Macro Roundup

Allysia Finley, "The Bearer of Good Coronavirus News,"Wall Street Journal, April 24, 2020, https://www.wsj.com/articles/the-bearer-of-good-coronavirus-news-11587746176

The Bearer of Good Coronavirus News

Defenders of coronavirus lockdown mandates keep talking about science. “We are going to do the right thing, not judge by politics, not judge by protests, but by science,” California’s Gov. Gavin Newsom said this week. Michigan Gov. Gretchen Whitmer defended an order that, among other things, banned the sale of paint and vegetable seeds but not liquor or lottery tickets. “Each action has been informed by the best science and epidemiology counsel there is,” she wrote in an op-ed.

But scientists are almost never unanimous, and many appeals to “science” are transparently political or ideological. Consider the story of John Ioannidis, a professor at Stanford’s School of Medicine. His expertise is wide-ranging—he juggles appointments in statistics, biomedical data, prevention research and health research and policy. Google Scholar ranks him among the world’s 100 most-cited scientists. He has published more than 1,000 papers, many of them meta-analyses—reviews of other studies. Yet he’s now found himself pilloried because he dissents from the theories behind the lockdowns—because he’s looked at the data and found good news.

In a March article for Stat News, Dr. Ioannidis argued that Covid-19 is far less deadly than modelers were assuming. He considered the experience of the Diamond Princess cruise ship, which was quarantined Feb. 4 in Japan. Nine of 700 infected passengers and crew died. Based on the demographics of the ship’s population, Dr. Ioannidis estimated that the U.S. fatality rate could be as low as 0.025% to 0.625% and put the upper bound at 0.05% to 1%—comparable to that of seasonal flu.

“If that is the true rate,” he wrote, “locking down the world with potentially tremendous social and financial consequences may be totally irrational. It’s like an elephant being attacked by a house cat. Frustrated and trying to avoid the cat, the elephant accidentally jumps off a cliff and dies.”

Dr. Ioannidis, 54, likes metaphors. A New York native who grew up in Athens, he also teaches comparative literature and has published seven literary works—poetry and fiction, the latest being an epistolary novel—in Greek. In his spare time, he likes to fence, swim, hike and play basketball.

Early in his career, he realized that “the common denominator for everything that I was doing was that I was very interested in the methods—not necessarily the results but how exactly you do that, how exactly you try to avoid bias, how you avoid error.” When he began examining studies, he discovered that few headline-grabbing findings could be replicated, and many were later contradicted by new evidence.

Scientific studies are often infected by biases. “Several years ago, along with one of my colleagues, we had mapped 235 biases across science. And maybe the biggest cluster is biases that are trying to generate significant, spectacular, fascinating, extraordinary results,” he says. “Early results tend to be inflated. Claims for significance tend to be exaggerated.”

An example is a 2012 meta-analysis on nutritional research, in which he randomly selected 50 common cooking ingredients, such as sugar, flour and milk. Eighty percent of them had been studied for links to cancer, and 72% of the studies linked an ingredient to a higher or lower risk. Yet three-quarters of the findings were weak or statistically insignificant.

Dr. Ioannidis calls the coronavirus pandemic “the perfect storm of that quest for very urgent, spectacular, exciting, apocalyptic results. And as you see, apparently our early estimates seem to have been tremendously exaggerated in many fronts.”

Chief among them was a study by modelers at Imperial College London, which predicted more than 2.2 million coronavirus deaths in the U.S. absent “any control measures or spontaneous changes in individual behaviour.” The study was published March 16—the same day the Trump administration released its “15 Days to Slow the Spread” initiative, which included strict social-distancing guidelines.

Dr. Ioannidis says the Imperial projection now appears to be a gross overestimate. “They used inputs that were completely off in some of their calculation,” he says. “If data are limited or flawed, their errors are being propagated through the model.... So if you have a small error, and you exponentiate that error, the magnitude of the final error in the prediction or whatever can be astronomical.”

“I love models,” he adds. “I do a lot of mathematical modeling myself. But I think we need to recognize that they’re very, very low in terms of how much weight we can place on them and how much we can trust them.... They can give you a very first kind of mathematical justification to a gut feeling, but beyond that point, depending on models for evidence, I think it’s a very bad recipe.”

Modelers sometimes refuse to disclose their assumptions or data, so their errors go undetected. Los Angeles County predicted last week that 95.6% of its population would be infected by August if social distancing orders were relaxed. (Confirmed cases were 0.17% of the population as of Thursday.) But the basis for this projection is unclear. “At a minimum, we need openness and transparency in order to be able to say anything,” Dr. Ioannidis says.

