“…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
“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 represents the most cogent and persuasive analysis of the Financial Crisis to date.” - Andrei Shleifer, 1999 John Bates Clark Medal Winner
“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
“…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 full-throated defense of economic dynamism.” - The Wall Street Journal
“…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
“…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
AI Summary.AI adoption has risen to 22.4%, with labor market effects visible but narrow: employment drags appear in marketing, graphic design, customer service, and some tech occupations, offset by 209k in added data center-exposed construction jobs. Corporate layoffs attributed to AI totaled 113k year-to-date, including 11k in a single month.
Sarah Dong and Joseph BriggsGoldman Sachs
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While finding a “slight positive correlation between AI adoption and unemployment among younger workers,” GS argues AI’s impact on the labor market remains “narrow.” The technology sector’s share of total employment continues to decline relative to its pre-2022 trend.
Is AI's labor market impact still narrow as adoption rises?
Core argument: AI adoption rose to 22.4%, with labor market effects remaining visible but narrow, concentrated in marketing, graphic design, customer service, and some tech occupations.
AI’s labor market impact remains visible but narrow. We continue to observe employment drags in specific industries where AI-use cases have been established such as marketing, graphic design, customer service, and some tech occupations. However, this is offset by construction job growth in data center-exposed categories, which is up 209k since 2022 (relative to broader construction employment trends). The tech share of total employment continues to decline relative to the pre-2022 trend. The correlation between AI adoption and broad labor market slack measures remains limited but has strengthened in recent months. Corporate layoffs attributed to AI affected 11k employees in June, bringing the 2026 YTD total to 113k.
Takeaways by Macro Roundup® AI
AI adoption rose to 22.4%, with labor market effects remaining visible but narrow, concentrated in marketing, graphic design, customer service, and some tech occupations.
Construction employment in data center-exposed categories is up 209k since 2022 relative to broader construction employment trends, offsetting employment drags in industries with established AI use cases.
Corporate layoffs attributed to AI affected 11k employees in June, bringing the 2026 year-to-date total to 113k, while the correlation between AI adoption and broad labor market slack measures remains limited though it has strengthened in recent months.
Related Articles:
Graduating into Disruption: Labor Market Outcomes for AI-Exposed College Majors— College graduates in the majors most exposed to artificial intelligence show statistically significant employment declines following the arrival of AI chatbots, with the most-exposed 10% of majors down about 5 percentage points and the seventh and eighth deciles down about 2 points. The authors note flat pre-2020 trends and precise timing argue against alternative explanations such as uneven post-pandemic recovery.
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.
Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence— Employment for workers aged 22–25 in the occupations most exposed to artificial intelligence has declined roughly 18pp relative to less-exposed peers, with no comparable decline in other age groups. The authors find this operates mainly through reduced hiring, concentrated where AI substitutes for human tasks rather than complements them.
AI Summary.College graduates in the majors most exposed to artificial intelligence show statistically significant employment declines following the arrival of AI chatbots, with the most-exposed 10% of majors down about 5 percentage points and the seventh and eighth deciles down about 2 points. The authors note flat pre-2020 trends and precise timing argue against alternative explanations such as uneven post-pandemic recovery.
Cody Orr, Lee Tucker and Lawrence WarrenU.S. Census Bureau
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A Census Bureau analysis of 29% of recent US college grads ranked majors by AI exposure using pre-ChatGPT occupational patterns. After ChatGPT’s November 2022 release, employment fell across the top 4 exposure deciles, with the top decile down 5pp compared to the bottom 6 deciles.
Are graduates in AI-exposed majors seeing employment declines?
Core argument: Employment for graduates in the most AI-exposed decile of college majors declined by an average of five percentage points relative to 2022 after ChatGPT’s release, with statistically significant declines of about two percentage points for deciles seven and eight.
In the quarters prior to 2020, the event study coefficients are usually statistically indistinguishable from zero across all deciles. Post-ChatGPT, there is a small to moderate decline in employment relative to the control group across the top four deciles with a particularly large effect for the tenth decile. For deciles seven, eight, and especially ten, these are statistically significant declines: an average of two percentage points for deciles seven and eight and five percentage points for decile ten relative to 2022. Decile nine also shows evidence of statistically significant declines, but this might reflect reversion to long-run trends rather than a negative effect of AI. Employment effects are clearly most acute among the most-exposed decile of majors. The relatively fat pre-trends and precise timing help to rule out some alternative explanations, such as heterogeneity in the post-pandemic recovery or the effects of a tight labor market in the reference period.
