Edward Conard

Top Ten New York Times Bestselling Author

  • “…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 offers deep and well-argued analyses on almost every issue.” - The New York Times
  • “…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
  • “Unintended Consequences represents the most cogent and persuasive analysis of the Financial Crisis to date.” - Andrei Shleifer, 1999 John Bates Clark Medal Winner
  • “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
  • “…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “A full-throated defense of economic dynamism.” - The Wall Street Journal
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “…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
  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
Upside of Inequality Oxford Unintended Consequences

Macro Roundup


Monday, November 24, 2025

AI Summary. A reanalysis using the full sample of firms, rather than one dropping observations with missing capital or overhead-cost data, finds that the measured rise in average markups at the end of the sample is driven almost entirely by the finance and insurance sector. Excluding that sector, average markups rise only modestly, from just under 1.30 to about 1.35.

C. Lanier Benkard, Nathan Miller and Ali Yurukoglu National Bureau of Economic Research
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De Loecker et al. found that aggregate markups surged from 18% to ~ 67% from 1980–2014. This comment argues that much of this finding is due to DEU’s deletion of observations with missing values for SG&A, and the rest is limited to Finance and Insurance.

Is the measured rise in markups driven by one sector?

Core argument: In the full sample excluding Finance & Insurance, the average markup rises only modestly, from just below 1.30 in the mid-1980s to about 1.35 in 2016.

Figure 1 plots three estimates of the average markup over time. The solid red line is the estimate provided in [The Rise of Market Power, Jan De Loecker, Jan Eeckhout and Gabriel Unger] (DEU). The dashed green line is what we obtain from the full sample—i.e., without dropping missing values for Capital or SG&A. Finally, the dash dot black line is what we obtain with the full sample excluding the Finance & Insurance sector. Using the DEU sample, the average markup increases rapidly throughout the 1980s and 1990s, pauses in the 2000s, then increases rapidly again in the final few years of the sample. In the full sample, the average markup increases more gradually through 2008. It then sharply increases in the last few years of the sample, ending at a similar level as in the DEU sample. Moreover, unlike the DEU results, which are robust to the exclusion of individual sectors, in the full sample the increase in markups at the end of the sample is driven almost entirely by a single sector: Finance & Insurance. Excluding F&I, the average markup increases only modestly, from just below 1.30 in the mid-1980s to about 1.35 in 2016. We conclude that the DEU methodology and data, as they are described in the article, do not support the conclusion that broad-based increases in market power have occurred in recent decades.

Takeaways by Macro Roundup® AI

  1. In the full sample excluding Finance & Insurance, the average markup rises only modestly, from just below 1.30 in the mid-1980s to about 1.35 in 2016.
  2. In the full sample, which retains observations with missing Capital or SG&A values, the late-sample increase in average markups is driven almost entirely by the Finance & Insurance sector, unlike the De Loecker, Eeckhout and Unger results, which are robust to excluding individual sectors.
  3. The comment concludes that the De Loecker, Eeckhout and Unger methodology and data, as described in their article, do not support the conclusion that broad-based increases in market power have occurred in recent decades.

Related Articles:

  • The Rise of Market Power and the Macroeconomic Implications — The authors estimate that aggregate markups over cost rose from 18% to nearly 67% between 1980 and 2014, concentrated among firms that already had high markups, which they interpret as a rise in market power. They associate this increase with declining labor and capital shares, lower low-skill wages, reduced labor force participation, fewer startups, and wider wage inequality.
  • Rising Markups and Declining Business Dynamism: Evidence From the Industry Cross Section — Across industries, larger increases in markups are not associated with larger declines in business dynamism—measured by new business entry rates and gross job or worker flows—and the correlation may even be positive. The authors conclude that rising markups as commonly measured are not a major explanatory factor behind declining dynamism.
  • The Economics of Inequality in High-Wage Economies — United States Income, Wealth, Consumption, and Inequality Diana Furchtgott-Roth

AI Summary. Barclays estimates that investment in software, computer equipment and data centers added about 1 percentage point to annualized US GDP growth in the first half of 2025, with artificial intelligence spending accounting for 0.8 points of the 1.6% annualized growth after subtracting imported chips.

Konrad Putzier Wall Street Journal
Date Posted:

Barclays estimates that AI-related investment constituted half of America’s 1.6% annualized GDP growth in the first half of 2025.

How much of US GDP growth comes from AI spending?

Core argument: Barclays estimates AI-related spending added 0.8 percentage points annualized to U.S. GDP growth in the first half of 2025, out of 1.6% annualized growth overall, implying growth of 0.8% absent that contribution.

