Category: politics

A schoolboy fight in the Middle East

The ongoing conflict between Israel and Hamas, in some ways, reminds me of my own childhood. And if I think about it, it relates to everyone’s childhoods, and to schoolboy fights in general.

A bit about myself – from early childhood I was mostly “topper types”. Yes, my school gave out “ranks” from the age of 6, and I had started topping then. This made me the teachers’ pet, and object of friends’ ire.

It didn’t help that I was the first person in class to wear spectacles, and was the slowest runner (and thus not very athletic), and had a stammer, and all this put together meant that I was an obvious target for other boys in the class to “tease” (I don’t know / remember why the girls didn’t participate in this. Maybe they had their own target).

Nowadays, I don’t have much patience for being troubled, and it was the same 35 years ago. After a little “teasing” (or bullying, if you might call it that), I would hit back. Literally. While I ran slow and was generally un-athletic, I was easily the tallest boy in the class. And so when I hit people, it hurt. On a physical 1-1 level, the fights were largely one-sided (I mostly remember whacking people, not getting whacked).

Soon this pattern emerged – someone would provoke me and in reply I would whack them. And then someone would complain to some teacher who would see that I had made a much bigger transgression than what the others had done, and then scold (or occasionally hit – my school allowed that) me, much to the joy of the others.

This kept happening, and there was seemingly no end to it. And then one day (or maybe over a period of time), ten years had gone behind us. We had grown up. We hit puberty. Our priorities in life changed. This wasn’t fun at all. We moved on. Nowadays I’m fairly good friends with many of the guys who used to tease me back then.

Thinking about it, there is nothing exclusive to me in this story. If you have siblings (I don’t), you might have seen this happen in your house. The smaller one provokes the bigger one, who hits back (mostly literally), causing a transgression much bigger than the provocation. This plays into the smaller one’s hands who then complains to the parent, who censures the bigger one, much to the joy of the smaller one. Again, this kind of stuff continues, until the kids grow up.

At some level (I know of the massive ongoing destruction and cruelty), the fight between Israel and terrorist groups such as Hamas can be thought of in a similar fashion. Israel is the “bigger kid” with an ability to whack the smaller kids to a level where they can’t hit back directly. Israel is also the kind of bigger kid who will just whack in retaliation without paying attention to “what people might think”. Hamas is like the mischievous little kid out to bug the bigger kid.

Over 75 years of fighting, the situation has now got to the point where the typical schoolboy fight gets played out, though at a much larger scale and with far far more damage. Hamas provokes Israel. Israel hits back with much greater force. It is clear that Hamas can’t whack back Israel with the same ferocity that Israel hit them. And so they go crying uncle. The “uncles” temporarily outrage. The situation (hopefully) comes back to some kind of an uneasy truce. And then it repeats.

Unfortunately, unlike schoolboys, countries (and terrorist groups) don’t grow up. I don’t know what the “puberty equivalent” for Israel and Hamas is, that will let them forget their mutual fight and unite for other common purposes. Until they find some such, the fighting will continue.

Modern Ganeshas

Om Ganeshaaya Namaha

There is this theory I have heard – just that I have forgotten the source – that Ganesha was not originally part of the Hindu pantheon, but was a local god who was coopted into the fold later on. In fact, the same is said of his “brothers” Karthikeya and Ayyappa, and it is interesting that all these cooptions happened as sons of Shiva.

Back to Ganesha, the story goes that he is “vighneshwara” not because he removes obstacles (“vighnas”) but because he is the “obstacle god” (direct translation of vighneshwara). The full funda is – the locals who had Ganesha as their god allowed him to become part of the Hindu pantheon (and thus themselves becoming Hindus) under the express condition that he be worshipped in advance of any of the other gods in the Hindu pantheon.

Now, as even most non-practising Hindus will know, pretty much every Hindu ritual starts with a worship of Ganesha. It doesn’t matter which other god you are trying to worship, you always start with a prayer to Ganesha (unless, of course, if you are a radical Vaishnavite – in which case, Ganesha, as a son of Shiva, is taboo).

