Edward Conard

Top Ten New York Times Bestselling Author

  • “A full-throated defense of economic dynamism.” - The Wall Street Journal
  • “Unintended Consequences should be read by anyone who takes for granted the superiority of progressive taxation and has not thought carefully about the trade-offs involved.” - The New Republic
  • “…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
  • “…reminds us that inequality sends a signal of what society lacks most, in America’s case, entrepreneurship and risk taking.” - Lawrence Lindsey, CEO, The Lindsey Group, former Director of the National Economic Council
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “There are an amazing number of good ideas and interesting points made in Unintended Consequences. The thinking underlying it, and the obvious depth of understanding of the author, are very impressive.” - Steven Levitt, coauthor of Freakonomics; 2004 John Bates Clark Medal
  • “…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
  • “…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “Unintended Consequences is far smarter and more thought-provoking than most economics written for the general public” - Greg Mankiw, Harvard University, Former Chairman of the Council of Economic Advisors
  • “Unintended Consequences provides a provocative interpretation of the causes of the global financial crisis and the policies needed to return to rapid growth. Whether you agree or not, this analysis is well worth reading.” - Nouriel Roubini, New York University; Chairman, Roubini Global Economics
Upside of Inequality Oxford Unintended Consequences

Macro Roundup


Thursday, July 23, 2026

AI Summary. A long-run model in which health spending, the federal budget, and capital investment are jointly determined by supply and demand projects U.S. federal debt reaching 151% of GDP in 2036 and 309% in 2056, versus the Congressional Budget Office's 120% and 175%. Rising real interest rates compound deficits and debt.

Mark Warshawsky American Enterprise Institute
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Warshawsky projects US debt-to-GDP will be 151% in 2036, well above CBO’s 120% projection, as higher real rates and a dynamic Baumol cost-disease effect will increase healthcare spending relative to GDP in an aging economy.

Does modeling health spending and interest rates jointly raise debt projections?

Core argument: The model projects U.S. federal debt-to-GDP at 151% in 2036, 218% in 2046, and 309% in 2056, above CBO's 120% in 2036 and 175% in 2056.

The Congressional Budget Office makes certain simplifying assumptions on health care spending, and is based on current law. By contrast, in the model presented in this paper, these variables are simultaneously determined by supply and demand, based on logical functional forms and parameter estimates from the literature or empirical analysis, and the model is based on current policy. This approach better reflects real economic relationships—between health care spending, the federal budget, and investment in capital—and changing underlying conditions, especially demographics. Within the next twenty or so years, the model predicts that federal government debt will grow significantly beyond historical experience, to be judged unsustainable because the real interest rate exceeds real economic growth. Debt-to-GDP will be 151% in 2036, 218% in 2046, and 309% in 2056, compared to CBO’s 120% in 2036 and 175% in 2056. Real interest rates rise for several decades, ratcheting interest payments, deficits, and debt in a vicious cycle.

Takeaways by Macro Roundup® AI

  1. The model projects U.S. federal debt-to-GDP at 151% in 2036, 218% in 2046, and 309% in 2056, above CBO's 120% in 2036 and 175% in 2056.
  2. The model judges the projected debt path unsustainable because the real interest rate exceeds real economic growth, with real rates rising for several decades and ratcheting up interest payments, deficits, and debt.
  3. Unlike CBO's current-law projections with simplifying assumptions on health care spending, the model determines these variables simultaneously through supply and demand under current policy, using functional forms and parameter estimates from the literature or empirical analysis.