Barclays estimates that investment in software, computer equipment and data centers boosted GDP growth by around 1 percentage point annualized in the first half of 2025. AI explained much, though not all, of that. Chips such as those sold by Nvidia make up the bulk of AI spending, but most are imported and must be subtracted from total investment to arrive at the impact on domestic production. Accounting for that, AI spending still increased output by an annualized 0.8% in the first half of the year, Barclays estimates GDP grew by an annualized 1.6% during the period. In other words, absent the growth in AI-related spending, growth would have been a sluggish 0.8%. Barclays senior U.S. economist Jonathan Millar estimates that a 20% to 30% stock-market decline could reduce GDP growth by 1 to 1.5 percentage points over roughly a year. If AI investment stopped growing, that could knock another 0.5 point off growth, Millar estimates. If it went to zero, that would knock a full percentage point off.

Takeaways by Macro Roundup® AI

  1. Barclays estimates AI-related spending added 0.8 percentage points annualized to U.S. GDP growth in the first half of 2025, out of 1.6% annualized growth overall, implying growth of 0.8% absent that contribution.
  2. Barclays estimates investment in software, computer equipment and data centers boosted GDP growth by about 1 percentage point annualized in the first half of 2025, with AI explaining much though not all of it.
  3. Barclays senior U.S. economist Jonathan Millar estimates a 20% to 30% stock-market decline could cut GDP growth by 1 to 1.5 percentage points over roughly a year, with flat AI investment removing another 0.5 point and a fall to zero removing a full point.

Related Articles:

  • Minsky Moments and AI CapEx — Capital-spending forecasts for the four largest cloud providers rose nearly 50% over the year, and AI data centers are increasingly funded through leveraged and off-balance-sheet structures, including $3.2bn in senior secured notes and a roughly $30bn asset-backed financing. The author argues this marks a speculative-finance phase where borrowers must roll over principal.
  • Why We Are Not In A Bubble….Yet — The 5 largest US technology companies are worth about 16% of the global public equity market, and the top 10 US stocks nearly 25%, almost $25tn. The author argues valuations are high but not bubble-level, with the "Magnificent 7" median forward price-to-earnings ratio at 27x, roughly half that of the late-1990s leaders.
  • SF FedViews: Solid Growth, but Small Cracks Have Emerged — Since the second half of 2023, four-quarter US productivity growth has averaged nearly 2.5%, well above the pre-pandemic trend of about 1% to 1.25%, supporting real GDP growth. It remains unclear whether this reflects a higher productivity regime fostered by rising AI use or a temporary post-pandemic reorganization surge.

AI Summary. Composite benchmarks show Google's Gemini roughly in line with GPT-5-class models, with any gap small, and the author argues it does not lead on strict low-leakage reasoning exams. The pattern reflects growing training costs yielding modest gains concentrated in vision, video, and some coding rather than broad reasoning advances.

Paul Kedrosky Applied Complexity
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Kedrosky argues Google’s Gemini 3 is “strong, but not transformative,” and ~ comparable to ChatGPT5. “The industry remains on the same trajectory: rising costs, slower progress, and diminishing returns on the problems that matter most.”

Are rising AI training costs producing only narrow capability gains?

Core argument: Composite benchmarks drawn from broader, cleaner task sets place Gemini roughly in line with GPT-5-class models, with any overall gap small in either direction.

Composite benchmarks built from broader, cleaner sets show Gemini roughly in line with GPT-5-class models. On the strictest reasoning tasks—low-leakage exams—Gemini almost certainly does not lead. In many cases it likely trails. Either way. whatever gap exists overall, if any, is small. The Gemini release fits the same trend seen across the industry: growing training costs and shrinking capability gains. Progress has slowed on the tasks that matter most for general reasoning. The gains that do appear come from domains still responsive to brute-force scaling—vision, video, and certain coding workflows—not from breakthroughs in abstraction, logic, or planning. If Gemini had made a clear leap in those harder areas, the missing benchmarks would have been on the above list. They were not, almost certainly because the gains are not there. The core pattern remains: large increases in compute and engineering produce modest, targeted improvements rather than broad advances in general capability.

Takeaways by Macro Roundup® AI

  1. Composite benchmarks drawn from broader, cleaner task sets place Gemini roughly in line with GPT-5-class models, with any overall gap small in either direction.
  2. On low-leakage reasoning exams, Gemini almost certainly does not lead and likely trails, and the absence of those benchmarks from its release suggests the gains are not there.
  3. Gemini's release fits an industry trend of rising training costs and shrinking capability gains, with improvements concentrated in vision, video, and certain coding workflows rather than abstraction, logic, or planning.

Related Articles:

  • Is AI Hitting A Wall? — Artificial intelligence gains have come from a formula of more data and more computing power, but companies have exhausted freely available internet training data and are now striking data-sharing deals with publishers and copyright holders. Training and running large models is energy-intensive, requiring vastly more Nvidia chips with each generation.
  • Why AI Capex isn’t a Bubble: A Perez-ian Perspective — Carlota Perez's framework of technological revolutions casts heavy artificial-intelligence infrastructure spending as a late-stage installation surge, in which financial capital overshoots and builds capacity ahead of demand, leaving both losses and infrastructure for later productivity. The author argues this model is descriptive rather than prescriptive, and that executives and policymakers invoking it rationalize losses in advance and erode market discipline.

