What is the Case Fatality Rate of Covid-19 in India?

The economist in me will give a very simple answer to that question – it depends. It depends on how long you think people will take from onset of the disease to die.

The modeller in me extended the argument that the economist in me made, and built a rather complicated model. This involved smoothing, assumptions on probability distributions, long mathematical derivations and (for good measure) regressions.. And out of all that came this graph, with the assumption that the average person who dies of covid-19 dies 20 days after the thing is detected.

 

Yes, there is a wide variation across the country. Given that the disease is the same and the treatment for most people diseased is pretty much the same (lots of rest, lots of water, etc), it is weird that the case fatality rate varies by so much across Indian states. There is only one explanation – assuming that deaths can’t be faked or miscounted (covid deaths attributed to other reasons or vice versa), the problem is in the “denominator” – the number of confirmed cases.

What the variation here tells us is that in states towards the top of this graph, we are likely not detecting most of the positive cases (serious cases will get themselves tested anyway, and get hospitalised, and perhaps die. It’s the less serious cases that can “slip”). Taking a state low down below in this graph as a “good tester” (say Andhra Pradesh), we can try and estimate what the extent of under-detection of cases in each state is.

Based on state-wise case tallies as of now (might be some error since some states might have reported today’s number and some mgiht not have), here are my predictions on how many actual number of confirmed cases there are per state, based on our calculations of case fatality rate.

Yeah, Maharashtra alone should have crossed a million caess based on the number of people who have died there!

Now let’s get to the maths. It’s messy. First we look at the number of confirmed cases per day and number of deaths per day per state (data from here). Then we smooth the data and take 7-day trailing moving averages. This is to get rid of any reporting pile-ups.

Now comes the probability assumption – we assume that a proportion p of all the confirmed cases will die. We assume an average number of days (N) to death for people who are supposed to die (let’s call them Romeos?). They all won’t pop off exactly N days after we detect their infection. Let’s say a proportion \lambda dies each day. Of everyone who is infected, supposed to die and not yet dead, a proportion \lambda will die each day.

My maths has become rather rusty over the years but a derivation I made shows that \lambda = \frac{1}{N}. So if people are supposed to die in an average of 20 days, \frac{1}{20} will die today, \frac{19}{20}\frac{1}{20} will die tomorrow. And so on.

So people who die today could be people who were detected with the infection yesterday, or the day before, or the day before day before (isn’t it weird that English doesn’t a word for this?) or … Now, based on how many cases were detected on each day, and our assumption of p (let’s assume a value first. We can derive it back later), we can know how many people who were found sick k days back are going to die today. Do this for all k, and you can model how many people will die today.

The equation will look something like this. Assume d_t is the number of people who die on day t and n_t is the number of cases confirmed on day t. We get

d_t = p  (\lambda n_{t-1} + (1-\lambda) \lambda n_{t-2} + (1-\lambda)^2 \lambda n_{t-3} + ... )

Now, all these ns are known. d_t is known. \lambda comes from our assumption of how long people will, on average, take to die once their infection has been detected. So in the above equation, everything except p is known.

And we have this data for multiple days. We know the left hand side. We know the value in brackets on the right hand side. All we need to do is to find p, which I did using a simple regression.

And I did this for each state – take the number of confirmed cases on each day, the number of deaths on each day and your assumption on average number of days after detection that a person dies. And you can calculate p, which is the case fatality rate. The true proportion of cases that are resulting in deaths.

This produced the first graph that I’ve presented above, for the assumption that a person, should he die, dies on an average 20 days after the infection is detected.

So what is India’s case fatality rate? While the first graph says it’s 5.8%, the variations by state suggest that it’s a mild case detection issue, so the true case fatality rate is likely far lower. From doing my daily updates on Twitter, I’ve come to trust Andhra Pradesh as a state that is testing well, so if we assume they’ve found all their active cases, we use that as a base and arrive at the second graph in terms of the true number of cases in each state.

PS: It’s common to just divide the number of deaths so far by number of cases so far, but that is an inaccurate measure, since it doesn’t take into account the vintage of cases. Dividing deaths by number of cases as of a fixed point of time in the past is also inaccurate since it doesn’t take into account randomness (on when a Romeo might die).