Takeaways by Macro Roundup® AI
Employment for graduates in the most AI-exposed decile of college majors declined by an average of five percentage points relative to 2022 after ChatGPT’s release, with statistically significant declines of about two percentage points for deciles seven and eight.
Event study coefficients were usually statistically indistinguishable from zero across all exposure deciles in the quarters before 2020, with small to moderate employment declines concentrated in the top four deciles afterward.
Flat pre-trends and the precise timing of the employment declines help rule out some alternative explanations, such as heterogeneity in the post-pandemic recovery or a tight labor market in the reference period, while decile nine’s decline may reflect reversion to long-run trends.
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.
Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence— Employment for workers aged 22–25 in the occupations most exposed to artificial intelligence has declined roughly 18pp relative to less-exposed peers, with no comparable decline in other age groups. The authors find this operates mainly through reduced hiring, concentrated where AI substitutes for human tasks rather than complements them.
The College Wage Premium in the Generative AI Era— 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.
AI Summary.Prime-age adults aged 25–54 and older workers aged 55–64 have higher employment rates in Europe than in the US, while US youth employment is higher because Europeans spend longer in education. The share of young people not in education, employment or training is higher in the US than in Europe.
Chris GilesFinancial Times
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Despite Europe’s high social spending relative to the US, Chris Giles notes that prime-age adult (25–54) labor force participation in the Eurozone has overtaken that of the US, and there has been a dramatic convergence in the LFP of older workers.
Do Europeans work less than Americans?
Core argument: Prime-age adults aged 25–54 in Europe are more likely to be employed than their US counterparts, whether measured with EU or Eurozone data, and Europeans aged 55–64 also have higher employment rates than Americans.
It does not matter whether you use EU or Eurozone data, prime-age adults (between 25 and 54) in Europe are more likely to be in work than those in the US. Older people (between 55 and 64) also have higher employment rates in Europe. Younger people (between 15 and 24) are more likely to have a job in the US, but that results from Europeans educating themselves for longer. The proportion of young people not in education, employment or training is higher in the US than in Europe. So welfare is not stopping work. More than that, the higher-welfare north of Europe tends to have higher employment rates than the south, although there is convergence within the Eurozone. Spain, in particular, has enjoyed rapid improvements.
Takeaways by Macro Roundup® AI
Prime-age adults aged 25–54 in Europe are more likely to be employed than their US counterparts, whether measured with EU or Eurozone data, and Europeans aged 55–64 also have higher employment rates than Americans.
Young people aged 15–24 are more likely to have a job in the US than in Europe, but that gap results from Europeans spending longer in education, and the share not in education, employment or training is higher in the US.
The higher-welfare north of Europe tends to have higher employment rates than the south, with convergence within the Eurozone and rapid improvement in Spain.
Related Articles:
Why Do Americans No Longer Work So Much More Than Non-Americans?— About half of the 1990s gap between American and European hours worked had reversed by the end of the 2010s. The authors estimate US hours per person declined after the 2000s because of rising benefits for the non-employed—especially health benefits like Medicaid—while non-US hours rose with higher wages and lower work costs.
The Future of European Competitiveness – A Competitiveness Strategy for Europe— A European Union competitiveness report attributes the EU's labour productivity growth gap with the US largely to weaker activity in information and communications technology and large-scale digital services; excluding main ICT sectors, EU productivity was broadly at par with the US from 2000-2019. It estimates Europe's investment share must rise about 5 percentage points of GDP.
Ed Conard Debates Furman On “The Expected Value of Risk Taking”— A debate on U.S. fiscal policy argues that spending increases, rather than the tax increases needed to fund them, slow growth, and that higher taxes would reduce the expected value of America's innovative risk-taking. The argument holds that cutting debt below 100% of GDP is critical.
AI Summary.Employment in five industries tied to the data-centre build-out, including electrical contracting and equipment manufacturing, has risen roughly 320,000 above what broader construction and manufacturing trends would suggest. Engineers, software developers, mathematicians and data scientists have added about 730,000 jobs above the trend for professional employment overall.
Economist StaffThe Economist
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The Economist estimates that so far the AI boom has created ~1mm new jobs in the US, exceeding their estimate of ~200,000 layoffs attributed to AI since mid-2023.