AI Summary. Pressure on technology company bonds has been uneven: Alphabet, Amazon and Microsoft fund most AI spending from quarterly cash flow, while Meta had to offer yields well above its existing debt on a $30bn bond sale. Oracle, already burning cash as it shifts toward AI cloud computing, carries yields higher than almost any investment-grade technology peer.

Sam Goldfarb Wall Street Journal
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Since September, hyperscalers have issued ~$90B of investment-grade debt at higher yields than expected. Post-issue prices of some of the bonds have declined. The cost of CDS’s on Oracle’s debt have climbed.

Does bond market pressure fall unevenly across AI-spending tech companies?

Core argument: Meta issued $30 billion of bonds at the end of October at yields comfortably above its existing debt, and despite double-A ratings those bonds now yield roughly the same as single-A-rated IBM debt after secondary-market prices edged lower.

Recent pressure on tech company bonds hasn’t been evenly applied across the sector. Alphabet, Amazon and Microsoft have been spared the worst, thanks to their ability to fund most of their AI expenditures with the huge amounts of cash that they generate each quarter. Meta has a slightly less massive cash hoard and is seen as likely needing more debt to realize CEO Zuckerberg’s large ambitions. When the social-media company issued $30 billion of bonds at the end of October, it had to lure investors with yields comfortably above those of its existing debt. Prices of some of those bonds then edged lower in the secondary market, pushing the yields higher. Despite carrying double-A ratings, they now yield roughly the same as IBM bonds, which are rated single-A by the major ratings firms. Oracle is in a more difficult position. It already is burning cash, with plans to burn tens of billions more over the next several years as it tries to transform itself from a leading software company into an AI cloud-computing giant—leasing out the vast clusters of advanced computer chips needed to power applications like OpenAI’s ChatGPT. Rated two notches above speculative-grade territory, Oracle’s bonds now carry yields that are higher than those of almost any of its investment-grade tech peers.

Takeaways by Macro Roundup® AI

  1. Meta issued $30 billion of bonds at the end of October at yields comfortably above its existing debt, and despite double-A ratings those bonds now yield roughly the same as single-A-rated IBM debt after secondary-market prices edged lower.
  2. Oracle, rated two notches above speculative grade and already burning cash with plans to burn tens of billions more over the next several years as it shifts toward AI cloud computing, carries bond yields higher than almost any investment-grade tech peer.
  3. Alphabet, Amazon and Microsoft have been spared the worst of the pressure on tech bonds, owing to their ability to fund most of their AI spending with the cash they generate each quarter.

Related Articles:

  • Minsky Moments and AI CapEx — Capital-spending forecasts for the four largest cloud providers rose nearly 50% over the year, and AI data centers are increasingly funded through leveraged and off-balance-sheet structures, including $3.2bn in senior secured notes and a roughly $30bn asset-backed financing. The author argues this marks a speculative-finance phase where borrowers must roll over principal.
  • Investor Angst Over Big Tech’s AI Spending Spills Into Bond Market — Investors have sold off bonds issued by large US technology companies building data centres, including Alphabet, Meta, Microsoft and Oracle, as concerns over artificial intelligence spending reached the bond market. Bank of America data show the extra yield investors demand to hold this debt over Treasuries rose to 0.78 percentage points from 0.5.
  • The AI Boom Is Looking More and More Fragile — Technology valuations are not extreme by historical standards, with the Nasdaq Composite's average forward price-to-earnings ratio near 29 versus above 32 before the artificial intelligence boom. Concern instead centers on leverage: AI-related companies issued $139bn of corporate bonds this year, about 9% of investment-grade issuance and 23% above last year.

AI Summary. Japan's gross debt is 240% of GDP versus Germany's 65%, yet both carry the same 30-year government bond yield, which the author argues shows Japanese long-term yields are massively mispriced by Bank of Japan bond buying. Letting markets set yields would stabilize the yen but risk a debt crisis; capping yields means further yen depreciation.

Robin Brooks Robin Brooks Substack
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Brooks argues BoJ intervention is still “massively” distorting Japan’s long-term yields, noting that Japan, with debt at 240% of GDP, and Germany, with debt at 65% of GDP, both have ~ the same 30-year government bond yield.

Are Japan's long-term bond yields mispriced by central bank buying?

Core argument: Japan's gross debt stood at 240% of GDP in 2024 versus Germany's 65%, yet both countries carry the same 30-year government bond yield, which the author calls a massive mispricing of Japanese yields.

