Edward Conard

Top Ten New York Times Bestselling Author

  • “…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “A full-throated defense of economic dynamism.” - The Wall Street Journal
  • “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
  • “Unintended Consequences is full of substance, it is one of the must-read books of the year, and once I finish it I will be giving it a second read through right away.” - Tyler Cowen, Professor, George Mason University
  • “…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
  • “Unintended Consequences represents the most cogent and persuasive analysis of the Financial Crisis to date.” - Andrei Shleifer, 1999 John Bates Clark Medal Winner
  • “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
  • “…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
  • “…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
  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
Upside of Inequality Oxford Unintended Consequences

Macro Roundup


Monday, August 17, 2026

AI Summary. Employment for workers aged 22–25 in the occupations most exposed to artificial intelligence has declined roughly 18pp relative to less-exposed peers, with no comparable decline in other age groups. The authors find this operates mainly through reduced hiring, concentrated where AI substitutes for human tasks rather than complements them.

Erik Brynjolfsson, Bharat Chandar and Ruyu Chen Stanford University
Date Posted:

If employment of the most AI-exposed workers aged 22–25 had kept pace with that of their less-exposed peers since January 2018, their employment would be 15% higher than at the July 2025 data vintage observed in our previous study, and 19% higher as of June 2026.

Is AI reducing hiring for young workers in exposed occupations?

Core argument: Event study estimates for workers aged 22–25 show employment in the most AI-exposed occupation quintile declining roughly 18pp relative to the least-exposed quintile by mid-2026, with no controls and under four control sets.

Figure 4 plots occupation event study estimates for the most exposed quintile (relative to quintile 1) for workers aged 22–25, tracing the long-difference coefficient as the endpoint of the difference is rolled forward month by month, with no controls and under four control sets: occupational interest rate exposure, education (college share), a work-from-home measure, and all three jointly. The relative decline grows to roughly 18pp by mid-2026. Other age groups show no comparable decline. This divergence, documented in prior versions of this study, has continued to widen; the shortfall was 15% below where it would have been had it kept pace with that of their less-exposed peers at the July 2025 data vintage, and 19% as of June 2026. It operates primarily through reduced hiring rather than increased separations, and declines are concentrated in occupations where AI usage substitutes for human tasks; complementary usage is associated with flat or rising employment.

Takeaways by Macro Roundup® AI

  1. Event study estimates for workers aged 22–25 show employment in the most AI-exposed occupation quintile declining roughly 18pp relative to the least-exposed quintile by mid-2026, with no controls and under four control sets.
  2. The shortfall for workers aged 22–25 relative to their less-exposed peers widened from 15% at the July 2025 data vintage to 19% as of June 2026, while other age groups show no comparable decline.
  3. The decline operates primarily through reduced hiring rather than increased separations and is concentrated in occupations where AI usage substitutes for human tasks, while complementary AI usage is associated with flat or rising employment.

Related Articles:

  • Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence — Using payroll records covering millions of US workers, the authors find employment for workers aged 22–25 in occupations most exposed to artificial intelligence, such as software developers and customer service representatives, declined 6%, while employment for older workers in those occupations rose 6–9%. Employment in less-exposed occupations grew comparably across age groups.
  • The Impact of AI on the U.S. Labor Market — Using observed AI usage data across 321 U.S. occupations, the authors estimate that highly exposed occupations saw a 6.7% decline in real wage growth with no detectable employment effects. The estimated declines are concentrated among the lowest earners, with service workers down 24.3% and the bottom wage quartile down 10.7%, while top earners show no significant effect.
  • You’re (Not) Hired: Artificial Intelligence and Early Career Hiring In The Quarterly Workforce Indicators — Employment among workers aged 22–24 in the most artificial-intelligence-exposed quintile of industry-state employers fell 15.2%, while other exposure quintiles were flat or posted modest gains. The decline is not generally observed among older workers; the 25-34 group's drop was less than one-third as large in percentage terms.

AI Summary. Artificial intelligence models are converging in capability and price, making switching easier and margins harder to defend, with US labs cutting prices on lower- and mid-tier offerings while keeping frontier models expensive. An 80% annual price decline requires 400% unit growth just to hold revenue flat, straining firms financing enormous capital spending.

