Category Archives: virus crisis

Bring on the Masks and Gloves!

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Scott Alexander looks into the question of whether a surgical mask can help keep you from getting infected by others. Short answer: very few reliable studies, but the answer seems to be “yes.”

But he says that this is reliable:

As far as I can tell, both sides agree on some points. They agree that surgical masks help prevent sick people from infecting others.

Well, that’s it then! What’s the purpose of social distancing? To keep sick people from infecting others. But it seems like you could accomplish the same thing by having everyone where a mask. That would settle the issue of “asymptomatic spreading.” Nobody could be a spreader. In that case, masks are an alternative to killing the economy.

The government could do this:

1. Have the taxpayers pay manufacturers to produce a zillion surgical masks. Maybe gloves also.
2. Dispense the masks, paid for by taxpayers.
3. Tell people they can mingle in public, but only if they wear masks–otherwise they are subject to fines. Maybe encourage the use of gloves, also.

It seems like a policy that is at least worth trying in some locations, to see if it works.

A story in Time Magazine Asia says,

Nearly everyone on Hong Kong’s streets, trains and buses has been wearing a mask for weeks—since news emerged of mysterious viral pneumonia in Wuhan, China

. . .Yet, in the U.S., wearing a face mask when healthy has become discouraged to the point of becoming socially unacceptable. The U.S. government, in line with World Health Organization recommendations, says only those who are sick, or their caregivers, should wear masks.

Are the official experts at WHO and in the U.S. idiots? Or am I the idiot? I am curious to know.

Two ways I could imagine the conversation going

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The WSJ reports,

The White House is discussing easing social-distancing guidelines as early as next week amid a broader debate over how much economic loss the country can bear to save an unknowable number of lives threatened by the novel coronavirus pandemic.

President Trump has told people that he wants to reopen the economy as soon as possible but his interest runs counter to the advice of public health experts, including in the administration, who have warned that the guidelines remain necessary.

Here are two imaginary conversations, one with a traditional health adviser named Suit, and one with an analytical health adviser named Geek.

—–1—–

President: I want to lift the social-distancing guidelines by the end of next week.

Suit: You can’t do that! The virus will spread! People will die!

President: But we’re killing the economy. I’m going to do it anyway.

Suit: I resign!

—–2—–

President: I want to lift the social-distancing guidelines by the end of next week.

Geek: You can’t do that! We need to first test a random sample in each of several geographical areas to see the distribution of the virus in the population. And we also need to run the experiment, probably a few times to get robust results. And we need to agree on benchmarks for the results that would have to be met in order to lift the guidelines.

President: What’s the point?

Geek: The random sample will tell us the prevalence of asymptomatic infected people in various geographic areas. The experiment will tell us how dangerous those people are, meaning how likely it is that they will infect others while asymptomatic.

If the experiment finds that the doorknob infection rate is less than 5 percent and the in-person infection rate for asymptomatic virus carriers is less than 5 percent, then we can safely lift many of the restrictions. Continue to encourage disinfecting and handwashing, and avoid large gatherings, but open up businesses.

If the experiment finds a result of more than 20 percent for either the doorknob infection rate or the in-person infection rate, then we should not lift the social-distancing guidelines. We would need to first meet benchmarks for treatment capacity. That might require training and equipping more health care workers, or better yet finding a reliable pharmaceutical treatment.

Suppose the experiment finds a result between 5 and 20 percent for either the doorknob infection rate or the in-person infection rate. Then decide differently for each region. If the prevalence of asymptomatic infection in a region is either very low (less than 5 percent) or very high (over 30 percent), you can lift the guidelines in those regions.

If the prevalence is currently low, then lifting the guidelines will cause the virus to spread in that region, but starting from a low baseline. So in several weeks you may have to recommend restoring restrictions in that region.

If the prevalence is high, then the people with the virus will already have encountered most of the people that they might infect (social interactions are not random). The spread rate going forward is likely to be low, as long as people stay stick to normal routines and avoid interactions with strangers or unfamiliar places.

———
Note: The numbers in the conversation are purely illustrative. I have not thought carefully about what the actual numbers ought to be.

Nice try, economists

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1. You asked for cost-benefit analysis? Here is Luigi Zingales.

we are talking about roughly $9 million per lost life, which, multiplied by the 7.2 million extra deaths, yields $65 trillion.

Thus, even the simplest cost-benefit analysis suggests that the US government should be willing to spend up to $65 trillion to avoid extra deaths. Since $65 trillion is 3 times the US GDP, the United States should be willing to stop production for up to 3 years in order to eliminate the extra deaths.

