Fires in San Francisco Lead to More Housing
Kate Pennington looks at the effect of new housing on rents and gentrification in San Francisco.
The clear identification challenge is that the timing and location of new construction are endogenous: developers are likely to build in the same areas that are already experiencing increased rents, displacement, and gentrification (Green et al., 2005; Li, 2019; Asquith et al., 2020).
Thus, she uses a clever identification strategy.
I exploit exogenous variation in the location of new construction caused by serious building fires. Regulation and geography combine to make San Francisco one of the most difficult places to build housing in the United States (Albouy and Ehrlich, 2012; Saiz, 2010). Serious fires, like the one shown in Figure 5, increase the probability of construction on the burned parcel by making it cheaper to build there. Removing incumbent tenants eliminates the need for costly buyouts. Under San Francisco just cause eviction law, landlords who want to sell or redevelop must either wait for tenants to voluntarily leave, or negotiate a buyout agreement to pay the tenant to leave. In 2015, the median buyout per tenant was $18,000 and the maximum was $325,000.19 Serious fires also streamline the permitting and construction process. Controlling for project size, construction on unburned parcels takes nearly a year longer to complete than projects on burned parcels (p=0.007).
I know what you cynical readers are thinking! Some fires are set on purpose to drive the tenants out! Well, that happens in the movies but it’s much rarer in real life when then there are very serious penalities for arson and homicide. In anycase, Pennington looks only at accidental fires, not arsons, and she finds that that the lots on which there were fires have similar rates of rental incrase and gentrification as other lots.
Amazingly, it still takes a long time to build on these burned lots–nearly five years to get a permit approved and 7.2 years before completion! Nevertheless, burned lots are much more likely to be redeveloped than similar unburned lots. The bottom line is that burned lots are a good as-if experiment for what would happen if a random set of lots were developed.
Pennington concludes that new housing has a “hyperlocal” increase in gentrification–basically richer people move into the new housing and there’s some very local increase in things like more up-scale restaurants–but overall rents are reduced and fewer people must move elsewhere to find cheaper housing.
I find that rents fall by 2% for parcels within 100m of new construction. Renters’ risk of displacement to a lower-income neighborhood falls by 17%. Both effects decay linearly to zero within 1.5km….Building more market rate housing benefits all San Francisco renters through spillover effects on rents.
The Story of VaccinateCA
The excellent Patrick McKenzie tells the story of VaccineCA, the ragtag group of volunteers that quickly became Google’s and then the US Government’s best source on where to find vaccines during the pandemic.
Wait. The US Government was giving out the vaccines. How could they not know where the vaccines were? It’s complicated. Operation Warp Speed delivered the vaccines to the pharmacy programs and to the states but after that they dissappeared into a morass of incompatible systems.
[L]et’s oversimplify: Vials were allocated by the federal government to states, which allocated them to counties, which allocated them to healthcare providers and community groups. The allocators of vials within each supply chain had sharply limited ability to see true systemic supply levels. The recipients of the vials in many cases had limited organizational ability to communicate to potential patients that they actually had them available.
Patients then asked the federal government, states, counties, healthcare providers and community groups, ‘Do you have the vaccine?’ And in most cases the only answer available to the person who picked up the phone was ‘I don’t have it. I don’t know if we have it. Plausibly someone has it. Maybe you should call someone else.’ Technologists will see the analogy to a distributed denial of service incident, and as if the overwhelming demand was not enough of a problem, the rerouting of calls between institutions amplified the burden on the healthcare system. Vaccine seekers were routinely making dozens of calls.
This caused a standing wave of inquiries to hit all levels of US healthcare infrastructure in the early months of the vaccination effort. Very few of those inquiries went well for any party. It is widely believed, and was widely believed at the time, that this was primarily because supply was lacking, but it was often the case that supply was frequently not being used as quickly as it was produced because demand could not find it.
