Category: Uncategorized
Emergent Ventures Covid-19 prizes, second cohort
There is another round of prize winners, and I am pleased and honored to announce them:
1. Petr Ludwig.
Petr has been instrumental in building out the #Masks4All movement, and in persuading individuals in the Czech Republic, and in turn the world, to wear masks. That already has saved numerous lives and made possible — whenever the time is right — an eventual reopening of economies. And I am pleased to see this movement is now having an impact in the United States.
Here is Petr on Twitter, here is the viral video he had a hand in creating and promoting, his work has been truly impressive, and I also would like to offer praise and recognition to all of the people who have worked with him.
The covid19india project is a website for tracking the progress of Covid-19 cases through India, and it is the result of a collaboration.
It is based on a large volunteer group that is rapidly aggregating and verifying patient-level data by crowdsourcing.They portray a website for tracking the progress of Covid-19 cases through India and open-sources all the (non-personally identifiable) data for researchers and analysts to consume. The data for the react based website and the cluster graph are a crowdsourced Google Sheet filled in by a large and hardworking Ops team at covid19india. They manually fill in each case, from various news sources, as soon as the case is reported. Top contributor amongst 100 odd other code contributors and the maintainer of the website is Jeremy Philemon, an undergraduate at SUNY Binghamton, majoring in Computer Science. Another interesting contribution is from Somesh Kar, a 15 year old high school student at Delhi Public School RK Puram, New Delhi. For the COVID-19 India tracker he worked on the code for the cluster graph. He is interested in computer science tech entrepreneurship and is a designer and developer in his free time. Somesh was joined in this effort by his brother, Sibesh Kar, a tech entrepreneur in New Delhi and the founder of MayaHQ.
3. Debes Christiansen, the head of department at the National Reference Laboratory for Fish and Animal Diseases in the capital, Tórshavn, Faroe Islands.
Here is the story of Debes Christiansen. Here is one part:
A scientist who adapted his veterinary lab to test for disease among humans rather than salmon is being celebrated for helping the Faroe Islands avoid coronavirus deaths, where a larger proportion of the population has been tested than anywhere in the world.
Debes was prescient in understanding the import of testing, and also in realizing in January that he needed to move quickly.
Please note that I am trying to reach Debes Christiansen — can anyone please help me in this endeavor with an email?
Here is the list of the first cohort of winners, here is the original prize announcement. Most of the prize money still remains open to be won. It is worth noting that the winners so far are taking the money and plowing it back into their ongoing and still very valuable work.
Policy for Covid-19, from Policy.NZ
Chris McIntyre from Wellington emails me:
“Pleased to share that COVID-19 Policy Watch is now live at www.covid19policywatch.org [links fixed]. We currently cover federal policies for 12 countries, including the US, UK, and China, with 15 more underway. We expect to expand our network of publishing partners from three, to five over the coming days. All feedback is most welcome.
A blurb for MR readers to edit as you see fit:
We aim for COVID-19 Policy Watch to be the most accessible source for governments’ policy responses to COVID-19 so that researchers, policymakers, journalists, and the general public can quickly learn about and compare governments’ responses. If you think this is important, we’d love your help: we’re seeking publishing partners (news media, universities, research groups) to keep country policies up to date, and experienced front- and back-end Drupal devs to help build new features. Interested parties should email [email protected].
TC again: I am pleased to announce that Policy.NZ is a new Emergent Ventures winner (not Fast Grants with its biomedical orientation, rather “classic” Emergent Ventures).
An econometrician on the SEIRD epidemiological model for Covid-19
There is a new paper by Ivan Korolev:
This paper studies the SEIRD epidemic model for COVID-19. First, I show that the model is poorly identified from the observed number of deaths and confirmed cases. There are many sets of parameters that are observationally equivalent in the short run but lead to markedly different long run forecasts. Second, I demonstrate using the data from Iceland that auxiliary information from random tests can be used to calibrate the initial parameters of the model and reduce the range of possible forecasts about the future number of deaths. Finally, I show that the basic reproduction number R0 can be identified from the data, conditional on the clinical parameters. I then estimate it for the US and several other countries, allowing for possible underreporting of the number of cases. The resulting estimates of R0 are heterogeneous across countries: they are 2-3 times higher for Western countries than for Asian countries. I demonstrate that if one fails to take underreporting into account and estimates R0 from the cases data, the resulting estimate of R0 will be biased downward and the model will fail to fit the observed data.
Here is the full paper. And here is Ivan’s brief supplemental note on CFR. (By the way, here is a new and related Anthony Atkeson paper on estimating the fatality rate.)
