Edward Conard

Top Ten New York Times Bestselling Author

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  • “…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 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
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “Unintended Consequences should be read by anyone who takes for granted the superiority of progressive taxation and has not thought carefully about the trade-offs involved.” - The New Republic
  • “…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “…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
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “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
  • “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
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Staff Report People's Republic Of China

Kenneth Kang International Monetary Fund
Date Posted:
May 10, 2021
Is Database:
Database

China’s economy only 30% as productive as advanced economies, with aggregate TFP growth slowing from 2.8% 1999-2008 to 0.7% in 2009-2018.

China's economy, while having experienced significant growth, remains only 30% as productive as the world's leading economies. This productivity gap is particularly pronounced in the services sector, where business services productivity is just 17% of the global frontier, partly due to high entry barriers. Aggregate Total Factor Productivity (TFP) growth has decelerated sharply, from 2.8% in the decade before the global financial crisis to 0.7% between 2009 and 2018. This slowdown has contributed to a decline in GDP growth, which fell below 7% for the first time since 1991 during 2015-18. Despite some signs of improving labor productivity and TFP growth in 2017, these metrics remain significantly lower than pre-crisis levels. The need for institutional reform is emphasized as a critical step towards enhancing resource allocation efficiency and addressing investment misallocation, which could otherwise hinder China's economic potential.

Kenneth Kang and Sanjaya Panth, "Staff Report People's Republic Of China," International Monetary Fund, January 8, 2021, https://www.imf.org/en/Publications/CR/Issues/2021/01/06/Peoples-Republic-of-China-2020-Article-IV-Consultation-Press-Release-Staff-Report-and-49992

“….China has seen remarkable growth over the last decades, but with average sectoral productivity at about one third of the global frontier, there is ample opportunity for more. Productivity gaps are especially large in the services sector—for example, business services productivity stands at only 17 percent of the frontier level, owing in part to high entry barriers…”

Loren Brandt John Litwack Elitza Mileva Luhang Wang Yifan Zhang Luan Zhao, " China’s Productivity Slowdown and Future Growth Potential," World Bank, June 2020, https://openknowledge.worldbank.org/bitstream/handle/10986/33993/Chinas-Productivity-Slowdown-and-Future-Growth-Potential.pdf

World Bank, “…China has experienced a marked slowdown in growth in output per worker since the global financial crisis. In 2015-18, average GDP growth fell below 7 percent for the first time since 1991, to a large extent due to slowing growth in total factor productivity (TFP). Aggregate TFP growth slowed from 2.8 percent in the 10 years before the global financial crisis to 0.7 percent in 2009-18. In 2017, signs of improving labor productivity and TFP growth emerged but both remain significantly lower than their pre-crisis levels. …High investment growth was in part driven by the large fiscal stimulus packages introduced first in in response to the 2009 global recession and later to cushion the 2015-16 growth slowdown (see below). In contrast, TFP growth decreased sharply to 0.7 percent a year in the same period. Other studies also estimate that aggregate TFP has declined in recent years (Wei et al., 2017; Wu, 2017). The TFP growth slowdown is explored further in the following sections…”

Ed, reviewed the IMF report. The report confirmed (see first chart) the WSJ claim, “…China’s economy is only 30% as productive as the world’s best-performing economies …”however it’s data didn’t support, “…The IMF estimates that annual productivity growth averaged just 0.6% between 2012 and 2017, a sharp decline from an average of 3.5% in the previous five years…”However Ben noted a World Bank report that ~ mirrored the WSJ report“…Aggregate TFP growth slowed from 2.8 percent in the 10 years before the global financial crisis to 0.7 percent in 2009-18…” (See second chart)

Michael Pettis, “China’s Economy Needs Institutional Reform Rather Than Additional Capital Deepening," China Financial Markets, July 24, 2021, https://carnegieendowment.org/chinafinancialmarkets/82362

Our friend Mike Pettis commented on the World Bank report when it was published:"...Except to the extent thatthe phrase “improving the efficiency of resource allocation” is carrying an extraordinarily heavy load, I think this is likely to be the wrong approach and will lead mainly to more investment misallocation in the country. What China really needs is a transformation of its institutions in a direction that some might argue is very different from the direction it is currently following...."

