Edward Conard

Top Ten New York Times Bestselling Author

  • “…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 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 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 comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “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
  • “There are an amazing number of good ideas and interesting points made in Unintended Consequences. The thinking underlying it, and the obvious depth of understanding of the author, are very impressive.” - Steven Levitt, coauthor of Freakonomics; 2004 John Bates Clark Medal
  • “Unintended Consequences is far smarter and more thought-provoking than most economics written for the general public” - Greg Mankiw, Harvard University, Former Chairman of the Council of Economic Advisors
  • “Unintended Consequences represents the most cogent and persuasive analysis of the Financial Crisis to date.” - Andrei Shleifer, 1999 John Bates Clark Medal Winner
  • “…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
Upside of Inequality Oxford Unintended Consequences

Macro Roundup


Thursday, November 12, 2020

AI Summary. A widely cited epidemiological model assumed a U.S. mask-adoption rate of 49%, implying large gains from moving to 85–95%, while surveys indicated adoption had been around 80%. The author argues this outdated assumption inflated the projected benefits used to justify a mask mandate.

Phillip Magness Wall Street Journal
Date Posted:
Is Database:
Database

The IHME model’s failure to update for behavioral changes led to flawed projections, undermining the case for a mask mandate.

Do outdated modeling assumptions overstate the benefits of mask mandates?

Core argument: The IHME model assumed a U.S. mask-adoption rate of 49% as of late September, implying large room for gains toward 85–95%, while more recent surveys indicate adoption has been around 80% since the summer.

The IHME [Institute for Health Metrics and Evaluation] model's failure to update for behavioral changes led to flawed projections, undermining the case for a mask mandate. The model erroneously assumed a U.S. mask-adoption rate of 49% as of late September, suggesting significant potential for increase to 95% or 85%. However, more recent surveys indicate that the mask-adoption rate has been around 80% since the summer. This discrepancy highlights the importance of using current data in economic modeling and policy-making, as outdated assumptions can lead to misguided decisions with significant economic implications. Accurate data is crucial for effective policy interventions, especially in public health, where behavioral changes can rapidly alter the landscape.

Takeaways by Macro Roundup® AI

  1. The IHME model assumed a U.S. mask-adoption rate of 49% as of late September, implying large room for gains toward 85–95%, while more recent surveys indicate adoption has been around 80% since the summer.
  2. The IHME model's failure to update for behavioral changes produced flawed projections that undermine the case for a mask mandate.
  3. Outdated assumptions in modeling rapidly changing behavior can lead to misguided policy decisions with significant economic implications, making current data essential to public health interventions.

IHME failed to update their model for behavioral changes"...Unfortunately, the IHME modelers’ findings contained an error that even minimal scrutiny should have caught. The projected number of lives saved, and the implied case for a mask mandate, are based on a faulty statistic. Using a months-old survey, IHME modelers assumed erroneously that the U.S. mask-adoption rate stood at only 49% as of late September, and therefore had plenty of room to increase to “universal adoption,” defined as 95%, or to a more plausible 85%. According to more recent survey findings, however, America’s mask-adoption rate has hovered around 80% since the summer...."

Phillip Magness, "Case for Mask Mandate Rests on Bad Data,"Wall Street Journal, November 11, 2020, https://www.wsj.com/articles/case-for-mask-mandate-rests-on-bad-data-11605113310

AI Summary. Research output per researcher has declined about 5% a year in Germany and 3% a year in China, even as research and development spending rose 20% and 30% respectively. The pattern points to diminishing returns on research investment, which the authors attribute to rising problem complexity and saturation in some fields.

Philipp Boeing Economics Letters
Date Posted:
Is Database:
Database

Research productivity in Germany and China has declined by 5% and 3% annually since 2010, respectively, despite significant R&D investments.

Is research productivity declining even as R&D spending rises?

Core argument: German research output per researcher has declined 5% annually since 2010 even as German R&D spending rose 20%, aligning with Bloom's findings on global research productivity trends.

