Edward Conard

Top Ten New York Times Bestselling Author

  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “…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
  • “Unintended Consequences offers deep and well-argued analyses on almost every issue.” - The New York Times
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
  • “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
  • “…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “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
  • “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 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 represents the most cogent and persuasive analysis of the Financial Crisis to date.” - Andrei Shleifer, 1999 John Bates Clark Medal Winner
  • “…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
Upside of Inequality Oxford Unintended Consequences

Macro Roundup


Tuesday, July 7, 2026

AI Summary. A 1pp lower birth rate 20 years earlier is associated with 22 log points higher output per worker 20 years later and 29 log points higher after 40 years, about 0.73pp faster annual growth. The authors find lower birth rates are also associated with more labor-saving patents, larger high-tech shares, and higher productivity growth.

Daron Acemoglu, David Autor, Keelan Beirne and Andrew Scott National Bureau of Economic Research
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Cross-country evidence from 1950 to 2020 shows that a 1pp lower birth rate is associated with 22 log points (~25%) higher GDP per worker 40 years later and 29 log points (~44%) after 60 years, likely driven by labor-saving technical change.

Do lower birth rates raise output per worker decades later?

Core argument: A 1pp lower birth rate 20 years earlier is associated with 22 log points higher GDP per worker at t+20 and 29 log points by t+40, roughly 0.73 percentage points faster annual growth over the 40-year interval.

Figure 3 traces out how GDP per worker evolves following a 1pp increase in birth rates at t−20. Since cohorts born at t−20 (e.g., 1970) do not enter the working-age population until t = 0 (e.g., 1990), we should observe no response before t = 0; [indeed] the estimated coefficients are ~0 and statistically insignificant before t = 0. Beginning at t = 0, however, higher birth rates are associated with lower GDP per worker, and the negative effects grow progressively larger over time. For example, a 1pp lower birth rate at t−20 is associated with 22 log points higher GDP per worker at t+20 and 29 log points higher by t+40. The latter corresponds to roughly 0.73 percentage points faster annual growth over the 40-year interval. We find that lower birth rates are associated with more labor-saving patents and a growing share of high-tech industries across countries and US commuting zones [Figure 11]. They are also predictive of higher TFP growth across countries and US industries and increased patenting in ICT and broader automation technology classes. Using cross-country variation in WWII-era military and civilian deaths, we present suggestive evidence that the positive growth effects of falling birth rates are primarily driven by the scarcity of younger workers rather than by reductions in population per se [Figure 14]. [Editor’s note: the paper does not grapple with the fiscal consequences of an aging population.]

Takeaways by Macro Roundup® AI

  1. A 1pp lower birth rate 20 years earlier is associated with 22 log points higher GDP per worker at t+20 and 29 log points by t+40, roughly 0.73 percentage points faster annual growth over the 40-year interval.
  2. Estimated coefficients for GDP per worker are near zero and statistically insignificant before affected cohorts enter the working-age population at t = 0.
  3. Lower birth rates are associated with more labor-saving patents, a growing high-tech industry share across countries and US commuting zones, higher TFP growth, and more ICT and automation patenting, with WWII-era death variation offering suggestive evidence that scarcity of younger workers, rather than smaller population, drives the growth effects.

Related Articles:

  • Can A Depopulating America Still Flourish? — US labor force participation among women aged 25-54 has reached all-time highs, exceeding its previous 1999 peak, yet remains over 4 points below Europe's level and nearly 6 points below Japan's. About 3.5m prime-age American women are neither working nor looking for work, without children at home or husbands, compared with roughly 7m such men.
  • Capital Is Making a Comeback — Tangible investment in property, factories and equipment fell from 12.5% to 8.5% of private gross domestic product, while intangible investment in intellectual property, software and process knowledge rose from about 11.5% to 16.75%. The author argues demand for tangible capital investment will now have to rise.
  • America Is Missing The New Labor Economy – Robotics Part 1 — Building an identical robotic arm in the US costs about 2.2x more than in China, and US-labeled components still depend heavily on Chinese parts and materials with no scalable alternative. The author argues China is the only country positioned to capture full-scale intelligent robotic automation, which would let it outcompete the US across production.