And here is a further paper on the IMHE model, by statisticians from CTDS, Northwestern University and the University of Texas, excerpt from the opener:
- In excess of 70% of US states had actual death rates falling outside the 95% prediction interval for that state, (see Figure 1)
- The ability of the model to make accurate predictions decreases with increasing amount of data. (figure 2)
Again, I am very happy to present counter evidence to these arguments. I readily admit this is outside my area of expertise, but I have read through the paper and it is not much more than a few pages of recording numbers and comparing them to the actual outcomes (you will note the model predicts New York fairly well, and thus the predictions are of a “train wreck” nature).
Let me just repeat the two central findings again:
- In excess of 70% of US states had actual death rates falling outside the 95% prediction interval for that state, (see Figure 1)
- The ability of the model to make accurate predictions decreases with increasing amount of data. (figure 2)
So now really is the time to be asking tough questions about epidemiology, and yes, epidemiologists. I would very gladly publish and “signal boost” the best positive response possible.
And just to be clear (again), I fully support current lockdown efforts (best choice until we have more data and also a better theory), I don’t want Fauci to be fired, and I don’t think economists are necessarily better forecasters. I do feel I am not getting straight answers.
Monday assorted links
1. Why isn’t the public more supportive of free trade?
2. How easy the spread on college campuses?
3. A bunch of medical claims, interesting, neither endorsing nor damning.
7. Are children’s face-to-face social skills declining?
8. “Church of England moves valuables to Tower of London amid fears of lockdown looting.”
9. Robin Hanson poll-based risk indicator.
10. An epidemiological account of how risk-spreader heterogeneity matters.
11. Harvard to sell $1.1 billion bonds. And pending cuts to higher ed.
12. Stephon Marbury, prophet for the NBA (WSJ). And Scott Gottlieb on employer testing (WSJ).
Sunday assorted links
1. Excellent thread, showing an actual understanding of public choice.
2. The mask as fashion statement.
3. Scott Sumner on the lower mortality estimates.
4. AMC movie theatres likely to go bankrupt soon.
6. The Belgium heterogeneity? And the Austrian heterogeneity?
Happy Easter

Credit: Saint Simeon Stylites the elder. Credit: Wellcome Collection. Attribution 4.0 International (CC BY 4.0)
What does this economist think of epidemiologists?
I have had fringe contact with more epidemiology than usual as of late, for obvious reasons, and I do understand this is only one corner of the discipline. I don’t mean this as a complaint dump, because most of economics suffers from similar problems, but here are a few limitations I see in the mainline epidemiological models put before us:
1. They do not sufficiently grasp that long-run elasticities of adjustment are more powerful than short-run elasticites. In the short run you socially distance, but in the long run you learn which methods of social distance protect you the most. Or you move from doing “half home delivery of food” to “full home delivery of food” once you get that extra credit card or learn the best sites. In this regard the epidemiological models end up being too pessimistic, and it seems that “the natural disaster economist complaints about the epidemiologists” (yes there is such a thing) are largely correct on this count. On this question economic models really do better, though not the models of everybody.
2. They do not sufficiently incorporate public choice considerations. An epidemic path, for instance, may be politically infeasible, which leads to adjustments along the way, and very often those adjustments are stupid policy moves from impatient politicians. This is not built into the models I am seeing, nor are such factors built into most economic macro models, even though there is a large independent branch of public choice research. It is hard to integrate. Still, it means that epidemiological models will be too optimistic, rather than too pessimistic as in #1. Epidemiologists might protest that it is not the purpose of their science or models to incorporate politics, but these factors are relevant for prediction, and if you try to wash your hands of them (no pun intended) you will be wrong a lot.
3. The Lucas critique, namely that agents within a model, knowing the model, will change how the model itself operates. Epidemiologists seem super-aware of this, much more than Keynesian macroeconomists are these days, though it seems to be more of a “I told you that you should listen to us” embodiment than trying to find an actual closed-loop solution for the model as a whole. That is really hard, either in macroeconomics or epidemiology. Still, on the predictive front without a good instantiation of the Lucas critique again a lot will go askew, as indeed it does in economics.
The epidemiological models also do not seem to incorporate Sam Peltzman-like risk offset effects. If you tell everyone to wear a mask, great! But people will feel safer as a result, and end up going out more. Some of the initial safety gains are given back through the subsequent behavioral adjustment. Epidemiologists might claim these factors already are incorporated in the variables they are measuring, but they are not constant across all possible methods of safety improvement. Ideally you may wish to make people safer in a not entirely transparent manner, so that they do not respond with greater recklessness. I have not yet seen a Straussian dimension in the models, though you might argue many epidemiologists are “naive Straussian” in their public rhetoric, saying what is good for us rather than telling the whole truth. The Straussian economists are slightly subtler.
4. Selection bias from the failures coming first. The early models were calibrated from Wuhan data, because what else could they do? Then came northern Italy, which was also a mess. It is the messes which are visible first, at least on average. So some of the models may have been too pessimistic at first. These days we have Germany, Australia, and a bunch of southern states that haven’t quite “blown up” as quickly as they should have. If the early models had access to all of that data, presumably they would be more predictive of the entire situation today. But it is no accident that the failures will be more visible early on.
