Tag: risk

When Institutions Decay

A few weeks back, I’d written about “average and peak skills“. The basic idea in this blogpost is that in most jobs, the level of skills you need on most days (or the “average skill” you need) is far far inferior to the “peak skill” level required occasionally.

I didn’t think about this when I wrote that blogpost, but now I realise that a lot of institutional decay can be simply explained by ignoring this gap between average and peak skills required.

I was at my niece’s wedding this morning, and was talking to my wife about the nadaswara players (and more specifically about this tweet):

“Why do you even need a jalra”, she asked. And then I pointed out that the jalra guy had now started playing the nadaswara (volga). “Why do we need this entire band”, she went on, suggesting that we could potentially use a tape instead.

This is a classic case of peak and average skill. The average skill required by the nadaswara player (whether someone sitting there or just operating a tape) is to just play, play it well and play in sync with the dhol guy. And if you want to maximise for the sheer quality of the music played, then you might as well just buy a tape and play it at the venue.

However, the “peak skill” of the nadaswara player goes beyond that. He is supposed to function without instruction. He is supposed to keep an eye on what is happening at the wedding, have an idea of the rituals (given how much the rituals vary by community, this is nontrivial) and know what kind of music to play when (or not play at all). He is supposed to gauge the sense of the audience and adjust the sort of music he is playing accordingly.

And if you consider all these peak skills required, you realise that you need a live player rather than a tape. And you realise that you need someone who is fairly experienced since this kind of judgment is likely to come more easily to the player.

The problem with professions with big gaps between average and peak skills, and where peak skills are seldom called upon, is that penny-pinching managers can ignore the peak and just hire for average skill (I had mentioned this in my previous post on the topic as well).

In the short run, there is an advantage in that people with average skills for the job are far cheaper than those with peak skills for the job (and the former are unlikely to suffer motivational issues as well). Now, over a period of time you find that these average skilled people are able to do rather well (and are much cheaper and much lower maintenance than peak skilled people).

Soon you start questioning why you need the peak skill people after all. And start replacing them with average people. The more rare the requirement of peak skill is in the job, the longer you’ll be able to go on like this. And then one day you’ll find that the job on that day required a little more nuance and skill, and your current team is wholly incapable of handling it.

You replace your live music by tapes, and find that your music has got static and boring. You replace your bank tellers with a combination of ATMs and call centres, and find it impossible to serve that one customer with an idiosyncratic request. You replace your software engineers with people who don’t have that good an idea of algorithmic theory, and one day are saddled with inefficient code.

Ignoring peak skill required while hiring is like ignoring tail risk. Because it is so improbable, you think it’s okay to ignore it. And then when it hits you it hits you hard.

Maybe that’s why risk management is usually bundled into a finance person’s job – if the same person or department in charge of cutting costs is also responsible for managing risk, they should be able to make better tradeoffs.

Darwin Nunez and missed chances

There is one “fact” I’m rather proud of – it is highly likely (there is absolutely no way to verify) that in CAT 2003-4 (scheduled for 2003; then paper got leaked and it was held in Feb 2004), among all those who actually joined IIMs that year, I had the most number of wrong answers.

By my calculations after the exam (yeah I remember these things) I had got 20 answers wrong (in a 150 question paper). Most of my friends had their wrong counts in the single digits. That I did rather well in the exam despite getting so many answers wrong was down to one thing – I got a very large number of answers right.

Most readers of this blog will know that I can be a bit narcissist. So when I see or read something, I immediately correlate it to my own life. Recently I was watching this video on striker Darwin Nunez, and his struggles to settle into the English Premier League.

“Nobody has missed more clear chances this season than Darwin Nunez”, begins JJ Bull in this otherwise nice analysis. Somewhere in the middle of this video, he slips in that Nunez has missed so many chances because he has created so many more of them in the first place – by being in the right place at the right time.

Long ago when I used to be a regular quizzer (nowadays I’m rather irregular), in finals I wouldn’t get stressed if our team missed a lot of questions (either with other teams answering before us, or getting something narrowly wrong). That we came so close to getting the points, I would reason, meant that we had our processes right in the first place, and sooner or later we would start getting those points.