Most important, “what we need is data. We need real data. We need data on how many people are infected so far, how many people are actively infected, what is really the death rate, how many beds do we have to spare, how has this changed.”

That will require more testing. Dr. Ioannidis and colleagues at Stanford last week published a study on the prevalence of coronavirus antibodies in Santa Clara County. Based on blood tests of 3,300 volunteers in the county—which includes San Jose, California’s third-largest city—during the first week of April, they estimated that between 2.49% and 4.16% of the county population had been infected. That’s 50 to 85 times the number of confirmed cases and implies a fatality rate between 0.12% and 0.2%, consistent with that of the Diamond Princess.

The study immediately came under attack. Some statisticians questioned its methods. Critics noted the study sample was not randomly selected, and white women under 64 were disproportionately represented. The Stanford team adjusted for the sampling bias by weighting the results by sex, race and ZIP Code, but the study acknowledges that “other biases, such as bias favoring individuals in good health capable of attending our testing sites, or bias favoring those with prior Covid-like illnesses seeking antibody confirmation are also possible. The overall effect of such biases is hard to ascertain.”

Dr. Ioannidis admits his study isn’t “bulletproof” and says he welcomes scrutiny. But he’s confident the findings will hold up, and he says antibody studies from around the world will yield more data. A study published this week by the University of Southern California and the Los Angeles County Department of Public Health estimated that the virus is 28 to 55 times as prevalent in that county as confirmed cases are. A New York study released Thursday estimated that 13.9% of the state and 21.2% of the city had been infected, more than 10 times the confirmed cases.

Yet most criticism of the Stanford study has been aimed at defending the lockdown mandates against the implication that they’re an overreaction. “There’s some sort of mob mentality here operating that they just insist that this has to be the end of the world, and it has to be that the sky is falling. It’s attacking studies with data based on speculation and science fiction,” he says. “But dismissing real data in favor of mathematical speculation is mind-boggling.”

In part he blames the media: “We have some evidence that bad news, negative news, are more attractive than positive news—they lead to more clicks, they lead to people being more engaged. And of course we know that fake news travels faster than true news. So in the current environment, unfortunately, we have generated a very heavily panic-driven, horror-driven, death-reality-show type of situation.”

The news is filled with stories of healthy young people who die of coronavirus. But Dr. Ioannidis recently published a paper with his wife, Despina Contopoulos-Ioannidis, an infectious-disease specialist at Stanford, that showed this to be a classic man-bites-dog story. The couple found that people under 65 without underlying conditions accounted for only 0.7% of coronavirus deaths in Italy and 1.8% in New York City.

“Compared to almost any other cause of disease that I can think of, it’s really sparing young people. I’m not saying that the lives of 80-year-olds do not have value—they do,” he says. “But there’s far, far, far more... young people who commit suicide.” If the panic and attendant disruption continue, he says, “we will see many young people committing suicide... just because we are spreading horror stories with Covid-19. There’s far, far more young people who get cancer and will not be treated, because again, they will not go to the hospital to get treated because of Covid-19. There’s far, far more people whose mental health will collapse.”

He argues that public officials need to weigh these factors when making public-health decisions, and more hard data from antibody and other studies will help. “I think that we should just take everything that we know, put it on the table, and try to see, OK, what’s the next step, and see what happens when we take the next step. I think this sort of data-driven feedback will be the best. So you start opening, you start opening your schools. You can see what happens,” he says. “We need to be open minded, we need to just be calm, allow for some error, it’s unavoidable. We started knowing nothing. We know a lot now, but we still don’t know everything.”

He cautions against drawing broad conclusions about the efficacy of lockdowns based on national infection and fatality rates. “It’s not that we have randomized 10 countries to go into lockdown and another 10 countries to remain relatively open and see what happens, and do that randomly. Different prime ministers, different presidents, different task forces make decisions, they implement them in different sequences, at different times, in different phases of the epidemic. And then people start looking at this data and they say, ‘Oh look at that, this place did very well. Why? Oh, because of this measure.’ This is completely, completely opinion-based.”

People are making “big statements about ‘lockdowns save the world.’ I think that they’re immature. They’re tremendously immature. They may have worked in some cases, they may have had no effect in others, and they may have been damaging still in others.”

Most disagreements among scientists, he notes, reflect differences in perspective, not facts. Some find the Stanford study worrisome because it suggests the virus is more easily transmitted, while others are hopeful because it suggests the virus is far less lethal. “It’s basically an issue of whether you’re an optimist or a pessimist. Even scientists can be optimists and pessimists. Probably usually I’m a pessimist, but in this case, I’m probably an optimist.”