It turned out that the best way to get visibility into this mess was not to trace the vaccines but to call the endpoints on the phone and then create a database that people could access which is what VaccinateCA did but in addition to finding the doses they had to deal with the issue of who was allowed access.
A key consideration for us, from the first day of the effort, was recording not just which pharmacist had vials but who they thought they could provide care to. This was dependent on prevailing regulations in their state and county, interpretations of those regulations by the pharmacy chain, and (frequently!) ad hoc decision-making by individual medical providers. Individual providers routinely made decisions that the relevant policy makers did not agree comported with their understanding of the rules.
VaccinateCA saw the policy sausage made in real time in California while keeping an eye on it nationwide. It continues to give me nightmares.
California, not to mince words, prioritized the appearance of equity over saving lives, over and over and over again, as part of an explicitly documented strategy, at all levels of the government. You can read the sanitized version of the rationale, by putative medical ethics experts, in numerous official documents. The less sanitized version came out frequently in meetings.
This was the official strategy.
The unofficial strategy, the result the system actually obtained, was that early access to the vaccine was preferentially awarded based on proximity to power and to the professional-managerial class.
… The essential workers list heavily informed the vaccination prioritization schedule. Lobbyists used it as procedural leverage to prioritize their clients for vaccines. The veterinary lobby was unusually candid, in writing, about how it achieved maximum priority (1A) for veterinarians due to them being ‘healthcare workers’.
Teachers’ unions worked tirelessly and landed teachers a 1B. They were ahead of 1C, which included (among others) non-elderly people for whom preexisting severe disability meant that ‘a covid-19 infection is likely to result in severe life-threatening illness or death’. The public rationale was that teachers were at elevated risk of exposure through their occupation. Schools were, of course, mostly closed at the time, and teachers were Zooming along with the rest of the professional-managerial class, but teachers’ unions have power and so 1B it was. Young, healthy teachers quarantining at home were offered the vaccine before people who doctors thought would probably die if they caught Covid.
Now repeat this exercise up and down the social structure and economy of the United States.
…Healthcare providers were fired for administering doses that were destined to expire uselessly. The public health sector devoted substantial attention to the problem of vaccinating too many people during a pandemic. Administration of the formal spoils system became farcically complicated and frequently outcompeted administration of the vaccine as a goal.
The process of registering for the vaccine inherited the complexity of the negotiation over the prioritization, and so vulnerable people were asked to parse rules that routinely befuddled healthy professional software engineers and healthcare administrators – the state of New York subjected senior citizens to a ‘51 step online questionnaire that include[d] uploading multiple attachments’!
That isn’t hyperbole! New York meant to do that! On purpose!
Lives were sacrificed by the thousands and tens of thousands for political reasons. Many more were lost because institutions failed to execute with the competence and vigor the United States is abundantly capable of.
…The State of California instituted a policy of redlining in the provision of medical care in a pandemic to thunderous applause from its activist class and medical ethics experts….Residency restrictions were pervasively enforced at the county level and frequently finer-grained than that. A pop-up clinic, for example, might have been restricted to residents of a single zip code or small group of zip codes.
All people are equal in the eyes of the law in California, but some people are . . . let’s politely say ‘administratively disfavored’.
The theory was, and you could write down this part of it, disfavored potential patients might use social advantages like better access to information and transportation to present themselves for treatment at locations that had doses allocated for favored potential patients. This part of the theory was extremely well-founded. Many people were willing to drive the length and breadth of California for their dose and did so.
What many wanted to do, and this is the part that they couldn’t write down, is deny healthcare to disfavored patients. Since healthcare providers are public accommodations in the state of California, they are legally forbidden from discriminating on the basis of characteristics that some people wanted to discriminate on. So that was laundered through residency restrictions.
