nation’s output is measured in the standard GDP accounts

The way the government’s contribution is measured in the GDP accounts (which are more properly referred to as the National Income and Product Accounts, or NIPA) and the contrast with how production is measured in the private sector accounts, is of interest as it goes to the basic concepts of what GDP is and how it is measured and estimated.  One purpose of this post is to review those basic concepts, and contrast how the value of private production is estimated versus how the value of government-provided services is estimated.

The measure of the impact of the government shutdown also brings out that viewing GDP as a total demand for goods and services (for private consumption, investment, government spending, and net exports) can be misleading.  GDP is in fact a measure of production (GDP = Gross Domestic Product), and that production is equal to what is counted in demand only due to the fact that investment includes changes in inventories.  If the total supplied (of an individual product as well as all products together) exceeds the demand for it, then the excess is accumulated as an increase in inventories – and an increase in inventories is an investment.  That investment in higher inventories is counted along with other investments, and for this reason we can estimate how much was produced (GDP) based on the demands.  And if total demand exceeds total supply then inventories are drawn down, leading to negative investment in inventories.

I should hasten to add that this does not imply that the demand side components of GDP are unimportant.  They are extremely important, as production in a modern economy is primarily driven by demand (up to capacity constraints).  It is just that the demand side and the supply side are different.  They should not be confused, and they often are.

While government spending on goods and services is, indeed, an important component of total (or aggregate) demand, what is often lost in the discussion is that government also provides services itself.  This is a direct contribution to GDP, and while the BEA provides a measure of it, few people pay much attention to it.  The BEA has to approach this estimation of the provision of government-produced services differently, however, than how it estimates the value of private goods and services produced.  The issue is that while private goods and services are sold at some price – with their value measured by that price – government services are not sold but are rather provided without charge.  The issue then is how to estimate the value of these government services.

Section B of this post will first review how the value of private goods and services produced – their contribution to GDP – is measured in the GDP accounts.  The section following that will then review how this is done for the government provision of services, with its contribution to GDP.  The section will also look more broadly at how the BEA arrives at its estimate of government expenditures as a demand item in the GDP accounts – the government spending concept that people focus on when considering GDP.

The penultimate section will then apply this to estimate the direct impact on GDP of the federal government shutdown that took place from October 1 to November 12, 2025, with a resulting impact on GDP in the fourth quarter of 2025.  We find that the direct impact of the shutdown was that the growth rate of GDP in the fourth quarter of 2025 would have been 0.6% point higher (0.57% higher to be more precise), i.e. growth would have been at a rate of 1.1% rather than the 0.5% estimated.  And then, had there been no shutdown, growth in the first quarter of 2026 would have been 0.6% point lower (as it would be starting from a 0.6% higher base), i.e. growth of 1.4% rather than the 2.0% announced.

These growth rates in the absence of a federal government shutdown – of 1.1% in the fourth quarter and 1.4% in the first quarter – are shown in red in the chart at the top of this post.  The growth rates are not high, but they tell a different story than what one might conclude from the unadjusted figures.  Instead of low growth at the end of 2025 with a significant bounce back in early 2026, growth was middling throughout.  But as seen in the chart at the top of this post, the growth – while on the low side – was within the range seen in recent years.

I should also emphasize that this measure of the impact of the government shutdown takes into account only the direct effects of the shutdown on the supply of production (i.e. on the supply of government services).  It does not attempt to measure what the indirect impacts might have been.  The BEA is referring to this direct impact in the brief notes it attached to its news releases of the GDP accounts for the fourth quarter of 2025.

The BEA provided a somewhat different estimate than the one obtained here of the direct impact of the government shutdown, saying that it reduced GDP growth by “about 1.0 percentage point” in the fourth quarter of 2025.  The reason for that discrepancy with the 0.57% point estimate calculated here is not clear.  And while each of the three releases of its estimates of GDP in the fourth quarter of 2025 (i.e. the Advance, Second, and Third Estimates) uses the same language and the same estimate of a 1.0% point reduction, the recent news release for the first quarter off 2026 failed to say that in the absence of the shutdown, GDP growth in the first quarter of 2026 was 1.0% point higher than it otherwise would have been.  That is, in the absence of the shutdown (which depressed GDP in the fourth quarter of 2025), growth in the first quarter of 2026 would have been 1.0% (at an annual rate) rather than the 2.0% reported.

The 1.0% point estimate for the direct impact of the shutdown is higher than the 0.6% estimate made here.  The reason for this difference is not clear.  A guess at why this is the case will be discussed in the concluding section of this post.

B.  The Measurement of the Contribution to GDP from Private Production

Previous posts on this blog (see here and here, for example) have discussed that GDP is estimated by the BEA in three separate ways.  The BEA discusses this in more detail in Chapter 2:  Fundamental Concepts, of its NIPA (National Income and Product Accounts) Handbook.  The three approaches should each lead to the same estimate of GDP.  In practice there will be differences due to statistical noise and other such issues, but the three approaches serve as a good check on each other.  They also provide for a better understanding of what GDP means as a concept.

The first – and most commonly discussed – approach estimates GDP from the demand side, by adding up estimates of how production is used (for private consumption, private investment, government consumption and investment, and exports net of imports).  As discussed above, with additions to inventories counted as an investment, this total will match the total supply of what is produced.  It is also the basis of the first estimate of GDP that the BEA releases, which comes out normally about one month after the end of each quarter in its release of the “Advance Estimate” of the GDP accounts.

A second method is to estimate the incomes generated.  Whatever is produced leads to income for someone – wages of labor and profits of the owners and investors – so total incomes should match GDP.  To reduce confusion, the BEA labels this estimate Gross Domestic Income (GDI).  In principle it should be the same as GDP and will differ only because these are all statistical estimates.  The BEA normally releases its first estimate of GDI about two months after the end of each quarter, with it included (along with updated estimates of GDP) in its “Second Estimate” of the GDP accounts.

The third method is to estimate the value created in each sector of production.  Aggregated across sectors, that value should also match GDP.  To avoid double-counting, the estimates are of the value that is added in each sector (i.e. the value created on top of the intermediate inputs used that were obtained from other sectors).  The BEA refers to this as the value added in each sector.  The total across the economy is Gross Value Added.  This Gross Value Added should also match the estimates of GDP (from the demand side) and GDI (from the income side), although there will be differences in the estimates themselves due to statistical issues.  The BEA normally releases its first estimate of value added by sector about three months after the end of each quarter, with it included (along with updated estimates of GDP and GDI) in its “Third Estimate” of the GDP accounts.

What is of interest to us here is how the BEA arrives at this third estimate of GDP, i.e. of value added by sector.  Based on monthly sample surveys of firms (and later updated by more comprehensive annual surveys and ultimately by censuses of firms undertaken every five years), the BEA obtains information at the firm level of what its total sales were, how much was spent on intermediate inputs, what was paid in wages and other compensation to its workers, and what then remained as profits.  This is in broad terms:  there will be more detail in what is gathered, but the basic categories are what are of interest to us here.  Note also that these are all measured in nominal terms, i.e. in terms of dollars spent and received.

From this, the BEA can determine for its sample of firms (selected to provide representative samples of each sector) what their gross production was during the period (equal to what was sold as adjusted for inventories) and the value of the intermediate inputs purchased.  The difference is the value added.  Part of that value added then goes to wages and other compensation for labor.  What then remains (after certain indirect taxes) is operating profit.  The profits are generated in part by the capital invested in the firm, and that capital will be used up over time – i.e. it depreciates.  Hence part of the profit is allocated (at least notionally) to cover an allowance for depreciation, and what remains after that allowance is termed the “net operating surplus”.  The net operating surplus will then be made up of what is paid in interest and in rents, and then in the remaining profits of firms (whether incorporated or unincorporated).