Paul Kedrosky Applied Complexity
Date Posted:

US labs have “conceded commoditization” for models behind the frontier, while still trying to “preserve scarcity rents” for their frontier models. Kedrosky warns “this market-ceding tactic has failed in most markets historically, especially when competing with China.”

Can AI firms defend margins as model prices converge?

Core argument: An 80% year-over-year decline in AI model prices requires 400% unit growth simply to hold revenue flat, and considerably more to generate growth.

Good-enough models are converging in capability and price. That makes model switching easier and durable margins harder to defend. US labs are cutting lower- and mid-tier offerings while keeping their best models expensive. They are conceding commoditization below the frontier while trying to preserve scarcity rents at the top. This market-ceding tactic has failed in most markets historically, especially when competing with China. This is irreconcilable with trillion-dollar valuations and high & growing debt loads. OpenAI and Anthropic must finance enormous capital spending while competition pushes the models likely to generate the most volume toward commodity pricing. An 80% year-over-year price decline requires 400% unit growth just to stand still, and much more to deliver overall growth.

Takeaways by Macro Roundup® AI

  1. An 80% year-over-year decline in AI model prices requires 400% unit growth simply to hold revenue flat, and considerably more to generate growth.
  2. US AI labs are cutting prices on lower- and mid-tier models while keeping frontier models expensive, conceding commoditization below the frontier while trying to preserve scarcity rents at the top.
  3. OpenAI and Anthropic must finance enormous capital spending as competition pushes the models likely to generate the most volume toward commodity pricing, a position irreconcilable with trillion-dollar valuations and high and growing debt loads.

Related Articles:

  • OpenAI and Anthropic In Price War as Chinese AI Rivals Gain Ground — OpenAI cut its GPT-5.6 Luna prices by 80% and Anthropic launched Opus 5 at half the price of its Fable 5 model while cancelling a planned Sonnet 5 price increase. Headline token prices are not directly comparable, since more capable models can finish tasks using fewer tokens or attempts.
  • Who’s Afraid of Chinese Models? — Demand for frontier artificial intelligence models exceeds supply because computing capacity is scarce, allowing chip suppliers and their customers to resell compute at high margins. The author argues alarm over Chinese models is overblown, expecting the inference market to grow faster than training costs so labs can thrive at lower prices.
  • The AI Trade Is Losing One of Its Key Signals — An index blending artificial intelligence token prices and usage is ambiguous when it softens, since token prices have fallen more than 90% since 2023 while total spending has roughly doubled over the past year. Allianz Research measures a nearly 46% growth gap between artificial intelligence investment and sales, wider than the 32% divergence during the 2001 telecom bust.

AI Summary. Nine large technology companies disclosed roughly $3tn of off-balance-sheet commitments, mostly tied to artificial intelligence, in the footnotes of their most recent securities filings. These obligations are growing faster than traditional capital spending, which totaled about $600bn over the companies' latest reported year, and were roughly 3x what they owe on outstanding leases and long-term borrowings.

Peter Rudegeair and Peter Santilli Wall Street Journal
Date Posted:
Is Database:
Database

A WSJ analysis finds 9 firms involved in the data center buildout have ~$3T in off-balance-sheet commitments largely tied to AI infrastructure. The growth in such obligations has outpaced the firms’ capex growth over the last year.

Do off-balance-sheet commitments understate how much Big Tech spends on AI?

Core argument: Nine top tech companies carried roughly $3 trillion of off-balance-sheet commitments mostly tied to AI, according to a Wall Street Journal analysis of footnotes in their most recent securities filings.

Nine top tech companies had some $3 trillion of off-balance-sheet commitments mostly related to AI, according to a Wall Street Journal analysis of footnotes in their most recent securities filings. Those obligations are growing faster than traditional “capex,” which totaled about $600 billion over the past year they reported, and were about triple what the companies owe under their outstanding leases and long-term borrowings.

Takeaways by Macro Roundup® AI

  1. Nine top tech companies carried roughly $3 trillion of off-balance-sheet commitments mostly tied to AI, according to a Wall Street Journal analysis of footnotes in their most recent securities filings.
  2. The off-balance-sheet obligations were about triple what the nine companies owe under their outstanding leases and long-term borrowings, according to the Wall Street Journal analysis.
  3. The commitments are growing faster than traditional capital expenditures, which totaled about $600 billion over the past year the nine companies reported.