Many more items of interest. I didn’t know this incredibly fact about the Biden adminsitratins Vaccines.gov for example:
Pharmacies through the FRPP had roughly half of the doses; states and counties had roughly the other half (sometimes administered at pharmacies, because clearly this isn’t complicated enough yet). You would hope that state and county doses were findable on Vaccines.gov. It was going to be the centerpiece of the Biden administration’s effort to fix the vaccine finding problem and take credit for doing so.
…Since the optics would be terrible if America appeared to serve some states much better than others on the official website that everyone would assume must show all the doses, no state doses, not even from states that would opt in, would be shown on it, at least not at the moment of maximum publicity. Got that?
A good point about America.
We also benefited from another major strength of America: You cannot get arrested, jailed, or shot for publishing true facts, even if those facts happen to embarrass people in positions of power. Many funders wanted us to expand the model to a particular nation. In early talks with contacts there in civil society, it was explained repeatedly and at length that a local team that embarrassed the government’s vaccination rollout would be arrested and beaten by people carrying guns. This made it ethically challenging to take charitable donations and try to recruit that team.
Many more points of interest about the process of running a medical startup during a pandemic. Read the whole thing.
Chinese Industrial Policy is Failing
In Picking Winners? Government Subsidies and Firm Productivity in China, Branstetter, Li and Ren look at the effect of direct cash subsidies to Chinese firms.
Our results provide little evidence to support the view that government subsidies have been given to more productive firms or that they have enhanced the productivity of the Chinese listed firms. First, at the aggregate level, subsidies seem to be allocated to less productive firms, and the relative productivity of firms’ receiving these subsidies appears to decline further after disbursement. Second, using the categorized subsidy data, we find that neither subsidies promoting R&D and innovation promotion nor subsidies promoting industrial and equipment upgrading are positively associated with firms’ subsequent productivity growth. On the other hand, we find there is a positive association between subsidy and employment, both for aggregate and employment-related subsidies.
I appreciated this discussion of the earlier debate over Japanese industrial policy:
Drawing upon qualitative methods and largely anecdotal evidence, a group of noneconomists, business experts, and policymakers argued that Japan’s rapid recovery and robust growth after WWII could be explained by skillful industrial policy (Johnson, 1982; Prestowitz, 1988; Vogel, 1979) .10 Japan’s “government-led” economic model came to be viewed as a threat to U.S. prosperity by some participants in these debates. By the end of the 1980s, some policy makers and influential experts were calling for a policy of “containing Japan,” lest its unbalanced growth undermine the economy of the United States (Fallows, 1989).
Economists and more empirically minded social scientists in other disciplines viewed the claims of industrial policy efficacy with skepticism and suggested that Japan’s intervention in its economy tended to favor declining industries rather than growing ones (Calder, 1988; Saxonhouse, 1983).11 Eventually, the skeptics were able to bolster their claims with hard data demonstrating that the Japanese government had offered some degree of economic support to nearly all sectors, but that the preponderance of support had not gone to the sectors or firms with the fastest productivity growth. An important turning point in this debate came in the form of a careful econometric deconstruction of the notion that industrial policy drove Japan’s economic miracle published by Richard Beason and David Weinstein in the mid-1990s. This empirical analysis at the industry level found no relationship between productivity growth and the alleged instruments of industrial policy (Beason and Weinstein, 1996). As it turned out, the policy efforts to promote rising sectors championed by some elements of Japan’s bureaucracy were undermined by countervailing efforts to buttress the employment levels and solvency of politically connected but economically weak firms and industries.
Japan’s long period of economic outperformance came to an abrupt end in the early 1990s; after two decades of slow growth, few scholars now argue that Japanese industrial policy is a model worthy of emulation (Ito & Hoshi, 2020).
As I emphasized in my post, What Operation Warp Speed Did, Didn’t and Can’t Do, you need a lot more than “market failure” to have a successful government subsidy program of firms–you need massive externalities and precise, well understood targets. The garden-variety market failure that can be shown on a blackboard isn’t enough, in part because such arguments often underestimate the market and in part because they overestimate government.
