Edward Conard

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Some Facts about Dominant Firms

German Gutierrez and Thomas Philippon National Bureau of Economic Research
Date Posted:
October 29, 2020
Is Database:
Database

Top firms by global sales and market cap are not more productive than the top firms of the past and their contribution to aggregate productivity growth has fallen by one third since 2000.

Despite the perception of dominant firms as productivity leaders, data reveals that their relative productivity has not increased over the past two decades. Since 2000, the contribution of top firms to aggregate labor productivity has decreased by about 40%, challenging the notion that today's market leaders are more productive than their predecessors. The market share and value gap between leaders and followers have remained constant, with no significant upward trend in the market value of equity or sales. This stagnation is evident across sectors, including ICT-intensive industries, where relative productivities have been flat or declining. The findings suggest that the productivity paradox may not exist, as the top firms of today are neither larger nor more productive than those of the past, and their contribution to overall growth has diminished significantly since the 1990s. Understanding the changes since 2000 is crucial, as factors like declining competition and rising barriers to entry may have impacted investment and innovation.

German Gutierrez and Thomas Philippon, “Some Facts About Dominant Firms,” National Bureau of Economic Research, https://www.nber.org/papers/w27985

Since 2000 relative productivities have been flat or declining“…Fact 3. The relative productivity of dominant firms has not increased over the past 20 years. This fact is important because the super-star literature is motivated by the examples of Google, Facebook and Amazon. Amazon was founded in 1994, Google in 1998, and Facebook in 2004. Since these years, the stars have become neither larger nor more productive than the rest of the economy. Figure 6 in the appendix breaks down the relative productivity by sectors: manufacturing, non manufacturing, ICT-intensive and non-ICT-intensive. The trends are broadly similar to the ones in Figure 3 but Figure 6 brings some new insights. Thefirst insight is that the results are the same if we use Census data instead of Compustat. The second insight is that ICT intensive industries lag other industries. We find that non-ICT top firms doubled their relative productivity advantage in the 1970s from 19% to 38%. ICT stars experienced the same increase later, between 1985 and 2000. Since 2000, however, relative productivities have been either flat or declining…”

There is no productivity gap btw leaders and followers since 1995 “…Fact 2. The value gap between leaders and followers has not increased. 4 Are Top Firms Becoming More Productive? Panel A of figure 3 shows the relative productivity of top US firms. The left graph compares their productivity to BEA average productivity in their respective industries. We observe an increasing trend in the 1970s and 1980s. After 1995, however, relative productivity remains roughly constant. The right graph compares star firms to other firms in Compustat. This comparison is thus within a group of large firms. As expected, the productivity gap is smaller, around 10% instead of 40%, but the trends are similar. In particular, star firms’ relative productivity has not increased since 1995. Panel B figure 3 considers global stars, starting in 1990. The productivity gap on the left figure is larger than in Panel A, reflecting higher overall dispersion of productivity outside the US, a fact which is consistent with Hsieh and Klenow (2009). The global trends, on the other hand, are similar to the trends in the US. In particular the relative productivity of the global stars has declined since 2000. This basic fact is inconsistent with models that assume that star firms enjoy a growing productivity advantage….”

Size is not a defining feature of top firms“…Fact 1. The market share of dominant firms has not increased. Fact 1 is important because it appears to be inconsistent with theories that argue that today’s stars have relatively higher productivity advantages than the stars of the past. In standard models an increase in the relative productivity of the top firms leads to increase in their relative size measured by sales. Relative productivity differences could of course be present lower in the distribution of firms and in specific sectors of the economy. But Fact 1 says that size is not a defining feature of today’s top firms….”

They go on to highlight these three facts:

I though market capitalization was more compelling then sales numbers as it should be a leading indicator. This is how they interpret the market value of equity, “…Panel A of Figure 2 looks at US stars. The first plot uses the market value of equity. Across all industries the top firm is roughly 75% more valuable that its direct follower and there is no upward trend. The red circle line expands the comparison group to the top 5 firms. On average, the top firm is 1.5 times more valuable than the average of its top 4 followers. Once again, we do not see an upward trend. The right plot shows relative sales. There is no overall trend but we do see a decline in the 1990s followed by a recovery, which is consistent with the view of the 1990s as a period of fast entry and growth by relatively young companies. Panel B considers global stars and reaches similar conclusions. Consider for instance the top 10 global firms of a particular industry. The average revenues of a top-3 firm are double those of a top 4-10 firm, and its market value is about three times higher. These are large differences indeed,but they have not increased in recent years…”