In general I like Nunez. Maybe because he’s rather unpredictable (“Chaos” as JJ Bull calls him in the above video), I identify with him more than some of the more predictable characters in the team (it’s another matter that this whole season has been a disaster for Liverpool -I knew it on the opening day when Virgil Van Dijk gave away a clumsy penalty to Fulham). He is clumsy, misses seemingly easy chances, but creates some impossible stuff out of nothing (in that sense, he is very similar to Mo Salah, so I don’t know how they together work out as a portfolio for Liverpool. That said, I love watching them play together).

In the world of finance, losing money is seen as a positive bullet point. If you have lost more money, it is a bigger status symbol. In most cases, that you lost so much money means that your bank had trusted you with that much money in the first place, and so there must be something right about you.

You see this in the startup world. Someone’s startup folds. Some get acquihired. And then a few months later, you find that they are back in the market and investors are showering them with funds. One thing is that investors trust that other investors had trusted these founders with much more money in the past. The other, of course, is the hope that this time they would have learnt from the mistakes.

Fundamentally, though, the connecting thread running across all this is about how to evaluate risk, and luck. Conditional on your bank trusting you with a large trading account, one bad trading loss is more likely to be bad luck than your incompetence. And so other banks quickly hire you and trust you with their money.

That you have missed 15 big chances in half a season means that you have managed to create so many more chances (as part of a struggling team). And that actually makes you a good footballer (though vanilla pundits don’t see it that way).

So trust the process. And keep at it. As long as you are in the right place at the right time a lot of times, you will cash on average.

Key Person Risk and Creative Professions

I’m coming to the conclusion that creative professions inevitably come with a “key person risk”. And this is due to the way teams in such professions are usually built.

I’ll start with a tweet that I put out today.

(I had NOT planned this post at the time when I put out this tweet)

I’ll not go into defining creative professions here, but I will leave it to say that you typically know it when you see one.

The thing with teams in such professions is that people who are good and creative are highly unlikely to get along with each other. Going into the animal kingdom for an analogy, we can think of dividing everyone in any such professions into “alphas” and “betas”. Alphas are the massively creative people who usually rise to lead their teams. Betas are the rest.

And given that any kind of creativity is due to some amount of lateral thinking, people good at creative professions are likely to hallucinate a bit (hallucination is basically lateral thinking taken to an extreme). And stretching it a bit more, you can say that people who are good at creative tasks are usually mad in one way or another.

As I had written briefly this morning, it is not usual for mad people (especially of a similar nature of madness) to get along with each other. So if you have a creative alpha leading the team, it is highly unlikely that he/she will have similar alphas in the next line of leadership. It is more likely that the next line of leadership will have people who are good complements to the alpha leader.

For example, in the ongoing World Cup, I’ve seen several tactical videos that have all said one thing – that Rodrigo De Paul’s primary role in the Argentinian team is to “cover for Messi”. Messi doesn’t track back, but De Paul will do the defending for him. Messi largely switches off, but De Paul is industrious enough to cover for Messi. When Messi goes forward, De Paul goes back. When Messi drops deep, De Paul makes a forward run.

This is the most typical creative partnership that you can get – one very obviously alpha creative supported by one or more steady performers who enable the creative person to do the creative work.

The question is – what happens when the creative head (the alpha) leaves? And the answer to this are going to be different in elite sport and the corporate world (and I’m mostly talking about the latter in this post).

In elite sport, when Messi retires (which he is likely to do after tomorrow’s final, irrespective of the result), it is virtually inconceivable that Argentina will ask De Paul to play in his position. Instead, they will look into others who are already playing in a sort of Messi role, maybe (or likely) at an inferior level and bring them up. De Paul will continue to play his role of central midfielder and continue to support whoever comes into Messi’s role.

In corporate setups, though, when one employee leaves, the obvious thing to do is to promote that person’s second in command. Sometimes there might be a battle for succession among various seconds in command, and the losers also leave the company. For most teams, where seconds in command are usually similar in style to the leader, this kind of succession planning works.

For creative teams, however, this usually leads to a disaster. More often than not, the second in command’s skills will be very different from that of the leader. If the leader had been an alpha creative (that’s the case we’re largely discussing here), the second in command is more likely to be a steady “water carrier” (a pejorative term used to describe France’s current coach Didier Deschamps).

