Category: Data Source
That is the topic of a new paper by Farboodi, Jarosch, and Shimer, published version in here. They favor ” Immediate social distancing that ends only slowly but is not overly restrictive.” Furthermore, they test the model against data from Safegraph and also from Sweden and find that their recommendations do not depend very much on parameter values.
Here is an excerpt from the paper:
…social distancing is never too restrictive. At any point in time, the effective reproduction number for a disease is the expected number of people that an infected person infects. In contrast to the basic reproduction number, it accounts for the current level of social activity and the fraction of people who are susceptible. Importantly, optimal policy keeps the effective reproduction number above the fraction of people who are susceptible,although for a long time only mildly so. That is, social activity is such that, if almost everyone were susceptible to the disease, the disease would grow over time. That means that optimal social activity lets infections grow until the susceptible population is sufficiently small that the number of infected people starts to shrink. As the stock of infected individuals falls,the optimal ratio of the effective reproduction number to the fraction of susceptible people grows until it eventually converges to the basic reproduction number.
To understand why social distancing is never too restrictive, first observe that social activity optimally returns to its pre-pandemic level in the long run, even if a cure is never found. To understand why, suppose to the contrary that social distancing is permanently imposed, suppressing social activity below the first-best (disease-free world) level. That means that a small increase in social activity has a first-order impact on welfare. Of course, there is a cost to increasing social activity: it will lead to an increase in infections. However,since the number of infected people must converge to zero in the long run, by waiting long enough to increase social activity, the number of additional infections can be made arbitrarily small while the benefit from a marginal increase in social activity remains positive.
Recommended, one recurring theme is that people distance a lot of their own accord. That means voluntary self-policing brings many of the benefits of a lockdown. Another lesson is that we should be liberalizing at the margin.
If I have a worry, however, it has to do with the Lucas critique. People make take preliminary warnings very seriously, when they see those warnings are part of a path toward greater strictness. When the same verbal or written message is part of a path toward greater liberalization however…perhaps the momentum and perceived end point really matters?
For the pointer I thank John Alcorn.
Yes, that is the title of a new paper and it is excellent indeed. Lydia Cox et.al bring you a fresh and original look at some properties of government spending:
“Big G” typically refers to aggregate government spending on a homogeneous good. In this paper, we open up this construct by analyzing the entire universe of procurement contracts of the US government and establish five facts. First, government spending is granular, that is, it is concentrated in relatively few firms and sectors. Second, relative to private expenditures its composition is biased. Third, procurement contracts are short-lived. Fourth, idiosyncratic variation dominates the fluctuation of spending. Last, government spending is concentrated in sectors with relatively sticky prices. Accounting for these facts within a stylized New Keynesian model offers new insights into the fiscal transmission mechanism: fiscal shocks hardly impact inflation, little crowding out of private expenditure exists, and the multiplier tends to be larger compared to a one-sector benchmark aligning the model with the empirical evidence.
Via the still excellent Kevin Lewis.
The U.S. higher education sector will also be hard hit, with U.S. universities increasingly dependent on tuition from Chinese students. According to the Institute of International Education, China has remained the largest source of international students for ten years running,44 with 369,548 Chinese students enrolled in U.S. higher education programs in 2018 and contributing $15 billion in tuition payments.45 The postponement or cancellation of U.S. college entrance examinations in China, indefinite travel restrictions, and continued uncertainty surrounding when U.S. college campuses will reopen are expected to reduce Chinese demand for U.S. higher education in the 2020-2021 academic year.46 University administrators report that cancelled recruitment events in China and inability to work with local recruitment agencies could further depress Chinese student enrollment in U.S. university programs.
Here is the full document, on cascading economic impacts from China more generally. For the pointer I thank a loyal MR reader.
“Health inspectors cited roughly 75% of nursing homes nationwide for failing to have or follow a plan to prevent the spread of infectious diseases in the past four years, between 2016 and January 2020”
“A report released by academics at the London School of Economics (LSE) on April 15 said between 42 percent and 57 percent of deaths from the coronavirus in Italy, Spain, France, Ireland and Belgium have been linked to care homes for the elderly.”
From the (since updated) report: “In the remaining 5 countries for which we have official data (Belgium, Canada, France, Ireland and Norway), and where the number of total deaths ranges from 136 to 17,167, the % of COVID-related deaths in care homes ranges from 49% to 64%).”
Those are all from an email from Michael A. Alcorn.
From a Kevin Kelly email to me:
Another weird data point on the highly heterogeneous nature of this virus.