Japan’s “shadow” long-term yield - the yield that would clear the market in the absence of BoJ bond buying - is much higher than what we observe. The easiest way to see how massively distorted Japan’s long-term yields are is in the chart. The horizontal axis shows gross debt in percent of GDP. The vertical axis shows 30-year government bond yields. Euro zone countries are in red, the rest of the G10 are in blue and Germany (DE) as well as Japan (JP) are in black. German debt in 2024 was 65% of GDP, while that of Japan was 240%. Yet both countries have the same 30-year government bond yield. That’s a sign of a massive mispricing in Japanese yields, which are way below where they should be. Japan has a choice. It can either let markets set yields without BoJ interference. That will stabilize the Yen, but at the cost of far higher long-term yields and a debt crisis. Or it can cap yields, in which case the Yen will continue its slide.

Takeaways by Macro Roundup® AI

  1. Japan's gross debt stood at 240% of GDP in 2024 versus Germany's 65%, yet both countries carry the same 30-year government bond yield, which the author calls a massive mispricing of Japanese yields.
  2. Japan's "shadow" long-term yield — the level that would clear the market without Bank of Japan bond buying — is far above observed yields, according to the author.
  3. The author argues Japan faces a choice between letting markets set yields, which would stabilize the Yen at the cost of far higher long-term yields and a debt crisis, or capping yields and allowing the Yen to keep sliding.

Related Articles:

  • Japan’s Borrowing Costs At Highest In Decades On Fears Of Public Spending Surge — Yields on Japan's 10-year government bonds reached their highest level since 2008 and 30-year yields hit a record intraday high of 3.35%. Traders attribute the sell-off to signs the Japanese government is preparing a supplementary budget of at least ¥25tn ($161bn) to boost the economy and shield households from rising prices.
  • Japan’s Debt Crisis — The Bank of Japan's holdings of Japanese government bonds are so large that maturing bonds exceed gross purchases, making it a net seller equal to -2.6% of GDP over a recent one-year period. Further cuts to gross buying would deepen the decline in its holdings and likely push longer-term Japanese yields higher.
  • Japan’s Debt Puzzle: Sovereign Wealth Fund from Borrowed Money — Japan's public sector operates as a highly leveraged sovereign wealth fund, funding risky assets by borrowing from banks and depositors at below-market rates, with returns sustaining its debt-to-GDP ratio above 200% without primary surpluses. The authors argue the resulting duration and currency mismatches expose Japan to mark-to-market losses that could trigger a fiscal crisis or a run on the yen.

AI Summary. Consumer prices rose about 20% during both the Biden and Reagan presidencies, yet voters punished Biden's party while Reagan won in a landslide. The author argues the difference is that Americans had grown accustomed to high inflation before Reagan, whereas the surge after decades of low inflation came as a shock.

Paul Krugman Krugman Wonks Out
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“Historical Context Matters.” Both Biden’s term and Reagan’s 1st term saw a ~20% rise in the price level, but only Biden’s saw a sharp drop in sentiment. Consumers had gotten used to inflation in the 1970s, while the Covid inflation came as a shock.

Why do voters punish similar price increases differently?

Core argument: Consumer prices in November 2024 stood about 20% above their level when Joe Biden took office, and despite a much lower inflation rate, voters punished his party at the polls.

In November 2024, consumer prices were about 20% higher than they had been when Joe Biden took office. Even though the rate of inflation was way down, Americans were outraged at these higher prices, and punished Biden’s party at the polls. But in November 1984 consumer prices were also about 20% higher than they had been when the president at the time, Ronald Reagan, took office. Yet voters didn’t punish Reagan and his party. On the contrary, he won in a landslide: It was “morning in America.” Why the difference? My answer is that when Reagan took office, America had already been through years of high inflation, which people had come to accept as normal. Nobody expected prices to go back down to their level years in the past, and people were happy simply to see inflation leveling off. Biden, by contrast, took office in a country where inflation had been very low for decades. The sudden surge in prices from 2021 to 2023 came as a huge shock. What will end the vibecession? As I’ve suggested, there has to be some statute of limitations on anger over past inflation. Inflation peaked more than 3 years ago, and eventually we should expect people to accept higher prices as normal.

Takeaways by Macro Roundup® AI

  1. Consumer prices in November 2024 stood about 20% above their level when Joe Biden took office, and despite a much lower inflation rate, voters punished his party at the polls.
  2. Prices in November 1984 were also roughly 20% higher than when Ronald Reagan took office, yet Reagan won re-election in a landslide.
  3. The author argues the difference lies in expectations: Reagan inherited a country accustomed to years of high inflation, while Biden inherited decades of very low inflation, so the 2021–2023 surge came as a shock.

