Edward Conard

Top Ten New York Times Bestselling Author

  • “…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
  • “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
  • “Unintended Consequences represents the most cogent and persuasive analysis of the Financial Crisis to date.” - Andrei Shleifer, 1999 John Bates Clark Medal Winner
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
  • “Unintended Consequences is far smarter and more thought-provoking than most economics written for the general public” - Greg Mankiw, Harvard University, Former Chairman of the Council of Economic Advisors
  • “Unintended Consequences 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
  • “There are an amazing number of good ideas and interesting points made in Unintended Consequences. The thinking underlying it, and the obvious depth of understanding of the author, are very impressive.” - Steven Levitt, coauthor of Freakonomics; 2004 John Bates Clark Medal
  • “Unintended Consequences offers deep and well-argued analyses on almost every issue.” - The New York Times
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
Upside of Inequality Oxford Unintended Consequences
Buy the Books
  • Macro Roundup
  • About Roundup
  • About Ed Conard
  • Highlights
  • Topics
  • Subscribe
Edward Conard
  • twitter
  • facebook
  • linkedin
  • youtube
  • Email
  • Text Message (SMS)
  • Twitter/X
  • LinkedIn
  • Facebook
  • WhatsApp Message
Subscribe to Macro Roundup Emails
  • Mentions 367
  • Primary focus 78
Showing 78 database articles primarily about Innovation/Research
Currently filtering by:
  • Remove Innovation/Research
  • Remove "primary topics only" restriction
  • Remove 'Database'
Show all 7,326 articles
For whatever topics you select (currently: Innovation/Research):
Choose search scope

Your importance filter 'Database' shows fewer articles.

Remove filters to see full article counts

Which Entrepreneurs Boost Productivity?

AI Summary. 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.

Ufuk Akcigit, Harun Alp, Jeremy Pearce, and Marta Prato Federal Reserve Bank of New York
Date Posted:
January 5, 2026
Is Database:
Database
Is Important:
Important

Danish data show that the propensity to become an R&D worker or a “transformative entrepreneur” who pursues innovation is highly correlated with IQ. Productivity growth is driven by the “symbiotic relationship” between such entrepreneurs and R&D workers.

Which entrepreneurs drive disproportionate productivity gains?

Core argument: A distinct minority of entrepreneurs are "transformative" — those hiring at least one R&D worker — and the authors find these founders generate disproportionate productivity gains while tending to have high IQ scores and strong education.

We study who becomes an entrepreneur, along with their hiring and business decisions, and find that a distinct minority are “transformative.” These individuals, who generate disproportionate productivity gains, tend to have high IQ scores, be well-educated, and hire technical (R&D) workers. We identify entrepreneurs as individuals who register as primary founders of firms with at least one employee. Among this group, we distinguish transformative entrepreneurs, those who hire at least one R&D worker, from non-transformative entrepreneurs, who do not employ technical personnel. R&D workers are defined as individuals employed in occupations with high patenting intensity, reflecting their direct involvement in innovation-related activities. The shares of entrepreneurs and R&D workers vary across IQ deciles. The pattern is reversed across the two groups: individuals with higher IQs are more likely to be R&D workers, but less likely to be entrepreneurs. The data support the idea of productivity growth being driven by the symbiotic relationship between transformative entrepreneurs and R&D workers. For policymakers, the lesson is that when an economy has more R&D workers and transformative entrepreneurs, they sustain higher long-run productivity growth.

Takeaways by Macro Roundup® AI

  1. A distinct minority of entrepreneurs are "transformative" — those hiring at least one R&D worker — and the authors find these founders generate disproportionate productivity gains while tending to have high IQ scores and strong education.
  2. Across IQ deciles the pattern reverses between the two groups: individuals with higher IQs are more likely to be R&D workers but less likely to be entrepreneurs, with entrepreneurs identified as primary founders of firms with at least one employee.
  3. The authors conclude that economies with more R&D workers and transformative entrepreneurs sustain higher long-run productivity growth, which they attribute to the symbiotic relationship between the two groups.

