I talk COVID-19 with David Beckworth on the latest episode of Macro Musings. We cover quite a bit of material including the real Corona threat that we are totally unprepared for and no one is talking about. Self-Recommending.
Our illustrative exercise implies a year-on-year contraction in U.S. real GDP of nearly 11 percent as of 2020 Q4, with a 90 percent confidence interval extending to a nearly 20 percent contraction. The exercise says that about 60 percent of the forecasted output contraction reflects a negative effect of COVID-induced uncertainty.
Here is much more, a full paper from Scott R. Baker, Nicholas Bloom, Steven J. Davis, and Stephen J. Terry, an all-star team for this project.
These estimates are from the Centre for the Mathematical Modelling of Infectious Diseases at the London School of Hygiene & Tropical Medicine. More details here.
Richard Lowery emails me this:
I saw your post about epidemiologists today. I have a concern similar to point 4 about selection based what I have seen being used for policy in Austin. It looks to me like the models being used for projection calibrate R_0 off of the initial doubling rate of the outbreak in an area. But, if people who are most likely to spread to a large number of people are also more likely to get infected early in an outbreak, you end up with what looks kind of like a classic Heckman selection problem, right? In any observable group, there is going to be an unobserved distribution of contact frequency, and it would seem potentially first order to account for that.
As far as I can tell, if this criticism holds, the models are going to (1) be biased upward, predicting a far higher peak in the absence of policy intervention and (2) overstate the likely severity of an outcome without policy intervention, while potentially understating the value of aggressive containment measures. The epidemiology models I have seen look really pessimistic, and they seem like they can only justify any intervention by arguing that the health sector will be overwhelmed, which now appears unlikely in a lot of places. The Austin report did a trick of cutting off the time axis to hide that total infections do not seem to change that much under the different social distancing policies; everything just gets dragged out.
But, if the selection concern is right, the pessimism might be misplaced if the late epidemic R_0 is lower, potentially leading to a much lower effective spread rate and the possibility of killing the thing off at some point before it infects the number of people required to create the level of immunity the models are predicted require. This seems feasible based on South Korea and maybe China, at least for areas in the US that are not already out of control.
I do not know the answers to the questions raised here, but I do see the debate on Twitter becoming more partisan, more emotional, and less substantive. You cannot say that about this communication. From the MR comments this one — from Kronrad — struck me as significant:
One thing both economists and epidemiologists seem to be lacking is an awareness for the problems of aggregation. Most models in both fields see the population as one homogenous mass of individuals. But sometimes, individual variation makes a difference in the aggregate, even if the average is the same.
In the case of pandemics, it makes a big difference how that infection rate varies in the population. Most models assume that it is the same for everyone. But in reality, human interactions are not evenly distributed. Some people shake hands all day, while others spend their days mostly alone in front of a screen. This uneven distribution has an interesting effect: those who spread virus the most are also the most likely to get it. This means that the infection rate looks very higher in the beginning of a pandemic, but sinks once the super spreaders has the disease and got immunity. Also, it means herd immunity is reached much earlier: not after 70% of the population is immune, but after people who are involved in 70% of all human interactions are immune. At average, this is the same. But in practice, it can make a big difference.
I did a small simulation on this and came to the conclusion that with recursively applied Pareto-distribution where 1/3 of all people are responsible for 2/3 of all human interaction, herd immunity is already reached when 10% of the population had the virus. So individual variation in the infection rate can make an enormous difference that are be captured in aggregate models.
My quick and dirty simulation can be found here:
See also Robin Hanson’s earlier post on variation in R0. C’mon people, stop your yapping on Twitter and write some decent blog posts on these issues. I know you can do it.
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?
Walmart, Amazon and other firms are developing safety protocols for the COVID workplace. Walmart, for example, will be doing temperature checks of its employees:
Walmart Blog: As the COVID-19 situation has evolved, we’ve decided to begin taking the temperatures of our associates as they report to work in stores, clubs and facilities, as well as asking them some basic health screening questions. We are in the process of sending infrared thermometers to all locations, which could take up to three weeks.
Any associate with a temperature of 100.0 degrees will be paid for reporting to work and asked to return home and seek medical treatment if necessary. The associate will not be able to return to work until they are fever-free for at least three days.