The polite explanation of this is “Ganesha is such a great god, and a remover of obstacles, you better worship him first so that the rest of your worship goes without obstacles”.

The more realist (and impolite, and controversial) explanation (again I’ve forgotten the source) is that if you started a worship without worshipping Ganesha at first, the locals who had “contributed” him to the pantheon would get pissed off and ransack your worship. And so the Ganesha worship at the beginning of every worship (and invocation ceremony) originally started as a form of blackmail, and then became part of culture. Eventually, it became lip service to Ganesha.

Earlier this year, I was watching the Australian Open. The finals ended, and it was time for the prizes. And at the beginning of the prize distribution, the announcer (Todd Woodbridge) said (paraphrasing) “we begin with a worship to the native peoples of Australia on whose lands we now stand”. It was similar to some episode of Masterchef Australia 2-3 years  back, which again started with the same “invocation”.

OK I actually found the video of Woodbridge from this year:

 

In this particular case, what has happened is that Australia has (finally) learnt about racism, and is now going overboard to identify all forms of overt or covert racism, past and present. The modern Ganesha-worshippers are the people whose job it is to point out every instance of overt or covert racism. If you don’t worship this Ganesha (talking about the “native peoples whose lands we stand upon”), the Ganesha-worshippers will come for you and maybe disturb the rest of your worship.

Ultimately, like the original Ganesha worship, this has turned into lip service.

“Modern Ganeshas” are not restricted to Australia. I just read this hilarious tweet (new Twitter rules means I have to copy paste here):

Have been on college tours in the Northeast. Every admissions officer and student volunteer starts with (1) a declaration of their pronouns, and (2) an acknowledgement of the stolen native lands their college is placed upon.

This is similar modern Ganesha worship, but practiced in the US. Lip service paid so that the “modern Ganesha worshippers” don’t come and disturb your worship.

When Colin Kaepernick knelt down during the playing of the (US) national anthem, he made a powerful statement. But then, when people started randomly taking the knee at the beginning of events (especially immediately after George Floyd’s murder), it turned into “modern Ganesha worship” (lip service so that the worthies don’t get offended).

And no political “wing” or party has a monopoly on modern Ganesha worship. In some places, ceremonies routinely start with praise being conferred on some “dear leader”. Literal Ganesha worship can also help in modern times, since that still has its guardians. You can include recitals of (whichever nation’s) national anthems, or readings from the constitution into this list.

The less memetically fit of these worships will fade away (or burn out, in case of a change in government). The more memetically fit of these worships will remain, but over a period of time turn into Ganesha worship – a token done out of habit and practice rather than due to fear of any contemporary reprisal.

Wokes and Jokes

Q: How do you know a woke is losing an argument?
A: They start talking about privilege.

No, this is not a post that seeks to make jokes about wokes. Instead, here, I seek to explore what kind of jokes wokes like, assuming there are jokes they like, that is.

A long time back, I had written here that the problem with the woke movement is that it denies people their jokes. Because jokes are inherently at the expense of someone (a person or group of people or thing), and because extreme political correctness means that making fun of a person or group of people is not polite, political correctness means a lot of jokes go out of the window.

Think of all the jokes that you enjoyed when you are in high school – it is likely that you won’t be able to put most of those jokes on social media nowadays – since it’s not kosher to make fun of the people / groups of people they make fun of.

And so, one day recently, I started thinking if wokes laugh at all – if making fun of people or groups of people is not done, how do they get their laughs? And then I realised that if you look at standup comedians, there are a bunch of them who can be broadly described as “woke” (as per today’s standards – I have NO CLUE how well this will hold up). So what gives? How can wokes have their jokes when most of our old jokes are not valid any more?

The interesting thing about the woke movement is that they largely depend on group identities. One <insert oppressed community (on whatever axis)> person gets beaten, it is seen as an act of violence against the community. Everything is spoken in group terms. The individual’s individuality doesn’t matter. Everything is analysed in group terms.