Related Articles:

  • Transformative and Subsistence Entrepreneurs: Origins and Impacts on Economic Growth — Only 17% of firms are "transformative" entrepreneurs, defined as those hiring a research-and-development worker at some point; higher IQ and educational attainment strongly predict transformative entrepreneurship, though the likelihood of becoming an entrepreneur overall declines with IQ. The authors argue education subsidies address financial constraints more effectively than uniform startup or research subsidies.
  • The Economics of Inequality in High-Wage Economies — Information technology created investment opportunities exceeding the supply of properly trained talent, while trade with low-wage economies, low-skilled immigration, trade deficits, and aging demographics eased constraints on low-skilled labor and risk-averse savings. The author argues this framework, rather than rising cronyism, explains high returns to success, slowing productivity growth, and low interest rates.
  • The Extreme Shortage of High IQ Workers — Semiconductor manufacturing requires workers of above-average intelligence, with the most demanding roles needing IQs in the top 1 in 1,000 — a pool of about 164,000 people in the US, of whom perhaps 100,000 can be placed in such professions. The author argues this scarcity explains reliance on high-IQ workers abroad and on smarter tools and non-human intelligence.
  • Innovation/Research
  • GDP
    • Growth
  • Productivity
Previous articleJanuary 5, 2026Africa’s Growth Could Outpace Asia’s This YearThe IMF projects sub-Saharan Africa will grow at 4.4% in 2026, overtaking Asia. Per capita growth, however, will be 2 to 3% – far short of the more than 7% necessary to “replicate Asian-style transformation.”Next articleJanuary 5, 2026America’s Drone DilemmaCiting favorably the FCC’s recent ban on foreign-made drones and key components, Chris Miller argues “targeted import restrictions could look more attractive than blanket tariffs” as a path to incubating an American drone industry.
Showing 77 database articles primarily about Innovation/Research

The College Wage Premium in the Generative AI Era

AI Summary. The U.S. college wage premium fell from 0.626 to 0.575 after four decades of expansion, implying the first sustained negative growth in relative demand for college-educated labor. The authors estimate that weaker wage growth in occupations highly exposed to artificial intelligence accounts for roughly 28% of that decline, though the patterns are not causal.

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 AI exposure driving the decline in the college wage premium?

Core argument: The U.S. college wage premium fell from 0.626 in 2022 to 0.575 in 2026 after expanding for four decades, with Current Population Survey data implying the first sustained negative relative demand growth for college labor.

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 in 2022 to 0.575 in 2026 after expanding for four decades, with Current Population Survey data implying the first sustained negative relative demand growth for college labor.
  2. By 2026, moving from zero occupational AI exposure to full exposure had a -0.086 effect on wages, and combined with the college–non-college exposure gap this mechanism accounts for roughly 28% of the 2022–2026 drop in the college wage premium.
  3. Post-2022 wage growth slowed disproportionately in high-exposure occupations that employ a disproportionate share of college graduates, patterns that, while non-causal, indicate task displacement in AI-exposed white-collar occupations plays a quantitatively meaningful role in compressing the skill premium.

Related Articles:

  • Looking for the Ladder — The article argues that the hiring downturn in occupations most exposed to generative AI reflects interest-rate sensitivity rather than AI itself, since roughly 38% of workers in the most AI-exposed quintile work in information, finance and insurance, and professional and technical services, versus under 2% in the least-exposed quintile.
  • How Students and Recent Grads are Responding to the Rise of AI — Undergraduate enrollment in degrees tied to the most AI-exposed jobs has risen 8%, indicating students are moving toward rather than away from fields exposed to artificial intelligence. A decline in computer science degrees is more than offset by growth in other exposed fields such as engineering.
  • AI and Young-adult Jobs: The Real Mystery — Employment rates have declined for young adults both with and without college degrees, with non-degree holders faring marginally worse, and employment growth for young workers of all education levels has lagged that of other workers. Artificial intelligence can replace some lower-skilled jobs, but those jobs are too few to drive youth labor market weakness.
  • Innovation/Research
  • Productivity
  • Workforce
    • Education
      • College
    • Unemployment/Participation

The AI Re-Acceleration That Wasn’t

AI Summary. An analysis of Epoch's Capabilities Index data, controlling for developer and model family, finds no statistically significant breakpoint in AI progress; a segmented model fits no better than a straight-line trend (p = 0.615), with the estimated slope change ranging from -8.4 to +23.4 points per year.

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.”

Has AI capability progress accelerated beyond its linear trend?

Core argument: Using all observations from Epoch's Capabilities Index and controlling for developer and model family, a piecewise model shows no statistically significant breakpoint in AI capability trends (p = 0.615) and does not improve on a purely linear trend.