And if this “water carrier” (no offence meant to anyone by this, but it is a convenient description) stays in the job for a long time, it is likely that the creative team will stop being creative. The thing that made it creative in the first place was the alpha’s leadership (this is especially true of small teams), and unless the new boss has recognised this and brings in a new set of alphas (or identifies potential alphas in the org and quickly promotes them), the team will start specialising in what was the new boss’s specialisation – which is to hold things steady and do all the right things and cover for someone who doesn’t exist any more.

So teams in creative professions have a key man risk in that if a particularly successful alpha leaves, the team as it remains is likely to stagnate and stop being creative. The only potential solutions I can think of are:

  • Bring in a new creative from outside to lead the team. The second in command remains just that
  • Coach the second in command to identify diverse (and creative alpha) talents within the team and recognise that there are alphas and betas. And the second in command basically leads the team but not the creative work
  • Organise the team more as a sports team where each person has a specific role. So if the attacking midfielder leaves, replace with a new attacking midfielder (or promote a junior attacking midfielder into a senior attacking midfielder). Don’t ask your defensive midfielders to suddenly become an attacking midfielder
  • Put pressure from above for alphas to have a sufficient number of other alphas as the next line of command. Retaining this team is easier said than done, and without betas the team can collapse.

Of course, if you look at all this from the perspective of the beta, there is an obvious question mark about career prospects. Unless you suddenly change your style (easier said than done), you will never be the alpha, and this puts in place a sort of glass ceiling for your career.

Risk and data

A while back a group of <a large number of scientists> wrote an open letter to the Prime Minister demanding greater data sharing with them. I must say that the letter is written in academic language and the effort to understand it was too much, but in the interest of fairness I’ll put a screenshot that was posted on twitter here.

I don’t know about this clinical and academic data. However, the holding back of one kind of data, in my opinion, has massively (and negatively) impacted people’s mental health and risk calculations.

This is data on mortality and risk. The kind of questions that I expect government data to have answered was:

  1. If I get covid-19 (now in the second wave), what is the likelihood that I will die?
  2. If my oxygen level drops to 90 (>= 94 is “normal”), what is the likelihood that I will die?
  3. If I go to hospital, what is the likelihood I will die?
  4. If I go to ICU what is the likelihood I will die?
  5. What is the likelihood of a teenager who contracts the virus (and is otherwise in good health) dying of the virus?

And so on. Simple risk-based questions whose answers can help people calibrate their lives and take calculated enough risks to get on with it without putting themselves and their loved ones at risk.

Instead, what we find from official sources are nothing but aggregates. Total numbers of people infected, dead, recovered and so on. And it is impossible to infer answers to the “risk questions” based no that.

And who fill in the gaps? Media of course.

I must have discussed “spectacularness bias” on this blog several times before. Basically the idea is that for something to be news, it needs to carry information. And an event carries information if it occurs despite having a low prior probability (or not occurring despite a high prior probability). As I put it in my lectures, “‘dog bites man’ is not news. ‘man bits dog’ is news”.

So when we rely on media reports to fill in our gaps in our risk systems, we end up taking all the wrong kinds of lessons. We learn that one seventeen year old boy died of covid despite being otherwise healthy. In the absence of other information, we assume that teenagers are under grave risk from the disease.

Similarly, cases of children looking for ICU beds get forwarded far more than cases of old people looking for ICU beds. In the absence of risk information, we assume that the situation must be grave among children.

Old people dying from covid goes unreported (unless the person was famous in some way or the other), since the information content in that is low. Young people dying gets amplified.

Based on all the reports that we see in the papers and other media (including social media), we get an entirely warped sense of what the risk profile of the disease is. And panic. When we panic, our health gets worse.

Oh, and I haven’t even spoken about bad risk reporting in the media. I saw a report in the Times of India this morning (unable to find a link to it) that said that “young are facing higher mortality in this wave”. Basically the story said that people under 60 account for a far higher proportion of deaths in the second wave than in the first.

Now there are two problems with that story.

  1. A large proportion of over 60s in India are vaccinated, so mortality is likely to be lower in this cohort.
  2. What we need is the likelihood of a person under 60 dying upon contracting covid. NOT the proportion of deaths accounted for by under 60s. This is the classic “averaging along the wrong axis” that they unleash upon you in the first test of any statistics course.