Related Articles:

  • The Hyperscalers’ Exploding ‘Purchase Commitments’ Reach $1.5tn — Goldman Sachs analysts tallied $1.5tn of lease commitments in the footnotes of large cloud computing companies' regulatory filings, of which $1tn had not yet commenced and so did not appear as conventional liabilities. The analysts noted this treatment can understate leverage and future liquidity needs as those obligations are eventually recognized.
  • The Market Is Asking Questions — Markets are weighing whether the trillions committed upfront to AI data centers, chips, and power will generate returns above the cost of capital before those assets depreciate. With the buildout increasingly funded by debt rather than internal cash flow and borrowing spreads widening, financing costs could rise until new data-center spending no longer clears its return hurdle.
  • Big Tech Credit Risks Rise Sharply As AI Spending Soars — The cost of insuring debt against default for Oracle, SpaceX, Alphabet, Amazon, Meta, Broadcom, and Nvidia has reached record highs, with Oracle's five-year credit protection quoted at 215 basis points versus 144 earlier in the year. S&P Global Ratings cut Oracle to one notch above junk, citing an uncertain path to profitability amid massive AI investments.

AI Summary. Investment-grade companies have sold nearly $1.5tn of bonds, up 36% from a year earlier, and some investors have sold US Treasuries to buy higher-yielding corporate debt. Bank of America economists estimate the surge in corporate-debt sales, along with more mortgage-backed securities issuance, pushed 10-year rates up by about 0.3 percentage point.

Davide Barbuscia, Ye Xie, and Michael MacKenzie Bloomberg
Date Posted:
Is Database:
Database

US investment-grade debt issuance is up 36% y/y to ~$1.5T. A BofA analysis argues the surge in this debt, alongside increased MBS issuance, has driven up the 10-year Treasury yield by ~.3pp this year.

Is a surge in corporate bond issuance raising Treasury yields?

Core argument: Investment-grade companies have sold nearly $1.5 trillion of bonds in 2026, a 36% jump from a year earlier, putting them on pace to eclipse the record set in 2020, when businesses rushed to seize on near-zero interest rates.

[In 2026] investment-grade companies have sold nearly $1.5 trillion of bonds, a 36% jump from a year earlier, putting them on pace to eclipse the record from 2020, when businesses were rushing to seize on near-zero interest rates. As the slew of longer-dated bonds keeps hitting the market, some investors have sold US Treasuries. That freed up cash to buy higher-yielding debt from immensely profitable companies like Alphabet, whose recent 30-year debt was issued at a yield of nearly 6.4%, 1.15 percentage points more than comparable Treasuries. Quantifying the precise impact on interest rates is difficult and estimates vary. Bank of America economists said the AI borrowing is “potentially crowding out long-end Treasury demand” and has played a major role in the rise of bond yields. They estimated that the surge in corporate-debt sales — along with a rise in issuance of mortgage-backed securities — pushed up 10-year rates by about 0.3 percentage point this year.

Takeaways by Macro Roundup® AI

  1. Investment-grade companies have sold nearly $1.5 trillion of bonds in 2026, a 36% jump from a year earlier, putting them on pace to eclipse the record set in 2020, when businesses rushed to seize on near-zero interest rates.
  2. Bank of America economists estimated that the surge in corporate-debt sales, along with a rise in mortgage-backed securities issuance, pushed 10-year rates up by about 0.3 percentage point this year, and said AI borrowing is "potentially crowding out long-end Treasury demand.".
  3. Alphabet issued 30-year debt at a yield of nearly 6.4%, 1.15 percentage points above comparable Treasuries, as some investors sold US Treasuries to free up cash for higher-yielding corporate debt.

Related Articles:

  • Costliest US Bond Sale Since ‘01 Is Investor Warning to Bessent — The US government sold $25bn of 30-year bonds at a yield of 5.216%, the highest at such a sale since 2001, reflecting investors' demand for compensation to finance a growing deficit. The yield came in slightly above the prevailing market level before the bidding deadline, indicating demand modestly lagged expectations.
  • U.S. Treasury Investors Are Long in AI — The authors find U.S. government debt has a large positive productivity beta, because the tax code makes federal revenue highly sensitive to productivity growth while spending commitments change little, leaving bondholders implicitly long productivity growth. They argue greater market confidence in sustained productivity gains from AI could substantially reprice government debt, lowering nominal yields.
  • The Market Is Asking Questions — Markets are weighing whether the trillions committed upfront to AI data centers, chips, and power will generate returns above the cost of capital before those assets depreciate. With the buildout increasingly funded by debt rather than internal cash flow and borrowing spreads widening, financing costs could rise until new data-center spending no longer clears its return hurdle.

