Tag: economics

Structures of professions and returns to experience

I’ve written here a few times about the concept of “returns to experience“. Basically, in some fields such as finance, the “returns to experience” is rather high. Irrespective of what you have studied or where, how long you have continuously been in the industry and what you have been doing has a bigger impact on your performance than your way of thinking or education.

In other domains, returns to experience is far less. After a few years in the profession, you would have learnt all you had to, and working longer in the job will not necessarily make you better at it. And so you see that the average 15 years experience people are not that much better than the average 10 years experience people, and so you see salaries stagnating as careers progress.

While I have spoken about returns to experience, till date, I hadn’t bothered to figure out why returns to experience is a thing in some, and only some, professions. And then I came across this tweetstorm that seeks to explain it.

Now, normally I have a policy of not reading tweetstorms longer than six tweets, but here it was well worth it.

It draws upon a concept called “cognitive flexibility theory”.

Basically, there are two kinds of professions – well-structured and ill-structured. To quickly summarise the tweetstorm, well-structured professions have the same problems again and again, and there are clear patterns. And in these professions, first principles are good to reason out most things, and solve most problems. And so the way you learn it is by learning concepts and theories and solving a few problems.

In ill-structured domains (eg. business or medicine), the concepts are largely the same but the way the concepts manifest in different cases are vastly different. As a consequence, just knowing the theories or fundamentals is not sufficient in being able to understand most cases, each of which is idiosyncratic.

Instead, study in these professions comes from “studying cases”. Business and medicine schools are classic examples of this. The idea with solving lots of cases is NOT that you can see the same patterns in a new case that you see, but that having seen lots of cases, you might be able to reason HOW to approach a new case that comes your way (and the way you approach it is very likely novel).

Picking up from the tweetstorm once again:

 

It is not hard to see that when the problems are ill-structured or “wicked”, the more the cases you have seen in your life, the better placed you are to attack the problem. Naturally, assuming you continue to learn from each incremental case you see, the returns to experience in such professions is high.

In securities trading, for example, the market takes very many forms, and irrespective of what chartists will tell you, patterns seldom repeat. The concepts are the same, however. Hence, you treat each new trade as a “case” and try to learn from it. So returns to experience are high. And so when I tried to reenter the industry after 5 years away, I found it incredibly hard.

Chess, on the other hand, is well-structured. Yes, alpha zero might come and go, but a lot of the general principles simply remain.

Having read this tweetstorm, gobbled a large glass of wine and written this blogpost (so far), I’ve been thinking about my own profession – data science. My sense is that data science is an ill-structured profession where most practitioners pretend it is well-structured. And this is possibly because a significant proportion of practitioners come from academia.

I keep telling people about my first brush with what can now be called data science – I was asked to build a model to forecast demand for air cargo (2006-7). The said demand being both intermittent (one order every few days for a particular flight) and lumpy (a single order could fill up a flight, for example), it was an incredibly wicked problem.

Having had a rather unique career path in this “industry” I have, over the years, been exposed to a large number of unique “cases”. In 2012, I’d set about trying to identify patterns so that I could “productise” some of my work, but the ill-structured nature of problems I was taking up meant this simply wasn’t forthcoming. And I realise (after having read the above-linked tweetstorm) that I continue to learn from cases, and that I’m a much better data scientist than I was a year back, and much much better than I was two years back.

On the other hand, because data science attracts a lot of people from pure science and engineering (classically well-structured fields), you see a lot of people trying to apply overly academic or textbook approaches to problems that they see. As they try to divine problem patterns that don’t really exist, they fail to recognise novel “cases”. And so they don’t really learn from their experience.

Maybe this is why I keep saying that “in data science, years of experience and competence are not correlated”. However, fundamentally, that ought NOT to be the case.

This is also perhaps why a lot of data scientists, irrespective of their years of experience, continue to remain “junior” in their thinking.

