Edward Conard

Top Ten New York Times Bestselling Author

  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
  • “A full-throated defense of economic dynamism.” - The Wall Street Journal
  • “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 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 fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “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 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
  • “…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
Upside of Inequality Oxford Unintended Consequences

AI Summary. An analysis of authors at a leading AI research conference finds China is the largest originating source of elite AI talent, with its share of researchers holding Chinese undergraduate degrees rising 11 percentage points to 57%, while the US share fell to 13%. The US remains a net importer of AI researchers, gaining 2,145 while China lost 1,729.

Damien Ma and Binyi Yang Carnegie Endowment for International Peace
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Surveying education/employment histories of authors at the Neural Information Processing Systems conference, Ma and Yang infer China educated 57% of elite AI talent as of 2025, versus 13% for the US. Yet the US gained 2,145 researchers y/y while China lost 1,729.

Does China produce the most elite AI talent while America attracts it?

Core argument: Damien Ma and Binyi Yang find China’s share of elite AI talent, defined by undergraduate degrees earned in China, rose 11 percentage points to 57% in 2025 versus 2022, while the U.S. share fell to 13%.

[Using a methodology based on paper authors from the Neural Information Processing Systems conference (NeurIPS) Damien Ma and Binyi Yang find] China has established a commanding lead as the largest originating source of elite AI talent globally. Compared to 2022, the share of Chinese-origin AI talent, defined as those who received undergraduate degrees in China, increased 11 percentage points to 57% in 2025, while the U.S. share fell to 13%. The United States remains a magnet for global AI talent, particularly Chinese talent. Despite U.S.-China tensions, among AI researchers working in the United States, the 2025 share of those holding undergraduate degrees from China rose by 4 percentage points. When it comes to brain gain vs. brain drain, the United States saw a net gain of +2,145 researchers in 2025 while China registered a net loss of -1,729 researchers.

Takeaways by Macro Roundup® AI

  1. Damien Ma and Binyi Yang find China’s share of elite AI talent, defined by undergraduate degrees earned in China, rose 11 percentage points to 57% in 2025 versus 2022, while the U.S. share fell to 13%.
  2. Among AI researchers working in the United States, the share holding Chinese undergraduate degrees rose 4 percentage points in 2025, despite U.S.-China tensions.
  3. The United States recorded a net gain of 2,145 AI researchers in 2025, while China registered a net loss of 1,729, under Ma and Yang’s methodology based on NeurIPS paper authors.

Related Articles:

  • China Lures Home Its Top AI Talent From Silicon Valley — Elite artificial intelligence researchers have left US firms including Google DeepMind and OpenAI for Chinese companies such as ByteDance, Tencent, and Alibaba. Three China- and San Francisco-based headhunters say they relocated more than 30 US-based researchers to China over 12 months, up from low single digits the prior year.
  • China and the Future Of Science — China graduates about 5x as many medical and biomedical students as the US each year, 7x as many engineers, and nearly double the number of science and engineering PhDs, while one-sixth to one-fifth of US science and engineering graduates are themselves Chinese. One study found returning Chinese scientists lead-author 2.5x more papers than peers who stay in the US.
  • Why Europe Produces Nobel Prize Winners but Not Elon Musks — Europe trains more engineering graduates per capita than China and more STEM PhDs than any region outside the United States, yet the only EU-born companies to reach $100bn market value in the past fifty years, Spotify and BioNTech, listed on US exchanges. Europe captures 6% of global AI startup funding versus 61% for the United States.

AI Summary. The authors estimate that more than half of the rise in the top 1% income share since 1985 — 5.8 of 10.5 percentage points — flowed through pass-through businesses, counting roughly 3m wealthy private business owners with average net worth near $25m running law firms, dealerships, and medical practices.

Torsten Sløk Apollo
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More than half the increase in the top 1% income share since 1985 flowed through pass-through businesses to ~ 3mm private business owners with a mean net worth of ~$25mm.

Did most of the top 1% income gain come from private businesses?

Core argument: Owen Zidar and Eric Zwick find that 5.8 of the 10.5 percentage-point rise in the U.S. top 1% income share since 1985 flowed through pass-through businesses, more than half of the increase.

