Tag Archives: Pandemic

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.

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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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Farrell On Who Pushes To Open

Recently I wrote:

While the public will uniformly push for more opening, elites and experts push in a dozen different directions. If elites would all back the same story and solution, as they did before, they would probably get it. … So elites and experts don’t speak with a unified voice, while the public does. And that’s why the public will win. While the public tends to defer to elites and experts, and even now still defers a lot, this deference is gradually weakening. We are starting to open, and will continue to open, as long as opening is the main well-supported alternative to the closed status quo. … public pushes will will tend to be correlated in a particular direction, in contrast with the elite pushes which are much more diverse.

Also, my poll on “If in your region, pandemic continues to grow, or decline very slowly, for N more months, you will support a weaker lockdown,” got a median answer N = 1.2.

Henry Farrell responded:

People … are in favor of stay at home orders, … It could be that people are lying …but it would be unprecedented for so many of them to be lying. … So why then, may the equilibrium break down? It’s clearly not because of express demand from the public. Nor because of cheating … The plausible answer is that private power asymmetries are playing a crucial role in undermining the equilibrium. Some people – employees with poor bargaining power and no savings – may find themselves effectively coerced into a return to work as normal. … this is certainly a situation where the state and private actors are looking to get into cahoots, but not in the ways that public choice economists have devoted significant analytic energy to. And if you want an illustration of Marxist arguments about the “structural power of capital” to threaten politicians … Where public choice people seem to perceive a “public” that collectively wants to return to work, I see something different – a set of asymmetric power relations that public choice scholars are systematically blind to …

So here’s my bet. If the public choice analysis is right, and this is about some kind of broad and diffuse “public” pushing back against impossible regulations, then we will see a return to the economy sooner rather than later. But we can reasonably presume that this return will be roughly symmetric. … In contrast, if I’m right, we will see a very different return to “normality.” The return to the economy will be sharply asymmetric. Those who are on the wrong end of private power relations – whether they are undocumented immigrants, or just the working poor – will return early and en masse. Those who have the choice and the bargaining power will tend instead to pick safety. …[This] highlights the frequently brutal power relations that public choice scholars shove under the carpet when they talk about the “public” wanting an end to lockdown and a return to past economic relations.

So, Farrell says that it might look like people want to work, but this is only because “power asymmetries” let bad firms force them. If those firms would just be good enough to keep paying full wages, why then workers would happily stay home forever. Thus proving its not ordinary people who push to open, it is really the evil capitalists pushing. As also proven by those polls. Economic theory (what “public choice” scholars use to study politics) just can’t explain why poor people with less savings might be more eager to work; for that you need “private power relations” theory.

Wow. As you can see, I am not making this up.

Look, even if we kept printing plenty of money to hand out to workers to all stay home, eventually there wouldn’t be anything to buy, because no one was making and distributing products and services. And then no one would be happy to stay home. The fact that someone needs to work in order to make our world function isn’t some conspiracy foisted on us by the asymmetrically powerful, it is just a basic fact about our world that would also apply in Marxist heaven, or any other social system.

There are many channels by which such fundamental pressures will be communicated to the political equilibrium, but they must all lead to the same place. We are paying a real price for not working, we will only continue if we see sufficient value in it, and even then for only a limited time. Every non-crazy theory of politics must predict this. And economists, including those who study politics, do in fact predict that the poor will push first and harder, as they run out of resources first. Maybe you blame the very existence of the poor on evil capitalists, but this outcome happens regardless of why there are poor, it only requires that they exist.

Who pushes the least? Rich elites who can work from home selling abstract arguments that blame all problems, even pandemics, on (other) evil rich.

Note that we also have many non-work social needs that push us to open. And employers are similarly willing to stay home if we keep sending them checks.

Added: Farrell quickly responded:

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Why Openers Are Winning

Three main relevant groups have vied lately to influence pandemic policy: public, elites, and experts. Initially, public health experts dominated, even when they screwed up. But then they seemed to publicly assume that it was too late to contain Covid19, and the only viable option was “flattening the curve” to get herd immunity. At that point, elite opinion worldwide objected loudly, and insisted that containment be the official policy.

