Edward Conard

Top Ten New York Times Bestselling Author

  • “Unintended Consequences is full of substance, it is one of the must-read books of the year, and once I finish it I will be giving it a second read through right away.” - Tyler Cowen, Professor, George Mason University
  • “…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
  • “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 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 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 comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “Unintended Consequences is far smarter and more thought-provoking than most economics written for the general public” - Greg Mankiw, Harvard University, Former Chairman of the Council of Economic Advisors
  • “…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
  • “…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
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The Great Resignation vs. the Great Reallocation Revisited

Serdar Birinci Federal Reserve Bank of St. Louis
Date Posted:
June 2, 2022
Is Database:
Database

The so-called Great Resignation is more accurately described as the Great Reallocation, as data shows that quits are primarily driven by job-to-job transitions rather than exits to unemployment.

The so-called Great Resignation, characterized by high voluntary job separations in 2021, is more accurately described as the Great Reallocation, as data shows that quits are primarily driven by job-to-job transitions rather than exits to unemployment. This trend is most pronounced in nonroutine manual occupations, which saw a 24% increase in job-to-job transitions by December 2021 compared to January. In contrast, nonroutine cognitive, routine cognitive, and routine manual occupations experienced more modest increases of 14%, 11%, and 16%, respectively. These transitions, particularly prevalent in the service sector, suggest a labor market reallocation where workers seek better employment opportunities, contributing to higher wage growth in these sectors. This shift underscores a dynamic labor market where workers are not resigning from work entirely but are reallocating to roles that better match their skills and aspirations.

We show that, for all occupation groups, quits are mostly driven by job-to-job transitions—indicative of what we call the Great Reallocation—rather than quits to unemployment, as the term the Great Resignation may suggest.Meanwhile, job-to-job transitions, and thereby total quits, increased the most in the type of occupations that are typically found in the service sector. The next four figures plot the normalized (January 2021 = 100) number of total quits (gray dashed lines), quits to unemployment (orange lines) and job-to-job transitions (blue lines) for nonroutine cognitive, nonroutine manual, routine cognitive and routine manual occupations, respectively. Nonroutine cognitive: managers, doctors, engineers, etc. Nonroutine manual: child care workers, janitors, food service workers, etc. Routine cognitive: cashiers, sales agents, postal service workers, etc. Routine manual: construction laborers, electricians, flight attendants, etc. Following a voluntary separation, a worker can flow into either unemployment or employment with another employer. A careful consideration of this movement reveals that the latter—that is, job-to-job transitions—drives most of the change in the total number of quits during 2021. The so-called Great Resignation may therefore be more appropriately dubbed the Great Reallocation, as quits largely don’t reflect a desire to stop working altogether, but to find new (and presumably better) employment.

Serdar Birinci and Ngân Trần, "The Great Resignation vs. the Great Reallocation Revisited,"Federal Reserve Bank Of St. Louis, June 2, 2022, https://www.stlouisfed.org/on-the-economy/2022/jun/great-resignation-vs-great-reallocation-revisited

The Great Resignation vs. the Great Reallocation Revisited

The Great Resignation—a term referring to the historically high levels of voluntary job separations during the economic recovery of 2021—continues to garner interest among journalists and social analysts. In a previous Economic Synopses essay, Serdar Birinci and Aaron Amburgey examined the Great Resignation hypothesis at the industry level. In this blog post, we continue this examination by looking at quits and job-to-job transitions across four broadly defined occupation groups.

For each group, we consider the total number of quits and two components: quits to unemployment and job-to-job transitions (or quits to other jobs). We show that, for all occupation groups, quits are mostly driven by job-to-job transitions—indicative of what we call the Great Reallocation—rather than quits to unemployment, as the term the Great Resignation may suggest. Meanwhile, job-to-job transitions, and thereby total quits, increased the most in the type of occupations that are typically found in the service sector.

efining Occupation Groups

Data for our analysis come from the Current Population Survey, which tracks employment status, occupation, change of employer and reason for unemployment of U.S. workers on a monthly basis.

Our focus is 2021, when quit rates climbed above pre-pandemic levels.1 We broke down occupations into four groups along two dimensions: whether an occupation is manual or cognitive and whether it is routine or nonroutine.2

Occupations are considered manual if they involve more physical activities and cognitive if they involve more mental tasks. Occupations are considered routine if the tasks involved can be accomplished by following a set of well-defined procedures and nonroutine if these tasks require more complex problem-solving and communication skills. Below are examples of occupations in each group:

Nonroutine cognitive: managers, doctors, engineers, etc.
Nonroutine manual: child care workers, janitors, food service workers, etc.
Routine cognitive: cashiers, sales agents, postal service workers, etc.
Routine manual: construction laborers, electricians, flight attendants, etc.

