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Despite Germany's trade surplus, manufacturing employment share of total employment has fallen in its industrial hub at a similar rate as in Ohio

Adam Posen Peterson Institute for International Economics

Date Posted:: October 11, 2021
Is Database:: Database

Germany’s trade surplus didn’t shield its industrial hub from manufacturing job losses. North Rhine-Westphalia’s manufacturing employment fell 11.7% points, mirroring Ohio’s decline, despite Germany’s trade surplus.

Despite Germany's persistent trade surpluses from 1992 to 2020, manufacturing employment in North Rhine-Westphalia, its industrial hub, fell by 11.7 percentage points, losing 658k jobs, mirroring Ohio's decline of 8.1 percentage points and 341k jobs despite the US's trade deficit. Both regions, with populations of 17.9m and 11.7m respectively, illustrate that the decline in manufacturing employment is not solely driven by trade balances. Instead, common factors such as labor-saving technologies and productivity improvements are likely responsible, reducing the need for manufacturing workers. This trend highlights the limitations of focusing on trade balances to protect manufacturing jobs, as technological advancements continue to reshape the labor market across high-income economies.

Takeaways by Macro Roundup^® AI

Manufacturing employment in North Rhine-Westphalia, Germany's industrial hub with 17.9m people, fell 11.7 percentage points between 1992 and 2020, a loss of 658k jobs, despite Germany's persistent trade surpluses over that period.
Ohio, with a population of 11.7m, saw manufacturing employment fall 8.1 percentage points and shed 341k jobs from 1992 to 2020 despite the US running trade deficits, a decline similar to North Rhine-Westphalia's.
The parallel declines in North Rhine-Westphalia and Ohio suggest manufacturing employment losses are not driven solely by trade balances, with labor-saving technologies and productivity improvements the likely common causes, limiting what trade-balance policy can do to protect manufacturing jobs.

Adam Posen draws a parallel between Ohio and North Rhine-Westphalia a comparable region in Germany, "...manufacturing employment as a share of total employment has been steadily declining in all high-income economies, even those with strong export sectors and trade surpluses, like Germany. State-level data provide a more complete picture. Germany persistently recorded trade surpluses in goods between 1992 and 2020, but manufacturing employment as a share of total employment in its industrial heartland has fallen at a similar rate to that in the United States, which recorded a trade deficit in goods in that time frame. Manufacturing employment in North Rhine-Westphalia, an industrial hub in western Germany that has recently been reinventing itself as a high-tech and services center, fell as a share of total employment by 11.7 percentage points across the period, losing 658 thousand jobs. In Ohio, a Midwestern state in the United States with a similar manufacturing history, manufacturing employment as a share of total employment dropped by 8.1 percentage points, or 341 thousand jobs. Both states are heavily populated. Ohio's population is 11.7 million and North Rhine-Westphalia's is 17.9 million. The parallel trends observed in both regions suggest the decline is driven by forces other than trade balances or policy. Common factors, including increased use of labor-saving technologies and improvements in productivity, may be responsible as more efficient technologies reduce the need for manufacturing workers...."

Adam Posen, "Despite Germany's trade surplus, manufacturing employment share of total employment has fallen in its industrial hub at a similar rate as in Ohio," Peterson Institute For International Economics, October 6, 2021, https://www.piie.com/research/piie-charts/despite-germanys-trade-surplus-manufacturing-employment-share-total-employment

Despite Germany's trade surplus, manufacturing employment share of total employment has fallen in its industrial hub at a similar rate as in Ohio

Large trade deficits are often blamed for the protracted decline in manufacturing employment in the United States. Critics claim cheap imports from low-wage countries erode the competitiveness of US producers, driving American manufacturing plants and jobs abroad. However, manufacturing employment as a share of total employment has been steadily declining in all high-income economies, even those with strong export sectors and trade surpluses, like Germany.

State-level data provide a more complete picture. Germany persistently recorded trade surpluses in goods between 1992 and 2020, but manufacturing employment as a share of total employment in its industrial heartland has fallen at a similar rate to that in the United States, which recorded a trade deficit in goods in that time frame. Manufacturing employment in North Rhine-Westphalia, an industrial hub in western Germany that has recently been reinventing itself as a high-tech and services center, fell as a share of total employment by 11.7 percentage points across the period, losing 658 thousand jobs. In Ohio, a Midwestern state in the United States with a similar manufacturing history, manufacturing employment as a share of total employment dropped by 8.1 percentage points, or 341 thousand jobs. Both states are heavily populated. Ohio's population is 11.7 million and North Rhine-Westphalia's is 17.9 million.