AI Summary. A proxy for strain at the long end of government bond markets — the gap between the 10-year yield expected in 10 years' time and today's 10-year yield, scored against its own history — shows Japan as an outlier among the G10 economies.

Robin Brooks Robin Brooks Substack
Date Posted:

Citing the difference between Japan’s artificially depressed 10-year yield and its 10y10y fwd rate, Brooks notes “Japan’s ‘shadow yield’ is much higher than observed yields, which means Japan is – de facto – in a debt crisis.”

Which advanced economy shows the most unusual long-end bond market strain?

Core argument: Japan stands out among G10 economies on a measure of long-end yield curve distortion, defined as the gap between the 10y10y forward rate and the 10-year yield, demeaned and divided by its historical standard deviation.

The blue lines are my proxy for how “broken” yield curves are at the long end. This is the difference between the 10y10y forward [which is what markets price for the 10-year yield in 10 years’ time] and 10-year yield. I demean this difference and divide it by its historical standard deviation. The resulting z-score measures how unusual the slope of the yield curve is at the long end relative to history. The black line in each chart is the median across all G10 z-scores. The gray shaded area is a two standard deviation confidence interval around the black line. If you’re outside this area, something very worrying is going on. Japan sticks out like a sore thumb on this metric.

Takeaways by Macro Roundup® AI

  1. Japan stands out among G10 economies on a measure of long-end yield curve distortion, defined as the gap between the 10y10y forward rate and the 10-year yield, demeaned and divided by its historical standard deviation.
  2. The z-score framework compares each country's long-end curve slope to its own history, with a two standard deviation confidence interval around the median G10 z-score marking the band beyond which conditions are described as very worrying.

Related Articles:

  • Shadow Government Bond Yields in the G10 — 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.
  • 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%.
  • Costliest US Bond Sale Since ‘01 Is Investor Warning to Bessent — The US government sold $25bn of 30-year bonds at a yield of 5.216%, the highest at such a sale since 2001, reflecting investors' demand for compensation to finance a growing deficit. The yield came in slightly above the prevailing market level before the bidding deadline, indicating demand modestly lagged expectations.

AI Summary. Tariff refunds are boosting US corporate earnings and economic growth, with the Atlanta Federal Reserve's GDPNow tracker pointing to 4.3% growth this quarter, of which roughly 0.2 percentage points is estimated to come from tariff refunds.

Torsten Sløk Apollo
Date Posted:

Net tariff receipts turned negative in May as refunds outpaced custom duties. The Atlanta Fed projects GDP growth of 4.3% at an annual rate in Q3, of which Sløk estimates ~.2pp will be driven by tariff refunds.

Are tariff refunds adding to corporate earnings and GDP growth?

Core argument: The Atlanta Fed's GDPNow points to 4.3% US growth this quarter, of which roughly 0.2 percentage points are estimated to come from tariff refunds.

The tailwinds behind the US economy are not just AI spending, the industrial renaissance and the One Big Beautiful Bill. Tariff refunds have now joined the list. Not only are tariff refunds boosting corporate earnings, they are also boosting GDP growth. The Atlanta Fed's GDPNow currently points to 4.3% growth this quarter, of which we estimate roughly 0.2 percentage points come from tariff refunds. The bottom line is that the US economy continues to be supported by a growing set of tailwinds. Rates will stay higher for longer.

Takeaways by Macro Roundup® AI

  1. The Atlanta Fed's GDPNow points to 4.3% US growth this quarter, of which roughly 0.2 percentage points are estimated to come from tariff refunds.
  2. Tariff refunds are boosting both corporate earnings and GDP growth, joining AI spending, the industrial renaissance and the One Big Beautiful Bill as tailwinds behind the US economy.
  3. With the US economy supported by a growing set of tailwinds, rates are expected to stay higher for longer.