Satisfied? I’m not. Forget the wild assumptions (7.2 million extra deaths). We need to be careful about thinking on the margin.

Consider three scenarios:

a) zero social distancing
b) social distancing decisions made entirely by the private sector
c) social distancing imposed by the government

Scenario (a) is irrelevant, but people tend to forget that. But relative to (a), (b) is going to both reduce deaths and impose economic costs.

The relevant policy choice is between (b) and (c). The economic case for (c) is that when I go to work I calculate the risk to myself, but I do not include the risk I impose on the people who ride mass transit with me. But describing the externality is not enough. We need to guess how much social benefit comes from moving from (b) to (c) and compare it to the cost.

My own view is that, to a first approximation, moving from (b) to (c) imposes no economic cost, because I think that if the death rate soars under (b) the economic cost will be enormous, also. That’s just my intuition, based on what I think is the importance of social capital, which in turn I think depends on people feeling that their fellow citizens care about them. So it’s a very soft concept, not something I can put a hard number to.

What I would like to better estimate is how many deaths (c) saves relative to (b). But you know how frustrated I am with the inability to do reliable analytics on something like this. Meanwhile, I am willing to assume that the marginal life savings are high.

2. Greg Mankiw writes,

Under this plan, a person whose earnings fall to zero this year keeps all of the social insurance payments and does not pay the surtax. A person whose earnings fall by half keeps half of the payments and returns half. A person whose earnings remain the same (or increase) returns everything: They will have just gotten a short-term loan.

The specifics are at the link. This is clever. Maybe too clever. Any scheme like this invites gaming. For example, suppose my earnings go up, so I should not get to keep any of the money. In that case, I just sell some stock, take the capital losses, and as far as the IRS is concerned my income did not increase. It is always somewhat advantageous to me to take losses, but this would produce a proportionately larger payoff from doing that.

Still, Mankiw’s idea is far better than what I’m guessing what Congress will come up with.

Honestly, I don’t know what everyone is worried about with all this stimulus stuff. In 1945, we had just been through four years of war, and the economy bounced back way more quickly than the Keynesians thought possible (please click on the link). I’d give it a chance to bounce back on its own after a war against a virus that has fewer casualties and shorter duration.

Suits vs. geeks in the virus crisis

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1. Allison Schrager writes,

Among the unknowns about the virus: the true hospitalization and death rates; how infectious it is; how many asymptomatic patients are walking around; how it affects young people; how risk factors vary among different countries with different populations, pollution levels and urban densities. It seems certain the virus will overwhelm hospitals in some places, as it has in China and Italy. We also don’t know how long these extreme economic and social disruptions will last. Without reliable information, predictions are based on incomplete data and heroic assumptions.

…The way forward is testing as many people as possible—not only people with symptoms. Some carriers are asymptomatic. California is starting to test asymptomatic young people to learn more about transmission and infection rates. Testing everyone may not be feasible, but regularly testing a random sample of the population would be informative.

This is the analytical mindset, which is sorely needed. What I called the “suits vs. geeks divide” in 2008 is haunting us again. Ten days ago, the challenge was to get the suits to understand exponential growth. Hence, they were two weeks behind. Now, the challenge is to get the suits to make decisions based on rational calculations as opposed to fears or whoever shouts the loudest in their ears.

But much needs to change. Think about the “analytics revolution” in baseball. In the 1980s, the revolution started*, with Bill James and others questioning the value of the routinely-calculated statistics. Just as one example, data geeks discovered that a batter’s value was better measured by on-base percentage than batting average, even though the latter was prominently featured in the newspapers and the former was not. Soon, the geeks started longing for statistics that weren’t even being kept, and they started efforts to track and record the desired metrics.

(*In 1964, Earnshaw Cook wrote an analytical book, but he drew no followers, probably because personal computers had not yet been invented.)

Based on what we are seeing now, I think that epidemiology is ripe for an analytics revolution. To me as an outsider, the field relies too much on simulations using hypothetical parameters and not enough on identifying the data that would be useful in real time and making sure that the such data gets collected.

2. James Stock writes,

A key coronavirus unknown is the asymptomatic rate, the fraction of those infected who have either no symptoms or symptoms mild enough to be confused with a common cold and not reported. A high asymptomatic rate is decidedly good news: it would mean that the death rate is lower, that the hospital system is less likely to be overrun, and that we are closer to achieving herd immunity. From an economic point of view, a high asymptomatic rate means it is safe to relax restrictions relatively soon, and that hospitalizations can be kept within limits as economic activity resumes.