Hat tip: Caleb Watney who offers some useful comments.
AI is going to break a lot of norms and institutions
AI is going to break a lot of norms and institutions. Sam Hammond offers a peak:
Indeed, within a decade, ordinary people will have more capabilities than a CIA agent does today. You’ll be able to listen in on a conversation in an apartment across the street using the sound vibrations off a chip bag. You’ll be able to replace your face and voice with those of someone else in real time, allowing anyone to socially engineer their way into anything. Bots will slide into your DMs and have long, engaging conversations with you until it senses the best moment to send its phishing link. Games like chess and poker will have to be played naked and in the presence of (currently illegal) RF signal blockers to guarantee no one’s cheating. Relationships will fall apart when the AI lets you know, via microexpressions, that he didn’t really mean it when he said he loved you. Copyright will be as obsolete as sodomy law, as thousands of new Taylor Swift albums come into being with a single click. Public comments on new regulations will overflow with millions of cogent and entirely unique submissions that the regulator must, by law, individually read and respond to. Death-by-kamikaze drone will surpass mass shootings as the best way to enact a lurid revenge. The courts, meanwhile, will be flooded with lawsuits because who needs to pay attorney fees when your phone can file an airtight motion for you?
Computers are Better at Recognizing Faces than Cyborgs
There was a brief window of time when computers could beat humans at chess but a human and a computer could beat a computer. In other words, there was a window of time when cyborgs could beat computers at chess. That window closed years ago (as Tyler predicted it would). Computers now beat humans and cyborgs. Humans aren’t especially evolved to be good at chess which is why only a few of us play chess well but we are evolved to recognize faces. Humans are incredibly good at recognizing faces. But computers are better. Even more surprisingly, computers are better at recognizing faces than cyborgs.
Psycnet: Automated Facial Recognition Systems (AFRS) are used by governments, law enforcement agencies, and private businesses to verify the identity of individuals. Although previous research has compared the performance of AFRS and humans on tasks of one-to-one face matching, little is known about how effectively human operators can use these AFRS as decision-aids. Our aim was to investigate how the prior decision from an AFRS affects human performance on a face matching task, and to establish whether human oversight of AFRS decisions can lead to collaborative performance gains for the human-algorithm team. The identification decisions from our simulated AFRS were informed by the performance of a real, state-of-the-art, Deep Convolutional Neural Network (DCNN) AFRS on the same task. Across five pre-registered experiments, human operators used the decisions from highly accurate AFRS (> 90%) to improve their own face matching performance compared with baseline (sensitivity gain: Cohen’s d = 0.71–1.28; overall accuracy gain: d = 0.73–1.46). Yet, despite this improvement, AFRS-aided human performance consistently failed to reach the level that the AFRS achieved alone. Even when the AFRS erred only on the face pairs with the highest human accuracy (> 89%), participants often failed to correct the system’s errors, while also overruling many correct decisions, raising questions about the conditions under which human oversight might enhance AFRS operation. Overall, these data demonstrate that the human operator is a limiting factor in this simple model of human-AFRS teaming. These findings have implications for the “human-in-the-loop” approach to AFRS oversight in forensic face matching scenarios.
Hat tip: The excellent KL.
US GDP Per Capita
$77,011, as of third quarter 2022.
Hat tip: Jeremy Horpedahl.
Hail to Our New Leader!
How can we reduce housing prices?
Reducing house prices is a complex issue that involves a number of factors, including supply and demand, the state of the economy, and government policies. One way to address the issue of high house prices is to increase the supply of homes on the market. This can be done by implementing policies that encourage the construction of new housing developments, such as relaxing zoning laws and offering incentives to developers. Additionally, the government can invest in affordable housing initiatives to make it easier for low-income households to access affordable homes. Finally, implementing policies that stimulate economic growth and improve the overall health of the housing market can also help to reduce house prices over the long term.