Anyway, so what kind of data would have helped?

  1. Age profile of people testing positive, preferably state wise (any finer will be noise)
  2. Age profile of people dying of covid-19, again state wise

I’m sure the government collects this data. Just that they’re not used to releasing this kind fo data, so we’re not getting it. And so we have to rely on the media and its spectacularness bias to get our information. And so we panic.

PS: By no means am I stating that covid-19 is not a risk. All I am stating is that the information we have been given doesn’t help us make good risk decisions

Monetising volatility

I’m catching up on old newsletters now – a combination of job and taking my email off what is now my daughter’s iPad means I have a considerable backlog – and I found this gem in Matt Levine’s newsletter from two weeks back  ($; Bloomberg).

“it comes from monetizing volatility, that great yet under-appreciated resource.”

He is talking about equity derivatives, and says that this is “not such a good explanation”. While it may not be such a good explanation when it comes to equity derivatives itself, I think it has tremendous potential outside of finance.

I’m reminded of the first time I was working in the logistics industry (back in 2007). I had what I had thought was a stellar idea, which was basically based on monetising volatility, but given that I was in a company full of logistics and technology and operations research people, and no other derivatives people, I had a hard time convincing anyone of that idea.

My way of “monetising volatility” was rather simple – charge people cancellation fees. In the part of the logistics industry I was working in back then, this was (surprisingly, to me) a particularly novel idea. So how does cancellation fees equate to monetising volatility?

Again it’s due to “unbundling”. Let’s say you purchase a train ticket using advance reservation. You are basically buying two things – the OPTION to travel on that particular day using that particular train, sitting on that particular seat, and the cost of the travel itself.

The genius of the airline industry following the deregulation in the US in the 1980s was that these two costs could be separated. The genius was that charging separately for the travel itself and the option to travel, you can offer the travel itself at a much lower price. Think of the cancellation charge as as the “option premium” for exercising the option to travel.

And you can come up with options with different strike prices, and depending upon the strike price, the value of the option itself changes. Since it is the option to travel, it is like a call option, and so higher the strike price (the price you pay for the travel itself), the lower the price of the option.

This way, you can come up with a repertoire of strike-option combinations – the more you’re willing to pay for cancellation (option premium), the lower the price of the travel itself will be. This is why, for example, the cheapest airline tickets are those that come with close to zero refund on cancellation (though I’ve argued that bringing refunds all the way to zero is not a good idea).

Since there is uncertainty in whether you can travel at all (there are zillions of reasons why you might want to “cancel tickets”), this is basically about monetising this uncertainty or (in finance terms) “monetising volatility”. Rather than the old (regulated) world where cancellation fees were low and travel charges were high (option itself was not monetised), monetising the options (which is basically a price on volatility) meant that airlines could make more money, AND customers could travel cheaper.

It’s like money was being created out of thin air. And that was because we monetised volatility.

I had the same idea for another part of the business, but unfortunately we couldn’t monetise that. My idea was simple – if you charge cancellation fees, our demand will become more predictable (since people won’t chumma book), and this means we will be able to offer a discount. And offering a discount would mean more people would buy this more predictable demand, and in the immortal jargon of Silicon Valley, “a flywheel would be set in motion”.

The idea didn’t fly. Maybe I was too junior. Maybe people were suspicious of my brief background in banking. Maybe most people around me had “too much domain knowledge”. So the idea of charging for cancellation in an industry that traditionally didn’t charge for cancellation didn’t fly at all.

Anyway all of that is history.

Now that I’m back in the industry, it remains to be seen if I can come up with such “brilliant” ideas again.

Optimal risk sharing

The wife moved to Ann Arbor over the weekend, where she will be spending three months. She took an Air France flight (AF191) in the wee hours of Sunday morning, and then switched to a Delta flight at the legendary Charles de Gaulle. I must mention upfront that she seems to have had a peaceful journey.

Except that people following the same schedule exactly twenty four hours earlier would not have. AF191 that departed from Bangalore i n the wee hours of Saturday morning returned to Bangalore after a bomb scare. The flight was subsequently cancelled.

