Category Archives: PSST and Macro

Virus update

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1. Larry Summers writes,

The question in assessing universal tax rebates is, what about the vast majority of families who are still working, and whose incomes have not declined or whose pension or Social Security benefits have not been affected by Covid-19? For this group, the pandemic has reduced the ability to spend more than the ability to earn.

In other words, we should not be applying conventional macroeconomics right now. Conventional macro sees as all working the same GDP factory, which is producing below capacity because of insufficient demand. Conventional macro says that it does not matter how the government directs spending, because any spending will inject more “aggregate demand.”

Even a conventional macroeconomist like Larry Summers is able to see that this model does not fit the current situation. I happen to think that conventional macroeconomics needs a much broader reassessment.

2. We continue to argue about asymptomatic spreading.

The secondary attack rate for symptomatic index cases was 18.0% (95% CI 14.2%-22.1%), and the rate of asymptomatic and presymptomatic index cases was 0.7% (95% CI 0%-4.9%), “although there were few studies in the latter group.” The asymptomatic/presymptomatic secondary attack rate is not statistically different from zero

Just run a test, for crying out loud.

3. Megan McArdle writes (WaPo),

Looking back over the past nine months, it’s as if the public health community deliberately decided to alienate large groups of Americans, usually in the name of saving someone else.

The World Health Organization told us travel bans don’t work, apparently because they harm tourist economies; then we were told masks don’t work, apparently because experts worried that hoarding them would leave health-care workers without personal protective equipment; the public health community fell suddenly silent about the dangers of large gatherings during the George Floyd protests; a presentation to a government advisory committee actually described thousands of potential additional deaths as “minimal” compared with pursuing racial and economic equity; Anthony S. Fauci admitted he’d been lowballing his estimates of the point at which we’ll reach herd immunity.

The Orwellian public health community notwithstanding, my nominee for villain of the crisis is the FDA, for two reasons.

First, the FDA placed a very high priority on accuracy in deciding whether to approve tests for the virus. For some purposes, such as estimating the prevalence of the virus, accuracy is a good thing. But for controlling the spread of the virus, an accurate test that takes a week to provide results is worthless. The FDA should have prioritized “faster and cheaper” over “reliable.”

Second, the FDA did not use human challenge trials (give one group the vaccine and one group the placebo, and then expose them to the virus). Instead it gave one group the vaccine and one group the placebo, and then waiting until enough people naturally were exposed to the virus to show efficacy. We could have been starting to take the vaccine in June, but instead we had to wait until now.

4. Mr. Biden got into the virus forecasting business a few days ago, saying that the U.S. will have 400,000 deaths by the time he is sworn in as President. According to this site, we had 329,000 as of December 29. To get to 400,000 by inauguration day, we would need to average about 3000 deaths per day. On a 7-day average basis, the highest that it has been is 2680 on December 22. It has been edging down over the past week. But if you are trying to forecast the closest round number, then 400,000 is right.

He rightly criticized the slow process of distributing the vaccine. If it were rationed by price rather than by government authorities, my guess is that there would not be such a large supply of vaccine sitting around waiting for someone to administer it.

5. Miles Kimball joins those of us criticizing peacetime bureaucrats.

Highly accurate tests whose results take many days to arrive are next to useless. But the US government was very slow to approve tests of lower accuracy that could have made a big difference because they gave results within minutes.

Pointer from Alex Tabarrok. He says that the problem is perfectionism. I think it’s blame-avoidance.

If you do something and harm results (e.g., somebody gets a wrong test result), then you can be blamed. If you do nothing (i.e., don’t allow fast but less-accurate tests), then the harm that results is God’s Will. It’s the trolley problem.

Virus update

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1. Casey Mulligan writes,

I was in the Oval Office with the president and his economic team in February (when COVID-19 cases were beginning to spread). His staff was worried that the FDA would not be interested in removing any more approval barriers. But the President was confident, telling us that “I’ve done it before and will do it again … bring the FDA management in here.” President Trump initiated his Operation Warp speed, led by HHS, to give many private companies incentives for “speed and scale” of vaccine production and to give all companies the opportunity for streamlined FDA approval.

