Edward Conard

Top Ten New York Times Bestselling Author

  • “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
  • “…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
  • “Unintended Consequences offers deep and well-argued analyses on almost every issue.” - The New York Times
  • “…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
  • “A full-throated defense of economic dynamism.” - The Wall Street Journal
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “…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
  • “…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
Upside of Inequality Oxford Unintended Consequences

Macro Roundup


Friday, August 15, 2025

AI Summary. Median household incomes grew about 15% more in cities already in the top 10% than in cities initially in the bottom half, and house prices rose well above consumer price inflation, with especially large gains in major cities. The authors argue that when in-demand, higher-productivity cities lack the space and zoning to absorb migrants, housing costs there rise steeply.

Greg Howard and Jack Liebersohn Journal of Economic Perspectives
Date Posted:

Between 2000 and 2018, high-income cities had greater income growth, raising the relative demand for housing in them. Because these cities also face more geographic and regulatory obstacles to new housing, house prices and rents rose in the aggregate.

Does regional income divergence drive up national housing prices and rents?

Core argument: From 2000 to 2018, median household incomes rose about 15% more in cities already in the top 10% than in cities initially in the bottom half, while the FHFA house price index rose 31 percentage points more than the overall CPI.

From 2000 to 2018, median household incomes increased by about 15% more in cities that were already in the top 10% as compared to cities that were initially in the bottom half. [Over the same period] the FHFA house price index rose by 31 ppt more than the overall CPI. This housing price increase is unevenly distributed and driven in large part by the higher rate of housing price increases in major cities. Relative to CPI, house prices rose by 49 percentage points in New York, 96 percentage points in Seattle, 129 percentage points in Los Angeles, and 113 percentage points in Miami. To understand the link between regional inequality and average housing prices, we note that if a location experiences relatively higher productivity growth, then the associated rise in income will increase demand to migrate there. If there is a mismatch between the places where people increasingly want to live and places with the space and zoning policies to absorb population, then housing costs can increase a lot in the in-demand cities, while decreasing only a little elsewhere.

Takeaways by Macro Roundup® AI

  1. From 2000 to 2018, median household incomes rose about 15% more in cities already in the top 10% than in cities initially in the bottom half, while the FHFA house price index rose 31 percentage points more than the overall CPI.
  2. Relative to CPI from 2000 to 2018, house prices rose by 49 percentage points in New York, 96 in Seattle, 129 in Los Angeles, and 113 in Miami, with the national housing price increase driven largely by major cities.
  3. Higher relative productivity growth in a location raises incomes and demand to migrate there, and a mismatch with the space and zoning policies needed to absorb population can raise housing costs a lot in in-demand cities while lowering them only slightly elsewhere.

Related Articles:

  • Housing and the American Worker — Wages net of housing costs were once substantially higher in high-density areas than low-density areas for workers both with and without college education, but over the past half-century that urban wage premium has persisted only for college-educated workers, not for those with a high school degree or less.
  • The Determinants Of Declining Internal Migration — A 10% increase in wages where people live is associated with 3.5% less migration, while a 10% increase in destination wages is associated with 7.8% more migration; a 10% rise in destination home prices is associated with 2.6% less migration. The authors find wage responses alone would have raised migration rates, but housing-related factors more than offset them.
  • Young Families Have Continued Leaving Big Cities Post-Pandemic — Large urban counties have continued to lose population of young children even as their overall population growth resumed, driven by immigration. Birth rates in large urban counties have declined twice as fast as in rural counties over the past decade, and the number of children under five fell in 58% of all US counties.

AI Summary. Alternative measures of underlying US consumer price inflation — including versions excluding food, energy, shelter, and used vehicles, trimmed-mean measures, "sticky-price" indexes, and individual service categories such as restaurant meals and haircuts — tell a similar story about inflation that differs from headline figures, a divergence attributed to housing costs.

Matt Klein The Overshoot
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The CPI for shelter – ⅓ of overall CPI – lags behind market rents. Thus, receding rent increases from the Covid period are creating the “illusion of disinflation,” while measures other than overall CPI imply stable or accelerating inflation in 2025.

Do alternative inflation measures tell a different story than headline figures?

Core argument: Alternative inflation gauges — the BLS CPI excluding food, energy, shelter, and used cars and trucks, the Cleveland Fed's trimmed measure, and the Atlanta Fed's sticky-price CPI excluding shelter — tell a similar story that differs from headline CPI figures.