Related Articles:

  • Can AI Avert the Impending Federal Budget Crisis? — U.S. GDP per capita, about $87,000, is projected to reach $102,000 by 2035 with 1.8% annual productivity growth, or $110,000 with 2.7% growth. Because the healthcare sector has been slow to adopt AI, health spending is projected to rise from 18.3% of GDP to 21% under slower growth and 21.7% under faster growth.
  • How Might Fiscal Policy Respond to the Rise of Artificial Intelligence? — The Congressional Budget Office estimates that a permanent 0.5 percentage point increase in annual productivity growth would lower publicly held federal debt after 30 years by 39% of GDP, offsetting roughly half the projected rise from 101% of GDP to 175%. Higher revenue and slower spending growth outweigh higher interest costs.
  • The Budget and Economic Outlook: 2026 to 2036 — The Congressional Budget Office projects the federal deficit will grow from $1.9tn (5.8% of GDP) to $3.1tn (6.7%) over a decade, above the 3.8% averaged over the past 50 years, with rising net interest costs driving much of the increase. Federal debt held by the public is projected to rise from 101% to 120% of GDP.

AI Summary. The author estimates "shadow" 30-year government bond yields for Germany, Spain, Italy, Greece and Japan — what long-term yields would be if central banks withdrew support such as Japanese government bond purchases and the European Central Bank's backstop facilities. These estimates assume no outright central bank selling, so they represent a lower bound on how high yields could go.

Robin Brooks Robin Brooks Substack
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Robin Brooks estimates that, given its high government debt to GDP ratio, Japan’s 30-year yield would, in the absence of BoJ intervention, be at least 300bps higher – a 7% yield as opposed to the observed 4%.

How much higher would long-term bond yields be without central bank support?

Core argument: The author estimates "shadow" 30-year government bond yields for Germany, Spain, Italy, Greece and Japan, comparing actual market yields as of July 21st with where yields would sit absent central bank yield caps.

The scatter plot shows my estimates for what 30-year shadow yields are for Germany (DE), Spain (ES), Italy (IT), Greece (GR) and Japan (JP). The solid red dots are actual yields in markets as of [July 21st], while the hollow dots are where I think yields would be if central banks pulled back from yield caps. In the case of Japan, this would be an end to gross purchases of JGBs. For the ECB, it means cancelling TPI and going back to the OMT as the only bailout facility. It would obviously also preclude the kind of comments President Lagarde is fond of making on French yields. I don’t anticipate actual selling of government bonds from anyone, so my shadow yields are a lower bound for how high yields might go if central banks really exited bond markets.

Takeaways by Macro Roundup® AI

  1. The author estimates "shadow" 30-year government bond yields for Germany, Spain, Italy, Greece and Japan, comparing actual market yields as of July 21st with where yields would sit absent central bank yield caps.
  2. For Japan, the author's shadow yield scenario assumes an end to gross purchases of Japanese government bonds, while for the ECB it assumes cancelling the Transmission Protection Instrument and reverting to Outright Monetary Transactions as the only bailout facility.
  3. The author treats the estimated shadow yields as a lower bound for how high yields might go if central banks exited bond markets, anticipating no actual selling of government bonds by any central bank.

Related Articles:

  • Global Debt Report 2026 — Gross central government borrowing across OECD countries rose 7% to a record $17tn, about 23% of GDP, and is projected to reach roughly $18tn, or 24% of GDP. The United States and Japan account for close to 80% of the area's refinancing requirements, with the US share rising from 57% to 70%.
  • Are Government Bonds Safe in Times of War and Pandemic? — Government bonds severely underperform in wartime, in absolute terms and relative to equities, housing, and economic growth, unlike in financial crises when bonds tend to outperform stocks. The U.S. and U.K. reduced war-driven debt-to-GDP ratios without explicit default, using surprise inflation and financial repression that shifted fiscal burdens onto bondholders.
  • Across The Rich World, Fiscal Crises Loom — An analysis estimates most rich countries could keep debt stable as a share of their economies with only small primary surpluses—Britain's required surplus is 0.3% of GDP—even if all debt were refinanced immediately at current rates, but the tightening needed to stabilize debt ratios exceeds 2% of GDP in Britain and America and 3% in France.

AI Summary. Japan's return to headline inflation of 1.5%, modest by global and historical standards, feels to many households like a severe cost-of-living crisis because real wage gains had until recently failed to keep pace with more expensive imports. Japanese households spent 30.75% of income on food, a 25-year peak and among the highest in the developed world.