As noted above, all these estimates are in current dollar terms, with the value added of the firms obtained by subtracting the purchases of intermediate inputs from gross production.  But we also want to see what the changes over time were in real terms – i.e. adjusted for price changes.  For this, the BEA estimates separately average price increases for each sector, drawing on a range of separate data sources – many from the Bureau of Labor Statistics (BLS).  These do not come from the firm-level surveys, but rather from separate surveys of changes in prices for standard goods and services of a given quality.

The estimation of value added by sector for private producers is therefore conceptually straightforward.  There will of course be challenges in its implementation, but one can start with the values of what is produced in each sector. Those values are known because the goods and services are sold on a market, and GDP is a measure of the market values of what was produced.  (Some have argued that market values are not a good measure of the “true” value of what the economy produces.  But the question then is what value to use?  How does one value a glass of drinking water, for example?  It is of enormous value to someone dying of thirst, and presumably of greater value to any individual than whatever they paid for it, but how much greater?  There is no way to know this.  Hence market values are used, i.e. what was paid for it.  But this is a separate debate.)

Sales on the market can thus provide a measure that can be used to determine the value (the market value) of what private firms produce, with GDP derived from and based on this.  But what to do when services are provided by government entities?  Those services are not sold in a market, but rather are provided without charge.  That will be addressed in the next section.

C.  The Measurement of the Contribution to GDP from Government Production

When government is discussed in relation to the NIPA accounts, the focus is almost always on government demand as one of the basic demand components of GDP (along with private consumption, private investment, and exports less imports).  The government’s role as a source of aggregate demand is certainly important.  In an economic downturn, an increase in government expenditures can and has played a critical role in returning the economy to growth and thus generating employment.  The contrasting experiences following the 2008 economic and financial collapse (with the later slow recovery, as the Republican-controlled Congress forced through government expenditure cuts) and that following the Covid crisis of 2020 (where massive government expenditures – in both 2020 under Trump and in 2021 under Biden – led to a quick recovery to full employment) are clear examples.

Keynes was right, and hopefully he will not once again be forgotten.  But as important as that is, there is more to government in the NIPA accounts that is often overlooked.  There is in particular a direct role of government on the supply side of the accounts.  It is in the government’s role as a supplier of services that there was a direct impact on GDP in the fourth quarter of 2025 as a result of the federal government shutdown.

Governments produce services.  Those services are valuable, and contribute to a nation’s well-being.  At the state and local level, those services will include the services of public school teachers, police officers, fire and other first responders, and others.  At the federal level, the services include those of the scientists who work at the weather bureau or NASA or the energy research labs; the medical researchers and officials at NIH, the CDC, and the FDA; those who take care of and manage our National Parks and other public resources; and the soldiers in the nation’s armed forces who provide for the common defense.  They also include the services of the administrators of programs such as Social Security and Medicare, as well the programs to build and maintain our public highways and other public infrastructure, and much more.  Their work is valuable and should be (and is) counted in GDP.

But in general there is no charge (or only a minimal charge) for those services.  There are some exceptions, where government entities may charge an amount that may fully cover their costs, but at the federal level there are not many.  An example would be the Tennessee Valley Authority, or the Post Office.  The accounts for such government enterprises are separated from what are referred to as the “general government” accounts.  The “government” figures in the NIPA accounts that are usually referred to are the accounts for general government and exclude government enterprises.

For general government, the question then is how to value the services provided, since no fee (or essentially no fee) is charged for those services.  While taxes are paid, those taxes are not linked directly to particular services used.  And while there may sometimes be fees for certain services (such as admission fees to national parks, or passport and other such application fees), such fees are modest in the government sector – especially at the federal level.

Given all this, the BEA (and indeed national statistical agencies around the world) estimate the value added from the public services provided based on the cost of providing those services.  There are two components to those costs.  One is the wages and other compensation paid to government employees.  The other is for the depreciation of the capital assets of the public sector.  Those capital assets include, for example, roads.  There is an initial investment cost to build those roads and over time those roads depreciate.

Comparing this to the estimation of value added in the private sectors of the economy, one can see similarities.  As discussed above, the value added generated by private firms (i.e. the value of the gross production minus expenditures on intermediate inputs) will equal the wages and other compensation paid to the labor employed, plus a charge for the depreciation of the capital used in the sector, plus remaining profits after wages and depreciation are subtracted.  In the case of the provision of government services, the value added is similar except that there is no charge for remaining profits after wages are paid and depreciation is accounted for.  The implicit assumption is that the rate of return on capital after depreciation is zero.

Thus the measure of value added from the provision of government services is built bottom-up from the cost of providing those services (i.e. the wage and depreciation costs).  This is in contrast to the top-down calculation in the private sector accounts that starts with the market value of what is produced and ends with a residual amount as after-depreciation profits accruing to the firm.  In the government accounts the after-depreciation profits are valued as if they were zero, but the rest is in essence the same.

This estimate of government value added when added to the intermediate purchases by government of goods and services from other sectors, will then equal the gross output of general government.  Again, this is similar to the concepts in the private sector accounts, but rather than going top-down (i.e. from gross total output less purchases of intermediates to reach value added), the process for the government accounts is bottom-up (i.e. from adding intermediate purchases to value added to yield gross output).

To provide a sense of the magnitudes and to make this concrete, these are the figures for the federal government accounts as provided by the BEA in its Advance Estimate for the accounts for the first quarter of 2026:

Federal Government NIPA Accounts – Advance Estimate for 2026Q1

   Federal Government, annual rates, $ billions 2026Q1
Gross output of general federal government 1,597.0
  Value added 1,037.5
    Compensation of general government employees 617.2
    Consumption of general government fixed capital 420.3
  Intermediate goods and services purchased 559.5
     Durable goods 67.6
     Nondurable goods 66.5
     Services 425.4
  Less: Own-account investment 69.9
  Less: Sales to other sectors 13.4
Equals Federal Consumption Expenditures 1,513.7
Federal Gross Investment 473.7
Fed Gov’t Consumption + Gross Investment 1,987.5

Source:  Interactive NIPA Accounts, mostly from Table 3.10.5, with Table 3.2 for Federal Gross Investment and Table 1.1.5 for the check on total Federal Consumption and Investment.  Downloaded May 4, 2026.  As for all of the NIPA accounts, the figures are shown at annualized rates.

Compensation of federal government employees ($617.2 billion at an annual rate) is added to an estimate of depreciation ($420.3 billion at an annual rate, where depreciation is more properly referred to in the accounts as “consumption of fixed capital), to yield value-added from the services the federal government produces and provides to the economy ($1,037.5 billion).  Adding in purchases of intermediate goods yields gross output of government ($1,597.0 billion).  From this a charge is subtracted for “own-account investment” ($69.9 billion).  This is a charge for the compensation that was paid to federal workers for work they did in supervising the building of new public capital, e.g. highways and such.  That cost is included in the cost of federal government gross investment ($473.7 billion), which is a few lines down and is removed here to avoid double-counting.  Also subtracted is a small charge ($13.4 billion) for the relatively minor fees that are collected by government for various services (such as admissions to national parks, as noted before).  Those fees are counted either in private household consumption expenditures or in the expenditures of businesses, depending on who pays them.

Federal government gross output ($1,597.0 billion) less the charge for own-account investment ($69.9 billion) and less the charge for sales to other sectors ($13.4 billion) will then yield federal government consumption expenditures ($1,513.7 billion).  Adding in federal government gross investment ($473.7 billion, which as noted above includes the cost of federal workers who managed such investment), yields total federal government consumption and investment expenditures of $1,987.5 billion.  It is this final figure that is the government expenditures figure found in the demand components of GDP that discussion almost always focuses on.