The BLS publishes a version of the CPI that excludes food, energy, shelter (housing+hotels, mostly), and used cars and trucks. The Federal Reserve Bank of Cleveland has a measure that recalculates the monthly change in the CPI excluding the components with the largest price increases and largest price decreases. The Federal Reserve Bank of Atlanta divides the CPI into two groups based on whether prices change frequently or not. They also publish versions of their “sticky-price” CPI excluding shelter. Another approach is simply to focus on select components that ought to be representative of the broader whole, such as “full service meals and snacks”, or “personal care services” (mostly haircuts). Tellingly, all of these measures tend to tell a similar story regarding inflation over the past few years—and this story is very different from what the headline figures have been showing, thanks to the impact of housing.

Takeaways by Macro Roundup® AI

  1. Alternative inflation gauges — the BLS CPI excluding food, energy, shelter, and used cars and trucks, the Cleveland Fed's trimmed measure, and the Atlanta Fed's sticky-price CPI excluding shelter — tell a similar story that differs from headline CPI figures.
  2. The divergence between headline CPI figures and alternative inflation measures over the past few years is attributed to the impact of housing.
  3. Select components such as "full service meals and snacks" and "personal care services," mostly haircuts, tell a similar inflation story to the broader alternative measures.

Related Articles:

  • Market Rents and CPI Shelter Inflation — Shelter, about 35% of the consumer price index, responds to market rents with a lag because of long-term leases, smoothing at lease renewal, and measurement using six-month rent changes. The authors' model reproduces the lagged, dampened response of shelter inflation to swings in market-rent inflation.
  • Inflation, Trump, and the Fed — Nominal wage income is growing about 1–1.5 percentage points faster than before the pandemic, implying somewhat more inflation than in 2017–2019 if most of that extra income is spent and businesses cannot ramp up production. Cutting interest rates enough to reverse the budget deficit increase, without offsetting fiscal tightening, would be extraordinarily inflationary.
  • When Is Shelter Services Inflation Coming Down? — A gap between housing demand and supply emerged after the COVID-19 pandemic that has likely put upward pressure on rents. Leading indicators including units under construction, the gap between completed units and net household formation, and market rents on new leases tend to foreshadow shelter inflation by about a year, and suggest it will keep declining toward more traditional levels.

AI Summary. The share of Americans moving each year has declined from about 20% in the 1950s and '60s to 7.8% in 2023, the lowest since U.S. Census records began in 1948, with moves within the same county down roughly 47% over three decades. An aging population and the rise of two-earner households are among the contributing factors.

Konrad Putzier and Rachel Louise Ensign Wall Street Journal
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During the 1950s and ’60s, some 20% of Americans would typically move each year. In 2024, only 7.8% of Americans moved, the lowest rate logged since 1948. Aging, two-earner couples, and high housing costs are all factors stalling geographic mobility.

Why are fewer Americans moving for homes and jobs?

Core argument: Only 7.8% of Americans moved in 2023, the lowest rate since U.S. Census records began in 1948, down from 9.8% in 2019 and about 20% annually in the 1950s and '60s.

In the 1950s and ’60s, some 20% of Americans would typically move each year. The share of people moving has steadily slowed since then, in part because the U.S. population has aged, and older people tend to move less. More Americans also live in households with two earners, which makes uprooting more challenging. By 2019, the year before the Covid pandemic, 9.8% of Americans moved. During Covid, there was a well-publicized increase in people decamping farther away from work and deeper into the suburbs. That surge was brief. In 2023, only 7.8% of Americans moved, the lowest rate logged since U.S. Census records began in 1948. That figure held relatively steady in 2024, the most recent data available. The biggest drop: a roughly 47% decline among people moving within the same county over the past three decades, according to census data. Declining mobility is “a big deal in so many dimensions,” said Chang-Tai Hsieh, an economics professor at the University of Chicago. His research has previously found that expensive housing dissuaded so many workers from moving for better jobs that it weighed on U.S. gross domestic product. He believes that link, seen from 1964 to 2009, likely still holds true.

Takeaways by Macro Roundup® AI

  1. Only 7.8% of Americans moved in 2023, the lowest rate since U.S. Census records began in 1948, down from 9.8% in 2019 and about 20% annually in the 1950s and '60s.
  2. Moves within the same county fell roughly 47% over the past three decades, according to census data, the largest drop within the broader decline in U.S. mobility.
  3. Research by Chang-Tai Hsieh, an economics professor at the University of Chicago, found that expensive housing dissuaded enough workers from moving for better jobs to weigh on U.S. GDP from 1964 to 2009, a link he believes likely still holds.