Leo Lewis Financial Times
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Reflation in Japan has left real wages below their 2020 level, though headline inflation is only running at a modest 1.5%.

Why does modest inflation feel severe to Japanese households?

Core argument: Japan's Engel coefficient, the share of household income spent on food, hit a 25-year peak of 30.75% in December, one of the highest levels in the developed world.

[Japan’s] return to what is, in global and historical terms, modest headline inflation of 1.5% feels, for many households, like a severe cost of living crisis, as real wage increases have failed, until recently, to match the costs of more expensive imports. Japan’s Engel coefficient, a “pain index” that measures the proportion of household income spent on food, stood at a 25-year peak of 30.75% in December — one of the highest in the developed world.

Takeaways by Macro Roundup® AI

  1. Japan's Engel coefficient, the share of household income spent on food, hit a 25-year peak of 30.75% in December, one of the highest levels in the developed world.
  2. Japan's headline inflation of 1.5%, modest in global and historical terms, registers for many Japanese households as a severe cost of living crisis.
  3. Real wage increases in Japan failed, until recently, to keep pace with the cost of more expensive imports.

Related Articles:

  • 2026 Japan Economic Outlook: Steady Fundamentals, Policy Risks Ahead — Japan's government debt-to-GDP ratio has continued to decline despite an ongoing primary deficit, because the government's interest payment rate (0.8%) is far below nominal GDP growth (3.9%), a gap the authors call the widest since 1980 and worth roughly -7 percentage points a year.
  • Yen Hits Four-Decade Low in Historic Slide That’s Rattled Japan — The yen weakened past 161.95 per dollar to its lowest level since 1986, putting traders on alert for Japanese intervention to support the currency. The weak yen is lifting exporters' profits and Japanese stocks to record highs while raising import costs for oil and gas and fueling inflation that hurts consumers.
  • Japan’s Debt Puzzle: Sovereign Wealth Fund from Borrowed Money — Japan's public sector operates as a highly leveraged sovereign wealth fund, funding risky assets by borrowing from banks and depositors at below-market rates, with returns sustaining its debt-to-GDP ratio above 200% without primary surpluses. The authors argue the resulting duration and currency mismatches expose Japan to mark-to-market losses that could trigger a fiscal crisis or a run on the yen.

AI Summary. A warning sign in a boom cycle is when companies closest to final demand weaken while capital spending beneficiaries keep thriving, as when telecom operators' shares rolled over while communications equipment stocks kept rising. Semiconductor stock prices and free cash flow are soaring while large cloud providers' shares stagnate and their free cash flow plummets.

Michael Cembalest J.P. Morgan
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Cembalest notes the rising stock prices and expanding free cash flow of firms supporting the AI buildout, even as the hyperscalers, who are closest to final demand, see their stock prices stagnating and their free cash flow plummeting.

Does weakening demand-side performance amid thriving capital spending signal a boom's end?

Core argument: In late 1999 and early 2000, communications services companies including Verizon, AT&T, WorldCom, Bell South, GTE and Sprint rolled over even as communications equipment stocks such as Cisco, Nokia, Nortel, Qualcomm and Motorola kept rising.

The worrisome part of a boom cycle is when companies closest to final demand roll over, even as capital spending beneficiaries of the cycle keep on thriving. In late 1999 and early 2000, communications services companies rolled over (Verizon, AT&T, WorldCom, Bell South, GTE, Sprint) even as communications equipment company stocks (Cisco, Nokia, Nortel, Qualcomm, Motorola) kept on rising. The obvious parallel to today is soaring stock prices and free cash flow for semiconductor companies (the caboose) even as the front end of the train (hyperscalers) is seeing stock prices stagnate and free cash flow plummet. Tech sector earnings typically track reasonably well with sectors that rely heavily on technology (finance, manufacturing, media, transport, and healthcare) and pay for all that equipment and software. The recent divergence begs the question: who is going to pay for the AI boom if the tech sector’s primary customers are not making more money as well?

