“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
“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
“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
“…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
“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 must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
“…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
“A full-throated defense of economic dynamism.” - The Wall Street Journal
“…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
“…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
AI Summary.The authors estimate roughly 57 gigawatts of operating US data center capacity against a planned development pipeline of about 509 gigawatts, which is not a completion forecast since projects may be delayed, scaled back, or abandoned. Their central scenario has 182.8 gigawatts of added capacity becoming operational through 2032, 117.2 gigawatts completed later, and 226.9 gigawatts never completed.
Stijn Van NieuwerburghBrookings Papers on Economic Activity
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Stijn Van Nieuwerburgh projects ~$10.3T will be spent on the AI buildout over 2025–2032, averaging 3.63% of GDP. He estimates 2/3 of that spending reflects IT equipment and one-third real estate and associated power infrastructure.
How much of the planned data center pipeline gets built?
Core argument: Using Cleanview project-level data from July 2026, the authors estimate roughly 57 GW of operating U.S. data center capacity against a planned development pipeline of roughly 509 GW, which they stress is not a completion forecast.
The required investment is enormous. A representative 200 MW AI training campus costs roughly $8.2 billion, about two-thirds of which reflects IT equipment and one-third the real estate and associated power infrastructure. We estimate that 183 GW is completed by 2032 with a further 118 GW completed after 2032. This pipeline will trigger about $10.3 trillion dollars of investment over 2025–2032. At an average of 3.63% of GDP per year, the projected buildout would be larger relative to the economy than the major U.S. canal, railroad, electrification, highway, and telecommunications investment booms. It would double the electricity consumption of the entire U.S. residential sector.
Takeaways by Macro Roundup® AI
Using Cleanview project-level data from July 2026, the authors estimate roughly 57 GW of operating U.S. data center capacity against a planned development pipeline of roughly 509 GW, which they stress is not a completion forecast.
The authors’ central scenario allocates the announced pipeline so that 182.8 GW of additional U.S. capacity becomes operational during 2025–32, including 19.0 GW tied to projects already operational in 2025 or 2026.
The authors’ scenario has 117.2 GW of planned data center capacity completed after 2032 and 226.9 GW never completed, applying lower realization rates to more distant and less fully documented projects.
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Rear Window— Major cloud providers posted annual revenue growth of 37% at Amazon Web Services, 43% at Microsoft Azure and 82% at Google Cloud, with rising revenue backlogs of $514bn and $496bn respectively at Google Cloud and Amazon Web Services. Earnings growth appears highly contingent on continued hyperscaler capital spending and research, which rose from $460bn to $870bn.
AI Set to be Largest CapEx Cycle Ever … and Soon Majority Externally Financed— Hyperscaler and data-center investment is tracking toward $1.5tn annually by 2030, which in real terms would exceed the 2005-06 housing boom, the 1990s fiber peak, and the 1882 railroad peak combined. External debt and equity are projected to fund close to 90% of the buildout, making it dependent on credit markets.
The Summer I Turned Pretty— A warning sign in a boom cycle is when companies closest to final demand weaken while capital spending beneficiaries keep thriving, as when telecom operators' shares rolled over while communications equipment stocks kept rising. Semiconductor stock prices and free cash flow are soaring while large cloud providers' shares stagnate and their free cash flow plummets.
AI Summary.The authors trace a channel from rising imports to lower domestic sales, employment, plant closures, profits, capacity utilization, and reduced investment in fixed capital and research. They note the computer and electronic products industry, which contributed most to the US manufacturing productivity slowdown, offshored production to Asia, reaching 84% import penetration.
Robert Gordon and Kenneth RyuNational Bureau of Economic Research
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Citing complementarities btw production and innovation, Gordon et al trace the origins of the end of productivity growth in U. S. manufacturing back to 2000, when sectoral output growth ceased amid increased import competition.
Do rising imports explain the US manufacturing productivity growth slowdown?
Core argument: The computer and electronic products industry, which contributed most to the U.S. manufacturing productivity growth slowdown, largely offshored production to Asia after 2005, reaching an import penetration ratio of 84% by 2018.