PS: The last few paragraphs apply equally well to quantitative finance and economics as well. They are ill-structured professions that some practitioners (thanks to well-structured backgrounds) assume are well-structured.

Beckerian Disciplines

When Gary Becker was awarded the “Nobel Prize” (or whatever its official name is) for Economics, the award didn’t cite any single work of his. Instead, as Justin Wolfers wrote in his obituary,

He was motivated by the belief that economics, taken seriously, could improve the human condition. He founded so many new fields of inquiry that the Nobel committee was forced to veer from the policy of awarding the prize for a specific piece of work, lauding him instead for “having extended the domain of microeconomic analysis to a wide range of human behavior and interaction, including nonmarket behavior.”

Or as Matthew Yglesias put it in his obituary of Becker,

Becker is known not so much for one empirical finding or theoretical conjecture, as for a broad meta-insight that he applied in several areas and that is now so broadly used that many people probably don’t realize that it was invented relatively recently.

Becker’s idea, in essence, was that the basic toolkit of economic modeling could be applied to a wide range of issues beyond the narrow realm of explicitly “economic” behavior. Though many of Becker’s specific claims remain controversial or superseded by subsequent literature, the idea of exploring everyday life through a broadly economic lens has been enormously influential in the economics profession and has altered how other social sciences approach their issues

Essentially Becker sort of pioneered the idea of using economic reasoning for fields outside traditional economics. It wasn’t always popular – for example, his use of economics methods in sociology was controversial, and “traditional sociologists” didn’t like the encroachment into their field.

However, Becker’s ideas endured. It is common nowadays for economists to explore ideas traditionally considered outside the boundaries of “standard economics”.

I think this goes well beyond economics. I think there are several other fields that are prone to “go out of syllabus” – where concepts are generic enough that they can be applied to areas traditionally outside the fields.

One obvious candidate is mathematics – most mathematical problems come from “real life”, and only the purest of mathematicians don’t include an application from “real life” (well outside of mathematics) while writing a mathematical paper. Immediately coming to mind is the famous “Hall’s Marriage Theorem” from Graph Theory.

Speaking of Graph Theory, Computer Science is another candidate (especially the area of algorithms, which I sort of specialised in during my undergrad).  I remember being thoroughly annoyed that papers and theses that would start so interestingly with a real-life problem would soon involve into inscrutable maths by the time you got to the second section. I remember my B.Tech. project (this was taken rather seriously at IIT Madras) being about what I had described as a “Party Hall Problem” (this was in Online Algorithms).

Rather surprisingly (to me), another area whose practitioners are fond of encroaching into other subjects is physics. This old XKCD sums it up

Complex Systems (do you know most complex systems scientists are physicists by training?) is another such field. There are more.

In any case, assuming no one else has done this already, I hereby christen all these fields (whose practitioners are prone to venturing into “out of syllabus matters”) as “Beckerian Disciplines” in honour of Gary Becker (OK I have a economics bias but I’m pretty sure there have been scientists well before Becker who have done this).

And then you have what I now call as “anti-Beckerian Disciplines” – areas that get pissed off that people from other fields are “invading their territory”. In Becker’s own case, the anti-Beckerian Discipline was Sociology.

When all university departments talk about “interdisciplinary research” what they really need is Beckerians. People who are able and willing to step out of the comfort zones of their own disciplines to lend a fresh pair of eyes (and a fresh perspective) to other disciplines.

The problem with this is that they can encounter an anti-Beckerian response from people trying to defend their own turf from “outside invasion”. This doesn’t help the cause of science (or research of any kind) but in general (well, a LOT of exceptions exist), academics can be a prickly and insecure bunch forever playing zero-sum status games.

With the covid-19 virus crisis, one set of anti-Beckerians who have emerged is epidemiologists. Epidemiology is a nice discipline in that it can be studied using graph theory, non-linear dynamics or (as I did earlier today) simple Bayesian maths or so many other frameworks that don’t need a degree in biology or medicine.