Takeaways by Macro Roundup® AI

  1. In late 1999 and early 2000, communications services companies including Verizon, AT&T, WorldCom, Bell South, GTE and Sprint rolled over even as communications equipment stocks such as Cisco, Nokia, Nortel, Qualcomm and Motorola kept rising.
  2. Semiconductor companies show soaring stock prices and free cash flow while hyperscalers, at the front end of the chain, see stock prices stagnate and free cash flow plummet.
  3. Tech sector earnings typically track reasonably well with sectors that rely heavily on technology — finance, manufacturing, media, transport and healthcare — raising the question of who pays for the AI boom if those customers are not making more money.

Related Articles:

  • AI: The ROI Runway Could Be Long Outside the Tech Sector — AI company valuations rest on the assumption that profit margins across non-tech firms will eventually rise, but outside a handful of sectors adoption is slow, creating a divergence between front-loaded valuations and slower cash flows. If the per-use costs of AI models converge toward zero, revenue may be insufficient for all major cloud providers.
  • AI Set to be Largest CapEx Cycle Ever … and Soon Majority Externally Financed — Hyperscaler and data-center investment is tracking toward $1.5tn annually by 2030, which in real terms would exceed the 2005-06 housing boom, the 1990s fiber peak, and the 1882 railroad peak combined. External debt and equity are projected to fund close to 90% of the buildout, making it dependent on credit markets.
  • Cover Ratios for Hyperscaler Bonds Declining — Investor demand for hyperscaler bond deals has weakened, with the cover ratio — dollars of investor orders per dollar of bonds issued — falling from nearly 5x to below 2x. This suggests investors may require wider spreads to absorb additional hyperscaler bond supply.

AI Summary. Alphabet raised its projected annual capital expenditures to $195bn–$205bn from a prior forecast of $190bn, and its shares fell amid concern over artificial intelligence costs. The Google parent reported negative free cash flow of $5.9bn in the quarter, its first negative quarterly cash flow since going public more than two decades ago.

Davey Alba and Julia Love Bloomberg
Date Posted:

Alphabet’s free cash flow was negative (-$5.9B) in Q2 for the first time since going public over 20 years ago, as the firm raised its forecast of 2026 capex to between $195–205B, up from $190B.

Are rising AI spending plans pressuring big tech cash flow?

Core argument: Alphabet raised its projected capital expenditures for the year to $195 billion–$205 billion, up from a prior forecast of $190 billion, and its shares fell amid concern over AI spending costs.

The Google parent company boosted its projected capital expenditures for the year to a range of $195 billion to $205 billion, up from a previous forecast of $190 billion. The company’s quarterly cash flow was negative for the first time since going public more than two decades ago, according to data compiled by Bloomberg. Negative free cash flow came in at $5.9 billion in the period.

Takeaways by Macro Roundup® AI

  1. Alphabet raised its projected capital expenditures for the year to $195 billion–$205 billion, up from a prior forecast of $190 billion, and its shares fell amid concern over AI spending costs.
  2. Alphabet posted negative free cash flow of $5.9 billion in the quarter, its first negative quarterly cash flow since going public more than two decades ago, according to data compiled by Bloomberg.

Related Articles:

  • More Money, More Problems — Public filings indicate AI compute backlogs—contracts for future supply generating no current revenue—exceed $2.2tn, but under reasonable assumptions the net present value of those rental payments after required capital spending equals only about 15% of the backlog. Slowing AI revenue or lost capital-markets access could raise risk premiums on these obligations.
  • Hyperscaler CapEx Sits on Thin Implied Equity — The present value of future rental payments from the roughly $2.2tn artificial intelligence compute backlog, after deducting the capital spending needed to deliver that compute, equals only about 15% of the backlog. That thin implied-equity layer means the contracts hold value only if customers stay creditworthy, capital remains available, and the infrastructure earns competitive returns for years.
  • AI Set to be Largest CapEx Cycle Ever … and Soon Majority Externally Financed — Hyperscaler and data-center investment is tracking toward $1.5tn annually by 2030, which in real terms would exceed the 2005-06 housing boom, the 1990s fiber peak, and the 1882 railroad peak combined. External debt and equity are projected to fund close to 90% of the buildout, making it dependent on credit markets.