The parallel trends observed in both regions suggest the decline is driven by forces other than trade balances or policy. Common factors, including increased use of labor-saving technologies and improvements in productivity, may be responsible as more efficient technologies reduce the need for manufacturing workers. The regional histories demonstrate the futility of fixating on trade balances to protect manufacturing jobs, a policy course that is doomed to fail while ignoring long-standing injustices.

US Labor Productivity Increases at Fastest Pace in a Year

Julia Fanzeres Bloomberg

Date Posted:: November 5, 2026
Is Database:: Database
Is Important:: Important

Nonfarm business productivity grew at a 3.6% annualized rate in Q3, the fastest pace in a year. Real hourly compensation rose 1.1%, while the labor share fell to 53%, the lowest in data going back to 1947.

US labor productivity accelerated in the third quarter at the fastest pace in a year, suggesting a capital spending boom is translating into greater efficiency at a time when input costs have soared. Productivity, or nonfarm business output per hour, increased at a 3.6% annualized rate after an upwardly revised 2.1% pace in the prior quarter, data from the Bureau of Labor Statistics showed Thursday. Unit labor costs — what businesses pay employees to produce one unit of output — fell 0.7%, the first decline since the second quarter of 2025.

Takeaways by Macro Roundup^® AI

US nonfarm business output per hour rose at a 3.6% annualized rate in the third quarter, the fastest pace in a year, following an upwardly revised 2.1% pace in the prior quarter, according to the Bureau of Labor Statistics.
Unit labor costs, what businesses pay employees to produce one unit of output, fell 0.7% in the third quarter, the first decline since the second quarter of 2025.
The third-quarter productivity acceleration suggests a capital spending boom is translating into greater efficiency at a time when input costs have soared.

Change Is Accelerating Because It’s All Interconnected

Greg Jensen Bridgewater Associates

Date Posted:: October 30, 2026
Is Database:: Database

Bridgewater estimates that AI equities are pricing in ~3.5% annual productivity growth, while real growth and productivity implied by breakeven earnings in the broader stock market are slightly below current levels. They conclude: “Either AI stocks or broader macro markets are very meaningfully mispriced.”

Different markets are pricing very different futures; most are far from pricing an unrecognizable world. The AI boom has contributed tens of trillions of dollars of market cap in AI companies. As in any gold rush, trillions of dollars of “picks and shovels” only make sense if a great deal of gold (profits) is found. The pricing of a broader universe of AI companies tells us something about the scale of transformation that is discounted. If we calculate the incremental revenue required to sustain the valuation of AI companies and estimate how much of that can be earned by cannibalizing the revenue of existing companies, we can back out a productivity boost required to generate the rest of the incremental revenues. Today, we estimate that AI equities are discounting something like 3.5% annual productivity growth. That’s more than we estimate is needed for the US to make its debts sustainable. Across a reasonable range of assumptions, we assess AI stocks to be pricing trend nominal GDP growth well above the recent past. By contrast, consensus estimates and broader markets imply a world that looks more like the recent past. Making sense of the pricing in AI stocks requires a boom in productivity and growth. Consensus estimates for growth and productivity (from economist and Fed estimates) are slightly lower than current measures, as are rates of real growth and productivity implied by breakeven earnings in the broader stock market.

Takeaways by Macro Roundup^® AI

AI equities are priced for roughly 3.5% annual productivity growth, more than is estimated as needed to make US debts sustainable, and across a reasonable range of assumptions imply trend nominal GDP growth well above the recent past.
Consensus growth and productivity estimates from economists and the Fed, along with real growth and productivity implied by breakeven earnings in the broader stock market, sit slightly below current measures, describing a world resembling the recent past.
Either AI stocks or broader macro markets are very meaningfully mispriced, since the AI boom has added tens of trillions of dollars of market cap that only makes sense if a great deal of profit is found.