A friend of mine who lives in Bali says there have been 2 confirmed Covid-19 deaths on their island of 4.3 million residents. Yet according to him:
That makes it around 25.000 tourists from mainland China every week.
And until mid-January 2020, before the outbreak of the Corona pandemic, there were 5 direct flights from Wuhan per week.
During January 2020, 113,000 tourists from China visited Bali. During December 2019 when the Coronavirus was already spreading the number of arrivals from China was even higher because December is very busy in Bali.
So during the months of December 2019 and January 2020, approximately 220,000 tourists arrived from China alone.
Here are the official Covid-19 numbers as of 17th April 2020.
Confirmed cases: 113 | Recovered: 32 | Deaths: 2
The Crematorium in Bali’s capital city Denpasar does not see any increase in the number of cremations.
The hospitals do not have a flood of patients. There is hardly any talk on Social Media by people reporting about folks falling ill with Corona like symptoms.
The only thing I could find in Social Media groups is that business owners in Bali have reported an unusually high number of employees falling ill during November and December 2019.
Our models demonstrate that while social distancing measures clearly do flatten the curve, strategic reduction of contact can strongly increase their efficiency, introducing the possibility of allowing some social contact while keeping risks low. Limiting interaction to a few repeated contacts emerges as the most effective strategy. Maintaining similarity across contacts and the strengthening of communities via triadic strategies are also highly effective. This approach provides empirical evidence which adds nuanced policy advice for effective social distancing that can mitigate adverse consequences of social isolation.
That is from a new paper by Per Block, et.al. I do not consider this a confirmed result, but it is consistent with how my intuitions have been developing, and the success in containing Covid-19 on various smallish islands.
Here is the audio and transcript, here is part of the summary:
He joined Tyler to discuss whether the world as a whole is becoming harder to predict, whether Goldman Sachs traders can beat forecasters, what inferences we can draw from analyzing the speech of politicians, the importance of interdisciplinary teams, the qualities he looks for in leaders, the reasons he’s skeptical machine learning will outcompete his research team, the year he thinks the ascent of the West became inevitable, how research on counterfactuals can be applied to modern debates, why people with second cultures tend to make better forecasters, how to become more fox-like, and more.
Here is one excerpt:
COWEN: If you could take just a bit of time away from your research and play in your own tournaments, are you as good as your own best superforecasters?
TETLOCK: I don’t think so. I don’t think I have the patience or the temperament for doing it. I did give it a try in the second year of the first set of forecasting tournaments back in 2012, and I monitored the aggregates. We had an aggregation algorithm that was performing very well at the time, and it was outperforming 99.8 percent of the forecasters from whom the composite was derived.
If I simply had predicted what the composite said at each point in time in that tournament, I would have been a super superforecaster. I would have been better than 99.8 percent of the superforecasters. So, even though I knew that it was unlikely that I could outperform the composite, I did research some questions where I thought the composite was excessively aggressive, and I tried to second guess it.
The net result of my efforts — instead of finishing in the top 0.02 percent or whatever, I think I finished in the middle of the superforecaster pack. That doesn’t mean I’m a superforecaster. It just means that when I tried to make a forecast better than the composite, I degraded the accuracy significantly.
COWEN: But what do you think is the kind of patience you’re lacking? Because if I look at your career, you’ve been working on these databases on this topic for what? Over 30 years. That’s incredible patience, right? More patience than most of your superforecasters have shown. Is there some dis-aggregated notion of patience where they have it and you don’t?
TETLOCK: [laughs] Yeah, they have a skill set. In the most recent tournaments, we’ve been working on with them, this becomes even more evident — their willingness to delve into the details of really pretty obscure problems for very minimal compensation is quite extraordinary. They are intrinsically cognitively motivated in a way that is quite remarkable. How am I different from that?
I guess I have a little bit of attention deficit disorder, and my attention tends to roam. I’ve not just worked on forecasting tournaments. I’ve been fairly persistent in pursuing this topic since the mid 1980s. Even before Gorbachev became general party secretary, I was doing a little bit of this. But I’ve been doing a lot of other things as well on the side. My attention tends to roam. I’m interested in taboo tradeoffs. I’m interested in accountability. There’re various things I’ve studied that don’t quite fall in this rubric.
COWEN: Doesn’t that make you more of a fox though? You know something about many different areas. I could ask you about antebellum American discourse before the Civil War, and you would know who had the smart arguments and who didn’t. Right?