There are many risks to flying. Schedules nowadays are packed so closely that your flight might be delayed. Occasionally it might be cancelled even, sometimes without a good reason. A delay might sometimes mean that you miss your connecting flight.

The question is who bears the risk on this one. If I’m booked on a flight that gets cancelled or delayed (because of which I miss my connection), whose responsibility is it that I’m transported to my destination? There are three possibilities – the passenger himself, the airline and an external insurer. The question is which of these is most optimal.

The traditional model in aviation as I understand it is that it is the airline’s responsibility. While this makes sense because a large number of delays/cancellations are on account of faults on account of the airline, even when the delay is not due to the airline’s fault, the airline is best placed in terms of mitigating the risk.

Leaving the risk on the passenger has the advantage that he can choose his own risk profile. If you are flexible about your trip, you might choose to go without insurance, and take the hit yourself. If you’re a frequent flyer, then the “insurance cost” thus saved will compensate for the occasional delay. Yet, the problem with this kind of a model is that people tend to underestimate the risks, and will more often than not not insure, and get hit badly when the delay happens.

Which brings us to the final absorber of risk – the insurance company. I’d purchased “travel insurance” for a recent trip, and there was a component on account of delayed or missed flights. If my flight was delayed by a certain amount of time, my insurer would pay me a fixed amount of money.

While this financial hedging is good, it may not adequately represent the costs of making a new booking (including the hassles) when my flight is delayed or cancelled. So this is not a workable solution at scale.

Another solution is for the insurer to guarantee that you will reach your destination by a certain time in case your flight gets delayed or cancelled. This might work out to be more expensive than a fixed cash payout but this removes the cost and hassle of figuring out the next best alternative on the part of the customer. The problem, however, is correlation. Insurance works when people’s risks are uncorrelated or negatively correlated. Here they are positively correlated – all passengers on Saturday’s AF191 to Paris were affected similarly, and this pushes up the cost for the insurer to rebook people.

Unless they tie up with the airline itself! If they reach an agreement with the airline such that the airline commits to transport the stranded passengers, then this “positive correlation” I mentioned earlier will be taken care of. Seems workable, right? Except that what is being insured here is the risk that the airline abandoned in favour of the passenger, who insured against it from an insurer, who reinsured it with the airliner! Can we just cut out the middle men?

From this rather unscientific argument above, it looks like airlines are best placed to insure passengers against disrupted flight schedules. Back in the days of regulated air fares where competition had to be “on service”, airlines would take responsibility. This might have disappeared with the move towards unbundling over the last 2-3 decades. For good reason – insuring a schedule results in an additional (albeit hidden) cost, and getting rid of it can result in cheaper (base) fares.

Yet, given that airlines are best placed to insure schedules, we need a solution. Maybe they can charge a premium for insuring schedules apart from the base fares? Or would they argue that the current “unrestricted fares” are such insured fares (implying the premium is rather high)?

Short of  government mandated regulation, what is the best way for allocating the risk of disrupted flight schedules, and pricing it appropriately?

Tailpiece: A decade ago, our valuation professor (at IIM Bangalore) had told us that “risk cannot be eliminated. It can only be mitigaged by selling it to someone who can handle it better”.

27% and building narratives using numbers

Some numbers scare you. Some numbers look so unreasonably large that it seems daunting to you, infeasible even. Other numbers, when wrapped in the right kind of narrative, seem so unreasonably small that they sway you (the Rs. 32 per person per day poverty line comes to mind). Thus, when you are dealing with numbers that intuitively look very large or very small, it is important that you build the right narrative around them. Wrap them well so that it doesn’t scare or haunt people. As the old Mirinda Lime ad used to say, “zor ka jhatka.. dheere se lage..”.

So the number in the headline of this blog post is the proposed rate of the Goods and Service Tax. While it is the revenue-neutral amount that needs to be charge should excise and sales and other taxes go, the number looks stupendously large. The way this number was reported on the front pages of business newspapers this morning, it looks so large and out of whack that people might decide that it is better to not have a GST at all.

I’m not blaming the papers for this – they have reported what they’ve been told. It is a question of building narratives by the government. The government, and the GST sub-panel, has done a lousy job of communicating this number, and guiding how it needs to be reported in the media. It is almost as if the way the number was reported is an attempt to further delay the implementation of the GST.