Related Articles:

  • Where is Standard of Living the Highest? Local Prices and the Geography of Consumption — The authors estimate that geographical differences in cost of living are especially large for low-income US households, and that expensive cities offer college graduates a standard of living similar to affordable cities while offering less-educated households a lower one. A high school dropout household moving from the most affordable to the most expensive commuting zone would see consumption fall 18.5%.
  • Housing Constraints and Spatial Misallocation — The authors estimate that housing supply constraints in high-productivity cities such as New York and San Francisco misallocated labor and lowered aggregate US growth by 36% over 1964–2009. Relaxing supply in those cities to the median US city's level is estimated to have raised output 3.7%, or about $3,685 in additional average annual earnings.
  • Americans Are Still Adventurous. They Just Hate Moving — The share of Americans moving across state lines each year has stopped declining, and by a measure tracking where 30-somethings were born, interstate mobility never fell much. In 2022, 49% of US residents in their 30s were born outside their state of residence — slightly below 2010 but above any point in the 20th century.

AI Summary. Hedge fund Bridgewater exited positions in 16 Chinese stocks worth a combined $1.41bn and closed its indirect exposure through China-focused exchange-traded funds, according to its US regulatory filing. The firm shifted toward US mega-cap technology stocks, raising its stakes in Nvidia by 154%, Microsoft by 112%, Meta Platforms by 90% and Alphabet by 84%.

Yulu Ao South China Morning Post
Date Posted:

Bridgewater Associates sold all of its holdings in the 16 US-listed Chinese firms in which it held positions. Bridgewater rotated into the hyperscalers, increasing its Microsoft stake by 112% and its Nvidia stake by 154% to 4.61% of its portfolio.

Is a major hedge fund exiting Chinese stocks for US tech?

Core argument: Bridgewater exited positions in 16 Chinese stocks worth US$1.41 billion in total, according to its 13F filing with the US Securities and Exchange Commission.

The fund exited positions in 16 Chinese stocks, which were worth US$1.41 billion in total, according to its 13F filing with the US Securities and Exchange Commission. The exit came after Bridgewater made headlines in recent quarters by slashing its exposure to US equities and piling into Chinese names. The firm had sharply increased its bet on e-commerce leader Alibaba in the first quarter – boosting its stake by more than 3,360 per cent to US$748.4 million from US$21.6 million at the end of 2024. It also closed out its indirect exposure to Chinese equities by exiting exchange-traded funds such as the iShares MSCI China ETF and the iShares China Large-Cap ETF, the filing showed, suggesting a broader strategic reallocation away from China. Bridgewater pivoted towards US mega-cap technology stocks. It raised its stake in Nvidia by 154% to 4.61% of its portfolio, while also significantly increasing positions in Microsoft by 112%, Alphabet by 84% and Meta Platforms by 90%.

Takeaways by Macro Roundup® AI

  1. Bridgewater exited positions in 16 Chinese stocks worth US$1.41 billion in total, according to its 13F filing with the US Securities and Exchange Commission.
  2. Bridgewater's exit followed quarters in which it slashed US equity exposure and piled into Chinese names, including boosting its Alibaba stake by more than 3,360 per cent to US$748.4 million from US$21.6 million at the end of 2024.
  3. Bridgewater closed its indirect China exposure by exiting the iShares MSCI China ETF and iShares China Large-Cap ETF, while raising Nvidia by 154 per cent to 4.61 per cent of its portfolio, Microsoft by 112 per cent, Alphabet by 84 per cent and Meta Platforms by 90 per cent.

Related Articles:

  • Extreme Concentration in the S&P 500 Continues — Nvidia holds the largest weight of any individual stock in the S&P 500 since the data series began in 1981, reflecting extreme concentration in AI-linked shares.
  • Summer Mailbag — The share of total US market capitalization held by young unprofitable companies has fallen from its 2021–2022 peak, while over 40% of small-cap companies have negative earnings and the negative-earnings share among large caps is falling. The 10 largest stocks hold about 35% of market cap, a high since 1990, but their share of market earnings has roughly kept pace.
  • Which U.S. Stocks Generated the Highest Long-Term Returns? — Of the 29,078 U.S. common stocks in a database spanning nearly a century, 51.6% had negative cumulative returns, while 17 stocks returned more than 5m% — led by Altria at 265m%. Those top performers averaged annualized returns of just 13.47%, underscoring the value of time invested.