Anyway, here is my code, for what it’s worth.

deathRate <- function(covid, avgDays) {
covid %>%
mutate(Date=as.Date(Date, '%d-%b-%y')) %>%
gather(State, Number, -Date, -Status) %>%
spread(Status, Number) %>%
arrange(State, Date) -> 
cov1

# Need to smooth everything by 7 days 
cov1 %>%
arrange(State, Date) %>%
group_by(State) %>%
mutate(
TotalConfirmed=cumsum(Confirmed),
TotalDeceased=cumsum(Deceased),
ConfirmedMA=(TotalConfirmed-lag(TotalConfirmed, 7))/7,
DeceasedMA=(TotalDeceased-lag(TotalDeceased, 7))/ 7
) %>%
ungroup() %>%
filter(!is.na(ConfirmedMA)) %>%
select(State, Date, Deceased=DeceasedMA, Confirmed=ConfirmedMA) ->
cov2

cov2 %>%
select(DeathDate=Date, State, Deceased) %>%
inner_join(
cov2 %>%
select(ConfirmDate=Date, State, Confirmed) %>%
crossing(Delay=1:100) %>%
mutate(DeathDate=ConfirmDate+Delay), 
by = c("DeathDate", "State")
) %>%
filter(DeathDate > ConfirmDate) %>%
arrange(State, desc(DeathDate), desc(ConfirmDate)) %>%
mutate(
Lambda=1/avgDays,
Adjusted=Confirmed * Lambda * (1-Lambda)^(Delay-1)
) %>%
filter(Deceased > 0) %>%
group_by(State, DeathDate, Deceased) %>%
summarise(Adjusted=sum(Adjusted)) %>%
ungroup() %>%
lm(Deceased~Adjusted-1, data=.) %>%
summary() %>%
broom::tidy() %>%
select(estimate) %>%
first() %>%
return()
}

Coming back to life

On Sunday, I met a friend for coffee. In normal times that would be nothing extraordinary. What made this extraordinary was that this was the first time since the lockdown started that I was actually meeting a non-family member casually, for a long in-person conversation.

I’m so tired of the three pairs of shorts and five T-shirts that I’ve been wearing every day since the lockdown started that I actually decided to dress up that day. And bothered to take a photo at a signal on the way to meeting him.

We met at a coffee shop in Koramangala, from where we took away coffees and walked around the area for nearly an hour, talking. No handshakes. No other touches. Masks on for most of the time. And outdoors (I’m glad I live in Bangalore whose weather allows you to be outdoors most of the year). Only issue was that wearing a mask and walking and talking for an hour can tire you out a bit.

The next bit of resurrection happened yesterday when I had an in-person business meeting for the first time in three months. Parking the car near these people’s office was easier than usual (less business activity I guess?), though later I found that my windshield was full of bird shit (I had parked under a tree).

For the first time ever while going into this office, I got accosted by a security guard at the entrance, asking where I was headed, taking my temperature and offering me hand sanitiser. Being a first time, I was paranoid enough to use the umbrella I was carrying to operate the lift buttons, and my mask was always on.

There were no handshakes. The room was a bit stuffy and I wasn’t sure if they were using the AC, so I asked for the windows to be opened (later they turned on the AC saying it’s standard practice there nowadays). Again, no handshakes or anything. We kept our masks on for a long time. They offered water in a bottle which I didn’t touch for a long time.

Until one of them suggested we could order in dosas from a rather famous restaurant close to their office (and one that I absolutely love). The dosas presently arrived, and then all masks were off. For the next half hour as the dosas went down it was like we were back in “normal times” again, eating together and talking loudly without masks. I must say I missed it.

I took the stairs down to avoid touching the lift. Walked back to the car (and birdshit-laden windshield) and quickly used hand sanitiser. I hadn’t carried my laptop or notebook for the meeting, and I quickly made notes using the voice notes app of my phone.

Yes, in normal times, a lot of this might appear mundane. But given that we’re now sort of “coming back to life” after a long and brutal lockdown, a lot of this deserves documentation.

Oh, and I’m super happy to meet people now. Given a choice, I prefer outdoors. Write in if you want to meet me.

covid-19 and mental health

I don’t know about you but the covid-19 pandemic and the associated lockdown have had a massive (negative) impact on my mental health. And from the small number of people I’ve spoken to about this, I don’t think I’m alone in this.