No Signs of AI Payoff Outside the Tech Sector

Torsten Slok Apollo

Date Posted:: October 26, 2026
Is Database:: Database

Citing flat revenue per worker, and profit margin growth for the S&P 493, Slok argues that so far, AI’s diffusion through the economy has delivered “zero financial payoff to corporate America outside of tech.”

With no impact on the bottom line, mainstream corporate America is pulling back and starting to view token spending not as a source of growth but as a cost center that weighs on margins. VC-backed companies are spending more and more on tokens, but mainstream corporate America does not need the latest model and is unwilling to pay for it. As long as agentic AI and token spending are confined to a small part of the economy, the macroeconomic effects of AI will be very limited. The question looking ahead is whether, even as models become commoditized, mainstream corporate America can turn increasingly cheap intelligence into higher revenue growth and higher profit margins. That may be harder than it sounds. The AI story will be decided in the S&P 493, not the Mag 7, and so far, there is no evidence that revenue growth or margins are improving outside the tech sector.

Takeaways by Macro Roundup^® AI

Revenue per worker and profit margins are rising for the Magnificent 7 but flat for the S&P 493, with no signs of AI boosting productivity growth or delivering financial payoff outside the tech sector.
Mainstream corporate America is pulling back from AI and treating token spending as a cost center that weighs on margins, while VC-backed companies spend increasingly more on tokens.
The macroeconomic effects of AI will remain very limited as long as agentic AI and token spending stay confined to a small part of the economy.

How Much Revenue Does The AI Buildout Need In 2030?

William Gildea and Azeem Azhar Exponential View

Date Posted:: October 1, 2026
Is Database:: Database

Beginning with capex projections, Gildea/Azhar derive for their high-growth case a $1.3T annual AI-infrastructure revenue hurdle for 2030. This covers $771B of depreciation and operating costs, plus a 15% return on $3.5T of net capital in service.

We estimate AI CapEx to be $560B in 2026, 64% of the total declared by the hyperscalers. Unfinished facilities can’t generate revenue, so the investment bill and the productive asset base will grow at different speeds. [In the high investment case], by 2030 we see $3.5T of net AI capital in service. The largest component [of the revenue hurdle] is the $555B in compute depreciation. We model lifespans at the current 5-6 years. Shell depreciation and OpEx combined contribute about a further $216B to the required revenue hurdle. [Adding a 15% return on net capital brings required annual infrastructure revenue to $1.3T]. Reaching the $1.3T hurdle would represent 7.4x growth (59% CAGR) from today’s annualized revenues. For its customers to earn 30-50% gross margins, downstream revenues need to be $1.9-2.6T. [Editor’s note: Van Nieuwerburgh’s far larger figure of $3.7T covers mature revenues from projects completed through 2032, using commissioning capital and a 50% infrastructure operating cash-flow margin. This estimate instead charges depreciation and operating costs plus 15% on net capital in service in 2030. Different horizons and cost conventions prevent a direct comparison. Van Nieuwerburgh also has a much higher capex estimate.]

Takeaways by Macro Roundup^® AI

In a high investment case with $3.5tn of net AI capital in service, the authors estimate AI infrastructure must generate $1.3tn of annual revenue by 2030, requiring 7.4x growth (59% CAGR) from today’s annualized revenues.
Compute depreciation of $555bn, modeled on 5-6 year lifespans, is the largest component of the 2030 revenue hurdle, with shell depreciation and operating expenses adding about $216bn and a 15% return on net capital completing the $1.3tn total.

AI and Entrepreneurship: What is Really Happening?

Nathan Goldschlag Economic Innovation Group

Date Posted:: September 30, 2026
Is Database:: Database

Nathan Goldschlag finds that available federal data do not yet establish that AI is boosting entrepreneurship. “Across different cuts of the data, there is no clear evidence of AI affecting entrepreneurship, either for likely employers or nonemployers.”

Employer entrepreneurship is at best rising modestly, and at worst has been falling for several years already. The number of business applications likely to become an employer business was flat in 2025 and began rising in 2026. Fewer of those applications have been the kind that have the highest chance of becoming an employer. Nonemployer entrepreneurship, or “solopreneurship,” is likely rising, but not as the main job of the solopreneurs. These are probably side hustles. Likely nonemployer business applications rose significantly in 2025 and early 2026. But the share of workers claiming to be self-employed nonemployers in their main job — in other words, business owners with no employees — is flat (though noisy). AI-intensive sectors and industries are no exception. Establishment entry in AI-intensive sectors (information and professional services) has been flat or falling in recent years.