AI Summary. Royalty receipts from intellectual property equal 0.07% of China's GDP versus 0.45% of South Korea's, but China's royalty-to-GDP ratio has grown considerably faster than that of advanced economies in recent years, narrowing the gap.

François de Soyres, Ana Maria Santacreu and Ethan Hunt Federal Reserve Bank of St. Louis
Date Posted:

Royalty payments to Chinese firms as a percentage of GDP still trail other advanced economies, though Chinese royalty payments are growing faster than other countries’ in recent years. Royalty payments to China from ROW rose 5-fold btw 2015–2022.

Is China closing the gap with advanced economies in intellectual property earnings?

Core argument: Royalty receipts represented 0.07% of China's GDP versus 0.45% of South Korea's GDP, leaving China's royalty intensity behind top-tier economies.

When we scale royalty receipts by gross domestic product (GDP), a clearer picture emerges. The figure plots royalties received as a share of GDP for China and four advanced economies. China’s royalty intensity still trails the top-tier countries: Royalties represented 0.07% of China’s GDP versus 0.45% of South Korea’s GDP. However, China’s growth rate is considerably faster than those of advanced countries in recent years. To make this trajectory more visible, China’s series is plotted on a separate (right-hand) axis. This helps highlight the acceleration in royalty-to-GDP terms, without compressing the variation across advanced economies. Even if China has not yet reached parity, it is catching up quickly, and the gap is narrowing.

Takeaways by Macro Roundup® AI

  1. Royalty receipts represented 0.07% of China's GDP versus 0.45% of South Korea's GDP, leaving China's royalty intensity behind top-tier economies.
  2. China's royalty-to-GDP ratio has grown considerably faster than those of advanced countries in recent years, narrowing the gap without reaching parity.
  3. Plotting China's royalty-to-GDP series on a separate right-hand axis makes its acceleration visible without compressing the variation across the four advanced economies.

Related Articles:

  • Was Made in China 2025 Successful? — Chinese firms have narrowed the gap with foreign competitors across targeted industries, with China's share of global patents rising by more than 4 percentage points in electric vehicles, new materials, electronics, and robotics. Gaps remain in areas such as advanced semiconductors, where Chinese firms still lag the global frontier.
  • US Efforts to Contain Xi’s Push for Tech Supremacy Are Faltering — Research by Bloomberg Economics and Bloomberg Intelligence finds that of 13 key technologies tracked, China has reached global leadership in five and is catching up in seven others, with clear leads in electric vehicles, automotive software and lithium battery technology. China still struggles to develop manufacturing processes for advanced semiconductors, the main focus of US export controls.
  • Underestimating China — China's productive capacity is three times that of the United States and exceeds the next nine countries combined, with its share of global manufacturing quintupling to 30% while the U.S. share halved to about 15%. The authors argue Washington must convert its alliances into a platform for pooled capacity building.

AI Summary. China is warning foreign buyers of rare earths and derived products such as magnets that building large inventories will bring supply restrictions, and is limiting approved export volumes to prevent stockpiling, according to people familiar with the matter.

Joe Leahy and Ryan McMorrow Financial Times
Date Posted:

China is limiting rare earths export volumes to prevent foreign stockpiling of rare earths and downstream products such as magnets used in electric motors. Chinese exports of rare earths are down 38% y/y.

Is China blocking foreign buyers from stockpiling rare earths?

Core argument: China exported 3,188 tonnes of rare earth permanent magnets in June, more than double May's volume but down 38% from a year earlier, with exports in the three months to June about half year-earlier levels by volume.

Beijing is warning buyers they are at risk of greater supply restrictions if they stockpile rare earths and derived products such as magnets used in electric motors, according to two people familiar with the matter. China “is telling companies they cannot go out and build huge inventories in rare earths, or they will face shortages”, one of the people said. The other person said Chinese authorities were deliberately limiting approved export volumes to prevent foreign stockpiling. “This will be a leverage point from now on,” they said. China exported 3,188 tonnes of rare earth permanent magnets in June, more than double export volumes in May, but down 38% from a year earlier. In the three months to June — since Beijing enacted the trade restrictions — magnet exports were about half the level by volume in the same period last year.

Takeaways by Macro Roundup® AI

  1. China exported 3,188 tonnes of rare earth permanent magnets in June, more than double May's volume but down 38% from a year earlier, with exports in the three months to June about half year-earlier levels by volume.
  2. Beijing is warning buyers that building large rare earth inventories will bring greater supply restrictions and shortages, according to two people familiar with the matter.
  3. Chinese authorities are deliberately limiting approved export volumes to prevent foreign stockpiling, according to a person familiar with the matter, who said this "will be a leverage point from now on".

