Tag Archives: Pandemic

My Old Man Rant

As a 62 year old man, I think I’m entitled to rant once in a while. But instead of “you kids get off my lawn!”, this is my rant:

In principle, economics can help advise most any decisions, like when to wake up, or whether to own a second car. But there are fixed costs to doing explicit econ analysis, and also persuasion costs when you try to influence the decisions of some audience. Thus econ analysis seems most valuable for the biggest decisions whose the audience respects economists for those decisions. Or perhaps many similar but smaller decisions which can all be analyzed at once in the same framework. As we economists are most known for our work evaluating institutions, and as our institutional choices are some of the biggest ones we have, this all suggests our biggest wins come there.

I was first exposed to economics and libertarianism at the same time, and what most excited me about both were similarities to science fiction: they let me imagine very different social worlds. One could see how we could have very different institutions from our current versions, ones that would also plausibly be better. Yes, one couldn’t be very sure that those worlds would be better. But they gave us new things to try, to test and see if they might be better.

When I was young, theory was king, and I tried to master theory. But since then data has come to be king (and queen), even in econ and libertarian circles. Yet I hadn’t realized just how far that trend had gone until this pandemic. To me the obvious theory question a pandemic raises is: what are good general institutions for dealing with pandemics? I wrote a bit on that early on, but was told then that we instead needed immediate help in a crisis. Which I also tried to offer, but which many hated.

Yet it is now two years into what is looking more and more like an eternal pandemic, and I still haven’t see economists or libertarians talking about better pandemic institutions. While this pandemic has done great damage to libertarian sympathies, I’ve only seen libertarians argue that in this particular pandemic, doing nothing officially would have been better than doing what we did. And I’ve seen economists argue about particular parameter settings of the usual government-run system: rules, subsidies and direct government management of masks, lockdowns, tests, and vaccines. Mostly via data, not theory, analysis.

But I’ve not seen work on if there are better institutional alternatives to these two categories, if not for this pandemic then for future ones. Which to me feels like a deep betrayal of what I most value in econ: our ability to imagine, test, and argue for big institutional changes. Even my immediate (and beloved) colleagues haven’t been interested.

To me, the obvious other category is: law. We are better off having law to deal with many harms we can each do to each other, such as assault, slander, and reneging on contracts. Better than ignoring them, and better than having government agencies more directly manage such behaviors. Yes, our society runs law centrally, and likely law would be better if offered privately. But even so, for many harms we are better off because we now apply law over the other two main solutions of doing nothing officially or direct government management.

For law to work for assault, slander, theft, or car accidents, we need it to be often feasible to bring sufficient evidence to convince a court that a particular person harmed a particular other person to a particular degree at a particular event. If so, we can then sufficiently discourage such harms merely via the threat of such legal penalties. At least if we can sufficiently punish those we find guilty, and if we make it easy enough for complainants to subpoena the evidence they need to make their case.

Law today often ensures sufficient punishment via jail and criminal law, which works even if not as well as would vouchers. Law usually allows parties to subpoena any info relevant to a live case, and it so happens that evidence needed to prove assaults and car accidents lasts long enough to let them be so subpoenaed. With vouchers and the level of surveillance likely soon, I don’t actually think we’d need most of our traffic laws; the threat of lawsuits would be enough.

The main policy problem with pandemics is that some people hurt other people by infecting them. Just like they do in assault, slander, theft, and auto accidents. So law could deal fine with pandemics if we could meet the same two conditions: (1) sufficiently able to punish those who found guilty, e.g. via jail or vouchers, and (2) often enough able to easily-enough subpoena sufficient info to show who did what to whom. It is on that last point that economists, and lawyers, have traditionally thrown up their hands and concluded that law can’t deal with pandemics.

That is, people have just assumed that it is not possible to tell who infected who in a pandemic. At least not often enough for law to be our main way to deal with severe pandemics. So for something like the flu we subsidize vaccines and little else, while for covid we go crazy with government managing many related details.

But today with smartphone tracking we can actually see who was close enough to whom when to have infected them. And if we have spit samples from two people infected with covid, we can compare the DNA in their viruses to see if they match. By combining these two pieces of information, one could make a sufficiently strong case that a particular person infected another particular person with the virus at a particular time and place.

So the question that remains is: should we actually induce sufficient information collection and subpoena power, and sufficient punishment ability, to let law deal with pandemics? That is, on the one hand we might make infecting others a punishable crime, require everyone to have their phone track their locations, to report their infections, and to save regular spit samples. And then let government police pour over these details. Which does sound like a pretty intrusive police state, though perhaps still better than the actual police state we’ve had during this last pandemic.

