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Daily Archives: July 5, 2022


Where Does the Wealth Go When Asset Prices Go Down?

Noah Smith wrote . . . . . . . . .

“Fugayzi, fugazi. It’s a whazy. It’s a woozie. It’s fairy dust. It doesn’t exist. It’s never landed. It is no matter. It’s not on the elemental chart.” — Mark Hanna

I’ve been writing a lot about the crashes in the stock and crypto markets. Sometimes I say stuff like “Over $2 TRILLION of notional value has now been wiped out compared to the peak in late 2021.” And some people have been asking me: Where did all that wealth go?

The short answer is: It didn’t “go” anywhere. It vanished. It stopped existing. That’s not a natural or intuitive idea — how can wealth just disappear? — so this post is an explainer of how that works. And as we’ll see, this has implications for policy, for how we think about inequality, and for how we plan our own financial futures.

Wealth isn’t like water

A natural — but wrong — way to think about wealth is like a liquid, getting poured from one container into another.

But this isn’t how wealth works. It’s not conserved, like energy or momentum. It’s not even usually conserved, like mass. It can be created and destroyed, and in fact it is created and destroyed in pretty large amounts every day.

Now when I say “wealth gets created and destroyed”, I don’t just mean that the economy grows, or houses get destroyed by fires, or new companies start up, or old companies go bankrupt. Yes, all those things do create or destroy wealth. But I’m talking about something else here. Financial wealth gets created and destroyed not just because the real economy changes, but because the amount we pay for financial assets changes.

Ultimately, wealth isn’t a physical property of the Universe itself. It’s just how much humans value stuff like stocks, crypto, bonds, houses, or gold.

Mark-to-market accounting: How market prices determine wealth

To understand how wealth works, we first need to understand what “mark-to-market accounting” means.

Mark-to-market accounting means that ALL shares or units of an asset are valued at the market price. The market price is the price of the shares that get TRADED.

Suppose there are 1 million total shares of stock in Noahcorp, but that only 1000 shares of Noahcorp get traded on any particular day. And most Noahcorp shares just sit in people’s accounts and never even get traded at all. Now suppose that the 1000 shares that DO get traded go for $300 a share. Mark-to-market accounting means that we value all 1 million Noahcorp shares at $300 a share, including all the ones that never get traded. So the total value of all 1 million shares of Noahcorp — which is called Noahcorp’s “market capitalization” or “market cap” — is $300 million.

Now suppose that tomorrow, those 1000 Noahcorp shares get traded for only $200 a share. The mark-to-market value of the traded shares and the non-traded shares alike goes down to $200 a share. So Noahcorp’s market cap goes down to $200 million.

Noahcorp’s market cap is wealth. So when Noahcorp’s market cap goes down, where did the wealth go? It vanished. It ceased to exist. There aren’t more dollars out there. The number of Noahcorp shares is the same. The only thing that changed is that now people decided to buy and sell Noahcorp shares at a lower price. So mark-to-market accounting says Noahcorp is worth less than before. There is simply less wealth in the world.

But don’t people “pull money out” of a stock and put it somewhere else?

“But Noah,” you may ask, “when the price of the Noahcorp shares fell from $300 to $200, doesn’t that mean that people pulled their money out of Noahcorp and put it somewhere else?”

First of all, the answer to that question is “No.” It doesn’t mean that. You can personally pull your money out of a stock and put it into another stock, sure. But the market as a whole doesn’t work like that, because when you pull your money out of a stock, someone else puts exactly the same amount of money into that stock.

Here’s how that works. Suppose I sell you a share at $300 today and tomorrow you sell it back to me for $200. All that happens is that $100 of cash went from your account to my account over the course of those two days (Thanks!). The value of Noahcorp has gone down but there’s no more cash in our combined accounts than there was two days ago. No money has been “put in” or “pulled out”, but the amount of wealth in the world has changed.

But the even deeper answer is that this doesn’t even matter, because most shares don’t even get traded. Remember, in this example, only 1000 shares out of a total of 1 million get bought and sold every day. When the price of the 1000 traded shares drops from $300 to $200, the other 999,000 shares go down in value even though they don’t change hands.

