“…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
“…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
“…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
“…reminds us that inequality sends a signal of what society lacks most, in America’s case, entrepreneurship and risk taking.” - Lawrence Lindsey, CEO, The Lindsey Group, former Director of the National Economic Council
“Unintended Consequences should be read by anyone who takes for granted the superiority of progressive taxation and has not thought carefully about the trade-offs involved.” - The New Republic
“…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
“…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
“Unintended Consequences offers deep and well-argued analyses on almost every issue.” - The New York Times
“…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
AI Summary.The structural unemployment rate fell from 7.8% to 4.8% between 1976 and 2024, with over half of the 3.3 percentage point decline driven by workforce composition shifts, particularly rising educational attainment, which alone accounts for 1 percentage point of the reduction.
Andreas Hornstein, Marianna Kudlyak, Greeshma Avaradi and Taerin KimFederal Reserve Bank of San Francisco
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Hornstein et al find that about half of the ~3pp drop in the trend unemployment rate since 1976 is due to compositional change in the work force towards lower unemployment “types ” – in particular, older and more educated workers.
Is rising education driving down structural unemployment?
Core argument: The U.S. trend unemployment rate fell 3.3 percentage points—from 7.8% in 1976 to 4.8% in 2024—with rising educational attainment alone accounting for 1.0 percentage point of that structural decline.
We find that the trend unemployment rate declined from 7.8% in 1976 to 4.8% in 2024 [Figure 1]. Roughly half of that decline reflects compositional change. Figure 2 separates the estimated cumulative change in the annual unemployment trend since 1976 into its components. The total decline was about 3.3 percentage points by 2024. Changes in workforce composition account for a little more than half of that decline. Rising educational attainment is the single largest compositional force, lowering the trend about 1pp. Figure 4 plots estimated education shares for entering cohorts of women, and these patterns are similar for male cohorts (not shown). The figure shows a steady long-run fall in the share of new female cohorts with less than high school education and a steady rise in the share with some college or a college degree. [Returning to Figure 2], changes in group-specific LFP rate trends contribute only ~0.3 pp. The remaining decline, a bit under 1pp, comes from lower group-specific trend unemployment.
Takeaways by Macro Roundup® AI
The U.S. trend unemployment rate fell 3.3 percentage points—from 7.8% in 1976 to 4.8% in 2024—with rising educational attainment alone accounting for 1.0 percentage point of that structural decline.
Workforce compositional shifts explain slightly more than half of the 3.3-percentage-point decline in trend unemployment since 1976, making demographic change the dominant driver over the period.
Group-specific labor force participation trends contributed only 0.3 percentage points to the trend unemployment decline, while lower group-specific unemployment rates drove the remaining approximately 1.0 percentage point reduction.
Related Articles:
Global Views: They’re Not Hiking— The decline in unemployment has been driven by lower labor force participation, not an increase in employment. Hatzius stresses continued weakness in wage…
Labor Force Participation Is High If You Measure It Right— The overall labor force participation rate is a misleading measure of labor market health because it does not account for age-related variation in work patterns, making it especially distorted when large population groups shift into retirement-age brackets.
AI Summary.The narrowing unemployment gap between young college graduates and non-graduates reflects rising labor force dropout among non-graduates, not equal job market outcomes; the share of young non-graduates who are employed is 1.7 percentage points below pre-pandemic levels and falling, while graduates are near recovery.
Justin FoxBloomberg
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Labor force participation is rising for American college graduates aged 25–29, and is now at 89.4% – above its pre-pandemic level. Participation among non-college members of that age cohort is falling; at 78.7%, it is now below its pre-pandemic level.
Are non-graduates disappearing from the job market?
Core argument: The narrowing education-unemployment gap among young adults reflects labor force withdrawal by non-degree holders—not diminished diploma value—as workers who stop seeking jobs are excluded from unemployment calculations.
