In Keynesian economics, “running the economy hot” boosts employment by lowering the real wage.
In Lucasian economics, “running the economy hot” boosts employment by fooling people into thinking that real wages are higher, when they are not.
In #thegreatforgetting economics, “running the economy hot” boosts employment and real wages by…boosting employment and real wages.
It is actually the much-maligned supply-side economics that, at least in principle, makes sense here.
From Ben Casselman: “…unlike with most measures of the economy, retail sales are actually ABOVE their prepandemic level. Up 2.6% from February, and 2.9% over the past year. So not a clean story like with jobs.”
And from Larry Summers: ” Total household income is 8% above what anybody thought it would be before Covid.”
There are very real macroeconomic problems right now, but please keep the following in mind while drawing up a “demand-based” stimulus plan. Focus on public health!
At the Uluwatu temple in Bali, monkeys mean business. The long-tailed macaques who roam the ancient site are infamous for brazenly robbing unsuspecting tourists and clinging on to their possessions until food is offered as ransom payment.
Researchers have found they are also skilled at judging which items their victims value the most and using this information to maximise their profit.
Shrewd macaques prefer to target items that humans are most likely to exchange for food, such as electronics, rather than objects that tourists care less about, such as hairpins or empty camera bags, said Dr Jean-Baptiste Leca, an associate professor in the psychology department at the University of Lethbridge in Canada and lead author of the study.
Mobile phones, wallets and prescription glasses are among the high-value possessions the monkeys aim to steal. “These monkeys have become experts at snatching them from absent-minded tourists who didn’t listen to the temple staff’s recommendations to keep all valuables inside zipped handbags firmly tied around their necks and backs,” said Leca.
After spending more than 273 days filming interactions between the animals and temple visitors, researchers found that the macaques would demand better rewards – such as more food – for higher-valued items.
Bargaining between a monkey robber, tourist and a temple staff member quite often lasted several minutes. The longest wait before an item was returned was 25 minutes, including 17 minutes of negotiation. For lower-valued items, the monkeys were more likely to conclude successful bartering sessions by accepting a lesser reward.
This is a bleg, could you please offer your suggestions in the comments? Citations would be great if you know of them, but I understand this may not be possible. Thanks for your help in this…
Here’s something from a paper that I am working on. The context is why first doses first makes more sense the greater the uncertainty but the point made is larger. No indent.
An important feature of First Doses First (FDF) and other policies such as fractional dosing is that they are reversible. In other words, FDF contains an option to switch back to Second Doses First (SDF). Options increase in value with uncertainty (Dixit and Pindyck 1994). Thus, contrary to many people’s intuitions, the greater the uncertainty the greater the value of moving to First Doses First. Indeed, the value of the option can be so high that one might want to move to First Doses First even if it were worse in expectation. For example, if the expected efficacy of the first dose were just 45% then in expectation it would be worse than Second Doses First (95% efficacy) but if there were lots uncertainty around the 45% expected efficacy it might still be better to switch to First Doses First. If there was a 75% chance that the efficacy of the first dose was 30%, for example, and a 25% chance that it was 90% (.75*.3+.25*.90=45%) then under reversibility one would still want to switch to First Doses First to learn whether the true efficacy was 30% or 90%.*
Put differently shifting away from the default strategy to an alternative such as FDF or fractional dosing might be considered to be “risky”. But in this context, learning requires risk. When learning is desirable, it is also desirable to take on risk. Risk aversion can prevent learning and thus can be dangerous.
If FDF is worse in expectation than SDF then it would be optimal to switch to the most minimal form of FDF necessary to learn about the true efficacy rate. In other words, to run an experiment. If FDF is superior in expectation to SDF then it might also be better to run an experiment before switching but not necessarily. If FDF is superior in expectation to SDF then the cost of running the experiment is keeping the policy with lower expected value while the experiment is running. If these costs are high then switching immediately is better.
It would take at least 16 weeks, for example, to run an experiment on extending dosing from 3 weeks to 12 weeks (including, optimistically, just 1 week to setup the experiment). As of early January 2021, confirmed cases in the United States are increasing at the rate of 200,000 per day or 1,400,000 per week. Thus there could be 22,400,000 new confirmed cases in the time it takes to run the experiment. At a case fatality rate of 1.7% that means 380,800 new deaths. If First Doses First reduces the infection rate in expectation by 10% that would imply that running the experiment has an expected cost of 38,080 lives.