The drop in the value of those other 999,000 non-traded shares happens not because they’re traded, but because we infer their price from the price of the few shares that do get traded. The “tail” of the traded shares wags the “dog” of the non-traded shares.

Liquidity: When mark-to-market accounting doesn’t work

So, it’s worth mentioning that not all assets can be valued using mark-to-market accounting. Some assets are illiquid — they almost never get traded. For these assets, we have to determine their value some other way.

The best example is your house. Every share of Noahcorp is identical, but every house is different, so the sale price of other houses doesn’t automatically tell you how much your own house is worth. And your own house almost never gets sold. Your own house is very “illiquid”.

So how does the value of your house get determined? By appraisal. An appraiser comes by and says how much they think your house would sell for if you did sell it.

For houses this is really the best we can do. For some assets, there’s an argument over whether or not to use mark-to-market accounting or some form of appraisal. For example, in the financial crisis of 2008, some banks tried to argue that because their financial assets (CDOs and the like) were highly illiquid, that they shouldn’t be forced to use mark-to-market accounting to value them. If they were forced to use mark-to-market accounting, they argued, there would be a fire sale and the price would be unrealistically low, making banks look less solvent than they were. (This debate was resolved when the Fed came in and bought all the illiquid assets from the banks, and ended up making a profit.)

So is wealth just fake?

The quote and the picture at the beginning of this post come from the movie The Wolf of Wall Street. In that scene, stockbroker Mark Hanna (played by Matthew McConaughey) explains to rookie Jordan Belfort (Leo DiCaprio) that stock prices aren’t real, and that only cash is real. Reading me talk about how the price of 1000 traded shares of a company can determine the price of the other 999,000 untraded shares, maybe you’re starting to wonder if Hanna is right, and the numbers we use for wealth are simply fake.

Well, in fact, it is a little bit fake. Not entirely, but a little bit. The reason is something called price impact.

To go back our previous example, imagine if one guy (let’s call him “Noah”) owned 999,000 of the shares of Noahcorp. The price of Noahcorp shares — and therefore, the value of Noah’s wealth — would be determined by the remaining 1000 of the shares that did get traded. So if the price is $300 per share, then Noah’s wealth is $299,700,000.

But now imagine that Noah tried to sell all his shares of Noahcorp at once. The price would probably go way down. This is because in real life, asset prices aren’t determined only by fundamental value (Noahcorp’s earnings and cash flows and such), but by supply and demand. When Noah dumps his stock onto the market, it increases supply by 1000x. That’s probably going to tank the price.

So Noah won’t get $300 a share. As he keeps selling more and more shares, the price will go lower and lower. By the time he sells all his shares, he’ll have much less than $299,700,000 in cash. In a sense, that means that some of his $299,700,000 in wealth was always somewhat “fake”. There was simply no way for him to get that much in cash, because of price impact.

In fact, there can be other reasons for price impact besides just increased supply. If Elon Musk decided tomorrow to dump all his Tesla shares, people might conclude that there was something deeply wrong with Tesla, and the price would go down.

Price impact can affect the value of whole asset classes, not just individual stocks. For example, our best estimates suggest that crypto ownership is extremely concentrated. That means that if the “whales” who own most of the Bitcoin and Ether all tried to cash out, the amount of cash they got would be significantly lower than their wealth today would indicate. This isn’t just true of the whales, either; if crypto owners as a whole tried to dump their crypto, there would be massive price impact, because the people who don’t currently own crypto would have to buy it all, and they will probably value it a lot lower than the people who currently own crypto.

So does this mean that “true” wealth inequality — that is, inequality of potential purchasing power — is less than the headline numbers suggest? Well, yes, a bit less.

“But Noah,” you may ask, “if price impact means the wealth numbers are somewhat fake, then why don’t we calculate wealth as the amount of cash you COULD get out if you DID sell?”

Well, the answer is: Because we can’t. We just don’t know. The only way to find out price impact is to actually sell. So we can’t really calculate how much cash people could get from selling all their stock or all their Bitcoin, because we don’t actually have any way of knowing how much it is in advance.