The income that lifted America's top 1% did not come mainly from tech or Wall Street, according to Owen Zidar and Eric Zwick. More than half of the rise in the top 1% income share since 1985, 5.8 of 10.5 percentage points, flowed through pass-through businesses. Zidar and Zwick, counted roughly 3 million wealthy private business owners, with average net worth near $25 million, running law firms, car dealerships, medical practices, commercial contractors and regional restaurant chains, the kind of businesses that make their owners wealthy and well known in their own communities without ever making them nationally famous.

Takeaways by Macro Roundup® AI

  1. Owen Zidar and Eric Zwick find that 5.8 of the 10.5 percentage-point rise in the U.S. top 1% income share since 1985 flowed through pass-through businesses, more than half of the increase.
  2. Zidar and Zwick count roughly 3 million wealthy private business owners with average net worth near $25 million, running law firms, car dealerships, medical practices, commercial contractors and regional restaurant chains.
  3. The income that lifted America’s top 1% did not come mainly from tech or Wall Street, according to Zidar and Zwick, but from businesses whose owners are well known locally without becoming nationally famous.

Related Articles:

  • What’s Missing in the Fed’s Data about Ultrarich Portfolios — The Federal Reserve's Distribution of Financial Accounts defines "private business" narrowly as proprietor's equity in noncorporate, nonfinancial businesses, excluding S-corporations, other private corporations, and private equity and hedge fund partnerships, which are lumped into "corporate equities." The Survey of Consumer Finances instead shows private business is roughly 45–50% of top 0.1% wealth.
  • They’re Rich but Not Famous—and They’re Suddenly Everywhere — An analysis of Federal Reserve data estimates about 430,000 U.S. households are worth $30m or more, including roughly 74,000 worth at least $100m, with growth in the number of very rich households outpacing overall population growth over recent decades. Nearly 72% of the top 0.1%'s wealth consists of corporate equities, mutual fund shares, and private businesses.
  • Capitalists in the Twenty-First Century — Most income at the top of the US distribution is non-wage income, primarily profits from closely-held, mid-market firms in skill-intensive industries owned by working-age individuals. The authors find private business profit falls by three-quarters after an owner retires or dies prematurely, indicating most top earners are "working rich" deriving income from human capital.

AI Summary. Wage gains have trailed inflation for five months, and consumer prices are 27% higher than at the start of 2021. The "misery index," the sum of the inflation and unemployment rates, stands at 7.5, up from 6.7 a year earlier.

David Uberti, Justin Lahart and Aaron Zitner Wall Street Journal
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Over the past five months, real wages as measured by average hourly earnings have declined. Okun’s “misery index” is now 7.5, just above its October 2024 level of 6.7.

Are wage gains falling behind rising consumer prices?

Core argument: Consumer prices are 27% higher than in January 2021, when Biden took office, and wage gains have failed to keep pace with inflation for five months.

For five months, wage gains haven’t kept pace with inflation and, especially with the sharp increase in gasoline prices over the past few weeks, they aren’t likely to keep up this month. University of Chicago Booth School of Business economist Christina Patterson said employees who achieve pay bumps often bear a burden. “You negotiate. You get an outside offer. You switch jobs. You threaten to leave,” she said. “All of those actions are costly. They disturb employer-employee relationships. You lose co-workers. You have to move.” Consumer prices are now 27% higher than they were in January 2021. One long-popular benchmark of economic conditions is the “misery index,” a simple summing up of the inflation and unemployment rate, it now stands at 7.5, higher than the 6.7 it was at in October 2024, just before Harris lost the presidency to Trump.

Takeaways by Macro Roundup® AI

  1. Consumer prices are 27% higher than in January 2021, when Biden took office, and wage gains have failed to keep pace with inflation for five months.
  2. The “misery index,” a summing of the inflation and unemployment rates devised by economist Arthur Okun, stands at 7.5, above the 6.7 recorded in October 2024 before Kamala Harris lost to Trump.
  3. University of Chicago Booth School of Business economist Christina Patterson said workers who win pay bumps often bear a burden through negotiating, obtaining outside offers, switching jobs, or threatening to leave, actions that disturb employer-employee relationships and can require moving.