Job-to-Job Transitions and Quits to Unemployment by Occupation Groups

The next four figures plot the normalized (January 2021 = 100) number of total quits (gray dashed lines), quits to unemployment (orange lines) and job-to-job transitions (blue lines) for nonroutine cognitive, nonroutine manual, routine cognitive and routine manual occupations, respectively.
The Great Resignation vs. the Great Reallocation Revisited: Extended Excerpt Image 1

We found that total quits in nonroutine manual occupations experienced the sharpest rise during 2021, while the growth in quits was more modest in other occupation groups. We also showed that across occupation groups, movements in the number of job-to-job transitions very closely track movements in the total number of quits, indicating that job-to-job transitions are the main driver behind changes in total quits. As a result, this trend is more indicative of labor market reallocations, i.e., the Great Reallocation, rather than quits to unemployment, as the term the Great Resignation may suggest.

Focusing on job-to-job transitions, all occupation groups except nonroutine manual did not see sizable increases until December 2021. Between January 2021 and November 2021, job-to-job transitions increased by only about 7% for nonroutine cognitive occupations, about 3% for routine cognitive occupations and about 9% for routine manual occupations. December, however, saw jumps in the number of job-to-job transitions for all three groups, with increases from January 2021 of approximately 14%, 11% and 16%, respectively.

On the other hand, in nonroutine manual occupations, job-to-job transitions were on a steady and significant increase throughout 2021. Job-to-job transitions grew by about 18% between January 2021 and November 2021 for this group—approximately twice the corresponding increase for its routine counterpart. By December, they had grown by almost 24% compared with January 2021.

Conclusion

In 2021, the Great Resignation narrative gained traction in the discourse on how COVID-19 permanently changed workers’ relationship to work. Following a voluntary separation, a worker can flow into either unemployment or employment with another employer. A careful consideration of this movement reveals that the latter—that is, job-to-job transitions—drives most of the change in the total number of quits during 2021. The so-called Great Resignation may therefore be more appropriately dubbed the Great Reallocation, as quits largely don’t reflect a desire to stop working altogether, but to find new (and presumably better) employment.

Consistent with our previous work using industry-level evidence, the most sizable increase in job-to-job transitions comes from nonroutine manual occupations, of which service industry jobs make up a large proportion. Job-to-job transitions, in turn, typically lead to higher wage growth, as we discussed in a previous blog post on job-switching rates during the COVID-19 recession. Taken together, these two pieces of empirical evidence are consistent with another empirical observation that recent average wage increases have been relatively high for workers in the service industry.

Notes and References

1 In January 2020, the quit rate for workers excluding farm laborers was 2.4%. By April, it had plummeted to 1.6%, but quickly rebounded and returned to its pre-pandemic level by the end of 2020. Throughout 2021, the quit rate continued its upward trend, reaching 3% in December. See this FRED figure of the U.S. quit rate.

2 We used the methodology found in Autor, David H.; and Dorn, David. “The Growth of Low-Skill Service Jobs and the Polarization of the U.S. Labor Market.” American Economic Review, August 2013, Vol. 103, No. 5,pp. 1553-97.

  • Business Cycle
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  • Workforce
    • Unemployment/Participation
Previous articleJune 2, 2022Can Americas Cities Make a Post-Pandemic Comeback?US office attendance is down 19% compared to pre-pandemic levels, with significant variations across major cities. Houston is at the mean, LA is 21% lower, NYC/Boston 32% lower, and SF 52% lower @TunkuVaradarajan @WSJ.Next articleJune 3, 2022The booming economy, not the 2017 tax act, is fueling corporate tax receiptsCorporate tax revenues surged in 2021, reaching 1.7% of GDP, driven by robust economic growth, high inflation, and fiscal expansion, not the 2017 TCJA. Strong profits, not business investment, fueled higher corporate tax receipts.
Showing 218 database articles primarily about Business Cycle

3% vs. 60%

AI Summary. Direct lending represents roughly 3% of total U.S. household and business debt, a fraction of the 60% share mortgages held at the peak of the housing bubble.

Torsten Sløk Apollo
Date Posted:
April 8, 2026
Is Database:
Database

Torsten Sløk notes the direct lending market is ~$2T or 3% of household and non-financial debt outstanding. To provide context, he shows that in 2006, on the eve of the crisis, mortgages accounted for ~60% of such debt.

Core argument: Direct lending represents a small but growing alternative to traditional bank financing for businesses and households.

The direct lending market is roughly $2 trillion, or about 3% of total debt outstanding for US households and businesses. By comparison, mortgages accounted for about 60% of total household and corporate debt at the peak of the housing bubble in 2006.

Takeaways by Macro Roundup® AI

  1. Direct lending represents a small but growing alternative to traditional bank financing for businesses and households.
  2. The mortgage market’s dominance has shifted dramatically since the 2006 housing peak, reducing systemic risk concentration.
  3. Non-bank lenders now capture meaningful market share in credit provision across the economy.