Their overall conclusions, “..Our results challenge the common wisdom about dominant firms in the new economy and shed light on the productivity paradox. The paradox is often framed as a gap between tremendous innovations at large digital companies and lackluster aggregate productivity growth. Our findings suggest there might not be a paradox after all: the top firms of today are neither larger, nor more productive than the top firms of the past. In fact, their contribution to overall growth has declined in recent years, therefore explaining (some of) the paradox instead of reinforcing it. If we are correct, the most important question is to understand what has changed between the 1990s and the 2000s. During the 1990s large firms made tremendous contributions to overall growth, both internally (Hulten) and externally (reallocation). In the 2000s these beneficial effects have all but disappeared. Why are star firms not contributing as much as they used to? We do not have a definite answer but it is clear that something changed around 2000. Perhaps ideas are becoming harder to find as Bloom et al. (2018) argue. Or perhaps declining competition and rising barriers to entry have allowed incumbents to cut genuinely useful investment and innovation as Gutiérrez and Philippon (2019) argue….”

Here are their findings1)The market share of dominant firms has not increased. 2)The value gap between leaders and followers has not increased. 3)The relative productivity of dominant firms has not increased.4)Their Hulten growth contribution has decreased to approximately zero(note this is contribution of an individual firm to aggregate productivity growth equals its own productivity times its Domar weight. The Hulten contribution is defined as the Domar weight times the firm level increase in log sales per employee) 5)The contribution of top firms to aggregate labor productivity has decreased by about 40%.

They also do this exercise using market capitalization. “…Firms’ Consolidated Sales, Profits and Market ValueWe use firm-level sales, market value and employment data to identify stars. All nominal quantities are converted to US dollars using the exchange rates provided by Compustat. Compustat covers 100% of market capitalization in the U.S. and Canada; over 96% in Europe and over 88% in Asia. Data for the US starts in 1950 but provides stable coverage since 1965. Global data starts in 1987 but provides good coverage for most countries starting in 1990….”

Bottomline they find using, “…Our contribution to the literature is simply to look directly at the largest, most valuable companies in the world. We define two main sets of global stars: the top 100 firms by global sales in any given year, and the top 20 firms in each of our 25 industries. Similarly, we define two set of US stars: the top 20 firms by (global) sales and the top 4 firms within each 3-digit industry…“…We measure the evolution of dominant firms in the U.S. economy since 1960, and globally since 1990. Contrary to common wisdom, dominant firms have not become larger, have not become more productive, and their contribution to aggregate productivity growth has fallen by more than one third since 2000…”

Just an FYI, Philippon pushing back on the idea that market leaders are more productive

Ed Comment:How do they arrive at such different conclusion than everyone else?
Ben Comment:No no, this time it actually was an honest mistake. I don’t really have much invested here since my own research showed *local* concentrations were declining anyway. I’m all for good measurements contradicting the conventional wisdom; I just think C4 is a bad measurement. Here is how philipon describes the difference in his work and others’: "Two issues explain the differences between our findings and those in the literature. The first difference is that we actually look at the largest companies, which, perhaps surprisingly, is not what the literature has done so far. The second difference is that we distinguish domestic and global sales. A commonly used metric in the literature is consolidated sales over domestic GDP. This metric does not provide a reliable picture of the evolution of large firms when there is an upward trend in globalization combined with rapid growth in emerging markets. We argue that one should use either global sales over global GDP, or domestic sales over domestic GDP."They got on:"The data shows that the reason leading firms appear larger today than in the past is because of their foreign revenues. In terms of domestic revenues, today’s stars are exactly the same as the stars of the past 40 years." General comment - this is a bit of an odd paper. It’s only 9 pages
Ed Comment:Several thoughts come to mind. Dividing global sales by domestic GDP to measure share gain is nuts. WTF? But to your point about C4, nor is it correct to say the Google's share (power to earn monopoly rent) is declining using global sales that include chinese search engines. It all seems so political and unreliable. If economists want to wade in on this important issue and be respected by serious people like me, (I get that, sadly most everyone is woefully sloppy, stupid, or just a clown),why aren't they doing the analysis as properly as they possible can?
Ben Comment:Nose too close to the page. They are doing things as carefully as they can but that means arguing about NAICS-4 vs NAICS-6 and defining markets more exactly. It doesn’t mean taking a step back and trying to determine the Value-added of the industries with increasing concentration vs the value-added of industries with decreasing concentration.

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Previous articleOctober 29, 2020Why Is The Labor Share Declining?Decline in labor share may be driven by software substituting for workers in cognitive-intensive occupations, particularly since 2000.Next articleOctober 30, 20202020 Update: For Every 100 Girls.. Part IFor every 100 girls who completed high school, only 94 boys did. Social dysfunction indicators showed that for every 100 girls with ADHD diagnoses, there were 237 boys, and for every 100 girls with autism diagnoses, there were 400 boys.
Showing 485 database articles primarily about either Productivity, Cronyism, Incentives/Risk-Taking, Innovation/Research, Institutional Capabilities, Intangibles, Investment, Startups, or Workforce Reorganization

Moonshot Capitalism: AI Rewrites The Venture Capital Playbook

AI Summary. Deep-tech investment outside AI has exceeded $150bn since early 2024, surpassing the $133bn invested across the entire prior decade. Falling valuations for traditional software companies and outsized returns from early bets on capital-intensive ventures are pushing investors toward riskier, science-driven deals.