Before I continue I must mention that in the past I’ve been diagnosed with ADHD, anxiety and depression, though I haven’t been under medication for any of them for a long time now.

For starters, there’s the anxiety related to the disease itself. Every three or four days I suffer from what I’ve now come to dub “psychological corona”. Most of the times this is triggered by an allergy I get (I’m allergic to pollen from the tree in front of my house, a fact I conveniently forgot until I had bought this house). I start sneezing and coughing, and start imagining the worst.

One time, though, this “psychological corona” was legit thanks to my own stupidity. I had accepted a sample that a nearby baker had offered me, taking off my mask to eat it, and then remembered that he had been coughing before I entered the shop. And then panicked. I had thought later that I should write a blogpost on “the importance of keeping a consistent risk level” but then forgot.

The next level of anxiety is work-related. I’m lucky enough that I had a medium-term ongoing project at the time the lockdown started. This anxiety is regarding whether these clients will continue to pay, and if so, for how long. I don’t think I want to comment much on this issue (beyond bringing this up).

What I have mentioned so far is possibly what everyone has been going through. And then there is the “next layer”.

I have a 3 3/4 year old at home, and her school has been shut for over three months now. We don’t employ any help to take care of her (in other words, we use her school as our “child care”), and in normal times, we had worked out a method where we could get work done while still hanging out with her adequately.

Now, with the lockdown, this is doubly hard. We have settled on a method where the wife and I work in alternating 90 minute bands, with the person who “isn’t working” in that time band hanging out outside the study with the child. One of the responsibilities of the “person outside” is to ensure that the child doesn’t knock on the door.

This worked fine for me as long as I mostly had “fighter work” to do, as I could switch on and off at will as I entered and exited the room (though sometimes I found it harder to switch off when exiting). For the last month or so, my work has been more stud than fighter, and this band-based system has been a disaster. Most times, by the time I get into the zone, my slot is over.

And not getting work done in my slot is the least of my problems. The thing is that I’m “always working”, either trying to work on my work, or parenting (school meant that the total hours of work were far fewer). And it can be tiring. And from the point of view of my ADHD (I can easily get distracted and lose my train of thought), getting constant outside stimulus (even if it’s from close family) can be extremely draining.

What makes the problem really bad is that most outlets that help me normally deal with life are now absent. All sport has been shut, though nowadays football has been trickling back to life (yes, next Sunday I’m staying up late to watch Everton-Liverpool).

Getting regular exercise has been a part of my usual protocol of managing my mental health and it doesn’t help that gyms are closed (my gym wants to open, the state government wnats to open gyms, but the union government isn’t giving permission).

Children under 10 aren’t allowed to go out here “except for essential purposes” (I don’t understand the reason behind this, since the pandemic hasn’t really been affecting children). This means we can’t go out as a family. My wife and I can’t go to a shop together. I can’t take my daughter to a park (which is a big way in which I’ve bonded with her over the years).

The list is not complete but I’ll stop here since this is turning into a long rant. I’m pretty sure you have your own list of how the pandemic has hurt your mental health. And the lockdown isn’t helping one big on this.

Oh, and if there are therapists you recommend, please recommend.

Mata Amrita Goes To New York Times

Remember that I had written recently that the pandemic is likely to change the practice of hugging, and the Mata Amrita Index? Now the New York Times has also covered it (possibly paywalled). It includes helpful graphics on “how to hug and how not to hug”.

It is an interesting article, quoting an expert on aerosols about what is the best way to hug. From what I gather, the key is to keep your faces turned away from each other. As long as you maintain this, hugging should still be fine.

[…] the safest thing is to avoid hugs. But if you need a hug, take precautions. Wear a mask. Hug outdoors. Try to avoid touching the other person’s body or clothes with your face and your mask. Don’t hug someone who is coughing or has other symptoms.

And remember that some hugs are riskier than others. Point your faces in opposite directions — the position of your face matters most. Don’t talk or cough while you’re hugging. And do it quickly. Approach each other and briefly embrace. When you are done, don’t linger. Back away quickly so you don’t breathe into each other’s faces. Wash your hands afterward.