AI Summary. Aging populations create structural incentives to automate, and the argument is that demographic change could become one of the strongest long-run drivers of artificial intelligence deployment. Technology-producing economies may benefit twice, offsetting their own labor shortages while exporting automation, robotics, and AI to other aging societies.

Paul Kedrosky Applied Complexity
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Kedrosky argues that labor scarcity may become a “permanent innovation driver” as capital increasingly substitutes for labor, driving automation. “AI adoption may be driven as much by labor scarcity as by breakthroughs in machine learning.”

Are aging populations becoming a leading driver of AI adoption?

Core argument: Kedrosky argues that aging societies create structural incentives to automate, which could make demographic change one of the strongest long-run drivers of AI deployment.

[Commenting on Autor's new research, Kedrosky argues] AI adoption will be driven by demographic change. Aging societies create structural incentives to automate. Demographics could become one of the strongest long-run drivers of AI deployment. The economic winners of aging will be technology-producing economies. Countries that develop automation, robotics, and AI may benefit twice. First, by offsetting their own labor shortages. Second, by exporting those technologies to every other aging society. Demographics could reinforce, rather than weaken, technological concentration. Measures of national economies may shift from scale to productivity. If aggregate GDP becomes less useful while output per worker continues to rise, economies may increasingly be judged by productivity, innovation, and living standards rather than by the size of their labor force or total output. Labor scarcity becomes a permanent innovation driver. Tight labor markets cease to be a short-term inflationary problem and instead direct capital toward automation. Persistent worker shortages could accelerate AI adoption across sectors that previously resisted technological change, like healthcare, compressing decades of automation into a few years.

Takeaways by Macro Roundup® AI

  1. Kedrosky argues that aging societies create structural incentives to automate, which could make demographic change one of the strongest long-run drivers of AI deployment.
  2. Kedrosky contends that technology-producing economies may benefit twice from aging, offsetting their own labor shortages and exporting automation, robotics, and AI to other aging societies, potentially reinforcing technological concentration.
  3. Kedrosky argues that persistent worker shortages direct capital toward automation rather than acting as a short-term inflationary problem, and could accelerate AI adoption in sectors that previously resisted it, such as healthcare.

Related Articles:

  • Baby Busts and Growth Booms: Demographic Change and the Macroeconomy — A 1pp lower birth rate 20 years earlier is associated with 22 log points higher output per worker 20 years later and 29 log points higher after 40 years, about 0.73pp faster annual growth. The authors find lower birth rates are also associated with more labor-saving patents, larger high-tech shares, and higher productivity growth.
  • Capital Is Making a Comeback — Tangible investment in property, factories and equipment fell from 12.5% to 8.5% of private gross domestic product, while intangible investment in intellectual property, software and process knowledge rose from about 11.5% to 16.75%. The author argues demand for tangible capital investment will now have to rise.
  • The Demographic Outlook: 2026 to 2056 — The Congressional Budget Office projects the US population rises from 349m to 364m between 2026 and 2056, with annual growth slowing from 0.3% over the next decade to 0.1% thereafter before reaching zero and then shrinking. Declining fertility is projected to push deaths above births starting in 2030, increasingly offsetting net immigration.

AI Summary. Europe consumes AI products like ChatGPT without capturing the capital gains, payroll taxes on high-paid engineers, or agglomeration effects, because the tax base accrues where high-value production occurs rather than where products are consumed. The author argues Europe's welfare commitments were implicitly backed by expected future growth that is no longer anticipated.

Hanno Lustig The Two Cents
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Lustig argues that Krugman is wrong to dismiss concerns about slower European growth relative to the US based on welfare measures: “Europe’s social model is not financed out of consumer surplus; it is financed out of taxable income and taxable profits.”

Does consuming AI without producing it erode Europe's tax base?

Core argument: Europe consumes ChatGPT but collects none of the capital gains, payroll taxes on million-dollar AI engineers, or agglomeration effects, because the tax base migrates to where high-value production occurs rather than where products are consumed.