And epidemiologists are not happy (I’m not talking about my tweet specifically but this is a more general comment) that their turf is being invaded upon. “Listen to the experts”, they are saying, with the assumption that the experts in question here are them. People are resorting to credentialism. They’re adding “, PhD” to their names on twitter (a particularly shady credentialist practice IMHO). Questioning credentials and locus standi of people producing interesting analysis.

Enough of this rant. Since you’ve come all the way, I leave you with this particularly awesome blogpost by Tyler Cowen, who is a particularly Beckerian economist, about epidemiologists. Sample this:

Now, to close, I have a few rude questions that nobody else seems willing to ask, and I genuinely do not know the answers to these:

a. As a class of scientists, how much are epidemiologists paid?  Is good or bad news better for their salaries?

b. How smart are they?  What are their average GRE scores?

c. Are they hired into thick, liquid academic and institutional markets?  And how meritocratic are those markets?

d. What is their overall track record on predictions, whether before or during this crisis?

e. On average, what is the political orientation of epidemiologists?  And compared to other academics?  Which social welfare function do they use when they make non-trivial recommendations?

Alchemy

Over the last 4-5 days I kinda immersed myself in finishing Rory Sutherland’s excellent book Alchemy.

It all started with a podcast, with Sutherland being the guest on Russ Roberts’ EconTalk last week. I’d barely listened to half the podcast when I knew that I wanted more of Sutherland, and so immediately bought the book on Kindle. The same evening, I finished my previous book and started reading this.

Sometimes I get a bit concerned that I’m agreeing with an author too much. What made this book “interesting” is that Sutherland is an ad-man and a marketer, and keeps talking down on data and economics, and plays up intuition and “feeling”. In other words, at least as far as professional career and leanings go, he is possibly as far from me as it gets. Yet, I found myself silently nodding in agreement as I went through the book.

If I have to summarise the book in one line I would say, “most decisions are made intuitively or based on feeling. Data and logic are mainly used to rationalise decisions rather than making them”.

And if you think about it, it’s mostly true. For example, you don’t use physics to calculate how much to press down on your car accelerator while driving – you do it essentially by trial and error and using your intuition to gauge the feedback. Similarly, a ball player doesn’t need to know any kinematics or projectile motion to know how to throw or hit or catch a ball.

The other thing that Sutherland repeatedly alludes to is that we tend to try and optimise things that are easy to measure or optimise. Financials are a good example of that. This decade, with the “big data revolution” being followed by the rise of “data science”, the amount of data available to make decisions has been endless, meaning that more and more decisions are being made using data.

The trouble, of course, is availability bias, or what I call as the “keys-under-lamppost bias”. We tend to optimise and make decisions on things that are easily measurable (this set of course is now much larger than it was a decade ago), and now that we know we are making use of more objective stuff, we have irrational confidence in our decisions.

Sutherland talks about barbell strategies, ergodicity, why big data leads to bullshit, why it is important to look for solutions beyond the scope of the immediate domain and the Dunning-Kruger effect. He makes statements such as “I would rather run a business with no mathematicians than with second-rate mathematicians“, which exactly mirrors my opinion of the “data science industry”.

There is absolutely no doubt why I liked the book.

Thinking again, while I said that professionally Sutherland seems as far from me as possible, it’s possibly not so true. While I do use a fair bit of data and economic analysis as part of my consulting work, I find that I make most of my decisions finally on intuition. Data is there to guide me, but the decision-making is always an intuitive process.

In late 2017, when I briefly worked in an ill-fated job in “data science”, I’d made a document about the benefits of combining data analysis with human insight. And if I think about my work, my least favourite work is where I’ve done work with data to help clients make “logical decision” (as Sutherland puts it).

The work I’ve enjoyed the most has been where I’ve used the data and presented it in ways in which my clients and I have noticed patterns, rationalised them and then taken a (intuitive) leap of faith into what the right course of action may be.