Many associates have already been taking their own temperatures at home, and we’re asking them to continue that practice as we start doing it on-site. And we’ll continue to ask associates to look out for other symptoms of the virus (coughing, feeling achy, difficulty breathing) and never come to work when they don’t feel well.
Our COVID-19 emergency leave policy allows associates to stay home if they have any COVID-19 related symptoms, concerns, illness or are quarantined – knowing that their jobs will be protected.
Amazon is even investing in their own testing labs.
Amazon Blog: A next step might be regular testing of all employees, including those showing no symptoms. Regular testing on a global scale across all industries would both help keep people safe and help get the economy back up and running. But, for this to work, we as a society would need vastly more testing capacity than is currently available. Unfortunately, today we live in a world of scarcity where COVID-19 testing is heavily rationed.
If every person, including people with no symptoms, could be tested regularly, it would make a huge difference in how we are all fighting this virus. Those who test positive could be quarantined and cared for, and everyone who tests negative could re-enter the economy with confidence.
Until we have an effective vaccine available in billions of doses, high-volume testing capacity would be of great help, but getting that done will take collective action by NGOs, companies, and governments.
For our part, we’ve begun the work of building incremental testing capacity. A team of Amazonians with a variety of skills – from research scientists and program managers to procurement specialists and software engineers – have moved from their normal day jobs onto a dedicated team to work on this initiative. We have begun assembling the equipment we need to build our first lab (photos below) and hope to start testing small numbers of our front line employees soon.
Actions and experiments like these will discover ways to work safely till we reach the vaccine era.
Here is the link, it is about one hour long, with questions interspersed throughout, the title is “The future social and political implications of COVID-19.” Self-recommending!
Shruti Rajagopalan and I have written a policy brief on pandemic policy in developing countries with specific recommendations for India. The Indian context requires a different approach. Even washing hands, for example, is not easily accomplished when hundreds of millions of people do not have access to piped water or soap. India needs to control the COVID-19 pandemic better than other nations because the consequences of losing control are more severe given India’s relatively low healthcare resources, limited state capacity, and large population of poor people, many of whom are already burdened with other health issues. We make 10 recommendations:
1: Any test kit approved in China, Japan, Singapore, South Korea, Taiwan, the United States, or Western Europe should be immediately approved in India.
2: The Indian government should announce a commitment to pay any private Indian lab running coronavirus tests at least the current cost of tests run at government labs.
3: All import tariffs and quotas on medical equipment related to the COVID-19 crisis should be immediately lifted and nullified.
4: Use mobile phones to survey, inform, and prescreen for symptoms. Direct any individual with symptoms and his or her family to a testing center, or direct mobile testing to them.
5: Keep mobile phone accounts alive even if the phone bills are not paid, and provide a subsidy for pay-as-you-go account holders who cannot afford to pay for mobile services.
6: Requisition government schools and buildings and rent private hotel rooms, repurposing them as quarantine facilities.
7: Rapidly scale up the production and distribution of masks and encourage everyone to wear masks.
8: Truck in water and soap for hand washing and use existing distribution networks to provide hand sanitizers.
9: Accept voter identification cards and AADHAAR cards for in-kind transfers at ration shops.
10: Announce a direct cash transfer of a minimum of 3000 rupees per month (equivalent to the poverty line of $1.25 a day or $38 a month) to be distributed through Jan Dhan accounts or mobile phone applications such as Paytm.
See the whole thing for more on the rationales.
Addendum: As we went to press we heard that India will lift tariffs on medical equipment. My co-author lobbied hard for this.
I “zoom bombed” a high school class that is using Modern Principles of Economics. I thought that it would be useful to relate some virus economics to some regular economics. Here’s what I said:
Why has the response to coronavirus been so poor? Exponential growth, rare events, and the necessity of using theory instead of experience.
Coronavirus infections, when unchecked, double approximately every three days. If we start out with 1000 infections that means in 10 doublings, just 30 days, there will be one million infections (1,2,4,8,16,32,64,256,512,1024). If you act early and stop just one doubling, you prevent 500,000 people from being infected. Speed is of the essence. But you need to act when the problem appears small. You need to use theory rather than observation which isn’t natural or easy.
People get good at something when they have repeated attempts and rapid feedback. People can get pretty good at putting a basketball through a hoop. But for other decisions we only get one shot. One reason South Korea, Hong Kong and Taiwan have been much better at handling coronavirus is that within recent memory they had the SARS and H1N1 flu pandemics to build experience. The US and Europe were less hit by these earlier pandemics and responded less well. We don’t get many attempts to respond to once-in-a-lifetime events.