Related Articles:

  • The State of U.S. Tariffs July 24, 2026 — 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.
  • The AI Capex Boom Is Building Twice as Fast as the Housing Boom — Data-center capital spending is projected to rise from 1.4% of GDP to 3.1% within two years, about 0.85 percentage points a year, roughly twice the pace of the housing boom's fastest stretch (0.5 points a year) and far above telecom fiber's (about 0.15). In level, the buildout exceeds twice the fiber peak but remains under half the housing peak.
  • The AI Investment Race — Technology-driven investment booms have historically ended in sharp corrections with wider economic fallout, and the current artificial intelligence build-out is on track to outgrow every previous episode within three years measured from its pre-boom trough. The expansion is increasingly funded by borrowing through bonds, special purpose vehicles and non-bank lenders, alongside circular financing arrangements that create interconnectedness among firms.

Tuesday, August 18, 2026

AI Summary. The yield on 30-year US Treasury bonds rose to as high as 5.34%, its highest level since 2007, amid investor concern over rising government spending, heavy sales of long-dated bonds, and inflation that has run above the Federal Reserve's target for five years. Long-term yields also rose in Canada and Europe.

Greg Ritchie Bloomberg
Date Posted:

The 30-year Treasury yield climbed more than 5bps to as high as 5.34%, its highest level since 2007.

Are rising government spending and inflation pushing long-term yields higher?

Core argument: The 30-year US Treasury yield rose five basis points to as high as 5.34%, surpassing the prior month's high and reaching its highest level since 2007.

The yield on 30-year US Treasuries hit the highest in almost two decades, reflecting investor angst over surging government spending, a flood of long-dated bond sales, and inflation that has been stuck over the Federal Reserve’s target for the past five years. The interest rate on the so-called long bond rose five basis points to as high as 5.34%, surpassing a high from last month to reach the loftiest since 2007. The move was echoed in the Canadian bond market, where the yield on 30-year securities rose to the highest since 2010, as well as in those in Europe.

Takeaways by Macro Roundup® AI

  1. The 30-year US Treasury yield rose five basis points to as high as 5.34%, surpassing the prior month's high and reaching its highest level since 2007.
  2. The long-bond selloff reflects investor angst over surging government spending, a flood of long-dated bond sales, and inflation stuck above the Federal Reserve's target for the past five years.
  3. Canada's 30-year bond yield rose to its highest since 2010, with European long-dated yields echoing the move.

Related Articles:

  • AI Is Driving Up Treasury Yields: ‘It Just Touches Everything’ — Investment-grade companies have sold nearly $1.5tn of bonds, up 36% from a year earlier, and some investors have sold US Treasuries to buy higher-yielding corporate debt. Bank of America economists estimate the surge in corporate-debt sales, along with more mortgage-backed securities issuance, pushed 10-year rates up by about 0.3 percentage point.
  • U.S. Treasury Investors Are Long in AI — The authors find U.S. government debt has a large positive productivity beta, because the tax code makes federal revenue highly sensitive to productivity growth while spending commitments change little, leaving bondholders implicitly long productivity growth. They argue greater market confidence in sustained productivity gains from AI could substantially reprice government debt, lowering nominal yields.
  • 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 proposes a medium-run natural interest rate measure that averages short-run projections with more weight on fluctuations up to five years ahead, and estimates the current medium-run real natural rate at about 1.5%. With the federal funds target at 3.5–3.75% and inflation near 3%, the implied real rate of 0.5–0.75% suggests a somewhat accommodative policy stance.

Vasco Cúrdia Federal Reserve Bank of San Francisco
Date Posted:

Curdia estimates the US medium-run real natural rate over the next 2 years is ~1.5%, up from .84% in Q4 2019. With FFR at ~3.5 to 3.75%, and inflation at ~3%, the real rate is .5 to .75bps below the natural rate, and policy is accommodative.

Is current monetary policy accommodative relative to the medium-run natural rate?

Core argument: The medium-run real natural rate of interest is estimated at approximately 1.5%, above the implied real federal funds rate of 0.5–0.75% derived from a 3.5–3.75% target and 3% inflation, suggesting a somewhat accommodative policy stance.

Short-run estimates of the natural rate track recent economic developments closely but are notoriously volatile and imprecise. Long-run estimates that reflect the expected natural rate after temporary economic pressures fade are more stable but provide limited guidance for current policy decisions. I propose a medium-run measure that averages short-run natural rate projections into the future, putting more weight on fluctuations up to five years ahead. The medium-run natural rate estimate captures the lowest points of economic activity, known as business cycle troughs, in the early 2000s, the 2008 financial crisis, and the 2020 pandemic. During the 2008 financial crisis,the medium-run natural rate fell nearly 5 percentage points in the first year, driven primarily by persistent disruptions from the financial crisis. The current estimate of the medium-run real natural rate is approximately 1.5%. With the federal funds rate target at 3.5 to 3.75% and inflation hovering at 3%, the implied real rate of 0.5 to 0.75% is below the natural rate, which suggests a somewhat accommodative monetary policy stance.