Friday, July 24, 2026

AI Summary. Across U.S. states and counties, income and life expectancy rose together from 1980 through 2010, but after 2010 incomes kept rising without commensurate longevity gains, and places diverged in life expectancy. The authors attribute this to a weakened relationship between socioeconomic conditions—income, education, wealth, insurance coverage—and longevity rather than deterioration in those conditions.

Ritikaa Khanna, Rourke O'Brien, Andrew Stokes, Atheendar Venkataramani, et al. National Bureau of Economic Research
Date Posted:

Between 2010 and 2019, states and counties became richer, but did not experience the commensurate longevity gains seen btw 1980 and 2009. This is not “because income, education, wealth, or insurance coverage simply moved in the wrong direction.”

Has rising income stopped translating into longer life expectancy?

Core argument: U.S. Preston curves relating life expectancy to log real per capita income shifted upward across states from 1980 to 2010 and across counties from 2000 to 2010, but stopped shifting upward between 2010 and 2019 even as incomes kept rising.

Figure 1 shows Preston curves relating life expectancy to log real per capita income across U.S. states and counties. Among states, the curves follow the classic pattern from 1980 through 2010: states became richer and longer-lived [as did counties from 2000 to 2010]. That pattern changed after 2010. Between 2010 and 2019, states and counties continued to become richer, but the curves did not shift upward: resource accumulation continued, but commensurate longevity gains did not. The post-2010 period involved both decoupling and divergence: rising income no longer translated into broad longevity improvement, and places became more unequal in their capacity to convert aggregate resources into longer lives. Figure 4 asks a simple counterfactual question: if earlier covariate–life expectancy relationships had persisted, how much life expectancy would later years have achieved, given the actual changes in these covariates? Together, the decomposition results sharpen the central interpretation. The 2010s mortality crisis did not occur because income, education, wealth, or insurance coverage simply moved in the wrong direction. In many respects, aggregate socioeconomic conditions improved. Instead, what changed was the relationship between those conditions and longevity. The United States became less successful at converting collective resources into population-level longevity gains.

Takeaways by Macro Roundup® AI

  1. U.S. Preston curves relating life expectancy to log real per capita income shifted upward across states from 1980 to 2010 and across counties from 2000 to 2010, but stopped shifting upward between 2010 and 2019 even as incomes kept rising.
  2. The post-2010 period combined decoupling and divergence: rising income no longer translated into broad longevity improvement, and places became more unequal in their capacity to convert aggregate resources into longer lives.
  3. Decomposition results indicate the 2010s mortality crisis did not stem from income, education, wealth, or insurance coverage moving in the wrong direction — many aggregate socioeconomic conditions improved — but from a changed relationship between those conditions and longevity.

Related Articles:

  • The Geography of American Exceptionalism — The US population is split evenly among urban, suburban, and rural areas, but 64% of those living at suburban densities reside in prosperous or comfortable ZIP codes, versus 36% in rural and 38% in urban ones. County distress scores correlate strongly with life expectancy, with prosperous counties averaging 5.3 years longer.
  • Comments On: “Accounting For the Widening Mortality Gap Between American Adults With and Without a BA” By Anne Case and Angus Deaton — Selection plausibly accounts for most or all of the widening mortality gap between US adults with and without a bachelor's degree: high school achievement measures have not risen since the early 1970s while the share earning degrees rose, and national youth survey data show non-graduates increasingly negatively selected on aptitude scores.
  • Accounting for the Widening Mortality Gap Between American Adults With and Without a BA — The mortality gap between American adults with and without a four-year college degree widened across three periods—falling mortality for both with larger gains for graduates, then diverging, then rising faster for the less educated during the pandemic—reaching an 8.5-year difference in adult life expectancy. Americans with a degree, treated as a separate country, would rank just below Japan.

