Tag: public policy

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”.

Covid-19 Prevalence in Karnataka

Finally, many months after other Indian states had conducted a similar exercise, Karnataka released the results of its first “covid-19 sero survey” earlier this week. The headline number being put out is that about 27% of the state has already suffered from the infection, and has antibodies to show for it. From the press release:

Out of 7.07 crore estimated populationin Karnataka, the study estimates that 1.93 crore (27.3%) of the people are either currently infected or already had the infection in the past, as of 16 September 2020.

To put that number in context, as of 16th September, there were a total of 485,000 confirmed cases in Karnataka (official statistics via covid19india.org), and 7536 people had died of the disease in the state.

It had long been estimated that official numbers of covid-19 cases are off by a factor of 10 or 20 – that the actual number of people who have got the disease is actually 10 to 20 times the official number. The serosurvey, assuming it has been done properly, suggests that the factor (as of September) is 40!

If the ratio has continued to hold (and the survey accurate), nearly one in two people in Karnataka have already got the disease! (as of today, there are 839,000 known cases in Karnataka)

Of course, there are regional variations, though I should mention that the smaller the region you take, the less accurate the survey will be (smaller sample size and all that). In Bangalore Urban, for example, the survey estimates that 30% of the population had been infected by mid-September. If the ratio holds, we see that nearly 60% of the population in the city has already got the disease.

The official statistics (separate from the survey) also suggest that the disease has peaked in Karnataka. In fact, it seems to have peaked right around the time the survey was being conducted, in September. In September, it was common to see 7000-1000 new cases confirmed in Karnataka each day. That number has come down to about 3000 per day now.

Now, there are a few questions we need to answer. Firstly – is this factor of 40 (actual cases to known cases) feasible? Based on this data point, it makes sense:

In May, when Karnataka had a very small number of “native cases” and was aggressively testing everyone who had returned to the state from elsewhere, a staggering 93% of currently active cases were asymptomatic. In other words, only 1 in 14 people who was affected was showing any sign of symptoms.

Then, as I might have remarked on Twitter a few times, compulsory quarantining or hospitalisation (which was in force until July IIRC) has been a strong disincentive to people from seeking medical help or getting tested. This has meant that people get themselves tested only when the symptoms are really clear, or when they need attention. The downside of this, of course, has been that many people have got themselves tested too late for help. One statistic I remember is that about 33% of people who died of covid-19 in hospitals died within 24 hours of hospitalisation.

So if only one in 14 show any symptoms, and only those with relatively serious symptoms (or with close relatives who have serious symptoms) get themselves tested, this undercount by a factor of 40 can make sense.

Then – does the survey makes sense? Is 15000 samples big enough for a state of 70 million? For starters, the population of the state doesn’t matter. Rudimentary statistics (I always go to this presentation by Rajeeva Karandikar of CMI)  tells us that the size of the population doesn’t matter. As long as the sample has been chosen randomly, all that matters for the accuracy of the survey is the size of the sample. And for a binary decision (infected / not), 15000 is good enough as long as the sample has been random.

And that is where the survey raises questions – the survey has used an equal number of low risk, high risk and medium risk samples. “High risk” have been defined as people with comorbidities. Moderate risk are people who interact a lot with a lot of people (shopkeepers, healthcare workers, etc.). Both seem fine. It’s the “low risk” that seems suspect, where they have included pregnant women and attendants of outpatient patients in hospitals.

I have a few concerns – are the “low risk” low risk enough? Doesn’t the fact that you have accompanied someone to hospital, or  gone to hospital yourself (because you are pregnant), make you higher than average risk? And then – there are an equal number of low risk, medium risk and high risk people in the sample and there doesn’t seem to be any re-weighting. This suggests to me that the medium and high risk people have been overrepresented in the sample.

Finally, the press release says:

We excluded those already diagnosed with SARS-CoV2 infection, unwilling to provide a sample for the test, or did not agree to provide informed consent

I wonder if this sort of exclusion doesn’t result in a bias in itself.