Or, only during an officially declared severe pandemic we could tell everyone that they must either strictly isolate, or, they can get a “pandemic passport” by agreeing to get a voucher, have their phone track their locations, and regularly save spit samples, all available only to be subpoenaed in case of lawsuits by people who claim to be harmed, but not for general browsing by a police state.

Yes, once a pandemic becomes nearly endemic, frequent infection events could clog up courts. But at such scale vouchers would streamline their processes and settle almost all cases out of court. I also know of ways to greatly cut court costs. And damages awarded might greatly fall once one could credibly argue that the victim would likely have caught it soon from someone else.

This idea of legally requiring people to save info so that it can be available to be subpoenaed for future lawsuits is not a particularly new idea. It is just the application to the case of pandemics that would be new. But in our new world of greatly increased surveillance and info of various sorts, we should in fact be thinking about how all that new info might help us solve problems. Like pandemics. Via new institutional changes

Come on, don’t any economists or libertarians out there want to think about new pandemic institutions?

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My 11 Bets at 10-1 Odds On 10M Covid deaths by 2022

In February 2020, I made many bets on Covid19, including 11 bets at ten to one odds on if it would cause 10 million deaths worldwide by 2022, as estimated by WHO.

WHO has a Q&A page on Covid excess deaths that includes this section:

Why is excess mortality the preferred measure? … aggregate COVID-19 case and death numbers … being reported to WHO … under-estimate the number of lives lost due to the pandemic … In light of the challenges posed by using reported data on COVID-19 cases and deaths, excess mortality is considered a more objective and comparable measure that accounts for both the direct and indirect impacts of the pandemic.

This WHO page, updated daily, lists reported deaths. This WHO page estimated “The true death toll of COVID-19”, or world covid excess deaths, as of Dec. 31, 2020. I expect them to post a page like it soon with death estimates as of Dec. 31, 2021. But I doubt those estimates will differ much from The Economist, which as of Dec. 30, 2021 said:

The pandemic’s true death toll; Our daily estimate of excess deaths around the world … Although the official number of deaths caused by covid-19 is now 5.4m, our single best estimate is that the actual toll is 18.6m people. We find that there is a 95% chance that the true value lies between 11.6m and 21.6m additional deaths.


For many bets we agreed that if there were two number estimates instead of one, we’d go with a geometric mean of them. The geometric mean of 5.4 and 18.6 is 10.02.

Here is the current status of my 11 bets, with a link to the bets and the amount I’m owed. (I’ll update this as things change.)

These claim to win, say I should pay them:

No response since 31Dec:

  • A Twitter msg bet that I’m keeping private for now, $5000

Paid to me:

Some say that it is rude of me to brag about winning. But I need to make this bet situation public in order to pressure bettors to make good on their promises.

Some say it is immoral to bet on death. But I didn’t cause these deaths, and my public bets helped convince many to take this problem more seriously, for which they’ve thanked me.

Added 12Jan: Many are talking as if the issue is direct vs. indirect deaths, but I’d be very surprised if more than a third of excess deaths are indirect. Most of them were caused directly by covid, but just not caught by official testing and diagnosis systems.

Added 18Jan: Nature article:

Demographers, data scientists and public-health experts are striving to narrow the uncertainties for a global estimate of pandemic deaths. … Among these models, the World Health Organization (WHO) is still working on its first global estimate, but the Institute for Health Metrics and Evaluation in Seattle, Washington, offers daily updates of its own modelled results, as well as projections of how quickly the global toll might rise. And one of the highest-profile attempts to model a global estimate has come from the news media. The Economist magazine in London has used a machine-learning approach to produce an estimate of 12 million to 22 million excess deaths.

That IHME 95% confidence interval is 9 to 18 million deaths.

Added 26Jan: This Sept. 2021 PLOS paper says

[In] the United States … in 2020 … there were 375,235 excess deaths, with 83% attributable to direct, and 17% attributable to indirect effects of COVID-19.

Added 9May: WHO finally speaks on 2021 excess deaths:

 

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Protecting Hypocritical Idealism

I’m told that soldiers act a lot more confident and brave when they are far from battle, relative to when it looms immediate in front of them.

When presented with descriptions of how most citizens of Nazi Germany didn’t resist or oppose the regime much, most people claim they would have done different. Which of course is pretty unlikely for most of them. But there’s an obvious explanation of this “social desirability bias”. Their subconscious expects a larger positive payoff from presenting an admirable view of themselves to associates, relative to the smaller negative payoff from making themselves more likely to actually do what they said, should they actually find themselves in a Nazi regime.