AI Summary. An analysis of about 7,000 billionaires over the past 25 years classifies wealth as "uncompetitive" when it derives from inheritance or from sectors such as gambling, construction, defence, and raw materials that often depend on political access. The authors find half of global billionaire wealth now comes from self-made entrepreneurs in competitive sectors, an all-time high.

Economist Staff The Economist
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An Economist analysis of the wealth of 7,000 billionaires finds that over the past decade, the share of billionaire wealth “derived from self-made entrepreneurs in competitive sectors has surged to an all-time high.”

Is a growing share of billionaire wealth self-made in competitive industries?

Core argument: For the first time, half the wealth of the world's billionaires is reasonably fairly earned, with the share derived from self-made entrepreneurs in competitive sectors reaching an all-time high over the past decade.

Drawing on data from Forbes, a magazine, Hurun, a research firm, and Gapminder, a Swedish foundation, we have assembled a list of about 7,000 billionaires from the past 25 years. We call a billionaire’s wealth “uncompetitive” when it mainly comes from industries such as gambling, construction, defence, and raw materials. These sectors often depend on political access. We count inheritors in the “uncompetitive” category. From 2001, when our data begin, to 2014, the uncompetitive share of billionaire wealth rose slightly. Yet over the past decade, the share derived from self-made entrepreneurs in competitive sectors has surged to an all-time high. For the first time, half the wealth of the world’s billionaires is reasonably fairly earned. And since 2021, the total wealth derived from uncompetitive sectors has declined.

Takeaways by Macro Roundup® AI

  1. For the first time, half the wealth of the world's billionaires is reasonably fairly earned, with the share derived from self-made entrepreneurs in competitive sectors reaching an all-time high over the past decade.
  2. A list of about 7,000 billionaires from the past 25 years, assembled from Forbes, Hurun and Gapminder data, classifies wealth as "uncompetitive" when it mainly comes from industries such as gambling, construction, defence and raw materials, which often depend on political access.
  3. The uncompetitive share of billionaire wealth, which counts inheritors, rose slightly from 2001 to 2014, and the total wealth derived from uncompetitive sectors has declined since 2021.

Related Articles:

  • AI is the Democratic Party’s Next Villain — Mentions of billionaires in Democratic fundraising emails quadrupled to a peak above 20% of all emails sent and have held near 15%, making anti-billionaire messaging a mainstay. Anti-AI populism remains far smaller and is entering Democratic rhetoric not as a jobs or safety issue but as something billionaires are building.
  • America’s Support for Capitalism Has Declined Over Last Decade — Just under half of Americans say capitalism is working very or somewhat well, down from 60% about a decade ago, according to a Wall Street Journal-NORC survey. Only 12% say democracy is working very or extremely well, and 16% say average citizens have considerable influence on politics.
  • The Economics of Inequality in High-Wage Economies — Information technology created investment opportunities exceeding the supply of properly trained talent, while trade with low-wage economies, low-skilled immigration, trade deficits, and aging demographics eased constraints on low-skilled labor and risk-averse savings. The author argues this framework, rather than rising cronyism, explains high returns to success, slowing productivity growth, and low interest rates.

AI Summary. The recent pickup in US labor productivity appears driven by higher utilization of existing capital—running factories, server racks, and hotel rooms harder—rather than by firm-level efficiency gains from AI. Adoption remains too thin and the cross-sectional evidence too weak to conclude that AI-driven productivity gains are already widespread.

Ernie Tedeschi Stripe Economics
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AI is showing up in the macro numbers primarily in increased utilization and labor and capital deepening rather than true total factor productivity gains.

Is the recent productivity pickup driven by AI or higher capital utilization?

Core argument: The rise in US labor productivity appears to reflect macroproductivity gains from higher capital utilization — longer runs at existing factories, fuller use of already-paid-for server racks and GPU clusters, and higher occupancy of existing hotel rooms — rather than microproductivity gains.