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. Using all observations from Epoch's Capabilities Index and controlling for developer and model family, a piecewise model shows no statistically significant breakpoint in AI capability trends (p = 0.615) and does not improve on a purely linear trend.
  2. The estimated change in slope of the AI capabilities trend carries a confidence interval of -8.4 to +23.4 points per year.
  3. Apparent AI acceleration results from selecting frontier observations only, choosing a breakpoint, ignoring variance collapse, and fitting separate lines on either side of that breakpoint.

Related Articles:

  • Why .400 Hitters Disappeared — and What It Means for AI — Artificial intelligence models' capability gains are shrinking in percentage terms, falling from about 15% relative to the baseline early on to roughly 10% for the same absolute increment, narrowing the gap between frontier and good-enough models. The author argues this points to a commoditizing market where price, not minor model differences, becomes the main differentiator, pressuring frontier firms' margins.
  • Chart of the Day: Small Models are Closing the Gap to Frontier AI — Achieving 60% on MMLU, a test of academic reasoning, once required a 540bn-parameter model but was reached by a 3.8bn-parameter model two years later, a 142x reduction in parameters. Models small enough to run on phones or sensors shift inference from data centers to devices, weakening the case for paying cloud providers per token.
  • Anthropic’s Best AI Model Struggles To Attract Users As Cheaper Tools Thrive — Spending on Anthropic's largest and priciest AI model has plateaued at about 11% of total outlay on the company's tools, according to payments data from 70,000 companies, breaking a pattern of corporate users defaulting to the most powerful models. Analysts and investors cite its high price and older models' adequacy.
  • Innovation/Research
  • Productivity
    • Investment

The AI Trade Is Losing One of Its Key Signals

AI Summary. An index blending artificial intelligence token prices and usage is ambiguous when it softens, since token prices have fallen more than 90% since 2023 while total spending has roughly doubled over the past year. Allianz Research measures a nearly 46% growth gap between artificial intelligence investment and sales, wider than the 32% divergence during the 2001 telecom bust.

Jan-Patrick Barnert and Michael Msika Bloomberg
Date Posted:
July 7, 2026
Is Database:
Database

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

Why does a softening AI token index send ambiguous signals?

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

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

Takeaways by Macro Roundup® AI

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

Related Articles:

  • AI: The ROI Runway Could Be Long Outside the Tech Sector — AI company valuations rest on the assumption that profit margins across non-tech firms will eventually rise, but outside a handful of sectors adoption is slow, creating a divergence between front-loaded valuations and slower cash flows. If the per-use costs of AI models converge toward zero, revenue may be insufficient for all major cloud providers.
  • Semiquincententacles — Leading artificial-intelligence labs are moving to usage-based token pricing on a cost-plus basis that includes total ownership costs, pushing token prices higher and giving some enterprises reason to migrate to cheaper open models. Frontier models remain necessary for cybersecurity, distillation training, agents, and scientific discovery, where maximum performance is required.
  • Chart of the Day: Small Models are Closing the Gap to Frontier AI — Achieving 60% on MMLU, a test of academic reasoning, once required a 540bn-parameter model but was reached by a 3.8bn-parameter model two years later, a 142x reduction in parameters. Models small enough to run on phones or sensors shift inference from data centers to devices, weakening the case for paying cloud providers per token.
  • Innovation/Research
  • GDP
    • Financial Markets
  • Productivity
    • Investment

The Age Of The Solopreneur

AI Summary. A surge in US business formation is being driven by solo entrepreneurs, with growth in AI tool usage and in solopreneur activity in high-AI-adoption sectors suggesting artificial intelligence accounts for a meaningful portion of the increase. The authors argue AI is lowering barriers to starting and growing a business by expanding what one-person firms can do.

Ernie Tedeschi, Marisa Rama and Chris Cruickshank Stripe Economics
Date Posted:
June 22, 2026
Is Database:
Database

New US business applications have surged since late 2024, driven mainly by “likely nonemployer” filings representing “solopreneurs” rather than businesses expected to hire. Data suggest AI may be enabling this by filling capability gaps without hiring.

Is AI driving the surge in one-person business formation?

Core argument: The recent surge in US business formation is not reflected in high-propensity applications, but the evidence points toward a structural increase in genuine small business activity over 2025 and 2026, driven by solopreneurs.