When the covid pandemic first appeared, elites and experts voiced their long-standing position that masks and travel restrictions were not effective in a pandemic. Which let them express their pro-inclusive global-citizen liberal attitudes. Their subconscious foresaw only a small chance that they’d actually face a real and big pandemic. And if that ever happened, they could and did lower the cost of this previous attitude by just suddenly and without explanation changing their minds.

For many decades it has been an article of faith among a large fraction of these same sort of experts and elites that advanced aliens must be peaceful egalitarian eco-friendly non-expansionist powers, who would if they saw us scold and lecture us about our wars, nukes, capitalism, expansion, and eco-damage. Like our descendants are presented to be in Star Trek or the Culture novels.

Because in this scenario aliens would be the highest status creatures around, and it is important to these humans that the highest in status agree with their politics. I confidently predict that their attitudes would quickly change if they were actually confronted with unknown but very real alien powers nearby.

This predictable hypocrisy could be exposed if people would back these beliefs with bets. But of course they don’t. They aren’t exactly sure why, but most just feel “uncomfortable” with that. Visible and open betting market odds that disagreed with them would also expose this hypocrisy, but most such also oppose allowing those, mostly also for vague “uncomfortable” reasons. Their unconscious knows better what are those reasons, but knows also not to tell.

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The Pandemic Monkey Trap

Back in 2007 I said “cut medicine in half”, as its marginal value is too low. (Since then, US spending is up 40%!) But prestigious health economists said yes on average marginal value is low, but don’t I agree that some identified treatments have high value, and as there must be more like that, we should wait and not cut until we can identify which are high vs low. I say cut now, and only add back good things once you can identify them. Those hidden good treatments are the nut in a medical monkey trap, which prevents us from letting go of a larger gourd of wasteful spending.

My colleague Bryan Caplan says we should cut school spending, as its social value is on average low. Many critics say yes value may be low now, but it must be possible to create high value school programs, and so instead of cutting we should work on figuring how to increase school value. Caplan says to cut now, and only add back spending when you can actually identify high value programs.

Regarding pandemic prevention spending, both Caplan and I say that we seem to be spending way too much, and so we should cut back.

Me in October:

I’m comfortable with … an estimate of 3.0 [for the ratio of life-year value over annual income]. But … [that gives] $1.08T and $0.64T for covid death and impairment harms, which gives a prevention to harm cost ratio of 5.31. (Which is close to the 5.2 median estimate from my most recent poll.) And its crazy to think that on average we are getting a 5.31% cut in covid harm for each 1% increase in prevention cost we pay! But that’s what it would take to justify this level of prevention spending.

Bryan Caplan on Tuesday:

total loss comes to about 37 million years of life. That’s about 15 times the reported estimate of the direct cost of COVID. … If normal life had continued unabated since March, how many additional life-years would have been lost? … fifteen times? No way. Upshot: The total cost of all COVID prevention has very likely exceeded the total benefit of all COVID prevention.

Tyler Cowen today:

I don’t agree with Bryan’s numbers, but the more important point is one of logic. The higher the costs of reaction to Covid, the stronger the case for subsidizing vaccines, therapeutics, and other corrective measures. Would you accept this Bryan? You have numerous posts about risk overreaction, but not one (if I recall correctly) calling for such subsidies. …

A second question is whether moral suasion — “don’t overreact to Covid!” — is likely to prove effective. … Sweden didn’t do any better on the gdp front, and the country had pretty typical adverse mobility reactions. … Brazil … have a denialist president, a weak overall response, and a population used to a high degree of risk. … about overreaction. What kinds of reaction are you expecting or viewing as feasible and attainable? If overreacting is indeed a public bad, why think you can talk people down out of it? … they don’t and indeed can’t tell you how most of those [overreaction] costs were to be avoided, given how the public reacts to risk. …

If we instead look to the relevant changes in relative prices, that means subsidies for vaccines and tests, most of all through advance market commitments, but not only. And a full-scale commitment to implementing testing and masks and therapeutics. The more you push home points about overreaction, the more you ought to favor these subsidies. Libertarians out there, do you? This chicken has come home to roost, so please fess up and give the right answer here. Do you favor these subsidies?

Cowen seems to divide pandemic prevention into two categories, the first of which is ineffective but simply cannot be avoided, while the second is highly effective and can in fact be changed, if only people like Bryan would speak up. In this case, the more there is of unavoidable ineffective prevention, the more valuable it is to spend more on effective prevention.

I question Cowen’s arbitrary claim that we intellectuals can only influence spending on very effective kinds of treatment, but not on others. We see variations in both kinds of policy across space and time, due both to private and government choices, all of which seem modestly influenceable by intellectuals like Caplan, Cowen, and I. There are people out there arguing to cut ineffective prevention, as well as people arguing to expand effective prevention, and both groups deserve our support. (“Spending” includes all choices that induce opportunity costs.)