We trace a channel of causation from the arrival of imports to declining domestic sales and employment, plant closures, lower profits and capacity utilization, and less investment in fixed capital and R&D. The effect of imports on domestic innovation is more nuanced. Some authors argue that domestic manufacturing and innovation are complements; when production of components is offshored the growing distance from the production process inhibits further improvements that combine design and process innovation. The computer and electronic products industry, which contributed most to the manufacturing productivity growth slowdown, largely offshored production to Asia after 2005. By 2018 its import penetration ratio had reached 84%. This did not occur primarily because of lower labor costs. Instead the attraction of Asia was its emphasis on process innovation. “Manufacturing optimization” leads to the ability to ramp up production quickly at massive scale. Further explanations include government subsidies, geographically concentrated supplier clusters, and support for worker training.
Takeaways by Macro Roundup® AI
The computer and electronic products industry, which contributed most to the U.S. manufacturing productivity growth slowdown, largely offshored production to Asia after 2005, reaching an import penetration ratio of 84% by 2018.
The authors trace a channel of causation from the arrival of imports to declining domestic sales and employment, plant closures, lower profits and capacity utilization, and less investment in fixed capital and R&D.
Asia's attraction lay less in lower labor costs than in its emphasis on process innovation, with "manufacturing optimization" allowing rapid production ramp-up at massive scale, alongside government subsidies, concentrated supplier clusters, and support for worker training.
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Why Is Manufacturing Productivity Growth So Low?— Manufacturing productivity growth fell behind other sectors after computer and semiconductor manufacturing productivity, which had grown at roughly 15% and 11% a year, decelerated substantially. The authors also present suggestive evidence that quality improvements are missed in price measures, estimating manufacturing productivity growth is understated by 0.8 percentage points, including 1.7 points for durable goods.
What Would It Take to Bring Back US Manufacturing? Part 1: America’s Structural Headwinds— The US imports roughly 40% of the goods it consumes, directly and through imported inputs, with labor-intensive goods like apparel almost entirely imported. The authors argue America's main comparative disadvantage is its cost of production, driven mainly by labor costs, with real estate costs and a strong dollar also contributing.
Speed Can Reindustrialize America— The US has a relative surplus of low-paid hourly workers and a shortage of high-skill workers, so many less productive US manufacturers could buy a robot but could not attract the talent to program it. China faces the opposite conditions, with underemployed STEM graduates, residency rules complicating coastal factory hiring, and robot programmers earning a fraction of US wages.
AI Summary.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.
Hamza Abdelrahman and Andrew FoersterFederal Reserve Bank of San Francisco
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US labor productivity is ~8pp above 2022 levels, while TFP has shown little growth since 2024, echoing 1990s pre-surge dynamics. A regime-switching model using labor productivity gives a 57% probability the US is in a high-growth regime vs 21% using TFP.
Do recent productivity gains reflect better tools rather than broader advances?
Core argument: A regime-switching model puts the implied probability that the U.S. economy is in a high-productivity growth regime at about 57% based on labor productivity and 21% based on total factor productivity as of the fourth quarter of 2025.
[While] labor productivity has shown solid gains in recent years, the San Francisco Fed’s Total Factor Productivity (TFP) index- which adjusts for how much equipment, technology, and other resources workers have at their disposal - has shown modest growth. A similar divergence between labor productivity and TFP emerged in the 1990s technology before the full productivity benefits became more definitive. Figure 1 plots how labor productivity and TFP have evolved relative to 2022 values, right before the recent surge in labor productivity. Their paths have since diverged amid a surge in business investment in AI technology and infrastructure, such as data centers, with labor productivity growing at a faster pace in recent years. The increased investment has coincided with more productive workers but has not yet produced the broader gains that TFP is designed to measure, suggesting that productivity improvements so far reflect better tools for workers rather than fundamental economic advancement. Figure 2 shows the implied probability [from a regime-switching model] of the U.S. economy being in a high-productivity growth regime using labor productivity (blue line) and TFP (green line). The probabilities based on these two measures have picked up to varying degrees: around 57% based on labor productivity and 21% based on TFP as of the fourth quarter of 2025.
Takeaways by Macro Roundup® AI
A regime-switching model puts the implied probability that the U.S. economy is in a high-productivity growth regime at about 57% based on labor productivity and 21% based on total factor productivity as of the fourth quarter of 2025.
Labor productivity has posted solid gains in recent years while the San Francisco Fed's Total Factor Productivity index, which adjusts for the equipment, technology, and other resources workers have at their disposal, has grown modestly.
The divergence between labor productivity and TFP has coincided with a surge in business investment in AI technology and infrastructure such as data centers, suggesting productivity improvements so far reflect better tools for workers rather than fundamental economic advancement.