Related Articles:

  • China’s Weaponisation of Rare Earths is a New Kind of Trade War — China's export controls on rare earth materials and magnets threatened to halt production across the auto industry, the largest manufacturing sector in most advanced economies, and forced Washington to negotiate on tariffs. Despite China's well-known dominance of rare earth production, Western governments and companies have not developed alternative suppliers.
  • China Found World’s Pain Point on Trade – and Will Use It Again — China's export permitting system for rare earths and other critical minerals is detailed enough that Beijing is developing a granular understanding of where every kilogram it produces ends up and why. Bottlenecks emerged as China slowed approvals for magnets containing even minuscule amounts of seven rare earths, alongside limits on sales of the raw elements.
  • Pentagon to Take Stake in Rare-Earth Company, Challenging China’s Control — Under a deal with the largest U.S. rare earths miner, the Defense Department will take a 15% stake, commit billions of dollars to investment and output purchases, and guarantee a price floor for rare earth minerals and magnets. The agreement calls for a new magnet factory, expected online in 2028, at a scale far exceeding current U.S. magnet production.

Monday, August 18, 2025

AI Summary. Artificial intelligence gains have come from a formula of more data and more computing power, but companies have exhausted freely available internet training data and are now striking data-sharing deals with publishers and copyright holders. Training and running large models is energy-intensive, requiring vastly more Nvidia chips with each generation.

Melissa Heikkilä, Tim Bradshaw, Cristina Criddle and George Hammond Financial Times
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The rate of performance improvement of large language models may be slowing, as models face both data availability and compute constraints. GPT-5’s launch underwhelmed many in the sector.

Are data and computing limits slowing AI progress?

Core argument: GPT-4 was trained on several thousand Nvidia chips in 2022, while estimates suggest GPT-5 was trained on hundreds of thousands of Nvidia's next-generation processors, with more powerful chips on the way.

For the past five years, companies such as OpenAI and Anthropic have been able to show consistent gains in the performance of their systems by using a simple formula: more data and more computing power equals bigger, better models. Many AI leaders still believe that these “scaling laws” can continue to hold for years to come. But the approach is starting to reach the limits of the available resources. First, AI companies have sucked up all of the available free training data on the internet. They are now seeking more fuel for their models by making data-sharing deals with publishers and copyright holders, but it is unclear if that is enough to push the frontier forward. AI labs are also constrained by computing power. It is very energy-intensive to train and run large AI models. Back in 2022, GPT-4 was trained on several thousand of Nvidia’s chips. Estimates suggest that GPT-5 was trained on hundreds of thousands of Nvidia’s next-generation processors, with even more powerful chips on the way.

Takeaways by Macro Roundup® AI

  1. GPT-4 was trained on several thousand Nvidia chips in 2022, while estimates suggest GPT-5 was trained on hundreds of thousands of Nvidia's next-generation processors, with more powerful chips on the way.
  2. AI companies have exhausted the freely available training data on the internet and are now striking data-sharing deals with publishers and copyright holders, though it is unclear whether that supply is enough to advance the frontier.
  3. The scaling formula of more data plus more computing power that delivered five years of AI performance gains is reaching the limits of available resources, though many AI leaders still expect scaling laws to hold for years.

Related Articles:

  • The Kanye/Data Center Crossover — Data center capital spending differs from fiber and railroad investment because most of the cost sits in processors that are replaced every 3-4 years rather than in long-lived shells, power, and cooling connections. That short replacement cycle requires returns high enough to exceed a risk-adjusted cost of capital of 12-14% before the hardware must be replaced.
  • ‘Absolutely Immense’: The Companies On The Hook For The $3tn AI Building Boom — Google, Amazon, Microsoft and Meta are projected to spend more than $400bn on data centres in 2026, following more than $350bn this year. Morgan Stanley analysts estimate global data centre spending will approach $3tn through 2029, of which about $1.4tn comes from Big Tech capital expenditure, leaving roughly $1.5tn to be financed by investors and developers.
  • The AI Boom’s Hidden Risk to the Economy — Net income at Alphabet, Amazon, Meta and Microsoft rose 73% to $91bn in a quarter two years on, while their combined free cash flow fell 30% to $40bn, as heavy AI investment absorbs cash these firms once recycled into financial markets. That spare cash previously helped hold down long-term interest rates despite large federal deficits.