Read the whole post. Pointer from a commenter.

Unless Mulligan’s account can be disputed, Mr. Trump successfully fought the bureaucracy on the vaccine issue. Thus, contrary to the standard view on the left, he is a hero rather than a villain of the virus crisis.

2. The daily average death rate seems to have finally leveled off, albeit at a high level of about 2500 deaths per day.

3. In a podcast with Russ Roberts, Jay Bhattacharya says,

if you’re under 70, the infection survival rate is something like 99.95%. 99.95%

He argues for a policy that I would call “expose the young, protect the old.” Let me play Devil’s Advocate on that.

–His numbers say that the chances of dying if you are under 70 and get the virus are 5 out of 10,000. Suppose that 200 million people get it with that death rate. That means that 100,000 of them die. Is that low for people in that age group? I don’t know.

–And what if he is a little off–and the death rate turns out to be 8 out of 10,000? That would mean 160,000 deaths in that age group.

–And what about survival but with long-term damage? I think it was Bret Weinstein who speculated that the virus takes an average of 10 years off of the life of everyone who gets it, or perhaps everyone who is symptomatic. That is a lot of life-years lost, even if it only kills people who otherwise had less than 10 years of expected life.

–And in practice can you really protect the old while the young are exposed?

Of course, he deals with these objections in the podcast.

Post-pandemic WFH

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1. Jose Maria Barrero, Nicholas Bloom, and Steven J. Davis write,

Our survey evidence says that 22 percent of all full work days will be supplied from home after the pandemic ends, compared with just 5 percent before. We provide evidence on five mechanisms behind this persistent shift to working from home: diminished stigma, better-than-expected experiences working from home, investments in physical and human capital enabling working from home, reluctance to return to pre-pandemic activities, and innovation supporting working from home. We also examine some implications of a persistent shift in working arrangements: First, high-income workers, especially, will enjoy the perks of working from home. Second, we forecast that the postpandemic shift to working from home will lower worker spending in major city centers by 5 to 10 percent. Third, many workers report being more productive at home than on business premises, so post-pandemic work from home plans offer the potential to raise productivity as much as 2.4 percent.

I would not be optimistic regarding the last point. But I do think that this will really accentuate the class divide. The people who work from home will be able to engage more with their children. They will have more flexibility in general for dealing with everything from medical issues to laundry.

2. I talk with Richard Reinsch about macroeconomics in the age of the virus. I offer the PSST perspective.

on net 10 million people not working. That’s an entrepreneurial opportunity to find something useful for them to do. But that means you have to encourage entrepreneurship. And under the Obama administration, you had discouraging entrepreneurship because they kept piling on regulation. And one of the quiet things that the Trump administration has done is to loosen those regulations so that entrepreneurs could work more quickly. But the current situation is so extreme in terms of the reconfigurations that are needed, that you need just an awful lot of entrepreneurial activity, and it’s going to take a long time for entrepreneurs to figure out how to use these extra 10 million or so people.

When there is no science to follow

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The question of whether you can get sick from someone with the virus who is asymptomatic is still not close to being settled, as far as I can see. I think it is safe to say that settling this question would make for much better-informed decisions by individuals and policy makers.

[UPDATE: Consider this study (pointer from a reader) vs. this study (pointer from a commenter).] One says that asymptomatics have as much viral load as symptomatics. The other says that there were no cases of asymptomatic transmission in their sample.]

The question could be settled by running experiments. You could find people who test positive for the virus and are asymptomatic. You could find volunteers willing to expose themselves to these asymptomatic folks under various conditions. Then you could evaluate the results.

I have been saying since the early days of March and April that we need this sort of science. But epidemiologists and public health “experts” do not think the way I do. As far as I am concerned, the call to “follow the science” is baloney sandwich. There is no science to follow.

Three endgames for the virus

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1. The treatment endgame. We learn to live with the virus. Deaths are prevented using treatments.

2. The suppression endgame. We keep people from coming into contact with the virus.

3. The immunity endgame. Enough people get the virus and/or a vaccine so that it has few people to infect.

It sounds like (2) works in some countries, using ots of testing, tracing, and quarantining. Many people are angry that the U.S. has not executed this strategy. But (a) a lot of other countries also have not been able to execute it and (b) it seems like a fragile strategy, in that at some point you could experience too many cases to deal with using testing and tracing, and then where are you?