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America Is Experiencing A Productivity Miracle— America's post-2019 productivity acceleration came largely from professional services and management—about 10% of output and heavy users rather than producers of new technology—and from oil and gas. The information industry grew productivity around 6% annually, no faster than its 2000-19 average, with its output share steady at 5.3–5.5%.
Concentrated Growth: The Role Of The IT Sector— Since 1988, total factor productivity in the information technology sector has risen about 178%, versus about 12% across all non-IT sectors combined, while aggregate productivity rose about 40%. The IT sector accounts for roughly 45% of cumulative aggregate productivity growth, once sectoral linkages to downstream industries are weighted in.
An AI Productivity Boom? Don’t Count Your (Productivity Data) Chickens— Measured productivity can rise from compositional effects when lower-wage, lower-productivity workers leave the labor force, as happened during Covid and 2009, and immigration policy changes could produce a similar effect. Much of the recent productivity boost appears to come from capital utilization tied to increased investment in AI rather than workers becoming more productive using AI.
AI Summary.Excluding artificial-intelligence-related categories, US non-residential fixed investment has contracted at roughly a 3% annual rate over the past year, versus average growth above 5% in the prior decade, running about $130bn below trend and cutting GDP growth by roughly 0.4 percentage points. Credit remains plentiful, with tight corporate bond spreads.
Economist StaffThe Economist
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The AI buildout drove ~ half of US GDP growth in 2025, according to an Economist analysis. Non-residential non-AI related fixed investment contracted ~3% over the past year.
Is business investment outside artificial intelligence shrinking?
Core argument: Excluding AI-related information-processing equipment and software, U.S. non-residential fixed investment has contracted at an annualised rate of roughly 3% over the past four quarters, against average growth above 5% in the previous decade.
Strip out the splurge on information-processing equipment and software—the categories most closely tied to AI—and the picture looks grim. Over the past four quarters non-residential fixed investment excluding ai-related categories has contracted at an annualised rate of roughly 3%, compared with average growth of more than 5% in the previous decade. Investment in industrial and transport equipment has fallen by more than 2% over the past year. Manufacturing construction is down by 20%. In total, non-AI investment is running about $130bn below its trend from the last decade. This capex recession is reducing gdp growth by roughly 0.4 percentage points. [A] potential explanation, in which strong demand or heavy government borrowing pushes up interest rates and crowds out other private investment, also looks unpersuasive. Credit remains plentiful. The spreads between investment-grade corporate bond and Treasuries have seldom been this tight since the 1990s.
Takeaways by Macro Roundup® AI
Excluding AI-related information-processing equipment and software, U.S. non-residential fixed investment has contracted at an annualised rate of roughly 3% over the past four quarters, against average growth above 5% in the previous decade.
Non-AI investment is running about $130bn below its trend from the last decade, with industrial and transport equipment investment down more than 2% over the past year and manufacturing construction down 20%.
The capex recession in non-AI investment is reducing U.S. GDP growth by roughly 0.4 percentage points, and crowding out by higher interest rates looks unpersuasive as an explanation, since credit remains plentiful and investment-grade corporate spreads over Treasuries have seldom been this tight since the 1990s.
Related Articles:
$14 Trillion in IG Supply Coming to the Market— About $14tn of investment-grade fixed income supply is coming to market, combining roughly $10tn of US government debt to be refinanced over the next 12 months, a budget deficit near $2tn, and about $2tn of gross corporate bond issuance boosted by data-center builders. The author argues this growing supply is putting upward pressure on interest rates and credit spreads.
Let Them Eat Compute: Data Centers’ Implications for Mortgage Finance— The author argues the housing affordability crisis stems from the sharp decline in 30-year fixed mortgage rates rather than their subsequent rise, since fixed rates let rate dips boost purchasing power, lifting existing-home sales 25% and prices 32% relative to rents. A costly data center buildout is crowding out for-sale housing.
Larry Summers and Bob Rubin Join Our CIOs to Discuss the Surprising Economic Cycle— Adopting the Simpson-Bowles fiscal consolidation when interest rates were at zero would have risked excessive disinflation, since long rates had no room to fall to provide an offsetting expansionary impulse. After the large run-up in debt and rise in real interest rates, the developing fiscal situation risks inflationary pressure, Treasury market disruption, or crowding out of private investment.
AI Summary.America's post-2019 productivity acceleration came largely from professional services and management—about 10% of output and heavy users rather than producers of new technology—and from oil and gas. The information industry grew productivity around 6% annually, no faster than its 2000-19 average, with its output share steady at 5.3–5.5%.