Recent data indicates a notable decline in research productivity in both Germany and China, aligning with Bloom's findings on global trends. In Germany, research output per researcher has decreased by 5% annually since 2010, despite a 20% increase in R&D [Research and Development] spending. Similarly, China's research productivity has fallen by 3% per year over the same period, even as R&D investment surged by 30%. These trends suggest diminishing returns on research investments, raising concerns about long-term economic growth and innovation potential. The decline in productivity is attributed to factors such as increased complexity of research problems and saturation in certain fields, which may require policy interventions to enhance efficiency and effectiveness in research activities.

Takeaways by Macro Roundup® AI

  1. German research output per researcher has declined 5% annually since 2010 even as German R&D spending rose 20%, aligning with Bloom's findings on global research productivity trends.
  2. China's research productivity has fallen 3% per year since 2010 despite a 30% increase in R&D investment, suggesting diminishing returns on research spending.
  3. The declines in German and Chinese research productivity are attributed to increased complexity of research problems and saturation in certain fields, raising concerns about long-term economic growth and innovation potential.

Philipp Boeing and Paul Hünermundd, "A global decline in research productivity? Evidence from China and Germany,"Economics Letters, December 2020, https://www.sciencedirect.com/science/article/abs/pii/S0165176520304067

Ed Comment:“We saw in recent paper that the quality of researchers declines as the share of researchers increases. For the same reason (.i.e., a shortage of talent) every increase in a research is a decrease in the quality of people employed to other critical endeavors such as commercialization.”

New paper replicates Bloom'sAre Ideas Getting Harder To Findfor China and Germany and finds evidence of a decline in research productivity in both countries providing support to Bloom's work

What they did, “….Following Bloom et al., we calculate the research productivity parameter,𝛼𝛼, in equation (1),by taking the average of output growth per firm and decade (1990s, 2000s, and 2010s), and dividing by average input levels. As measures for output we use sales revenue, employment, revenue labor productivity, and market capitalization (monetary units deflated by the GDP implicit price deflator). Market capitalization is not available for Germany’s predominantly privately owned companies and we substitute it with sales revenue from innovative products and services. Regarding inputs, Bloom et al. (2020) show theoretically that research inputs in (1) can be measured by𝑆𝑆̃𝑡𝑡, the“effective number of researchers”, by deflating a firm’s R&D expenditures, 𝑆𝑆with the nominal wage rate for high-skilled workers in the economy…”

Bottom line, “….Table 1 depicts our results. In Germany, the effective number of researchers grows at an annual rate of 1.5% to 4.9%. Like Bloom et al.’s findings for the U.S., however, such input growth is not met with a proportional growth in output.As a result, we find declines in research productivity ranging from3.7% to 7.8% per year. The average of the four estimates, equal to -5.225%, implies that research productivity halves every fourteen years, which is very close to the estimated halflife of thirteen years for the U.S. (Bloom et al., 2020). In China, we observe an extremely rapid expansion of research activities during the first and second decades of the 21st century, with growth rates for effective researchers ranging between 21% and 24%.5 The resulting output growth, again, is not proportional to such inputs, which is reflected in a decrease in research productivity estimated between 15.4% and 29.3%. Averaged across estimates, this amounts to a decline of -23.775% per year, or a half-life of around 3 years….”