Conversely, a low asymptomatic rate would require trading off losing many lives against punishing
economic losses.

Neither the asymptomatic rate nor the prevalence of the coronavirus can be estimated if tests are prioritized to the symptomatic or if the included asymptomatic are unrepresentative (think NBA players).

Instead, we need widespread randomized testing of the population.

It may seem counterintuitive that we should be rooting for a high number of people running around with the virus without symptoms. But that would mean, among other things, that their presence is not creating huge risks for the rest of the population. You want the ratio of mild cases to emergency-room cases to be high.

3. Larry Brilliant says,

We should be doing a stochastic process random probability sample of the country to find out where the hell the virus really is.

Note that he has a lot of anger against President Trump. I won’t push back at Mr. Brilliant (I’m not being sarcastic, that is his name), but I think his rhetoric is stronger than his case. See my post on anger.

4. Dan Yamin says,

But there is one country we can learn from: South Korea. South Korea has been coping with corona for a long time, more than most Western countries, and they lead in the number of tests per capita. Therefore, the official mortality rate there is 0.9 percent. But even in South Korea, not all the infected were tested – most have very mild symptoms.

The actual number of people who are sick with the virus in South Korea is at least double what’s being reported, so the chance of dying is at least twice as low, standing at about 0.45 percent – very far from the World Health Organization’s [global mortality] figure of 3.4 percent.

He is at least taking care not to take statistics at face value. But don’t be satisfied with trying to guess based on data that don’t measure what you want. Try to get the authorities to provide you with the numbers you need.

Computer models and the ADOO loop

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When you ask a question of a computer model, it provides out to several decimal places answers that can be off by several orders of magnitude. Give me a clear, logical back-of-the-envelope calculation grounded in real-world data over a model simulation any day.

Several people have sent me links to papers that use computer models to purport to simulate the economic consequences of alternative strategies for dealing with the virus. I don’t bother reading them. When I see that Jeffrey Shaman’s pronouncements about the rate of asymptomatic spreading are based on a simulation model, I assign them low confidence.

Once you build a model that is so complex that it can only be solved by a computer, you lose control over the way that errors in the data can propagate through the model. For me, it is important to look at data from a perspective of “How much can I trust this? What could make it misleadingly high? What could make it misleadingly low?” before you incorporate that data into a complex model with a lot of parameters.

I read that in the U.S. we have done 250,000 tests for the virus, and yet we have only 35,000 positive cases. But before we jump to any conclusions based on this, we ought to get an idea of how many of these tests are re-tests. If the average person who is tested is tested three times, then almost half of the people being tested are positive. I have no idea what the average number of tests per person actually is–it probably isn’t as high as three, but it isn’t as low as one, either.

A lot of people are quoting lines from Gene Krantz in the movie Apollo 13. One of my favorites is when he warns against “guessin’.” Computer models are just “guessin'” in my view. Making decisions based on models is approximately as bad as making them based on blind panic.

I am constantly calling for taking a random sample of the population, say 5000 people, and testing them on a repeated bases. I am quite willing to take some testing resources away from being used for people walking in with symptoms. If people have symptoms and we don’t have resources to test them, then isolate them as if they were infected, in a non-hospital setting. You can base the decision about when to hospitalize the person on how their symptoms progress.

We don’t yet have a proven drug treatment, so you don’t really help an infected person by testing them. Testing helps reassure the uninfected people that they don’t need to be totally isolated. That is a benefit, but not enough to justify putting all our resources into people with symptoms, leaving no resources for random testing.

The OODA loop says, “Observe, Orient, Decide, Act.” Right now, our public policy seems like we’re in an ADOO loop–“act, decide, orient, observe.” I find it frustrating.

Nonbank liquidity now, solvency later

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Tyler Cowen posts an email he received that illustrates what I think most people get wrong about the appropriate policy for the crisis. Without getting into the specifics of he letter, I want to emphasize where I believe people are going wrong.

1. Worrying about bank liquidity before we worry about nonbank liquidity is exactly backward. This is not the financial crisis of 2008, where the big need for liquidity was on Wall Street. The big need now is on Main Street.

2. Worrying about solvency of the non-bank sector now, rather than worrying about liquidity, is exactly backward.

Government grants to keep individuals and businesses solvent are needed after we are ready to re-start the economy, not before. As Tyler and others have pointed out, our short-term goal is to reduce economic activity in order to slow the spread of the virus. That in turn creates a liquidity crisis for individuals who miss paychecks and businesses that miss revenue. As you know, I think we can deal with the liquidity crisis through government-backed credit lines. We should focus on the liquidity crisis now.