But we should also consider the very real possibility that the political and policy worlds aren’t very capable of listening to our advice about which particular policies are more effective than others. They may well mostly just hear us say “more” or “less”, such as seems to happen in medical and education spending debates. In this case, we should consider the value of more or less prevention spending overall, holding constant the relative proportions of different kinds of spending. And in this case the clear answer seems to be: less; we should do less. Let go the nut of effective treatment in the pandemic-money-trap gourd of over-prevention. Don’t you agree Tyler?

Added 8pm: Though Tyler criticizes Caplan and my posts which are directly on the topic of overall covid over-prevention, he refuses to say if we are spending too much overall; he simply rejects the “framing” of this question. Seems a question he’d rather not talk about.

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Optimum Prevention

Assume you use prevention efforts P to reduce a harm H, a harm which depends on those efforts via some function H(P). If you measure these in the same units, then at a prevention optimum you should minimize P+H(P) with respect to P, giving (for an interior optimum) dH/dP = -1.  And since in general dlnX = dX/X, this implies:

-dlnH/dlnP = P/H.

That is, the elasticity of harm with respect to prevention equals the ratio of losses from prevention to losses from harm. (I previously showed that this applies when H(P) is a power law, but here I’ve shown it more generally.)

Yesterday I estimated that for Covid in the U.S., the ratio P/H seems to be around 5.3. So to be near an optimum of total prevention efforts, we’d need the elasticity -dlnH/dlnP to also be around 5.3. Yet when I’ve done polls asking for estimates of that elasticity, they have been far lower and falling. I got 0.23 on May 26, 0.18 on Aug. 1, and 0.10 on Oct. 22. That most recent estimate is a factor of 50 too small!

So you need to argue that these poll estimates are far too low, or admit that in the aggregate we have spent far too much on prevention. Yes, we might have spent too much in some categories even as we spent too little in others. But overall, we are spending way too much.

Note that if you define P to be a particular small sub-category of prevention efforts, instead of all prevention efforts, then you can put all the other prevention efforts into the H, and then you get a much smaller ratio P/H. And yes, this smaller ratio takes a smaller elasticity to justify. But beware of assuming a high enough elasticity out of mere wishful thinking.

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We Are Over-Preventing Covid

In the Oct. 12 JAMA, David Cutler and Lawrence Summers estimate the total costs that the U.S. will suffer from the covid pandemic. Here is their key table:

So their numbers imply that we will lose $6.95T due to covid harms (deaths and impairments), and $9.17T from efforts to prevent those harms (lost income and mental impairment). Note that Cutler and Summers didn’t divide these costs into prevention versus harm prevented; that’s my division. And note that while they included the main covid harms, they missed some big costs from prevention, such as children getting worse schooling, less socializing, and a general dislike of wearing masks.

If we just look at the costs that Cutler and Summers consider, we get a prevention to harm cost ratio of 1.32, which is optimal if on average 1.0% more prevention effort cuts health harms by about 1.3%. I don’t know if that’s right, but at least it doesn’t seem crazy.

However, those virus harm estimates come from assuming a $7M value for each of these lives lost, and that I say does seem crazy. (They also assume 625K total virus deaths, 2 people impaired by 35% per death, and $7.6T income lost over the next ten years.)

As I’ve discussed before, it makes much more sense to value life-years lost, relative to income. And as we’ve so far seen 9.2 life-years lost on average per covid death (in U.S. through July 11), this $7M value estimate translates to a life-year lost being worth 12.1 years of income. And that’s crazy high.

For such crazy high estimates, we have no one to thank more than Kip Viscusi. He arguably started the literature estimating the value of life, and he’s been a big influence on it ever since, via co-authoring a huge number of papers (a big % of papers he cites in his review articles are by him), and editing a major journal that publishes stuff on the topic (J. of Risk & Uncertainty). And Viscusi is proud to present himself as a crusader for higher life-value estimates:

The watershed event that led to the adoption of the [Value of Statistical Life] was the 1982 conflict between the US Department of Labor and the Office of Management and Budget (OMB) over the proposed hazard communication regulation. The agency had valued the reduced mortality risks by the ‘cost of death’, leading to relatively modest benefit values. In its review of the regulatory proposal, the OMB concluded that the proposed regulation failed the required test that benefits must exceed costs. The Department of Labor appealed the decision to then Vice President George H.W. Bush, and I was asked to resolve the dispute. The only change I made to the OMB’s critique is that I introduced my VSL estimates into the agency’s analysis. Use of my VSL figure of US$7.4 million (in 2015 dollars) increased the estimated benefits of the regulation by a factor of 10. (more)

As I’ve noted before, since a lot of government regulation is justified as protecting citizen lives, those who seek to justify more regulation tend to seek higher value of life estimates. Just a year before, in 1981, Viscusi had just published an estimate that each U.S life was worth $17.9M in 1976$, implying a life-year lost was worth 20.3 years of income! That’s much higher than most of the estimates he’s published since, and his estimates tend to be higher than those of other authors. Also, Viscusi has long been skeptical of the idea of adjusting the value of life for the number of life years left – he says the value of life doesn’t depend on age.