Note they theorize that China might see a quicker decline in research productivity due to internal constraints, “….Overall, ideas are not only getting harder to find in the U.S., but that the same holds true for the largest R&D-spending countries in Europe and Asia respectively. Although estimates are difficult to compare, due to differences between data sources, negative growth rates are, in fact, remarkably similar across Germany and the U.S. China has undergone an even larger decline in research productivity in the last two decades, which reflects its rapid transformation from principally capital-driven growth toward more innovation-led growth. It remains to be seen whether China will start to follow productivity trends of advanced economies. The increasingly inward looking and mission-driven nature of Chinese innovation policy (Chinese State Council, 2020), however, suggests that research productivity might continue to decline faster in China than elsewhere. Knowledge production at the technology frontier crucially relies on creative freedom, serendipitous discovery, and exchange…”

A global decline in research productivity? Evidence from China and Germany: Comments Image 1

AI Summary. US investment in intellectual property now exceeds 5% of GDP, as firms shift spending toward research and development, software, and workforce training that financial statements do not immediately capture. Because such intangible investment can leave earnings and book value understating a company's worth, traditional value-investing metrics are becoming less informative.

Economist Staff The Economist
Date Posted:
Is Database:
Database

US Intellectual Property Investment Now Over 5% Of GDP.

Does rising intangible investment make traditional value metrics less informative?

Core argument: US intellectual property investment exceeds 5% of GDP, a shift in corporate resource allocation that leaves book value a diminishing guide to company worth.

Intellectual property investment in the US now exceeds 5% of GDP, reflecting a significant shift in how companies allocate resources. This trend underscores the diminishing relevance of traditional metrics like book value in assessing company worth, as earnings and book value often understate true value due to intangible investments. Firms increasingly invest in areas like R&D, software development, and workforce training, which are not immediately reflected in financial statements but are crucial for future growth. Consequently, a small number of leading firms dominate capital spending, reshaping industries and challenging the principles of value investing. This evolution highlights the need for investors to focus on understanding the magnitude and returns of intangible investments to accurately gauge a company's potential.

Takeaways by Macro Roundup® AI

  1. US intellectual property investment exceeds 5% of GDP, a shift in corporate resource allocation that leaves book value a diminishing guide to company worth.
  2. Firms' spending on R&D, software development, and workforce training is not immediately reflected in financial statements, causing earnings and book value to understate true value.
  3. A small number of leading firms dominate capital spending, reshaping industries and challenging the principles of value investing, making the magnitude and returns of intangible investments central to assessing a company's potential.

Economist mentions the MS Mauboussin report on intangible we looked at in a discussion of the implications of the rise of intangible investment in terms of valuation (it has made book value a less useful gauge of value)

Intellectual property investment as a % of GDP:

Their argument on book value, “….The third aspect of intangibles to consider is their implications for investors. A big one is that earnings and accounting book value have become less useful in gauging the value of a company. Profits are revenues minus costs. If a chunk of those costs are not running expenses but are instead spending on intangible assets that will generate future cashflows, then earnings are understated. And so, of course, is book value.The more a firm spends on advertising, r&d, workforce training, software development and so on, the more distorted the picture is. The distinction between a running expense and investment is crucial for securities analysis. An important part of the stock analyst’s job is to understand both the magnitude of investment and the returns on it. This is not a particularly novel argument, as Messrs Mauboussin and Callahan point out. It was made nearly 60 years ago in a seminal paper by Merton Miller and Francesco Modigliani, two Nobel-prize-winning economists. They divided the value of a company into two parts. The first—call it the “steady state”—assumes that that the company can sustain its current profits into the future. The second is the present value of future growth opportunities—essentially what the firm might become. The second part depends on the firm’s investment: how much it does, the returns on that investment and how long the opportunity lasts. To begin to estimate this you have to work out the true rate of investment and the true returns on that investment….”

Good graph of how investment has shifted to a small number of leading firms“….The big business successes of the past decade—Google, Amazon and Facebook in America; and Alibaba and Tencent in China—have grown to a size that was not widely predicted. But there are plenty of older asset-light businesses that were built on such network effects—think of Visa and Mastercard. The result is that industries become dominated by one or a few big players. The same goes for capital spending. A small number of leading firms now account for a large share of overall investment (see chart 3)….”
Value investing is struggling to remain relevant: Extended Excerpt Image 1

Economist Staff, "Value investing is struggling to remain relevant,"The Economist, November 12, 2020, https://www.economist.com/briefing/2020/11/12/value-investing-is-struggling-to-remain-relevant

AI Summary. The average lag between scientific research and clean energy patents has lengthened from 5 to 8 years since the 1980s, while dirty energy technologies draw on science that is 11 years old on average. The authors argue this indicates clean technologies depend more on recent science, making timely research investment important.