And this also means that over time I’ve been moving away from work that involves building models (the output is too “precise” to interest me), and take on more “strategic” stuff where there is a fair amount of intuition riding on top of the data.

Back to the book, I’m so impressed with it that in case I was still living in London, I would have pestered Sutherland to meet me, and then tried to convince him to let me work for him. Even if at the top level it seems like his work and mine are diametrically opposite..

I leave you with my highlights and notes from the book, and this tweet.

Here’s my book, in case you are interested.

 

Magnus Carlsen’s Endowment

Game 12 of the ongoing Chess World Championship match between Magnus Carlsen and Fabiano Caruana ended in an unexpected draw after only 31 moves, when Carlsen, in a clearly better position and clearly ahead on time, made an unexpected draw offer.

The match will now go into a series of tie-breaks, played with ever-shortening time controls, as the world looks for a winner. Given the players’ historical record, Carlsen is the favourite for the rapid playoffs. And he knows it, since starting in game 11, he seemed to play towards taking it into the playoffs.

Yesterday’s Game 12 was a strange one. It started off with a sharp Sicilian Pelikan like games 8 and 10, and then between moves 15 and 20, players repeated the position twice. Now, the rules of chess state that if the same position appears three times on the board, the game is declared a draw. And there was this move where Caruana had the chance to repeat a position for the third time, thus drawing the game.

He spent nearly half an hour on the move, and at the end of it, he decided to deviate. In other words, no quick draw. My suspicion is that this unnerved Carlsen, who decided to then take a draw at the earliest available opportunity available to him (the rules of the match state that a draw cannot be agreed before move 30. Carlsen made his offer on move 31).

In behavioural economics, Endowment Effect refers to the bias where you place a higher value on something you own than on something you don’t own. This has several implications, all of which can lead to potentially irrational behaviour. The best example is “throwing good money after bad” – if you have made an investment that has lost money, rather than cutting your losses, you double down on the investment in the hope that you’ll recoup your losses.

Another implication is that even when it is rational to sell something you own, you hold on because of the irrationally high value you place on it. The endowment effect also has an impact in pricing and negotiations – you don’t mind that “convenience charge” that the travel aggregator adds on just before you enter your credit card details, for you have already mentally “bought” the ticket, and this convenience charge is only a minor inconvenience. Once you are convinced that you need to do a business deal, you don’t mind if the price moves away from you in small marginal steps – once you’ve made the decision that you have to do the deal, these moves away are only minor, and well within the higher value you’ve placed on the deal.

So where does this fit in to Carlsen’s draw offer yesterday? It was clear from the outset that Carlsen was playing for a draw. When the position was repeated twice, it raised Carlsen’s hope that the game would be a draw, and he assumed that he was getting the draw he wanted. When Caruana refused to repeat position, and did so after a really long think, Carlsen suddenly realised that he wasn’t getting the draw he thought he was getting.

It was as if the draw was Carlsen’s and it had now been taken away from him, so now he needed to somehow get it. Carlsen played well after that, and Caruana played badly, and the engines clearly showed that Carlsen had an advantage when the game crossed move 30.

However, having “accepted” a draw earlier in the game (by repeating moves twice), Carlsen wanted to lock in the draw, rather than play on in an inferior mental state and risk a loss (which would also result in the loss of the Championship). And hence, despite the significantly superior position, he made the draw offer, which Caruana was only happy to accept (given his worse situation).

 

 

How markets work

A long time back, there was this picture that was making the rounds on Twitter and (more prominently) LinkedIn. It featured three boys of varying heights trying to look over a fence to see a ball game.

Here is what it looked like:

Source: http://www.freshshropshire.org.uk/about-us/equality-and-diversity/equality-of-opportunity/

These pictures were used to illustrate that equality of outcomes is not the equality of opportunity, or some such things, and to make a case for “justice”.