These federal government expenditures are for goods and services that it either produces itself (the $1.0 trillion of value added) or has purchased from other sectors (whether as intermediates used in government consumption or for investment – close to $1.0 trillion as well).  But as some may realize, such expenditures account for only a relatively small share of total federal budget expenditures.  There are also transfer payments from the federal government to individuals (about $3.8 trillion at an annual rate currently for programs such as Social Security and Medicare) and to state and local governments ($1.0 trillion).  The demands for goods and services arising from those transfers are counted in the NIPA accounts in the accounts for households or for state and local governments.  There are also payments of interest on the federal debt ($1.2 trillion) and various other payments, bringing total federal expenditures to $8.5 trillion as of the first quarter of 2026 (at an annual rate).  The $1,987.5 billion of federal expenditures on goods and services derived above (the federal government demand component of GDP) are less than one-quarter of that total.

With federal government demand for goods and services close to $2.0 trillion, a bit over half ($1,037.5 billion) comes from value-added produced in the government sector itself.  Of this, $617.2 billion reflects the compensation paid to federal government employees.  That is, government is a provider of services (that are then treated as being “purchased” by government itself) as well as a purchaser of goods and services from other sectors (of intermediates and for government investment expenditures).

It is government as a producer of services where the federal government shutdown had a direct impact on GDP in the fourth quarter of 2025.  The next section of this post will look at how that impact was calculated.

D.  The Direct Impact of the Federal Government Shutdown on Government Production

With the federal government shutdown of October 1 to November 12, 2025, most (although not all) federal workers were told to stay home.  They were not paid during the shutdown, but based on a law passed in January 2019 (in response to an earlier shutdown), federal workers furloughed during a shutdown are paid following the end of the congressional impasse.  (Prior to the January 2019 law approving this for all future shutdowns, Congress had always approved legislation to provide for payment, but passed such legislation each time there was a shutdown.)

The federal government wages and other compensation paid for the period of the shutdown were thus the same as they would have been in the absence of a shutdown.  While the payments then came in November (i.e. still within the calendar quarter), all payments in the NIPA accounts are in any case accounted for on an accrual basis – i.e. when the payment obligation is incurred.  Thus they are always reflected in the NIPA accounts in the quarter when the shutdown took place, even if the timing was such that the back payments came only later, in a subsequent quarter.

How, then, did the BEA reflect the loss in government produced services as a consequence of the shutdown?  It provided a very brief, one paragraph, explanation as a technical note included with its Advance Estimate of 2025Q4 GDP (released on February 20, 2026), and then with the same note in the Second Estimate (released on March 13) and the Third Estimate (released on April 9).  It provides a more detailed, one page, explanation on its Frequently Asked Questions page, and an even more complete explanation on the principles followed in estimating the government accounts more generally as Chapter 9 of the NIPA Handbook.

The basic principles are simple.  Since the wages will (in the end) be paid to all federal government employees (whether furloughed or not), the total paid in nominal terms is simply the same, regardless of the shutdown.  The BEA obtains those figures from the Department of the Treasury.  But the BEA then adjusts what the real labor input was based on the proportion that working hours of federal workers were reduced during the quarter, due to some share of the federal workers being placed on furlough.  The implicit assumption is that the real provision of government services during the period was reduced in that proportion.

This will then lead to a mechanical increase in the price index in that quarter for the provision of the federal labor services provided, as the price index (in essence a wage index) will equal the compensation paid in nominal terms (unchanged by the shutdown) divided by the real index of labor services provided in the quarter (reduced by the reduction in hours reporting to work in the quarter).  That is, the price index for government compensation in the quarter will shoot up, as the ratio of the compensation payments made (unchanged) divided by an index of the hours worked (reduced due to the shutdown) will go up.

The question, then, is what effect the government shutdown had on GDP (and hence its growth) in the quarter.  For this, one needs to specify a counterfactual as a basis for comparison.  One cannot simply take what the change was in the figure for the real compensation of federal workers in the quarter, as there are always quarter-to-quarter changes in those figures independent of any government shutdown.  Also, the price index for government workers changes from one quarter to the next (like for any price index, and generally going up).

But one can specify a reasonable counterfactual from the fact that there was no government shutdown in the first quarter of 2026.  Thus the price index for government workers, after shooting up in the figures for the fourth quarter of 2025, will revert to its previous path in the figures published for the first quarter of 2026.  We have these.  A reasonable assumption to make would be that in the absence of the shutdown, the price index in the fourth quarter of 2025 would have gone up at the same pace as it did over the six-month period from the third quarter of 2025 to the first quarter of 2026 (adjusted, of course, to the quarterly equivalent).

From this, one can calculate what the direct impact was of the government shutdown on the provision of government services and hence on GDP:

Direct Impact of Federal Government Shutdown on GDP

$ billions or index; annualized change 2025Q3 2025Q4 Change
A) BEA Estimates
1)  Nominal Gov’t Compensation $632.5 $617.2 -$15.3
2)  Price Index / % Change 148.779 163.688 46.5%
3)  Real Gov’t Compensation $425.1 $377.1 -$48.0
B) No Gov’t Shutdown Scenario
1)  Nominal Gov’t Compensation $632.5 $617.2 -$15.3
2)  Price Index / % Change 148.779 150.225 3.9%
3)  Real Gov’t Compensation $425.1 $410.9 -$14.3
C) Impact on GDP
1)  Difference in Real Gov’t VA        0.0     $33.8 $33.8
2)  BEA Estimate of GDP $24,026.8 $24,055.7 0.48%
3)  GDP if No Shutdown $24,026.8 $24,089.5 1.05%
Difference in GDP Growth 0.57%

Note:  “Government” in this table refers to Federal Government only.  “Compensation” refers to compensation of federal government employees.

Source:  Based on data derived from the Interactive NIPA Accounts.  Downloaded May 4, 2026.

Panel A in the table provides the figures directly from the BEA released accounts for the periods (as of April 30, 2026).  Nominal compensation of federal workers fell in the fourth quarter – from $632.5 billion in the third quarter to $617.2 billion in the fourth – with this independent of the shutdown.  Keep in mind that – as in all of the NIPA accounts – the financial flows are shown at annual rates.  The actual flows in any given quarter will be one-fourth of these.

The BEA then estimates that the real input of government labor (based on the number of hours reporting to work, and expressed in terms of 2017 prices) fell from $425.1 billion in the third quarter to $377.1 billion in the fourth.  From this, it calculated the implicit price index for this compensation (i.e. the nominal payment divided by the payment in constant 2017 prices), and found that it rose from 148.779 in the third quarter to 163.688 in the fourth.

While the growth rate in the price index looks scary at 46.5%, keep in mind again that the BEA figures (including for growth rates) are all shown in annualized terms in the NIPA accounts.  The actual increase in the index in the quarter itself (i.e. from 148.779 to 163.688) is a 10.0% rise.  But when compounded as if it were that for a full year (four quarters), the rate is the 46.5% shown.

As noted above, a reasonable counterfactual to estimate the direct impact on GDP from the government shutdown would be to assume the price index for government compensation would have risen in the fourth quarter of 2025 at the same pace as it did between the third quarter of 2025 and the first quarter of 2026 (when it reverted back to its previous path from the special conditions of the fourth quarter).  That rate – in annual terms – was just 3.9% – far less than the 46.5% arising due to the shutdown.

Panel B of the table then works out the implications.  Nominal federal government wages will be the same.  The price index will, however, only rise to 150.225 from the 148.779 of the third quarter (an increase of just under 1.0% – keep in mind that the 3.9% is an annual rate, and is 3.945% to be more precise).  The figure for real input of federal workers would thus fall only to $410.9 billion ( = $617.2 / 1.50225) from the $425.1 billion of the third quarter.  It still fell, due to the ongoing reductions in the federal labor force, but with no shutdown that fall will be less:  a reduction (relative to the third quarter) of $14.3 billion rather than the reduction of $48.0 billion that the BEA estimated with the shutdown (all at annual rates).  The difference due to the shutdown is $33.8 billion (in figures before rounding).