Europe consumes ChatGPT; it does not collect the capital gains, the payroll taxes on million-dollar AI engineers, or the agglomeration effects. The worst possible combination for a welfare state: contented consumers committed to the status quo, eroding public finances. The tax base migrates to where the high-value production takes place, not where the products are ultimately consumed. All this insurance that European states are committed to providing was implicitly backed by the promise of future growth. And that growth is no longer expected to materialize, but the promises remain. The problem is not simply that Europeans chose a different point on the efficiency-equity frontier. Their choices have shifted the frontier itself inward over time, leaving future generations of Europeans with a trade-off that is much worse.

Takeaways by Macro Roundup® AI

  1. Europe consumes ChatGPT but collects none of the capital gains, payroll taxes on million-dollar AI engineers, or agglomeration effects, because the tax base migrates to where high-value production occurs rather than where products are consumed.
  2. Europe's welfare-state insurance was implicitly backed by a promise of future growth that is no longer expected to materialize, while the promises themselves remain in place.
  3. European choices have shifted the efficiency-equity frontier itself inward over time, leaving future generations with a worse trade-off rather than merely a different point on the same frontier.

Related Articles:

  • European vs. U.S. Economic Performance: An Update — Productivity growth from 2000-2019 was higher in the US than in western Europe, but almost half the gap disappears when information and communications technology is excluded, even though tech was 9.2% of US output versus 5.4% in the EU. The author estimates this difference in industrial mix accounts for roughly 40% of the overall productivity growth gap.
  • The Future of European Competitiveness – A Competitiveness Strategy for Europe — A European Union competitiveness report attributes the EU's labour productivity growth gap with the US largely to weaker activity in information and communications technology and large-scale digital services; excluding main ICT sectors, EU productivity was broadly at par with the US from 2000-2019. It estimates Europe's investment share must rise about 5 percentage points of GDP.
  • European Stagnation Is Real — Comparing the United States, France, and Germany at current purchasing power parity prices shows the European economies roughly constant relative to America since 2000, but current-price measures miss output gains in sectors where prices fall, such as software. The author argues constant-price comparisons, the standard approach for growth over time, reveal greater European stagnation.

AI Summary. 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.

Jan-Patrick Barnert and Michael Msika Bloomberg
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The Silicon Data LLM Token Expenditure Index, which tracks AI token prices, is down ~20% from its recent peak in May. The metric is a rough “proxy for marginal willingness to pay.”

Why does a softening AI token index send ambiguous signals?

Core argument: Token prices have fallen more than 90% since 2023 while total token spend has roughly doubled over the past year, supporting a benign read in which cheaper tokens expand the market and an index pause reflects digestion.

A softer index doesn’t mean AI is getting cheaper. The gauge blends prices and usage, meaning a dip can imply very different scenarios: either list prices are falling, or demand is shifting toward cheaper models. It could also point to a genuine softening in what buyers are prepared to shoulder. Each of these possibilities carries different implications. Let’s start with a benign read: While token prices have collapsed more than 90% since 2023, total spend has roughly doubled since last year. Cheaper tokens have expanded the market. This means that an index pause is simply digestion, while demand is real and capex is money well spent. The bull case for Nvidia rests here. Now for the interpretation that’s keeping people up at night: Bears warn that sustained weakness in the index could end the trade that saw nearly the entire AI cohort rally hard this cycle. It’s token spending that justifies the next capex order, and the bill is already looking stretched. Allianz Research said there’s nearly a 46% growth gap between AI investment and sales. That’s worse than the 32% divergence measured during the 2001 telecom bust.

Takeaways by Macro Roundup® AI

  1. Token prices have fallen more than 90% since 2023 while total token spend has roughly doubled over the past year, supporting a benign read in which cheaper tokens expand the market and an index pause reflects digestion.
  2. Allianz Research put the growth gap between AI investment and sales at nearly 46%, wider than the 32% divergence measured during the 2001 telecom bust.
  3. The index blends prices and usage, so a dip can imply falling list prices, demand shifting toward cheaper models, or a genuine softening in what buyers are prepared to shoulder.