At these rates, more lives could be saved in expectation by switching to the policy with higher expected value and simultaneously running experiments. Randomized trials that explicitly test the impact of dosing timing, fractional dosing and different timings of additional doses on severe, symptomatic and asymptomatic infections, and also on transmission should be incorporated as part of roll-out plans (Kominers and Tabarrok 2020, Bach 2021). However, roll-out of modified plans should not wait until these trial results are known; instead, plans should be adjusted as new information emerges. Most notably the British moved to First Doses First and they approved the AstaZeneca vaccine on December 30, 2020 and the consequences of both of these decisions should be monitored very closely to help improve decisions in other countries.
*This assumes that one could learn the true efficacy rate quickly enough relative to the ongoing pandemic to benefit from the new information. One might respond that in principle SDF also contains an option to switch to FDF but this option is valueless since Second Doses First provides no opportunity to learn. Only under First Doses First do we learn valuable new information.
Here’s Marty Makary, M.D., a professor of surgery and health policy at the Johns Hopkins University School of Medicine:
Finally, the FDA needs to stop playing games and authorize the Oxford-AstraZeneca vaccine. It’s safe, cheap ($2-$3 a dose), and is the easiest vaccine to distribute. It does not require freezing and is already approved and being administered in the United Kingdom.
Sadly, the FDA is months away from authorizing this vaccine because FDA career staff members insisted on another clinical trial to be completed and are punishing the company for inadvertently giving a half-dose of the vaccine to some people in the trial.
It’s like the FDA is holding out, pontificating existing excellent data and being vindictive against a company for making a mistake while thousands of Americans die each day.
Ironically, those in the Oxford-AstraZeneca trial who inadvertently received half the initial vaccine dose had lower infection rates. And this week Dr. Moncef Slaoui, the chief adviser to Operation Warp Speed, acknowledged that using half a dose might be a good broader strategy for the U.S. to double our supply as long our supply is severely constrained. That’s a good strategy that makes sense.
See also my post The AstraZeneca Factory in Baltimore. Thousands of people are dying every day. We have a vaccine factory ready to go. The FDA should lifts its ban on the AstraZeneca vaccine.
That is the title of a new paper by Isaiah Hull, Or Sattath, Eleni Diamanti, and Goran Wendin. Much of it I did not understand, but maybe you will. Here is one excerpt:
Our overview of quantum money starts with a full description of the original scheme, which was introduced circa 1969, but only published later in Wiesner (1983). We will see that it achieves what is called “information-theoretic security,” which means that an attacker with unbounded classical and quantum resources will not be able to counterfeit a unit of the money. Since this original scheme was proposed, the term “quantum money” has come to refer to a broad variety of different payment instruments, including credit cards, bills, and coins, all of which use of quantum physical phenomena to achieve security.The real promise of quantum money is that it offers the possibility of combining the beneficial features of both physical cash and digital payments, which is not possible without the use of the higher standard of security quantum money offers.In particular, a form of currency called “public-key” quantum money would allow individuals to verify the authenticity of bills and coins publicly and without the need to communicate with a trusted third party. This is not possible with any classical form of digital of money, including cryptocurrencies, which at least require communication with a distributed ledger. Thus, quantum money could restore the privacy and anonymity associated with physical money transactions, while maintaining the convenience of digital payment instruments.
Makes those crypto people look like David Laidler! See also this Behera and Sattath paper.
The federal government was unprepared for the pandemic, despite multiple, loud and clear warnings. State and local governments were unprepared for vaccines, despite multiple, loud and clear warnings. The Capitol Police were unprepared for rioters, despite multiple, loud and clear warnings.