Except for the jokes.

Wokes get their jokes because they target particular people. And identification of such people is rather easy. Start with choosing a politician (or politicians) who are definitely anti-woke (Modi, Trump, Johnson, Jair, Orban – at the time of writing). And then build a social network around them, on people who hang out with them, agree with them, retweet them, get retweeted by them, and so on. All of them are worth making fun of.

If you make a joke about Modi, you are NOT making a joke about Gujaratis. If you make a joke about Trump, you are NOT making a joke about builders, or blondes. And these jokes are kosher because the target of the jokes are reviled, or are strongly associated with the reviled.

And a person’s status on whether they can be made fun of or not depends on their associations. You cross the proverbial political floor, you can suddenly gain indemnity or get exposed to being made fun of, spending upon the direction in which you’ve crossed the floor.

I’ve never really been a fan of standup comedy (I think it has a rather low “bit rate”). But this possibly explains why I find it even less tolerable nowadays – most of the jokes are political, and it gets boring after a while.

Then again, as the wokes say, everything is political.

Finite and infinite games, and questioning elections

I came across this snippet of an interview of Dr. S Jaishankar, India’s foreign minister.

 

In this, among other things, he says that “in India, nobody questions an election” (in the context of some reports that India is not really a democracy).

This can be simply explained by the concept of finite and infinite games, something I’ve spoken here about for a long time now, ever since I read the book of the same name by James Carse.

In general, in a stable democracy, parties don’t question election results because they know that the only way they can get back to power at a later point in time is by winning a similar election. In other words, if a party that loses an election were to question its legitimacy, it’s own victory in a subsequent election can be similarly undermined.

In other words, in a stable democracy, parties play an infinite game, where the potential short-term benefit of questioning an election gets trumped by the long-term benefit of using the same apparatus for winning subsequent elections.

So what explains America and Donald Trump’s questioning of the elections?

Notice that above, I said that “parties play an infinite game”. Individual politicians, on the other hand, can also play finite games. Given his age, Trump pretty much knew that the 2020 election (that he lost to Biden) was likely going to be his last. If he lost these elections (as he did), he would be out of power for the rest of his life. And so it made sense to him to question the results.

I’m pretty sure that the Republican party establishment (or whatever is left of it) wouldn’t have wanted to question the election, because as a party they are playing an infinite game, and what they need is the same election apparatus to come back to power next time round, or some time in the future.

The difference, in this regard, between India and the US, is the form of government. In a parliamentary system (at least in theory), and one with anti-defection laws, the party is supreme. However much a leader tries, he can never be superior to the party. And so the party’s incentives (infinite game) trump’s the leader’s (possibly finite game), and elections are not questioned.

The presidential system in the US means the leader trumps the party, at least within an election cycle, and so Trump’s finite game trumped the Republican party’s infinite game, and the results were questioned.

Why I quit public policy

This is yet another of those posts that elaborates something I’ve put on twitter.

I remember getting interested in public policy sometime in 2005. I think that was around the time when I stopped solely talking about gossip (and random “life issues”) on this blog, and started commenting about random “issues” here.

That was also the time when Madman Aadisht introduced me to his blog circle that he called the “libertarian cartel”. Reading blogposts by this cartel (included the likes of Ravikiran Rao, Amit Varma, Gaurav Sabnis (who was once a libertarian), Nitin Pai, etc.), I was hooked. I too wanted in on this “libertarian cartel”.

Soon enough, I started work and did one project that involved the study of some economic reforms. I soon quit that job but wrote about this, and other issues. I started getting into the “econ blogosphere”. Between the libertarian cartel, the opinion pages of the Business Standard (back when TN Ninan was the editor) and “econ blogs” (the likes of Marginal Revolution and EconLog), I got deeply interested in “policy issues”. And I thought I wanted to do public policy.