Most of this seems fine. Only the last bit seems a bit difficult to implement – how do you wash your hands soon after hugging someone without offending them? I mean – I face this problem already. There are many people I come across whose hands I shake (this is all pre-pandemic) which leave me queasy and at unease until I have washed my hands. The challenge in this situation is how to efficiently wash your hands without making it explicit that the handshake wasn’t a pleasant one.

My favourite bit in the article, however, is the last one. It pertains to the “quality of hugs” that I’ve been talking about for a while now, and also happens to bring in Marie Kondo into the picture.

Dr. Marr noted that because the risk of a quick hug with precautions is very low but not zero, people should choose their hugs wisely.

“I would hug close friends, but I would skip more casual hugs,” Dr. Marr said. “I would take the Marie Kondo approach — the hug has to spark joy.”

Covid-19 superspreaders in Karnataka

Through a combination of luck and competence, my home state of Karnataka has handled the Covid-19 crisis rather well. While the total number of cases detected in the state edged past 2000 recently, the number of locally transmitted cases detected each day has hovered in the 20-25 range.

Perhaps the low case volume means that Karnataka is able to give out data at a level that few others states in India are providing. For each case, the rationale behind why the patient was tested (which is usually the source where they caught the disease) is given. This data comes out in two daily updates through the @dhfwka twitter handle.

There was this research that came out recently that showed that the spread of covid-19 follows a classic power law, with a low value of “alpha”. Basically, most infected people don’t infect anyone else. But there are a handful of infected people who infect lots of others.

The Karnataka data, put out by @dhfwka  and meticulously collected and organised by the folks at covid19india.org (they frequently drive me mad by suddenly changing the API or moving data into a new file, but overall they’ve been doing stellar work), has sufficient information to see if this sort of power law holds.

For every patient who was tested thanks to being a contact of an already infected patient, the “notes” field of the data contains the latter patient’s ID. This way, we are able to build a sort of graph on who got the disease from whom (some people got the disease “from a containment zone”, or out of state, and they are all ignored in this analysis).

From this graph, we can approximate how many people each infected person transmitted the infection to. Here are the “top” people in Karnataka who transmitted the disease to most people.

Patient 653, a 34 year-old male from Karnataka, who got infected from patient 420, passed on the disease to 45 others. Patient 419 passed it on to 34 others. And so on.

Overall in Karnataka, based on the data from covid19india.org as of tonight, there have been 732 cases where a the source (person) of infection has been clearly identified. These 732 cases have been transmitted by 205 people. Just two of the 205 (less than 1%) are responsible for 79 people (11% of all cases where transmitter has been identified) getting infected.

The top 10 “spreaders” in Karnataka are responsible for infecting 260 people, or 36% of all cases where transmission is known. The top 20 spreaders in the state (10% of all spreaders) are responsible for 48% of all cases. The top 41 spreaders (20% of all spreaders) are responsible for 61% of all transmitted cases.

Now you might think this is not as steep as the “well-known” Pareto distribution (80-20 distribution), except that here we are only considering 20% of all “spreaders”. Our analysis ignores the 1000 odd people who were found to have the disease at least one week ago, and none of whose contacts have been found to have the disease.

I admit this graph is a little difficult to understand, but basically I’ve ordered people found for covid-19 in Karnataka by number of people they’ve passed on the infection to, and graphed how many people cumulatively they’ve infected. It is a very clear pareto curve.

The exact exponent of the power law depends on what you take as the denominator (number of people who could have infected others, having themselves been infected), but the shape of the curve is not in question.

Essentially the Karnataka validates some research that’s recently come out – most of the disease spread stems from a handful of super spreaders. A very large proportion of people who are infected don’t pass it on to any of their contacts.

Meetings from home

For the last eight years, I’ve worked from home with occasional travel to clients’ offices. How occasional this travel has been has mostly depended on how far away the client is, and how insistent they are on seeing my face. Nevertheless, I’ve always made it a point to visit them for any important meetings, and do them in person.

Now, with the Covid-19 crisis, this hybrid model has broken down. Like most other people in the world, I work entirely from home nowadays, even for important meetings.

At the face of this, this seems like a good thing – for example, nowadays, however important a meeting is, the transaction cost is low. An hour long meeting means spending an hour for it (the time taken for prep is separate and hasn’t changed), and there’s no elaborate song-and-dance about it with travel and dressing up and all that.