…I had a very interesting correspondence with William Safire in the 1980s about forecasting tournaments. We could talk a little about it later. The upshot of this is that young people who are upwardly mobile see forecasting tournaments as an opportunity to rise. Old people like me and aging baby-boomer types who occupy relatively high status inside organizations see forecasting tournaments as a way to lose.
If I’m a senior analyst inside an intelligence agency, and say I’m on the National Intelligence Council, and I’m an expert on China and the go-to guy for the president on China, and some upstart R&D operation called IARPA says, “Hey, we’re going to run these forecasting tournaments in which we assess how well the analytic community can put probabilities on what Xi Jinping is going to do next.”
And I’ll be on a level playing field, competing against 25-year-olds, and I’m a 65-year-old, how am I likely to react to this proposal, to this new method of doing business? It doesn’t take a lot of empathy or bureaucratic imagination to suppose I’m going to try to nix this thing.
COWEN: Which nation’s government in the world do you think listens to you the most? You may not know, right?
New York City’s multitentacled subway system was a major disseminator – if not the principal transmission vehicle – of coronavirus infection during the initial takeoff of the massive epidemic that became evident throughout the city during March 2020. The near shutoff of subway ridership in Manhattan – down by over 90 percent at the end of March – correlates strongly with the substantial increase in the doubling time of new cases in this borough. Maps of subway station turnstile entries, superimposed upon zip code-level maps of reported coronavirus incidence, are strongly consistent with subway-facilitated disease propagation. Local train lines appear to have a higher propensity to transmit infection than express lines. Reciprocal seeding of infection appears to be the best explanation for the emergence of a single hotspot in Midtown West in Manhattan. Bus hubs may have served as secondary transmission routes out to the periphery of the city.
That is from a new NBER working paper by Jeffrey E. Harris.
You may recall that some time ago MR posted an anonymous account of how the coronavirus problem actually was much worse in Japan than was being admitted by the Japanese government and broader establishment. It is now clear that this Cassandra was correct.
I can now reveal to you the full story of that posting behind the first link, including my role in it. Here is the opening excerpt:
By March 22nd, I strongly suspected there was a widespread coronavirus epidemic in Japan. This was not widely believed at the time. I, working with others, conducted an independent research project. By March 25th we had sufficient certainty to act. We projected that the default course of the epidemic would lead to a public health crisis.
We attempted to disseminate the results to appropriate parties, out of a sense of civic duty. We initially did this privately attached to our identities and publicly but anonymously to maximize the likelihood of being effective and minimize risks to the response effort and to the team. We were successful in accelerating the work of others.
The situation is, as of this writing, still very serious. In retrospect, our pre-registered results were largely correct. I am coming forward with them because the methods we used, and the fact that they arrived at a result correct enough to act upon prior to formal confirmation, may accelerate future work and future responses here and elsewhere.
I am an American. I speak Japanese and live in Tokyo. I have spent my entire adult life in Japan. I have no medical nor epidemiology background. My professional background is as a software engineer and entrepreneur. I presently work in technology. This project was on my own initiative and in my personal capacity.
I am honored to have played a modest role in this story, though full credit goes elsewhere, do read the whole thing. Hashing plays a key role in the longer narrative.
Here is a new paper by Seth Benzell, Avinash Collis, and Christos Nicolaides:
To prevent the spread of COVID-19, some types of stores and gathering places have been shut down while others remain open. The decision to shut down one type of location and leave another open constitutes a judgement about the relative danger and benefits of those locations. Using location data from a large sample of smartphones, nationally representative consumer preference surveys, and government statistics, we measure the relative transmission risk benefit and social cost of closing 30 different location categories in the US. Our categories include types of shops, schools, entertainments, and public spaces. We rank categories by those which should face stricter regulation via dominance across eight dimensions of risk and importance and through composite indexes. We find that from February to March, there were larger declines in visits to locations that our measures imply should be closed first. We hope this analysis will help policymakers decide how to reopen their economies.
Here is a summary picture:
MOMA take note!
Obviously such rankings are somewhat speculative, but sooner or later some kind of disaggregated road map like this is going to be necessary. And I would say sooner.
As a consequence of missing data on tests for infection and imperfect accuracy of tests, reported rates of population infection by the SARS CoV-2 virus are lower than actual rates of infection. Hence, reported rates of severe illness conditional on infection are higher than actual rates. Understanding the time path of the COVID-19 pandemic has been hampered by the absence of bounds on infection rates that are credible and informative. This paper explains the logical problem of bounding these rates and reports illustrative findings, using data from Illinois, New York, and Italy. We combine the data with assumptions on the infection rate in the untested population and on the accuracy of the tests that appear credible in the current context. We find that the infection rate might be substantially higher than reported. We also find that the infection fatality rate in Italy is substantially lower than reported.