Is the AI build-out adding jobs above trend?
Core argument: Employment in five industries tied to the data-centre build-out, from electrical contracting to equipment manufacturing, has risen by roughly 320,000 more since 2023 than broader construction and manufacturing trends would suggest.
[We] tracked five industries at the heart of the data-centre build-out, from electrical contracting to equipment manufacturing. Since 2023 employment in them has risen by roughly 320,000 more than broader construction and manufacturing trends would suggest. Not all of those jobs owe their existence to AI—grid upgrades and other factory building matters too. [We also] tracked employment in professional occupations closest to the AI boom—engineers, software developers, mathematicians and data scientists—and compared their growth since 2022 with professional employment overall. These roles have added roughly 730,000 jobs above trend in recent years. AI will not have created every single one of them. But it has almost certainly created quite a few.
Takeaways by Macro Roundup® AI
Employment in five industries tied to the data-centre build-out, from electrical contracting to equipment manufacturing, has risen by roughly 320,000 more since 2023 than broader construction and manufacturing trends would suggest.
Professional occupations closest to the AI boom—engineers, software developers, mathematicians and data scientists—have added roughly 730,000 jobs above the trend for professional employment overall since 2022.
Not all of these above-trend gains are attributable to AI, since grid upgrades and other factory building also matter, though AI has almost certainly created quite a few of them.
Related Articles:
The College Wage Premium in the Generative AI Era— 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.
Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence— Employment for workers aged 22–25 in the occupations most exposed to artificial intelligence has declined roughly 18pp relative to less-exposed peers, with no comparable decline in other age groups. The authors find this operates mainly through reduced hiring, concentrated where AI substitutes for human tasks rather than complements them.
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.
AI Summary.The unemployment rate for workers ages 22 to 34 without a college degree has rarely been lower in the past two decades, and relative to their own historical ranges the job market looks much better for blue-collar and manual-service workers. Degree-holders still face lower unemployment rates overall.
Theo Francis and Ray SmithWall Street Journal
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In 2026, the 12-month moving-average unemployment for college-educated 22–34-year-olds is above its post-2003 mean, while the rate for non-college peers is historically low. Prime-age college grads still have lower unemployment than those with no degree.
Is the job market unusually strong for workers without college degrees?
Core argument: The unemployment rate for workers ages 22 to 34 who never graduated from college has rarely been lower in the past two decades, based on data through July.
The unemployment rate for workers ages 22 to 34 who never graduated from college has rarely been lower in the past two decades. To gauge how the job market has shifted for each cohort, [Gad Levanon, Burning Glass’s chief economist] compared current unemployment rates for the different groups with their own range of unemployment rates since 2003. The analysis included data through July. By that measure, the job market looks much better for blue-collar workers, including those in construction and on manufacturing lines, and manual-service workers. It is [however] still easier to find a job with a college degree. The unemployment rate for degree-holders in their prime working years—ages 25 to 54—averaged 2.7% for the 12 months ending in July - well below the 3.6% rate for workers with just some college education, and 4.7% for people with a high-school diploma only.
Takeaways by Macro Roundup® AI
The unemployment rate for workers ages 22 to 34 who never graduated from college has rarely been lower in the past two decades, based on data through July.
Burning Glass chief economist Gad Levanon compared each cohort's current unemployment rate against its own range since 2003, and by that measure the job market looks much better for blue-collar workers in construction and manufacturing and for manual-service workers.
College graduates ages 25 to 54 averaged a 2.7% unemployment rate for the 12 months ending in July, below the 3.6% rate for workers with some college education and 4.7% for those with only a high-school diploma.
Related Articles:
College Grads Struggle to Find Jobs. Non-Grads Are Giving Up— The narrowing gap in unemployment rates between young college graduates and non-graduates appears driven mainly by non-graduates dropping out of the labor force rather than by diplomas losing value, since people not seeking work aren't counted as unemployed. The share of young non-graduates employed is 1.7 percentage points below pre-pandemic levels.
To Fix Education, Fix The Economy First— 30% of US adults have literacy skills typical of a 10-year-old, yet US workers scoring at that low level earn an average of nearly $30 an hour and two-thirds are employed, compared with about $20 and fewer than half employed in Britain. The author argues this reflects stronger economic growth rather than vocational education policy.