What appears to be lifting US labor productivity, rather than microproductivity gains, are macroproductivity gains from AI—specifically companies running their existing capital harder. Think longer runs of factories already built, more utilization of server racks and GPU clusters already paid for, and more occupancy of existing hotel rooms. Economists call this “capital intensity” or “utilization.” Higher capital utilization represents real economic gains, but it’s not the same as microproductivity. This is not the same as “capital deepening” (growth in measured productivity from expanding capital supply—for example, through capital investment). What the current numbers do not yet support is the claim that this transmission is already well underway at the aggregate level: adoption is too thin, the cross-sectional signal is too weak, downstream bottlenecks remain, and the productivity pickup is too attributable to utilization to be confident in that conclusion. Instead, the acceleration we’re seeing so far is largely a result of companies trying to meet the demand for AI capacity by pushing the limits of their existing infrastructure.

Takeaways by Macro Roundup® AI

  1. The rise in US labor productivity appears to reflect macroproductivity gains from higher capital utilization — longer runs at existing factories, fuller use of already-paid-for server racks and GPU clusters, and higher occupancy of existing hotel rooms — rather than microproductivity gains.
  2. Higher capital utilization represents real economic gains but differs from both microproductivity and capital deepening, the growth in measured productivity that comes from expanding capital supply through investment.
  3. The current numbers do not yet support the claim that AI's productivity transmission is well underway at the aggregate level, given thin adoption, a weak cross-sectional signal, downstream bottlenecks, and a pickup largely attributable to utilization.

Related Articles:

  • Have We Entered an Era of High Productivity Growth? — Labor productivity has grown solidly while the San Francisco Federal Reserve's total factor productivity index, which adjusts for the equipment and technology workers use, has grown modestly, suggesting gains so far reflect better tools rather than broader advances. A regime-switching model puts the probability of a high-productivity regime at 57% using labor productivity and 21% using total factor productivity.
  • America Is Experiencing A Productivity Miracle — America's post-2019 productivity acceleration came largely from professional services and management—about 10% of output and heavy users rather than producers of new technology—and from oil and gas. The information industry grew productivity around 6% annually, no faster than its 2000-19 average, with its output share steady at 5.3–5.5%.
  • AI and the Pitfalls of Innovation — The post-World War II productivity boom, which doubled real wages and living standards in a generation, came largely from technologies that already existed decades earlier rather than radical new inventions. It was followed by sluggish productivity growth from 1973 to 1995, then an information-technology boom that lasted roughly a decade before measured productivity growth slowed again.

AI Summary. Aerospace leads all US manufacturing industries in job growth and posted the largest manufacturing trade surplus at $116.7bn, supported by strong commercial airline demand and a record-high order backlog. Electrical equipment and components employment has also grown, with dedicated data centers more than doubling their real consumption of electrical equipment and components.

Jiaxin (Jason) He Agglomerations
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The US has lost 300,000 manufacturing jobs since their recent peak in Q2 2023. Two subsectors have seen strong job growth: aerospace manufacturing added 43,000 jobs, and electrical equipment, cable and battery manufacturing added 36,200.

Which US manufacturing industries are adding jobs despite the overall decline?

Core argument: Aerospace leads all U.S. manufacturing industries in job growth since 2023 and posted the largest nominal trade surplus of any manufacturing industry last year, at $116.7 billion.

Aerospace leads all manufacturing industries in job growth since 2023. Last year it also posted the largest nominal trade surplus of any manufacturing industry, at $116.7 billion. Driving the growth has been strong commercial and passenger airline demand, including a record-high order backlog. Boeing, the world’s leading airplane manufacturer, returned to profitability and high delivery volumes in 2025. The employment surge in the electrical components subsector tells a different story. It owes its strength to two industries: electrical equipment manufacturing and batteries, wires, cables, and miscellaneous electrical components. These are among the industries that bore the brunt of the China Shock and suffered acute decline in the 2000s. Green energy and electric vehicles likely contributed to their initial recovery, raising demand for batteries and electrical infrastructure. But their continued growth even after the cancellation of EV subsidies in 2025 points to a different, potent, and entirely unsurprising driver: Artificial Intelligence. AI’s manufacturing footprint runs the length of the supply chain. Dedicated data centers more than doubled their real consumption of electrical equipment and components from 2019 to 2024.