While this seems far more efficient use of my time, I’m not sure I’m so happy about it. Essentially, I miss the sense of occasion. Now, an important meeting feels no different from an internal meeting with partners, or some trivial update.

Travel to and from an important meeting was a good time to mentally prepare for it, and then take stock of how it was gone. Now, until ten minutes before a meeting, I’m living my life as usual, and the natural boundaries that used to help me prep are also gone.

The other problem with remotely being there in large but important meetings is that it’s really easy to switch off. If you’re not the one who is doing a majority of the talking (or even the listening), it becomes incredibly hard to focus, and incredibly easy to get distracted elsewhere in the computer (it helps if your camera is switched off).

In a “real” physical meeting, however, large the gathering is, it is naturally easy for you to focus (and naturally more difficult to be distracted), and also easier to get involved in the meeting. An online meeting sometimes feels a bit too much like a group discussion, and without visual cues involved, it becomes really hard to butt in and make a point.

So once we are allowed to travel, and to meet, I’m pretty certain that I’ll start travelling a bit for work again. I’ll start with meetings in Bangalore (inter-city travel is likely to be painful for a very long time).

It might involve transaction cost, but a lot of the transaction cost gets recovered in terms of collateral benefits.

Expertise

During the 2008 financial crisis, it was fairly common to blame experts. It was widely acknowledged that it was the “expertise” of economists, financial markets people and regulators that had gotten us into the crisis in the first place. So criticising and mocking them were part of normal discourse.

For example, most of my learning about the 2008 financial crisis came from following blogs written by journalists, such as Felix Salmon, and generalist academics such as Tyler Cowen or Alex Tabarrok or Arnold Kling, rather than blogs written by financial markets experts or practitioners. I don’t think it was very different for too many people.

Cut to 2020 and the covid-19 crisis, and the situation is very different. You have a bunch of people mocking experts (epidemiologists, primarily), but this is in the minority. The generic Twitter discourse seems to be “listen to the experts”.

For example, there was this guy called Tomas Pueyo who wrote a bunch of really nice blog posts (on Medium) about the possible growth of the disease. He got heavily attacked by people in the epidemiology and medicine professions, and (surprisingly to me)  the general twitter discourse backed this up. “We don’t need a silicon valley guy telling us epidemiology”, went the discourse. “Listen to the experts”.

That was perhaps the beginning of the “I’m not an epidemiologist but” meme (not a particularly “fit” meme in terms of propagation, but one that continues to endure). For example, when I wrote my now famous tweetstorm about Bayes’s theorem and random testing 2-3 weeks back, a friend I was discussing with it advised me to “get the thing checked with epidemiologists before publishing”.

This came a bit too late after I’d constructed the tweetstorm, and I didn’t want to abandon it, and so I told him, “but then I’m an expert on Probability and Bayes’s Theorem, and so qualified to put this” and went ahead.

In any case, I have one theory as to why “listen to the expert” has become the dominant discourse in this crisis. It has everything to do with politics.

Two events took place in 2016 that the “twitter establishment” (the average twitter user, weighted by number of followers and frequency of tweeting, if I can say) did not like – the passing of the Brexit referendum and the election of President Trump.

While these two surprising events took place either side of the Atlantic, they were both seen as populist movements that were aimed at the existing establishment. Some commentators saw them as a backlash “against the experts”. The rise of Trump and Brexit (and Boris Johnson) were seen as part of this backlash against expertise.

And the “twitter establishment” (the average twitter user, weighted by number of followers and frequency of tweeting, if I can say) doesn’t seem to like either of these two gentlemen (Trump and Johnson), and they are supposed to be in power because of a backlash against experts. Closer home, in India, the Modi government allegedly doesn’t trust experts, which critics blame for ham-handed decisions like Demonetisation and pushing through of the Citizenship Amendment Act in the face of massive protests (the twitter establishment doesn’t like Modi either).

Essentially we have a bunch of political leaders who are unpopular with the twitter establishment, and who are in place because of their mistrust of expertise, and multiplying negative with negative, you get the strange situation where the twitter establishment is in love with experts now.