Of course, what public policy pays is nothing comparable to what post-MBA jobs pay, so I never explored it seriously as a career. I kept moving from one highly paid job to another, though I kept writing about “policy issues” on this blog, and then on Twitter (when I opened an account there in 2008). I even wrote on the “Indian Economy Blog”. And while the libertarian cartel never admitted me as a member, when they did form a mailing list, I got invited to join it soon enough (thanks to Aadisht once again).

“Policy work”, or “policy blogging” (which might be a more accurate term), in the late noughties was enjoyable because most people (at least those I bothered to read) were issue driven. So you had the aforementioned libertarians who analysed issues through a libertarian lens. You had leftists like the Jagadguru Krish and “Jihvaa”.  You had right wingers like SandeepWeb. Each class largely evaluated each issue based on their own philosophies, and commented about them. People avoided being partisan.

And so, in 2011, when I quit full time employment and decided to lead a portfolio life, I decided that public policy should be part of my portfolio. And the Takshashila Institution was kind enough to appoint me as its “resident quant” (for the most part, there were no formal responsibilities for the role and I wasn’t paid. However, we mutually enjoyed it, I would like to think).

That was a fantastic opportunity. I didn’t have to commit that much time, but got the optionality to participate in a large number of fairly interesting discussions with fairly interesting people. I did some work here and there, doing some research and teaching and course designing and lecturing, and it was most enjoyable. More enjoyable, of course, was the set of people I met through this assignment.

Somewhere down the line, maybe in 2015 or 2016 (or maybe even earlier), things changed. Basically policy became partisan. Out went the libertarians and totalitarians and right wingers and left wingers. In came the “Congressis” and “bhakts”, and republicans and democrats.

Output of policy analysis everywhere, except in academic journals (which I can’t comment on since I don’t bother reading them), became a function of the author’s political preferences. One year, an author might be favourable to the BJP and everything he/she wrote would nicely tally with the BJP’s view of the world. And then maybe the author would change political preferences, and there was a 180 degree turn on most issues!

On twitter, on mainstream media, on blogs, even on Instagram – “policy analysis” became rather predictable. Once you knew a person’s political preferences and leanings, it became clear what their view on any topic would be – it was identical to the view of their chosen party at that point in time. This partisanship meant there was “no information content” in any of this writing.

And that is how I started getting disillusioned. And the disillusionment grew over time, until a point when I started actively avoiding policy discussions (I’ve even muted the word “policy” on twitter).

I’m happy living my life, and doing my work, and earning my money, and paying my taxes. In the spirit of 2020, I’ll “leave public policy to the experts”.

Start the schools already

Irrespective of when you open the schools, there will be a second wave at that point in time. So we might as well reopen sooner rather than later and put children (and parents of young children) out of their misery.

OK, I admit I have a personal interest in this one. Being a double income, single kid, no nanny, nuclear family, we have been incredibly badly hit by the school shutdown for the last nine months. The wife and I have been effectively working at 50% capacity since March, been incredibly stressed out, and have no time for anything.

And now that I’ve begun a “proper job”, her utilisation has dropped well below 50%. This can’t last for long.

Then again, this post is not being driven solely by personal agendas or interests. The more perceptive of you might know that on my twitter account, I publish a bunch of graphs every morning, based on the statistics put out by covid19india.org . And every day, even when I don’t log into twitter, I go and take a look at the graphs to see what’s happening in the country.

And the message is clear – the pandemic is dying down in India. It is a pretty consistent trend. The Levitt Model might not really be true (my old friend’s comment that it is “random curve fitting” when I first came across it holds true, I would think), but it gives a great picture of how the pandemic has been performing in India. This is the graph I put out today.

In most states in India, the Levitt measure is incredibly close to 1, indicating that the pandmic is all but over. However, you might notice that the decline in this metric is not monotoniuc.

However, if you look at the Delhi numbers on the top right, notice how nicely the Levitt metric shows the three “waves” of the disease in the city. And you can see here that the third wave in Delhi is all but over. And while you see the clear effect of Delhi’s third wave in the Levitt metric, you can also see that it coincided with a second wave in Haryana, and a (barely noticeable) second wave in Uttar Pradesh and Rajasthan.