Economist StaffThe Economist
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The 2019–2024 uptick in US productivity wasn’t driven by the information sector, whose growth and scale is on par with its 2000–2019 mean, but rather by professional services and management, which together make up ~10% of the economy.
Which industries are driving America's recent productivity acceleration?
Core argument: America's "information" industry, spanning software, telecoms, publishing and film-making, led productivity growth at roughly 6% a year in 2019–2024, no higher than its 2000–2019 annual average.
Between 2019 and 2024 the “information” industry—which covers areas from software and telecoms to publishing and film-making—came top with an annual rate of around 6%. That was no higher than the annual average in 2000-19. Nor is America’s recent uptick the consequence of this particularly efficient industry accounting for a bigger slice of the economy: in the past six years the sector’s share of total American output has hovered between 5.3% and 5.5%. Instead, some of the biggest jumps in productivity growth have come in professional services and management. Together, these make up around 10% of the American economy, up a little from 2019. They are the sorts of businesses that do not produce new technology, but are voracious users of it. Productivity growth also sped up in oil and gas. The shale-fracking revolution of the 2010s turned America from a net energy importer to an exporter. In 2023 it sold half as much energy abroad, net of imports, as Saudi Arabia. Since then, construction of new liquefaction plants for natural gas has allowed America to send the fuel to Europe and Asia, where it fetches higher prices than at home.
Takeaways by Macro Roundup® AI
America's "information" industry, spanning software, telecoms, publishing and film-making, led productivity growth at roughly 6% a year in 2019–2024, no higher than its 2000–2019 annual average.
The information sector's share of total American output stayed between 5.3% and 5.5% over the past six years, so the productivity uptick did not come from that industry taking a larger slice of the economy.
Some of the biggest productivity jumps came in professional services and management—about 10% of the American economy, up a little from 2019—which use technology heavily rather than produce it, and in oil and gas.
Related Articles:
Killer Incentives: Status Competition and Pilot Performance during World War II— Analysis of over 5,000 German World War II fighter pilots finds that public peer recognition was associated with higher victory scores among skilled pilots without higher mortality, while average pilots gained little in victories but died at higher rates. The authors conclude status-based awards can have uneven effects in high-risk settings.
Attention (And Money) Is All You Need: Why Universities Are Struggling to Keep AI Talent— The annual compensation gap between top industry and academic artificial intelligence researchers widened from roughly $294,000 to $1.5m, coinciding with accelerating academic out-migration toward large incumbent technology firms. Researchers who move to industry cut paper production by about 63% while increasing patent filings nearly sixfold, consistent with a shift from open science toward proprietary innovation.
An AI Productivity Boom? Don’t Count Your (Productivity Data) Chickens— Measured productivity can rise from compositional effects when lower-wage, lower-productivity workers leave the labor force, as happened during Covid and 2009, and immigration policy changes could produce a similar effect. Much of the recent productivity boost appears to come from capital utilization tied to increased investment in AI rather than workers becoming more productive using AI.
AI Summary.The authors find that startups with a larger share of tasks doable by generative AI cut employment beginning immediately after ChatGPT's release, with technically proficient founders restructuring teams toward higher-skill roles. Venture investors made smaller but more numerous deals, and new firm entry offset the decline in total startup employment.
Abhinav Gupta, Franklin Qian, Elena Simintzi and Yifan SunWorking Paper
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The release of ChatGPT was followed by rapid employment declines in startups with a large share of tasks that could be done by AI. The subsequent restructuring saw many new, smaller startups that employed a higher share of senior workers.
How does generative AI reshape startup hiring and venture investment?
Core argument: Startups with a larger share of tasks accomplishable by generative AI reduced employment sharply in the first quarter after ChatGPT's release, with no evidence of anticipation and monotonic declines thereafter, which the authors read as supporting a causal interpretation.
Using variation in task exposure across firms, industries, and markets, we show that startups with higher exposure to Gen AI [more precisely, a larger share of tasks accomplishable by AI], adjusted rapidly after the introduction of ChatGPT. A [negative] employment effect appears sharply in the first quarter following the release, with no evidence of anticipation, supporting a causal interpretation. Employment declines monotonically thereafter, through the end of the sample [Figure 1]. The reorganization of startup teams was concentrated among technically proficient founders, who adopted AI tools more aggressively and restructured their workforces around higher-skill, more flexible roles [Figure 3]. VCs in Gen AI-intensive sectors made smaller initial investments per firm but expanded the number of deals, broadening their exposure across a larger set of AI-related startups. This shift toward a more diversified, exploratory investment approach fueled a wave of new startup formation [Figure 4]. Employment among incumbent startups declined, but the surge in new firm entry muted the net effect on total startup employment. However, the share of managerial workers increased, indicating a shift in the structure of employment rather than a simple change in its level [Figure 2].