Robert Perrons National Bureau of Economic Research
Date Posted:
Is Database:
Database

The lag btw scientific research & clean energy patents has increased from 5 to 8 years since the 1980s, highlighting a growing dependency on underlying science.

Do clean energy technologies rely more on recent scientific research?

Core argument: The lag between scientific research and clean energy patents has lengthened from 5 years to 8 years since the 1980s, indicating a growing dependency of clean energy patents on underlying science.

The lag between scientific research and clean energy patents has increased from 5 to 8 years since the 1980s, highlighting a growing dependency on underlying science. This delay underscores the urgency for timely investments in clean energy research to effectively mitigate climate change. Despite calls for faster innovation, the process of translating scientific knowledge into patents remains lengthy, with clean technologies taking 8 years on average, compared to 11 years for dirty technologies. This suggests clean technologies rely more on recent science, emphasizing the critical need for accelerated scientific advancements to meet future energy demands.

Takeaways by Macro Roundup® AI

  1. The lag between scientific research and clean energy patents has lengthened from 5 years to 8 years since the 1980s, indicating a growing dependency of clean energy patents on underlying science.
  2. Clean energy technologies draw on science that is 8 years old on average, compared with 11 years for dirty technologies, suggesting clean technologies rely more on recent scientific advances.
  3. The length of the translation from scientific knowledge into patents underscores the urgency of timely investment in clean energy research to mitigate climate change and meet future energy demand.

New NBER suggests innovation getting more “science” intensive. Paper looks at the average lag between publication of scientific findings and the incorporation of those findings in clean energy patents. Data shows that the lag btw the pure research and a patent has risen from about five to about eight years since the 1980.

“…The challenge of mitigating climate change has focused recent attention on basic scientific research feeding into the development of new energy technologies….Energy innovation tends to consist of a series of partially overlapping processes involving: (1) the production of scientific and technological knowledge, (2) the translation of that knowledge into working technologies or artifacts, and (3) the introduction of the artifacts into the marketplace, where they are matched with users’ requirements. However, relatively little data are available showing how long each of these processes takes for energy technologies. Here we combine information from patent applications with bibliographic data to shine light on the second process —that is, the translation of scientific knowledge into working prototypes. Our results show that “clean” energy technologies are more dependent on underlying science than “dirty” technologies, and that the average lag between publication of scientific findings and the incorporation of those findings in clean energy patents has risen from about five to about eight years since the 1980s….”

Tracing the Linkages Between Scientific Research and Energy Innovations: A Comparison of Clean and Dirty Technologies: Extended Excerpt Image 1


They warn that means that the “science” that drives potential clean energy innovation needs to happen soon if it’s to be useful in mitigating climate change“….our evidence shows that a significant part of the problem lies further upstream before the capital-intensive nature of the industry is materially relevant. This finding also highlights the time-critical nature of investments in clean energy technologies. If significantly new technologies will be required to mitigate climate change in a useful timeframe…then the scientific research laying the foundations for these new technologies needs to be undertaken very soon….”