Lockdowns were supposed to be part of (1), the idea being to “flatten the curve” and ensure enough treatment resources. I have speculated on a super-strict short-term lockdown to achieve (2), but that is probably a fantasy. Meanwhile, many people seem to have come to believe mistakenly that the lockdowns that we actually have can achieve (2).

I thought that (1) was more likely to work than (3). But events seem to be moving in the other direction. We see have seen deaths rise pretty dramatically in recent weeks. Not to NY/NJ nursing home levels, but still alarming. So the treatments have not yet reached the point where we can just live with the virus as we can with the flu.

To my knowledge, herd immunity has not emerged anywhere. That leaves the vaccine.

Vaccine trials seem to show efficacy. As you know, I worry that the results might not be reliable, because even in the placebo sample there were not many cases. But my guess is that since mid-November there have been many more cases, and if the results are still strong then that would be pretty convincing.

I suggested the other day that challenge trials could have been used to more efficiently demonstrate efficacy of the vaccine. But there is an argument that there is no way to evaluate safety quickly. If the vaccine is going to have harmful effects, these may take a while to show up. So perhaps we could not have evaluated a vaccine in a matter of weeks, even with challenge trials.

Vaccine testing and the trolley problem

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As you know, I am not ready to proclaim a vaccine a success based on what I see as a small number of cases in the sample population. A lot of you think I am wrong about that, and I hope I am.

But my intuition is still that there is something unsatisfying about the testing protocol.

The actual protocol seems to be to give the vaccine to a large sample and wait for people to get exposed to the virus as they go about their normal business. My inclination is to deliberately expose a smaller sample to the virus and see how well the vaccine works.

One argument against deliberate exposure is that the response of people to deliberate exposure may not be the same as their response to normal-business exposure.

But in favor of deliberate exposure:

–you can clearly test how well the vaccine does in a specific populations, such as people with and without obesity.
–you can clearly test how well the vaccine does at two levels of viral load.
–you can get results quickly, rather than wait for months for people in the sample population to become exposed the the virus
–you could identify the main contacts of people to whom the virus is exposed and make sure that the vaccine reduces spreading.

Whether you deliberately expose 100 people or wait for 100 cases to emerge, that is still 100 cases either way. It seems to me that deliberate exposure is equivalent to throwing the switch in the trolley problem. Those 100 cases are 100 cases either way, it’s just that the experimenter didn’t specifically choose them.

I think that the case for deliberate exposure as a testing protocol ends up being pretty strong. What am I missing?

UPDATE: a reader points me to a story about a proposed challenge trial.

Challenge trials are controversial because of the risks involved with infecting patients with a potentially lethal virus

But again I ask, what is the difference between infecting a group of people and waiting for a group of people to become infected?

Virus policy and inflation

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Subsidize demand, restrict supply.

In Specialization and Trade, I make the claim that most government intervention does not conform to the textbook model of dealing with market failure. In that model, when the market produces too little of something (use of masks in a pandemic), the government is supposed to subsidize either supply or demand. When the market produces too much of something (air pollution from automobiles), the government should penalize either supply or demand.

In practice, politicians don’t follow the textbooks. They obey interest groups. Every interest group once to see its supply restricted and its demand subsidized. So the national government subsidizes home purchases, but then at a local level it restricts housing construction. The government subsidizes the demand for health care and higher education, but it uses licensing and accreditation rules to restrict supply.

It occurs to me that virus policy is “subsidize demand, restrict supply” writ large. Lockdown-type rules restrict supply. And “stimulus” subsidizes demand.

What do you get when you subsidize demand and restrict supply? Higher prices. Eventually.

Further thoughts on a vaccine

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UPDATE: Thanks to a commenter for finding the study protocol for the Pfizer study. That helps a lot.