Even as coronavirus swept through China and Italy, many people dismissed the threat by thinking that we were somehow different. We weren’t. Even within the United States some people think that New York is different. It’s not. Most people learn, if they learn at all, from their own experiences, not from the experiences of others–even others like them. Learning from your own mistakes and experiences is a good skill. Many people make the same mistakes over and over again. But learning from other people’s mistakes or experiences is a great skill of immense power. It’s rare. Cultivate it.
Now let’s apply these issues to another one close to your life. Savings and retirement. Savings also follow an exponential process, albeit one neither as rapid nor as certain as those involving viruses. The same principles apply, however. But in this case instead of wanting to avoid the gains at the end you want to start saving early in order to capture the big gains in your 50s and 60s as you approach retirement. You don’t get many attempts at retirement so you need to use theory rather than experience. And because you don’t get many attempts you need to learn from other people, including other people’s mistakes, to guide your savings decisions today.
The students asked good questions and we also talked about aggregate demand and supply and how to think about the economic crisis.
Hat tip: Joel Cohen and Dr. Brian Dille.
P.S. I didn’t actually zoom bomb the class. I was invited but it was a surprise to the students.
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…
In a short-sighted blunder, India’s Supreme Court has ruled that private labs cannot charge for coronavirus tests:
NDTV: “The private hospitals including laboratories have an important role to play in containing the scale of pandemic by extending philanthropic services in the hour of national crisis…We thus are satisfied that the petitioner has made out a case…to issue necessary direction to accredited private labs to conduct free of cost COVID-19 test,” the court said.
Whether the private labs should be reimbursed by the government, will be decided later, Justices Ashok Bhushan and S Ravindra Bhat said in a hearing conducted via video conferencing.
The Supreme Court’s ruling will reduce the number of tests and dissuade firms from rushing to develop and field new drugs and devices to fight the coronavirus. A price is a signal wrapped up in an incentive. Instead of incentivizing investment, this order incentives firms to invest resources elsewhere.
Nor do private labs have a special obligation that mandates their conscription–an obligation to fund testing for all, falls on all.
The ruling is especially unfortunate because as Rajagopalan and Choutagunta document, India’s health care sector is predominantly private:
…India must rely primarily on the private sector and civil society to lead the response to COVID-19,…the role of the government should be financing and subsidizing testing and treatment for those who cannot afford to pay. India’s private healthcare system is better funded and better staffed than the government healthcare system, and it serves more people. It is estimated to be four times bigger in overall healthcare capacity, and it has 55 percent of the total hospital bed capacity, 90 percent of the doctors, and 80 percent of the ventilators.
The temptation to requisition private resources for state use in an emergency is ever present—but Indian policymakers must resist that temptation because it will compromise instead of increase capacity.
Benevolence is laudatory but even in a pandemic we should not rely on the benevolence of the butcher, brewer or baker for our dinner nor on the lab for our coronavirus tests. If we want results, never talk to suppliers of our own necessities, but only of their advantages.
China bent the curve, Italy bent the curve and I believe that the curve is bending in the United States. Suppression is working and the second part of the strategy of test, trace and isolate will start to come into play in a few weeks. The states are gearing up to test, trace and isolate and several large serological surveys are already underway which will gives us a much better idea of how widely the virus has spread. Ideally, we will move from test, trace and isolate to a situation where we can conduct millions of tests weekly which will take us into the vaccine time.
Before testing is fully operational, however, we will need to follow safety protocols. We can learn about what works from what essential workers are doing now. Green Circuits in CA, for example, redesigned the shift schedule:
His first move was to redesign the plant’s work schedule. The company, owned by the Dallas-based private equity firm Evolve Capital, always had the first and second shifts overlap for a half-hour. That allowed workers arriving in the afternoon to confer with colleagues as they handed off duties.
But O’Neil said they realized that would risk their whole workforce getting quarantined for 14 days, if someone got infected by the coronavirus and spent time at the factory as part of this larger group.
The solution was to create three separate teams of 40 workers each. The first shift now ends at 2 p.m., and then there’s an hour when the workspaces, tools, and breakrooms are sanitized. The third team does not work at all, but rather is held in reserve and available to jump in if an illness hampers one of the two other teams of workers.