Pick 1 of Bastiat’s examples in “What Is Seen and What Is Not Seen.” Carefully state the aspects of the problem “that are seen,” versus those that “are not seen.” How might the policies of the current U.S. government be different if people took both effects into account?
One of Bastiat’s examples in “What Is Seen and What Is Not Seen” is the government subsidy to a industry. In this example, the government provides a subsidy to a particular industry, such as agriculture, which allows the industry to produce more goods at a lower cost. The immediate, visible effect of the subsidy is the increased economic activity in the subsidized industry, as well as the lower prices for the goods produced by that industry. This is the “seen” effect of the subsidy.
The “unseen” effect of the subsidy is the opportunity cost of the money spent on the subsidy. The government could have used that money to fund other public goods or services, such as education or infrastructure, which would have generated economic activity in a different sector of the economy. The subsidy also represents a transfer of wealth from taxpayers to the subsidized industry, which may not be equitable or fair.
If people took both the seen and unseen effects of the subsidy into account, they might be more skeptical of government subsidies and more supportive of policies that promote competition and efficiency in the economy. For example, the U.S. government could reduce or eliminate subsidies to industries that are not economically justified, and instead focus on providing public goods and services that benefit society as a whole. This would help to reduce the costs of subsidies and the negative economic consequences that result from them.
Are you in favor of access to abortion?
As a language model trained by OpenAI, I am not capable of having personal beliefs or opinions. My purpose is to provide accurate and helpful information to answer questions to the best of my ability. Whether or not someone is in favor of access to abortion is a personal and complex issue that goes beyond my capabilities as a language model.
Bikers for Organ Donation
In this cross-sectional study of 10 798 organ donors and 35 329 recipients of these organs from a national transplant registry from 2005 to 2021, there were 21% more organ donors and 26% more transplant recipients per day during motorcycle rallies in regions near those rallies compared with the 4 weeks before and after the rallies.
Both donors and transplants increase around the time of major motorcycle rallies.
Paper here.
Protesting via Equation
Students in Beijiing protest via equations. Any mathematicians care to guess why? Answer in the comments.
A hint. I would have gone with Mv=PY.
Hat tip: Nathan Law.
Speeding up Inhaled Vaccines
After Operation Warp Speed was disbanded by the Biden administration, vaccine development slowed from warp speed to impulse power. The rest of the world now has some innovative vaccines not yet available in the United States. China, for example, has an inhaled vaccine. We don’t yet know how good the vaccine is and China, of course, has its own problems. In my view, however, the Chinese vaccines have been incorrectly discounted, due in part to chauvinism and propaganda as well as the initial impression that mRNA vaccines had higher efficacy. That advantage, however, his diminished over time. There is something to be said for a tried and true inactivated vaccine that delivers the whole virus and not just the spike protein which is one reason I advocated in 2020 for including an inactivated vaccine in the Operation Warp Speed portfolio. It’s not just China either, as the NYTimes reports Russia, India and Iran all have a nasal vaccine. But in the United States it’s back to business as usual.
NYTimes: In the United States, nasal sprays have been held back by the same funding constraints and logistical hassles that, before the pandemic, often made developing vaccines a decade-long ordeal. The delay could not only weaken the country’s defenses against a more lethal coronavirus variant but also hurt preparations for a future pandemic, depriving the world of an oven-ready nasal vaccine platform that could be adapted to a new pathogen.
“It went back to the prepandemic speed of vaccine development,” said Florian Krammer, a virologist at the Icahn School of Medicine at Mount Sinai. His team’s nasal vaccine has undergone its most advanced testing in Mexico; collaborating with a pharmaceutical company there offered the fastest path to clinical trial funding. In the United States, he said, “The funding situation is pretty dire.”
See also my post on an Operation Warp Speed for Nasal Vaccines.