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.
  • Semiquincententacles — Leading artificial-intelligence labs are moving to usage-based token pricing on a cost-plus basis that includes total ownership costs, pushing token prices higher and giving some enterprises reason to migrate to cheaper open models. Frontier models remain necessary for cybersecurity, distillation training, agents, and scientific discovery, where maximum performance is required.
  • Chart of the Day: Small Models are Closing the Gap to Frontier AI — Achieving 60% on MMLU, a test of academic reasoning, once required a 540bn-parameter model but was reached by a 3.8bn-parameter model two years later, a 142x reduction in parameters. Models small enough to run on phones or sensors shift inference from data centers to devices, weakening the case for paying cloud providers per token.

AI Summary. 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.

Jason Thomas Carlyle
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Tech-related net fixed investment had risen 30% y/y by the end of H1, while non-tech investment fell, suggesting the datacenter buildout is crowding out non-tech related fixed investment.

Is the AI investment boom crowding out other capital spending?

Core argument: Tech-related investment rose 30% from year-ago levels while all other capex fell, a record divergence implying the AI boom is bidding away electricians, grid capacity, transformers, metals, engineering talent, and construction labor in quantities that stress rival projects' economics.

Overall tech-related investment increased 30% from year-ago levels, while all other capex fell. This record divergence implies that the AI boom is bidding away physical inputs – electricians, grid capacity and transformers, metals and materials, engineering talent, and construction labor – in quantities that stress the economics of rival projects elsewhere. Perhaps the best illustration of binding real resource constraints was observed inside the AI data center complex itself. In the first half of the year, the average price of semiconductors, labor for assembly and integration, and related materials exploded, as physical supply constraints interacted with insatiable demand. As more economic rent accrued to the compute layer, the stock prices of the semiconductor manufacturers upstream from this physical chokepoint outperformed those of their top customers by 56%. When AI spending gets financed out of internally generated cash flow, “crowding out” isn’t a pressing issue for investors. Credit spreads have not widened materially, financing conditions remain accommodative, and the cost of capital for non-software borrowers doesn’t suggest displacement. But the hyperscalers’ free cash flow has now been exhausted. Over 80% of the projected capex over the next few years will have to be externally financed, including fresh equity issuance.

Takeaways by Macro Roundup® AI

  1. Tech-related investment rose 30% from year-ago levels while all other capex fell, a record divergence implying the AI boom is bidding away electricians, grid capacity, transformers, metals, engineering talent, and construction labor in quantities that stress rival projects' economics.
  2. Semiconductor manufacturers' stock prices outperformed those of their top customers by 56% as more economic rent accrued to the compute layer, with average prices for chips, assembly and integration labor, and related materials rising sharply in the first half of the year.
  3. Hyperscalers' free cash flow has been exhausted, and over 80% of projected capex over the next few years will have to be externally financed, including fresh equity issuance, though credit spreads have not widened materially.

Related Articles:

  • The Macro Implications of the AI Capex Boom — Artificial intelligence capital spending is estimated to add roughly 1.4 percentage points to US growth in one year and about 1.5 points the next, comparable to business investment's contribution during the tech bubble. A material part of that estimate reflects profits at Nvidia and a few other chipmakers that are unlikely to be recycled back into the economy.
  • The Other US Capex Question — Business investment outside artificial intelligence has been weak, contributing just 1.54% to total capital expenditure growth. The author argues AI investment is not crowding out other projects, since the spread between return on capital and cost of capital has stayed positive and US nonfinancial corporations' capital spending is below their gross savings.
  • America’s Data Center Build-Out Is Falling Way Behind Schedule — A JPMorgan analysis found that more than 60% of US data-center capacity planned for completion in 2027 has not yet begun construction, with another 7% delayed. Analysts and power-industry experts say Google's strategy of owning generation—including its $4.75bn purchase of wind and solar developer Intersect—and shifting computing loads to follow power supply could speed its grid connections.

Wednesday, July 8, 2026

AI Summary. The authors find cognitive ability test scores predict capital income, with the gradient in log capital income about 3x steeper than for labor income, and only partly explained by labor earnings. Higher-ability individuals are associated with higher saving rates and higher risk-adjusted returns while holding portfolios with lower systematic risk.