Related Articles:

  • Vibecessions, Part I — Consumer sentiment in the long-running University of Michigan survey is worse than during periods of roughly 9% inflation, double-digit unemployment after the financial crisis, and the 1980 recession, even though unemployment stands at 4.3% and inflation at 3%. The article argues people credit themselves for wage gains while blaming inflation for taking those gains away.
  • Why Do We Dislike Inflation? — Survey evidence finds 80% of respondents believe prices rise faster than wages, though real weekly earnings for the median worker grew 1.7% between 2019 and 2023. About 40% think their nominal income would be higher had inflation been lower, and half expect wages to take over a year to catch up if inflation doubled.
  • Zero-Sum Thinking and the Roots of U.S. Political Divides — Zero-sum thinking — viewing gains for one group as losses for another — is associated with greater support for liberal economic policies, stronger alignment with the Democratic Party, and weaker alignment with the Republican Party. The authors also find it linked to believing the QAnon conspiracy theory holds some truth and to sympathy for the attack on the U.S. Capitol.

Tuesday, November 25, 2025

AI Summary. The US would gain relative to China from a fracturing world economy because its bloc of allies includes nearly all advanced countries, accounting for 68% of world GDP at market prices versus 26% for China's bloc. China's structural current account surplus also leaves it reliant on the US and its allies as safe places to invest those funds.

Martin Wolf Financial Times
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Neil Shearing argues that as the global economy fractures, the US is poised to benefit; most countries will ultimately choose the US-centered bloc (currently 50% of PPP-adjusted world GDP) over a China-centered one (currently 32% of world GDP).

Would the US gain relative to China from a fracturing world economy?

Core argument: Neil Shearing argues the US bloc accounts for 68% of world GDP at market prices against the China bloc's 26%, and 50% against 32% at purchasing power parity, because it includes almost all advanced countries.

[Neil Shearing] argues that the US would come out ahead from a fracturing of the world economy. The main justification is that America’s allies are more economically powerful than China’s because they include almost all advanced countries. At market prices, the US bloc’s share of world GDP is 68%, against the China bloc’s 26%. Even at purchasing power parity, the US bloc’s share is 50%, against the China bloc’s 32%. In the end, the most economically potent countries in the world will stay closer to the US, because they depend on its security umbrella, its markets and its currency, or because, in the last resort, they distrust China more. China would also lose more than the US from a fractured world economy. One reason is that it has a structural current account surplus. The only countries where these funds can be safely invested are the US and its allies. The alternative is to lend vastly to developing countries, which may be unable to service their debts.

Takeaways by Macro Roundup® AI

  1. Neil Shearing argues the US bloc accounts for 68% of world GDP at market prices against the China bloc's 26%, and 50% against 32% at purchasing power parity, because it includes almost all advanced countries.
  2. Neil Shearing argues the most economically potent countries would stay closer to the US in a fractured world economy because they depend on its security umbrella, markets and currency, or distrust China more.
  3. Neil Shearing contends China would lose more than the US from a fractured world economy, since its structural current account surplus can be safely invested only in the US and its allies, the alternative being heavy lending to developing countries that may not service their debts.

Related Articles:

  • Sticking with What Works: Raising GDP Forecast on China’s Manufacturing Push — Stronger Chinese growth driven by export-oriented manufacturing may not produce net positive spillovers: for every 1pp export-driven boost to China's GDP, other economies may see a 0.1–0.3pp drag, with high-tech producers such as Europe and Japan facing greater pressure. Cheaper Chinese goods continue to exert a disinflationary impulse.
  • Underestimating China — China's productive capacity is three times that of the United States and exceeds the next nine countries combined, with its share of global manufacturing quintupling to 30% while the U.S. share halved to about 15%. The authors argue Washington must convert its alliances into a platform for pooled capacity building.
  • Power Politics: Energy Self-Sufficiency in a Modern Mercantilist World — China leads in adding power to the grid, with more than 30,000 miles of ultra-high-voltage transmission lines versus zero in the US and lead times for large transformers of 48 weeks against a US average of 143 weeks. This positions China to accommodate new demand sources such as artificial intelligence.

AI Summary. The share of U.S.-born men out of the labor force typically grows after each recession and generally has not returned to pre-recession levels once the economy recovered. Immigrant men, once more likely than U.S.-born men to be out of the labor force, are now less likely, according to Census survey data.

Steven Camarota and Karen Zeigler Center For Immigration Studies
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The share of native-born prime-age men (25–54) not in the labor force – 12.1% – has returned to its pre-pandemic level, but is elevated relative to prior economic expansions.

Are U S -born men leaving the labor force more than immigrants?

Core argument: The share of U.S.-born men not in the labor force typically rises after each recession, and labor force participation generally did not return to pre-recession levels for the U.S.-born once the economy recovered.