While the recent surge in US business formation is not reflected in high-propensity applications, the evidence points toward a structural increase in genuine small business activity over 2025 and 2026, driven by solopreneurs. Preliminary evidence of growth in AI tool usage and solopreneur growth in high-AI-adoption sectors suggests that advances in AI are responsible for a meaningful portion of this growth. We believe AI is lowering barriers to business formation and growth by expanding the capabilities of solopreneurs, further improving tools and platforms that cater to new businesses, and creating a new set of opportunities for entrepreneurs to pursue. Whether the magnitude of this effect is as large as the most optimistic readings of the data suggest remains to be seen, but we believe we might be in the early innings of a fundamental acceleration in business formation—which could have ripple effects throughout the economy.

Takeaways by Macro Roundup® AI

  1. The recent surge in US business formation is not reflected in high-propensity applications, but the evidence points toward a structural increase in genuine small business activity over 2025 and 2026, driven by solopreneurs.
  2. Preliminary evidence of growth in AI tool usage and solopreneur growth in high-AI-adoption sectors suggests that advances in AI are responsible for a meaningful portion of this growth.
  3. AI appears to be lowering barriers to business formation and growth by expanding solopreneur capabilities, improving tools and platforms serving new businesses, and creating opportunities for entrepreneurs, with possible ripple effects throughout the economy.

Related Articles:

  • Solopreneurs, Solow, and the SaaSpocalypse — For three decades after commercial electricity delivery began in 1882, inflation-adjusted output per worker grew just 0.5% annually, accelerating to more than double that pace only after factories were redesigned around electric motors rather than retrofitted from steam. The author argues a near-term absence of productivity acceleration from AI is not evidence none is coming.
  • AI-Native Firms∗ — The authors find AI-native U.S. startups are 25% smaller than non-AI startups in the same industry and cohort, with 13% higher engineer shares, roughly 15% lower shares of entry-level workers and managers, flatter hierarchies, and comparable valuations. They argue embedding AI into products lets firms scale knowledge work without large teams.
  • 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.
  • Innovation/Research
  • Growth
    • US Business Dynamism
  • Productivity
    • Startups
  • Workforce
    • Unemployment/Participation

Chart of the Day: Small Models are Closing the Gap to Frontier AI

AI Summary. Achieving 60% on MMLU, a test of academic reasoning, once required a 540bn-parameter model but was reached by a 3.8bn-parameter model two years later, a 142x reduction in parameters. Models small enough to run on phones or sensors shift inference from data centers to devices, weakening the case for paying cloud providers per token.

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

Small models have increasingly closed the performance gap with frontier models. To Kedrosky, this implies “edge compute becomes real,” which in turn, at the margin, means reduced relative demand for data center-based versus local compute.

Are small AI models closing the gap with frontier models?

Core argument: Reaching 60% on MMLU, a test of academic reasoning, required a 540bn-parameter model in 2022 but only a 3.8bn-parameter model by 2024, a 142-fold parameter reduction in two years.

A quiet story in AI isn't that large models keep getting better, it's that small ones are catching up quickly. In 2022, reaching 60% on MMLU, a test of academic reasoning, required a 540-billion-parameter model. By 2024, a 3.8-billion-parameter model hit the same threshold — a 142-fold parameter reduction in two years. The direction is clear, and the implications are wide-ranging. Edge compute becomes real. Models that fit on a phone or a sensor don't need a data center. Inference shifts from hyperscaler to device, which is a different supply chain and a different competitive moat than anyone has built for. The data center API business model has a half-life. If a fine-tuned 7B model running locally can handle 80% of enterprise use cases, the case for paying per-token to a cloud provider shrinks fast. The most exposed providers are those whose moats are model quality rather than distribution, i.e., the capex-heavy frontier models. Compute becomes less scarce.

Takeaways by Macro Roundup® AI

  1. Reaching 60% on MMLU, a test of academic reasoning, required a 540bn-parameter model in 2022 but only a 3.8bn-parameter model by 2024, a 142-fold parameter reduction in two years.
  2. Models small enough to run on a phone or sensor shift inference from hyperscalers to devices, which involves a different supply chain and competitive moat than providers have built for.
  3. The case for paying per-token to a cloud provider shrinks if a fine-tuned 7B model running locally can handle 80% of enterprise use cases, leaving capex-heavy frontier providers whose moats rest on model quality rather than distribution most exposed.