GDP in the fourth quarter would thus have been $33.8 billion higher than otherwise.  The BEA had estimated that GDP in the fourth quarter was $24,055.7 billion (in terms of constant 2017 prices), an increase of just 0.48% (at an annual rate) from the $24,026.8 billion in the third quarter.  Without the direct impact of the shutdown, GDP would have been $33.8 billion higher in the fourth quarter, at $24,089.5 billion.  The (annual) growth rate would then have been 1.05%.  The difference in the (annual) growth rate was 0.57%, or just under 0.6%.

This impact is significant, although not overwhelming.  Growth in the fourth quarter still would have been slow.  Indeed, the 0.6% direct impact on growth is less than the change in the BEA’s estimate for GDP growth in the fourth quarter as it gained more data on the quarter.  In the Advance Estimate, the BEA estimated GDP in the fourth quarter had grown at a 1.4% annual rate.  This was reduced to 0.7% in its Second Estimate and to 0.5% (i.e. rounded from 0.48%) in its Third Estimate.  Such changes in the BEA estimates for growth in GDP are not unusual, and the BEA is open about this.  But while the BEA receives a substantial amount of additional data on the private sector accounts in the months following its initial set of NIPA estimates, the federal government accounts (which it obtains directly from the Treasury) normally do not change much.  Indeed, there were essentially no changes between the three releases in the federal government data used in the table above.

Furthermore, while the direct impact of the shutdown reduced the growth rate of real GDP (as measured) in the fourth quarter by 0.6%, the reversion to the prior path means that the growth in real GDP was a similar 0.6% higher in the first quarter than what it otherwise would have been.  That is, to be consistent, one should recognize that while the direct impact of the shutdown would have meant 1.05% growth in the fourth quarter rather than 0.48%, there would then also have been a similar reduction in growth in the first quarter of 2026.  This is due to simple arithmetic, as GDP would have started from a higher point in the fourth quarter.  The result would have been that instead of 2.0% growth in the first quarter of 2026 (the BEA Advance Estimate), the growth in GDP would have been only 1.4%.  Those figures on GDP growth are shown in red in the chart at the top of this post.

E.  Concluding Points

The direct impact of the federal government shutdown was small.  Furthermore, an honest accounting would recognize that the impact was temporary – any reduction in GDP growth in a given quarter would then be offset (by simple arithmetic) by an increase in GDP growth in the next quarter of a similar amount.  But while the Trump White House highlighted the first, it ignored the second.

The BEA calculation of that direct impact has, however, served as a “teachable moment” that can lead to a better understanding of what makes up GDP.  While government spending is commonly recognized as an important contributor to GDP demand, many are not aware that government is also a significant contributor to GDP supply.  Government workers provide important services, and those services are part of the supply of goods and services that enrich a country.  That supply does not come solely from the private sector.

Valuing the supply of services provided by government workers is a challenge.  For private production – where goods and services are sold in the market – the statistical agencies producing the national income accounts can use their market values as the basis for the valuation of what is produced.  Presumably the true valuation by consumers is even higher, as they will purchase some good or service if their valuation is higher than the price but not if their valuation is lower.  But there is no way to know what those consumer valuations are, plus they will be different for each individual as they will depend on that individual’s circumstances.

But government provided services are not sold and thus there is not even that basis for estimating the value of what is produced.  The best that can be done is a bottom-up valuation based on the cost of providing those services (i.e. the labor cost and the cost of capital depreciation), where a political process is followed to determine what and how much of such services will be provided.  But the top-down valuation that is done for privately produced goods and services is not all that different, once one recognizes how value-added is determined (where GDP is equal to the total of value-added in the economy).  The main difference is that while for private production there is a residual profit that accrues to the entity that organized the production, there is no such residual value that can be ascertained for government production.  It is implicitly set to zero instead.

The limitations in how the BEA estimated the direct impact on GDP from the shutdown should also be recognized.  The BEA had little choice other than to assume that there would be a reduction in federal government output in proportion to the reduction in the number of official working hours of federal workers during the quarter.  While it’s probably the only assumption it could make for the estimation, it’s not really a good one.  I suspect that many (and probably most) of the federal workers put on furlough continued to work while at home, in order to catch up on reports and other tasks, and to prepare for when they would return to the office.  Furthermore, once they did return, there would be a period when they would be doing more than the usual in order to catch up.  Thus the BEA estimate of the impact on GDP based on the reduction in the number of formal working hours is probably an overestimate.

On the other side, the overall impact on GDP from the shutdown was almost certainly greater than whatever the direct impact was.  Government spending was reduced during the period of the shutdown by some amount, and this will have an impact.  Certain contractors were also dismissed during this period (including low-paid workers in the cafeterias and as janitors, as well as more highly paid consultants), and the impact of this on GDP will depend on whether they found alternative employment during that period.

But any assessment of the overall impact can only be done by a model of the impacts, and different analysts will have different models.  And the BEA does not do models: they are a statistical agency.  Thus they provided an estimate of the direct impact – subject, as discussed above, to the limitation that assumptions would still need to be made on what the counterfactual was.  They did not try to provide an estimate of the overall impact on GDP.

The BEA estimate of the direct impact of the shutdown – expressed initially in its news release of the Advance Estimate for the fourth quarter of 2025 (on February 20, 2026), and then repeated in its news releases of the Second and Third estimates – was that the direct impact was a reduction in the growth rate of real GDP in the quarter by “about 1.0 percentage point”.  This is somewhat higher than the 0.6 percentage point impact calculated in Section D above.  A question is why?

I puzzled over this for some time.  Any difference in the calculations should have been well less than a difference between an impact on GDP growth of 0.57% point and an impact of 1.0% point.  And working backwards, in order to have an impact on growth of 1.0% point, the real compensation of federal government workers during 2025Q4 would have had to increase from $425.1 billion in the third quarter (in 2017 prices, at an annual rate) to $437.2 billion in the fourth, rather than fall to the BEA’s estimate of $377.1 billion.  There would have been no reason for such an increase in the absence of a shutdown, especially as nominal spending on compensation for government workers fell from $632.5 billion in the third quarter to $617.2 billion in the fourth.  Compensation of federal workers would also then need to fall back to $406.9 billion – BEA’s estimate of real compensation of federal government workers in the first quarter of 2026.  This is not plausible.

In the calculations in Section D above – where it was assumed that the price index for compensation of federal workers would have grown at the same rate in the fourth quarter of 2025 as it did between the third quarter of 2025 and the first quarter of 2026 – real compensation of federal government workers would have gone in the absence of a shutdown from $425.1 billion in the third quarter, to $410.9 billion in the fourth quarter.  The fall would then have continued to the $406.9 billion BEA figure for the first quarter of 2026.  That would be a reasonable path in the absence of a shutdown.  A big increase in the fourth quarter in the absence of a shutdown, followed then by a sharp fall, is highly improbable.

Why then did the BEA releases on GDP in the fourth quarter of 2025 (all three) state that the impact of the government shutdown on the growth in GDP was to subtract “about 1.0 percentage point from real GDP growth in the fourth quarter”?  I can only speculate; what follows is purely a guess.  As the GDP estimates were being prepared, political appointees in the White House and/or in the Department of Commerce may well have asked BEA staff for their estimate of the impact on GDP growth due to the shutdown.  Such a request would not be surprising, but professional staff in the BEA would have to respond that they really cannot say what the overall impact might have been.  They are statisticians working with data, and all they could estimate would be what the direct impact would be as a consequence of most federal workers being placed on furlough (with the assumption that their real output would be reduced in proportion to the reduction in the formal number of hours at their work sites).