A solvency crisis may emerge later, particularly if the public health crisis lasts a long time, leading to an extended period of shutdowns. But the solvency crisis is not what the next few weeks are all about. Yes, a few businesses are on such thin margins that they may be insolvent now, but most can survive a few weeks provided that they have liquidity. Any business that has already become insolvent is probably not a business you can save in the long term.

3. If we are lucky, we won’t even have a solvency crisis. Suppose that in three weeks it looks like we can re-start the economy. This could happen if treatment capacity ramps up, which probably requires that we obtain success from one of the drugs we are trying. A more probable favorable scenario would be a dramatic slowdown of the virus, caused by a combination of the measures currently being taken and warmer weather. That might give us a few months’ respite, and perhaps in the meantime we can scale up hospital capacity and improve other public health policies, so that we won’t need to use shutdowns as a tool ever again.

I know this is an optimistic scenario rather than the most likely scenario. But there is no reason to rush into throwing money blindly at solving a solvency crisis that may not occur. If we wait a few weeks, we will have a better idea of the nature and scope of whatever solvency crisis lies ahead.

4. Let me add that Wall Street will clamor for Congress to do something. But a big stimulus package will make investors a little happier for a few hours, and then they will go back to watching the dashboards showing case spread rates and death rates. Congress will be told that they have to choose between plunging share prices and taking bold action today. They will take bold action today. After a few hours, we will be back to plunging share prices. Then what?

Deadlocked on the Stimulus! (Don’t get your hopes up)

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I remember how thrilled I was in 2008 when Congress voted down TARP. But then the stock market tanked, and Warren Buffett said that Congress had to act, and next thing you know. . .well, I won’t revisit all my bitter criticisms of that bailout.

I just read that Congress deadlocked on a stimulus. In any rational world, they would not be voting so quickly to spend money. The economy is going to sleep for a while no matter what. We have time to think about what the economy really needs before Congress enacts another whopping spending bill.

Keep in mind my idea to give everyone a short-term line of credit. I think that could inject support quickly where it would help most in the near term, without costing very much.

Unfortunately, I don’t think that deadlock will last. We will probably see something like a re-run of 2008, with the stock market threatening to plummet and all sorts of economists and financial dignitaries standing on the sidelines yelling “Spend! Spend! Spend! There is no time left to lose!”

Feeling helpless, I dissent. I readily concede that there will be needs that justify spending, and perhaps a lot of it. But I have the quaint idea that we should gain an understanding of the needs first, then vote for the spending.

If the public was not on board with TARP and the Obama Stimulus, I don’t expect that the support for this new spending spree is going to be any better. There is a non-zero probability that this revives either the Tea Party, Occupy Wall Street, or both. I have no desire to side with the populists, but I sure will not be defending what I see as a reckless, irresponsible spending bill.

Enjoy deadlock while it lasts. By the time you read this, it will probably be over.

Calibrating anger

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I am trying not to let my emotions influence my analysis of the virus crisis. I am trying to stick close to what I know, which is economics and basic probability theory. As I look back on my posts, on this topic, I see quite a bit to be proud of and very little that I would want to walk back. With that as background, below are a few thoughts about anger. Continue reading →

We need outside-in liquidity

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I am going to elaborate on the idea I first proposed as nationwide overdraft protection. Here I will offer more arguments in favor of the proposal and add some more details. I think I will take a suggestion from a commenter and call it a credit line instead of overdraft protection. The credit line is something that is clearly limited.

1. We face a liquidity crisis, but it is in some ways the opposite of the 2008 financial crisis. 2008 was what I would call an “inside” financial crisis–the center of the crisis was the financial sector. The 2020 liquidity crisis is an “outside” financial crisis. The center of this crisis is the nonfinancial sector, including small business and individuals who are missing out on paychecks.

2. So if we focus on banks now, we are not putting relief where it is most directly needed.

3. Even forbearance, which is an idea that I have endorsed, is an inside-out approach to trying to solve the problem. But we need outside-in liquidity. We should guarantee liquidity for the nonfinancial sector, and trust that this will take care of the banks, rather than try to do it the other way around. The other way around would consist of backstopping banks and hoping that they relieve the nonfinancial sector. That would at best be fighting the last war.