This wave was due to increased pollution, primarily on the account of crop burning in Punjab and Haryana in October-November. The reason the second waves in Uttar Pradesh and Rajasthan (as seen in terms of the Levitt measures) were small is that they are rather large states, and the areas affected by the bad pollution was fairly small.

And along with this, consider the serosurveys in Karnataka (both the government one and the IDFC-sponsored one), which estimated that the number of actual infections in the state are higher than the official counts of infections by a factor of 40 to 100 (we had initially assumed 10-20 for this factor). In other words, an overwhelmingly large number of cases in India are “asymptomatic” (which is to say that the people are, for all practical purposes, “unaffected”).

In other words, we know cases only when someone is affected badly enough to get themselves tested, or has a family member affected badly enough to get themselves tested. And what happened in Delhi and surrounding states in October-November was that with higher pollution, everyone who got affected got affected more severely than they would have otherwise.

Some people who might have otherwise been unaffected showed symptoms and got themselves tested. Some people who might have not been affected seriously enough ended up in hospital. Pollution meant that some people who might have recovered in hospital ended up dying. And as the crops finished burning and pollution levels dropped, you can see the Levitt metric dropping as well.

And lest you argue that I’m making an argument based on a mostly discredited metric, here is the actual number of known cases in the most affected states in the country. The graph is a Loess smoothing, and the points can be seen here.

See the precipitous decline in Delhi (green line) and Karnataka (orange) and Andhra Pradesh (pink) in the last couple of months. The pandemic has pretty much burnt through in most states. We can start relaxing, and opening schools.

You might be tempted to ask, “but won’t there be a second wave when schools reopen?”. That is a very fair concern, since people who have so far been extremely conservative might relatively relax when the schools open. The counterpoint to that is, “irrespective of when you open the schools, there will be a second wave at that point in time“.

It doesn’t matter if we reopen the schools now, or in April, or in August, or in next December. There will always be a few vestigial (possibly unaffected) cases going around, and there will be a spike in known cases at that point. And by quickly dialling up and down, we can control that.

So I hereby strongly urge the state governments (especially looking at you, Government of Karnataka) to permit schools to reopen. A few vocal and overly conservative parents should not be able to hold the rest of the country (or state) to ransom.

69 is the answer

The IDFC-Duke-Chicago survey that concluded that 50% of Bangalore had covid-19 in late June only surveyed 69 people in the city. 

When it comes to most things in life, the answer is 42. However, if you are trying to rationalise the IDFC-Duke-Chicago survey that found that over 50% of people in Bangalore had had covid-19 by end-June, then the answer is not 42. It is 69.

For that is the sample size that the survey used in Bangalore.

Initially I had missed this as well. However, this evening I attended half of a webinar where some of the authors of the survey spoke about the survey and the paper, and there they let the penny drop. And then I found – it’s in one small table in the paper.

The IDFC-Duke-Chicago survey only surveyed 69 people in Bangalore

The above is the table in its glorious full size. It takes effort to read the numbers. Look at the second last line. In Bangalore Urban, the ELISA results (for antibodies) were available for only 69 people.

And if you look at the appendix, you find that 52.5% of respondents in Bangalore had antibodies to covid-19 (that is 36 people). So in late June, they surveyed 69 people and found that 36 had antibodies for covid-19. That’s it.

To their credit, they didn’t highlight this result (I sort of dug through their paper to find these numbers and call the survey into question). And they mentioned in tonight’s webinar as well that their objective was to get an idea of the prevalence in the state, and not just in one particular region (even if it be as important as Bangalore).

That said, two things that they said during the webinar in defence of the paper that I thought I should point out here.

First, Anu Acharya of MapMyGenome (also a co-author of the survey) said “people have said that a lot of people we approached refused consent to be surveyed. That’s a standard of all surveying”. That’s absolutely correct. In any random survey, you will always have an implicit bias because the sort of people who will refuse to get surveyed will show a pattern.