Experts and the public demurred, and elites got their way. Everywhere in the world, all at once, strong lockdown polices began, and containment became the official goal. But elites did not insist on any particular standard containment policy. Such as, for example, the packages of polices that seem to have worked initially in Wuhan or South Korea. Instead elites seemed satisfied to let the politicians and experts in each jurisdiction craft their own policy packages, as long as they seemed “strong”, involving much public sacrifice. And they allowed official public messages suggesting that relatively short durations would be sufficient.

A few months later, those duration periods are expiring. And in the different jurisdictions, the diverse policies now sit next to quite diverse outcomes. In some places, infections are low or declining, while in others they are flat or increasing. The public is feeling the accumulated pain, and itching to break out. If these flat or increasing trends continue, containment will fail, and lockdown harms will soon exceed plausible future gains from preventing medical system overload.

Elites are now loudly and consistently saying that this is not time to open; we must stay closed and try harder to contain. When confronted with the discouraging recent trends, elites respond with a blizzard of explanations for local failures, and point to a cacophony of prophets with plans and white papers declaring obvious solutions.

But, and this is the key point, they mostly point to different explanations and solutions. For example, this polls shows very little agreement on the key problem:

So while the public will uniformly push for more opening, elites and experts push in a dozen different directions. If elites would all back the same story and solution, as they did before, they would probably get it. If they would say “We agree that this is what we did wrong over the last few months, and this is the specific policy package that will produce much different outcomes over the next few months.” But they aren’t saying this.

So elites and experts don’t speak with a unified voice, while the public does. And that’s why the public will win. While the public tends to defer to elites and experts, and even now still defers a lot, this deference is gradually weakening. We are starting to open, and will continue to open, as long as opening is the main well-supported alternative to the closed status quo, which we can all see isn’t working as fast as expected, and plausibly not fast enough to be a net gain. Hearing elites debate a dozen other alternatives, each supported by different theories and groups, will not be enough to resist that pressure to open.

Winning at politics requires more than just prestige, good ideas, and passion. It also requires compromise, to produce sufficient unity. At this game, elites are now failing, while the public is not.

Added 3p: Many are reading me as claiming that the public is unified in the sense of agreeing on everything. But I only said that the public pushes will will tend to be correlated in a particular direction, in contrast with the elite pushes which are much more diverse. Some also read me as claiming that strong majorities of the public support fast opening, but again that’s not what I said.

Added 6May: Here is data suggesting people are getting out more. Here is data suggesting increasing support for opening.

Added 7May: This poll suggests patience is thin. Lognormal fit says only willing to wait median of 1.2 months (mode 0.08, mean 4.7).

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The Plan A Winner’s Circle Alliance

Plan A is how to contain Covid19 until a vaccine or other strong treatment shows up. Plan B is how to deal with Plan A failing, and a substantial fraction (>20%) of the population getting infected.

While I’ve mostly focused on Plan B analysis, many nations and sub-regions seem to be doing okay so far at Plan A, keeping infections low and now declining in rate. It isn’t at all clear that they can maintain this until a vaccine arrives, but it is clear that they will keep trying for a while. And most other places have been shamed into giving lip service to Plan A. So much so that they may well induce much more damage from such efforts than they’d suffer from quitting and moving on to Plan B.

Soon the places that are doing well under Plan A will consider carefully relaxing their controls, while standing ready to reassert them should infections rise. And one of their key choices will be how much contact to have with other places. They will be much more willing to interact with places that also seem to be doing well at Plan A.

If X opens to Y who opens to Z, that makes X vulnerable to Z. Thus a winner’s circle of places doing well at Plan A will want to coordinate on who opens to who. They may want to share monitoring efforts re who is worthy to join their circle, and standards re how to grade their openings. And how to prevent hidden openings to the contagious centers that are failing at Plan A. And eventually, how if at all to open to places that fail so much as to achieve local “herd immunity”.

