Edward Conard

Top Ten New York Times Bestselling Author

  • “Unintended Consequences is full of substance, it is one of the must-read books of the year, and once I finish it I will be giving it a second read through right away.” - Tyler Cowen, Professor, George Mason University
  • “Unintended Consequences is far smarter and more thought-provoking than most economics written for the general public” - Greg Mankiw, Harvard University, Former Chairman of the Council of Economic Advisors
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “There are an amazing number of good ideas and interesting points made in Unintended Consequences. The thinking underlying it, and the obvious depth of understanding of the author, are very impressive.” - Steven Levitt, coauthor of Freakonomics; 2004 John Bates Clark Medal
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
  • “…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
  • “…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “…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
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Leisure-Enhancing Technological Change

Lukasz Rachel Working Paper
Date Posted:
April 26, 2020
Is Database:
Database

Leisure technologies, such as the internet & TV, have contributed to a decline in TFP growth by altering time allocation patterns. Average hours worked have decreased by -0.5% annual growth rate across advanced economies, while leisure hours have increased.

The rise of leisure technologies, such as the internet and television, has contributed to a decline in Total Factor Productivity (TFP) growth by altering time allocation patterns. Over the past seven decades, average hours worked have decreased by an annual growth rate of -0.5% across advanced economies, while leisure hours have increased. Despite their small share of GDP, leisure technologies have a significant macroeconomic impact due to their non-rival nature and ability to divert time from productive activities. This shift is linked to the Solow Paradox, where rapid technological advancements coexist with weak productivity growth, as leisure innovations are not fully captured in GDP statistics. The proliferation of digital technologies has further accelerated these trends, highlighting the need for new economic models to address the implications of leisure-enhancing innovations on labor supply and productivity.

Leisure-Enhancing Technological Change: Extended Excerpt Image 1


“…The theory highlighted that the size of the economy - both in terms of its population and the level of income-per-capita - determines the conditions under which non-rival leisure technologies emerge and gain importance. The evidence suggests that this is indeed an important feature of the leisure economy. But other factors play a role too....One such factor is the share of time that households can feasibly allocate to marketable leisure. The first half of the 20th century saw a dramatic increase in this share. Two historical events were key: the introduction of the two-day weekend in the 1930s and the gradual adoption of household appliances - the washing machine, the flush toilet, the vacuum and others - from 1920s through 1940s and beyond. Both of these freed up time for other activities. It is plausible that this has acted to direct resources towards inventions and activities that complement leisure and leisure time, in the spirit of the directed technical change literature...... Incorporating these mechanisms in the model and assessing their empirical validity is an interesting avenue for future work. Second, the audio-visual entertainment revolution that started in the 1920s and rapidly accelerated in the 1950s has been propelled by the introduction of general purpose technologies that allowed for the signal to be transmitted to households: the radio receiver and the television set. More recently, invention of PCs, smartphones and tablets made it possible for the free leisure technologies to spread far and wide....To analyze these one could bring in the insights from Fernald and Jones (2014) on modeling general purpose technologies into the framework developed here. An alternative that stays closer to the current model is to view the arrival of these technologies as exogenous shifts in the productivity parameters of the leisure technology production functions: each of these GPTs made the delivery of free leisure services easier and cheaper and boosted the ability of the platform to turn consumers’ time and attention into brand equity...... It shows that such changes effectively turbo-charge the development of leisure technologies, exacerbating the effects discussed in the main body of the paper and potentially explaining the rise of the ever-powerful platforms. Relatedly, the arrival of the internet, social media and smartphones has arguably been associated with a culture shift towards online activities among consumers. And the dominance of search engines in consumer search, targeted advertising, and the ability to hook people onto devices have likely shifted firms’ perception about the importance and effectiveness of brand equity investments. Again, the present theory makes it possible to provide a preliminary picture..... shows that these changes are associated with further rapid increase in leisure technologies, sharper slowdown in hours worked, and lost ground in terms of productivity of the non-leisure part of the economy. These patterns ring true with the facts across advanced economies over the past 15 years or so. An open question is how the rise of the leisure sector interacts with heterogeneity, both at the household and at the firm level. On the household side, it is interesting to study how time allocation decisions interact with income and wealth inequality. For example, disaggregated evidence on time allocation across the income distribution shows that poor individuals increased their leisure more than the rich.... Allowing for household and income heterogeneity in the presence of leisure-enhancing technologies could bring out new insights and aid the debate on leisure-inequality. Considering firm heterogeneity may be important, too: the current setting is well suited to analyze equilibrium outcomes when heterogenous firms compete not only in prices but also in intangible assets. More productive firms may devote more resources to brand building, cementing their market share, with interesting implications for market power (De Loecker and Eeckhout, 2017). As leisure economy becomes ever more important going forward, the framework built here can be used as a base for explorations of some of the pressing policy questions, such as optimal taxation of platforms or competition- and anti-trust policy in presence of zero-price services. These ideas formulate an exciting research agenda for economics in general, and macroeconomics specifically, in the years to come….”