So what’s the upshot here?

So why does any of this matter? Well, first of all, don’t assume that the price of some asset class going down means that money is “flowing” somewhere else in the economy. That just isn’t how it works.

And sometimes people will assure you that drops in asset markets don’t mean anything because no actual wealth was destroyed. But paper wealth is as “real” as wealth gets, and people whose assets get marked down may cut back on spending, which affects the real economy.

Second, take wealth numbers with a grain of salt. Yes, the rich people are all actually rich, but the net worth numbers you read in the Forbes 400 or on Wikipedia are more like indicators than exact measures of how much stuff someone could buy.

Third, in my opinion this should make people reconsider their support for taxing unrealized capital gains. There are some good arguments in favor of this approach, but at the end of the day, A) price impact, and B) the fact that asset values fluctuate based on the price of just a few traded shares mean that some portion of the gains you’ll be taxing will just be “fugazi”. (Allowing tax-loss carry-forwards alleviates some of this issue, but not all of it, since not every gain is preceded by a loss.)

Anyway, I hope this explainer was helpful (and to the people who already knew all this stuff, at least not boring). Pretty basic stuff, but fun and important to know!

Source : Noahpinion

A Province-by-province Look at Excess Deaths in Canada During the Pandemic

Imagine the COVID-19 pandemic never happened — people still would have died across Canada, and the number of deaths would have been somewhat predictable based on data from previous years.

In a new study, Dr. Kimberlyn McGrail, a professor in UBC’s school of population of public health, examined all “excess deaths” across Canadian provinces during the first 19 months of the pandemic, and how many of those were attributed specifically to COVID-19. Excess deaths are deaths above and beyond what would have been expected under normal circumstances.

Dr. McGrail spoke about the findings, published today in the Canadian Medical Association Journal.

Why look at “excess deaths” rather than just COVID-19 deaths when trying to understand the pandemic?

The pandemic had a direct effect on deaths, in that people got the virus and unfortunately some died from it, but the pandemic also had other effects. People delayed care, or had surgeries, diagnostics and appointments cancelled, which can lead to poorer outcomes. We also had other public health events going on — particularly in B.C. with the ongoing tainted drug supply and the heat dome in the summer of 2021. Those things were potentially affected by the pandemic. So overall mortality is a better indication of what’s actually happening at the population level.

How do we know how many deaths were expected?

I used data provided by Statistics Canada. They look at trends in the population’s size and age in the five years preceding the pandemic to model what would have been expected in 2020-21, absent the pandemic.

What did you learn about excess deaths across Canada during the pandemic?

Excess mortality is just an estimate, but the experience across the provinces, according to what Statistics Canada is telling us, is very different. We saw very little to no excess death in the Atlantic provinces, and quite high excess deaths in western provinces.

What are some possible explanations for this wide variation?

One of the challenges is that a number of different things could contribute to this variation, and it’s probably not one or another, but a combination.

For COVID-19 deaths, differences in COVID-19 reporting practices could be contributing. Each province defines and counts COVID-19 deaths in different ways, and reports them at different speeds.

For overall excess deaths, it could be because provinces differed in their responses to COVID-19. It could be because of the additional public health events going on. Or it could be the broader implications of COVID-19, like cancelled surgeries and delayed diagnostics. There may also be some inaccuracy in the modelling of expected deaths by Statistics Canada.

Your study shows that excess deaths far exceeded COVID-19 deaths in some provinces. How is that possible?

I’ll use the heat dome in the summer of 2021 as a particular example. The B.C. Coroners Service has now attributed almost 600 deaths to that event. You might say that’s not related to COVID-19. However, so much policy attention was being placed on COVID-19 at the time. Some of the precautions, with people being locked down, limited activity and so on, might have contributed to how we responded to the heat dome, which in turn could have contributed to deaths. For example, lots of older people living alone didn’t have the usual level of social support and people checking on them.

Source : The University of British Columbia

Read also at Statistics Canada

Provisional death counts and excess mortality, January 2020 to February 2022 . . . . .