Takeaways by Macro Roundup® AI

  1. The medium-run real natural rate of interest is estimated at approximately 1.5%, above the implied real federal funds rate of 0.5–0.75% derived from a 3.5–3.75% target and 3% inflation, suggesting a somewhat accommodative policy stance.
  2. The proposed medium-run measure averages short-run natural rate projections into the future with more weight on fluctuations up to five years ahead, capturing business cycle troughs in the early 2000s, the 2008 financial crisis, and the 2020 pandemic.
  3. During the 2008 financial crisis, the medium-run natural rate fell nearly 5 percentage points in the first year, driven primarily by persistent disruptions from the crisis.

Related Articles:

  • The Post‑Pandemic Global R* — Using an econometric model extracting the common trend in real interest rates across advanced economies, the authors estimate that the global neutral real interest rate fell about 3.5 percentage points from 1990 to 2019, then rose about 1 percentage point after COVID, leaving global and U.S. estimates near 0.5%.
  • Recent Movements in R*: The Most Important Interest Rate That You Have Never Heard Of — The natural rate of interest — the inflation-adjusted rate prevailing when the economy grows at trend with inflation at target — is unobservable and uncertain, but most estimates have risen and sit above pre-pandemic levels. The data lean toward a long-run natural rate higher than pre-pandemic and possibly above Federal Reserve projections.
  • The Race Between Asset Supply and Asset Demand — The authors estimate that demand for assets has outpaced supply by roughly 400% of GDP versus 100%, making rising asset demand the leading contributor to falling interest rates. They project that debt sustainability requires fiscal consolidation exceeding 10% of GDP, though debt could reach 250% of GDP without raising interest rates.

AI Summary. Long-term forward bond yields, which began rising when central banks raised rates to counter pandemic-era inflation, are now being driven by risk and term premia rather than expectations for monetary policy. Countries with high debt and political dysfunction, including Japan, the UK, and France, are affected more than low-debt countries such as Switzerland.

Robin Brooks Robin Brooks Substack
Date Posted:

Contrasting high-debt countries, on the one hand, with Switzerland and a few other low-debt outliers on the other, Brooks infers that the rise in 10y10y forward yields globally is “all about risk and term premia, not monetary policy expectations.”

Are rising long-term yields now driven by risk premia rather than policy?

Core argument: Long-term global yields, measured by 10y10y forwards, began rising in 2022 as central banks hiked to rein in COVID inflation, but the increase has shifted from front-end-driven to long-term yields rising autonomously.

The chart shows what’s going on with 10y10y forward yields. Long-term yields started rising in 2022 when central banks were hiking to rein in COVID inflation. That rise was driven by the front end of the yield curve, but that’s morphed into long-term yields rising autonomously. What’s happening now is all about risk and term premia, not monetary policy expectations. Countries with lots of debt and political dysfunction - like Japan, the UK, and France - are being hit harder than others. Initial conditions turn out to be massively important. The only outlier in all this is Switzerland and a handful of other low-debt countries. The benefits of responsible fiscal policy and things like Germany’s debt brake (that’s been hollowed out in recent years) have never been more apparent.

Takeaways by Macro Roundup® AI

  1. Long-term global yields, measured by 10y10y forwards, began rising in 2022 as central banks hiked to rein in COVID inflation, but the increase has shifted from front-end-driven to long-term yields rising autonomously.
  2. The rise in long-term yields now reflects risk and term premia rather than monetary policy expectations, with heavily indebted, politically dysfunctional countries such as Japan, the UK, and France hit harder than others.
  3. Switzerland and a handful of other low-debt countries stand as outliers to the global rise in long-term yields, pointing to the benefits of responsible fiscal policy and measures like Germany's debt brake, which has been hollowed out in recent years.