AI Summary. Benchmark scores show only modest capability gains in the latest large language model releases compared with the larger jump seen two generations earlier, alongside a broader convergence in performance across competing AI models. Commentators attribute this to inherent limits of the technology and the exhaustion of new training data, raising doubts that the spending on AI hardware is justified.

Joseph Wang Fed Guy Blog
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Following tepid reactions to the release of GTP-5, Joe Wang observes, “It is looking more like companies are spending hundreds of billions on rapidly depreciating GPUs that produce a commoditized product that most clients only modestly benefit from.”

Are AI capability gains slowing enough to question hardware spending?

Core argument: GPT-5 users widely expressed disappointment with the release, which seemed in some ways a step back, and benchmarks show only modest capability gains since the significant improvement delivered by version 4 two years earlier.

GPT-5 users widely expressed disappointment in the capabilities of the new release, which seemed in some ways a step back. This sentiment is reflected in benchmarks that show a modest improvement in capabilities since the significant improvement in version 4 released two years ago. In addition, the benchmarks suggest a broader convergence in the capabilities of AI models. Commentary suggests this could be due to inherent limitations in the LLM technology and exhaustion of new training data. AI is fascinating technology, but it may not justify the enormous sums spent in its pursuit. It is looking more like companies are spending hundreds of billions on rapidly depreciating GPUs that produce a commoditized product that most clients only modestly benefit from. The entire macro landscape would look very different without the support of the AI boom.

Takeaways by Macro Roundup® AI

  1. GPT-5 users widely expressed disappointment with the release, which seemed in some ways a step back, and benchmarks show only modest capability gains since the significant improvement delivered by version 4 two years earlier.
  2. Benchmarks suggest a broader convergence in AI model capabilities, with commentary attributing this to inherent limitations of large language model technology and exhaustion of new training data.
  3. Companies appear to be spending hundreds of billions on rapidly depreciating GPUs producing a commoditized product that most clients only modestly benefit from, and the entire macro landscape would look very different without the support of the AI boom.

Related Articles:

  • Honey, AI Capex is Eating the Economy — Capital spending on AI data centers is around 20% of railroads' peak spending as a share of GDP and is rising quickly, having already surpassed the dot-com era peak in telecom investment. Unlike long-lived rail infrastructure, data centers are short-lived assets requiring frequent hardware replacement, and the capital is being reallocated from other sectors.
  • Back to Our Regularly Scheduled Programming — The four largest US hyperscalers spent $440bn on capital expenditure and research in 2024, with $596bn projected for 2025, a 35% increase, leaving large gaps between AI spending and AI revenues. These firms account for more than half of all capital expenditure growth in the US equity market.
  • AI’s $600B Question — Comparing spending on AI chips to historical railroad construction overlooks that high-fixed-cost, low-marginal-cost businesses without monopoly or oligopoly power tend to see prices competed down to marginal cost, and that semiconductors continually improve in cost versus performance, unlike physical infrastructure. Speculative frenzies accompany technology, but the payoff will take a long time.

AI Summary. Investment-grade corporate borrowing costs stand 0.75 percentage points above benchmark government bond yields in the US and 0.76 points in the Eurozone, the narrowest spreads since 1998 and 2018 respectively. US companies issued $910bn of highly rated debt in the first half of the year, the second-largest first-half total on record.

Ian Smith and Will Schmitt Financial Times
Date Posted:

Borrowing costs for investment grade firms in the US and Eurozone are .75 and .76pp above the benchmark government bond yields, respectively, the lowest levels since 1998 and 2018, respectively.

Are corporate credit spreads at their narrowest levels in decades?

Core argument: US investment-grade credit spreads stood at 0.75 percentage points over benchmark government bond yields, their tightest since 1998, while Eurozone spreads reached 0.76 points, the narrowest since 2018, according to Ice BofA data.

The cost of borrowing for investment-grade companies in US and Eurozone credit markets is 0.75 and 0.76 percentage points above benchmark government bond yields, respectively, according to Ice BofA data. This took spreads in the two markets — a proxy for the risk of default — on Friday to their lowest levels since 1998 and 2018, respectively. “The credit markets are again weirdly confident that the global economy is going to be just fine, and I don’t know why they are as confident as they are,” said Ben Inker, co-head of asset allocation at asset manager GMO. “You just don’t seem to be getting paid that much for taking risk.” US companies have taken advantage of tight spreads to borrow heavily this year, issuing the second-most highly rated debt in the first half of a year on record at $910bn — even with April’s trade war lull.