The share of U.S.-born men not in the labor force typically grows after each recession, which is to be expected since, like unemployment, it is a lagging indicator of the economy. Participation in the labor force generally did not make it back to pre-recession levels for the U.S.-born once the economy recovered. In 1994, the first year for which the CPS ASEC has nativity data, there was only a modest difference in rates between immigrants and the U.S.-born, but that changed thereafter with the foreign-born improving and the U.S.-born deteriorating. We see the same general trend in Figure 11, which shows the share of prime-age men 25 to 54 not in the labor force based on the CPS ASEC for every year. In fact, prime-age immigrant men were actually more likely to be out of the labor force in 1994 than their U.S.-born counterparts, which is completely reversed today.

Takeaways by Macro Roundup® AI

  1. The share of U.S.-born men not in the labor force typically rises after each recession, and labor force participation generally did not return to pre-recession levels for the U.S.-born once the economy recovered.
  2. In 1994, the first year for which the CPS ASEC has nativity data, the difference in labor force non-participation rates between immigrants and the U.S.-born was modest, after which the foreign-born improved and the U.S.-born deteriorated.
  3. Prime-age immigrant men 25 to 54 were more likely than their U.S.-born counterparts to be out of the labor force in 1994, a pattern that is completely reversed today.

Related Articles:

  • Post-Pandemic Recovery for America’s Prime Age Labor Force: A Tale of Two Sexes — Prime-age women's labor force participation climbed about 5 points above its pre-pandemic level over a decade, while prime-age men's rose barely a point, only just regaining pre-pandemic levels. Despite economic expansion and labor shortages, work rates for prime-age American men are barely as high as at the trough of the severe Reagan-era recession.
  • Wage Inequality and the Rise in Labor Force Exit: The Case of US Prime-Age Men — Among US prime-age men without college degrees, a 10% rise in expected earnings is associated with a 0.12 percentage point lower labor force exit rate, while a 10% rise in peers' reference earnings is associated with a 0.13 percentage point higher exit rate, fully offsetting the earnings effect.
  • Bachelors Without Bachelor’s: Gender Gaps in Education and Declining Marriage Rates — Marriage rates for college-educated women have remained stable over the past fifty years even as the relative supply of college-educated men declined, with the overall decline in marriage driven almost entirely by fewer marriages between non-college women and non-college men. The education gap in women's marriage rates is larger in areas where non-college men face the highest joblessness and incarceration.

AI Summary. Among unemployed workers aged 22–27, the rate of exiting unemployment for college graduates trended upward from 1976 to 2000 and then declined sharply, while the rate for high school graduates was stable until 2008 and now sits near its long-run average. The gap between the two groups narrowed after 2000 and closed entirely around 2019.

Alexander Cline and Barış Kaymak Federal Reserve Bank of Cleveland
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The gap btw high school and college graduates in the rate at which individuals leave unemployment, either by finding a job or dropping out of the labor force, began to fall around 2000. By 2019, HS grads left unemployment sooner than college grads.

Have young college graduates lost their advantage in exiting unemployment?

Core argument: The gap in unemployment exit rates between high-school-educated and college-educated workers aged 22–27 narrowed after 2000 and closed entirely around 2019.

We examine unemployment trends for high school and college graduates aged 22–27. Figure 3 shows two different measures of unemployment exit rates for young high school and college graduates. Dashed lines are smoothed to capture short-run movements primarily from business cycle fluctuations; both groups show strongly procyclical exit rates with comparable amplitudes, meaning that economic cycles do not affect the exit rates of the two groups differentially. Solid lines reveal longer-term trends: High school exit rates remained stable until 2008, dropped during the jobless recovery from the 2008 recession, and now sit near long-run averages. College exit rates trended upward from 1976 to 2000 and then declined sharply. The gap between the exit rates for high-school-educated and college-educated workers narrowed after 2000, closing entirely around 2019.

Takeaways by Macro Roundup® AI

  1. The gap in unemployment exit rates between high-school-educated and college-educated workers aged 22–27 narrowed after 2000 and closed entirely around 2019.
  2. College graduates' unemployment exit rates trended upward from 1976 to 2000 and then declined sharply, while high school graduates' rates held stable until 2008, fell during the jobless recovery from the 2008 recession, and now sit near long-run averages.
  3. Smoothed exit rates for both young high school and college graduates are strongly procyclical with comparable amplitudes, indicating that economic cycles do not affect the two groups' exit rates differentially.

Related Articles:

  • What the Graduate Unemployment Story Gets Wrong — Comparing recent graduates with others newly entering the labor market, rather than with all non-graduates of similar age, shows those without a degree are faring worse: US unemployment among recent college graduates is up 1.3 percentage points from its low, versus 2.4 points for recent entrants without a degree.
  • Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence — Using payroll records covering millions of US workers, the authors find employment for workers aged 22–25 in occupations most exposed to artificial intelligence, such as software developers and customer service representatives, declined 6%, while employment for older workers in those occupations rose 6–9%. Employment in less-exposed occupations grew comparably across age groups.
  • Military Officer Quality in the All-Volunteer Force — The authors find a steady decline in U.S. Marine Corps officers' General Classification Test scores, which is statistically associated with the expansion of college attendance and explains 96% of the measured change. They also find a positive association between African American officers and mean test scores, arguing affirmative action has not lowered officer quality.