The College Wage Premium in the Generative AI Era— 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.
AI Summary.Unemployment has stayed at or below the Federal Reserve's estimated non-accelerating inflation rate of unemployment of under 4.5% for 57 months, tied for the longest such streak on record. The author argues this persistent labor market tightness is a key reason inflation has remained elevated and that keeping interest rates higher for longer is needed to reach the 2% target.
Torsten SløkApollo
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US unemployment has been under the Fed’s 4.5% estimate of the non-accelerating inflation rate of unemployment (NAIRU) for 57 months. Sløk argues “persistent tightness is a key reason inflation has remained elevated.”
Does a persistently tight labor market keep inflation above target?
Core argument: U.S. unemployment has stayed at or below the Fed's estimated NAIRU of under 4.5% for 57 months, tied for the longest such streak on record, leaving the labor market in excess-demand territory for an unusually long time.
With the Fed estimating the non-accelerating inflation rate of unemployment (NAIRU) at below 4.5%, and unemployment having stayed at or below that level for 57 months, tied for the longest such streak on record, the labor market has been operating in excess-demand territory for an unusually long time. That persistent tightness is a key reason inflation has remained elevated: when unemployment runs below NAIRU, wages and prices face sustained upward pressure. Prior episodes of sub-4.5% unemployment were typically far shorter. The current one is one of the longest on record, which helps explain why the ongoing inflation overshoot since 2021 has been so stubborn. The bottom line is that a strong economy is the reason why inflation has been high, and only by keeping rates higher for longer can the Fed cool inflation down towards the FOMC’s 2% inflation target.
Takeaways by Macro Roundup® AI
U.S. unemployment has stayed at or below the Fed's estimated NAIRU of under 4.5% for 57 months, tied for the longest such streak on record, leaving the labor market in excess-demand territory for an unusually long time.
When unemployment runs below NAIRU, wages and prices face sustained upward pressure, which the author identifies as a key reason inflation has remained elevated since the overshoot began in 2021.
Prior episodes of sub-4.5% unemployment were typically far shorter, and the author argues that only by keeping rates higher for longer can the Fed cool inflation toward the FOMC's 2% target.
Related Articles:
The U.S. Job Market is (Still) Inflationary— Prices for locally produced U.S. services are rising about 1–1.5 percentage points faster than in the years just before the pandemic, leaving underlying inflation as far from the Federal Reserve's goals as it was three years ago. Prices for manufacturing components and data-center-related loaded circuit boards have also risen.
Labor Force Participation Is High If You Measure It Right— The headline labor force participation rate — the share of people 16 and older working or looking for work — is a limited gauge of labor market health because participation varies greatly by age. Aging baby boomers and the addition of nearly 1.7m Americans 65 and older to population estimates add distortion.
Baby Busts and Growth Booms: Demographic Change and the Macroeconomy— A 1pp lower birth rate 20 years earlier is associated with 22 log points higher output per worker 20 years later and 29 log points higher after 40 years, about 0.73pp faster annual growth. The authors find lower birth rates are also associated with more labor-saving patents, larger high-tech shares, and higher productivity growth.
AI Summary.The headline labor force participation rate — the share of people 16 and older working or looking for work — is a limited gauge of labor market health because participation varies greatly by age. Aging baby boomers and the addition of nearly 1.7m Americans 65 and older to population estimates add distortion.
Justin FoxBloomberg
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Adjusted for the age composition of the population, US labor force participation, at 61.8%, is near an all-time high, reflecting delayed retirement of older workers.
Does the headline labor force participation rate accurately measure labor market health?
Core argument: The Bureau of Labor Statistics added nearly 1.7 million Americans aged 65 and older to its population estimates this year, reflecting belated adjustments stemming from the 2020 Census, which adds to distortions in the labor force participation rate.
The labor force participation rate is the number of people 16 and older who have jobs or are actively looking for them, divided by the civilian (that is, not uniformed military) noninstitutional (that is, not in prison or the like) population 16 and older. While this percentage can tell us useful things about, say, the sustainability of Social Security or the overall availability of labor, its validity as a gauge of the health of the labor market is limited by the reality that labor force participation varies greatly by age. It’s a flawed metric that is currently more flawed than usual as the youngest members of the giant baby boom generation age into their mid-60s. The addition this year of nearly 1.7 million 65-and-older Americans to the BLS population estimates, because of belated adjustments stemming from the 2020 Census, just adds to the distortions.