Takeaways by Macro Roundup® AI

  1. US investment-grade credit spreads stood at 0.75 percentage points over benchmark government bond yields, their tightest since 1998, while Eurozone spreads reached 0.76 points, the narrowest since 2018, according to Ice BofA data.
  2. Ben Inker, co-head of asset allocation at GMO, said credit markets are "weirdly confident that the global economy is going to be just fine" and that "you just don't seem to be getting paid that much for taking risk.".
  3. US companies issued $910bn of highly rated debt in the first half of the year, the second-most on record for that period, even with April's trade war lull.

Related Articles:

  • Has Private Credit Broken the High-Yield Spread Signal? — The high-yield bond market has stayed at about $1.4tn while private credit assets have grown to as much as $2tn, and the highest-rated BB portion of the high-yield index has risen from 41% to 51%. Private markets have shifted riskier borrowers out of high yield, making that index safer.
  • The Growing Role of Private Credit: The Outlook for Corporate Finance — Banks provide a smaller share of corporate credit than before the financial crisis, with bank lending falling from about 27% of total non-financial corporate debt to about 20%.
  • Private Credit: Characteristics and Risks — Private credit loans are negotiated bilaterally outside traditional regulatory requirements and are illiquid because no secondary market exists, leaving investors expected to hold them to maturity or face steep losses if they need to exit. Scarce data on this growing form of nonbank lending makes assessing its risks difficult.

AI Summary. US interest rates have exceeded euro-area rates for well over a decade, with the federal funds rate target range at 4.25%–4.5% versus the European Central Bank's 2% deposit rate. The author argues this gap largely reflects stronger underlying US trend growth relative to Europe's underperforming economy.

Jesper Rangvid Rangvid's Blog
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Due to stronger growth, the underlying real short-term interest rate in the US is ~ 1pp higher than in the euro area. US inflation is also ~1pp higher. Jesper Rangvid concludes the current FFR is appropriate.

Do higher US interest rates than Europe's reflect stronger US growth?

Core argument: The federal funds target range stands at 4.25%–4.5% while the ECB's deposit facility rate is 2%, leaving US policy rates more than two percentage points above the euro area's.

The target range for the federal funds rate currently stands at 4.25%–4.5%. At present, the ECB’s key policy rate, the deposit facility rate, is 2%, more than two percentage points below the fed funds rate.The key point I want to make in this analysis is that US interest rates have been consistently higher than those in the euro area for well over a decade, dating back to 2012, and before that were usually higher too. Since rates began diverging in 2012, underlying US trend growth has risen relative to Europe’s. In essence, higher US interest rates thus largely reflect a stronger US economy. By the same token, there are solid reasons for lower rates in the euro area – namely, that its economy has underperformed relative to the United States.

Takeaways by Macro Roundup® AI

  1. The federal funds target range stands at 4.25%–4.5% while the ECB's deposit facility rate is 2%, leaving US policy rates more than two percentage points above the euro area's.
  2. US interest rates have been consistently higher than euro-area rates since 2012 and were usually higher before that as well.
  3. Higher US rates largely reflect a stronger US economy, as underlying US trend growth has risen relative to Europe's since rates began diverging in 2012.

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AI Summary. The authors decompose the gender earnings gap among college-educated workers into college major, graduate degree attainment, and graduate field, estimating that education differences were large for early birth cohorts and declined substantially. They estimate changes in fields of study contributed 0.128 to the gap's decline, reduced to 0.055 once shifts in field-specific returns favoring men are included.

Joseph Altonji, John Eric Humphries, Yagmur Yuksel and Ling Zhong National Bureau of Economic Research
Date Posted:
Is Database:
Database

The bulk of the decline in the gender gap among full-time college educated workers born btw the 1931 and 1984 birth cohorts is within occupation. Changes in fields of study contribute on net only ~5.5pp out of a more than 40pp drop.

How much have education choices narrowed the gender earnings gap?

Core argument: Holding relative returns to fields constant, changes in fields of study contribute 0.128 to the decline in the gender earnings gap, but cohort trends in relative returns to specific fields favored men over women, reducing that contribution to 0.055.