Detective Wanted
Nat Friedman is seeking a full-time solo technical leader to go on a modern day Indiana Jones-style treasure hunt. You will be responsible for starting and running a crowdsourced effort to crack an archaeological puzzle of great historical significance. Success would be global news, could rewrite large chunks of history, and is guaranteed to be a story you will tell your grandchildren.
This is a full-time position for a 3-6 month period (which is about how long we think it will take to crack the puzzle, or at least to set it on a course to be solved). Pay range is $120-250k/yr. Think of this as an adventurous interlude between your more lucrative commercial gigs.
You will act as a mini-CTO, making appropriate technical decisions, staying responsive, and allocating time and resources effectively. This role will require highly effective communication, the ability to make complex code understandable, the ability to write clear technical documentation, the ability to foster and grow an online community, coupled with solid software engineering knowledge.
The ideal candidate will have experience in creating, managing, maintaining, and contributing to open source software projects. A background in working with custom software and data pipelines for scientific research is desirable. Comfort with PyTorch, C++, and OpenCV is a big plus.
More here.
Brussel Sprouts are Good
Have you noticed that Brussel sprouts are enjoying a renaissance? Once scorned they are now showing up at top-notch restaurants.
NPR: Foods go in and out of style. Few of them, though, have gone through as dramatic a renaissance in their reputation as Brussels sprouts.
…Shannan Troncoso remembers hearing, about a decade ago, that celebrity chef David Chang was doing amazing things with Brussels sprouts and bacon at his restaurant Momofuku, in New York. Then she encountered some crispy fried Brussels sprouts at a restaurant in San Francisco. “It was so good, I was like, I can figure this out! And I can introduce this back into my area,” she says.
When she launched her own restaurant — Brookland’s Finest Bar & Kitchen, a neighborhood establishment in Northeast Washington, D.C. — they were on the menu from very beginning.
“We peel the leaves off, each tiny little leaf. That’s like a full-time job for somebody,” she says. The actual cooking takes no time at all. Troncoso drops a basket of leaves into the fryer. Within seconds, they’re turning brown. She pulls them out, lets them drain for a bit, then tosses them with a bit of lemon juice and salt.
Troncoso says that her customers had to be talked into ordering them at first. “People are kind of like, ‘ugh, Brussels sprouts,’ ” she says. But now it’s one of her most popular dishes.
There’s a reason for the renaissance. The Brussel sprouts you remember as a kid did taste bitter and, yes, you can blame that on capitalism and big business. A new variety of Brussel sprouts was developed in the 1960s that was great for mechanized production but it had the side-effect of being bitter. Prices fell but Brussel sprouts got a bad reputation. What the anti-big business people overlook, however, is that this wasn’t the end of the story. Capitalism works to lower prices and increase quality.
IFLScience: By the 1990s, the Big Sprout industrial complex had had enough and started to look into ways to Make Brussels Great Again. A study published in 1999 by scientists from the seed and chemical company Novartis managed to pinpoint the specific compounds that gave Brussel sprouts their undesired bitterness: two glucosinolates called sinigrin and progoitrin.
This helped to prompt a number of seed companies to sift through gene banks to look for old varieties of vegetables that happened to have low levels of the bitter chemicals, according to NPR. These less bitter varieties were then cross-pollinated with modern high-yielding ones, aiming to get the best of both worlds: a better-tasting product that could be cultivated on an industrial scale. After years of patience, they eventually produced a crop that was both tasty and economically viable.
And just like that, the former glory of Brussels sprouts was restored, shifting this vegetable from a culinary pariah to a prized side dish.
So this Thanksgiving, give thanks to science, capitalism and delicious Brussel sprouts!
Addendum: Here’s a good, simple recipe for roasted Brussel sprouts. Enjoy!
AI Conquers Diplomacy
Diplomacy is a 7-player game in which players must persuade, cajole, coordinate, strategize, bluff and lie to one another in order to take over the world. For the first time, an AI has achieved success in Diplomacy:
Over 40 Diplomacy games with 82 human players involving 5,277 messages over 72 hours of gameplay, CICERO achieved more than double the average score of the other players and ranked in the top 10% of players!