Ed Comment:Surprising. Mark important and add to data base. What accounts for the 3x (=1/0.30) difference in our productivity vs China—capital per worker, human capital per worker (i.e. education), or TFP? Perhaps they can close much of the gap with our productivity by investing more capital per worker. That would be relatively easy. So would increasing levels of education. It might be hard to close a TFP gap if they are only growing TFP 0.6% a year (about the same rate as us). What do your 2 china buddies say about this.

Steve Comment:If you are interested Nick Lardy book from a few years ago The State Strikes Back does a great job flushing out the dynamics driving what you are describing. Under Xi they have tacked away from private market driven growth and are currently shrinking the role for private firms (so raising the role of SOE) and the market in the overall economy (Jack Ma’s disappearance is a good illustration), Lardy forecast that was going to slow productivity growth which the World Bank and IMF are now picking up. The men who run China understand that market economies diffuse power in a way that would threaten the CCP control, and it’s a good bet they will always optimize towards maintaining the CCP monopoly on power despite the tradeoffs, slower productivity growth for example. The shift under Xi shows it’s in fact a deliberate choice. Now that is likely enough for their middle term goals (for example in practical terms the PLAN likely has the USN beat today in terms of the distribution of relevant resources) as this chart of military spending inppP terms implies:

Ben Comment:The report also mentions that some of the service industries with the highest productivity gap are the least competitive: "Productivity gaps are especially large in the services sector—for example, business services productivity stands at only 17 percent of the frontier level, owing in part to high entry barriers.” It will also come as no surprise that the state operated enterprises (SOEs) are less productive than private firms. The graph I’m attaching says, while SOEs are declining in relative importance, they’re still important users of capital and credit. My interpretation is that SOEs are not allowing private firms to reach the scale they’d need to become big drivers of productivity and productivity growth. There also seems to be a geographic mobility issue: "Urbanization has helped rural workers to find work in economically active regions, where household incomes are twice as high as in rural areas. A more holistic reform to further improve the hukou system, preserve migrant worker land ownership rights, improve the efficiency of rural land markets, and increase spending on public services and social safety nets, would facilitate labor market mobility and raise growth.”Taken together - it looks to me like China’s productivity lag is unlikely to disappear because the reforms necessary (SOE reform, opening markets, mobility, etc) are unlikely to be popular among the control-obsessed Chinese government. I think will take a lot more than just capital deepening. The World Bank Report graph Steve pulled out shows that the Chinese are adding tons of capital per worker but not very much human capital and very little TFP. The increased capital is unlikely to be that productive if, as the IMF claims, it’s going into poorly run SOEs. So it seems like the Chinese are behind in all three: capital per worker, human capital per worker (i.e. education), and TFP, and they’re struggling to close the gap because they have institutional and structural issues that would make catch up hard even if capital per worker and human capital (education) investment increase.

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Previous articleMay 6, 2021The Growing Importance of Decision-Making on the JobBtw 1960-2020, total wage bill paid to managers in the US more than doubled from 15% to 32%, also leading to an uptick in age of peak earnings.Next articleMay 12, 2021The Rise of Market Power and the Macroeconomic ImplicationsAccording to @DeLoockerJan @JanEeckhout @GabrielUnger, aggregate markups have surged from 18% to nearly 67% since 1980, driven by firms with already high markups, indicating a rise in market power.
Showing 484 database articles primarily about either Productivity, Cronyism, Incentives/Risk-Taking, Innovation/Research, Institutional Capabilities, Intangibles, Investment, Startups, or Workforce Reorganization

The College Wage Premium in the Generative AI Era

AI Summary. S. 575 between 2022 and 2026, the first sustained decline in relative demand for college-educated labor in four decades. AI exposure in white-collar occupations accounts for roughly 28% of that drop, as wage growth slowed disproportionately in high-AI-exposure jobs where college graduates are concentrated.

José Azar, Mireia Gine and Javier Sanz-Espín Social Science Research Network
Date Posted:
September 4, 2026
Is Database:
Database

The college wage premium flattened in the mid-2010s and has fallen ~8% since 2022. The authors argue that this compression reflects a broad decline in the returns to formal schooling, rather than a decline in the upper tail.

Is the college degree losing its economic value to artificial intelligence?