The GST is too important a piece of legislation to be derailed by bad narratives. The government must make every attempt to build a narrative that shows the GST as being conducive to people and to businesses, to show how the transaction costs it reduces will result in better prices for both consumers and businesses, and why it makes lives better. Reporting numbers that look really large doesn’t help matters.

Also, the quant in me is disappointed to see one precise number being put out as the “revenue neutral rate”. Since different goods and services which are now being taxed at differential rates are going to be brought into this one umbrella rate, the real revenue neutral rate is actually a function of the mix of the contribution of each of these goods and services to the GDP. Given that in a dynamic economy these rates are constantly changing, reporting one revenue neutral rate simply doesn’t make sense. A range would be a better way of going about it.

Related to this, given that the revenue neutral rate is a function of mix of goods and services, and this mix will change over time, the assumptions and forecasts that need to be taken into account in the process of fixing the rate are important. The GST panel would do well to take into account the risk of product-and-service mix changing that can make all calculations go awry!

PS: If only they were to hire me as a consultant to this panel 😛

 

Sigma and normal distributions

I’m in my way to the Bangalore airport now, north of hebbal flyover. It’s raining like crazy again today – the second time in a week it’s raining so bad.

I instinctively thought “today is an N sigma day in terms of rain in Bangalore” (where N is a large number). Then I immediately realized that such a statement would make sense only if rainfall in Bangalore were to follow a normal distribution!

When people normally say something is an N sigma event what they’re really trying to convey is that it is a very improbable event and the N is a measure of this improbability. The relationship between N and the improbability implied is given by the shape of the normal curve.

However when a quantity follow a distribution other than normal the relationship between the mean and standard deviation (sigma) and the implied probability breaks down and the number of sigmas will mean something totally different in terms of the implied improbability.

It is good practice, thus, to stop talking in terms of sigma and talk in terms of of odds. It’s better to say “a one in forty event” rather than saying “two sigma event” (I’m assuming a one tailed normal distribution here).

The broader point is that the normal distribution is too ingrained in people’s minds which leads then to assume all quantities follow a normal distribution – which is dangerous and needs to be discouraged strongly.

In this direction any small measure – like talking odds rather than in terms of sigma – will go a long way!

Volleyball

It’s been over eight years since I last played the game, but if I were to pick one outdoor game in which I’m best at (relative to other games I’ve played) it’s volleyball. And when I say I’m best at that, it’s on a strict relative basis – in undergrad, I struggled to get into my hostel team (let alone college team). It just goes to show how bad I’ve been in other outdoor games! I’m a successful cricket and football-watcher, though!

The thing with volleyball is that my game runs counter to how i play other games, and my life in general. In general, I’m an extremely high-risk person – I’m not into adventure sports, though, but have a Royal Enfield motorcycle – I take chances where possible and go for the spectacular. It is hard for me to be “accurate” and “correct”, and given that I know that I’m prone to making mistakes I try to maximize the outputs from the times when I don’t make mistakes, and thus go on a high risk path.

So I’ve quit my job without something else in hand four times, now freelance as a management consultant, blog about every damn thing – things that have promises of big upsides, but also risks of downsides. It also reflects in how I sometimes talk to people – I sometimes try too hard to make an impression – which can potentially get me big returns, but end up saying something stupid at times, and end up sounding arrogant at other times. Those are risks I willingly take.

And this risky nature has reflected in most games I’ve played, also – again nothing in the recent past. In chess, I get bored of slow technical Carlsen-esque positions, and am prone to go on Morphy-esque attacks that can backfire spectacularly. Playing bridge, I finesse way more than I’m supposed to – making some otherwise unmakeable contracts, but going down in contracts I should have otherwise made.

Back in school, when we played cricket with rubber and tennis balls, I would bowl leg spin, and using a light bat, would try to hit every ball for four or six, rather than trying to bat steadily. And while playing basketball (my “second best” outdoor game, after volleyball) I have a propensity to go for long shots.

What sets volleyball apart is that my game completely runs counter to who I am. In volleyball I’m a solid player – don’t spike too much (can’t jump!!), but can set spikes well, block well and can lead a team well from the back line. In fact, my best volleyball games have been those when the team has had to carry some weak links, and I’ve led from the centre of the back line, lending solidity and helping build up attacks. It definitely doesn’t reflect what I’m like otherwise.