The BEA may then have arrived at an estimate of an impact of about 0.6% points – as per above.  This might have been taken as sounding low, so it was suggested to round this to 1%.  And then at some point later, some senior person may have made it 1.0%.

This is pure speculation.  But it is unusual that the BEA would have provided such an estimate in its news releases for the GDP estimates for the fourth quarter of 2025.  And the BEA did not say in the release of its Advance Estimate for GDP in the first quarter of 2026 that its estimated figure on growth in the quarter (of 2.0%) would have been reduced by 1.0% (or 0.6%) in the absence of the federal government shutdown in the prior quarter.  Instead, it only included the qualitative remark that there was an increase in federal government nondefense spending, that this was mainly from an increase in federal employee compensation following its reduction in the fourth quarter of 2025, and that this was “impacted” by the shutdown in the fourth quarter.  Furthermore, it would be easy to mistakenly misunderstand the increase in federal government nondefense “spending” in the first quarter (relative to the depressed level in the fourth quarter) as a demand-side impact.  The use of the word “spending” would imply this.  Rather, it was a supply-side effect from the contribution to GDP supply from the services provided by government employees.

But political appointees may be becoming more involved in what the BEA provides.  Starting with the April 9 release of the Third Estimate of GDP for the fourth quarter of 2025, the news releases for the GDP estimates no longer include the standard sets of tables that were provided before.  Those tables could be easily scanned to see what the important developments were.  Instead, the releases now only include links to different tables (not those given before in the news releases) on the BEA website, where more comprehensive data has always been posted and updated with each news release.

They assert, in the paragraph announcing that they are no longer providing those standard tables, that not making those tables available is an “improvement”, reflecting “modernization” and “streamlining”.  It is, of course, anything but.  It will now be difficult to quickly find the key developments in the quarter.  The data tables now being linked to were not designed for this.  The data will all be there, but buried with all the other data in the NIPA accounts.  Those tables were designed for reference purposes.

It would have been easy and at essentially no cost to have continued to provide the standard tables of the news releases.  The computer programs are written, and they are all then posted as PDF files online.  But by ending the publication of these tables, analysts will now focus more of their attention on the “spin” the officials provide in the news releases.  What they provide can highlight the points that they want to see emphasized.

This is unfortunate, but consistent with an administration that wishes to better control what news is released and how it is interpreted, including on figures produced by the nation’s statistical agencies.  These have not been subject to such political interference before.

The Delayed BLS Employment Report Confirms the Labor Market Weakened Sharply Under Trump

Chart 1

A.  Introduction

The delayed Employment Situation report of the BLS for November 2025 was released on December 16, 2025.  It includes estimates of the job numbers also for October 2025 – figures that had not been compiled and released before due to the government shutdown.  The new figures confirm that the labor market has weakened substantially this year.

With this new data, this post will compare what happened to employment since Trump took office with its growth during the latter part of the Biden administration.  As seen in the chart above, there has been a dramatic slowdown.  Indeed, outside the health and social assistance sector, there are now fewer jobs in the economy than when Trump took office – 134,000 fewer.  There was still reasonable monthly job growth in the first few months of the Trump administration, as it takes some time before a new administration’s policies will have an impact.  Measured from April 2025 (the month that started with Trump’s “Liberation Day” with the announcement of his so-called “reciprocal tariffs”), the number of jobs in the entire economy other than in health and social assistance fell by 311,000.

These comparisons are based on the change in employment relative to what it was early in Trump’s term – from January and April, respectively.  More meaningful is a comparison to what employment would have been had it continued to grow from January at the pace it had in Biden’s last year in office.  Had that growth continued as it had under Biden, there would have been 1.2 million more jobs in the economy as a whole by November 2025 than in fact there were.

The turnaround from the robust growth in employment under Biden has been remarkable.

This post will focus on the job numbers as well as what has happened to the average wages paid to employees.  Both come from the Current Employment Statistics (CES) survey of the Bureau of Labor Statistics.  The CES data come from a sample of employers reporting to the BLS on the number of workers on their payroll at mid-month and the wages paid.

A follow-up post on this blog will examine the figures in the BLS report that derive from its survey of households – the Current Population Survey (CPS).  Figures on unemployment, characteristics of workers (such as age and race), and related issues can only be identified at the household level and thus come from the CPS.  The survey was not undertaken in October due to the government shutdown – so no estimates will ever be available for October – but the survey resumed in November and figures are now available for that month.  They also show a weakening labor market.

The first section below will look at the growth in total employment under Trump compared to what it was in the latter part of the Biden administration.  Early in the Biden administration (2021 and 2022), employment growth was far higher as the economy recovered from the sharp downturn in the last year of Trump’s first administration during the Covid pandemic.  But even when compared to the more steady growth in an economy already at full employment in the latter part of the Biden administration – as we will do here – Trump’s record is poor.

The section that follows will then examine what happened to the growth in average nominal and real wages of workers on employer payrolls.  While still growing – as they had under Biden – that growth slowed under Trump.  The penultimate section of this post will then look at the assertion made by Trump administration officials that BLS data show employment of native-born Americans soared in 2025.  They are wrong.  As numerous analysts pointed out already last August – when Trump officials first started to make this claim – the officials do not understand how the BLS figures are estimated.  As one of them – Jed Kolko – noted, their mistaken assertions “are a multiple-count data felony”.

A concluding section will compare what the new BLS figures actually show to what a White House press release asserted they show.  While one can expect any White House press release will try to put a favorable spin on newly released figures, the contortions they had to go through here are amusing.  In the end, they could only make up assertions that are simply not true.

As I was finalizing this blog post, the BEA released (on December 23) its first estimate of GDP growth for the third quarter of 2025 (i.e. July to September).  The estimate was of growth in real GDP of 4.3% at an annual rate when measured by the demand components of GDP – the measure that most people focus on.  Real GDP was estimated to have grown at a 2.4% rate when measured by the income components of GDP (with this measure of GDP referred to as Gross Domestic Income, or GDI).  In principle, the two measures (GDP and GDI) should come out exactly the same, as whatever is produced and sold will be someone’s income.  But typically they do not due to measurement error and statistical noise.  The 4.3% growth rate is certainly high, and the highest since the third quarter of 2023 when real GDP grew at a rate of 4.7%.  Estimated inflation in the third quarter of 2025 was also high, with the price index for GDP rising by 3.8% at an annual rate – up from 2.1% in the second quarter and 3.6% in the first, and the highest since 2023.  The core Personal Consumption Expenditures price index (i.e. the price index excluding food and energy items) rose at a rate of 2.9% – an increase from the 2.6% rate in the second quarter and above the Fed’s target rate of 2.0%.

This high rate of real GDP growth (when measured by the demand components of GDP) is especially surprising given the lack of significant growth in employment.  For a proper comparison, one should compare the growth in GDP to the growth in average employment in the third quarter (the average number employed in July to September) over that in the second (the April to June average).  Between those periods, average employment rose by 0.2% (at an annual rate).

No one really knows why this first estimate of GDP growth in the third quarter was so much higher than the growth in employment in that period.  With labor productivity growth of 2% per annum (not far from the long-term average in the US before around 2008), then to get real GDP growth of 4% would require additional employment of about 2% (using rounded figures).  But as noted, employment grew only at a rate of 0.2% in the third quarter.

There are many possible reasons.  I may put up a post on this blog to discuss such issues, and on the new GDP report more broadly.  This current blog post will remain focused on what has happened to employment this year.