Related Articles:

  • The Cost of Money is Part of the Cost of Living: New Evidence on the Consumer Sentiment Anomaly — Variation in the University of Michigan consumer sentiment index that inflation and unemployment cannot explain is strongly correlated with growth in consumer borrowing costs, such as mortgage and car loan rates. The authors estimate that including interest payments and homeownership costs in the consumer price index would raise measured inflation from 3% to 9%.
  • Sticky Wage Norms and the Real Wage Cost of Unexpected Inflation — Firms set nominal wage increases by round-number rules—about 42% of increases below 6% fell within 0.01 percentage points of a whole or half number—and these rules rose only from about 2.7% to 3.5% as inflation peaked near 7%, contributing to falling real wages for job stayers.
  • Do Voters Punish Inflation or Pay Cuts? Inflation and Real Wages in U.S. Elections — An analysis of U.S. counties estimates that where wages grew faster than prices, voters supported the incumbent party more, while counties with larger real wage losses than average shifted more toward the opposition. A one-standard-deviation larger real wage loss is associated with shifts of roughly 0.10 and 0.19 percentage points in vote share and margin.

AI Summary. The official U.S. poverty rate fell 0.5 percentage points to 10.2%, a third consecutive annual decline and among the lowest on record, with 34.5m people in poverty. Poverty rates hit historic lows for Hispanic individuals, down 1.2 points to 13.9%, and children under 18, down 1.0 point to 13.4%.

Christina Bijou and Emily Shrider U.S. Census Bureau
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According to the Census Bureau, the poverty rate in the US fell .5pp in 2025 to 10.2%, one of the lowest rates on record. The poverty rate for children fell to a historical low of 13.4%. Census’s income measure does not include taxes or non-cash government transfers.

Is the U.S. poverty rate falling to record lows?

Core argument: The official U.S. poverty rate fell 0.5 percentage points to 10.2% in 2025, the third consecutive annual decline and one of the lowest rates on record, with 34.5 million people in poverty.

In 2025, the official poverty rate fell 0.5 percentage points to 10.2%, the third consecutive annual decline and one of the lowest rates on record. There were 34.5 million people in poverty in 2025. Poverty rates for Hispanic individuals and children under age 18 both fell to historic lows in 2025. Between 2024 and 2025, the poverty rate for Hispanic individuals fell 1.2 percentage points to 13.9%. The child poverty rate fell 1 percentage point to 13.4%.

Takeaways by Macro Roundup® AI

  1. The official U.S. poverty rate fell 0.5 percentage points to 10.2% in 2025, the third consecutive annual decline and one of the lowest rates on record, with 34.5 million people in poverty.
  2. The poverty rate for Hispanic individuals fell 1.2 percentage points to 13.9% between 2024 and 2025, a historic low.
  3. The poverty rate for children under age 18 fell 1.0 percentage point to 13.4% in 2025, a historic low.

Related Articles:

  • Poverty and Dependency in the United States, 1939–2023 — Extending a consistent poverty measure back to 1939, the authors estimate US absolute poverty fell from 48.5% to 19.5% between 1939 and 1963, driven by growth in market income rather than government transfers net of taxes. Poverty fell no faster in the 24 years after the War on Poverty than before.
  • Evaluating the Success of the War on Poverty since 1963 Using an Absolute Full-Income Poverty Measure — The authors estimate that an absolute full-income poverty measure, which counts a fuller set of income sources and adjusts thresholds only for inflation, fell from 19.5% to 1.6%, versus an official decline to 10.5%. They also find relative poverty reductions modest and government dependence more than doubled among working-age adults.
  • The Great “Transfer”-mation — Government transfers rose from 8.2% of Americans' total personal income in 1970 to 17.6% in 2022, making them the third largest income source after work and investments. The share of U.S. counties drawing a quarter or more of personal income from transfers rose from under 1% in 1970 to 53% in 2022.

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 Briggs Goldman 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

  1. 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.
  2. 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.
  3. 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. Median U.S. household income rose 2.6% to $87,460, the highest level on record in data going back to 1967. Median income increased 3% for White households, 2.9% for non-Hispanic White households, and 4.8% for Black households, while changes for Asian and Hispanic households were not statistically significant.

Melissa Kollar and Zach Scherer U.S. Census Bureau
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Real US median household income increased 2.6% year over year in 2025 to a record high of $87,460.

Has median U.S. household income reached its highest level on record?