Tim Bradshaw Financial Times
Date Posted:
September 10, 2026
Is Database:
Database

Since the start of 2024, more than $150B of venture capital has been invested into non-AI “deep tech” firms whose products are rooted in significant engineering advances, exceeding the $133B invested in such firms btw 2010 and 2019.

Are investors abandoning software for capital-intensive science bets?

Core argument: Deep-tech investment excluding AI exceeded $150bn since early 2024, surpassing the entire $133bn deployed across the prior decade (through end-2019), as falling valuations for traditional software push venture capital toward capital-intensive scientific bets.

The AI boom is fuelling a resurgence in ambitious “moonshot” bets, as early SpaceX backers’ huge returns and falling valuations for traditional software companies force tech investors to embrace riskier and more capital-intensive dealmaking. Excluding the giant sums ploughed into AI start-ups, global investment in “deep tech” — companies whose products are rooted in big scientific or engineering advances — has exceeded $150bn since the start of 2024, more than the $133bn in the entire decade to the end of 2019, according to Dealroom. This year’s deep-tech investments have not yet surpassed 2021’s peak, which was propelled by battery and electric vehicle deals for the likes of Rivian and Northvolt — many of which turned sour, highlighting the risks involved in moonshot dealmaking.

Takeaways by Macro Roundup® AI

  1. Deep-tech investment excluding AI exceeded $150bn since early 2024, surpassing the entire $133bn deployed across the prior decade (through end-2019), as falling valuations for traditional software push venture capital toward capital-intensive scientific bets.
  2. The 2021 deep-tech peak — driven by battery and electric vehicle deals including Rivian and Northvolt — has not yet been surpassed, and the subsequent losses from those deals underscore the capital destruction risk inherent in moonshot dealmaking.

Related Articles:

  • 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…
  • Public to Private Equity in the United States: A Long-Term Look — Global venture capital returns are highly skewed: 62% of deals lose money, more than half lose 50–100% of invested capital, but fat-tailed outliers drive overall returns. This pattern mirrors historical whaling voyages, where payoffs were similarly variable and driven by rare outsized outcomes.
  • Gross and Net US Investment — 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.
  • Investment
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The College Wage Premium in the Generative AI Era

AI Summary. 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 in four decades. AI exposure in white-collar occupations accounts for ~28% of this drop, as moving from zero to full occupational AI exposure reduced wages by 0.086.

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 in 2022 to 0.575 in 2026—the first sustained negative relative demand growth for college labor in four decades, per Current Population Survey data.

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—the first sustained negative relative demand growth for college labor in four decades, per Current Population Survey data.
  2. Moving from zero to full occupational AI exposure reduced wages by 8.6 percentage points by 2026; because college graduates concentrate in high-exposure white-collar roles, this mechanism accounts for roughly 28% of the premium’s compression.

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.

Related Articles:

  • US Stock Market To Stop Shrinking For First Time In 23 Years — US equity supply is turning positive for the first time in over two decades, as a surge in IPOs and large share sales by major technology companies outweighs the buybacks and privatizations that have shrunk the stock market since 2003.
  • 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…
  • The Transition to a Higher Cost of Capital — Bridgewater Associates co-CIO Karen Karniol-Tambour expects 10-year Treasury yields to rise from the current ~4.5% to compensate for structurally higher fiscal…
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The AI Re-Acceleration That Wasn’t

AI Summary. AI capability growth follows a linear trend with no statistically significant acceleration; apparent re-acceleration results from cherry-picking frontier observations, selecting a breakpoint, ignoring variance collapse, and fitting separate trend 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.”

Does AI capability growth actually accelerate or just appear to?

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.

Related Articles:

  • The Two Europes — The European Union contains two divergent economies: a reforming frontier energized by security threats, and a stagnant interior where structural reform pressure remains absent.
  • Ed Conard Debates Furman On “The Expected Value of Risk Taking” — I debate @JasonFurman—Pres. Obama’s Chair of the Council of Economic Advisors—at Harvard over the effect of tax increases on the expected value of innovative…
  • The Future of European Competitiveness – A Competitiveness Strategy for Europe — An EC study of European competitiveness finds that EU gross value-added per hour worked increased by 0.7%/year from 2000-19, vs. 1.2%/year in the US. “Europe…
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