A few months ago I went through five other papers, mostly lit reviews, on the value of life, and I translated the favored median estimate in each paper into a median life-year-to-income ratio:

  • A 2002 review of 33 job papers found 1.76.
  • A 2003 review of 30 road safety papers found 2.92.
  • A 2011 review of 850 stated preference estimates found 2.98.
  • A 2003 study of 76 US federal regulations (the ones Viscusi is proud to have influenced) found 3.34.
  • A 2003 Viscusi review found 4.53 re 30 US labor market papers, 5.94 re 22 non-US labor market papers, and 1.88 re 11 U.S. housing and product market papers.

Setting aside the estimates Viscusi influenced, I’m comfortable with accepting an estimate of 3.0. But if we apply that to the covid estimates Culter and Summers used, we’d get only $1.08T and $0.64T for covid death and impairment harms, which gives a prevention to harm cost ratio of 5.31. (Which is close to the 5.2 median estimate from my most recent poll.)

And its crazy to think that on average we are getting a 5.31% cut in covid harm for each 1% increase in prevention cost we pay! But that’s what it would take to justify this level of prevention spending relative to harms prevented. In fact, in most recent poll, the median estimate is that we’d instead get only a 0.10% cut in covid harm from 1% more prevention. Over a factor of 50 weaker than needed. And that’s why I say we are way over-preventing covid. (A scenario I warned against back in March.)

Note that I’m not blaming all this excess prevention on government; private choices clearly drive much of it. I’m talking about the U.S.; prevention elsewhere may have been more effective. I’m not saying all prevention efforts are equally effective. And I’m not weighing in here on exactly what are the best prevention strategies, nor on what is their maximum possible effectiveness. Sure, the best probably are far more effective than what we are and have been doing. But, alas, doing less than what we have been doing seems to me a far more politically feasible option than proposing to identify and switch to far better strategies. (Like maybe trying variolation or vouching.)

I’ve long said that we spend way too much on medicine, because the marginal value is far below our marginal costs.  (A topic on which Cutler and I sparred before.) Seems that sort of thing continues to hold in a pandemic. And governments, instead of correcting for this problem, mainly just make it worse.

Added 26Oct: Tyler says this sort of marginal analysis is irrelevant due to “non-linearities”. Doesn’t really say more than that. But most econ systems are not linear, yet that doesn’t stop econ elsewhere. He could say because of “non-convexities”, but this requires that either we are at a boundary or that there be big moves from the status quo that beat all small moves. Yet for that to be a relevant critique here, Tyler needs to be able to point to big changes that we should adopt soon, and likely will adopt soon. Absent that, critiques of small changes to the status quo remain quite relevant.

Added 3Jan2022: A new paper estimates $1.3M value of life for 67yo , $0.25M for 87yo, as expressed via housing prices.

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School Vouchers As Pandemic Response

Politico asked me and 17 others:

If you were in charge of your school district or university, how would you design the fall semester?

My answer:

Let 1,000 vouchers bloom. Schools face very difficult choices this fall, between higher risks of infection and worse learning outcomes. We should admit we don’t know how to make these choices well collectively, and empower parents to choose instead. Take the per-student school budget and offer a big fraction of it to parents as a voucher, to pay for home schooling they run themselves, for a neighbor to set up a one-house schoolhouse, for a larger private school, or to use at a qualifying local public school. Each option would set its own learning policies and also policies on distancing and testing. Let parents weigh family infection risks against learning quality risks, using what they know about available options, and their children’s risks, learning styles and learning priorities.

Yes, schools may suffer a large initial revenue shortfall this way; maybe they could rent out some rooms to new private school ventures. Yes, some children will end up with regretful schooling outcomes, though that seems inevitable no matter what we do. Yes, there should be some limits on teaching quality, but we should be forgiving at first; after all, public schools don’t know how to ensure quality here either. And maybe let any allowed option start a month or two late, if they also end later next summer; after all, we aren’t giving them much time to get organized.