Infographic: Three Different Types of Inflation

‘An Invisible Cage’: How China Is Policing the Future

Paul Mozur, Muyi Xiao and John Liu wrote . . . . . . . . .

The more than 1.4 billion people living in China are constantly watched. They are recorded by police cameras that are everywhere, on street corners and subway ceilings, in hotel lobbies and apartment buildings. Their phones are tracked, their purchases are monitored, and their online chats are censored.

Now, even their future is under surveillance.

The latest generation of technology digs through the vast amounts of data collected on their daily activities to find patterns and aberrations, promising to predict crimes or protests before they happen. They target potential troublemakers in the eyes of the Chinese government — not only those with a criminal past but also vulnerable groups, including ethnic minorities, migrant workers and those with a history of mental illness.

They can warn the police if a victim of a fraud tries to travel to Beijing to petition the government for payment or a drug user makes too many calls to the same number. They can signal officers each time a person with a history of mental illness gets near a school.

It takes extensive evasive maneuvers to avoid the digital tripwires. In the past, Zhang Yuqiao, a 74-year-old man who has been petitioning the government for most of his adult life, could simply stay off the main highways to dodge the authorities and make his way to Beijing to fight for compensation over the torture of his parents during the Cultural Revolution. Now, he turns off his phones, pays in cash and buys multiple train tickets to false destinations.

While largely unproven, the new Chinese technologies, detailed in procurement and other documents reviewed by The New York Times, further extend the boundaries of social and political controls and integrate them ever deeper into people’s lives. At their most basic, they justify suffocating surveillance and violate privacy, while in the extreme they risk automating systemic discrimination and political repression.

For the government, social stability is paramount and any threat to it must be eliminated. During his decade as China’s top leader, Xi Jinping has hardened and centralized the security state, unleashing techno-authoritarian policies to quell ethnic unrest in the western region of Xinjiang and enforce some of the world’s most severe coronavirus lockdowns. The space for dissent, always limited, is rapidly disappearing.

“Big data should be used as an engine to power the innovative development of public security work and a new growth point for nurturing combat capabilities,” Mr. Xi said in 2019 at a national public security work meeting.

The algorithms, which would prove controversial in other countries, are often trumpeted as triumphs.

In 2020, the authorities in southern China denied a woman’s request to move to Hong Kong to be with her husband after software alerted them that the marriage was suspicious, the local police reported. An ensuing investigation revealed that the two were not often in the same place at the same time and had not spent the Spring Festival holiday together. The police concluded that the marriage had been faked to obtain a migration permit.

The same year in northern China, an automated alert about a man’s frequent entry into a residential compound with different companions prompted the police to investigate. They discovered that he was a part of a pyramid scheme, according to state media.

The details of these emerging security technologies are described in police research papers, surveillance contractor patents and presentations, as well as hundreds of public procurement documents reviewed and confirmed by The Times. Many of the procurement documents were shared by ChinaFile, an online magazine published by the Asia Society, which has systematically gathered years of records on government websites. Another set, describing software bought by the authorities in the port city of Tianjin to stop petitioners from going to neighboring Beijing, was provided by IPVM, a surveillance industry publication.

China’s Ministry of Public Security did not respond to requests for comment faxed to its headquarters in Beijing and six local departments across the country.

The new approach to surveillance is partly based on data-driven policing software from the United States and Europe, technology that rights groups say has encoded racism into decisions like which neighborhoods are most heavily policed and which prisoners get parole. China takes it to the extreme, tapping nationwide reservoirs of data that allow the police to operate with opacity and impunity.

A New York Times analysis of over 100,000 government bidding documents found that China’s ambition to collect digital and biological data from its citizens is more expansive and invasive than previously known.
Often people don’t know they’re being watched. The police face little outside scrutiny of the effectiveness of the technology or the actions they prompt. The Chinese authorities require no warrants to collect personal information.

At the most bleeding edge, the systems raise perennial science-fiction conundrums: How is it possible to know the future has been accurately predicted if the police intervene before it happens?