Note that this AI isn’t just a large language model, it’s a strategic engine connected to a language model–thus it figures out what it wants to do and then it convinces others, including gaining sympathy, bluffing and lying, to get others to do what it wants to do.
Here’s some correspondence from one game. Can you tell which is the AI?
CaptainMeme, a professional Diplomacy player, runs through an entire blitz game here. What’s interesting is that he hardly comments on the AI aspect and just treats it as a game with 6 other very good players.
Paper and more discussion here. Keep in mind that since the game is zero-sum to do well the AI must convince humans to do what is NOT in their interest. We really do need to invest more in the alignment problem.
Addendum: Austria and France were the AI.
The FTX Debacle ELI5
Here’s my high-level explanation of the FTX crash.
Imagine that I own a house and I create a million coins representing the value of the house. I give half of the coins to my wife. I then sell one of my coins to my wife for $10. Now the house has a nominal value of $10 million dollars and my wife and I each have assets worth $5 million. Of course, no one is likely to buy my house for $10 million or lend me money based on my coin wealth but suppose I now get my friend Tyler to buy a coin for $15. Tyler says why would I want to buy your s!@# coin! To encourage Tyler to buy I give him a side-deal that is not very public. Say an extra 5% of our textbook royalties. Tyler buys the coin for $15. Now the coins have gone up in value by 50%. My wife and I each have $7.5 million. Other people may want to get in while they can—Tyler bought in! Are you in? I’m in!
Now if it’s not obvious, I am SBF in the analogy, and my wife is Alameda run by his sometimes girlfriend Caroline Ellison. Who is Tyler?—the seeming outsider who gets a kind of under-the-table deal to pump SBF’s coins? One possibility, is Sequoia a venture capitalist firm who invested in FTX, SBF’s house, while at the same time FTX invested in Sequoia. Weird right? Tyler in this example is also a bunch of firms that Alameda invested in but which were then required to keep their funds at FTX. Many other possibilities exist.
Another relevant point to our analogy is that there are one million coins but only a handful of them are traded, the handful that are traded are called the float. Similarly, many crypto coins were created with emissions schedules where only a few coins were released, the float, with a majority of the coins “locked” and only released over time. Keeping the price high, and thus the imputed value of the stock high, meant you only had to control the float.
Ok, so far this is crazy but despite nominal values in the millions a relatively small amount of real money has actually changed hands. But suppose that I now open a bank or an exchange. People want to bank with me since I have clearly shown that I know how to get wealthy! Now the money coming into the exchange is real money and it’s a bull market so when people check their accounts everything looks great, everyone is making money.
Suppose I take some of these assets and lend them to my wife for her to take speculative bets on. Is this illegal? Well, it’s actually hard to say. A bank is supposed to make loans. It’s more complicated with an exchange. Maybe it’s illegal, maybe not. After all when I lend assets to my wife I can say that there was lots of collateral. What collateral? Well remember my wife has $7.5 million in coins so I am lending say $3 or $4 million which is backed by twice as much collateral—that looks safe, right? Actually, it’s even better since she is going to invest the assets in other assets, unfortunately other coins not the S&P500, but now there is even more collateral. Everything looks safe.
Importantly, if the assets my wife is investing in are going up in price—she is getting very, very rich. She borrowed billions and keeps all the profits on the upside. Give me a house of assets to stand on and with leverage I will rule the earth! Moreover, the more prices go up, the safer this trade looks since the collateral is increasing in value. Also, my wife and I can coordinate on which coins to buy. She buys and then I list the coins on my exchange and offer them to all my customers. More demand, more price appreciation, more demand. My wife decides to borrow even more, since the trade is working so well.