And so when mathematicians or computer scientists or economists (or other “Beckerians“) opine on covid-19, they are dismissed as being “not expert enough”. Because any criticism of expertise of any kind is seen as endorsement of the kind of politics that got Trump, Johnson or Modi into power. And the twitter establishment (the average twitter user, weighted by number of followers and frequency of tweeting, if I can say) doesn’t like that.

The corner Bhelpuri guy

There’s this guy who sells Bhelpuri off a cart that he usually stations at the street corner 100 metres from home. His wife (I think) sells platters of cut fruit from another (taller, and covered) cart stationed next to him.

I don’t have any particular fondness for them. I’ve never bought cut fruit platters, for example (I’m told by multiple people that I’m not part of the target segment for this product). I have occasionally bought bhelpuri from this guy, but it isn’t the best you can find in this part of town. Nevertheless, every afternoon until mid-March he would unfailingly bring his cart to the corner every afternoon and set up shop.

He has since fallen victim to the covid-19 induced lockdown. I have no clue where he is (I don’t know where he lives. Heck, I don’t even know his name). All I know is that he has already suffered a month and half of revenue loss. I don’t know if he has had enough stash to see him through this zero revenue period.

The lockdown, and the way it has been implemented, has resulted in a number of misalignments of incentives. The prime minister’s regular exhortations to businesses to not lay off employees or cut salaries, for example, has turned the lockdown into a capital versus labour issue. Being paid in full despite not going to work, (organised) labour is only happy enough to demand an extension of the lockdown. Capital is running out of money, with zero revenues and having to pay salaries, and wants a reopening.

Our bhelpuri guy, running a one-person business, represents both capital and labour. In fact, he represents the most common way of operating in India – self employment with very limited (and informal) employees. Whether he pays salaries or not doesn’t matter to him (he only has to pay himself). The loss of revenue matters a lot.

The informality of his business means that there is pretty much no way out for him to get any sort of a bailout. He possibly has an Aadhaar card (and other identity cards, such as a voter ID), and maybe even a bank account. Yet, the government (at whatever level) is unlikely to know that he exists as a business. He might have a BPL ration card that might have gotten him some household groceries, but that does nothing to compensate for his loss of business.

If you go by social media, or even comments made by politicians to the media or even to the Prime Minister, the general discourse seems to be to “extend the lockdown until we are completely safe, with the government providing wage subsidies and other support”. All this commentary completely ignores the most popular form of employment in India – informal businesses with a small number of informal employees.

If you think about it, there is no way this set of businesses can really be bailed out. The only way the government can help them is by letting them operate (even that might not help our Bhelpuri guy, since hygiene-conscious customers might think twice before eating off a street cart).

One friend mentioned that the only way these guys can exert political power is through their caste vote banks. However, I’m not sure if these vote banks have a regular enough voice (especially with elections not being nearby).

It may not be that much of a surprise to see some sort of protests or “lockdown disobedience” in case the lockdown gets overextended, especially in places where it’s not really necessary.

PS: I chuckle every time I see commentary (mostly on social media) that we need a lockdown “until we have a vaccine”. It’s like people have internalised the Contagion movie a bit too literally.

Fulfilling needs

We’re already in that part of the crisis where people are making predictions on how the world is going to change after the crisis. In fact, using my personal example, we’ve been in this part of the crisis for a long time now. So here I come with more predictions.

There’s a mailing list I’m part of where we’re talking about how we’ll live our lives once the crisis is over. A large number of responses there are about how they won’t ever visit restaurants or cafes, or watch a movie in a theatre, or take public transport, or travel for business, for a very very long time.

While it’s easy to say this, the thing with each of these supposedly dispensable activities is that they each serve a particular purpose, or set of purposes. And unless people are able to fulfil these needs that these activities serve with near-equal substitutes, I don’t know if these activities will decline by as much as people are talking about.

Let’s start with restaurants and cafes. One purpose they serve is to serve food, and one easy substitute for that is to take the food away and consume it at home. However, that’s not their only purpose. For example, they also provide a location to consume the food. If you think of restaurants that mostly survive because working people have their midday lunch there, the place they offer for consuming the food is as important as the food itself.

Then, restaurants and cafes also serve as venues to meet people. In fact, more than half my eating (and drinking) out over the last few years has been on account of meeting someone. If you don’t want to go to a restaurant or cafe to meet someone (because you might catch the virus), what’s the alternative?