Core argument: The U.S. college wage premium fell from 0.626 to 0.575 between 2022 and 2026—the first sustained decline in relative demand for college labor after four decades of uninterrupted expansion.

After expanding for four decades, the U.S. college wage premium [dropped] sharply from 0.626 in 2022 to 0.575 in 2026. Current Population Survey data through 2026 implies an unprecedented drop in relative demand for college labor—the first sustained negative relative demand growth. Post-2022 wage growth slowed disproportionately in high-exposure occupations, which employ a disproportionate share of college graduates. By 2026, going from zero occupational AI exposure to full exposure had a negative effect on wages of−0.086. Combined with the college–non-college exposure gap, this mechanism accounts for roughly 28% of the total drop in the college wage premium from 2022 to 2026. While non-causal, these patterns indicate that task displacement in AI-exposed white-collar occupations plays a quantitatively meaningful role in the recent compression of the aggregate skill premium.

Takeaways by Macro Roundup® AI

  1. The U.S. college wage premium fell from 0.626 to 0.575 between 2022 and 2026—the first sustained decline in relative demand for college labor after four decades of uninterrupted expansion.
  2. Moving from zero to full occupational AI exposure reduced wages by 0.086 log points by 2026.
  3. the college–non-college AI-exposure gap accounts for roughly 28% of the total premium compression over that period.

Related Articles:

  • Looking for the Ladder — The downtick in hiring in AI-exposed occupations started 6 months prior to the release of ChatGPT, and is “perfectly” aligned with the start of Fed rate hikes…
  • How Students and Recent Grads are Responding to the Rise of AI — Far from shying away from AI, American undergraduates “are flocking towards the most-AI-exposed degrees,” with enrollment in these majors up 8% last year…
  • AI and Young-adult Jobs: The Real Mystery — Since the summer of 2023, the employment rate for Americans 22–25 has declined for both college grads and non-college workers, a phenomenon beyond both…
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Gross and Net US Investment

AI Summary. Net investment has fallen from ~40% of gross investment in the 1970s to ~25% today, meaning three-quarters of gross investment merely replaces depreciating assets. The shift toward faster-depreciating information technology assets requires larger gross investment increases to achieve any given gain in productive capital per worker.

Timothy Taylor Conversable Economist
Date Posted:
September 4, 2026
Is Database:
Database

U.S. real net private domestic investment—which adds to the American capital stock—is now only ~25% as large as gross investment, down from ~40% in the 1970s. Taylor suggests the widening gap between gross and net investment reflects the relatively rapid depreciation of IT-related capital.

Does faster asset depreciation explain slowing productivity growth?

Core argument: Net investment has fallen from ~40% of gross investment in the 1970s to ~25% today, meaning three-quarters of gross investment now merely replaces depreciating capital rather than expanding the productive stock.

The figure divides net investment by gross investment. Back in the 1970s, net investment was often around 40% of gross investment, but the share has been slumping over time. For the last decade or so, net investment has been about 25% of the gross–that is, about three-quarters of gross investment is just making up for depreciation of the pre-existing capital stock. The likely reason for the growing gap between gross and net investment is that modern investment is more likely to be related to information technology [which] depreciates more rapidly and thus needs to be replaced and updated more often. If we want the average US worker to be using a greater amount of capital on the job–which was one of the key drivers of rising labor productivity in the past–it now takes a bigger rise in gross investment to lead to a given rise in net investment.

Takeaways by Macro Roundup® AI

  1. Net investment has fallen from ~40% of gross investment in the 1970s to ~25% today, meaning three-quarters of gross investment now merely replaces depreciating capital rather than expanding the productive stock.
  2. The shift toward information technology — which depreciates faster than physical machinery — is the primary driver of the widening gap between gross and net investment.
  3. Raising capital per worker, a historic engine of labor productivity growth, now requires a substantially larger increase in gross investment than it did several decades ago.

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  • Capital Is Making a Comeback — Btw 1985-2021 the capital intensity of the American economy was relatively flat as a rise in intangible investment was offset by a decline in tangible…
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The AI Re-Acceleration That Wasn’t

AI Summary. 615). Claims of re-acceleration result from cherry-picking frontier observations, selecting a breakpoint, ignoring variance collapse, and fitting separate lines on either side.