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Top 10% of Earners Drive a Growing Share of US Consumer Spending

Jonnelle Marte Bloomberg
Date Posted:
September 17, 2025
Is Database:
Database

Mark Zandi finds Americans in the top 10% of the income distribution accounted for 49.2% of consumer spending in Q2, the highest level since 1989.

Consumers in the top 10% of the income distribution accounted for 49.2% of total spending in the second quarter, up from 48.5% in the first quarter, reaching the highest level in data going back to 1989, according to an analysis of Federal Reserve data by Mark Zandi, chief economist for Moody’s Analytics. In contrast, the bottom 80% of the income distribution, or consumers making less than roughly $175,000 a year, have seen their spending merely keep pace with inflation since the pandemic.

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Litigation Nation, Engineering Empire

Jonathon Sine Cogitations
Date Posted:
September 2, 2025
Is Database:
Database
Is Important:
Important

Jonathon Sine argues China “is moving beyond its breakneck industrial prime, facing similar dilemmas to those America confronted in the 1960s and 70s.” The ratio of science/engineering to humanities undergraduate majors is 2:1 in both the PRC and US.

Dan Wang’s “big idea” [is] “China is an engineering state, building big at breakneck speed, in contrast to the United States’ lawyerly society, blocking everything it can, good and bad.” I re-group US college majors according to Chinese disciplines to allow for rough comparison. Surprisingly, the ratio of science/engineering to humanities/social sciences is 2:1, the same as in China (if one groups management with science/engineering, as I also do for China). As with China today, America’s breakneck building phase was decidedly winding down by the 1960s. Urbanization went from 40% in 1900 to 70% by 1960, and grew much more incrementally over the next 60 years to 85% by 2020. The country simply did not need to continue building dams, expressways, and energy production facilities at breakneck pace. It became much more a matter of maintaining and upgrading (which has not gone well, at least according to the American Society of Civil Engineers’ report card). The American [building/investment slowdown that started after the 1970s] may be more about structural economic shifts than lawyers.

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How America’s AI Boom Is Squeezing The Rest Of The Economy

Economist Staff The Economist
Date Posted:
August 19, 2025
Is Database:
Database
Is Important:
Important

As AI-related investment has risen since 2023, residential and nonresidential investment have declined or flatlined. This may suggest that a relatively rate-insensitive AI buildout is crowding out more interest-sensitive forms of investment.

Something like a sixth of the 2% rise in American real GDP over the past year has come from investments in computer and communications equipment, including chips, and data centres. Add in the grid upgrades to power AI models, plus the intellectual-property value of the software itself, and one estimate puts the boom’s contribution to real GDP growth at 40%. The trouble is that the very sector powering so much of America’s economic growth is squeezing the rest of its output. Housebuilders, for instance, cannot afford to be blithe about higher borrowing costs. Data centres have also constrained the rest of the economy by keeping energy prices high. Average American electricity bills have risen by 7% so far in 2025, at least in part due to the extra strain data centres have put on the grid. Real consumption has flatlined since December. Housebuilding has slumped, as has non-AI business investment.

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Is it Over?

Joseph Wang Fed Guy Blog
Date Posted:
August 18, 2025
Is Database:
Database

Following tepid reactions to the release of GTP-5, Joe Wang observes, “It is looking more like companies are spending hundreds of billions on rapidly depreciating GPUs that produce a commoditized product that most clients only modestly benefit from.”

GPT-5 users widely expressed disappointment in the capabilities of the new release, which seemed in some ways a step back. This sentiment is reflected in benchmarks that show a modest improvement in capabilities since the significant improvement in version 4 released two years ago. In addition, the benchmarks suggest a broader convergence in the capabilities of AI models. Commentary suggests this could be due to inherent limitations in the LLM technology and exhaustion of new training data. AI is fascinating technology, but it may not justify the enormous sums spent in its pursuit. It is looking more like companies are spending hundreds of billions on rapidly depreciating GPUs that produce a commoditized product that most clients only modestly benefit from. The entire macro landscape would look very different without the support of the AI boom.

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    • Innovation/Research
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US Households and Firms Are in Great Shape

Torsten Sløk Apollo
Date Posted:
March 31, 2025
Is Database:
Database

​​Torsten Sløk notes that US household and banking sector debt has fallen to its lowest level in decades as a % of GDP, while corporate leverage has moved sideways. “The bottom line is that the private sector in the US is in incredibly good shape.”

Household sector leverage and banking sector leverage have declined significantly since 2008. Over the same period, federal government leverage has increased significantly, and corporate leverage has moved sideways. The bottom line is that the private sector in the US is in incredibly good shape.

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