“…Yet most criticism of the Stanford study has been aimed at defending the lockdown mandates against the implication that they’re an overreaction. “There’s some sort of mob mentality here operating that they just insist that this has to be the end of the world, and it has to be that the sky is falling. It’s attacking studies with data based on speculation and science fiction,” he says. “But dismissing real data in favor of mathematical speculation is mind-boggling.” In part he blames the media: “We have some evidence that bad news, negative news, are more attractive than positive news—they lead to more clicks, they lead to people being more engaged. And of course we know that fake news travels faster than true news. So in the current environment, unfortunately, we have generated a very heavily panic-driven, horror-driven, death-reality-show type of situation.”…”

“…In the 1980s, two out of 10 Americans worked for themselves. By 2016, that number fell to about one in 10….”

Randall Stross, “‘The Soul of an Entrepreneur’ Review: Doing It for Themselves,”Wall Street Journal, April 26, 2020, https://www.wsj.com/articles/the-soul-of-an-entrepreneur-review-doing-it-for-themselves-11587930639

‘The Soul of an Entrepreneur’ Review: Doing It for Themselves

In the 1980s, two out of 10 Americans worked for themselves. By 2016, that number fell to about one in 10. Who knows how much more it will shrink before the Covid-19 pandemic passes? In “The Soul of an Entrepreneur: Work and Life Beyond the Startup Myth,” David Sax speaks up for the many entrepreneurs around the country who have scratched out a living in the shadow of the privileged few, the venture-backed startup founders in Silicon Valley.

Though the book understandably omits the recent appearance of the new coronavirus, Covid-19 has given it a timely relevance. Mr. Sax says he would like to “shift the conversation back to the businesses that actually touch us every day”—that is, touch us in the physical world rather than the digital one. As we shelter in place, we cannot help but become concerned about those businesses. Their ability to recover and serve us is in grave peril.

Mr. Sax provides seven anchor stories of entrepreneurs, with shorter profiles of others mixed in, spanning widely varying industries and circumstances. Some were already struggling financially when the author paid them a visit.

We meet, for instance, Tracy Obolsky, the proprietor of Rockaway Beach Bakery in Queens, N.Y., a “lifestyle business” that Ms. Obolsky closes whenever she feels an urge to go surfing. Mr. Sax, a freelance writer who lives in Toronto, is an especially passionate defender of lifestyle businesses, which are meant “to fund the living expenses and lifestyle ambitions” of their owners. The author himself savors the freedom he enjoys to grab a friend and go paddleboarding around Toronto’s downtown harbor whenever he wants. A mutual friend asks them, “Do you two even work?” His answer: “Of course we work… but on our terms.”

Other entrepreneurs profiled by Mr. Sax are not so lucky: the recent immigrants who own a tiny Syrian restaurant in Toronto; the operators of an unprofitable grass-fed-cattle ranch in California; the fourth-generation farmers running a highly efficient but money-losing dairy.

Mr. Sax has sought out profile subjects whose collected stories show us the many facets of entrepreneurship. Bodega y Cavas de Weinert, a winery in Mendoza, Argentina, provides a short course on the conflicts that can arise in family-owned businesses. A chapter introducing us to Jesseca Dupart, the founder of New Orleans-based Kaleidoscope Hair Products, explains that for the past two decades minority women have constituted the fastest-growing group of entrepreneurs in the country. But firms owned by African-American women barely generate an average of $25,000 in revenue a year; for firms owned by white females, the amount is more than $200,000 annually.

In one of the book’s most moving accounts, Mr. Sax tells of an all-hands meeting at NCC Automated Systems, a conveyor manufacturer located outside Philadelphia. The firm’s owner, Kevin Mauger, assembled the group to make a surprise announcement. After asking his colleagues to stand up, he announced: “If you are standing and you are an employee of NCC you are an owner.” Mr. Mauger had put in place an employee stock-ownership plan, which allows workers to gain shares in the company over time. Mr. Sax connects the NCC story to the growth of social entrepreneurship, and includes a grateful nod to Yvon Chouinard, founder of Patagonia and author of the influential (and delightfully titled) memoir, “Let My People Go Surfing.”

The weakest portions of the book are those that talk about Silicon Valley. The chapter centered on two Stanford undergraduates, aspiring entrepreneurs who have nothing more than a wisp of an idea for a startup, does not show us much of anything. Errors abound: Stanford undergraduates cannot, as the author claims, “spend their entire time at Stanford” taking classes on entrepreneurship; the university offers but a handful and with limited enrollments. Mr. Sax garbles Steve Jobs’s marital history, misspells the name of Stripe co-founder Patrick Collison and fails to understand that infamous flameout Theranos was never backed by the Valley’s “top venture capitalists.” Most inexplicably, Mr. Sax makes no mention of the “techlash” against Amazon, Facebook, Google and others that has dimmed Silicon Valley’s former luster. Silicon Valley is simply no longer the foil the author intended it to be.