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The High Cost of Masks

In my debate last night with Tomas Pueyo, after I emphasized the high costs of lockdowns he said lockdowns are over. From now on we’ll let people go out, as long as they (M) wear masks and keep 6 feet apart when not at home. Which let me to wonder: just how much cheaper is M than lockdowns?

So I tried some Twitter polls. I asked:

  1. [1.14%] If you by law had to M unless you paid, what % of income would you pay?
  2. [4.6%] If you didn’t have to, but were offered % of income to M, what would it take?
  3. [19.6%] Like (2), except it is 2019 and there is no pandemic.
  4. [8.0%] Like (3), except half of the folks in your area are already M-ing
  5. [5.2%] Like (4), except only masks, no 6ft distancing.
  6. [15.8%] Like (4), except both at home and away.

In brackets before each question is the median answer (using lognormal fits to % responses re theses 4 options: <3%, 3-6%, 6-12%, >12%). Note how widely these estimates vary!

The following factors plausibly influence these responses: (A) personal pain and trouble of wearing masks and keeping apart, (B) endowment effect of preferring to stick with what current law seems to endorse, (C) not wanting to look or act too differently from others nearby, (D) wanting to be and seem pro-social and helpful in slowing the pandemic, and (E) wanting to support your side of current culture wars.

The big variation in median answers suggests that non-A effects are big! And the big variation within each poll also suggests that these costs vary greatly across individuals. We might gain lots from policies that let some pay to avoid M-like policies.

Question (4) seems to me to offer the best estimate of the real social cost of doing M. It has the best chance of avoiding effects C & D, and I don’t see a way to avoid E. Regarding B, I think we do want the endowment effect to go this direction, because in fact M was not the legal default a year ago. Yes we enjoy helping our community in a crisis, but we wouldn’t endorse creating crises just to enjoy such effects. So we shouldn’t include then when calculating how much to avoid or reduce crises.

Now this 8.0% median (27% mean) of income cost of masks and public distancing doesn’t include all costs; businesses and other places must also pay to accommodate your distancing. But it is probably a big fraction of costs. And it is quite a bit lower than the 32% of GDP estimate for recent strong lockdowns.

However, my estimate of the total cost of having 50% of the population infected at 0.5% IFR was 3 weeks of income, or 6% of one year’s income. So if we wear masks for 9 months, that single cost equals the entire cost of failing to contain. So as with lockdowns, we should be wary of spending more to prevent infections than the infections would cause. Yes, we should be willing to overspend for very effective preventions, with high elasticity. But masks aren’t plausibly such a very effective method.

Added 30May: I added new polls, included on the above list as #5,6. Seems more than half the cost is from the masks alone. It also seems that masks at home would give similar benefits, and the above suggests that it costs about the same too. So why isn’t there more of push to require that?

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Time to Mitigate, Not Contain

In a few hours, I debate “Covid-19: Contain or Mitigate?” with Tomas “Hammer & Dance” Pueyo, author of many popular pandemic posts (e.g. 1 2 3 4 5 6 7). Let me try to summarize my position.

We have long needed a Plan B for the scenario where a big fraction of everyone gets exposed to Covid19, and for this plan I’ve explored variolation and other forms of deliberate exposure. To be ready, variolation just needs a small (~100) short (1-2mo.) trial to verify the quite likely (>75%) case that it works (cuts harm by 3-30x), but alas, while funding and volunteers can be found, med ethics panels have consistently disapproved. (Months later, they haven’t even allowed the widely praised human challenge trials for vaccines, some of which would include variolation trials.)

One big reason we aren’t preparing enough for Plan B is that many of us are mentally stuck in a Plan A “monkey trap.” Like a monkey who gets caught because it won’t let go of a tasty nut held in its fist within a gourd, we are so focused on containing this pandemic that we won’t explore options premised on failing to contain.

Containment seeks to keep infections permanently low, via keeping most from being exposed, for at least several years until a strong lasting vaccine is widely applied. Mitigation, in contrast, accepts that most will be exposed, and seeks only to limit the rate or exposure to keep medical systems from being overwhelmed, and to maintain enough critical workers in key roles.

Succeeding at containment is of course a much bigger win, which is why containment is the usual focus early in a pandemic. Catch it fast enough, and hit it hard enough with testing, tracing, and isolation, and the world is saved. But eventually, if you fail at that Plan A, and it grows big enough across across a wide enough area, you may need to admit failure and switch to a Plan B.

And, alas, that’s where we seem to be now with Covid-19. Over the last 7 weeks since the cases peak the official worldwide case count has been rising slowly, while official deaths are down ~25%. In the US, deaths are down ~1/2 since he peak 6 weeks ago. You might think, “yay, declines, we are winning!” But no, these declines are just too slow, as well as too uncertain.