The narrowing of [the unemployment gap btw college and non-college workers] in recent years has lent support to narratives that college diplomas are losing their value amid the rise of large language models, the purported return of blue-collar work, and other job market changes. But while it may well be that diplomas have lost value, the recent shrinking of the young-adult education-unemployment gap seems to be driven mainly by a different phenomenon. Growing numbers of young adults without college degrees simply aren’t trying to find work and thus aren’t counted in unemployment calculations. The employment-population ratio for young college grads is not far off from where it was just before the pandemic and seems as if it might be headed upward again after a sharp drop in 2023 and 2024, for non-grads it is 1.7 percentage points lower than before the pandemic and clearly trending downward.
Takeaways by Macro Roundup® AI
The narrowing education-unemployment gap among young adults reflects labor force withdrawal by non-degree holders—not diminished diploma value—as workers who stop seeking jobs are excluded from unemployment calculations.
Young adults without college degrees carry an employment-population ratio 1.7 percentage points below pre-pandemic levels and trending downward, while college graduates have nearly recovered to pre-pandemic parity.
Related Articles:
Labor Force Participation Is High If You Measure It Right— The overall labor force participation rate is a misleading measure of labor market health because it does not account for age-related variation in work patterns, making it especially distorted when large population groups shift into retirement-age brackets.
How Labor Force Participation Has Diverged Across Genders— Male labor force participation has fallen steadily since the 1970s, while female participation rose sharply before plateauing, driven by shifts in education levels and age composition rather than a single cause.
AI, Immigration, and Collapsing Labor Force Participation— Declining labor force participation concentrates economic output among fewer workers, raising the return on automation and making productivity growth the primary driver of expansion. Any productivity shortfall carries greater consequences because a shrinking worker base cannot compensate through increased participation.
The decline in unemployment has been driven by lower labor force participation, not an increase in employment. Hatzius stresses continued weakness in wage growth as evidence that the labor market is not tightening, and sees no hikes on the horizon.
With nonfarm payrolls and household employment down on the month, our estimate of underlying trend job growth slowed further to just 5k in July. Since this is below our estimate of breakeven job growth of 50k, similar numbers in coming months would probably reverse some of the decline in the unemployment rate from 4.5% in December to 4.1% in July. We think this decline deserves less weight than it normally would because it has been driven by lower labor force participation, not higher employment. This is visible not only in the headline employment/population ratio (whose sharp decline is partly driven by changes in the age structure of the population) but to some degree also in a composition-adjusted version that holds the age structure constant over time. The continued weakness in wage growth also argues against the notion that the labor market is tightening.
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Labor Force Participation Is High If You Measure It Right— The overall labor force participation rate is a misleading measure of labor market health because it does not account for age-related variation in work patterns, making it especially distorted when large population groups shift into retirement-age brackets.
The Jobless Boom Has Arrived— Corporate earnings are growing faster than stock prices, indicating the current market rally is driven by real profit gains rather than inflated valuations. Broad earnings growth across the economy is outpacing overall economic output, with inflation-adjusted revenue for large companies running well above GDP growth.
AI, Immigration, and Collapsing Labor Force Participation— Declining labor force participation concentrates economic output among fewer workers, raising the return on automation and making productivity growth the primary driver of expansion. Any productivity shortfall carries greater consequences because a shrinking worker base cannot compensate through increased participation.
AI Summary.China's youth unemployment rate rose to 17.9% in July, driven by a record influx of university graduates entering an already saturated labour market.
Sherry WangSouth China Morning Post
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China’s youth unemployment (16–24) climbed to 17.9% in July, on par with July 2025 and up 3pp from the previous month, as 12.7mm new university graduates entered the workforce – ~4% more than last year, superimposed on a slowing economy.
Does record graduate supply outpace job creation in China's economy?
Core argument: China’s youth unemployment rate (ages 16–24, excluding students) jumped 3 percentage points to 17.9% in July, as a record graduate cohort entered a saturated labour market and reversed three consecutive months of decline.
China’s youth jobless rate climbed to 17.9% in July, as a record wave of university graduates enters an already crowded labour market. The jobless rate for those aged 16 to 24, excluding students, rose 3 percentage points from 14.9% in June – the last of three straight months of contraction – according to data released by the National Bureau of Statistics on Wednesday. The rate last July was 17.8% and it then climbed to 18.9% the following month – the highest level since Beijing revised its methodology to exclude students in December 2023.