Here is a very good tweet storm on their methods, excerpt: “What I love about this paper is its humility in the face of uncertainty.” And: “…rather than trying to get exact answers using strong assumptions about who opts-in for testing, the characteristics of the tests themselves, etc, they start with what we can credibly know about each to build bounds on each of these quantities of interest.”
I genuinely cannot give a coherent account of “what is going on” with Covid-19 data issues and prevalence. But at this point I think it is safe to say that the mainstream story we have been living with for some number of weeks now just isn’t holding up.
For the pointer I thank David Joslin.
Hawaii Department of Health officials said today that the state’s tally of coronavirus cases has risen to 553, up 12 from Thursday.
Of all the confirmed cases in Hawaii since the start of the outbreak, 48 have required hospitalizations, with three new cases reported today, health officials said.
The state’s coronavirus death toll stands at nine, unchanged from Thursday. Six of the deaths were on Oahu, while three were in Maui.
The population of Hawaii is about 1.4 million. Three days ago, Hawaii was the lowest infection rate in the United States, but of course more and better data are needed. We’ll see, with the passage of time, if this remains a true heterogeneity. But do note this:
It is also noteworthy that Hawaii tests for coronavirus at a considerably higher rate than most states. According to data compiled by Vox, Hawaii continues to rank among the top 10 states for testing per capita, which suggests Hawaii’s infection rate may be more accurate than rates reported by some other states.
…Swedish state epidemiologist Anders Tegnell remains calm: he is not seeing the kind of rapid increase that might threaten to overwhelm the Swedish health service, and unlike policymakers in the UK, he has been entirely consistent that that is his main objective.
That is from a new piece by Freddie Sayers, asserting that “the jury is still out” when it comes to Sweden. I cannot reproduce all of the graphs in that piece, but scroll through and please note that in terms of per capita deaths Sweden seems to be doing better than Belgium, France, or the United Kingdom, all of which have serious lockdowns (Sweden does not). If you measure extant trends, Sweden is in the middle of the pack for Europe. And here is data on new hospital admissions:
Now I understand that ideally one should compare similar “time cohorts” across countries, not absolute numbers or percentages. That point is logically impeccable, but still as the clock ticks it seems less likely to account for the Swedish anomaly.
Of course we still need more days and weeks of data.
To be clear, I am not saying the United States can or should copy Sweden. Sweden has an especially large percentage of people living alone, the Swedes are probably much better at complying with informal norms for social distancing, and obesity is much less of a problem in Sweden than America, probably hypertension too.
But I’d like to ask a simple question: who predicted this and who did not? And which of our priors should this cause us to update?
I fully recognize it is possible and maybe even likely that Sweden ends up being like Japan, in the sense of having a period when things seem (relatively) fine and then discovering they are not. (Even in Singapore the second wave has arrived, from in-migration, and may well be worse than the first.) But surely the chance of that scenario has gone down just a little?
And here is a new study on Lombardy by Daniil Gorbatenko:
The data clearly suggest that the spread had been trending down significantly even before the initial lockdown. They invalidate the fundamental assumption of the Covid-19 epidemiological models and with it, probably also the rationale for the harshest measures of suppression.
One possibility (and I stress that word possibility) is that these Lombardy data, shown at the link, are reflecting the importance of potent “early spreaders,” often family members, who give Covid-19 to their families fairly quickly, but after which the average rate of spread falls rapidly.
I’ll stand by my claim that the pieces on this one show an increasing probability of not really adding up. In the meantime, I am very happy to pull out and signal boost the best criticisms of these results.
There is a new NBER working paper (by economists) on Covid-19:
We use anonymized and aggregated data from Facebook to show that areas with stronger social ties to two early COVID-19 “hotspots” (Westchester County, NY, in the U.S. and Lodi province in Italy) generally have more confirmed COVID-19 cases as of March 30, 2020. These relationships hold after controlling for geographic distance to the hotspots as well as for the income and population density of the regions. These results suggest that data from online social networks may prove useful to epidemiologists and others hoping to forecast the spread of communicable diseases such as COVID-19.
That is by Theresa Kuchler, Dominic Russell, and Johannes Stroebel.