Putting all this together – that there are qual samples of low, medium and high risk, that the “low risk” sample itself contains people of higher than normal risk, and that people who have refused to participate in the survey have been excluded – I sense that the total prevalence of covid-19 in Karnataka is likely to be overstated. By what factor, it is impossible to say. Maybe our original guess that the incidence of the disease is about 20 times the number of known cases is still valid? We will never know.

Nevertheless, we can be confident that a large section of the state (may not be 50%, but maybe 40%?) has already been infected with covid-19 and unless the ongoing festive season plays havoc, the number of cases is likely to continue dipping.

However, this is no reason to be complacent. I think Nitin Pai is  bang on here.

And I know a lot of people who have been aggressively social distancing (not even meeting people who have domestic help coming home, etc.). It is important that when they do relax, they do so in a graded manner.

Wear masks. Avoid crowded closed places. If you are going to get covid-19 anyway (and many of us have already got it, whether we know it or not), it is significantly better for you that you get a small viral load of it.

The Two Overton Windows

If you want to appear intelligent when discussing something about public policy, you could do worse than uttering the phrase “Overton Window”. The Overton Window, “invented” by one Joseph Overton, suggests that there is a “range of policies acceptable to political mainstream”.

And so you frequently have political commentators talking about the Overton Window “shifting” whenever a new political idea (or person) comes to the fore. This was bandied about much when Modi became Prime Minister of India, or when Trump became President of the US, or when Jeremy Corbyn became the Labour Party leader.

While “shifting Overton window” is something you come across rather often in policy discourse, my argument is that with the rise of subscription media, the Overton window is not shifting as much as it is “splitting”. In other words, we now have not one but two Overton Windows.

Without loss of generality, let us call them the “Jamie Overton Window” and the “Craig Overton Window”. Since both the twins are right arm fast bowlers, it doesn’t matter which brother is associated with which Overton Window.

So how did we get here, and what does it mean for us?

We started with the classic Overton Window. Let’s assume that all politics can be reduced to one axis (if we do a Principal Component Analysis of political views, the principal axis is certain to account for a large share of the variance, so this is not a bad assumption). So the Overton Window can be referred to by a line which the shifts.

As long as the world was “ruled by mainstream media”, this Overton Window kept moving back and forth, expanding and contracting, but it remained united. And then with the start of subscription ad-free media (maybe a decade or decade and half ago), the Overton Window started expanding.

The “left media” (that’s a convenient term isn’t it?) started admitting stuff that was left to the then Overton Window. The “right media” started admitting stuff that was to the right of the then Overton Window. And so over time, the Overton Window started expanding. And things can’t get into the media Overton Window unless they’re part of the mainstream political Overton Window.

The thing is that as the media became subscription-heavy and hence biased, political ideas that were once on the fringe now got a voice. And so the Overton Window got larger and larger.

Until a point when it got so unwieldy that it split, giving rise to Jamie and Craig. The image on the right is an approximate illustration of what happened.

And once the Overton Window split, there was no looking back. They started moving away from each other well-at-a-faster-rate. The Jamies could not come to terms with the policies of the Craigs, and vice versa. Political analysts and commentators started getting associated with the Jamie and Craig camps.

For a while, a few commentators continued to write for both sides, but the extreme fringes, which were getting more and more extreme, started overreacting. “How can we have someone who has written 10 articles for Craigs write for us”, the Jamies asked. “Most of our commentators are Craigs, so we might as well become a Craig newspaper”, the other side reasoned.

And that’s where mainstream media is going. The Overton Window has split down the middle. Crossing this gap is considered a crime worse than crossing the floor in Parliament.

Sadly, it is not just media that is getting Jamie and Craig. Mainstream politics reflects this as well, and so across countries we get political opponents who just cannot talk to each other, since everything one says is outside the Overton Window of the other.

Maybe the only way this can end is by going across axes, or inventing a new axis even. With the current spectrum politics, there is no hope of the two Overton Windows coming to meet.

 

The Crane-Mongoose Theory of Public Policy

I have several favourite stories from the Panchatantra (which perhaps explains my lack of appreciation of modern children’s fiction). One of them involves a crane and a mongoose. And I think it is a good lesson on when and where to call for regulation, and government or legal intervention.