And note that right now some of the very worst countries (Mexico, Brazil, possibly India?) are not far enough along on the data side to yield useful inputs into the models. So currently those models might be picking up too many semi-positive data points and not enough from the “train wrecks,” and thus they are too optimistic.
On this list, I think my #1 comes closest to being an actual criticism, the other points are more like observations about doing science in a messy, imperfect world. In any case, when epidemiological models are brandished, keep these limitations in mind. But the more important point may be for when critics of epidemiological models raise the limitations of those models. Very often the cited criticisms are chosen selectively, to support some particular agenda, when in fact the biases in the epidemiological models could run in either an optimistic or pessimistic direction.
Which is how it should be.
Now, to close, I have a few rude questions that nobody else seems willing to ask, and I genuinely do not know the answers to these:
a. As a class of scientists, how much are epidemiologists paid? Is good or bad news better for their salaries?
b. How smart are they? What are their average GRE scores?
c. Are they hired into thick, liquid academic and institutional markets? And how meritocratic are those markets?
d. What is their overall track record on predictions, whether before or during this crisis?
e. On average, what is the political orientation of epidemiologists? And compared to other academics? Which social welfare function do they use when they make non-trivial recommendations?
f. We know, from economics, that if you are a French economist, being a Frenchman predicts your political views better than does being an economist (there is an old MR post on this somewhere). Is there a comparable phenomenon in epidemiology?
g. How well do they understand how to model uncertainty of forecasts, relative to say what a top econometrician would know?
h. Are there “zombie epidemiologists” in the manner that Paul Krugman charges there are “zombie economists”? If so, what do you have to do to earn that designation? And are the zombies sometimes right, or right on some issues? How meta-rational are those who allege zombie-ism?
i. How many of them have studied Philip Tetlock’s work on forecasting?
Just to be clear, as MR readers will know, I have not been criticizing the mainstream epidemiological recommendations of lockdowns. But still those seem to be questions worth asking.
Saturday assorted links
1. MIE: “This Man Owns The World’s Most Advanced Private Air Force After Buying 46 F/A-18 Hornets.”
2. Romer tweet storm states his plan.
4. Is American innovation speeding up? (WSJ)
6. Non-exemplary lives (ouch). And what do the humanities do in a crisis?
7. Instagram strippers (NYT). And Bret Stephens: our regulatory state is failing us (NYT).
8. “Believe women,” selectively.
9. BloombergQuint on Alex and Shruti.
10. A proposal for releasing British young people (ever listen to early Clash?).
11. Arnold Kling annotates (and likes) my Princeton talk.
12. A Swede explains Sweden to an Israeli: “Some maintain that the Swedish policy can succeed only in Sweden, because of its distinctive characteristics – a country where population density is low, where a high percentage of the citizenry live in one-person households and very few households include people over 70 cohabiting with young people and children. Those are mitigating circumstances which the Swedes hope will work to their advantage.”
What should I ask Adam Tooze?
I will be doing a Conversation with him, no associated public event. He has been tweeting about the risks of a financial crisis during Covid-19, but more generally he is one of the most influential historians, currently being a Professor at Columbia University. His previous books cover German economic history, German statistical history, the financial crisis of 2008, and most generally early to mid-20th century European history. Here is his home page, here is his bio, here is his Wikipedia page.
So what should I ask him?
Friday assorted links
1. Balaji on heterogeneities and data integration.
2. Citizen’s handbook for nuclear attack and natural disasters. Do we need a new version of this?
3. The Amazon: “We show that, starting at around 10,850 cal. yr BP, inhabitants of this region began to create a landscape that ultimately comprised approximately 4,700 artificial forest islands within a treeless, seasonally flooded savannah.”
4. How much distance do you need when exercising? And against crowded spaces.
5. Dan Wang letter from Beijing in New York magazine.
6. Trump pushing to reopen by May 1.
7. Lots of new testing results from Germany, consider these as hypotheses but still a form of evidence.
8. Good and subtle piece on Tiger King (NYT). And betting markets in everything.
9. The Vietnamese response seems pretty good so far.
10. Joe Stiglitz discusses his love of fiction (NYT)
12. Ronald Inglehart on the shift to tribalism.
13. Explaining the Fed lending programs.
14. MIT Press preprint of new Joshua Gans book on Covid-19, open for public comment.
Does working from home work?