There’s a certain set of people we might be inclined to meet at home (or office), but there’s a large section of people you’re simply not comfortable enough with to meet at a personal location, and a “third place” surely helps (also now we’ll have a higher bar on people we’ll invite home or to offices). If restaurants and cafes are going to be taboo, what kind of safe “third places” can emerge?

Then there is the issue of the office. For six to eight months before the pandemic hit, I kept thinking about getting myself an office, perhaps a co-working space, so that I could separate out my work and personal lives. NED meant I didn’t execute on that plan. However, the need for an office remains.

Now there’s greater doubt on the kind of office space I’ll get. Coworking spaces (at least shared desks) are out of question. This also means that coffee shops doubling up as “computer classes” aren’t feasible any more. I hate open offices as well. Maybe I have to either stick to home or go for a private office someplace.

As for business travel – they’ve been a great costly signal. For example, there had been some clients who I’d been utterly unable to catch over the phone. One trip to their city, and they enthusiastically gave appointments, and one hour meetings did far more than multiple messages or emails or phone calls could have done. Essentially by indicating that I was willing to take a plane to meet them, I signalled that I was serious about getting things done, and that got things moving.

In the future, business travel will “become more costly”. While that will still serve the purpose of “extremely costly signalling”, we will need a new substitute for “moderately costly signalling”.

And so forth. What we will see in the course of the next few months is that we will discover that a lot of our activities had purposes that we hadn’t thought of. And as we discover these purposes one by one, we are likely to change our behaviours in ways that will surprise us. It is too early to say which sectors or industries will benefit from this.

Post-Covid Stimulus

There are two ways in which businesses have been adversely affected by the ongoing Covid-19 crisis. Using phrases from my algorithmic trading days, let me call this “temporary impact” and “permanent impact”.

For some businesses, the Covid-19 crisis and the associated lockdown means about three months or so of zero (or near-zero) revenues. There is nothing inherently unsafe about these businesses that makes their sales take a “permanent hit” after the crisis has passed us by. Once the economy opens up again, these businesses can do businesses like they used to before, except that they are staring at a three-odd month revenue hole at the top of their P&L.

The second kind of businesses are going to be “permanently impacted”. They involve stuff that are going to be labelled as “unsafe” even after the crisis is over, and people are going to do less of these.

For example, bars and restaurants are going to see a “permanent impact” because of the crisis – people are not going to relish sitting in a public place with strangers in the next one year, and a large proportion of restaurants will have to go out of business.

Similarly any industry associated with travel – such as transport (airlines, railways, buses), hotels and taxis will see a permanent impact from the crisis. Real estate is also likely to be hit hard by the crisis. For all these sectors (and more), even after the economy is otherwise back in full swing, it will be a very long time before they see the sort of demand seen before the crisis.

Now that distinction is clear (I mean there will always be sectors that will sort of lie in the borderline), but at least we have a classification, we can use this to determine how governments respond to stimulate economies after the crisis.

Based on all the commentary going around, it seems like a given that governments and central banks need to do their bit to stimulate the economies. The collapse in both demand and supply thanks to the crisis means that governments will collect less taxes this year than expected. So while to some extent they will be able to possibly borrow more, or monetise deficit, or set aside money from other budgeted items, the funds available for stimulating businesses are likely to be limited.

So what sectors of the economy should the governments (and central banks) choose to spend this precious stimulus on? My take is that they should not bother about businesses that will be permanently impacted by the crisis – at best, the money will go into delaying the inevitable at some of these companies, and if structured in the form of a loan, will be highly unlikely to be unpaid.

Instead, the government should spend to stimulate sections of the economy where the impact of the crisis is temporary – in order to make the crisis “more temporary”. By giving cash to sectors that are going to be fundamentally solvent, this cash can be more assured to “travel around the economy”, thus giving more of the proverbial bang for the buck.

This essentially means that sectors most affected by the current crisis should not get any help from the governments – this might sound counterintuitive, but if the true intention of the government stimulus is to stimulate the economy rather than helping a particular set of companies, this makes eminent sense.

Oh, and in the Indian context, this seems like the perfect time to “let go” of Air India.