AI Summary. Among young adults aged 22-27, the unemployment rate for those with only a high school degree is consistently higher than for college graduates, but the gap is now the smallest in half a century. Much of that convergence reflects how sharply high-school-only unemployment spiked during the Great Recession.

Timothy Taylor Conversable Economist
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Noting the convergence of the employment rates of young college and non-college workers since 2010, Tim Taylor concludes that today, “the main labor advantage [of a college degree] is higher wages after finding a job.”

Is the unemployment gap between young high school and college graduates narrowing?

Core argument: The unemployment gap between young adults aged 22–27 with only a high school degree and those with a college degree is the smallest it has been in a half-century, having generally converged since roughly 2010 apart from the pandemic.

The left-hand figure shows that unemployment rates for those with only a high school degree are consistently higher than for those with a college degree. This data includes only young adults in the 22-27 age group. However, the size of the unemployment gap varies. Since the aftermath of the Great Recession back around 2010, the two rates have been generally converging (except during the pandemic). At present, the gap is the lowest it has been for a half-century. A glass-half-full type would also point out that although the pattern over the last 15 years is striking, most of the convergence between these unemployment rates has happened because the unemployment rate for those with only a high school degree spiked so violently during the Great Recession. Also, although the gap between unemployment rates for the two groups is small, it was also quite low in the late 1970s, late 1980s, and late 1990s. Also, comparing unemployment rates for these two groups doesn’t take into account how the share of those attending college has risen in the last half-century.

Takeaways by Macro Roundup® AI

  1. The unemployment gap between young adults aged 22–27 with only a high school degree and those with a college degree is the smallest it has been in a half-century, having generally converged since roughly 2010 apart from the pandemic.
  2. Unemployment rates for young adults aged 22–27 with only a high school degree remain consistently above those for college graduates, though the size of that gap varies over time.
  3. Most of the convergence in these unemployment rates traces to the sharp spike in unemployment for high-school-only young adults during the Great Recession, and comparably small gaps also occurred in the late 1970s, late 1980s, and late 1990s.

Related Articles:

  • Are Young College Graduates Losing Their Edge in the Job Market? — Among unemployed workers aged 22–27, the rate of exiting unemployment for college graduates trended upward from 1976 to 2000 and then declined sharply, while the rate for high school graduates was stable until 2008 and now sits near its long-run average. The gap between the two groups narrowed after 2000 and closed entirely around 2019.
  • Jobless Growth — Labor demand growth may be too weak to keep pace with even modest labor supply growth: the labor market is somewhat weaker than before the pandemic, job growth has turned negative outside healthcare, and firms are focused on using AI to cut labor costs. Economists estimate most employment losses in routine manual occupations since the 1970s occurred during recessions.
  • Americans With Four-Year Degrees Now Comprise a Record 25% of Unemployed Workers — Americans with at least a bachelor's degree make up a record 25% of the unemployed, over 1.9m workers aged 25 and up, according to US Bureau of Labor Statistics data going back to 1992. Their unemployment rate rose half a percentage point from a year earlier to 2.8%, while other education levels showed little increase.

AI Summary. China installed 295,000 industrial robots in a year, nearly 9x the US total and more than the rest of the world combined, with its operational robot stock surpassing 2m, the most of any country. Of 131 sites recognized globally for using technologies such as AI to lift productivity, 45 are in mainland China and three in the US.

Brian Spegele Wall Street Journal
Date Posted:

China installed 295,000 industrial robots in 2024, ~9x as many as the U.S. and more than the rest of the world combined, according to data from the International Federation of Robotics.

Is China leading the world in factory automation?

Core argument: China installed 295,000 industrial robots last year, nearly nine times the U.S. total and more than the rest of the world combined, according to the International Federation of Robotics.

China installed 295,000 industrial robots last year, nearly nine times as many as the U.S. and more than the rest of the world combined, according to the International Federation of Robotics. China’s stock of operational robots surpassed two million in 2024, the most of any country. Of 131 factories and industrial sites recognized by the World Economic Forum globally for lifting productivity through cutting edge technologies such as AI, 45 are in mainland China, while three are in the U.S.

Takeaways by Macro Roundup® AI

  1. China installed 295,000 industrial robots last year, nearly nine times the U.S. total and more than the rest of the world combined, according to the International Federation of Robotics.
  2. China's stock of operational robots surpassed two million in 2024, the most of any country.
  3. Of 131 factories and industrial sites recognized globally by the World Economic Forum for lifting productivity through cutting-edge technologies such as AI, 45 are in mainland China and three are in the U.S.