I’m not sure what names people will give to these two groups. Competent circle versus incompetent basket case centers? Clean versus unclean? High vs low state capacity? But whatever they are called, the US seems a likely candidate to stay in the bad group, with Europe a close second, and US rivals will eagerly push for a new international alignment that keeps them there.

US and European prestige will take a huge hit if this situation persists long. You might think this would shame us into reorganizing better to succeed, but apparently not. Yet we seem inclined to pay large costs to pursue plan A with our poor organization. So we risk the worst of both: trashing our economy far more than do they, and yet still ending up excluded from the winner’s circle.

Of course if success at Plan A is only temporary, and infections from the contagious centers infect most of the world before vaccines arrive, it will be the Plan B places who achieve herd immunity that will form the growing and relatively successful alliance. In that case, the Plan A alliance will shrink and suffer while they limit their connections to the Plan B group.

Note that places that achieve herd immunity have less need to coordinate on who they open to. And if these really organized into hostile alliances of nations, some may be willing to do the extreme things that nations have long done against competing alliances. The Plan A alliance may accuse the Plan B group of trying to infect them on purpose, and in at least some cases, they may be right.

Added 2May: Here’s an article on a Plan A alliance.

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Constant Elasticity Prevention

While many engaged Analysis #1 in my last post, only one engaged Analysis #2. So let me try again, this time with a graph.

This is about a simple model of prevention, one that assumes a constant elasticity (= power law) between harm and prevention effort. An elasticity of 1 means that 1% more effort cuts harm by 1%. For an elasticity of 2, then 1% more effort cuts harm by 2%, while for an elasticity of 0.5, 1% more effort cuts harm by 0.5%.

Such simple “reduced form” models are common in many fields, including economics. Yes of course the real situation is far more complex than this. Even so, reduced forms are typically decent approximations for at least small variations around a reference policy. As with all models, they are wrong, but can be useful.

Each line in the following graph shows how total loss, i.e., the sum of harm and prevention effort, varies with the fraction of that loss coming from prevention. The different lines are for different elasticities, and the big dots which match the color of their lines show the optimum choice on each line to min total loss. (The lines all intersect at prevention = 1/20, harm = 20.)

As you can see, for min total loss you want to be on a line with higher elasticity, where prevention effort is more effective at cutting harm. And the more effective is prevention effort, then the more effort you want to put in, which will result in a larger fraction of the total harm coming from prevention effort.

So if locks are very effective at preventing theft, you may well pay a lot more for locks on than you ever suffer on average in theft. And in the US today, the elasticity of crime with respect to spending on police is ~0.3, explaining why we suffer ~3x more losses from crime than we spend on police to prevent crime.

Recently, I asked a few polls on using lockdown duration as a way to prevent pandemic deaths. In these polls, I asked directly for estimates of elasticity, and in this poll, I asked for estimates of the ratio of prevention to health harm loss. And here I asked if if the ratio is above one.

In the above graph there is a red dot on the 0.5 elasticity line. In the polls, 56% estimate that our position will be somewhere to the right of the red dot on the graph, while 58% estimate that we will be somewhere above that grey 0.5% elasticity line (with less elasticity). Which means they expect us to do too much lockdown.

Fortunately, the loss at that red dot is “only” 26% higher than at the min of the grey line. So if this pandemic hurts the US by ~$4T, the median poll respondent expects “only” an extra $1T lost due to extra lockdown. Whew.

Added 26May: Follow-up surveys on US find (via lognormal fit) median effort to harm ratio of 3.6, median elasticity of 0.23. For optimum these should be equal – so far more than optimal lockdown!

Added 1Aug: Repeating same questions now gives median effort to harm ratio of 4.0, median elasticity of 0.18. That is, they see the situation as even worse than they saw it before.

Added 22Oct: Repeating the questions now gives median effort to harm ratio of 5.2, median elasticity of 0.10. The estimated deviation between these two key numbers has continued to increase over time.

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