Lukasz Rachel, "Leisure-Enhancing Technological Change," Working Paper, April 24, 2020, https://drive.google.com/file/d/14SbcIolO3EBpgPtE466oty351tOkp9Xo/view

“….leisure-enhancing innovations are ubiquitous and yet their cost of production as a share of GDP is relatively small. TV channels, websites, social media and free newspapers are products that billions of people use every day, and businesses involved in supplying these services are highly profitable and very large….Measured at the cost of production the value of free services has been relatively stable at about 1-1.5% of GDP over much of the past century in the United States, rising recently due to the arrival of digital technologies: not negligible, but certainly not large either. The theory developed in this paper stresses the non-rival nature of leisure technologies, and underscores that leisure-enhancing technological change may be of first-order macro importance even if the share of the sector is negligible in aggregate.... The second feature of the data that this theory speaks to is the decline in average hours worked and a corresponding rise in leisure hours across developed economies over the past seven decades. Over the entire post-WW2 period average hours worked have been on a downward trend, with annual growth averaging around −0.5% across advanced world....The model developed here offers a novel way to reconcile the falling hours with other features of balanced growth. It does so bylinking the time allocation decisions to the advancement of leisure technologies. This mechanism is backed up by the evidence that changes in time allocation patterns have indeed been tightly linked to technology....Third, this paper can shed new light on the Solow Paradox (Solow, 1987). Few would disagree that current period features rapid technological change, and yet economy-wide productivity growth remains weak. This paper offers a way to reconcile the paradox: while clearly visible and salient to the consumer, leisure-enhancing innovations can indirectly diminish productivity growth through their impact on the effective growth of labor supply, and their true value is unlikely to be captured in the GDP statistics….”

Interesting new paper (complicated) argues that the growth of leisure technologies (Internet, Television) because the tech is so cheap has pushed people to spend more time on leisure, and this time diversion has contributed to the decline in TFP growth through changes in time allocation.

But I like the theory. I have often wondered why, if consumption is getting better, consumption doesn’t go up and investment go down. I get why work might go down. But both should go down. What is it about investment that allows it to compete with consumption, which keeps getting better? I’ve never seen anyone try to address the question.

Ed Comment:I’m skeptical of the analysis however. These trends occurred simultaneously, so of course it correlates. You need a lot of data with circumstances where the casual factors did and didn’t occur. I’m not sure they really have much.

  • Productivity
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Previous articleApril 26, 2020How Inequality Hurts the Economy and Complicates Policy Responses to the PandemicAt the lower bound, the economy cannot maintain equilibrium unless output falls.Next articleApril 27, 2020The Bearer of Good Coronavirus NewsDr. John Ioannidis highlights how biases in scientific studies can lead to exaggerated claims, particularly during the coronavirus pandemic. @AllysiaFinley @WSJ.
Showing 484 database articles primarily about either Productivity, Cronyism, Incentives/Risk-Taking, Innovation/Research, Institutional Capabilities, Intangibles, Investment, Startups, or Workforce Reorganization

The College Wage Premium in the Generative AI Era

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

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

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

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

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

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

Takeaways by Macro Roundup® AI

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

Related Articles:

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

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

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

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

Does faster asset depreciation explain slowing productivity growth?

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

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

Takeaways by Macro Roundup® AI

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

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  • Investment
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The AI Re-Acceleration That Wasn’t

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

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

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

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

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

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

Takeaways by Macro Roundup® AI

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

Related Articles:

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

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

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

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

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

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

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

Takeaways by Macro Roundup® AI

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

Related Articles:

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

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  • Investment
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    • Europe USA Relative Performance
  • GDP
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Why Big Tech’s AI Spending Is $3 Trillion Higher Than It Seems

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

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

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

Are technology companies hiding the true cost of artificial intelligence?

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

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  • The Market Is Asking Questions — AI infrastructure debt spreads are widening as markets question whether returns on massive, front-loaded capital spending will outpace financing costs before assets depreciate. If compute demand plateaus from efficiency gains or slow adoption, the industry faces a glut of expensive, rapidly depreciating capacity.
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