1. If you assume that everyone is identical, and if you want to try to eradicate the virus by giving everyone the vaccine, then you don’t need to show that it is 90 percent effective. My guess is that 20 percent effectiveness will do the trick. The problem you face is a PR problem. Most people hear the word “vaccine” and think “super-power that keeps me from getting sick.” But to be honest you would have to say “You might still get sick, but if everyone takes the vaccine and also exercises some degree of precaution, the virus will die out and eventually you won’t have to worry about it.”

2. But if your goal with the virus is to target high-risk populations or people whose working conditions might expose them to high viral loads and make them safe, then you want the super-power story to be true. I would want to see that high viral load does not degrade the effectiveness of the vaccine, and I would want to see that being in a high-risk category does not degrade the effectiveness of the vaccine.

3. The most I can find out about the Pfizer study is from this press release.

The first primary objective analysis is based on 170 cases of COVID-19, as specified in the study protocol, of which 162 cases of COVID-19 were observed in the placebo group versus 8 cases in the BNT162b2 group. Efficacy was consistent across age, gender, race and ethnicity demographics. The observed efficacy in adults over 65 years of age was over 94%.

There were 10 severe cases of COVID-19 observed in the trial, with nine of the cases occurring in the placebo group and one in the BNT162b2 vaccinated group.

There are a number of comparisons you could make between the placebo group and the treatment group.

–number of people who tested positive for the virus
–number of people who tested positive and showed symptoms (this appears to be what they used)
–number of “severe cases” (this was reported in the press release)
–number of hospitalizations (is this the same as “severe cases”?)
–number of deaths

Suppose that nobody in either group died from the virus. A headline that says “Vaccine prevents zero deaths” would not be very inspiring, would it?

When a study can look at many possible outcome measures and chooses to report only those that favor the drug, this is known as p-hacking. I don’t know that Pfizer was p-hacking, but I don’t know that they weren’t.

UPDATE: the protocol specified the outcome measures in advance, so no p-hacking.

The context here is one in which people who spread the virus differ greatly in their ability to spread it (at least, that seems like a good guess), and people who come in contact with the virus differ greatly in the extent to which they get sick. In that context, a ratio of 162/22000 compared to 8/22000 is promising but not definitive. I would be much more impressed if it were 1620/22000 and 80/22000. With the smaller numbers, I can think of a dozen ways to get those results without the vaccine actually being effective.

UPDATE: Now that I have seen the protocol, I can go back to the first two points. For the purpose of trying to eradicate the virus with universal vaccination, you don’t need much efficacy. But I think you want to be really, really, confident that there is some efficacy, because otherwise you will have blown it with the public if your vaccine campaign does not eradicate the virus. If I were the public official in charge of making the decision, I would want to see a larger number of cases in the sample before I would make this kind of a bet.

For the purpose of protecting vulnerable populations, you need to conduct a different protocol, in my opinion. Again, you want to know how the vaccine does as viral load goes up and as vulnerability of the individual goes up. I think that would argue for a different study protocol altogether.

Testing a vaccine

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A follow-up/clarification to my earlier post:

I believe in what I call the Avalon-Hill model of how the virus affects people. That is, it depends on a combination of viral load and patient vulnerability. Accordingly, I would like to see a vaccine tested on various combinations of these factors. That means that the experimenter should control the viral load rather than leave it to chance in the context of selection bias (people who volunteer for the trial may be behaving in ways that reduce their probability of being exposed to high viral load).

In principle, that means assigning a high viral load to some high-risk subjects in both the control group and the placebo group. That could discomfit the experimenter, not to mention the experimental subjects.

But if you don’t do that, what have you learned? If the most severe cases in the real world come from people exposed to high viral loads, and almost no one in your trial was exposed to a high viral load, then you have at best shown that the vaccine is effective under circumstances where it is least needed.

Virus update

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1. I remain a vaccine skeptic. Consider these two recent reports.

First

Out of 170 adult volunteers in the nearly 44,000-subject trial who developed Covid-19 with at least one symptom, 162 received a placebo, while eight got the vaccine, Pfizer and BioNTech said.

Second

Ninety-five people in a 30,000-subject study developed Covid-19 with symptoms; of those, 90 had received a placebo and only five Moderna’s vaccine.