Related Articles:

  • The Coming Great Repression? — Across countries and over time, higher public debt burdens are associated with lower relative returns on government bonds, the opposite of standard risk-premium predictions. The authors estimate financial repression—making banks captive bond buyers and loading them with low-return reserves—contributed 90.7 percentage points of GDP to Britain's postwar debt reduction, versus 20.3 from surprise inflation.
  • 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.
  • 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%.

AI Summary. American Time Use Survey diary data show that on an average day in 2023, one in 10 Americans aged 23 to 29 spent no time interacting face to face with another person. The most isolated group's time around others fell from roughly a third of the day to essentially zero.

John Burn-Murdoch Financial Times
Date Posted:

According to American Time Use Survey data, on a three-year rolling average basis in 2023, 10% of Americans aged between 23 and 29 reported spending zero time interacting face-to-face with another person.

How isolated have young Americans become in daily face-to-face contact?

Core argument: On an average day in 2023, one in 10 Americans aged 23–29 spent no time at all interacting face to face with another person, according to American Time Use Survey diary data.

While exploring data from the American Time Use Survey, which draws on detailed diaries to track how and with whom people spend every minute of a randomly selected day, I came across the following: on an average day in 2023, one in 10 young Americans aged between 23 and 29 did not spend a single minute interacting face to face with another person. The most socially connected quarter of young people used to spend 87% of their day with other people, whether family, friends, co-workers, or others. During the pandemic, that dipped to 78%, but it has rebounded to 83%. The most isolated segment used to still spend roughly a third of their day around others, but that has now collapsed to essentially zero.

Takeaways by Macro Roundup® AI

  1. On an average day in 2023, one in 10 Americans aged 23–29 spent no time at all interacting face to face with another person, according to American Time Use Survey diary data.
  2. The most isolated segment of young Americans once spent roughly a third of the day around other people, a share that has collapsed to essentially zero.
  3. The most socially connected quarter of young Americans spent 87% of the day with others before the pandemic, dipping to 78% during it and rebounding to 83%.

Related Articles:

AI Summary. Business records show revenue scale varies widely across industries: of 21,622 new-car dealerships with employees, more than half are estimated to take in at least $5m a year, versus about a quarter of 4,742 beer, wine, and spirits wholesalers and roughly 4% of 780,257 construction businesses.

Owen Zidar and Eric Zwick The Everywhere Millionaire
Date Posted:

Owen Zidar and Eric Zwick estimate that as of 2021, 4,075 American auto dealers, 479 beverage distributors, and 6,418 contractors generated at least $25mm in annual revenue

Which local businesses most often generate millions in revenue?

Core argument: Of the 21,622 new-car dealerships with employees in the Census's County Business Patterns, an estimated more than half take in at least $5 million a year; Dun & Bradstreet records show 13,826 dealer locations above $5 million and 4,114 above $25 million.

There are 21,622 new-car dealerships with employees, according to the Census’s County Business Patterns. We estimate that more than half of them take in at least $5 million a year; in the Dun & Bradstreet business records, 13,826 dealer locations clear $5 million and 4,114 clear $25 million. There are 4,742 beer, wine, and spirits wholesalers with employees, according to the Census’s County Business Patterns. We estimate that about a quarter of them take in at least $5 million a year; in the Dun & Bradstreet business records, 1,232 distributor locations clear $5 million and 491 clear $25 million. There are 780,257 construction businesses with employees, according to the Census’s County Business Patterns—building, heavy civil, and specialty trade contractors. Anyone with a truck and a license can enter. We estimate that only about 4% of them take in at least $5 million a year. In the Dun & Bradstreet business records, 33,752 contractor locations clear $5 million and 6,525 clear $25 million.

Takeaways by Macro Roundup® AI

  1. Of the 21,622 new-car dealerships with employees in the Census's County Business Patterns, an estimated more than half take in at least $5 million a year; Dun & Bradstreet records show 13,826 dealer locations above $5 million and 4,114 above $25 million.
  2. Among the 780,257 construction businesses with employees in the Census's County Business Patterns—building, heavy civil, and specialty trade contractors—an estimated 4% take in at least $5 million a year; Dun & Bradstreet records show 33,752 contractor locations above $5 million and 6,525 above $25 million.
  3. Of the 4,742 beer, wine, and spirits wholesalers with employees in the Census's County Business Patterns, an estimated quarter take in at least $5 million a year; Dun & Bradstreet records show 1,232 distributor locations above $5 million and 491 above $25 million.

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