Core argument: Median U.S. household income reached $87,460 in 2025, up 2.6% from $85,210 in 2024 and the highest on record in data dating back to 1967.

In 2025, median household income was $87,460, an increase of 2.6% from the 2024 estimate of $85,210. Household income in 2025 was the highest on record dating back to 1967. For all demographic characteristics of the householder the median household income estimates in 2025 were either higher than the 2024 estimates or were not statistically different from 2024. Median household income increased by 3% for White households, 2.9% for non-Hispanic White households, and 4.8% for Black households. Median income did not change significantly for Asian or Hispanic households.

Takeaways by Macro Roundup® AI

  1. Median U.S. household income reached $87,460 in 2025, up 2.6% from $85,210 in 2024 and the highest on record in data dating back to 1967.
  2. Median household income rose 4.8% for Black households, 3% for White households, and 2.9% for non-Hispanic White households in 2025, while Asian and Hispanic households saw no statistically significant change.
  3. Across all demographic characteristics of the householder, 2025 median household income estimates were either higher than or not statistically different from 2024 estimates.

Related Articles:

  • Income in the United States: 2024 — U.S. median household income was $83,730, not statistically different from the prior year's $82,690, according to Census Bureau estimates. Across most demographic subgroups, median household income was higher or not statistically different from the prior year, with the exception of Black households, which saw a decline.
  • US Focus: The Effect Of Soaring Profits — US nominal pre-tax corporate profits in the national accounts grew 23% over the past year, a pace not seen outside post-recession spikes since the mid-2000s. Wider margins were the key driver, as domestic profit growth far exceeded the 8% rise in corporate value added, pushing margins near all-time highs while the labor share hit new lows.
  • 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. The authors estimate the heritability of labor market outcomes by comparing the excess similarity of same-sex over opposite-sex twins with the corresponding difference among ordinary siblings. They caution that social interaction between twins, assortative mating, genetic dominance, population stratification, or indirect parental effects can bias such estimates upward or make them reflect non-genetic influences.

David Card, Jesse Rothstein and Moises Yi National Bureau of Economic Research
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Analysis of 44,500 twin pairs suggests ~one-third of the variation in log earnings is due to genetic differences – likely an upper bound, because same-sex twins may influence one another and share more similar environments than ordinary same-sex siblings.

How reliable are twin-based estimates of heritability in earnings?

Core argument: The authors identify heritability of U.S. labor market outcomes from a difference-in-differences comparing same-sex versus opposite-sex twins against the corresponding difference among ordinary siblings, adjusting for the estimated share of same-sex twins who are identical.

Our model identifies heritability from the difference-in-differences of same-sex vs. opposite-sex twins vs. siblings. [Specifically], the model attributes to genetic differences the excess similarity of same-sex over opposite-sex twins over and above the corresponding difference among ordinary siblings, adjusting for the estimated share of same-sex twins who are identical. The ordinary-sibling comparison is intended to remove family influences that differ between sons and daughters. Social interactions may lead to an over-estimate of the genetic component in earnings. The estimated heritability of outcomes may be biased in the presence of assortative mating, genetic dominance, population stratification, or violations of the assumption that the home environment is shared to the same degree for MZ as for DZ twins. Even when estimated without bias, heritability need not indicate direct genetic effects – it also may reflect indirect or cultural effects (e.g., effects of the parents’ genes on a child’s outcomes, not operating through the child’s own genes. Twins may [also] influence each other directly – for example in their choice of jobs -- inflating the correlation in their outcomes.

Takeaways by Macro Roundup® AI

  1. The authors identify heritability of U.S. labor market outcomes from a difference-in-differences comparing same-sex versus opposite-sex twins against the corresponding difference among ordinary siblings, adjusting for the estimated share of same-sex twins who are identical.
  2. Social interactions between twins, such as influencing each other’s job choices, may inflate the correlation in their outcomes and lead to an over-estimate of the genetic component in earnings.
  3. Estimated heritability may be biased by assortative mating, genetic dominance, population stratification, or unequal sharing of the home environment between identical and fraternal twins, and even unbiased estimates may reflect indirect or cultural effects such as parents’ genes.

Related Articles:

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 Warren U.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

  1. 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.
  2. 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.
  3. 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.