Robert Perrons, Adam Jaffe and Trinh Le, "Tracing the Linkages Between Scientific Research and Energy Innovations: A Comparison of Clean and Dirty Technologies," National Bureau Of Economic Research, September 2020, https://www.nber.org/papers/w27777

Core finding, “…Figure 4 also reveals that the process of turning scientific and technological knowledge into working artifacts is not getting shorter, despite the chorus of voices that has been calling over many years for the acceleration of energy-related innovation…Figure 4 shows that, during the past decade, it took on average approximately 8 years for scientific discoveries to appear in patent applications related to clean energy technologies. Interestingly, the average for dirty energy technologies in the most recent decade was about 11 years. This may reflect a tendency for clean technologies to rely somewhat more on the most recent science, while dirty technologies have more of a reliance on older science. But whatever the factors underlying the difference, these relatively long lags suggest that the time needed for the middle stage of Figure 1 represents a significant fraction of the overall time lags necessary to bring new cleaner technologies into actual use.…. It should be emphasized, too, that this figure only considers the middle process of the three shown in Figure 1, and includes neither the amount of time it will take for these investments in science to actually generate published results (i.e., the first process), nor how long it will take for the new technology to be matched with user requirements in the marketplace (i.e. the third process)….”

Friday, November 13, 2020

AI Summary. The share of safe assets in the US economy has remained stable at roughly 30–35% of total assets over the past 70 years, with a standard deviation of 2.3%. Their composition has shifted, with banks' role in producing safe assets diminishing and a move back toward depositories and away from shadow banking.

Sara Almadani Federal Reserve Board
Date Posted:
Is Database:
Database

The supply of safe assets in the US has been relatively stable over the past 70 years, ranging btw 30-35% of total assets.

Has the share of safe assets in the economy stayed stable?

Core argument: Safe assets have held at 30–35% of total US assets over the past 70 years, with a standard deviation of 2.3%.

Over the past 70 years, the proportion of safe assets in the US economy has remained stable, consistently ranging between 30-35% of total assets. This stability is underscored by a standard deviation of 2.3%, indicating minimal fluctuation. Despite this constancy, the composition of these assets has evolved, particularly since the Great Financial Crisis (GFC). The role of banks in producing safe assets has diminished, with a notable shift back to depositories and away from shadow banking post-GFC. Government-issued safe assets continue to play a significant role, as depicted by the red area in updated analyses through 2020. This enduring stability in the share of safe assets highlights their critical role in the financial landscape, providing a reliable foundation amidst changing economic conditions.

Takeaways by Macro Roundup® AI

  1. Safe assets have held at 30–35% of total US assets over the past 70 years, with a standard deviation of 2.3%.
  2. The composition of US safe asset production has shifted even as its share stayed constant, with banks' role diminishing and a move back toward depositories and away from shadow banking after the Great Financial Crisis.
  3. Government-issued safe assets remain a significant component of US safe asset production in analyses updated through 2020.

New Fed research finds that the percentage of safe assets in the United States been relatively stable staying btw 30-35 percent of total US assets over the past 70 years, though there have been shifts in the makeup of those safe assets (and obviously demand is not just domestic)“….One remarkable claim is the percentage of safe assets in the United States has remained very stable over time…In particular,the fraction of safe assets to total assets in the economy has remained in a range between 30-35 percent during the past 7 decades. Here, we update the data on safe assets, as defined using the Financial Accounts of the United States (Z.1), and show thatthis claim is still true. We also update the components of safe assets and show that while the overall proportion has remained the same, there has been a significant shift in the components. For many years, the role of banks in the production of safe assets has been declining. But since the Great Financial Crisis (GFC), safe asset production has shifted back to depositories and away from shadow banking. Still, the declining contribution of banks remains notable….Figure 1 below updates the analysis through 2020. The red area represents the safe assets issued by U.S. federal, state, and local governments as a share of total assets in the U.S. economy, the blue area represents safe assets issued by the financial sector, and the dotted line indicates the average proportion of safe assets over the years 1952 to 2020 (31.1 percent). The standard deviation of the safe asset share has been 2.3 percent, suggesting that thehypothesis of a relatively constant proportion of safe assets remain true….”

Sara Almadani, Michael Batty, Danielle Nemschoff, and Wayne Passmore, "The Stability of Safe Asset Production," Federal Reserve Board, November 9, 2020, https://www.federalreserve.gov/econres/notes/feds-notes/the-stability-of-safe-asset-production-20201109.htm