The record isn’t good but as a Queen’s Scout I persist. We now have multiple, loud and clear warnings that new variants of the SARS-COV II virus are more transmissible and thus much more dangerous. But we can do something. As wrote in The New Strain and the Need for Speed
One of the big virtues of mRNA vaccines is that much like switching a bottling plant from Sprite to 7-Up we could tweak the formula and produce a new vaccine using exactly the same manufacturing plants. Moreover, Marks and Hahn at the FDA have said that the FDA would not require new clinical trials for safety and efficacy just smaller, shorter trials for immune response (similarly we don’t do new large-scale clinical trials for every iteration of the flu vaccine.) Thus, if we needed it, we could modify mRNA vaccines (not other types) for a new variant in say 8-12 weeks.
Thus, let’s start doing much more sequencing to discover new strains–and also think about potential new strains–and start phase I and phase II trials of new vaccines. Florian Krammer suggested an even more ambitious plan to do the same thing for all potential pandemic viruses:
From each of the identified virus families, which should certainly include the Paramyxoviridae, Orthomyxoviridae, and Coronaviridae families, a handful of representative strains with the highest pandemic potential should be selected for vaccine production. Up to 50–100 different viruses could be selected and this would broadly cover all phylogenies that may give rise to pandemic strains….It should be possible to choose candidates that are close to viruses that might emerge in the human population. The idea is that once viruses are selected, vaccines can be produced in different platforms and tested in phase 1 and phase 2 trials with some of the produced vaccine being stockpiled. This would likely cost 20–30 million US dollars per vaccine candidate resulting in a cost of 1–3 billion US dollars.
What I am suggesting is less ambitious–just do this for Sars-COV-3, 4, 5 and 6. But do it now!
Hat tip: Daniel Bier.
Broken Record Addendum: We should make better use of our limited vaccine supply by moving to First Doses First and/or fractional dosing and approve the AstraZeneca vaccine immediately and spend billions to increase the rate of vaccinations and to speed new vaccines (such as those from J&J and Novavax) to market.
Neal Katyal has argued 43 cases before the Supreme Court. Until the coronavirus pandemic hit, he hadn’t once enlisted his son as an assistant.
Now, Mr. Katyal and other lawyers appearing in the nation’s highest court have to argue their cases remotely, which often means from home. In November, as Mr. Katyal prepared in his home office to represent the city of Philadelphia in a case about religious objections to same-sex parents, he worried about the street noise.
So he gave his 19-year-old son $100 and instructed him to go outside and dole out cash to quiet down any noisemakers. Sure enough, minutes before the hearing began, a truck rolled up, idling loudly.
“Oh my God, the justices are going to be so mad at me,” Mr. Katyal, who served as acting solicitor general in the Obama administration, recalled thinking. Fortunately, the truck drove away without his son having to intercede.
Here is the full WSJ article.
I think your column agrees with my mental model in that the actual crypto networks may not be regulated, but the on-ramps and off-ramps will be heavily regulated (and already are).
If you are an exporter being paid in crypto assets by a Nigerian importer, the obvious thing to do is hedge that crypto against your currency of choice. Because of the volatility, this is maybe most analogous to oil companies hedging oil sales. It is a common practice that most energy lenders provide as part of their menu.
If you tried to set up a service to do this without following the current regulations, I’m sure you’d end up in prison. Just like Coinbase or USDC is already regulated under current rules, hedging crypto against the dollar would easily fall under CFTC rules. Your bank that already provides a line of credit and knows your order book would be the one to offer the service.
I think this is a good outcome in that for those so inclined, the crypto networks provide high risk but low regulation pathways to do business. Everybody else that wants to straddle the dollar and crypto world to get some benefits of crypto will still use the same institutions that manage the massive amounts of regulation that exists in the dollar world.
Maybe the best way to look at the future of crypto, especially outside of bitcoin, is that it is the perfect open-source software ecosystem. Everything is easily interoperable, security is high, and there is a business model for paying developers. Linux and Unix never became consumer operating systems, but they underly every website you use, every popular phone operating system, and now both macOS and Windows. Crypto can do that for financial systems and other applications by providing the infrastructure for advanced (and regulated) consumer and enterprise apps to be built on. It is more like Marc Andreessen’s “software is eating the world” than crypto anarchy. The crypto-anarchists will always have Monero!