Takeaways by Macro Roundup® AI
Startups with a larger share of tasks accomplishable by generative AI reduced employment sharply in the first quarter after ChatGPT's release, with no evidence of anticipation and monotonic declines thereafter, which the authors read as supporting a causal interpretation.
Venture capitalists in generative AI-intensive sectors made smaller initial investments per firm but expanded their number of deals, a more diversified, exploratory approach that fueled a wave of new startup formation.
Employment among incumbent startups declined while a surge in new firm entry muted the net effect on total startup employment, and the share of managerial workers rose, indicating a shift in the structure of employment rather than its level.
Related Articles:
Continued Rise in Business Formation— The gap between growth-oriented and value-oriented new business applications is widening, a divergence likely driven by a proliferation of startups built around artificial intelligence. Large language models are lowering the barriers to starting a business.
Attention (And Money) Is All You Need: Why Universities Are Struggling to Keep AI Talent— The annual compensation gap between top industry and academic artificial intelligence researchers widened from roughly $294,000 to $1.5m, coinciding with accelerating academic out-migration toward large incumbent technology firms. Researchers who move to industry cut paper production by about 63% while increasing patent filings nearly sixfold, consistent with a shift from open science toward proprietary innovation.
Which Entrepreneurs Boost Productivity?— The authors find that a minority of entrepreneurs are "transformative" — generating disproportionate productivity gains, scoring high on IQ tests, being well-educated, and hiring research and development workers. They argue that economies with more such entrepreneurs and R&D workers sustain higher long-run productivity growth.
AI Summary.About half of the 1990s gap between American and European hours worked had reversed by the end of the 2010s. The authors estimate US hours per person declined after the 2000s because of rising benefits for the non-employed—especially health benefits like Medicaid—while non-US hours rose with higher wages and lower work costs.
Serdar Birinci, Loukas Karabarbounis, and Kurt SeeNational Bureau of Economic Research
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Work hours of Americans and Europeans were ~ equal in the early 1970s, but by the mid 1990s, Europeans worked much less than Americans. Half of this hours gap had vanished by 2019. The drop in US hours was collinear with an ~ doubling of Medicaid enrollment.
Why has the gap in hours worked between Americans and others narrowed?
Core argument: About half of the U.S.–Europe gap in hours worked that opened by the 1990s had reversed by the end of the 2010s, with the convergence concentrated on the extensive margin and observed for both men and women.
Whereas Americans and Europeans were working roughly the same hours in the early 1970s, by the mid 1990s, Americans were working much more than Europeans. We update Prescott’s observations on hours worked for advanced economies and document that about half of the hours gap in the 1990s has reversed by the end of the 2010s. While the decline in the U.S. hours is well documented in the literature, the increase in non-U.S. hours in the past two decades, both relative to the United States and in absolute levels, has not yet been analyzed systematically. The convergence in hours worked is concentrated on the extensive margin and is observed for both men and women. We offer a comparative study on the convergence of hours worked and ask, “Why do Americans no longer work so much more than non-Americans?” [Employing both nonstructural correlation analysis and a structural model of labor supply, we suggest that] U.S. hours per person declined after the 2000s because of the rise of benefits provided to the non-employed. Among these benefits, we find the most important role for health benefits and, in particular, Medicaid. For non-U.S. countries, the rise of labor supply is generally accounted for by a rise of wages and falling fixed costs and disutility of work.
Takeaways by Macro Roundup® AI
About half of the U.S.–Europe gap in hours worked that opened by the 1990s had reversed by the end of the 2010s, with the convergence concentrated on the extensive margin and observed for both men and women.
The authors attribute the post-2000s decline in U.S. hours per person to the rise of benefits provided to the non-employed, assigning the most important role to health benefits and, in particular, Medicaid.
Rising labor supply outside the United States is generally accounted for by higher wages and falling fixed costs and disutility of work, according to the authors' correlation analysis and structural model of labor supply.