Takeaways by Macro Roundup® AI
China’s youth unemployment rate (ages 16–24, excluding students) jumped 3 percentage points to 17.9% in July, as a record graduate cohort entered a saturated labour market and reversed three consecutive months of decline.
At 17.9%, China’s July youth unemployment rate approaches the post-methodology-revision peak of 18.9% set in August 2023, confirming that annual graduate-cycle labour market stress remains structurally unresolved.
Related Articles:
China’s Great Jobs Squeeze— Over 53mn Chinese workers are employed in food delivery and ridesharing, with flexible and gig work projected to reach 320mn workers, reflecting weak aggregate demand that reduces worker bargaining power and forces acceptance of underemployment.
Chinese Student High-Flyers Set Sights On Police and Military Academies— Top-scoring Chinese students are increasingly choosing police and military academies over elite universities, with admission scores for some programmes reaching 650+/750 — placing them within 3 points of the country's most prestigious university. Public-sector job security is driving high achievers toward state institutions, with military academy entrants ranking on average
AI Summary.Young workers in the most AI-exposed occupations face an employment shortfall ~19% below less-exposed peers, driven by reduced hiring rather than job losses, and concentrated in roles where AI replaces rather than complements human tasks.
Erik Brynjolfsson, Bharat Chandar and Ruyu ChenStanford University
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If employment of the most AI-exposed workers aged 22–25 had kept pace with that of their less-exposed peers since January 2018, their employment would be 15% higher than at the July 2025 data vintage observed in our previous study, and 19% higher as of June 2026.
Is artificial intelligence reducing job opportunities for young workers in exposed occupations?
Core argument: no comparable decline appears in any older age group.
Figure 4 plots occupation event study estimates for the most exposed quintile (relative to quintile 1) for workers aged 22–25, tracing the long-difference coefficient as the endpoint of the difference is rolled forward month by month, with no controls and under four control sets: occupational interest rate exposure, education (college share), a work-from-home measure, and all three jointly. The relative decline grows to roughly 18pp by mid-2026. Other age groups show no comparable decline. This divergence, documented in prior versions of this study, has continued to widen; the shortfall was 15% below where it would have been had it kept pace with that of their less-exposed peers at the July 2025 data vintage, and 19% as of June 2026. It operates primarily through reduced hiring rather than increased separations, and declines are concentrated in occupations where AI usage substitutes for human tasks; complementary usage is associated with flat or rising employment.
Takeaways by Macro Roundup® AI
no comparable decline appears in any older age group.
The Impact of AI on the U.S. Labor Market— A difference-in-differences design finds 6.7% slower real-wage growth in AI-exposed occupations since 2023 than in low-exposure ones, with no detectable job…
AI Summary.Over 53mn Chinese workers are employed in food delivery and ridesharing, with flexible and gig work projected to reach 320mn workers, reflecting weak aggregate demand that reduces worker bargaining power and forces acceptance of underemployment.
Thomas Hale and William LangleyFinancial Times
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There are ~320mm “flexible workers” in China including ~53mm food delivery or rideshare drivers whose ranks have grown by ~10mm in the last two years. Flexible work is serving as a “shock absorber” in the face of broad-based labor market weakness.
Does weak demand push workers into lower-paying gig jobs?
Core argument: Flexible employment in China is on track to reach 320mn workers in 2025, up from 280mn the prior year—a 14% rise that reflects broad labour market weakness rather than platform-driven opportunity.
Flexible employment, an official term that is vaguely defined, implies a broader scope than gig work. It stood at 200mm in 2021 [including] part-time work and self-employment as well as “new forms of employment.” More than 53mm people as of 2025 work as food delivery or ridesharing drivers in China, up 10mm in two years, estimates the China New Employment Forms Research Center. [They] estimate that flexible employment will hit 320mm this year, up from 280mm last year. Andrew Batson, China research director at Gavekal, suggests flexible employment and gig work are “more of a symptom of broad-based labour market weakness in China than a totally independent development…Because aggregate demand is low, the bargaining power of workers is weaker, and they have to accept more underemployment and less favourable working conditions."
Takeaways by Macro Roundup® AI
Flexible employment in China is on track to reach 320mn workers in 2025, up from 280mn the prior year—a 14% rise that reflects broad labour market weakness rather than platform-driven opportunity.