Paul Kedrosky Applied Complexity
Date Posted:
September 3, 2026
Is Database:
Database

Kedrosky argues AI capabilities continue to improve, but “the full composite data shows flattening relative gains, not acceleration…rolling relative model gains have fallen from their 2024 peak, while model dispersion has narrowed sharply.”

Are AI performance gains accelerating or just appearing to through selective measurement?

Core argument: Epoch’s Capabilities Index shows no statistically significant AI performance acceleration when controlling for developer and model family, with a breakpoint test returning p = 0.615 and a slope-change confidence interval of -8.4 to +23.4 pts per year.

Using all Epoch’s Capabilities Index observations, and controlling for developer and model family, there is no statistically significant breakpoint. A piecewise model—which splits the series into intervals and applies a sub-function to each segment—does not improve on a purely linear trend: p = 0.615, The estimated change in slope has a confidence interval of -8.4 to +23.4 points per year. In short, the maths shows there is no model acceleration, contrary to claims, and as expected. The result comes from selecting frontier observations only, choosing a breakpoint, ignoring variance collapse, and fitting separate lines on either side.

Takeaways by Macro Roundup® AI

  1. Epoch’s Capabilities Index shows no statistically significant AI performance acceleration when controlling for developer and model family, with a breakpoint test returning p = 0.615 and a slope-change confidence interval of -8.4 to +23.4 pts per year.
  2. Claims of AI re-acceleration rest on a methodological artifact: selecting only frontier model observations, pre-choosing a breakpoint, ignoring variance collapse, and fitting separate trend lines on each side of that breakpoint.

Related Articles:

  • Why .400 Hitters Disappeared — and What It Means for AI — As AI model performance converges toward a ceiling, relative gains per improvement cycle shrink, transforming frontier capability from a pricing moat into a commodity where price becomes the primary differentiator and margin pressure intensifies across leading providers.
  • Chart of the Day: Small Models are Closing the Gap to Frontier AI — Small AI models are closing the gap with large ones, achieving the same reasoning benchmarks with 142x fewer parameters than required two years ago. This makes on-device AI viable without data centers, compressing the economic case for cloud-based, per-query AI services.
  • Anthropic’s Best AI Model Struggles To Attract Users As Cheaper Tools Thrive — Spending on the most expensive AI model from a leading provider has plateaued at 11% of total outlay, as cheaper, older models prove capable of handling most business tasks.
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Understanding AI and Productivity

AI Summary. U.S. productivity growth has accelerated to ~2.2% annually since mid-2022, above the 2010s baseline, though pandemic-era labor market and business formation dynamics likely contributed alongside AI. Historical general-purpose technology booms sustained labor productivity growth above 2.5% for a decade or more, making the current acceleration substantial but not unprecedented.

Chad Syverson Economic Innovation Group
Date Posted:
August 28, 2026
Is Database:
Database

Syverson is skeptical that AI initiated the rise in productivity growth that began in 2023. The acceleration began while AI investment was small, and pandemic-era labor market churn and business dynamism match the acceleration’s start.

Is AI-driven productivity growth sustainable at historical technology boom levels?

Core argument: U.S. labor productivity has grown at roughly 2.2% annually since mid-2022, a pace exceeding the 2010s trend and, if sustained, implying GDP per capita roughly 7% higher within a decade than the prior trajectory.

Productivity from mid-2022 on has maintained a faster-than-2010s trajectory involving annual growth of about 2.2%. Could this acceleration be due to AI? Perhaps. The timing leans against AI being the sole initial cause. Additionally, there were well-documented increases in economic dynamism (labor market churn and business formation) during the pandemic emergence whose timing matches the acceleration’s start. Regardless of AI’s current effect, the longer the aggregate productivity acceleration continues, the more plausible it is that AI is an important driver. As for the magnitude, a sustained increase from 1.5 to 2.2% annual productivity growth would be substantial (after a decade, GDP per capita would be 7% higher than otherwise), but hardly unprecedented. The 1995–2004 productivity boom saw annual productivity growth of nearly 3% per year, and other past general-purpose-technology-related productivity boosts saw labor productivity growth in excess of 2.5% for a decade or longer.