As it must be very clear, the allocation of blocks on the right is more efficient than the allocation of the blocks on the left – the tallest guy simply doesn’t need any blocks, while the shortest guy needs two.

And if you think about it, you don’t need any top-down “justice” to allocate the blocks in the right manner. All it takes is a bit of logical thinking and markets – and not even efficiently.

Think about how this scenario might play out at the ball park. The three boys go to see the ball game, and see three blocks at the fence. Each of them climbs a block, and we get the situation on the left.

Shortest boy realises he can’t see and starts crying. There are many ways in which this story can play out from here onward:

  1. Tallest boy realises that he doesn’t really need that extra block, and steps down and gives it to the shortest guy, giving the picture on the right.
  2. Tallest boy continues to stand on his block. Shortest boy realises that the tallest boy doesn’t need it, and requests him for the block. Assuming tallest boy likes him, he will give him the block.
  3. Tallest boy continues on the block. Shortest boy requests for it, but tallest boy refuses saying “this is my block why should I give it to you?”. Shortest boy negotiates. Tells tallest boy he’ll give him a chocolate or some such in return for the block. And gets the block.
  4. Tallest boy doesn’t want chocolate or anything else the shortest boy offers. In fact he might want to settle a score with the shortest boy and refuses to give the block. In this case, the shortest boy realises there is no point being there and not watching the ball game, and makes an exit. In some cases, the middle boy might negotiate with the tallest boy on his behalf, leading to the transfer of the block. In other situations, the shortest boy simply goes away.

Notice that in none of these situations (all of them reasonably “spontaneous”) does the picture on the left happen. In other words, it’s simply unrealistic. And you don’t need any top down notion of “justice” to enable the blocks to be distributed in a “fair” manner.

Service charges

So the Indian government has said that it is not mandatory for customers to pay “service charges” at restaurants. It will be interesting to see how the restaurant industry will react to this.

The basic idea of a “service charge” is a “forced tip”. Given that Indians aren’t big tippers, restaurants, about a decade ago, started levying a service charge on top of the bill, ranging from 5% to 15%. Some restaurants mention this on the menu explicitly. In others, the print is fine. Some customers have come to accept the service charge. Others fight it.

The National Restaurants Association of India hasn’t taken too kindly to the notification, and has said they’ll take the government to court on this matter. It sounds like a rather extreme reaction, but illustrates the effect of behavioural studies.

Lower end eateries typically publish menus with “all inclusive” prices. If a cup of coffee is listed at Rs. 10, you pay Rs. 10 for it. Mid-priced and higher-end restaurants, however, have defaulted to showing prices exclusive of taxes and charges. With a 5% VAT, 15% Service Tax and (typically) 5% service charge, the final bill comes out to about 25-30% higher than the labelled price.

Now, frequent restaurant goers are aware of all these charges, and that the bill will be much higher than the sticker price. If they are rational, they should be taking into account these additional charges when deciding whether to go to restaurants, and when they do, what to order.

The problem, however, is that these charges are not immediately visible at the time of ordering, and so the customers end up ordering more expensive food than they had budgeted for (after controlling for the overall price level of the restaurant itself). It is a behavioural effect, where the customers’ minds are tricked by the number in front of them rather than what they will immediately end up paying.

The order that service charge is not mandatory will now push restaurants to include them in the sticker price of the food itself (it doesn’t matter what you call it – it’s ultimately revenue to the restaurant). The immediate impact of this will be that sticker prices will have to go higher, which will put a “bigger price” in front of the customers’ eyes, and they will order less.

How much less is not clear, but the fact that the restaurants association wants to take the government to court suggests it’s not insignificant. The high end restaurant business runs on extremely low margins (think what you may of the pricing), and even a less than 5% impact on revenues can have a significant impact on the bottom line.

It will be interesting to see if the government next mandates menus to print prices inclusive of taxes. It will be another behavioural nudge, but will end up ruining the restaurant business even more.