Related Articles:
Hours Worked and Lifetime Earnings Inequality— US survey data show large differences in lifetime hours of work, and a calibrated model of labor supply and human capital accumulation implies that nearly 20% of the variance in lifetime earnings is accounted for by these differences, with 90% of that effect due to heterogeneity in preferences for work.
Comparing EU-to-US Output Per Hour— European Union output per hour worked has risen from about 72% of the US level in the early 2000s to about 82%. The EU's 28% shortfall in GDP per capita versus the US is roughly two-thirds attributable to lower output per hour and one-third to fewer hours worked; Germany's shortfall reflects fewer hours alone.
Europeans ‘Less Hard-Working’ Than Americans, Says Norway Oil Fund Boss— Norway's sovereign wealth fund argues that American companies are outpacing European rivals on innovation and technology because the US more readily accepts failure and risk and because Americans work harder and are more ambitious. US shares now make up almost half of the fund's equity holdings, up from 32% a decade earlier, while UK holdings fell from 15% to 6%.
AI Summary.The Federal Reserve's Distribution of Financial Accounts defines "private business" narrowly as proprietor's equity in noncorporate, nonfinancial businesses, excluding S-corporations, other private corporations, and private equity and hedge fund partnerships, which are lumped into "corporate equities." The Survey of Consumer Finances instead shows private business is roughly 45–50% of top 0.1% wealth.
Owen Zidar and Eric ZwickThe Everywhere Millionaire
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Zidar and Zwick find SCF data show ownership share in private businesses make up between 45–50% of top 0.1% American wealth, and ~80% of US households worth $30mm+ are business owners.
Does official wealth data overstate how much the ultrarich own public stocks?
Core argument: The Survey of Consumer Finances shows private business at roughly 45–50% of top 0.1% wealth, far more than the Federal Reserve's Distribution of Financial Accounts "private business" label implies.
“Private businesses” make up a declining and now-modest share of top 0.1% wealth in the Federal Reserve’s Distribution of Financial Accounts (DFA), with corporate equities dominating portfolios at the very top. The misleading implication is that the rich are primarily stockholders. In fact, they are not. The issue, we learned, is that the DFA’s “private business” category is much narrower than what most people mean by the term. It covers only proprietor’s equity in noncorporate businesses — partnerships and sole proprietorships. Not S-corporations. Not other private corporations. Not financial partnerships like private equity or hedge funds. Just noncorporate, nonfinancial businesses. Private corporations are valued separately, but the Fed can’t distinguish households’ holdings of private corporate equity from their holdings of publicly traded stocks. They’re all lumped together into “corporate equities.” The Survey of Consumer Finances (SCF) tells a different story about what the top 0.1% actually owns. Private business shows up as roughly 45 to 50% of top wealth — far larger than the DFA’s “private business” label suggests. About 80% of households worth $30 million or more are business owners.
Takeaways by Macro Roundup® AI
The Survey of Consumer Finances shows private business at roughly 45–50% of top 0.1% wealth, far more than the Federal Reserve's Distribution of Financial Accounts "private business" label implies.
The Distribution of Financial Accounts' "private business" category covers only proprietor's equity in noncorporate, nonfinancial partnerships and sole proprietorships, excluding S-corporations, other private corporations, and private equity and hedge fund partnerships.
The Federal Reserve cannot distinguish households' private corporate equity from publicly traded stocks, lumping both into "corporate equities," which makes the top 0.1% appear to be primarily stockholders.
Related Articles:
Top Wealth in America: New Estimates and Implications for Taxing the Rich— New estimates put the top 0.1%'s share of US wealth at 15%, up from 12.9% over a 15-year span, a smaller concentration than some earlier estimates but still leaving the top 1% holding nearly as much wealth as the bottom 90%. Pass-through business and public equity dominate top-end wealth, while pension and housing wealth dominate for the bottom 90%.
They’re Rich but Not Famous—and They’re Suddenly Everywhere— An analysis of Federal Reserve data estimates about 430,000 U.S. households are worth $30m or more, including roughly 74,000 worth at least $100m, with growth in the number of very rich households outpacing overall population growth over recent decades. Nearly 72% of the top 0.1%'s wealth consists of corporate equities, mutual fund shares, and private businesses.
Capitalists in the Twenty-First Century— Most income at the top of the US distribution is non-wage income, primarily profits from closely-held, mid-market firms in skill-intensive industries owned by working-age individuals. The authors find private business profit falls by three-quarters after an owner retires or dies prematurely, indicating most top earners are "working rich" deriving income from human capital.