B.  Growth in Employment

Chart 1 at the top of this post shows average monthly growth in total employment since May 2023 and in the monthly average outside of the health and social assistance sector.  Growth from the May 2023 date was chosen as the unemployment rate reached a trough in the prior month of just 3.4% of the labor force – the lowest unemployment rate in more than 50 years.  The economy was then at essentially full employment through the end of Biden’s term.  Four-month averages are taken to smooth out the normal month-to-month fluctuation in the figures (due in part simply to statistical noise), with a three-month average for the September to November 2025 figures.

The figures come from the CES survey of employers on the number of employees on their payroll (as of the payroll period that includes the 12th day of each month).  The survey does not include those employed in the farm sector.  Thus the figures are more properly referred to as the “nonfarm payroll”.  But since agriculture employees account only for 0.8% of the labor force (based on CPS numbers), the difference – especially when looking at month-to-month changes in employment – is not significant and is typically ignored.  Of much greater significance is that the nonfarm payrolls also exclude the self-employed in unincorporated enterprises.  The self-employed account for 6.0% of the labor force (based again on CPS data).  One cannot know if they are self-employed by choice or because they cannot find a job on some firm’s payroll.

Employment growth during Biden’s term in office was high.  Total employment grew at a rate of 603,000 per month in 2021 and 380,000 per month in 2022 as the economy recovered rapidly from the downturn in the last year of Trump’s first administration.  But setting this aside and limiting the analysis to job growth during Biden’s term in office from May 2023, employment grew at a good and sustainable pace under Biden.  Total employment grew by 1.3% in 2024, in the last year of Biden’s term.

Employment growth then fell sharply under Trump, especially since May.  This is seen in Chart 1 at the top of this post.  Overall job growth in the economy as a whole fell from 217,000 per month in September to December 2024 under Biden, to 123,000 per month in January to April 2025, just 13,000 per month from May to August, and 22,000 per month from September to November.  And more than all of the growth in 2025 was due to growth in the health and social assistance sector.  Other than in just this one sector, job growth fell from 138,000 per month in September to December 2024 under Biden, to 56,000 per month in January to April 2025, and then to a fall of 48,000 per month from May to August and again a fall of 40,000 per month from September to November.

Trump’s policies of high tariffs and other measures have also failed in their stated aims of raising employment in the manufacturing sectors and in particular in the motor vehicles sector.  Jobs in manufacturing fell by a total of 58,000 between January and November 2025, while jobs in the motor vehicles sector fell by a total of 15,000.

Trump’s press people have also been proud to assert that “100% of the job growth” under Trump “has come in the private sector”.  It is true that job growth – such as it was – was greater in the private sector than in the economy as a whole (i.e. including the public sector).  But the reason is that while the growth in private sector jobs fell in the first ten months of Trump’s term in office by 45% compared to what it was during Biden’s last ten months in office, total employment in the economy as a whole fell by an even greater 68% under Trump:

Chart 2

It is not clear why this is a record one should be proud of.  It is true that public sector jobs – particularly in the federal government – have fallen under Trump.  This was a consequence of the chaotic federal job cuts that Trump empowered Musk and DOGE to force through.  But the federal workers who were dismissed have not been able to transition easily to private employment in a robust job market.  Private employment grew at a far slower pace than it had before.

Another issue to consider is the extent to which Trump’s policies to deport migrants in the US and block new ones from entering the country may account for some share of the reduction in employment in 2025.  There is little doubt that it accounts for some share of the fall, but when one looks at the numbers, it is clear it can only account for a small share of it.  There is also no indication that the reduction in the number of migrants employed led to greater employment of native-born Americans – at least at the aggregate level.  The unemployment rate for native-born Americans rose in 2025.  It did not fall, as it would have if migrants taking jobs had kept native-born Americans from finding employment.

I will address these issues related to migrants in the labor force in the penultimate section below.  But first, we will look at what has been happening to the growth in nominal and real wages under Trump.

C. Nominal and Real Wages

In addition to the employment figures, the CES survey of employers gathers data on the average wages paid by the surveyed firms.  From this the BLS can calculate what has happened to average nominal wages.  Coupled with estimates for inflation (the CPI – also estimated by the BLS), one can then obtain an estimate of what has happened to average real wages.

By definition, such changes need to be measured over some period of time.  Using changes over the same month one year earlier, one has:

Chart 3

Over this period, average nominal wages over the same month in the previous year grew at a pace of between 4.0 and 4.2% in the period leading up to the end of Biden’s term in office.  In more recent months, that growth has slowed to a pace of 3.5% to 3.7%.  The change is not huge, but it is on a declining trend.  The 12-month increase in the CPI has varied more – within a range of 2.4 and 3.0% over the period – going up some over the 12 months ending in January 2025, then declining in the 12 months ending in March to May, and then rising again.  The 12-month increase in real wages – the combination of the changes in nominal wages and in inflation – has since May been on a falling trend.

A few cautions should be noted regarding the recent data.  First, no CPI data was collected for October 2025 due to the government shutdown.  For the calculations here, I assumed the CPI index for October was simply the average of the estimates for September and November.  Second, analysts have noted that the November data for the CPI should be treated cautiously as it may be biased low.  The figure published indicated inflation as measured by the overall CPI was 2.7% over the year-earlier period, when most analysts were expecting an increase of 3.0 or 3.1%.  Two main issues have been highlighted.  First, some specialists on such data believe that inflation in the Shelter component of the CPI (which accounts for 35% of the overall CPI index) may have been underestimated due to an assumption (possibly implicit) of zero inflation in October in the Owners’ Equivalent Rent of Residences component of Shelter (accounting for three-quarters of the Shelter index).  No data had been gathered for October due to the government shutdown, but whatever it may have been was almost certainly not zero.

Second, field data on prices only began to be collected on November 14, when the federal government reopened.  That meant that the November data used to estimate inflation came only from the second half of the month.  That meant that a higher than normal share of the prices would have come from a period when many items are on sale due to the holidays (Black Friday and such).  While seasonal adjustment factors for November would normally take into account the late November sales, they would have undercompensated this year as the historically determined seasonal adjustment factors are estimated for the month as a whole, not just for the second half of the month.  That is, had the BLS been able to collect data over the full month rather than just the second half, the resulting inflation estimate may have been higher.

It is difficult to know how significant these possible biases in the CPI data might have been.  But while we cannot estimate the magnitude, they point in the direction of a higher rate of inflation.  At a higher rate of inflation, real wages in October and November grew by something less than what is shown in Chart 3 above.

But even without such corrections, real wages have not been rising as fast as they were before.  They are still increasing, but at a somewhat slower pace.  That pace has certainly not risen.

D.  The Impact of Immigrants

One factor that will account for a share of the lower employment figures in 2025 (relative to the trend under Biden), will be the reduction in immigrant labor due to Trump’s aggressive policies on migration.  Immigrants resident in the US (often long-time residents in the US) are being deported, while new immigration is being blocked (other than by White South Africans).

A first question is how large an impact this might have.  It is difficult to come up with hard data on this, but perhaps the best estimate can be found in a study published by the American Enterprise Institute (a center-right think tank in Washington, DC).  It came out in July 2025 and is thus a forecast of what net migration may be in the context of Trump’s new policies.  Estimates are provided for 2025 as well as the next several years.

It provides its estimates as a range.  For 2025, it estimates that net migration may be somewhere between net outward migration of 525,000 and net inward migration of 115,000.  That is a broad range, but gives a sense of what the magnitude may be.  One must then make several adjustments.  First, multiplying by 11/12 as November is the 11th month of the year, the range would be (with all figures rounded) net migration of – 480,000 to + 105,000.  Second, these are figures for the total number of migrants, not just those employed.  It will include spouses, children, university students, retirees, and others not seeking employment.  Among adults, the labor force participation rate has been around two-thirds.  Adjusting for children, the share is likely less than half.  Assuming one-half, the range is then – 240,000 to + 52,500, with a mid-point of – 94,000.