So the story goes like this. A snake lives at the bottom of the tree where a crane has built its nest. Each time the crane lays eggs, the snake slithers up the tree and devours them. And the crane doesn’t know what to do. Ultimately it receives some “brilliant advice”.

There is a mongoose living somewhere nearby, and the crane lays out a Hansel-and-Gretel like path of fish from the mongoose’s house to the snake’s house. The mongoose duly follows the trail of fish and finishes off the snake. The next day, the mongoose is hungry again, and it climbs up the tree and devours the crane’s eggs.

It is common political discourse nowadays to call for the government’s or court’s intervention to solve what seems to be private problems. The governments and courts are of course happy to oblige – any new source for intervention and rent-seeking are good news for the people involved. And then you get a solution that temporarily solves the problem (slaughtering the snake). And then in the long term, what you get is a bigger problem (mongoose eating the crane’s eggs). The only difference is that in real life it is not just the crane that gets negatively affected – the regulations hurt everyone.

The examples that come to my mind at this point in time are all “local”. Some residents in Indiranagar in Bangalore weren’t happy about the noise from nearby pubs. They asked the government to “do something”. And the government “did something” – it banned the playing of live music in restaurants, killing off what was then a budding industry in Bangalore.

Some other residents somewhere else in Bangalore were unhappy that their neighbours had dogs that barked. They asked the government to do something. The government did something – coming up with an elaborate document to regulate dogs that people can own.

And there are more involved (and dangerous) examples of this as well.

Don’t be like the crane.

Correlation in defence purchases

Nitin Pai has a nice piece on defence procurement in Business Standard today. He writes:

Even if the planning process works as intended, it still means that the defence ministry merely adds up the individual requirements and goes about buying them. This is sub-optimal: consider a particular emerging threat that everyone agrees India needs to be prepared for. The army, navy and air force then prepare their own strategies and operational plans, for which they draw up a list of requirements. At the back of their minds, they know that the defence budget is more-or-less divided in a fixed ratio among them.

What he is saying, in other words, is that the defence ministry simply takes the arithmetic sum of demands from various components of the military, rather than taking correlation into account.

Let me explain using a toy example.

Let’s say that the Western wing of the Indian army (I’m making this up), the one that guards the border with Pakistan, wants 100 widgets that will come useful in case of a war. Let’s say that the Eastern wing of the Indian army, which guards the China border, wants 150 such widgets for the same purpose. The question is how many you should purchase.

According to Nitin, the defence ministry now doesn’t think. It simply adds up and buys 250. The question is if we actually need 250.

Let’s assume that these widgets are easily transportable, and let’s assume that the probability of a simultaneous conventional conflict with Pakistan and China is zero (given all three are nuclear states, this is a fair assumption). Do we still need 250 widgets? The answer is no, we only need 150, since we can quickly swing them over to where they are most required, and at the maximum, we need 150!

This is a case of negative correlation. There could be a case of positive correlation also – perhaps the chance of an India-China conventional conflict actually goes up when an India-Pakistan conventional conflict is on, and this might lead to more prolonged battles, meaning we might need more than 250 widgets! Or we have positive correlation.

The most famous example of ignoring correlation was the 2008 financial crisis, when ignored positive correlation led to mortgage backed securities and their derivatives blowing up. The Indian defence ministry can’t afford such a mistake.

In which I thulp the RBI

I’m still so pissed off with the Reserve Bank of India doing a Ramanamurthy that I’ve written a serious editorial in Pragati – the Indian National Interest Review (published by the Takshashila Institution). In this piece I take on measures by the RBI to limit ATM transactions and the thing on two factor authorization.

I claim that both these decisions are economically unsound and there is only possibly a farcical explanation for them:

There is perhaps only one idea (more a conspiracy theory) that possibly explains the above decisions from the RBI. Both these decisions, it might be noticed, help push up the usage of hard currency and decrease the levels of bank deposits. Less bank deposits means less money available for banks to lend out, which means that the cost of borrowing from a bank implicitly goes up. Could it be that the above regulations are a move by the RBI to curtail money supply without necessarily doing the politically tricky task of raising interest rates?

If it is (and it is a very remote possibility), we should commend the RBI for what will then amount to be a sneaky decision

Link