We decompose the education gap into the contributions from college major choice, graduate degree attainment, and graduate field. When evaluated at fixed relative returns to each degree type, we find that the education gap is large for the early cohorts but declines substantially and is an important part of the narrowing of the gender gap. We find that much of the large gap in earnings between the 1931 and 1950 cohorts is due to a cohort specific “residual component” that shifts the gender gap in earnings by the same amount for all college graduates. Most of the decline is within occupation, especially for the early cohorts. The residual gap varies little for the 1951 to late 1970s birth cohorts, after which it resumes its decline. When holding the relative returns to fields constant, changes in fields of study contribute 0.128 to the decline in the gender gap, but this decline is partially offset by cohort trends in the relative returns to specific fields that favored men over women, reducing the contribution of field-of-study changes to the decline to 0.055. Degree combinations contribute to the [overall level of the] gender gap, but very little to the decline in the gender gap over the full time period.

Takeaways by Macro Roundup® AI

  1. Holding relative returns to fields constant, changes in fields of study contribute 0.128 to the decline in the gender earnings gap, but cohort trends in relative returns to specific fields favored men over women, reducing that contribution to 0.055.
  2. Much of the large gender earnings gap between the 1931 and 1950 birth cohorts is attributed to a cohort-specific residual component that shifts the gap by the same amount for all college graduates, with most of the decline occurring within occupation.
  3. The residual gender gap varies little across the 1951 through late-1970s birth cohorts before resuming its decline, while degree combinations contribute to the level of the gender gap but very little to its decline over the full period.

Related Articles:

  • One Cohort at a Time: A New Perspective on the Declining Gender Pay Gap — The authors find the entire decline in the gender pay gap comes from newer cohorts entering with smaller-than-average gaps and older cohorts exiting with larger ones, with entry convergence driven by younger men's positional losses in the wage distribution, especially at higher-paying firms. They find labor-market exit became the sole contributor, implying no full convergence ahead.
  • Women’s Evolving Careers Helped Shrink the Gender Pay Gap — The share of women in their late 20s working in the four highest-paying broad occupation groups rose from about 30% to 55%, while the share in professional occupations rose from about 19% to 26%. Women early in their careers shifted away from predominantly administrative work toward better-paying occupations.
  • The ‘Gender Pay Gap’ Is a Myth That Won’t Go Away — The commonly cited figure that women earn 84% of men's pay compares average annual earnings of full-time, year-round workers, a category defined broadly enough to obscure differences in hours, experience, and occupation. Women over 40 average three fewer years of experience than men the same age, which the author says explains about a third of the gap.

AI Summary. The US government is in discussions to take roughly a 10% stake in chipmaker Intel, potentially by converting some or all of the company's federal semiconductor grants into equity, which could make the government Intel's largest shareholder. Intel has been slated to receive $10.9bn in such grants for commercial and military production.

Mackenzie Hawkins, Ryan Gould, and Josh Wingrove Bloomberg
Date Posted:

The Trump administration is considering taking a 10% stake in Intel via converting ~$10.9B in grants committed under the US Chips and Science Act into an equity stake. The USG would be the company’s largest shareholder.

Should the government convert chip grants into an equity stake?

Core argument: The Trump administration is in talks to take a stake of about 10% in Intel, a move that could make the US government the chipmaker's largest shareholder, according to a White House official and others familiar with the matter.

The Trump administration is in discussions to take a stake of about 10% in Intel, according to a White House official and other people familiar with the matter, in a move that could see the US become the beleaguered chipmaker’s largest shareholder. The federal government is considering a potential investment in Intel that would involve converting some or all of the company’s grants from the US Chips and Science Act into equity, said the people, who asked not to be identified because the information is confidential. Intel has been slated to receive a combined $10.9 billion in Chips Act grants for commercial and military production.

Takeaways by Macro Roundup® AI

  1. The Trump administration is in talks to take a stake of about 10% in Intel, a move that could make the US government the chipmaker's largest shareholder, according to a White House official and others familiar with the matter.
  2. The potential federal investment would involve converting some or all of Intel's US Chips and Science Act grants into equity, according to people familiar with the discussions.
  3. Intel has been slated to receive a combined $10.9 billion in Chips Act grants for commercial and military production.

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  • Nippon Steel Considers ‘Golden Share’ Idea For US Steel Deal — Nippon Steel is considering granting the U.S. government a "golden share" in U.S. Steel to advance its acquisition, a special class of stock whose holder can control board composition and wield influence over major management decisions. U.S.-listed companies generally cannot issue golden shares, but Nippon Steel plans to delist U.S. Steel.
  • The Chips Act Has Been Surprisingly Successful So Far — The US government has spent over half of its $39bn in Chips Act semiconductor incentives, and chip companies and supply chain partners have announced $327bn of investments over the next 10 years, according to Semiconductor Industry Association calculations. US data show a 15-fold increase in construction of computing and electronics manufacturing facilities.