Spencer Bastani, Kristina Karlsson, Jonas Kolsrud and Daniel Waldenström Uppsala University
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Cognitive ability positively predicts capital as well as labor income, with the capital-income gradient ~3x as large in % terms. This reflects both higher saving rates and higher risk-adjusted returns, neither fully explicable by earnings.

Does cognitive ability predict capital income more strongly than labor income?

Core argument: Cognitive ability, measured on a 1–9 test-score scale, predicts capital income, with a log-income gradient roughly three times steeper than the labor income gradient; in rank terms the labor gradient is steeper, because capital income's heavy right tail compresses rank differences.

We document three results. First, cognitive ability predicts capital income. Figure 2 plots mean log income and mean income rank against the nine cognitive ability scores reporting test performance on a 1–9 scale, with both series normalized to zero at the lowest score. In the log specification (Panel a), the capital income gradient is roughly three times steeper than the labor income gradient. In the rank specification (Panel b), the ordering reverses: the labor income gradient is steeper, because the heavy right tail of capital income compresses rank differences. Figure 3 provides a complementary perspective, plotting average cognitive ability across percentiles of the labor and capital income distributions. [The relationship between ability and rank flattens at the top of the labor distribution but strengthens at the top of the capital distribution.] Second, the capital-income gradient is only partially explained by labor income: a decomposition shows that ability is associated with higher saving rates and investment returns through channels beyond labor income. Third, the investment return channel is consistent with skill rather than risk compensation, as high-ability individuals earn higher risk-adjusted excess returns while holding portfolios with lower systematic risk.

Takeaways by Macro Roundup® AI

  1. Cognitive ability, measured on a 1–9 test-score scale, predicts capital income, with a log-income gradient roughly three times steeper than the labor income gradient; in rank terms the labor gradient is steeper, because capital income's heavy right tail compresses rank differences.
  2. The capital-income gradient is only partially explained by labor income: a decomposition shows cognitive ability is associated with higher saving rates and higher investment returns through channels beyond labor income.
  3. High-ability individuals earn higher risk-adjusted excess returns while holding portfolios with lower systematic risk, a pattern consistent with skill rather than risk compensation.

Related Articles:

  • Cognitive Skills Beyond Childhood — Wages at age 50 rise more steeply with cognitive skills measured at age 16 than with cognitive skills measured at age 50; for women, moving from average to two standard deviations above average raises expected hourly wages from £11 to £22 using age-16 skills, versus £12 to £15 using age-50 skills.
  • Steeper At The Top: Cognitive Ability and Earnings in Finland and Norway — Administrative data on over 350,000 men each in Finland and Norway show a convex relationship between earnings rank and cognitive ability measured by military conscription tests: top-percentile earners score about 1 standard deviation above median earners, who score about 0.5 above bottom-percentile earners. The lowest scores are almost absent among top earners.
  • Toward An Understanding Of The Returns To Cognitive Skills Across Cohorts — The authors find that the estimated decline in labor market returns to pre-labor-market cognitive skills for white men across two U.S. youth cohorts depends critically on the widening and increased left-skewness of the test-score distribution, with declines appearing only at the lower end of that distribution.

AI Summary. Researchers constructed an Inflation Shock Momentum Index that measures the spending-weighted share of personal consumption categories with three consecutive monthly inflation surprises above expectations minus the share with three consecutive surprises below. The authors find the index improves forecasts of overall personal consumption inflation one to three years ahead, even after accounting for standard predictors.

Kevin Lansing and Adam Shapiro Federal Reserve Bank of San Francisco
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The FRBSF has launched a new Inflation Shock Momentum Index, which uses disaggregated data to isolate persistent positive and negative components of inflation, improving PCE forecasts at 1 to 3 year horizons relative to standard predictions.

Can patterns of repeated inflation surprises improve inflation forecasts?

Core argument: The Inflation Shock Momentum Index (ISMI) helps improve forecasts of overall PCE inflation at horizons of one to three years, even after accounting for standard predictor variables.