Are we more inclined to take risks for ourselves rather than on someone else’s behalf? The current study reviews and summarizes 28 effects from 18 studies (n=4,784). Across all studies, choices for others were significantly more risk-averse than choices for self (d=0.15, p=0.012). Two objective features of the choices moderated these effects: potential losses and reciprocal relationships. First, self-other differences in risk preferences were significant in the presence of potential losses (k=14, d=0.33, p<.001), and not significant (k=14, d=-0.06, p=0.473) in the gains-only domain (Q=12.56, p=<0.001). Choices for others were significantly more risk-averse when decision makers were reciprocally related to recipients (k=6, d=0.33, p=0.018) but no different in the absence of such a relationship (d=0.11, p=0.115). Reciprocal relationship was a marginally significant predictor (Q=2.02, p=0.155). Results are shown separately by publication status and by context (medical, economic game, hypothetical choice). A relational model of surrogate risk taking is proposed to explain the pattern of results, which emphasizes the importance of chooser-recipient relationships, and the tendency of choosers to minimize anticipated blame from losses, rather than maximizing credit for gains. Implications for benefits design, medical and managerial decision making are discussed.
At least that is what the science says.
Putting things into some perspective, Geanakoplos also said that the $250 million Yale lost as a result of COVID-19 represents one day’s average fluctuation in the value of the endowment. The salary freeze that accompanied the hiring freeze, meanwhile, saved the Faculty of Arts and Sciences $5 million.
Geanakoplos, for instance, said at an October Senate meeting, “I hope the Yale administration will listen to the science of financial crises and take the right calculated risk to deal with the COVID financial crisis.”
Yale, he continued, is “unlikely in the next 50 years to have so good an opportunity to make progress in faculty excellence and diversity as it has right now.” Many peer institutions, especially public ones, continue to face the financial fallout of COVID-19, and so Yale’s “opportunity is now huge,” Geanakoplos urged. “Seize it … Seeing an opportunity while having the money at the same time is truly extraordinary.”
Here is the full story, via Mike.
I will be doing a Conversation with him, in case you do not know Brian is co-founder and CEO at Coinbase.
So what should I ask him? And to be clear, this is the conversation I want to have with him, namely one that maximizes my selfish learning, not your mood affiliation. Here is the Wikipedia page for Coinbase, here is Brian on Twitter, why does a major CEO and person with 410k Twitter followers have no Wikipedia page of his own?
Emergent BioSolutions has a factory in Baltimore that operates under an innovative long-term private-partnership agreement with BARDA. Essentially BARDA subsidized the factory in return for an option to use it in an emergency–Operation Warp Speed exercised that option and in June-July AstraZeneca signed a licensing agreement with Emergent for large-scale manufacturing of its vaccine.
According to the Baltimore Sun the AZ vaccine is already being made at the facility. I hope they are making millions of doses. I want the AZ vaccine approved in the United States immediately but if we won’t take it (yet) they can still export it to Britain and the many other countries which will approve the vaccine.
More generally, there are three vaccines in the near term pipeline. AstraZeneca, Johnson and Johnson and Novavax. If there is anything that we can do to speed these vaccines to people it would be worth billions. All of these vaccine manufacturers should be making and storing millions of doses now.
It’s important to understand that a policy like First Doses First works best when capacity is increasing rapidly so approving these additional vaccines is part of an integrated plan.
Here’s the factory in Baltimore. It’s capable of producing tens to hundreds of millions of vaccine doses a year. Isn’t it beautiful?
Addendum: One more thing. Stop telling me that the problem is vaccine distribution not supply. Guess what? I am thinking ahead.
Alex has been arguing for a “First Doses First” policy, and I find his views persuasive (while agreeing that “halfsies” may be better yet, more on that soon). There are a number of numerical attempts to show the superiority of First Doses First, here is one example of a sketched-out argument, I have linked to a few others in recent days, or see this recent model, or here, here is an NYT survey of the broader debate. The simplest numerical case for the policy is that 2 x 0.8 > 0.95, noting that if you think complications overturn that comparison please show us how. (Addendum: here is now one effort by Joshua Gans).