Related Articles:

  • America Is Missing The New Labor Economy – Robotics Part 1 — Building an identical robotic arm in the US costs about 2.2x more than in China, and US-labeled components still depend heavily on Chinese parts and materials with no scalable alternative. The author argues China is the only country positioned to capture full-scale intelligent robotic automation, which would let it outcompete the US across production.
  • Homegrown Robots Help Drive China’s Global Export Surge — Chinese factories install about 280,000 industrial robots a year, half the global total, with roughly half made by domestic producers that undercut foreign rivals on price, lifting China's robot-to-worker density above Germany's. Trade data show China's global export share in labour-intensive goods rose, including small manufactured goods up 9 percentage points to 52.3%.
  • What Would It Take to Bring Back US Manufacturing? Part 2: Making American Manufacturing More Productive — The authors argue the United States is a decade away from the scale of automated "lights-out" manufacturing needed to meet its own end-to-end demand at comparable cost. With 98% of US manufacturers being small or medium enterprises, low deployment of mature productivity technologies reflects limited commercial adoption and investment rather than technical readiness.

AI Summary. The authors respond to a critique of their finding of rising market power, arguing that the critique's results are driven entirely by outlier small firms in pharmaceutical and medicine manufacturing that report very low sales, no overhead costs, and negative profits. They restrict their sample to firms reporting the overhead costs needed to measure both markups and profits.

Jan De Loecker, Jan Eeckhout and Gabriel Unger National Bureau of Economic Research
Date Posted:
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De Loecker et al. note that the deleted observations in their paper are small pharmaceutical companies with ~0 sales, unsuitable for production function estimation. With these excluded, the results are invariant to the inclusion of finance and insurance.

Does the finding of rising market power hold up against outlier firms?

Core argument: De Loecker, Eeckhout and Unger reply that Benkard, Miller and Yurukoglu's findings are entirely driven by outliers in one four-digit NAICS industry, 3254 (Pharmaceutical and Medicine Manufacturing), whether or not Finance, Insurance and Real Estate is excluded.

Benkard, Miller, and Yurukoglu (BMY) argue that the results in De Loecker, Eeckhout and Unger (DLEU) are not robust for two reasons: 1. the inclusion of Finance and Insurance and Real Estate (FIRE); 2. sample selection due to the exclusion of firms not reporting SGA (Sales, General and Administrative expenses). Here we show that the findings in BMY are entirely driven by outliers in one four-digit NAICS industry 3254 (Pharmaceutical and Medicine Manufacturing, whether or not FIRE is excluded. These small firms have very low sales, some costs, no SGA, and negative profits. In essence, these firms do not produce, and therefore report extremely low sales-to-COGS ratios. In DLEU we use a sample with firms reporting all variables required to measure markups and profits, since our key insight is that both markups and profits are rising. To establish the latter, we need overhead costs, which we show are increasing, but not enough to offset the rise in markups, and hence profits are rising too. [The presence of industry 3254] leads to implausibly low output elasticities dropping from about 0.85 in 1990 to about 0.5 by the end of the sample. To aid in seeing the full effect of the outliers [on the time series of estimated markups], we reproduce the aggregate markup series but exclude [two-digit] sector 32 all together, in the right panel of Figure A.4.

Takeaways by Macro Roundup® AI

  1. De Loecker, Eeckhout and Unger reply that Benkard, Miller and Yurukoglu's findings are entirely driven by outliers in one four-digit NAICS industry, 3254 (Pharmaceutical and Medicine Manufacturing), whether or not Finance, Insurance and Real Estate is excluded.
  2. The outlier firms in industry 3254 have very low sales, some costs, no reported SGA, and negative profits, and their extremely low sales-to-COGS ratios accompany output elasticities falling from about 0.85 in 1990 to about 0.5 by the end of the sample.
  3. De Loecker, Eeckhout and Unger restrict their sample to firms reporting all variables needed to measure markups and profits, and show overhead costs are rising but not enough to offset rising markups, so profits rise as well.

Related Articles:

  • The Rise of Market Power and the Macroeconomic Implications: Comment — A reanalysis using the full sample of firms, rather than one dropping observations with missing capital or overhead-cost data, finds that the measured rise in average markups at the end of the sample is driven almost entirely by the finance and insurance sector. Excluding that sector, average markups rise only modestly, from just under 1.30 to about 1.35.
  • The Rise of Market Power and the Macroeconomic Implications — The authors estimate that aggregate markups over cost rose from 18% to nearly 67% between 1980 and 2014, concentrated among firms that already had high markups, which they interpret as a rise in market power. They associate this increase with declining labor and capital shares, lower low-skill wages, reduced labor force participation, fewer startups, and wider wage inequality.
  • The Economics of Inequality in High-Wage Economies — United States Income, Wealth, Consumption, and Inequality Diana Furchtgott-Roth