This Economic Letter describes a new indicator called the Inflation Shock Momentum Index (ISM), which provides a timely signal of emerging inflationary or disinflationary pressures. We construct the ISMI by sorting categories of the personal consumption expenditures (PCE) basket of goods and services into groups based on the pattern of recent monthly surprises, called inflation shocks. Positive momentum categories are identified as those with three consecutive positive inflation shocks, while negative momentum categories are identified as those with three consecutive negative inflation shocks. The ISMI represents the expenditure-weighted share of inflation categories with positive momentum minus the share with negative momentum.The ISMI helps improve forecasts of overall PCE inflation at horizons of one to three years, even after accounting for standard predictor variables. Panel A of Figure 1 plots the ISMI together with 12-month PCE inflation. Pronounced positive or negative fluctuations of the index align well with historical inflation narratives.

Takeaways by Macro Roundup® AI

  1. The Inflation Shock Momentum Index (ISMI) helps improve forecasts of overall PCE inflation at horizons of one to three years, even after accounting for standard predictor variables.
  2. The ISMI is constructed as the expenditure-weighted share of PCE categories with three consecutive positive monthly inflation shocks minus the share with three consecutive negative shocks.
  3. Pronounced positive or negative fluctuations of the ISMI align well with historical inflation narratives when plotted alongside 12-month PCE inflation.

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%.
  • Why Did Inflation Rise And Fall In 2021-24? Channels And Evidence From Expectations — Across countries, unexpectedly higher government deficits relative to earlier International Monetary Fund forecasts are associated with unexpectedly higher inflation, measured by each factor's contribution to the increase in public debt, a relationship the author finds statistically significant. This evidence is consistent with a fiscal channel for inflation.
  • 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%.

AI Summary. Japan's 10-year government bond yield reached 2.87%, its highest since 1996, in a sell-off investors attribute to a $2.3tn, 14-year government spending plan and concern the Bank of Japan will let inflation exceed its 2% target. The premium for borrowing 10 years rather than two widened to 1.4 points.

Ian Smith and Leo Lewis Financial Times
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The yield on the benchmark Japanese 10-year government bond rose to 2.87%, its highest level since 1996, on budgetary concerns and doubts about the BOJ. The premium of the 10yr bond over the 2yr has risen sharply, from 1pp in April to 1.4pp today.

Are government spending plans and inflation concerns driving up Japan's borrowing costs?

Core argument: Japan's benchmark 10-year government bond yield reached 2.87%, its highest level since 1996, after a sell-off in Japanese government bonds this year.

A bruising sell-off in Japanese government bonds this year pushed the country’s benchmark 10-year yield to 2.87% on Wednesday, its highest since 1996. Investors say the policies of Prime Minister Sanae Takaichi — centred on a $2.3tn spending plan spread over 14 years — are stoking the sell-off in long-term debt. Some also worry that the Bank of Japan, which raised interest rates to 1% last month, will fall behind the curve and let inflation rise above its 2% target. Market concern over long-term risks is reflected in the premium that investors are charging Japan to borrow for 10 years rather than two, which has risen from less than 1 percentage point in April to 1.4 percentage points today. This premium has recently been flat or falling in other big bond markets such as the US and Germany.

Takeaways by Macro Roundup® AI

  1. Japan's benchmark 10-year government bond yield reached 2.87%, its highest level since 1996, after a sell-off in Japanese government bonds this year.
  2. Investors attribute the sell-off in long-term Japanese debt to Prime Minister Sanae Takaichi's policies, centred on a $2.3tn spending plan spread over 14 years.
  3. The premium investors charge Japan to borrow for 10 years rather than two rose from less than 1 percentage point in April to 1.4 percentage points, while that premium has been flat or falling in the US and Germany.

Related Articles:

  • The Quiet Implosion of Japan — Across G10 economies, Japan carries a far higher gross public debt burden yet its 30-year government bond yield is barely above Germany's, which the author argues shows Japanese long-term yields should be much higher. As long as that is prevented, the yen stays under depreciation pressure toward 170 per dollar and intervention grows less effective.
  • 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. As labor force participation falls, a shrinking group of workers generates the income, taxes, and innovation supporting a growing number of non-workers, leaving the economy more fragile because labor shocks are concentrated among fewer participants. Labor scarcity raises the return on automation, robotics, and software, making growth depend on productivity rather than participation.