Over 53mn Chinese workers are employed as food delivery or ridesharing drivers as of 2025, a figure that has grown by 10mn in two years, driven by weak aggregate demand forcing workers into underemployment.
Related Articles:
China’s Youth Unemployment Rate Edges Down In May As Economic Strains Persist— China's youth unemployment rate (ages 16–24, excluding students) fell to 15.6% in May but remains above the 14.9% recorded a year earlier. A record 12.7m graduates entering the labor market this summer is expected to tighten conditions further.
Chinese Student High-Flyers Set Sights On Police and Military Academies— Top-scoring Chinese students are increasingly choosing police and military academies over elite universities, with admission scores for some programmes reaching 650+/750 — placing them within 3 points of the country's most prestigious university. Public-sector job security is driving high achievers toward state institutions, with military academy entrants ranking on average
China Says Hostile Foreign Forces Are Driving Its Youth to Slack Off— Youth unemployment and disillusionment in China have driven millions of young people to reject work and ambition entirely. The Chinese government attributes this trend to foreign ideological influence rather than domestic economic conditions such as scarce well-paying jobs.
AI Summary.Guaranteed income transfers reduce total household earnings by more than the transfer amount, as other household members—particularly partners—work fewer hours and are less likely to advance in their jobs.
Elizabeth Rhodes, David Broockman, Eva Vivalt, Patrick Krause, et al.National Bureau of Economic Research
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In a randomized guaranteed-income experiment, giving one adult a transfer of $1,000/month for two years cut the other household members’ income by ~$1,700/year. Partners worked less and advanced less at work, while schooling and training among others rose.
Does guaranteed income reduce household work effort beyond the transfer amount?
Core argument: Guaranteed income transfers narrowed the gap between participant income and total household income by approximately $1,700 per year, a reduction driven by lower earnings among other household members rather than collective income gains.
Figure 4 summarizes treatment effects on the standardized family-level indices. The transfers’ effects reshaped the income and employment of other household members. The gap between participant income and total household income fell by about $1,700 per year (s.e. $800). The decline appears to reflect lower earnings among other household members. Effects on employment outcomes are consistent with this interpretation. Partner promotions and transitions to better jobs decrease significantly, but these effects are very small in magnitude. Partner hours and employment show more meaningful declines but are not significant in the unconditional analysis. Several other measures provide supporting evidence of negative effects on labor supply. Net transfers—the value given [to extended family] minus the value received—increased by roughly $135 per year. Estimates for household stability, decision-making, and the division of labor cluster near zero.
Takeaways by Macro Roundup® AI
Guaranteed income transfers narrowed the gap between participant income and total household income by approximately $1,700 per year, a reduction driven by lower earnings among other household members rather than collective income gains.
Guaranteed income transfers reduced partner labor supply, with statistically significant declines in promotions and job transitions, though effect sizes were small.
partner hours and employment showed larger but statistically insignificant declines.
A difference-in-differences design finds 6.7% slower real-wage growth in AI-exposed occupations since 2023 than in low-exposure ones, with no detectable job loss. The largest effects were for the lowest quartile (-10.7%) and service occupations (-24.3%).
We examine the wage and employment effects of AI adoption across U.S. occupations using observed usage data from the Anthropic Economic Index rather than the theoretical exposure measures that dominate prior work. Using a difference-in-differences design with occupation and year fixed effects across 321 matched occupations from 2015 to 2025, we find that high-exposure occupations experience a 6.7% decline in real wage growth post-2023 with no detectable employment effects. The effect is concentrated among the lowest earners: service workers face a 24.3% decline and the bottom wage quartile a 10.7% decline, while top earners show no significant effect.Today, 5.8 million workers are affected, but as AI adoption deepens across corporate America, this figure is likely to grow substantially, with significant implications for income inequality and labor market policy in the years ahead. Only 321 of roughly 800 BLS occupations were matched, and the post-2023 period may be partially confounded by post-pandemic labor market dynamics. [Editor’s note: Figure 3 shows both wage and employment growth and decline among high-exposure workers, but the exposure measure combines automated and augmentative use, and thus cannot distinguish substitution from complementarity.]
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