Macro Roundup

Mian and Sufi summarize both their “savings glut of the rich” paper and “indebted demand” papers (attached) in a short blog post for University of Chicago arguing that lowering inequality is needed to maintain demand

“…..This is where the traditional view breaks down. The investment to national income ratio (which includes both private and government investment) has actually fallen considerably since the 1980s. The savings of the rich have not translated into more capital investment. So where did the savings of the rich go? The answer: more borrowing by the non-rich (think bottom 90 percent of the income distribution) and the government. The large rise in household debt up to the Great Recession was directly tied to the large increase in income inequality through the saving glut of the rich….The only way for the economy to maintain equilibrium in light of this depressed demand is to lower interest rates, which is exactly what has happened across the world as income inequality has risen. For a while, this lower interest rate strategy may work, as financial markets encourage more and more borrowing by the non-rich to sustain demand. Unfortunately, this debt dynamic has a dead end (or perhaps we should say a “debt end”). When interest rates hit their effective lower bound, it is impossible to lower interest rates further to encourage even more borrowing and consumption by the non-rich. The economy cannot maintain equilibrium unless output falls.The world then sees depressed economic growth. This is exactly what we see in the data. When economies around the world approached the effective lower bound on interest rates, economic growth weakened considerably. This simple logic explains how the persistent and large rise in income inequality leads to (a) rising debt levels, (b) falling interest rates, and (c) depressed economic growth. This simple logic also implies that attacking the rise in inequality directly is the most effective way of achieving a healthy economy in the long-term. Reduced inequality is good for fairness and political economy concerns, but policymakers should also consider policies that help dampen income inequality for the sake of the overall economy….”

Atif Mian, Ludwig Straub, and Amir Sufi, “How Inequality Hurts the Economy and Complicates Policy Responses to the Pandemic,” University Of Chicago, April 24, 2020, https://promarket.org/how-inequality-hurts-the-economy-and-complicates-policy-responses-to-the-pandemic/

How Inequality Hurts the Economy and Complicates Policy Responses to the Pandemic

In the years leading up to the pandemic, rising inequality created a saving glut of the rich which pushed down interest rates and fueled a large rise in government and household debt. But with interest rates now hitting their effective lower bound, it is impossible to encourage even more borrowing and consumption by the non-rich.

The Covid-19 health crisis has struck a world economy already suffering from sustained weakness. Extremely low interest rates, lackluster growth, and historically high levels of debt have characterized almost all of the advanced economies of the world even before the health crisis.

Very low interest rates have complicated the economic policy response to the Covid-19 health crisis, as central banks have had little room to maneuver before hitting the effective lower bound.

Our recent research argues that rising income inequality has played a central role in explaining such weakness. Many have argued that rising inequality is problematic for both fairness and political economy reasons, and we agree. However, our recent research argues that rising income inequality is also bad for the overall economy. (The two research studies are The Saving Glut of the Rich and the Rise in Household Debt and Indebted Demand.)

This argument may sound puzzling.

A typical argument in macroeconomics is that inequality is good for growth. According to this argument, the rich save more of their income, and such saving boosts capital investment. More capital investment means that we can produce more goods and services, and therefore have a stronger economy.

Is this argument empirically accurate? Let’s focus on the United States. From the 1980s to recent years, the top 1 percent share of national income rose significantly. And indeed, the rich saved more of this income, generating a substantial rise in saving.

So far, so good. But did investment in capital goods rise?

This is where the traditional view breaks down. The investment to national income ratio (which includes both private and government investment) has actually fallen considerably since the 1980s. The savings of the rich have not translated into more capital investment.

So where did the savings of the rich go? The answer: more borrowing by the non-rich (think bottom 90 percent of the income distribution) and the government.

The large rise in household debt up to the Great Recession was directly tied to the large increase in income inequality through the saving glut of the rich.

After the Great Recession, the savings of the rich financed dissaving by the government. Inequality has driven the rise in both household and government debt through this “saving glut of the rich.”

This game of lending is not sustainable. As the non-rich and the government continue to borrow from the rich, such borrowing creates a stream of debt service payments that go to the rich. This amplifies income inequality.

Furthermore, the rich have a saving rate that is higher than the non-rich, and as a result these debt service payments that go from the non-rich to the rich weaken aggregate demand.

The only way for the economy to maintain equilibrium in light of this depressed demand is to lower interest rates, which is exactly what has happened across the world as income inequality has risen.

For a while, this lower interest rate strategy may work, as financial markets encourage more and more borrowing by the non-rich to sustain demand.

Unfortunately, this debt dynamic has a dead end (or perhaps we should say a “debt end”).

When interest rates hit their effective lower bound, it is impossible to lower interest rates further to encourage even more borrowing and consumption by the non-rich.

The economy cannot maintain equilibrium unless output falls. The world then sees depressed economic growth. This is exactly what we see in the data. When economies around the world approached the effective lower bound on interest rates, economic growth weakened considerably.

This simple logic explains how the persistent and large rise in income inequality leads to (a) rising debt levels, (b) falling interest rates, and (c) depressed economic growth.

This simple logic also implies that attacking the rise in inequality directly is the most effective way of achieving a healthy economy in the long-term.

Reduced inequality is good for fairness and political economy concerns, but policymakers should also consider policies that help dampen income inequality for the sake of the overall economy.

The framework we have developed implies that a sustained and successful effort to boost income levels of the non-rich would strengthen aggregate demand, boost output, and help economies leave the effective lower bound on interest rates.

Lukasz Rachel, "Leisure-Enhancing Technological Change," Working Paper, April 24, 2020, https://drive.google.com/file/d/14SbcIolO3EBpgPtE466oty351tOkp9Xo/view

“….leisure-enhancing innovations are ubiquitous and yet their cost of production as a share of GDP is relatively small. TV channels, websites, social media and free newspapers are products that billions of people use every day, and businesses involved in supplying these services are highly profitable and very large….Measured at the cost of production the value of free services has been relatively stable at about 1-1.5% of GDP over much of the past century in the United States, rising recently due to the arrival of digital technologies: not negligible, but certainly not large either. The theory developed in this paper stresses the non-rival nature of leisure technologies, and underscores that leisure-enhancing technological change may be of first-order macro importance even if the share of the sector is negligible in aggregate.... The second feature of the data that this theory speaks to is the decline in average hours worked and a corresponding rise in leisure hours across developed economies over the past seven decades. Over the entire post-WW2 period average hours worked have been on a downward trend, with annual growth averaging around −0.5% across advanced world....The model developed here offers a novel way to reconcile the falling hours with other features of balanced growth. It does so bylinking the time allocation decisions to the advancement of leisure technologies. This mechanism is backed up by the evidence that changes in time allocation patterns have indeed been tightly linked to technology....Third, this paper can shed new light on the Solow Paradox (Solow, 1987). Few would disagree that current period features rapid technological change, and yet economy-wide productivity growth remains weak. This paper offers a way to reconcile the paradox: while clearly visible and salient to the consumer, leisure-enhancing innovations can indirectly diminish productivity growth through their impact on the effective growth of labor supply, and their true value is unlikely to be captured in the GDP statistics….”

Interesting new paper (complicated) argues that the growth of leisure technologies (Internet, Television) because the tech is so cheap has pushed people to spend more time on leisure, and this time diversion has contributed to the decline in TFP growth through changes in time allocation.

But I like the theory. I have often wondered why, if consumption is getting better, consumption doesn’t go up and investment go down. I get why work might go down. But both should go down. What is it about investment that allows it to compete with consumption, which keeps getting better? I’ve never seen anyone try to address the question.

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