Elasticity and Discounted Pricing

The common trend among startups nowadays is to give away their product for a low price (or no price), and often below what it costs them to make it. The reasoning is that this helps them build traction, and marketshare, quickly. And that once the market has taken to the product, and the product has become a significant part of the customer’s life, prices can be raised and money can be made.

The problem with this approach is the beast known as elasticity. Elasticity means that when you increase your price, quantity demanded falls. Some products are highly elastic – a small increase in price can result in a large drop in quantity. Others are less so. Yet, it is extremely rare to find a product whose elasticity is zero, that is, whose quantity demanded does not vary with price. And even if such products exist, it is extremely unlikely that a product produced by a startup will fall in that category.

A good example of elasticity hitting is the shutting down of this American company called HomeJoy. As this piece in Forbes explains, the chief reason for HomeJoy shutting down is that it couldn’t hold on to its customers when it started charging market rates:

Not only did that kind of discounting make Homejoy lose significant money, it also brought in the wrong kind of customer. Many never booked again because they weren’t willing or able to pay the full price, which ranged from $25 to $35 an hour. Homejoy changed its pricing last year to make recurring cleanings cheaper and encourage repeat business. In response, some customers simply booked at the cheaper price and cancelled future appointments.

Based on the above explanation, it seems like subsidising customers to gain traction is a bad idea, and that a business should not be willing to make losses in the initial days in order to gain market. Yet, that would be like throwing out the baby with the bathwater, for subsidising at the “right level” can help ramp up significantly without elasticity hitting later. The question is what the right level is.

A feature of many businesses, and especially marketplace kind of businesses that startups nowadays are getting into, is economies of scale. This means that as the number of units “sold” increases, the cost per unit falls drastically. In other words, such businesses work well when they have built up sufficient scale, but collapse at lower levels. For such businesses, the thinking goes, it is impossible to bootstrap, and the solution is to subsidise customers until the requisite scale can be built up, at which point in time you can start making money.

The question is regarding the “sweet spot” of subsidy that should be given to the customer in order to build up the business. If you subsidise the customer too much in the initial days, there is the risk of elasticity hitting you at steady state, and things rapidly unravelling. If you subsidise too little, you may never build the scale.

The answer is rather straightforward, and possibly intuitive – start out by charging the price to the customer at which the business will be profitable and sustainable in the steady state. This will imply losses in the initial days, since your unit costs will be significantly higher (due to lack of scale). Yet, as you ramp up and hit steady state, you don’t have the problem of raising the price which might result in elasticity hitting your business.

What if, on the other hand, the subsidy you are giving out is not enough, and you are not willing to build traction? That is answered with the “Queen of Hearts” paradigm. The paradigm says that if the only way you can make your contract is if West holds the Queen of Hearts (talking about contract bridge here), you simply assume that West holds the card and play on. If he held the card, you would win. If not, you would have never won anyway!

Similarly, the only way your business might be long-run-sustainable is if you can generate sufficient traction at your long-run-sustainable price. If you need to drop the price below this in order to gain initial traction, it means that you will have the risk of losing customers when you eventually raise the price to the long-run-sustainable-price, which means that your business is perhaps not long-run-sustainable, and it is best for you to cut your losses and move on.

 

Now think of all the heavily-discounted startups out there and tabulate who are the ones who are charging what you think is a long-run-sustainable price, and who runs the risk of getting hit by elasticity.

The Box: A review

So over the weekend I started and finished reading “The Box: How the shipping container made the world smaller and the world economy bigger” by Mark Levinson. It’s a fascinating book, and one that I had been intending to read for a very long time. Somehow it always kept slipping my mind whenever I wondered what book to buy next, and I’d pushed buying it for a long time now.

Finally, a few days back, when “unknown twitter celebrity” Krish Ashok asked his followers to send him reading recommendations, and when he published the list, and I saw this book on the list, and I saw that the book was available on Kindle for Rs. 175, I just bought it. This is the first book in a very long time that I’ve bought “straight” off the Kindle Store, not bothering with a sample.