Most of the US decline has been in New York, which has just now reached bottom, with no more room to decline. And even if US could maintain the rate of declining by 1/3 every 6 weeks, and repeat that 5 times over 30 weeks (= 7 months), not at all a sure thing, that would only bring daily US cases from 31K at the peak down to 4.2K. Which isn’t clearly low enough for test and trace to keep it under control without lockdown. And, more important, we just can’t afford to lockdown for that long.

You see, lockdown is very expensive. On average, around the world, lockdowns seems to cost about one third of local average income. Yet I estimate the cost of failing to contain, and letting half the population get infected with Covid-19 (at IFR ~0.5%), to be about 3 weeks of income. (Each death loses ~8 QALY.) So 9 weeks of strong lockdown produces about the same total harm as failing to contain! And where I live, we have almost had 10 weeks of lockdown.

If without lockdown the death rate would double due to an overloaded medical system, then paying for less than 9 weeks of added lockdown to prevent that is a good deal. But at that point, paying more than an additional 9 weeks of strong lockdown to prevent all deaths would not be a good deal. So our willingness to pay for lockdowns to cut deaths should really be quite limited. Sure, if we were at a tipping point where spending just a bit more would make all the difference between success and failure, then sure we should spend that bit more. But that’s just not plausibly where we are now.

Yes, sometimes we pay more to prevent harm than we suffer on average from such harms. For example, we pay more for door locks, and security guards, than we lose on average from theft. But those are very effective ways to prevent harm; paying 10% more there cuts harms by much more than 10%. Yet according to my Twitters polls, most see 10% more spent on lockdown as producing much less than 10% fewer deaths. If so, we should spend much less on lockdowns than we suffer from pandemic deaths.

Now if Pueyo is reading this, I suspect he’s screaming “But we’ve been doing it all wrong! Other possible policies exist that are far more effective, and if we use them containment becomes cost-effective. See Taiwan or South Korea.” And yes, other places have achieved better outcomes via better policies. We might not be able to do as well as them now that we’ve lost so much time, but we might well do much better than currently. Pueyo has sketched out plans, and they even seem to be good sketches.

So if we suddenly made Tomas Pueyo into a policy czar tomorrow, with an unlimited budget and able to run roughshod across most related laws or policies, we’d probably get much better Covid-19 outcomes, perhaps even cost-effective containment. But once such a precedent was set, I’d fear for the effectiveness of future czars. Ambitious politicians and rent-seekers would seek to manufacture crises and pseudo-Pueyo-czar candidates, all to get access to those unlimited budgets and legal powers.

Which is a big part of why we have the political systems we do. All around the world, we have created public health agencies tasked with pandemic policy, and political systems that oversee them. These agencies are staffed with experts trained in various schools of thought, who consult with academic experts of varying degrees of prestige. And all are constrained by local legal precedent, and by public perceptions, distrust, and axes of political conflict. These are the people and systems that have produced the varying policies we have now, all around the world.

Yes, those of us in places which have seen worse outcomes should ask ourselves how strong a critique that fact offers of our existing institutions and political cultures, and what we might do to reform them. But there is no easy and fast answer there; good reforms will have to be carefully considered, tested, and debated. We can hope to eventually improve via reforms, but, and this is the key point, we have no good reason to expect much better pandemic policy in the near future than we have seen in the near past. Even when policy makers have access to well-considered policy analyses by folks like Pueyo.

Now it might be possible to get faster political action if Pueyo and many other elites would coordinate and publicly back one specific standard plan, say the “John Hopkins Plan”, that specifies many details on how to do testing, tracing, isolation, etc. Especially if this plan pretty directly copied a particular successful policy package from, say, Taiwan. If enough people yelled in unison “We must do this or millions will die!”, why then politicians might well cave and make it happen.

But that is just not what is happening. Instead, we have dozens of essays and white papers pushing for dozens of related but different proposals. So there’s no clear political threat for politicians to fear defying. Whatever they do, come re-election time politicians can point to some who pushed for some of what they did. So all these divergent essays have mainly limited short term political effects, though they may do much more to raise the status of their authors.

So if political complexity argues against containment now in many places, why doesn’t that same argument apply equally well to mitigation? After all, mitigation can also be done well or badly, and it must be overseen by the same agencies and politicians that would oversee containment. As there is no escaping the fact that many detailed policy choices must be made, why not push for the best detailed packages of choices that we know?