Takeaways by Macro Roundup® AI
The Bureau of Labor Statistics added nearly 1.7 million Americans aged 65 and older to its population estimates this year, reflecting belated adjustments stemming from the 2020 Census, which adds to distortions in the labor force participation rate.
The labor force participation rate divides people 16 and older who are working or actively looking for work by the civilian noninstitutional population 16 and older, a measure whose validity as a gauge of labor market health is limited because participation varies greatly by age.
The aging of the youngest baby boomers into their mid-60s makes the participation rate more flawed than usual, though the measure still tells us useful things about Social Security sustainability and the overall availability of labor.
Related Articles:
Baby Busts and Growth Booms: Demographic Change and the Macroeconomy— A 1pp lower birth rate 20 years earlier is associated with 22 log points higher output per worker 20 years later and 29 log points higher after 40 years, about 0.73pp faster annual growth. The authors find lower birth rates are also associated with more labor-saving patents, larger high-tech shares, and higher productivity growth.
AI, Immigration, and Collapsing Labor Force Participation— As labor force participation falls, a shrinking group of workers generates the income, taxes, and innovation supporting a growing number of non-workers, leaving the economy more fragile because labor shocks are concentrated among fewer participants. Labor scarcity raises the return on automation, robotics, and software, making growth depend on productivity rather than participation.
How Labor Force Participation Has Diverged Across Genders— Between 1976 and 2024 the trend labor force participation rate fell 10.1 percentage points for men and rose 8.2 points for women. Population aging lowered participation by about 4 points for each gender and rising education raised it (+6 for women, +3 for men), but the divergence is dominated by participation trends within age-gender groups.
AI Summary.The author argues that negative narratives, including pessimism about new technology, played an underappreciated role in the economic climate of the Great Depression, alongside more widely studied causes such as sweeping bank failures, a weak initial Federal Reserve response, and a tariff regime that reduced global trade.
Robert ShillerNew York Times
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Shiller argues that fears around the potential impact of AI – often fostered by the words of AI executives themselves – are reducing consumer confidence and risk a negative feedback loop. “There is a risk that fear can actually help birth the reality.”
Did pessimistic narratives about technology deepen the Great Depression?
Core argument: The Great Depression, the decade of economic ruin following the 1929 stock market crash, stemmed from multiple factors including a lack of banking regulation and monetary policy that led to sweeping bank failures.
Such pessimism was present in the Great Depression, the decade of economic ruin that followed the stock market crash of 1929. There were many factors that contributed to making that period as difficult as it was. A lack of banking regulation and monetary policy led to sweeping bank failures, the Federal Reserve’s initial response was weak and ineffective, and a punishing tariff regime reduced global trade. Many of these causes have been widely studied; less understood is the role I believe negative narratives, including negative narratives about new technology, had on the economic climate.
Takeaways by Macro Roundup® AI
The Great Depression, the decade of economic ruin following the 1929 stock market crash, stemmed from multiple factors including a lack of banking regulation and monetary policy that led to sweeping bank failures.
The Federal Reserve's initial response to the Great Depression was weak and ineffective, and a punishing tariff regime reduced global trade.
The author argues that negative narratives, including those about new technology, shaped the economic climate of the Great Depression, a role less understood than the widely studied causes.
Related Articles:
Emotions and Subjective Crash Beliefs— Using investor survey data and option prices, the authors decompose beliefs about stock market crashes into fundamental and subjective components, finding the subjective part is volatile and almost always positive. Investor sentiment, measured from survey narratives with a large language model, is correlated with this subjective component and with non-fundamental variation.
US Consumer Sentiment Picks Up on Easing Gasoline Prices— US consumer sentiment rose for the first time in four months as lower gasoline prices offered some relief from a surge in inflation, with the University of Michigan's preliminary sentiment index increasing to 48.9 from a record low of 44.8. The reading beat most economists' expectations but remains the second lowest in data going back to the 1970s.
Have We Entered an Era of High Productivity Growth?— Labor productivity has grown solidly while the San Francisco Federal Reserve's total factor productivity index, which adjusts for the equipment and technology workers use, has grown modestly, suggesting gains so far reflect better tools rather than broader advances. A regime-switching model puts the probability of a high-productivity regime at 57% using labor productivity and 21% using total factor productivity.