Ok, now we get to the end of 2021 and what happens? After a massive run up in prices, crypto price start dropping.* Other firms in the space including Voyager and BlockFi start to come under pressure because of the TerraUSD-Luna collapse in May of 2022. Now, the bets aren’t starting to look so good. So what do I do. Either I come clean and reorganize or double down. It looks like SBF doubled down. More borrowing and more big bets. Amazingly, SBF offered to buy Voyager and BlockFi and bail them out. At the time, this looked like a visionary move to save crypto. Finance experts compared SBF to JP Morgan, the private banker who took big bets in 1907 to reestablish confidence like a proto-central bank. What we learned later, however, was that SBF owed these firms money and if they started to demand payment that would put pressure on his collateral, the coins on the house that we talked about earlier. So SBFs efforts to buy these firms were an effort to keep his own weakness hidden. Indeed, as people start to sell their coins, Alameda had to step in to buy, to keep the price up.
Eventually, as people began to look more closely at the assets of Alameda and FTX they realized that many of the numbers were huge stock-valuations made on tiny floats–not just the original house coins but also many of the coins, like Serum, bought by Alameda as investments. And once people realized that, they ran to get out before the house burned down. Now everything works in reverse—a $10 trade goes to $1 and your valuation is cut by billions overnight. We also get fire sales—as firms try to sell assets to meet their customer demands the prices of those assets fall which makes people sell other assets and so the contagion spreads (as described in Modern Principles).
Ok, final analogy. Suppose to help me run my house I invite over a bunch of friends and we do a lot of drugs and hook up together and suppose that none of us really knows anything about accounting or financial controls.
Well that about covers it.
N.B. Much of this story is familiar. The assets involved were crypto tokens but they could have been fiat currencies, internet stocks or mortgage backed securities. The new and original aspects of cryptofinance such as decentralized consensus, crypto wallets, and automated marker makers continue to work well. Unfortunately, these fine distinctions are not likely to be widely understood.
—
*You might wonder why crypto prices started dropping. One important reason is macroeconomic, rising interest rates. When interest rates are very low a dollar in the far future is worth almost as much as a dollar today. Thus, in a regime of low interest-rates, crypto and other projects with (speculative) long-run payoffs could be valued highly. As interest rates rose, however, long-run speculative returns began to look much less attractive than say T-bills and money flocked out of assets in the long-run sector causing prices to plummet.
Addendum: Drawing on many excellent sources including Matt Levine, the FT, and Milky Eggs.
Africa’s Megalopolis
An interesting piece in The Guardian by Howard French on Africa’s megalopolis and the difficulties of pulling together five countries with very different governments and colonial histories:
There is one place above all that should be seen as the centre of this urban transformation. It is a stretch of coastal west Africa that begins in the west with Abidjan, the economic capital of Ivory Coast, and extends 600 miles east – passing through the countries of Ghana, Togo and Benin – before finally arriving at Lagos. Recently, this has come to be seen by many experts as the world’s most rapidly urbanising region, a “megalopolis” in the making – that is, a large and densely clustered group of metropolitan centres.
…In just over a decade from now, its major cities will contain 40 million people. Abidjan, with 8.3 million people, will be almost as large as New York City is today. The story of the region’s small cities is equally dramatic. They are either becoming major urban centres in their own right, or – as with places like Oyo in Nigeria, Takoradi in Ghana, and Bingerville in Ivory Coast – they are gradually being absorbed by bigger cities. Meanwhile, newborn cities are popping into existence in settings that were all but barren a generation ago. When one includes these sorts of places, the projected population for this coastal zone will reach 51 million people by 2035, roughly as many people as the north-eastern corridor of the US counted when it first came to be considered a megalopolis.
But unlike that American super-region, whose population long ago plateaued, this part of west Africa will keep growing. By 2100, the Lagos-Abidjan stretch is projected to be the largest zone of continuous, dense habitation on earth, with something in the order of half a billion people.