Most of the book is given over to the entrepreneurs with whom Mr. Sax spent time during the course of one year’s reporting. But he also frequently injects himself into their midst, along with his wife, his parents and his wife’s parents—hardy entrepreneurs one and all, Mr. Sax wants us to know and appreciate. When he quotes his father as saying “The day you stop working is the day you start dying,” he is wholly insensible to how well-worn this adage is. Still, several times in the book he declares “I am an entrepreneur.” And he tells those readers who, like him, take everything related to work personally that they should realize that they, too, are entrepreneurs. “Say it out loud. Take pride in it.” Like any self-help guide, this will be appreciated by those looking for a pep talk. But it will grate on the ears of everyone else.

When we reach the end of the book, we do not have an explanation of why entrepreneurship has declined dramatically in recent decades. But we do come away with an appreciation of the daunting challenges faced by small businesses and, most poignantly, by those who supply our food.

Thirteen years after its founding, that grass-fed-cattle ranch nets only $30,000 annually; its owner, Seth Nitschke, has to work part-time as a cattle-feed salesman to make ends meet. And yet he will not let go of his dream of independence. “I don’t want to go back to working for someone else,” Mr. Nitschke says.

We can only be grateful to him and his kindred spirits.

Mulligan looks to quantify the value of innovation in terms of the current pandemic, finds that innovation that could “accelerate the end of a nationwide shutdown by a single workday” would be worth ~ $30 Billioin. One takeaway from paper is the value of medical innovation is not saving lives, not even primarily.

".... As shown in Table 1, if any one of the innovations in medicine, testing, manufacturing, or workplace mitigation could accelerate the end of a nationwide shutdown by a single workday, that would be worth almost $30 billion (plus the value of health and longevity). If a combination of these innovations could accelerate the end of a nationwide shutdown by just a month, they would be worth $590 billion.Accelerating the end by a quarter is worth almost $2 trillion...The reward to finding alternative protections that allow more normal economic activity is therefore commensurately tremendous. Medical innovation may provide such alternatives, but is currently handicapped by Federal and state regulations, including the shutdowns themselves. Although this report does not address the question of whether a shutdown is better than no policy response, it offers some of the essential ingredients for such an analysis. No policy response would itself involve economic contraction, especially because work is so heavily taxed. Shutdown policy therefore costs less relative to no policy response than it does relative to a normally functioning economy. On the other hand, the costs of economic contraction are convex, which suggests that the amount of contraction without a policy response would be less than shutdown, especially in the metric of welfare costs...."

As of last night, “The cumulative costs through April 26, 2020 were $8,096 per household ($1.0 trillion in aggregate).24 percent of these are mortality costs (valued at $4.3 million VSL). The rest are the market and nonmarket costs of shutting down the economy, for the categories documented. The costs above do not necessarily reflect the drop in stock prices, because much of that drop indicates costs expected to be incurred after April 26.”

Link to Mulligan’s implied cumulative Covid costs (this will be updated each evening)http://pandemiccosts.com/

“….This paper provides a formal evaluation of the predictive performance of a model (and its various updates) developed by the Institute for Health Metrics and Evaluation (IHME) for predicting daily deaths attributed to COVID19 for each state in the United States. The IHME models have received extensive attention in social and mass media, and have influenced policy makers at the highest levels of the United States government. For effective policy making the accurate assessment of uncertainty, as well as accurate point predictions, are necessary because the risks inherent in a decision must be taken into account - especially in the present setting of a novel disease affecting millions of lives. To assess the accuracy of the IHME models, we examine both forecast accuracy using tools from the econometric literature, as well as the predictive performance of the 95% prediction intervals provided by the IHME models. We find that the initial IHME model underestimates the uncertainty surrounding the number of daily deaths substantially. Specifically, the true number of next day deaths fell outside the IHME prediction intervals as much as 70% of the time, in comparison to the expected value of 5%.In addition, we note that theperformance of the initial model does not improve with shorter forecast horizons. Regarding the updated models, our analyses indicate that the later models do not show any improvement in the accuracy of the point estimate predictions. In fact, there is some evidence that this accuracy has actually decreased over the initial models. Moreover, when considering the updated models, while we observe a larger percentage of states having actual values lying inside the 95% prediction intervals (PI), our analysis suggests that this observation may be attributed to the widening of the PI’s. The width of these intervals calls into question the usefulness of the predictions to drive policy making and resource allocation….”