Other safety protocols include:
- Shift work for white collar workers as well as for blue collar workers. Including spreading work over the weekends.
- Senior shopping hours.
- Temperature checks, perhaps via passive fever cameras at work and public transport.
- Mandatory masks for public transportation.
- Masks for workers.
- Sanitation breaks for mandatory hand washing.
- Quarantining at work for essential workers, as the MLB is thinking of doing despite not being essential.
- Reducing touch surfaces (even with simple things like propping up bathroom doors) and copper tape for hi-touch surfaces that cannot be eliminated.
It will take longer to reopen restaurants, clubs and sports stadiums but I believe that applying these protocols will allow many of us to work safely. We aren’t ready yet but now is the time to plan for our return.
There is a new paper by Benjamin Enke, Uri Gneezy, Brian Hall, David Martin, Vadim Nelidov, Theo Offerman, and Jeroen van de Ven:
Despite decades of research on heuristics and biases, empirical evidence on the effect of large incentives – as present in relevant economic decisions – on cognitive biases is scant. This paper tests the effect of incentives on four widely documented biases: base rate neglect, anchoring, failure of contingent thinking, and intuitive reasoning in the Cognitive Reflection Test. In preregistered laboratory experiments with 1,236 college students in Nairobi, we implement three incentive levels: no incentives, standard lab payments, and very high incentives that increase the stakes by a factor of 100 to more than a monthly income. We find that cognitive effort as measured by response times increases by 40% with very high stakes. Performance, on the other hand, improves very mildly or not at all as incentives increase, with the largest improvements due to a reduced reliance on intuitions. In none of the tasks are very high stakes sufficient to debias participants, or come even close to doing so. These results contrast with expert predictions that forecast larger performance improvements.
Via Kadeem Noray (EV winner, btw). This is perhaps related to behavior during and leading up to the lockdown…
We present a theory of Keynesian supply shocks: supply shocks that trigger changes in aggregate demand larger than the shocks themselves. We argue that the economic shocks associated to the COVID-19 epidemic—shutdowns, layoffs, and firm exits—may have this feature. In one-sector economies supply shocks are never Keynesian. We show that this is a general result that extend to economies with incomplete markets and liquidity constrained consumers. In economies with multiple sectors Keynesian supply shocks are possible, under some conditions. A 50% shock that hits all sectors is not the same as a 100% shock that hits half the economy. Incomplete markets make the conditions for Keynesian supply shocks more likely to be met. Firm exit and job destruction can amplify the initial effect, aggravating the recession. We discuss the effects of various policies. Standard fiscal stimulus can be less effective than usual because the fact that some sectors are shut down mutes the Keynesian multiplier feedback. Monetary policy, as long as it is unimpeded by the zero lower bound, can have magnified effects, by preventing firm exits. Turning to optimal policy, closing down contact-intensive sectors and providing full insurance payments to affected workers can achieve the first-best allocation, despite the lower per-dollar potency of fiscal policy.
All NBER papers on Covid-19 are open access, by the way.
That is the title of a new working paper by Tania Babina, Asaf Bernstein, and Filippo Mezzanotti, here is the abstract:
The effect of financial crises on innovative activity is an unsettled and important question for economic growth, but one difficult to answer with modern data. Using a differences-in-differences design surrounding the Great Depression, we are able to obtain plausible variation in local shocks to innovative ecosystems and examine the long-run impact of their inventions. We document a sudden and persistent decline in patenting by the largest organizational form of innovation at this time—independent inventors. Parallel trends prior to the shock, evidence of a drop within every major technology class, and consistent results using distress driven by commodity shocks all suggest a causal effect of local distress. Despite this negative effect, our evidence shows that innovation during crises can be more resilient than it may appear at a first glance. First, the average quality of surviving patents rises so much that there is no observable change in the aggregate future citations of these patents, in spite of the decline in the quantity of patents. Second, the shock is in part absorbed through a reallocation of inventors into established firms, which overall were less affected by the shock. Over the long run, firms in more affected areas compensate for the decline in entrepreneurial innovation and produce patents with greater impact. Third, the results reveal no significant brain drain of inventors from the affected areas. Overall, our findings suggest that financial crises are both destructive and creative forces for innovation, and we provide the first systematic evidence of the role that distress from the Great Depression played in the long-run innovative activity and the organization of innovation in the U.S. economy.
Further data coming your way…