Edward Conard

Top Ten New York Times Bestselling Author

  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
  • “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
  • “…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
  • “A full-throated defense of economic dynamism.” - The Wall Street Journal
  • “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
  • “…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
  • “…a 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 represents the most cogent and persuasive analysis of the Financial Crisis to date.” - Andrei Shleifer, 1999 John Bates Clark Medal Winner
  • “…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard 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
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 886
  • Primary focus 486
Showing 486 database articles primarily about Productivity
Currently filtering by:
  • Remove Productivity
  • Remove "primary topics only" restriction
  • Remove 'Database'
Show all 7,218 articles
For whatever topics you select (currently: "Productivity", "Cronyism", "Incentives/Risk-Taking", "Innovation/Research", "Institutional Capabilities", "Intangibles", "Investment", "Startups", "Workforce Reorganization"):
Choose search scope

Your importance filter 'Database' shows fewer articles.

Remove filters to see full article counts

Within-Job Wage Inequality: Performance Pay and Job Relatedness

Rongsheng Tang, Yang Tang and Ping Wang, National Bureau of Economic Research
Date Posted:
June 25, 2020
Is Database:
Database

Btw 1983 and 2013, 80% of US wage inequality was driven by within-job factors, primarily performance pay & job relatedness. Performance pay rewards individual productivity, while job relatedness reduces inequality.

Btw 1983 and 2013, 80% of US wage inequality was driven by within-job factors, primarily performance pay & job...
Between 1983 and 2013, 80% of residual wage inequality in the US was driven by within-job factors, primarily performance pay and job relatedness. Performance-pay positions, which include bonuses and commissions, significantly contribute to wage dispersion as they reward individual productivity. Jobs with higher performance-pay incidence exhibit greater wage inequality. Conversely, job relatedness, defined as the alignment between one's field of study and occupation, positively impacts wages and reduces within-job inequality. A decline in job relatedness has been linked to increased wage disparity. The model calibrated to the US economy in 2000 attributes 42% of within-job inequality changes to performance pay and 26% to job relatedness, surpassing the impact of job-specific productivity. These factors are particularly influential in business/professional industries and sales occupations.

~ 80% of US residual wage inequality is driven by rising performance-pay incidence and “relatedness” note they use education as opposed to skills (on the job training for example) as a proxy for “relatedness”

“…We classify individuals into the high and low education groups according to their years of schooling. We then compute residual wage inequality for both groups during 1983-2013. The results suggestinequality within the high education group not only appears to be higher but also increases faster than the low education groupthe pattern becomes more prominent in the 1990s and the 2000s. In the high education group, even if we control for more job characteristics including industry, occupation, firm size, location, citizenship etc., about 90% of residual wage inequality still remains. This implies that wage inequality is primarily driven by within-industry/occupation inequality. To further confirm this finding, we decompose residual wage inequality into between-job and within-job components over all industry-occupation pairs. The decomposition result shows that within-job inequality accounts for more than 80% of residual wage inequality between 1983 and 2013,and its contribution to the change ranges from 70% to 110% between 1990 and 2002. To the best of our knowledge, this pattern has not been explored in the literature. In order to explain the aforementioned facts, we propose performance-pay incidence and job relatedness as the two potential causes of within-job inequality, in addition to differential job productivities. Workers in performance-pay position are paid according to how much they contribute, and such payments usually include bonus, commission, piece-rate and tips. The counter-part to this is the payment of a fixed hourly wage….we further examine the relationship between within-job wage inequality and performance-pay incidence. We find a significant positive relationship: jobs with higher performance-pay incidence usually have higher wage inequality. This hints the importance of the rising performance-pay incidence for the widening wage dispersion as observed. With regard to job relatedness, we measure it as the relatedness between the field of study of the highest degree earned and the occupation at the current job. We show that job relatedness has positive wage effect: among workers with similar schooling levels, those whose majors are more related to their jobs usually receive higher compensation than others. Moreover, we find a negative relationship between job relatedness and within-job wage inequality: jobs with more related matches are paid more equally. This implies that a reduction in job relatedness as observed in data could also serve to explain within-job wage inequality.…Over the past few decades, we find that about 80% of the widening residual wage inequality to be within jobs. We propose performance-pay incidence and job relatedness as two primary factors driving within-job inequality and embed them into a sorting equilibrium framework. We show that equilibrium sorting is positive assortative both within-job and across jobs. While performance-pay position amplifies within-job wage inequality through self-selection, the overall relationship between job relatedness and within-job wage inequality is found generally ambiguous. To quantify the role played by these factors, we calibrate the model to the US economy in 2000, where the model can account around 92% of the changes in within-job inequality among the highly educated from 1990 to 2000. Counterfactual analysis shows the contributions of performance-pay incidence and job relatedness are about 42% and 26%, respectively, both higher than that of job-specific productivity. While performance-pay incidence is particularly crucial for within-job wage dispersion in business/professional industry and professional occupation, job relatedness is the most important for mining/goods/construction industry and sales occupation….”

Rongsheng Tang, Yang Tang and Ping Wang, "Within-Job Wage Inequality: Performance Pay and Job Relatedness," National Bureau Of Economic Research, June 2020, https://www.nber.org/papers/w27390

  • Productivity
    • Workforce Reorganization
      • High vs Low Skill
  • Comparisons
    • Historical
    • Skill Level
  • Workforce
    • Inequality
    • Wages/Income
Previous articleJune 25, 2020Empty Cradles Means a Bleaker FutureIf birthrates remain at 2020 levels, we’ll forgo 9m more births over this decade, totaling 16m missed births over two decades compared to 2007 levels, according to @LymanStoneJ in @Newsweek.Next articleJune 25, 2020Tax Myths Of Warrenomics@LaurenceKotlikoff, The analysis of wealth inequality by Kotlikoff highlights key misconceptions in tax progressivity assessments, focusing on gross rather than net taxes & overlooking transfer payments like Social Security that benefit the poor.
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
  • GDP
    • Financial Markets
  • Productivity
    • 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 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…
  • Innovation/Research
  • Productivity
  • Workforce
    • Education
      • College
    • Unemployment/Participation

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…
  • Investment
  • GDP
    • Financial Markets
    • Growth
  • Productivity
    • Innovation/Research

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.
  • Innovation/Research
  • Productivity
    • Investment

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…
  • Investment
  • GDP
    • Growth
  • Productivity
    • Innovation/Research

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…
  • Investment
  • Comparisons
    • Europe USA Relative Performance
  • GDP
    • Growth
  • Productivity
    • Innovation/Research
© Copyright 2026 Coherent Research Institute · All Rights Reserved · Privacy · Terms