Takeaways by Macro Roundup^® AI

Business applications likely to become employer businesses were flat in 2025 and began rising in 2026, with fewer applications of the kind having the highest chance of becoming an employer business.
Likely nonemployer business applications rose significantly in 2025 and early 2026, going vertical in the most AI-intensive sectors of information and professional services, while other sectors kept pace until early 2026 and then flattened.
The share of workers reporting self-employment as nonemployers in their main job is flat, though noisy, including among workers in AI-exposed occupations in 2025 and early 2026.

Tech's Trillion-Dollar Internal Inconsistency

Torsten Sløk Apollo

Date Posted:: September 29, 2026
Is Database:: Database

Tech analysts expect sector operating cash flow to more than double to ~$2.4T by 2028. Analysts covering other S&P 500 sectors (tech’s customers) see far less cash growth in their sectors. Slok “raises the question of who exactly will be writing all those checks to buy AI services.”

Wall Street equity analysts work in sector silos, and when you add up their forecasts, the numbers are internally inconsistent. The analysts covering tech expect the sector's operating cash flow to more than double to roughly $2.4 trillion by 2028, an increase of over $1.2 trillion. Meanwhile, the analysts covering the other sectors in the S&P 500, which are tech's customers, expect those companies to add much less operating cash flow. In other words, the tech silo is betting on a future in which demand for AI and tech services explodes, while the silos covering the companies that would pay for those services see a much more modest outlook. Both cannot be right at the same time. The bottom line is that either tech's customers will generate a lot more cash than their analysts expect, or tech's cash flow forecasts are too optimistic, which raises the question of who exactly will be writing all those checks to buy AI services.

Takeaways by Macro Roundup^® AI

Analysts covering the tech sector expect its operating cash flow to more than double to roughly $2.4tn by 2028, an increase of over $1.2tn.
Analysts covering the other S&P 500 sectors, which are tech's customers, expect those companies to add much less operating cash flow, leaving the combined forecasts internally inconsistent.
Either tech's customers will generate much more cash than their analysts expect, or tech's cash flow forecasts are too optimistic, which raises the question of who will pay for AI services.

Germany’s trade surplus didn’t shield its industrial hub from manufacturing job losses. North Rhine-Westphalia’s manufacturing employment fell 11.7% points, mirroring Ohio’s decline, despite Germany’s trade surplus.

Do trade surpluses prevent manufacturing employment from declining?

Takeaways by Macro Roundup® AI

Nonfarm business productivity grew at a 3.6% annualized rate in Q3, the fastest pace in a year. Real hourly compensation rose 1.1%, while the labor share fell to 53%, the lowest in data going back to 1947.

Is a capital spending boom lifting worker productivity?

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Do AI stock valuations and broader markets imply incompatible growth expectations?

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Citing flat revenue per worker, and profit margin growth for the S&P 493, Slok argues that so far, AI’s diffusion through the economy has delivered “zero financial payoff to corporate America outside of tech.”

Is artificial intelligence boosting revenue and margins outside big tech?

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Beginning with capex projections, Gildea/Azhar derive for their high-growth case a $1.3T annual AI-infrastructure revenue hurdle for 2030. This covers $771B of depreciation and operating costs, plus a 15% return on $3.5T of net capital in service.

How much annual revenue must AI infrastructure generate to justify investment?

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Nathan Goldschlag finds that available federal data do not yet establish that AI is boosting entrepreneurship. “Across different cuts of the data, there is no clear evidence of AI affecting entrepreneurship, either for likely employers or nonemployers.”

Is AI driving a boom in new employer businesses?

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Tech analysts expect sector operating cash flow to more than double to ~$2.4T by 2028. Analysts covering other S&P 500 sectors (tech’s customers) see far less cash growth in their sectors. Slok “raises the question of who exactly will be writing all those checks to buy AI services.”

Are tech cash flow forecasts inconsistent with customers' projected growth?

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