Roman Marchant, Noelle Samia, Ori Rosen, Martin Tanner, and Sally Cripps, "Learning as We Go - An Examination of the Statistical Accuracy of COVID19 Daily Death Count Predictions," ARC Centre for Data Analytics for Resources and Environments, University of Sydney, University of Texas, Northwestern University, April 24, 2020, https://www.sydney.edu.au/content/dam/corporate/documents/centre-for-translational-data-sience/marchant-et-al---2020---learning-as-we-go.pdf

There is difference between prime debt, the first 80%, and subprime debt, leverage beyond 80%. Most of this represent prime debt.

Created prosperity is transferred to people three ways 1) as return on business investment 2) as increased wages and 3) as real estate appreciation. As the wages and incomes of the real estate occupiers rise, the value of their real estate rises, namely the convenience value of the underlying land, if land is unconstrained. Even the rising wages of white collar workers can get transferred to blue collar real estate owners through the appreciation of their land, if their land becomes more desirable to white collar workers as it has in many cities. Land values also rise when interest rates fall, and they have fallen for decades, which can not be repeated even if low rates can be sustained.

New paper supports your view that equity extraction from housing gains driven by low and middle class borrowing was key driver of crisis.

Paper shows (Using a new Survey of Consumer Finances dataset) that equity extraction was the key driver of aggregate debt increase,without this borrowing housing debt with have increased by only half as much btw 1981-2007(See Figure 20) Home upgrading and home equity extraction accounted for 70% of additional housing debt btw 1981-2007 (Figure 17) Debt increase rose most dramatically for households btw the 50th and 90th percentiles of income distribution. Low and middle class families extracted equity by borrowing against their gains, wealth to income ratios grew faster than debt to income ratios. Home equity borrowing accounts for half the increase in American household debt btw the 1970's and 2007. Equity extraction of the middle class accounts for the lion’s share of equity extraction & the largest part of the increase in household debt creating the financial instability that drove crisis.

Great charts.

" the secular increase in U.S. household debt and its relation to growing income inequality and financial fragility. We exploit a new household level dataset that covers the joint distributions of debt, income, and wealth in the United States over the past seven decades. The data show that increased borrowing by middle-class families with low income growth played a central role in rising indebtedness. Debt-to-income ratios have risen most dramatically for households between the 50th and 90th percentiles of the income distribution. While their income growth was low, middle-class families borrowed against the sizable housing wealth gains from rising home prices. Home equity borrowing accounts for about half of the increase in U.S. household debt between the 1970s and 2007.The resulting debt increase made balance sheets more sensitive to income and house price fluctuations and turned the American middle class into the epicenter of growing financial fragility...In Figure 1 shows that the share of the richest 10% of households in total household income increased from below 35% to almost 50% between 1950 and 201...Figure 4 shows the share of total debt owed by the three different income groups. Debt shares have been rather stable over time. Over the entire postwar period, middle-class households have always accounted for the largest share of total debt, on average about 50% to 60% of total outstanding debt. Low-income households in the bottom half make up another 20%. The debt share of the top 10% fluctuated around 20% before the 1980s and then increased to around 30%. It is clear from Figure 4 that the upper half of the income distribution has always accounted for about 80% of total household debt outstanding...Figure 5 confirms this visually. From 1950 to 2007, middle-class households accounted for 55% of the total debt increase, whereas households from the bottom 50% of the income distribution contributed only 15%, even less than the top 10% with almost 30%. This insight is important in itself. We see that 85% of the increase in U.S. household debt occurred within the upper 50% of the income distribution. The explanation for soaring household debt in the United States lies in the borrowing behavior of these incomes groups, and in particular of middle-class households...To do so, Figure 10a decomposes the change in debt-to-income ratios into the extensive and intensive margins stratified by income. The figure shows two boom phases (1950-1965 and 1983-2007), followed by two periods of deleveraging (1965- 1983 and 2007-2016). Figure 10b shows a similar picture for loan-to-value ratios. There are substantial differences between the four periods.....The second important result is that home equity extraction has played the key quantitative role in driving the debt boom. It accounts for about 49% of the total increase in housing debt. In other words, about half of the increase in housing debt is driven by incumbent owners borrowing against their home equity. New owners account for a slightly smaller share, around 43%. Upgraders account for about 23%, while new renters contribute negatively to the total increase. The net contribution of downgraders was negligible over the considered period. Together, upgrading and home equity extraction account for more than 70% of additional housing debt since 1981. This corroborates our previous finding that the intensive margin of housing debt is the key driver of the debt boom.. Without equity extraction, housing debt would have increased by half as much over the 1981 to 2007 period. Debt-to-income ratios would have stayed at around 40% until 2001 and increased only during the boom of the 2000s, when new homeowners increased aggregate housing debt (see also Figure 17). Compared to the observed increase, the counterfactual increase would have been much more modest. By 2007, we estimate that the housing debt-to-income ratio would barely have exceeded 50% of income….”