That is, with Trump’s policies in place, the net outmigration of workers in 2025 may be on the order of perhaps 100,000, although perhaps up to 240,000 or even a net inmigration of 94,000.  While not trivial, these figures are small compared to the reduction in employment of 1.2 million that one has seen under Trump (through November) compared to what it would have been had employment continued to grow as it had during Biden’s last year in office.  And 100,000 fewer workers is just 0.06% of the US labor force of over 171 million.

Net outmigration of that magnitude – or even several times that magnitude – is too small to have a major impact on the number of native-born American citizens employed.  Further, the unemployment rate of native-born Americans has been rising in 2025 rather than falling – from a rate of 3.8% in November 2024 to a 4.3% rate in November 2025.  This will be discussed further below.

There is thus no evidence at the macro level that employing fewer migrants has led to an observable increase in employment of native-born American citizens.  What has happened instead under Trump’s policies is that some number of migrants – who had been working at jobs and paying their taxes (including Social Security taxes, even though they will not be eligible for Social Security benefits) – will no longer be producing goods and services for the American economy.  That work – at the overall level – is just not being done.

Trump administration officials have nevertheless repeatedly claimed that BLS data can be used to show that the number of native-born Americans employed jumped dramatically in 2025.  They are wrong.  They do not understand how the BLS data are constructed.  Jed Kolko, a senior fellow at the Peterson Institute and who has explained their error in detail, has called those assertions a “multiple-count data felony”.

A full explanation will not be provided here.  It is a technical issue, and a mistake that non-specialists can make if they are unfamiliar with how the BLS estimates are constructed.  Dean Baker provides an easy to follow explanation of the issues here ahd here, while Jed Kolko explains the issues in more detail here and here.

Briefly, the figures often cited (incorrectly) come from the standard Table A-7 of the BLS monthly Employment Situation report.  That table provides figures from the CPS survey of households for native-born citizens and separately for the foreign-born (whether citizen or not) on the adult population, the number in the labor force, the number employed and unemployed, those not in the labor force, and the unemployment rate as well as the employment/population ratio.

The issue arises because the population controls to go from the survey results to the aggregate figures for the adult population as a whole are set annually and then not changed.  These controls for the total adult population (native and foreign-born together) come from the Census Bureau, and it is then forecast to grow at some steady rate from month to month over the year from the figure fixed in January.

There are then two major problems.  One is that when the population control figures are updated each January, the BLS does not go back to revise the CPS estimates (on anything) in the prior year.  Thus the BLS clearly warns people not to make comparisons of figures on totals (such as the number employed) from one year to the next (such as between November 2024 and November 2025).  In contrast, the number employed in prior years in the CES estimates – the nonfarm payroll estimates – are revised each January when the population and other controls are updated.  That is why figures such as those above in Charts 1 and 2 are comparable over time.

The second major issue is that the BLS estimates the number of foreign-born in the adult population from figures obtained through the CPS, and then calculates the number of native-born by subtracting the foreign-born from the estimated population totals.  Thus if the number of foreign born respondents in the CPS household survey goes down in some month (which might happen because those in the household were deported, or were worried they might be deported if they responded honestly and hence decided either not to respond at all or to indicate they were native born – understandable given that the Trump administration has openly violated the confidentiality rules that are supposed to apply to such surveys), then the BLS estimate of the number of foreign-born in the adult population will go down.  And since the totals for the adult population derived from the Census Bureau figures each January are not changed (but rather grow from month to month at some pre-set level), a smaller estimate for the foreign-born population from the CPS responses will lead by simple arithmetic to an increase in the figure provided for the native-born population.

Year-to-year comparisons of the number of native-born Americans in the BLS figures can thus jump around and are not meaningful.  For example, between November 2024 and November 2025 the figure for the adult population of native-born Americans jumped by 5.3 million, or 2.5%.  The year before (November to November) it grew by 346,000, or 0.2%.  And the year before that by 1.9 million, or 0.9%.  In reality, the native-born population of adults in the US does not jump around like that from one year to the next.  As Kolko has said, to make such year-over-year comparisons in these BLS figures is a “multiple-count data felony”.  The error in such comparisons will carry over to comparisons across years in the labor force and employment figures.

As Kolko has noted, the most meaningful way to track what may be happening to the native-born and foreign-born populations in the labor market is to look at their reported unemployment rates.  These rates come directly from the household surveys, are independently determined for each, and will indicate whether employment prospects are improving or worsening.  An issue is that none of the figures in the BLS Table A-7 of its monthly Employment Situation report are seasonally adjusted.  Thus the month-to-month reported changes in the unemployment rates will vary due to seasonal effects.  It is better (although still not ideal) to compare the reported unemployment rate to that of the same month the year before.  And these have been going up in 2025 for native born Americans.  The November 2025 rate was 4.3%, up from 3.9% in Novermber 2024.

A seasonally adjusted series would be more useful to track the trends.  Jed Kolko has calculated an estimate of this for the unemployment rates of the native-born and foreign-born, using standard software for making seasonal adjustments from historical data.  The estimates he has released go through July 2025, and show a rising trend in 2025 (and since mid-2023 in his chart) for the unemployment rate of the native-born labor force.  While there is still a good deal of month-to-month fluctuation in the figures, the trend is basically the same from mid-2023 to mid-2025.  That is, Trump’s aggressive policies on immigrants have not affected this trend.

Trump administration officials continue to claim that the BLS data show that the employment of native-born Americans soared in 2025.  Despite analysts pointing out already last August the error in making such year-to-year comparisons in the BLS CPS data, Trump administration officials continue to make this mistake.  It would be understandable that originally they may have misunderstood the basis of the BLS figures.  It is a technical issue, and non-specialists would likely not be aware of it.  But by failing to correct their understanding of the issue once it was pointed out to them, their continued and repeated claims (most recently for the November figures) can only be viewed as moving from misunderstanding to misrepresentation to outright lying.

E.  Summary and Conclusion

The labor market has weakened substantially this year.  Employment had been growing at a good pace under Biden.  But in the first ten months of Trump’s second term in office, the country ended up with 1.2 million fewer jobs than there would have been had they grown at the pace achieved during Biden’s last year in office.  And if one excludes just the growth in employment in the health and social assistance sector, there were 134,000 fewer employed by November than there were in January, and 311,000 fewer compared to the number that were in April.

Trump’s deportations and other aggressive policies on migrants likely accounted for some share of this drop in employment.  But the fall in employment under Trump (relative to what it would have been had it continued to grow as under Biden) has been far more than can be accounted for by fewer migrants being employed.  And there is no evidence that fewer migrants being employed led to more native-born Americans being employed.  The unemployment rate of native-born Americans has gone up under Trump.  Furthermore, the growth in nominal and in real wages has diminished under Trump.  Deporting migrants did not lead to higher wages for those remaining.

Trump’s White House claims otherwise.  The White House press release issued on the day the BLS Employment Situation report for November was released opened by saying (in bold in the original):

“The strong jobs report shows how President Trump is fixing the damage caused by Joe Biden and creating a strong, America First economy in record time. Since President Trump took office, 100% of the job growth has come in the private sector and among native-born Americans — exactly where it should be. Workers’ wages are rising, prices are falling, trillions of dollars in investments are pouring into our country, and the American economy is primed to boom in 2026.”
— White House Press Secretary Karoline Leavitt

Breaking this down by phrase, with then what has in fact happened:

The strong jobs report shows how President Trump is fixing the damage caused by Joe Biden and creating a strong, America First economy in record time. Not true.  Job growth was substantial under Biden, and this growth then collapsed under Trump.  By November, there were 1.2 million fewer jobs under Trump than there would have been had growth continued at the pace it had in the last year of Biden’s term.