However, in this particular case, the point to note is the extremely high number of people who refused to be surveyed – over half the households in the panel refused to be surveyed, and in a further quarter of the panel households, the identified person refused to be surveyed (despite the family giving clearance).

One of the things with covid-19 in India is that in the early days of the pandemic, anyone found having the disease would be force-hospitalised. I had said back then (not sure where) that hospitalising asymptomatic people was similar to the “precogs” in Minority Report – you confine the people because they MIGHT INFECT OTHERS.

For this reason, people didn’t want to get tested for covid-19. If you accidentally tested positive, you would be institutionalised for a week or two (and be made to pay for it, if you demanded a private hospital). Rather, unless you had clear symptoms or were ill, you were afraid of being tested for covid-19 (whether RT-PCR or antibodies, a “representative sample” won’t understand).

However, if you had already got covid-19 and “served your sentence”, you would be far less likely to be “afraid of being tested”. This, in conjunction with the rather high proportion of the panel that refused to get tested, suggests that there was a clear bias in the sample. And since the numbers for Bangalore clearly don’t make sense, it lends credence to the sampling bias.

And sample size apart, there is nothing Bangalore-specific about this bias (apart from that in some parts of the state, the survey happened after people had sort of lost their fear of testing). This further suggests that overall state numbers are also an overestimate (which fits in with my conclusion in the previous blogpost).

The other thing that was mentioned in the webinar that sort of cracked me up was the reason why the sample size was so low in Bangalore – a lockdown got announced while the survey was on, and the sampling team fled. In today’s webinar, the paper authors went off on a rant about how surveying should be classified as an “essential activity”.

In any case, none of this matters. All that matters is that 69 is the answer.

 

More on Covid-19 prevalence in Karnataka

As the old song went, “when the giver gives, he tears the roof and gives”.

Last week the Government of Karnataka released its report on the covid-19 serosurvey done in the state. You might recall that it had concluded that the number of cases had been undercounted by a factor of 40, but then some things were suspect in terms of the sampling and the weighting.

This week comes another sero-survey, this time a preprint of a paper that has been submitted to a peer reviewed journal. This survey was conducted by the IDFC Institute, a think tank, and involves academics from the University of Chicago and Duke University, and relies on the extensive sampling network of CMIE.

At the broad level, this survey confirms the results of the other survey – it concludes that “Overall seroprevalence in the state implies that by August at least 31.5 million residents had been infected by August”. This is much higher than the overall conclusions of the state-sponsored survey, which had concluded that “about 19 million residents had been infected by mid-September”.

I like seeing two independent assessments of the same quantity. While each may have its own sources of error, and may not independently offer much information, comparing them can offer some really valuable insights. So what do we have here?

The IDFC-Duke-Chicago survey took place between June and August, and concluded that 31.5 million residents of Karnataka (out of a total population of about 70 million) have been infected by covid-19. The state survey in September had suggested 19 million residents had been infected by September.

Clearly, since these surveys measure the number of people “who have ever been affected”, both of them cannot be correct. If 31 million people had been affected by end August, clearly many more than 19 million should have been infected by mid-September. And vice versa. So, as Ravi Shastri would put it, “something’s got to give”. What gives?

Remember that I had thought the state survey numbers might have been an overestimate thanks to inappropriate sampling (“low risk” not being low risk enough, and not weighting samples)? If 20 million by mid-September was an overestimate, what do you say about 31 million by end August? Surely an overestimate? And that is not all.

If you go through the IDFC-Duke-Chicago paper, there are a few figures and tables that don’t make sense at all. For starters, check out this graph, that for different regions in the state, shows the “median date of sampling” and the estimates on the proportion of the population that had antibodies for covid-19.

Check out the red line on the right. The sampling for the urban areas for the Bangalore region was completed by 24th June. And the survey found that more than 50% of respondents in this region had covid-19 antibodies. On 24th June.