Alina Bartscher, Moritz Kuhn, Moritz Schularick and Ulrike Steins, "Modigliani Meets Minsky: Inequality, Debt and Financial Fragility In America 1950-2016," April 27, 2020, https://www.wiwi.uni-bonn.de/kuhn/paper/Household_Debt.pdf

Excellent review ofThe Industrial Revolution in Services

“…The reason for increased concentration appears to be technological, not anticompetitive. And, as might be expected from that cause, its effects are beneficial. Indeed, the story is both intuitive and positive....This means that competition is increasing, not decreasing, whether it is accompanied by an increase in concentration or not....The net effect is a decrease in the power of top firms relative to the economy as a whole, as the largest firms specialize more, and are dominant in fewer industries...Thus, summing up, technology has led to increased productivity as well as greater specialization by large firms, especially in relatively concentrated industries (exactly the opposite of the pessimistic stories):...”

Geoffrey Manne, "What if rising concentration were an indication of more competition, not less?,"Truth On The Market, December 14, 2019, https://truthonthemarket.com/2019/12/14/what-if-rising-concentration-were-an-indication-of-more-competition-not-less/

What if rising concentration were an indication of more competition, not less?

An oft-repeated claim of conferences, media, and left-wing think tanks is that lax antitrust enforcement has led to a substantial increase in concentration in the US economy of late, strangling the economy, harming workers, and saddling consumers with greater markups in the process. But what if rising concentration (and the current level of antitrust enforcement) were an indication of more competition, not less?

By now the concentration-as-antitrust-bogeyman story is virtually conventional wisdom, echoed, of course, by political candidates such as Elizabeth Warren trying to cash in on the need for a government response to such dire circumstances:

In industry after industry — airlines, banking, health care, agriculture, tech — a handful of corporate giants control more and more. The big guys are locking out smaller, newer competitors. They are crushing innovation. Even if you don’t see the gears turning, this massive concentration means prices go up and quality goes down for everything from air travel to internet service.

But the claim that lax antitrust enforcement has led to increased concentration in the US and that it has caused economic harm has been debunked several times (for some of our own debunking, see Eric Fruits’ posts here, here, and here). Or, more charitably to those who tirelessly repeat the claim as if it is “settled science,” it has been significantly called into question.

Most recently, several working papers looking at the data on concentration in detail and attempting to identify the likely cause for the observed data, show precisely the opposite relationship. The reason for increased concentration appears to be technological, not anticompetitive. And, as might be expected from that cause, its effects are beneficial. Indeed, the story is both intuitive and positive.

What’s more, while national concentration does appear to be increasing in some sectors of the economy, it’s not actually so clear that the same is true for local concentration — which is often the relevant antitrust market.

The most recent — and, I believe, most significant — corrective to the conventional story comes from economists Chang-Tai Hsieh of the University of Chicago and Esteban Rossi-Hansberg of Princeton University. As they write in a recent paper titled, “The Industrial Revolution in Services”:

We show that new technologies have enabled firms that adopt them to scale production over a large number of establishments dispersed across space. Firms that adopt this technology grow by increasing the number of local markets that they serve, but on average are smaller in the markets that they do serve. Unlike Henry Ford’s revolution in manufacturing more than a hundred years ago when manufacturing firms grew by concentrating production in a given location, the new industrial revolution in non-traded sectors takes the form of horizontal expansion across more locations. At the same time, multi-product firms are forced to exit industries where their productivity is low or where the new technology has had no effect. Empirically we see that top firms in the overall economy are more focused and have larger market shares in their chosen sectors, but their size as a share of employment in the overall economy has not changed. (pp. 42-43) (emphasis added).

This makes perfect sense. And it has the benefit of not second-guessing structural changes made in response to technological change. Rather, it points to technological change as doing what it regularly does: improving productivity.

The implementation of new technology seems to be conferring benefits — it’s just that these benefits are not evenly distributed across all firms and industries. But the assumption that larger firms are causing harm (or even that there is any harm in the first place, whatever the cause) is unmerited.

What the authors find is that the apparent rise in national concentration doesn’t tell the relevant story, and the data certainly aren’t consistent with assumptions that anticompetitive conduct is either a cause or a result of structural changes in the economy.