Related Articles:

  • Commoditization, Orchestration, and the New AI Stack — The author argues that as incremental gains from larger AI training runs shrink, the return on additional computing power falls, weakening the case for the training spending that dominates data center demand. Survivors of model commoditization will be those owning the orchestration layer or the applications where outcomes are priced.
  • The Geopolitics of AI: Decoding the New Global Operating System — The performance gap between the top American and Chinese AI models narrowed from 9.3% to 1.7%, with Chinese firms relying on cheaper open-source models—DeepSeek prices usage 20-40x below OpenAI's ChatGPT. This price advantage could win adoption in developing countries and build dependency on Chinese AI ecosystems.
  • Misanthropic: On Mythos, Bad Human Behaviors and Systems Vulnerabilities — Anthropic reports that its Mythos model has detected thousands of high-severity cyber vulnerabilities, including ones found by chaining multiple obscure software weaknesses, and that these hacking skills are emergent rather than a designed capability. Mythos completed a corporate network attack simulation estimated to take an expert over 10 hours, which no other frontier model had solved.
  • Innovation/Research
  • GDP
    • Financial Markets
  • Productivity
    • Investment

Challenging the Narrative of European Decline, Continued

AI Summary. Euro-area income per person falls behind the US when measured in constant 2021 prices but gains on the US when measured using each year's prices, suggesting Europe is holding its own in applying technology. The author argues Europe's real risk is being cut off from strategically important technologies.

Paul Krugman Paul Krugman Wonks Out
Date Posted:
May 19, 2026
Is Database:
Database

In 2021 prices, per capita GDP in the Euro area is falling relative to that of the US, but the opposite is true if one takes into account the benefits to Europe from falling tech prices. “The big benefits of IT come from applying it, [not] creating it.”

Does Europe's income gap with the US depend on price measurement?

Core argument: Euro-area GDP per capita relative to the US declines over the past 25 years when measured in constant 2021 prices, as the World Bank does, but rises when measured at each year's prices (PPP).

Let’s start by looking at GDP per capita in Europe (actually the euro area) as a percentage of GDP per capita in the US. If we do this using constant prices — the World Bank uses 2021 prices — we get the line in Chart 1 labeled “2021 prices.” This line shows Europe falling behind over the past 25 years. If, however, we simply use prices in each given year, we get the line labeled “PPP,” which shows Europe gaining on the US. We get a similar picture if we look at GDP per worker-hour, where the black line is calculated using 2021 prices and the blue line is calculated using PPP. IT progress is passed on to all consumers via lower prices. The big benefits of IT come from applying it, rather than creating it. And as I’ve tried to show, the data show Europe holding its own in the relative value of the goods it produces, indicating that European economies are doing fine when it comes to applying technological advances. What should worry Europe, instead, are the geopolitical implications of US/Chinese leadership in advanced technology. The risk of being cut off from strategically important technologies, once minimal, is now very real. And that risk, rather than misleading numbers about trends in real GDP per worker hour, is what should concern European policymakers.

Takeaways by Macro Roundup® AI

  1. Euro-area GDP per capita relative to the US declines over the past 25 years when measured in constant 2021 prices, as the World Bank does, but rises when measured at each year's prices (PPP).
  2. The same divergence appears in GDP per worker-hour, where the constant-2021-price series and the PPP series point in opposite directions for Europe relative to the US.
  3. The author argues that Europe is holding its own in the relative value of the goods it produces and in applying technological advances, and that the real concern is the risk of being cut off from strategically important technologies amid US and Chinese leadership.

Related Articles:

  • Europe v America: Who’s Really Winning? — Real GDP data show the US economy growing substantially faster than the European Union's, yet on a purchasing-power basis adjusting for price levels, the EU economy remains only slightly smaller than the US, as it was earlier. The author argues US dominance in information technology, where productivity growth is rapid and passed on as lower prices, reconciles the two measures.
  • Europe’s Tech Lag: Does It Matter? — If technology producers face competition, faster measured productivity growth in a tech-specialized country is passed to consumers in both countries through lower prices rather than higher relative living standards. Real GDP per worker over 1998-2024 diverged more between California and the rest of the US than between the US and Europe, largely reflecting industry composition.
  • From IT to AI: What Explains US Productivity Outperformance? — Larger information-technology quality adjustments and hours-worked measurement distortions explain about 30% of the average annual US-Euro area total factor productivity growth gap since 1995, leaving an adjusted gap near 0.25pp. The authors estimate greater US intangible investment spillovers contribute about 0.1pp, more efficient resource allocation about 0.1pp, and better management and larger firms about 0.05pp.
  • Innovation/Research
  • Productivity
© Copyright 2026 Coherent Research Institute · All Rights Reserved · Privacy · Terms