Even when the software fails to deduce human behavior, it can be considered successful since the surveillance itself inhibits unrest and crime, experts say.

“This is an invisible cage of technology imposed on society,” said Maya Wang, a senior China researcher with Human Rights Watch, “the disproportionate brunt of it being felt by groups of people that are already severely discriminated against in Chinese society.”

‘Nowhere to Hide’

In 2017, one of China’s best-known entrepreneurs had a bold vision for the future: a computer system that could predict crimes.

The entrepreneur, Yin Qi, who founded Megvii, an artificial intelligence start-up, told Chinese state media that the surveillance system could give the police a search engine for crime, analyzing huge amounts of video footage to intuit patterns and warn the authorities about suspicious behavior. He explained that if cameras detected a person spending too much time at a train station, the system could flag a possible pickpocket.

“It would be scary if there were actually people watching behind the camera, but behind it is a system,” Mr. Yin said. “It’s like the search engine we use every day to surf the internet — it’s very neutral. It’s supposed to be a benevolent thing.”

He added that with such surveillance, “the bad guys have nowhere to hide.”

Five years later, his vision is slowly becoming reality. Internal Megvii presentations reviewed by The Times show how the start-up’s products assemble full digital dossiers for the police.

“Build a multidimensional database that stores faces, photos, cars, cases and incident records,” reads a description of one product, called “intelligent search.” The software analyzes the data to “dig out ordinary people who seem innocent” to “stifle illegal acts in the cradle.”

A Megvii spokesman said in an emailed statement that the company was committed to the responsible development of artificial intelligence, and that it was concerned about making life more safe and convenient and “not about monitoring any particular group or individual.”

Similar technologies are already being put into use. In 2022, the police in Tianjin bought software made by a Megvii competitor, Hikvision, that aims to predict protests. The system collects data on legions of Chinese petitioners, a general term in China that describes people who try to file complaints about local officials with higher authorities.

It then scores petitioners on the likelihood that they will travel to Beijing. In the future, the data will be used to train machine-learning models, according to a procurement document.

Local officials want to prevent such trips to avoid political embarrassment or exposure of wrongdoing. And the central government doesn’t want groups of disgruntled citizens gathering in the capital.

A Hikvision representative declined to comment on the system.

Under Mr. Xi, official efforts to control petitioners have grown increasingly invasive. Zekun Wang, a 32-year-old member of a group that for years sought redress over a real estate fraud, said the authorities in 2017 had intercepted fellow petitioners in Shanghai before they could even buy tickets to Beijing. He suspected that the authorities were watching their communications on the social media app WeChat.

The Hikvision system in Tianjin, which is run in cooperation with the police in nearby Beijing and Hebei Province, is more sophisticated.

The platform analyzes individuals’ likelihood to petition based on their social and family relationships, past trips and personal situations, according to the procurement document. It helps the police create a profile of each, with fields for officers to describe the temperament of the protester, including “paranoid,” “meticulous” and “short tempered.”

Many people who petition do so over government mishandling of a tragic accident or neglect in the case — all of which goes into the algorithm. “Increase a person’s early-warning risk level if they have low social status or went through a major tragedy,” reads the procurement document.

Automating Prejudice

When the police in Zhouning, a rural county in Fujian Province, bought a new set of 439 cameras in 2018, they listed coordinates where each would go. Some hung above intersections and others near schools, according to a procurement document.

Nine were installed outside the homes of people with something in common: mental illness.

While some software tries to use data to uncover new threats, a more common type is based on the preconceived notions of the police. In over a hundred procurement documents reviewed by The Times, the surveillance targeted blacklists of “key persons.”

These people, according to some of the procurement documents, included those with mental illness, convicted criminals, fugitives, drug users, petitioners, suspected terrorists, political agitators and threats to social stability. Other systems targeted migrant workers, idle youths (teenagers without school or a job), ethnic minorities, foreigners and those infected with H.I.V.

The authorities decide who goes on the lists, and there is often no process to notify people when they do. Once individuals are in a database, they are rarely removed, said experts, who worried that the new technologies reinforce disparities within China, imposing surveillance on the least fortunate parts of its population.