Hsieh and Rossi-Hansberg point out that increased concentration is not happening everywhere, but is being driven by just three industries:

First, we show that the phenomena of rising concentration... is only seen in three broad sectors - services, wholesale, and retail.... op firms have become more efficient over time, but our evidence indicates that this is only true for top firms in these three sectors. In manufacturing, for example, concentration has fallen.

Second, rising concentration in these sectors is entirely driven by an increase the number of local markets served by the top firms. (p. 4) (emphasis added).

These findings are a gloss on a (then) working paper — The Fall of the Labor Share and the Rise of Superstar Firms — by David Autor, David Dorn, Lawrence F. Katz, Christina Patterson, and John Van Reenan (now forthcoming in the QJE). Autor et al. (2019) finds that concentration is rising, and that it is the result of increased productivity:

If globalization or technological changes push sales towards the most productive firms in each industry, product market concentration will rise as industries become increasingly dominated by superstar firms, which have high markups and a low labor share of value-added.

We empirically assess seven predictions of this hypothesis: (i) industry sales will increasingly concentrate in a small number of firms; (ii) industries where concentration rises most will have the largest declines in the labor share; (iii) the fall in the labor share will be driven largely by reallocation rather than a fall in the unweighted mean labor share across all firms; (iv) the between-firm reallocation component of the fall in the labor share will be greatest in the sectors with the largest increases in market concentration; (v) the industries that are becoming more concentrated will exhibit faster growth of productivity; (vi) the aggregate markup will rise more than the typical firm’s markup; and (vii) these patterns should be observed not only in U.S. firms, but also internationally. We find support for all of these predictions. (emphasis added).

This is alone is quite important (and seemingly often overlooked). Autor et al. (2019) finds that rising concentration is a result of increased productivity that weeds out less-efficient producers. This is a good thing.

But Hsieh & Rossi-Hansberg drill down into the data to find something perhaps even more significant: the rise in concentration itself is limited to just a few sectors, and, where it is observed, it is predominantly a function of more efficient firms competing in more — and more localized — markets. This means that competition is increasing, not decreasing, whether it is accompanied by an increase in concentration or not.

No matter how may times and under how many monikers the antitrust populists try to revive it, the Structure-Conduct-Performance paradigm remains as moribund as ever. Indeed, on this point, as one of the new antitrust agonists’ own, Fiona Scott Morton, has written (along with co-authors Martin Gaynor and Steven Berry):

In short, there is no well-defined “causal effect of concentration on price,” but rather a set of hypotheses that can explain observed correlations of the joint outcomes of price, measured markups, market share, and concentration. As Bresnahan (1989) argued three decades ago, no clear interpretation of the impact of concentration is possible without a clear focus on equilibrium oligopoly demand and “supply,” where supply includes the list of the marginal cost functions of the firms and the nature of oligopoly competition.

Some of the recent literature on concentration, profits, and markups has simply reasserted the relevance of the old-style structure-conduct-performance correlations. For economists trained in subfields outside industrial organization, such correlations can be attractive.

Our own view, based on the well-established mainstream wisdom in the field of industrial organization for several decades, is that regressions of market outcomes on measures of industry structure like the Herfindahl-Hirschman Index should be given little weight in policy debates. Such correlations will not produce information about the causal estimates that policy demands. It is these causal relationships that will help us understand what, if anything, may be causing markups to rise. (emphasis added).

Indeed! And one reason for the enduring irrelevance of market concentration measures is well laid out in Hsieh and Rossi-Hansberg’s paper:

This evidence is consistent with our view that increasing concentration is driven by new ICT-enabled technologies that ultimately raise aggregate industry TFP. It is not consistent with the view that concentration is due to declining competition or entry barriers..., as these forces will result in a decline in industry employment. (pp. 4-5) (emphasis added)

The net effect is that there is essentially no change in concentration by the top firms in the economy as a whole. The “super-star” firms of today’s economy are larger in their chosen sectors and have unleashed productivity growth in these sectors, but they are not any larger as a share of the aggregate economy. (p. 5) (emphasis added)

Thus, to begin with, the claim that increased concentration leads to monopsony in labor markets (and thus unemployment) appears to be false. Hsieh and Rossi-Hansberg again:

e find that total employment rises substantially in industries with rising concentration. This is true even when we look at total employment of the smaller firms in these industries. (p. 4)

ectors with more top firm concentration are the ones where total industry employment (as a share of aggregate employment) has also grown. The employment share of industries with increased top firm concentration grew from 70% in 1977 to 85% in 2013. (p. 9)