Takeaways by Macro Roundup® AI

  1. U.S. labor productivity has grown at roughly 2.2% annually since mid-2022, a pace exceeding the 2010s trend and, if sustained, implying GDP per capita roughly 7% higher within a decade than the prior trajectory.
  2. The 1995–2004 productivity boom averaged nearly 3.0% annual growth, establishing that a durable AI-driven acceleration to 2.2% would be meaningful but well within historical precedent for general-purpose-technology cycles.
  3. Pandemic-era surges in labor market churn and business formation align more precisely with the productivity acceleration’s start date than AI adoption does, complicating AI-as-sole-cause narratives.

Related Articles:

  • AI and Productivity — Rising US labor productivity is driven by higher capital utilization—factories, servers, and hotel rooms running harder—rather than new investment or efficiency gains at the individual task level.
  • Google’s AI & Economy ATLAS v1.0: Mapping Gemini Usage in the Economy — Google’s new AI & Economy ATLAS maps 15M AI interactions to occupations, tasks, and activities, showing AI use is pervasive but not intensive…
  • Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools — Event studies indicate that adoption of AI coding tools raised “commits” (saved code updates) ~180%, but releases by only ~30%. Large upstream…
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US Widens AI-Driven Investment Gap With Europe

AI Summary. US corporate investment in equipment and facilities is projected to grow 40% in real terms by the end of next year, versus 12% in the euro area, widening a productivity gap where output per hour worked rose $14 in the US compared with $2 in Europe since 2018.

Sam Fleming, Amy Borrett and Olaf Storbeck Financial Times
Date Posted:
August 24, 2026
Is Database:
Database

Oxford Economics projects US real business investment will rise 40% over 2021–2027, ~3x the euro area’s 12%. US investment growth since 2024 has been largely information processing and software, but high US growth in GDP/hour is not “merely digital.”

Is artificial intelligence investment widening the transatlantic productivity divide?

Core argument: U.S. corporate investment in equipment and facilities is projected to rise 40% in real terms between 2021 and end-2026, versus 12% in the euro area and near-zero growth in Germany, sharply widening the transatlantic capital-spending gap.

Corporate spending on new equipment and facilities in the US is projected to increase 40% in real terms between 2021 and the end of next year, according to forecasts from Oxford Economics. The US surge compared with a real-terms increase of just 12% in the euro area, while German business investment is expected to have all but stagnated over the same period. Europe also faces a large and growing productivity gap with the US. “The United States has recently pulled further ahead of Europe,” Bart van Ark, a professor at the University of Manchester, told policymakers at the ECB Forum in Sintra. GDP per hour worked increased $14 in the US between 2018 and 2025, compared with just $2 in Europe. “The gap is not only a digital sector story,” added van Ark, stressing that the US outperformance extended to other sectors, including wholesale and retail as well as professional services.

Takeaways by Macro Roundup® AI

  1. U.S. corporate investment in equipment and facilities is projected to rise 40% in real terms between 2021 and end-2026, versus 12% in the euro area and near-zero growth in Germany, sharply widening the transatlantic capital-spending gap.
  2. U.S. labor productivity rose $14 per hour worked between 2018 and 2025, versus $2 in Europe, with outperformance spanning wholesale, retail, and professional services—not solely the digital sector.

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Why Big Tech’s AI Spending Is $3 Trillion Higher Than It Seems

AI Summary. Nine major technology companies carry ~$3tn in off-balance-sheet AI commitments — 5x their ~$600bn in reported capital spending — obligations that are growing faster than traditional investment and triple their combined lease and debt liabilities.

Peter Rudegeair and Peter Santilli Wall Street Journal
Date Posted:
August 17, 2026
Is Database:
Database

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

Are technology companies hiding the true cost of artificial intelligence?

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

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  • The Market Is Asking Questions — AI infrastructure debt spreads are widening as markets question whether returns on massive, front-loaded capital spending will outpace financing costs before assets depreciate. If compute demand plateaus from efficiency gains or slow adoption, the industry faces a glut of expensive, rapidly depreciating capacity.
  • Big Tech Credit Risks Rise Sharply As AI Spending Soars — The cost of insuring major technology companies' debt against default has reached record highs, driven by surging AI infrastructure spending that is straining balance sheets and pushing credit ratings toward junk status.
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