It’s a fascinating book, as it takes us through the 50-odd years of history of the shipping box. And on the way, it gives us insights into the development of the world economy through the 50s and 60s, and factors that led to the logistic revolution ushered in by the box.

We think of post world war America as this capitalist haven, where markets were free, and you could get jailed for communist leanings. We tend to think about this time as one of innovation and freedom of business, leading to high economic growth.

This wasn’t the case, though. While the US was nominally capitalist and markets were supposedly free, this was a time of heavy regulations, and the presence of cartels. International shipping rates, for example, till the mid-1970s, were set by “conferences” (basically cartels), after which the cartels broke down. It was not possible for a carrier to quote an integrated source-to-destination rate, and rates had to be quoted by leg. Someone who wanted to start a new train route had to prove to the regulators that it would not harm existing players!

And then there were the unions. Levinson devotes an entire chapter to how the unions were managed. Basically containerisation meant greater mechanisation and a reduction in demand for labour. And this was obviously not acceptable to the dockworker unions, and led to protracted battles which needed to be resolved before containerisation could take off. The most interesting story came from the UK, where unions in most established ports (primarily London and Liverpool) blocked containerisation, and went on strike in the specially developed container port at Tilbury. Felixstowe, which had hitherto been too obscure a port to attract unions’ attention, now unencumbered by unions, jumped on to the container business and is now by far the UK’s biggest port.

Levinson also pays much attention to how the container shaped economies in general. Prior to containerisation, the cost of changing mode of transport was very high, since individual items needed to be unloaded from one means of transport and loaded to another. Industries were usually located based on access to port, and ports came up to service nearby industries. Containerisation changed all that. Now that it was easy to transport using a series of different means of transport, the location advantage of being close to port was lost. And this had massive effects on the economy of regions.

Massive effects on economies also happened due to the scale factor that containerisation brought in. Small ports didn’t make any sense any more, since the transaction cost of berthing was too high. And so small ports started dying, with business being soncolidated into a few larger ports. The game changed into a winner take all mechanism.

In the 1950s and 60s, before the coming of the container, shipping was a low-capex high-opex (operational expenditure) business. Most ships were old and cheap, but costs in terms of labour and other things was high. With the coming of the containership, the cost structure inverted, with the capital expenditure now being extremely high, but opex being quite low. This led to “revenue management”, and a drop in prices, and ultimately the breaking of the cartels.

The book is full of insights, and chapters are organised by subject rather than in chronological order. It gets a little repetitive at times, but is mostly crisp (I read it in a weekend), and the insights mentioned above are only a sample. And it tells us not only the story of the box (which it does) but also the story of the world economy, and regulation, and competition, and unionisation and economies of scale. Highly recommended.

 

Aggregate quality of life

I was going through some discussions on the “Bangalore – Photos from a Bygone Era” (membership required to view) group on Facebook. From some of the discussions, it is evident that people are nostalgic about the quality of life in Bangalore in “those bygone days” compared to now (irrespective of your definition of bygone).

For example, someone was marvelling about how empty the HAL airport used to be in those days, until it became intolerably crowded in the late 1990s necessitating the construction of the new airport in Devanahalli. Someone else, perhaps in the same thread, wondered about how one could make a dash from HAL airport to Commercial street and back in 30 minutes “back in those days”. Outside of the group, I remember Vijay Mallya mention in an interview a couple of years back about how when he was young he could drive from his home in the middle of town to HAL airport in 15 minutes, and it’s not possible any more.

Reading such reports, you start thinking that life back in those days was truly superior to life today.