4. Implement the idea for every bank account in the country. For each account, add up all the money deposited into that account in January and February of this year. We are not referring to the bank balance at any one time. Instead, we are measuring the revenue stream going into the account as deposits. For an ordinary worker, it might be $10,000, consisting of four paychecks for $2500 each. For a small business, it might be $200,000, consisting of receipts deposited. Have the government guarantee a credit line for each account’s revenue stream.

5. People and businesses that need to draw on the credit line can do so. Make the incentive to repay the loan very strong. For example, an individual would lose rights to some future government benefits if the individual does not repay the loan.

6. There would be a self-triage with using the credit line. People and businesses with no need for it will not use it. People and businesses that cannot hope to repay a loan if they use it will not draw on it, either. They will have to raise money from other sources, which might be charity or investors or outright grants from the government. The users of the credit line will be temporarily strapped individuals and businesses who expect to get back on their feet once things return to normal.

7. Use of the credit line will limit the adverse feedback effects of a few weeks of reduced economic activity. In theory, you might add to the credit line if economic activity is going to be curtailed for a longer period. But honestly, I don’t think anyone has a good policy for the scenario in which we keep the economy in a coma for months. So focus on this as an approach for dealing with a short period of sharply curtailed economic activity.

8. One advantage of this approach is that the government won’t have to write regulations for forbearance for all types of contracts. The government doesn’t have to impose mortgage forgiveness, utility bill forgiveness, car payment forgiveness, and so on. People can use their credit lines to pay their bills.

9. Another advantage is that the cost to the future taxpayers will be low. Taxpayers only have to make good on defaulted loans. The government is not obligated to bail out every single person or every single business.

10. Another advantage is that there will be less incentive for individual sectors to lobby for more. In this case, “more” would be a larger credit line. Since you have to pay back what you borrow using the credit line, it is not like you want to spend a fortune on K street lawyers trying to get it, the way you would if there were government grants up for grabs.

I am going to keep pushing this idea, because I think that we have an outside-in liquidity problem, and so far this seems like the best solution for it.

Addressing the issue of asymptomatic spreading

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From the WSJ.

“Certainly there is some degree of asymptomatic transmissibility,” Anthony Fauci, the director of the National Institute of Allergy and Infectious Diseases, said at a news conference Friday. “It’s still not quite clear exactly what that is. But when people focus on that, I think they take their eye off the real ball, which is the things you do will mitigate against getting infected, no matter whether you are near someone who is asymptomatic or not.”

I think Dr. Fauci has missed the point. It’s one thing for me as an individual to treat everyone around me as if they could be a spreader, and act accordingly. I don’t shut down the economy by washing my hands a lot and staying 6 feet away from people.

But when public officials treat everyone as a spreader and order people to shelter in place, that does shut down the economy. So I think it is important to make an informed decision about whether treating everyone as if they could be spreaders is wise. That is, it would help to be able to know the results of the experiment, or to be able to anticipate the results.

The article goes on to say,

Researchers have posted to the open-access site MedRxiv their own recent studies that used data from the outbreak that suggest people can be infectious sometimes days before they show symptoms of Covid-19. Some reports suggest some carriers never experience any.

But being asymptomatic only makes you dangerous if you can be a spreader. The story gives numbers from one research paper.

. . .early in China’s outbreak, 86% of infections went undetected. The paper also noted that because they were so numerous, stealth infections were the source for roughly 80% of known ones.

This isn’t quite the answer we need, though.

Let C be the event “come in contact with someone with the virus who is asymptomatic.”

Let I be the event “become knowingly infected with the virus.”

What the quoted paragraph gives is the claim that P(C|I)= 80/100. That says that of every 100 people knowingly infected, 80 got the infection from coming in contact with an asymptomatic carrier. What I want to know is P(I|C). Out of 100 people who come in contact with an asymptomatic carrier, how many will become knowingly infected? P(I|C) = P(C|I)*P(I)/P(C).

At first, I thought that there cannot be more asymptomatic carriers than there are people infected, so P(I) has to be greater than than P(C). So if the report is correct, out of every 100 people who come into contact with an asymptomatic carrier, more than 80 will become infected. That would seem to justify a lockdown policy.

But remember the important modifier knowingly infected. If not everyone is tested, then certainly there can be more asymptomatic carriers than there are people knowingly infected. If there are 10 times more, then out of 100 people who come in contact with an asymptomatic carrier, only 8 will themselves become infected, and that might not be enough to justify crippling the economy by telling everyone to shelter in place.

So I still think we need harder data. And yet once again, I make a plea for random testing. Since we know P(I), if we also knew P(C), we could make an intelligent estimate of the key probability, P(I|C). That in turn would help inform public policy decisions that are of huge import.