Better than you might think. Here is a paper from a few years back, by Nicholas Bloom, James Liang, John Roberts, and Zhichun Jenny Ying:
A rising share of employees now regularly engage in working from home (WFH), but there are concerns this can lead to ‘‘shirking from home.’’ We report the results of a WFH experiment at Ctrip, a 16,000-employee, NASDAQ-listed Chinese travel agency. Call center employees who volunteered to WFH were randomly assigned either to work from home or in the office for nine months. Home working led to a 13% performance increase, of which 9% was from working more minutes per shift (fewer breaks and sick days) and 4% from more calls per minute (attributed to a quieter and more convenient working environment). Home workers also reported improved work satisfaction, and their attrition rate halved, but their promotion rate conditional on performance fell. Due to the success of the experiment, Ctrip rolled out the option to WFH to the whole firm and allowed the experimental employees to reselect between the home and office. Interestingly, over half of them switched, which led to the gains from WFH almost doubling to 22%. This highlights the benefits of learning and selection effects when adopting modern management practices like WFH.
Via Matt Notowidigdo. Of course in that paper, the schools were not all closed…
The economics of supply cut-offs
As a number of people have pointed out, cable TV, cable internet connections, and cable streaming have been remarkably robust throughout this crisis. Why might that be? Let’s think through a few basic points about the economics of supply cut-offs. This will not be a complete model, but it will focus attention on perhaps one possible factor of interest.
Imagine a seller with market power who comes close to perfect price discrimination. That supplier will take great care to avoid supply cut-offs (imagine an electric utility investing in emergency capacity, for instance). If a cut-off were to happen, the profits of the supplier would be much lower. As a first-order approximation, such suppliers will invest a near-optimal amount of resources to prevent such supply interruptions.
Alternatively, imagine a nearly perfectly competitive situation where all of the surplus goes to consumers and producers earn the going rate of return. Fixed costs are not significant. A market collapse or supply cut-off doesn’t cut much into profits, and in essence the suppliers do not care about the losses of the inframarginal consumers, were a supply interruption to occur.
As a simple theorem, if the market is good for the producers in the first place, supply interruptions are less likely. If the market is good for consumers in the first place, supply interruptions are more likely.
Might this also apply to health care systems? The U.S. hospital system, in normal times, spends way too much. Still, it has the “cultural mentality” for making capital expenditures, far more than say Britain’s NHS does. And so the United States has far more ICU units per capita than does Britain. Whether justly or not, the U.S. health care system might come out of this crisis looking not entirely bad.
Fiction and classics to read under lockdown
A number of you asked me for a list of books to read during lockdown, mostly novels and fiction (like Plato, right?). Here is a list I drew up maybe fifteen (?) years ago, with only slight revisions since. I feel a current list might be quite different, but actually the early list is perhaps closer to most of your tastes? Here it is. It starts with classics and then goes through more recent novels maybe up through 2000 or so.
Thursday assorted links
1. “We are at a critical juncture for the market.”
2. Pandemic insurance for Wimbledon cancellation.
3. Borjas on who is undertested, from NYC data.
4. Japanese cook draws every meal he eats.
5. How to close a bag of chips with no clip.
7. How is the Swedish approach working out?
8. Re-entry stickers for the Florida Keys — get the picture?
9. Stapp and Watney, masks for all.
10. Hong Kong quarantine diary.
11. How the Faroe Islands aced it (so far).
12. “Many brands are using keyword blocklists to stop their adverts appearing next to stories about Covid-19, meaning that even though news websites are getting record traffic from readers they are barely earning any money from the clicks.” Link here.
13. The Pandemic Challenge, from Singularity University.
14. Will Covid-19 induce a decline in religiosity?
Where we stand
I thought it useful to sum up my current views in a single paragraph, here goes:
I don’t view “optimal length of shutdown” arguments compelling, rather it is about how much pain the political process can stand. I expect partial reopenings by mid-May, sometimes driven by governors in the healthier states, even if that is sub-optimal for the nation as a whole. Besides you can’t have all the banks insolvent because of missed mortgage payments. But R0 won’t stay below 1 for long, even if it gets there at all. We will then have to shut down again within two months, but will then reopen again a bit after that. At each step along the way, we will self-deceive rather than confront the level of pain involved with our choices. We may lose a coherent national policy on the shutdown issue altogether, not that we have one now. The pandemic yo-yo will hold. At some point antivirals or antibodies will kick in (read Scott Gottlieb), or here: “There are perhaps 4-6 drugs that could be available by Fall and have robust enough treatment effect to impact risk of another epidemic or large outbreaks after current wave passes. We should be placing policy bets on these likeliest opportunities.” We will then continue the rinse and repeat of the yo-yo, but with the new drugs and treatments on-line with a death rate at maybe half current levels and typical hospital stays at three days rather than ten. It will seem more manageable, but how eager will consumers be to resume their old habits? Eventually a vaccine will be found, but getting it to everyone will be slower than expected. The lingering uncertainty and “value of waiting,” due to the risk of second and third waves, will badly damage economies along the way.
So there you have it.