But volleyball has also been the game where I’ve had a large number of spectacular failures. At every level I’ve played, I’ve had some responsibility thrust upon me, and I’ve buckled under the pressure. It’s volleyball that comes to mind every time I let down people’s trust because I do badly a something I’m supposed to be good at.

1. Voyagers versus pioneers, 1999: This was the school inter-house tournament. We go two sets up. They win the next two. Down to the decider. We lead 14-13, and its our turn to serve. Our captain purposely messes up our rotation such that I can serve (I had a big serve – one attacking aspect of my volleyball). The serve clips the net on its way across (back then, a let was a foul serve in volleyball). We lose.

2. NPS Indiranagar versus NPS Rajajinagar, 1999: Then I get selected to represent my school. I’m on the bench, and am subbed in right on time to serve. I decide to warm up with an underarm serve (before I start unleashing my overarm thunders). Hit it into the net. Opponent’s serve comes to me and I receive it badly. Get subbed out.

3. G block versus F block, 2004-05: Semi finals of the IIMB inter-hostel championship. We have two big spikers, two decent lifters and defenders (including me) and two who had never played volleyball in their lives, but were chosen on the basis of their physical fitness alone. Down to third set (best of three). We lead 25-24 (new scoring system). I’m playing right forward. Ball comes across the net. All I need to do is to set it up for a big spike, but I decide to spike it directly myself. And miss. Then I serve on the next match point. Decide to go for a safe serve, gets returned. We lose.

4. Section C versus Section A, 2004-05: Again similar story. I don’t remember the specifics of this, but again it was heartbreak, and I think I missed my serve on match point.

I guess you get the drift..

Pricing railway safety

Yet another railway accident has happened. As someone on twitter pointed out,

The problem with the Indian Railways is that there is no real measure of safety. How do we know how much safer the trains and tracks are compared to last year? Given the way the Railway finances are put out currently, there is no way to figure this out. Without the railways putting out more disclosures, is there a way to put a number on how safe the Indian Railways are? In other words, is there a way to “price” railway safety?

As you are well aware, and as the above tweet points out, it is standard practice in Indian Railway accidents for the Railway Minister to announce an ex-gratia payment to the families of the dead and the injured in case of any accident. I’m not sure if there is a formula to this but one cannot rule out the arbitrariness of this amount. As I had pointed out in an earlier post on RQ, accident compensation needs to be predictable and automatic. Can we use this to price railway safety?

First of all, we need to point out that the railways follows a cash accounting system, and thus doesn’t need to account for any contingent liabilities such as ex-gratia payment (last weekend I sat through an awesome lecture by Prof. Mukul Asher (councillor to Takshashila) on public finances, and he pointed this out). Hence, it would be prudent on behalf of the Indian Railways to hedge out this contingent liability.

How do you hedge a contingent liability? By buying insurance! What the Indian Railways needs to do is to buy group accident insurance – all the ex-gratia payments will then by paid out by the insurance company, and the railways will only pay a premium to these companies, thus hedging out the risk! And this process will help put a price on railway safety!

How is that? Let us say that given the railways’ bad record in safety, and its continued promises that safety will be improved each year, the railways decides to take up group accident insurance on an annual basis. Let us say that there is a competitive bidding process among general insurers in India (both public and private sector) to provide this insurance (railways is a large organization, and insuring them will be a matter of prestige, so companies will bid for it). The premium as determined by this competitive bidding process is the price of railway safety!

We can do better – instead of buying one overall policy, the Railways can think of insuring different routes separately, or perhaps zones. This will help put a price on the safety of each route or zone! There will be some transaction cost, of course, but price discovery will happen, and we will be able to put a price on risk!

But then, this is all wishful thinking. It is unlikely this will happen because:

1. Given the cash accounting system followed by the railways, there is no incentive to hedge contingent liabilities
2. Buying insurance means increasing scrutiny. The railways will not want to be scrutinized too hard. It is currently an opaque organization and it will want to be that way.
3. Given the railways are wholly government owned and there are government owned general insurers, there might be some collusion which might  result in underpricing the risk.
And so forth…

Nevertheless, the point of this post is that it is possible to put a price on safety!