Takeaways by Macro Roundup® AI

  1. Aerospace leads all U.S. manufacturing industries in job growth since 2023 and posted the largest nominal trade surplus of any manufacturing industry last year, at $116.7 billion.
  2. Strong commercial and passenger airline demand, including a record-high order backlog, has driven aerospace growth, and Boeing returned to profitability and high delivery volumes in 2025.
  3. Employment growth in electrical equipment manufacturing and in batteries, wires, cables, and miscellaneous electrical components continued after the 2025 cancellation of EV subsidies, pointing to artificial intelligence as a driver, with dedicated data centers more than doubling real consumption of electrical equipment and components from 2019 to 2024.

Related Articles:

  • Does the Import Invasion Explain the Mysterious Disappearance of Productivity Growth in U.S. Manufacturing? — The authors trace a channel from rising imports to lower domestic sales, employment, plant closures, profits, capacity utilization, and reduced investment in fixed capital and research. They note the computer and electronic products industry, which contributed most to the US manufacturing productivity slowdown, offshored production to Asia, reaching 84% import penetration.
  • Midyear Outlook — Tech-related investment rose 30% from year-ago levels while all other capital spending fell, a divergence the author argues reflects the AI boom bidding away scarce physical inputs such as electricians, grid capacity, metals, and construction labor. With hyperscalers' free cash flow now exhausted, over 80% of projected capital spending in coming years will require external financing.
  • What Would It Take to Bring Back US Manufacturing? Part 1: America’s Structural Headwinds — The US imports roughly 40% of the goods it consumes, directly and through imported inputs, with labor-intensive goods like apparel almost entirely imported. The authors argue America's main comparative disadvantage is its cost of production, driven mainly by labor costs, with real estate costs and a strong dollar also contributing.

AI Summary. The average statutory US tariff rate stands at 11.1% and, under current law including scheduled increases, is projected to reach 11.8% by year-end. The authors estimate current-law tariffs will raise consumer prices by about 0.7% and generate roughly $1.9tn over ten years, with revenue somewhat lower after accounting for tariffs' negative effect on economic output.

John Iselin Yale University
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The US’s average statutory tariff rate stands at 11.1% and is set to rise to 11.8% by the end of year. John Iselin projects that the current regime will raise ~$1.9T in revenue over 10 years.

How much will current tariffs raise prices and revenue?

Core argument: The average U.S. statutory tariff rate stands at 11.1%, down about 0.3pp after Section 122 tariffs expired and were replaced with new tariffs under Section 301.

As of July 24th, the average statutory tariff rate stands at 11.1% after falling by about 0.3pp when Section 122 tariffs expired and were replaced with new tariffs under Section 301. Under current law, which includes several scheduled tariff increases in the coming months, this figure is set to be 11.8% by the end of this year. We estimate the ultimate consumer price impact of current-law tariff policy to be about 0.7%. Over the next ten years, we estimate that current-law tariffs will raise about $1.9 trillion. These numbers are somewhat lower, though, after taking into account the negative impact of tariffs on GDP.

Takeaways by Macro Roundup® AI

  1. The average U.S. statutory tariff rate stands at 11.1%, down about 0.3pp after Section 122 tariffs expired and were replaced with new tariffs under Section 301.
  2. Under current law, which includes several scheduled tariff increases in the coming months, the average statutory tariff rate is set to reach 11.8% by the end of this year.
  3. Current-law tariff policy is estimated to raise consumer prices by about 0.7% and to bring in about $1.9 trillion over ten years, with both figures somewhat lower after accounting for the negative impact of tariffs on GDP.

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