OK. Assume half received the placebo (Does anyone know the actual percent that received the placebo?). So with no vaccine, 162 out of 22,000 got the disease in the Pfizer study. That is less than 0.75 percent. 90 out of 15,000 got the disease in the Moderna study. That is 0.3 percent.

Extrapolate that to the entire population. Of 330 million people, if 0.5 percent get the disease, that would be 1.65 million people. If the fatality rate is 0.5 percent, then that would mean just over 8000 deaths, which is about one week’s worth in reality. If the whole country were like the sample that received the placebo, this disease would never have made it into the public consciousness.

Another way to look at it: The Pfizer study followed participants for more than three months, starting in late July. During that time, more than 6 million new U.S. cases were reported, or about 2 percent of the entire population (the percentage would be even higher if you exclude children from the numerator and the denominator). So more than twice as many people got it in the general population as got it in the placebo sample studied by Pfizer.

Still another way to look at it: the number of cases that emerged in the placebo population was less than what can emerge from a single super-spreader event. Apparently, there were zero super-spreader events in either study. So these studies tells us nothing about the ability of the vaccine to work against a super-spreader. I also suspect that they tell us almost nothing about the ability of the vaccine to work for vulnerable populations.

Maybe the vaccines are 90 percent effective, in which case it is easy to recommend them. Maybe they are 0 percent effective, in which case it is easy to dismiss them. But what if in reality they are 50-70 percent effective? That would create a dilemma. From a central planner perspective, you want everyone to take a vaccine even if it is only 50 percent effective, because that would dramatically slow the spread of the virus. But meanwhile, a lot of individuals who got the vaccine will still get sick and die. That would put the agencies in charge in an awkward position, without any credibility left to deal with the next pandemic.

2. While I am being contrarian, let me go after the “keep R below 1” theory. That is the theory that if we can get the reproduction rate below 1 and keep it there, we can eradicate the disease. Ergo, even a mostly-ineffective intervention, such as an inaccurate test, or an unreliable vaccine, or a mostly-useless lockdown, if it brings R below 1, can achieve eradication.

My problem with “keep R below 1” is that it is a representative-agent model. That is, it treats everyone the same, with identical probability of getting or spreading the disease. But in fact people differ greatly in terms of vulnerability and in terms of propensity to spread the disease. Inferences that “scientists” draw from the representative-agent model are generally bogus. I don’t trust anyone who would make policy based on a representative-agent model, and that includes anyone who uses the “keep R below 1” theory.

3. I am still not impressed with “the science.” They (scientists) are still arguing over the extent of asymptomatic transmission. They are still arguing about the effectiveness of masks. Katya Simon, to whom Tyler Cowen provides a link, writes,

Implement indoor mask mandates for public spaces. Outdoor mask mandates are ridiculous. COVID19 does not appear to transmit outdoors. Enjoy our great outdoors!

Heather Heying and Bret Weinstein say the same thing. But Cambridge Massachusetts, which is where a lot of leading-edge biological research takes place, is a paragon of outdoor mask-wearing.

They are still arguing about the effectiveness of lockdowns.

They are still arguing about how long immunity lasts.

They are still arguing about long-term effects.

I believe that they are still arguing about the mechanism by which the virus causes illness.

All of this reinforces my doubts that a vaccine will prove as effective in practice as it has in trials.

Note that many of the issues about which there are arguments could be clarified, if not completely settled, by careful controlled experiments. As I pointed out more than 6 months ago, experiments would be really useful, but the people you would count on to do them are not willing to do so.

Experimental results are signal. Pronouncements that are not based on experiments are noise. Don’t tell me to “listen to the science” when what I am being asked to listen to is noise.

4. So where are we today? As of the other day, the average daily death totals were higher than at any time since early May. (Tyler Cowen shows a chart.) Unless you are more impressed than I am about the vaccine test results, it is appropriate to be a virus pessimist right now.

I think that there is at least a 25 percent chance that we will be as fearful of the virus a year from now as we are now. And if our fears have declined, this may be due mostly to a change in reporting about the virus. Perhaps someone with congestive heart failure who dies with the virus will no longer be counted as a virus death. Perhaps the press will no longer report cases of long-term damage from the virus. Should such a change in reporting take place, a cynic might call it the “Biden effect.”