Imagine that you were driving from A to B, and your first instinct was to take a simple route via two interstate freeways, both in big valleys. Your friend instead suggests that you take a backroad mountain short cut, using eight different road segments, many of them with only one lane, and some very wiggly. (Assume no phone or GPS.) That plan might look like it would take less total time, but you should worry about your competence to follow it. If you are very tired, bad at following road directions, or bad at sticking to wiggly roads, you might prefer to instead take the interstates. Especially if it was your tired 16 year old teen son who will do the driving.

Like the wiggly backroad short cut, containment is a more fragile plan, more sensitive to details; it has to be done more exactly right to work. To contain well, we need the right combination of the right rules about who can work and shop, and with what masks, gloves, and distance, tests going to the right people run by the right testing orgs, the right tracing done the right way by the right orgs with the right supporting apps, and the right rules requiring who gets isolated where upon what indications of possible infection. All run by the right sort of people using the right sort of local orgs and legal authority. And coordinated to right degree with neighboring jurisdictions, to avoid “peeing section of the pool” problems.

Yes, we might relax lockdown badly, but we are relaxing toward a known standard policy: no lockdown. So there are fewer ways to go wrong there. In contrast, there are just more ways to go wrong in trying to lockdown even more strictly. And that’s why it can make sense for the public to say to the government, “you guys haven’t been doing so well at containment, so let’s quit that and relax lockdown faster, shooting only for mitigation.” Yes, that might go badly, but it can’t go quite as badly as the worse possible scenario, where we trash the economy with long painful lockdowns, and yet still fail and most everyone gets exposed.

And that’s my argument for mitigation, relative to containment.

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Pandemic Futarchy Design

Researchers … use … feeds from a global network of students, staff and alumni to construct a “stringency index” that boils down to a single number how strictly governments in 160 countries are locking down their economies and societies to contain the spread of the virus. … plans to include state-by-state measures of stringency in the U.S. … latest version … draws on 17 indicators to determine the stringency of the government response. (More)

Not that I hear anyone eagerly clamoring to try, but let me sketch out how one would use decision markets to set pandemic policy. Just to plant another flag on how widely they could be usefully applied, if only enough folks cared about effective policy.

As you may recall, a decision market is a speculative (i.e., betting) market on a key outcome of a decision, conditional on which discrete decision is made. To apply these to the current pandemic, we need to pick

  1. key ex-post-measurable outcome(s) of interest,
  2. likely-enough key decisions which could substantially influence those outcomes,
  3. participants capable of learning a bit about how decisions related to outcomes,
  4. sponsors who care enough about informing these decisions, and
  5. legal jurisdictions that may allow such markets.

Regarding participants, sponsors, and permission, it makes sense to be opportunistic. Seek any sponsors interested in relevant questions, any participants you can get to trade on them, and any jurisdiction that let you want to do. Alas I have no sponsor leads.

For key decisions, we could consider using bills before legislatures, administrative rulings, or election results. But there are a great many of these, we don’t get much warnings about many, and most have little overall impact. So I’d prefer to aggregate decisions, and summarize policy via three key choice metrics per region:
Lockdown Strictness. As described in the quote above, some have created metrics on lockdown strictness across jurisdictions. Such metrics could be supplemented by cell-phone based data on trips outside the home.
Testing Volume. The number of tests per unit time, perhaps separated into the main test types, and perhaps also into accuracy classes.
Tracing Volume. The number of full-time equivalent tracers working to trace who infected whom. Perhaps supplemented by the % of local folks use apps that report their travels to tracing authorities.

Yes, worse pandemic outcomes will likely cause more lockdown, tests, and tracing. But one could look at outcomes that happen after decisions. Such as how average future outcomes depend on the decisions made this month or quarter.

For key outcomes, the obvious options are deaths and economic growth.

For deaths, we can avoid testing problems by looking at total deaths, or equivalently “excess” deaths relative to prior years. It helps to note the ages of deaths, which can be combined with local mortality tables to estimate life-years lost. Even better, if possible, note the co-morbidities of those who died, to better estimate life-years lost. And even more better, have estimates of the relative quality of those life-years.

For economic growth, just take standard measures of regional income or GDP, and integrate them many years into the future, using an appropriate discount factor. Assuming that the temporary disruption from a pandemic is over within say 10 years, one could end the bets after say ten years, projecting the last few years of regional income out into the indefinite future.

As usual, there will be a tradeoff here re how far to go in accounting for these many complexities. I’d be happy to just see measures of life years lost related to lockdown strictness, perhaps broken into three discrete categories of strictness. But I’d of course be even happier to include economic growth as an outcome, and tests and tracing as decisions. Either aggregate all outcomes into one overall measure (using values of life years), or have different markets estimate different outcomes. For decisions, either separate markets for each type of decision. Or, ideally, combinatorial markets looking at all possible combinations of outcomes, decisions, and regions.

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