Since President Trump took office, 100% of the job growth has come in the private sector:  The growth in private sector jobs was 45% less in the first ten months of Trump’s second term in office than it was in the last ten months of Biden’s term in office.  Private job growth was greater than job growth in the economy as a whole (including the public sector) only because that growth fell by an even greater 68% under Trump.  This is not a record to be proud of.

and among native-born Americans — exactly where it should be.:  As explained in Section D above, this conclusion is based on a mistaken understanding of how the BLS figures on employment of the native-born and the foreign-born are estimated.  Such year-to-year comparisons are not meaningful.  What we do know from the BLS figures is that the unemployment rate of the native-born labor force has gone up in 2025.

Workers’ wages are rising,:  They are rising at a slower rate than they were during the Biden administration.

prices are falling,:  No.  Prices are rising.

trillions of dollars in investments are pouring into our country,:  While not something addressed in the BLS report, this reference is to promises made by various countries – as part of their trade negotiations with the Trump administration – to increase their investment into the US.  Figures “promised” range up to $1.4 trillion (by the United Arab Emirates), $1.2 trillion (by Qatar), and $1.0 trillion (by Japan), along with promises from other nations as well.  The investments would largely be made by private firms from the respective countries, even though it is not clear how public officials can commit their private firms to make investments of the magnitude promised.  The time frames are also not always clear.

There is no evidence that such investment is “pouring into” the US.  They are certainly not “pouring into” new fixed investments being made.  Total private fixed investment expenditures in the US from all sources (almost entirely domestic) were only $126 billion higher in the first three-quarters of 2025 than they were in the last quarter of 2024.  This is far from “trillions” even if it were entirely by foreign investors (which it was not).  Unless the vision is that foreign investors will displace domestic American investors – and take over control of the American economy – foreign investment of such magnitude will never happen.

Nor is it something most would want.  Recall the worries in the late 1980s (such as depicted in the popular book and movie Rising Sun) that Japanese investment would soon take ownership and control over significant assets in the US.  Recall also the concerns that arose after Japanese investors had purchased existing assets such as Rockefeller Center, Columbia Records, and the Pebble Beach Golf Course.

If anything close to the scale of investments by foreign firms the Trump White House is citing eventually materialize, the Japanese investment in the late 1980s will look puny.

Furthermore, for such foreign investment into the US to materialize on anything close to the scale the Trump White House is claiming, the trade deficit of the US would have to increase sharply.  This is the exact opposite of the claim that the negotiated trade agreements will lead the US trade deficit to go down.  Foreign investors will only be able to get the dollars to make the additional investments in the US if the US imports more from others.  This illustrates the confusion and lack of coherence in the Trump administration’s trade policies.

The discussion is, however, academic.  There will never be anything close to an increase in foreign investment into the US at the scale being claimed.

and the American economy is primed to boom in 2026.:  That remains to be seen.

A Comparison of the Net New Job Estimates of the ADP and the BLS: The Differences are Large

Chart 1

With the government shutdown, the Bureau of Labor Statistics (BLS) is no longer issuing its often eagerly awaited monthly report on employment growth.  Institutions such as the Fed are flying blind, even though the Fed is now reducing interest rates out of concern the job market is weak.  Private markets know even less.

In the absence of the BLS reports, the reports issued by the private firm ADP (a large processor of payrolls for firms) have taken on greater importance. The ADP has long issued a report (normally two days before the BLS issues its monthly report) that provides a first look at what the employment numbers might be that month.  It is now producing these reports in a collaboration with the Digital Economy Lab of Stanford University.  ADP bases its estimates on the payrolls it processes for firms, now covering 26 million employees.  While not as large as the sample of employers who report their payrolls to the BLS each month (roughly one-third of all workers – including government employees – or around 53 million currently), those are both still huge “samples”.

But the ADP does not have the advantages the BLS has.  The BLS obtains its estimates from a stratified random sample of employers, while the ADP must base its sample on the firms that have contracted with it for payroll processing services.  While both the BLS and the ADP try to correct their samples to reflect a proper weighting of different types of firms (e.g. by size of the firm, by sector of activity and region of the country, by type of ownership, and by other factors that may be relevant), there can still be biases in the data they have to work with.  Such biases are likely significantly more of a factor for ADP.

It is also important to recognize that while the focus of the media is typically on the number of “new jobs created” in the month of the report, that is a confused term on many levels.  There is constant turnover in the job market, with workers starting new jobs and leaving old ones (mostly by resignation, but also from layoffs, retirement, and so on).  The estimates of neither the ADP nor the BLS are directly of “new jobs”.  Rather, they both estimate the total number of workers who were employed (i.e. were on the payroll) in some specific period of each month (for the BLS, for the payroll period that included the 12th day of the month).  The number of “new jobs created” is then the difference between the estimated total number of workers on a payroll in the given month less the separately estimated total number of workers on a payroll in the prior month.  This difference is thus a net figure, and is estimated by taking the difference between two total employment estimates.

Both ADP and the BLS also revise their monthly estimates each year in a rebenchmarking exercise, based on results from the Quarterly Census of Employment and Wages of the BLS.  This report is comprehensive  – a census – that provides a good count of the number of workers employed as of a given month each year (mostly from available data on who is covered by unemployment insurance).

Are the ADP monthly estimates on net employment growth then close to what the BLS estimated?  Not really.  The chart at the top of this post shows the difference between net private employment growth as estimated by ADP and as estimated for the month by the BLS.  The currently issued ADP series on private employment begins in January 2010, and hence one can find the change in employment starting in February.  The last estimate issued by the BLS before the government shutdown was for August of this year.  The BLS figures used here are its estimates for private employment (to be comparable to the ADP estimates), not the more commonly cited figures for overall employment – including government – that the BLS also provides.

I have removed from the chart the figures that would apply for the period from March 2020 to July 2020.  This was during the peak of the Covid disruptions, and the differences in the monthly net new jobs estimates were huge.  The largest was for April, where ADP estimated private employment fell by 6.1 million while the BLS estimated it fell by 19.6 million – a difference of 13.5 million.  But these were far from normal circumstances, and including these months on the chart would have distorted the scale.  They were instead set to zero.

Leaving out the March 2020 to July 2020 figures, the average absolute deviation (i.e. the average deviation regardless of whether higher or lower) was 108,000 workers.  That is, on average the ADP and BLS estimates of net new private jobs in a given month differed by 108,000.  To put this in perspective, the average monthly growth in net new jobs over this period was 152,000 in the BLS figures.  The average deviation between the ADP and BLS estimates (of 108,000) was more than two-thirds of this.

The standard deviation was 180,000, meaning that (in the long run) in two-thirds of the months the deviation can be expected to be less than that, and hence that in one-third they can be expected to be more.  These monthly differences in the employment estimates between the ADP and the BLS are large.  It would not be safe to assume the ADP figures provide a good estimate of what the BLS figures would have been.

The chart at the top of this post covers the full period from February 2010 to August 2025, a period when there were other major disruptions and not just those of the Covid period.  If one limits the analysis to the period since 2022, when growth has been relatively smooth and unemployment has been low, one has:

Chart 2

While the deviations between the two sets of estimates are less than over the full period going back to 2010 (with an average absolute deviation of 68,000 workers and a standard deviation of 99,000), the differences are still large.

It is commendable that ADP issues employment estimates based on the payroll data they have access to.  With no figures from the BLS, there is currently nothing else to go on to ascertain whether employment is growing or not.  But the ADP figures are not a terribly good approximation to what the BLS provides in its monthly net new employment estimates, and users should be aware of the often large differences.