Let’s put that in context. As of 24th June, Bangalore Urban had 1700 confirmed cases. The city’s population is north of 10 million. I understand that 24th June was the “median date” of the survey in Bangalore city. Even if the survey took two weeks after that, as of 8th of July, Bangalore Urban had 12500 confirmed cases.

The state survey had estimated that known cases were 1 in 40. 12500 confirmed cases suggests about 500,000 actual cases. That’s 5% of Bangalore’s population, not 50% as the survey claimed. Something is really really off. Even if we use the IDFC-Duke-Chicago paper’s estimates that only 1 in 100 cases were reported / known, then 12500 known cases by 8th July translates to 1.25 million actual cases, or 12.5% of the city’s population (well below 50% ).

My biggest discomfort with the IDFC-Duke-Chicago effort is that it attempts to sample a rather rapidly changing variable over a long period of time. The survey went on from June 15th to August 29th. By June 15th, Karnataka had 7200 known cases (and 87 deaths). By August 29th the state had 327,000 known cases and 5500 deaths. I really don’t understand how the academics who ran the study could reconcile their data from the third week of June to the data from the third week of August, when the nature of the pandemic in the state was very very different.

And now, having looked at this paper, I’m more confident of the state survey’s estimations. Yes, it might have sampling issues, but compared to the IDFC-Duke-Chicago paper, the numbers make so much more sense. So yeah, maybe the factor of underestimation of Covid-19 cases in Karnataka is 40.

Putting all this together, I don’t understand one thing. What these surveys have shown is that

  1. More than half of Bangalore has already been infected by covid-19
  2. The true infection fatality rate is somewhere around 0.05% (or lower).

So why do we still have a (partial) lockdown?

PS: The other day on WhatsApp I saw this video of an extremely congested Chickpet area on the last weekend before Diwali. My initial reaction was “these people have lost their minds. Why are they all in such a crowded place?”. Now, after thinking about the surveys, my reaction is “most of these people have most definitely already got covid and recovered. So it’s not THAT crazy”.

Election Counting Day

At the outset I must say that I’m deeply disappointed (based on the sources I’ve seen, mostly based on googling) with the reporting around the US presidential elections.

For example, if I google, I get something like “Biden leads Trump 225-213”. At the outset, that seems like useful information. However, the “massive discretisation” of the US electorate means that it actually isn’t. Let me explain.

Unlike India, where each of the 543 constituencies have a separate election, and the result of one doesn’t influence another, the US presidential election is at the state level. In all but a couple of small states, the party that gets most votes in the state gets all the votes of that state. So something like California is worth 55 votes. Florida is  worth 29 votes. And so on.

And some of these states are “highly red/blue” states, which means that they are extremely likely to vote for one of the two parties. For example, a victory is guaranteed for the Democrats in California and New York, states they had won comprehensively in the 2016 election (their dominance is so massive in these states that once a friend who used to live in New York had told me that he “doesn’t know any Republican voters”).

Just stating Biden 225 – Trump 213 obscures all this information. For example, if Biden’s 225 excludes California, the election is as good as over since he is certain to win the state’s 55 seats.

Also – this is related to my rant last week about the reporting of the opinion polls in the US – the front page on Google for US election results shows the number of votes that each candidate has received so far (among votes that have been counted). Once again, this is highly misleading, since the number of votes DOESN’T MATTER – what matters is the number of delegates (“seats” in an Indian context) each candidate gets, and that gets decided at the state level.

Maybe I’ve been massively spoilt by Indian electoral reporting, pioneered by the likes of NDTV. Here, it’s common to show the results and leads along with margins. It is common to show what the swing is relative to the previous elections. And some publications even do “live forecasting” of the total number of seats won by each party using a variation of the votes to seats model that I’ve written about.

American reporting lacks all of this. Headline numbers are talked about. “Live reports” on sites such as Five Thirty Eight are flooded with reports of individual senate seats, which to me sitting halfway round the world, is noise. All I care about is the likelihood of Trump getting re-elected.