Firms throughout the size distribution increase employment in sectors with increasing concentration, not only the top 10% firms in the industry, although by definition the increase is larger among the top firms. (p. 10) (emphasis added)

Again, what actually appears to be happening is that national-level growth in concentration is actually being driven by increased competition in certain industries at the local level:

93% of the growth in concentration comes from growth in the number of cities served by top firms, and only 7% comes from increased employment per city.... verage employment per county and per establishment of top firms falls. So necessarily more than 100% of concentration growth has to come from the increase in the number of counties and establishments served by the top firms. (p.13)

The net effect is a decrease in the power of top firms relative to the economy as a whole, as the largest firms specialize more, and are dominant in fewer industries:

Top firms produce in more industries than the average firm, but less so in 2013 compared to 1977. The number of industries of a top 0.001% firm (relative to the average firm) fell from 35 in 1977 to 17 in 2013. The corresponding number for a top 0.01% firm is 21 industries in 1977 and 9 industries in 2013. (p. 17)

Thus, summing up, technology has led to increased productivity as well as greater specialization by large firms, especially in relatively concentrated industries (exactly the opposite of the pessimistic stories):

op firms are now more specialized, are larger in the chosen industries, and these are precisely the industries that have experienced concentration growth. (p. 18)

Unsurprisingly (except to some…), the increase in concentration in certain industries does not translate into an increase in concentration in the economy as a whole. In other words, workers can shift jobs between industries, and there is enough geographic and firm mobility to prevent monopsony. (Despite rampant assumptions that increased concentration is constraining labor competition everywhere…).

Although the employment share of top firms in an average industry has increased substantially, the employment share of the top firms in the aggregate economy has not. (p. 15)

It is also simply not clearly the case that concentration is causing prices to rise or otherwise causing any harm. As Hsieh and Rossi-Hansberg note:

he magnitude of the overall trend in markups is still controversial... and... the geographic expansion of top firms leads to declines in local concentration... that could enhance competition. (p. 37)

Indeed, recent papers such as Traina (2018), Gutiérrez and Philippon (2017), and the IMF (2019) have found increasing markups over the last few decades but at much more moderate rates than the famous De Loecker and Eeckhout (2017) study. Other parts of the anticompetitive narrative have been challenged as well. Karabarbounis and Neiman (2018) finds that profits have increased, but are still within their historical range. Rinz (2018) shows decreased wages in concentrated markets but also points out that local concentration has been decreasing over the relevant time period.

None of this should be so surprising. Has antitrust enforcement gotten more lax, leading to greater concentration? According to Vita and Osinski (2018), not so much. And how about the stagnant rate of new firms? Are incumbent monopolists killing off new startups? The more likely — albeit mundane — explanation, according to Hopenhayn et al. (2018), is that increased average firm age is due to an aging labor force. Lastly, the paper from Hsieh and Rossi-Hansberg discussed above is only the latest in a series of papers, including Bessen (2017), Van Reenen (2018), and Autor et al. (2019), that shows a rise in fixed costs due to investments in proprietary information technology, which correlates with increased concentration.

So what is the upshot of all this?

First, as noted, employment has not decreased because of increased concentration; quite the opposite. Employment has increased in the industries that have experienced the most concentration at the national level.
Second, this result suggests that the rise in concentrated industries has not led to increased market power over labor.
Third, concentration itself needs to be understood more precisely. It is not explained by a simple narrative that the economy as a whole has experienced a great deal of concentration and this has been detrimental for consumers and workers. Specific industries have experienced national level concentration, but simultaneously those same industries have become more specialized and expanded competition into local markets.

Surprisingly (because their paper has been around for a while and yet this conclusion is rarely recited by advocates for more intervention — although they happily use the paper to support claims of rising concentration), Autor et al. (2019) finds the same thing:

Our formal model, detailed below, generates superstar effects from increases in the toughness of product market competition that raise the market share of the most productive firms in each sector at the expense of less productive competitors.... An alternative perspective on the rise of superstar firms is that they reflect a diminution of competition, due to a weakening of U.S. antitrust enforcement (Dottling, Gutierrez and Philippon, 2018). Our findings on the similarity of trends in the U.S. and Europe, where antitrust authorities have acted more aggressively on large firms (Gutierrez and Philippon, 2018), combined with the fact that the concentrating sectors appear to be growing more productive and innovative, suggests that this is unlikely to be the primary explanation, although it may important in some specific industries (see Cooper et al, 2019, on healthcare for example). (emphasis added).

The popular narrative among Neo-Brandeisian antitrust scholars that lax antitrust enforcement has led to concentration detrimental to society is at base an empirical one. The findings of these empirical papers severely undermine the persuasiveness of that story.