In many cases the software goes further than simply targeting a population, allowing the authorities to set up digital tripwires that indicate a possible threat. In one Megvii presentation detailing a rival product by Yitu, the system’s interface allowed the police to devise their own early warnings.

With a simple fill-in-the-blank menu, the police can base alarms on specific parameters, including where a blacklisted person appears, when the person moves around, whether he or she meets with other blacklisted people and the frequency of certain activities. The police could set the system to send a warning each time two people with a history of drug use check into the same hotel or when four people with a history of protest enter the same park.

Yitu did not respond to emailed requests for comment.

In 2020 in the city of Nanning, the police bought software that could look for “more than three key people checking into the same or nearby hotels” and “a drug user calling a new out-of-town number frequently,” according to a bidding document. In Yangshuo, a tourist town famous for its otherworldly karst mountains, the authorities bought a system to alert them if a foreigner without a work permit spent too much time hanging around foreign-language schools or bars, an apparent effort to catch people overstaying their visas or working illegally.

In Shanghai, one party-run publication described how the authorities used software to identify those who exceeded normal water and electricity use. The system would send a “digital whistle” to the police when it found suspicious consumption patterns.

The tactic was likely designed to detect migrant workers, who often live together in close quarters to save money. In some places, the police consider them an elusive, and often impoverished, group who can bring crime into communities.

The automated alerts don’t result in the same level of police response. Often, the police give priority to warnings that point to political problems, like protests or other threats to social stability, said Suzanne E. Scoggins, a professor at Clark University who studies China’s policing.

At times, the police have stated outright the need to profile people. “Through the application of big data, we paint a picture of people and give them labels with different attributes,” Li Wei, a researcher at China’s national police university, said in a 2016 speech. “For those who receive one or more types of labels, we infer their identities and behavior, and then carry out targeted pre-emptive security measures.”

Toward Techno Totalitarianism

Mr. Zhang first started petitioning the government for compensation over the torture of his family during the Cultural Revolution. He has since petitioned over what he says is police targeting of his family.

As China has built out its techno-authoritarian tools, he has had to use spy movie tactics to circumvent surveillance that, he said, has become “high tech and Nazified.”

When he traveled to Beijing in January from his village in Shandong Province, he turned off his phone and paid for transportation in cash to minimize his digital footprint. He bought train tickets to the wrong destination to foil police tracking. He hired private drivers to get around checkpoints where his identification card would set off an alarm.

The system in Tianjin has a special feature for people like him who have “a certain awareness of anti-reconnaissance” and regularly change vehicles to evade detection, according to the police procurement document.

Whether or not he triggered the system, Mr. Zhang has noticed a change. Whenever he turns off his phone, he said, officers show up at his house to check that he hasn’t left on a new trip to Beijing.

Even if police systems cannot accurately predict behavior, the authorities may consider them successful because of the threat, said Noam Yuchtman, an economics professor at the London School of Economics who has studied the impact of surveillance in China.

“In a context where there isn’t real political accountability,” having a surveillance system that frequently sends police officers “can work pretty well” at discouraging unrest, he said.

Once the metrics are set and the warnings are triggered, police officers have little flexibility, centralizing control. They are evaluated for their responsiveness to automated alarms and effectiveness at preventing protests, according to experts and public police reports.

The technology has encoded power imbalances. Some bidding documents refer to a “red list” of people whom the surveillance system must ignore.

One national procurement document said the function was for “people who need privacy protection or V.I.P. protection.” Another, from Guangdong Province, got more specific, stipulating that the red list was for government officials.

Mr. Zhang expressed frustration at the ways technology had cut off those in political power from regular people.

“The authorities do not seriously solve problems but do whatever it takes to silence the people who raise the problems,” he said. “This is a big step backward for society.”

Mr. Zhang said that he still believed in the power of technology to do good, but that in the wrong hands it could be a “scourge and a shackle.”

“In the past if you left your home and took to the countryside, all roads led to Beijing,” he said. “Now, the entire country is a net.”

Source : New York Times