Reports talk about “swing states” and how each party has performed in these, but neglect mentioning which party had won it the last time. So “Biden leading in Arizona” is of no importance to me unless I know how Arizona had voted in 2016, and what the extent of the swing is.

So what would I have liked? 225-213 is fine, but can the publications project it to the full 538 seats? There are several “models” they can use for this. The simplest one is to assume that states that haven’t declared leads yet have voted the same way as they did in 2016. One level of complexity can be using the votes to seats model, by estimating swings from the states that have declared leads, and then applying it to similar states that haven’t given out any information. And then you can get more complicated, but you realise it isn’t THAT complicated.

All in all, I’m disappointed with the reporting. I wonder if the split of American media down political lines has something to do with this.

Opinion polling in India and the US

(Relative) old-time readers of this blog might recall that in 2013-14 I wrote a column called “Election Metrics” for Mint, where I used data to analyse elections and everything else related to that. This being the election where Narendra Modi suddenly emerged as a spectacular winner, the hype was high. And I think a lot of people did read my writing during that time.

In any case, somewhere during that time, my editor called me “Nate Silver of India”.

I followed that up with an article on why “there can be no Nate Silver in India” (now they seem to have put it behind a sort of limited paywall). In that, I wrote about the polling systems in India and in the US, and about how India is so behind the US when it comes to opinion polling.

Basically, India has fewer opinion polls. Many more political parties. A far more diverse electorate. Less disclosure when it comes to opinion polls. A parliamentary system. And so on and so forth.

Now, seven years later, as we are close to a US presidential election, I’m not sure the American opinion polls are as great as I made them out to be. Sure, all the above still apply. And when these poll results are put in the hands of a skilled analyst like Nate Silver, it is possible to make high quality forecasts based on that.

However, the reporting of these polls in the mainstream media, based on my limited sampling, is possibly not of much higher quality than what we see in India.

Basically I don’t understand why analysts abroad make such a big deal of “vote share” when what really matters is the “seat share”.

Like in 2016, Hillary Clinton won more votes than Donald Trump, but Trump won the election because he got “more seats” (if you think about it, the US presidential elections is like a first past the post parliamentary election with MASSIVE constituencies (California giving you 55 seats, etc.) ).

And by looking at the news (and social media), it seems like a lot of Americans just didn’t seem to get it. People alleged that Trump “stole the election” (while all he did was optimise based on the rules of the game). They started questioning the rules. They seemingly forgot the rules themselves in the process.

I think this has to do with the way opinion polls are reported in the US. Check out this graphic, for example, versions of which have been floating around on mainstream and social media for a few months now.

This shows voting intention. It shows what proportion of people surveyed have said they will vote for one of the two candidates (this is across polls. The reason this graph looks so “continuous” is that there are so many polls in the US). However, this shows vote share, and that might have nothing to do with seat share.

The problem with a lot (or most) opinion polls in India is that they give seat share predictions without bothering to mention what the vote share prediction is. Most don’t talk about sample sizes. This makes it incredibly hard to trust these polls.

The US polls (and media reports of those) have the opposite problem – they try to forecast vote share without trying to forecast how many “seats” they will translate to. “Biden has an 8 percentage point lead over Trump” says nothing. What I’m looking for is something like “as things stand, Biden is likely to get 20 (+/- 15) more electoral college votes than Trump”. Because electoral college votes is what this election is about. The vote share (or “popular vote”, as they call it in the US (perhaps giving it a bit more legitimacy than it deserves) ), for the purpose of the ultimate result, doesn’t matter.

In the Indian context, I had written this piece on how to convert votes to seats (again paywalled, it seems like). There, I had put some pictures (based on state-wise data from general elections in India before 2014).

An image from my article for Mint in 2014 on converting votes to seats. Look at the bottom left graph

What I had found is that in a two-cornered contest, small differences in vote share could make a massive difference in the number of seats won. This is precisely the situation that they have in the US – a two cornered contest. And that means opinion polls predicting vote shares only should be taken with some salt.