While narratives like the above might indeed make you believe that life in a “bygone era” was significantly superior, what that doesn’t take into account is that life was possibly superior for only certain people back then – airports were empty because tickets were prohibitively expensive and the monopolist Indian Airlines ran few flights out of Bangalore. Traffic was smooth because there were few cars, so if you were lucky to have one you could zip around the city. However, if you were not as lucky, and one of the many who didn’t have access to a personal vehicle, things could be really bad for you, for you had to either walk, or wait endlessly for a perpetually crowded bus!

One of the ostensible purposes of the socialist model followed by India in the early decades after independence was to limit inequality. Yet, the shortages that the system led to led to widening inequality rather than suppressing it. By conventional metrics of inequality – such as the Gini coefficient, it might be that wealth/income inequality in India today is significantly higher than in the decades immediately after independence.

However, if you were to take into account consumption and access to living a certain way, inequality today is far lower than it was in those socialist years. In the 1970s you could get an asset only if you knew someone that mattered (my father waited four years (1976-80) before he was “allotted” his scooter. His first telephone connection took six years (1989-95) to arrive), and this only served to exacerbate the inequality between those that had access to the “system” and those that didn’t. Today on the other hand you are able to purchase any asset on demand as long as you can afford it! And so a lot more people can afford a “reasonable” quality of life that was beyond them (or their ancestors) back in those days!

What we need is a redefinition of the concept of inequality from a strictly monetary one to one based on consumption and access to certain goods and services. While wealth inequality is indeed a problem (because of lower marginal utility of money the super-rich don’t spend as much as the less rich), what matters more is inequality in terms of quality of life. And this is something standard measures such as the Gini coefficient cannot measure.

I tried getting some students work on a “quality of life index” to show the improvements in quality of life (as explained above) since the “bygone era”. Perhaps I didn’t communicate it well enough, but they just stuck to standard definitions like per capita income, education, life expectancy, etc. What I want to build is an index that captures and tracks “true inequality”.

Weak ties and job hunting

As the more perceptive of you would have figured out by now, the wife is in her first year of business school, and looking for an internship. I’m at a life stage where I have friends in most companies she is interested in who are in roles that are at a level where it is possible for them to make a decision to hire her.

Yet, so far I’ve made few recommendations. I’ve made the odd connection but that’s been mostly of the “she is applying to your company and wants to get to know the company better. Can you speak to her about it?” variety. I don’t think there’s a single person to whom I’ve written saying that the wife is in the market for an internship and they should consider hiring her.

I initially thought it was some inherent meanness in me, or lack of desire to help, that prevented me from recommending my wife to potential hirers who I know well. But then a little bit of literature survey pointed out an economic rationale to my behaviour – it is the phenomenon of “weak ties”. Now I was aware of this weak ties research earlier – but I had assumed that it had only referred to the phenomenon where acquaintances are more likely to help than friends because the former’s networks are much more disjoint from yours than the latter’s.

Anyway, in a vain attempt at defence, I hit “weak ties and job hunting” into google, and that led me to this wonderful post on the social capital blog that contained exactly what I was looking for. Here is the money quote:

It turns out, that people generally don’t refer their close friends to jobs for two reasons: 1) they are more worried that it will reflect badly on them if it doesn’t work out; and 2) they are more likely to know of the warts and foibles of their close friends and believe these could interfere with being a good worker (e.g., Jim stays up late to watch sports, or Charles has too much of an attitude, or Jane is too involved with her sick father).  Weak friends one can more easily project good attributes onto and believe this will work out.

So if I were to request you to hire my wife and it doesn’t work out, it can affect the relationship between you and me, so I wouldn’t risk that. When I’m recommending someone very close to me, I’m putting my own reputation on the line and I don’t like that. I’m happy referring cousins or other slightly distant acquaintances because there I have no skin in the game and hopefully some good karma can get created.

Now, while I’m loathe to recommend my wife to people I know well,  I wouldn’t be so hesitant recommending her to people I don’t know that well! For while my tie with my wife is strong, my tie with these people is weak enough that it not working out won’t affect me, and there is little reputational risk also. The problem is when the ties on both sides are strong!