Paul Kedrosky Applied Complexity
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Overall LFP is at a 50-year low. Kedrosky warns, “As fewer adults work or seek work, a larger fraction of voters experience the economy mostly as consumers, retirees, or rentiers, not as workers…[which] changes incentives around….taxation and redistribution.”

How does falling labor force participation reshape growth and fragility?

Core argument: Falling labor force participation leaves a shrinking core of workers generating the income, taxes, and innovation that support a growing number of non-workers, and makes the economy more fragile because labor shocks are concentrated among fewer participants.

As fewer adults work or seek work, a larger fraction of voters experience the economy mostly as consumers, retirees, or rentiers, not as workers. That changes political incentives around wages, immigration, AI, taxation, and redistribution. The economy becomes increasingly dependent on a shrinking core. A shrinking group of workers generates the income, taxes, and innovation that support a growing number of non-workers. The economy becomes more fragile because labor shocks are concentrated among fewer participants. Capital must increasingly substitute for labor. Labor scarcity raises the return on automation, AI, robotics, and software. The AI bet becomes as much about compensating for workers' absence as simple replacement. Growth increasingly depends on productivity, on not participation. Any [productivity] miss will be much more consequential than in the past, given that labor no longer picks up the slack.

Takeaways by Macro Roundup® AI

  1. Falling labor force participation leaves a shrinking core of workers generating the income, taxes, and innovation that support a growing number of non-workers, and makes the economy more fragile because labor shocks are concentrated among fewer participants.
  2. As more voters experience the economy as consumers, retirees, or rentiers rather than workers, political incentives shift around wages, immigration, AI, taxation, and redistribution.
  3. Labor scarcity raises the return on automation, AI, robotics, and software, making the AI bet as much about compensating for workers' absence as replacing them, with growth depending on productivity rather than participation and any productivity miss more consequential than in the past.

Related Articles:

  • Baby Busts and Growth Booms: Demographic Change and the Macroeconomy — A 1pp lower birth rate 20 years earlier is associated with 22 log points higher output per worker 20 years later and 29 log points higher after 40 years, about 0.73pp faster annual growth. The authors find lower birth rates are also associated with more labor-saving patents, larger high-tech shares, and higher productivity growth.
  • 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.
  • 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. Gallup polling shows the share of Democrats who are extremely proud to be American has fallen from an average of 58% to 14%. Republicans report higher pride overall, but their responses also shift with which party holds the presidency, ranging from an average of 79% down to 60% and back to 74%.

Eli McKown-Dawson and Nate Silver Silver Bulletin
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Gallup finds only 17% of Democrats are “extremely proud” to be an American, versus 30% of independents and 74% of Republicans – notable declines from 2005–2009, when 58% of Democrats/independents and 79% of Republicans were “extremely proud” to be an American.

Has American pride declined and shifted with the ruling party?

Core argument: The share of Democrats extremely proud to be American fell from an average of 58% during George W. Bush's second term to 30% during Trump's first term and 14% in the most recent Gallup poll.

During George W. Bush’s second term, an average of 58% of Democrats were extremely proud to be American according to Gallup, as was an identical share of independents — though Republicans were higher. The rough parity between Democrats and independents lasted through Barack Obama’s second term, but the share of extremely proud Democrats fell to an average of 30% during Trump’s first term and was just 14% in the most recent Gallup poll. Although Republicans are generally more patriotic, their opinions can shift based on who occupies 1600 Pennsylvania Avenue too. The share of Republicans extremely proud to be American fell from 79% on average during Bush’s second term to 60% during Biden’s term. What happened after Trump retook office? It jumped right back up to 74%.

Takeaways by Macro Roundup® AI

  1. The share of Democrats extremely proud to be American fell from an average of 58% during George W. Bush's second term to 30% during Trump's first term and 14% in the most recent Gallup poll.
  2. Republicans extremely proud to be American averaged 79% during Bush's second term, fell to 60% during Biden's term, and rebounded to 74% after Trump retook office, according to Gallup.
  3. Gallup found Democrats and independents at an identical 58% extremely proud to be American during Bush's second term, with that rough parity lasting through Obama's second term.

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