On Twitter I have been asking people to provide comparable back-of-the-envelope calculations against First Doses First. What is remarkable is that I cannot find a single example of a person who has done so. Not one expert, and at this point I feel that if it happens it will come from an intelligent layperson. Nor does the new FDA statement add anything. As a rational Bayesian, I am (so far) inferring that the numerical, expected value case against First Doses First just isn’t that strong.
Show your work people!
One counter argument is that letting “half-vaccinated” people walk around will induce additional virus mutations. Florian Kramer raises this issue, as do a number of others.
Maybe, but again I wish to see your expected value calculations. And in doing these calculations, keep the following points in mind:
a. It is hard to find vaccines where there is a recommendation of “must give the second dose within 21 days” — are there any?
b. The 21-day (or 28-day) interval between doses was chosen to accelerate the completion of the trial, not because it has magical medical properties.
c. Way back when people were thrilled at the idea of Covid vaccines with possible 60% efficacy, few if any painted that scenario as a nightmare of mutations and otherwise giant monster swarms.
d. You get feedback along the way, including from the UK: “If it turns out that immunity wanes quickly with 1 dose, switch policies!” It is easy enough to apply serological testing to a control group to learn along the way. Yes I know this means egg on the face for public health types and the regulators.
e. Under the status quo, with basically p = 1 we have seen two mutations — the English and the South African — from currently unvaccinated populations. Those mutations are here, and they are likely to overwhelm U.S. health care systems within two months. That not only increases the need for a speedy response, it also indicates the chance of regular mutations from the currently “totally unvaccinated” population is really quite high and the results are really quite dire! If you are so worried about hypothetical mutations from the “half vaccinated” we do need a numerical, expected value calculation comparing it to something we already know has happened and may happen yet again. When doing your comparison, the hurdle you will have to clear here is very high.
When you offer your expected value calculation, or when you refuse to, here are a bunch of things you please should not tell me:
f. “There just isn’t any data!” Do read that excellent thread from Robert Wiblin. Similar points hold for “you just can’t calculate this.” A decision to stick with the status quo represents an implicit, non-transparent calculation of sorts, whether you admit it or not.
g. “This would risk public confidence in the vaccine process.” Question-begging, but even if true tell us how many expected lives you are sacrificing to satisfy that end of maintaining public confidence. This same point applies to many other rejoinders. It is fine to cite additional moral values, but then tell us the trade-offs with respect to lives. Note that egalitarianism also favors First Doses First.
h. “We shouldn’t be arguing about this, we should be getting more vaccines out the door!” Yes we should be getting more vaccines out the door, but the more we succeed at that, as likely we will, the more important this dosing issue will become. Please do not try to distract our attention, this one would fail in an undergraduate class in Philosophical Logic.
i. Other fallacies, including “the insiders at the FDA don’t feel comfortable about this.” Maybe so, but then it ought to be easy enough to sketch for us in numerical terms why their reasons are good ones.
j. All other fallacies and moral failings. The most evasive of those might be: “This is all the more reason why we need to protect everyone now.” Well, yes, but still show your work and base your calculations on the level of protection you can plausibly expect, not on the level of protection you are wishing for.
At the risk of venturing into psychoanalysis, it is hard for me to avoid the feeling that a lot of public health experts are very risk-averse and they are used to hiding behind RCT results to minimize the chance of blame. They fear committing sins of commission more than committing sins of omission because of their training, they are fairly conformist, they are used to holding entrenched positions of authority, and subconsciously they identify their status and protected positions with good public health outcomes (a correlation usually but not always true), and so they have self-deceived into pursuing their status and security rather than the actual outcomes. Doing a back of the envelope calculation to support their recommendation against First Doses First would expose that cognitive dissonance and thus it is an uncomfortable activity they shy away from. Instead, they prefer to dip their toes into the water by citing “a single argument” and running away from a full comparison.
It is downright bizarre to me — and yes scandalous — that a significant percentage of public health experts are not working day and night to produce and circulate such numerical expected value estimates, no matter which side of the debate they may be on.
How many times have I read Twitter threads where public health experts, at around tweet #11, make the cliched call for transparency in decision-making? If you wish to argue against First Doses First, now it is time to actually provide such transparency. Show your work people, we will gladly listen and change our minds if your arguments are good ones.