liquidity – Pertinent Observations

It’s not just about status

Rob Henderson writes that in general, relative to the value they add to their firms, senior employees are underpaid and junior employees are overpaid. This, he reasons, is because senior employees trade off money for status.

Quoting him in full:

Robert Frank suggests the reason for this is that workers would generally prefer to occupy higher-ranked positions in their work groups than lower-ranked ones. They’re forgoing more earnings to hold a higher-status position in their organization.

But this preference for a higher-status position can be satisfied within any given organization.

After all, 50 percent of the positions in any firm must always be in the bottom half.

So the only way some workers can enjoy the pleasure inherent in positions of high status is if others are willing to bear the dissatisfactions associated with low status.

The solution, then, is to pay the low-status workers a bit more than they are worth to get them to stay. The high-status workers, in contrast, accept lower pay for the benefit of their lofty positions.

I’m not sure I agree. Yes, I do agree that higher productivity employees are underpaid and lower productivity employees are overpaid. However, I don’t think status fully explains it. There are also issues of variance and correlation and liquidity (there – I’m talking like a real quant now).

One the variance front – the higher you are in the organisation and the higher your salary is, the more the variance of your contribution to the organisation. For example, if you are being paid $350,000 (the number Henderson hypothetically uses), the actual value you are bringing to your firm might have a mean of $500,000 and a standard deviation of $200,000 (pulling all these numbers out of thin air, while making some sense checks that broadly risk pricing holds).

On the other hand, if you are being paid $35,000, then it is far more likely that the average value you bring to the firm is $40,000 with a standard deviation of $5,000 (again numbers entirely pulled out of thin air). Notice the drastic difference in the coefficient of variation in the two cases.

Putting it another way, the more productive you are, the harder it is for any organisation to put a precise value on your contribution. Henderson might say “you are worth 500K while you earn 350K” but the former is an average number. It is because of the high variance in your “worth” that you are paid far lower than what you are worth on average.

And why does this variance exist? It’s due to correlation.

More so at higher ranked positions (as an aside – my weird career path means that I’ve NEVER been in middle management) the value you can add to a company is tightly coupled with your interactions with your colleagues and peers. As a junior employee your role can be defined well enough that your contributions are stable irrespective of how you work with the others. At senior levels though a very large part of the value you can add is tied to how you work with others and leverage their work in your contributions.

So one way a company can get you to contribute more is to have a good set of peers you like working with, which increases your average contribution to the firm. Rather paradoxically, because you like your peers (assuming peer liking in senior management is two way), the company can get away with paying you a little less than your average worth and you will continue to stick on. If you don’t like working with your colleagues, there is the double whammy that you will add less to the company and you need to be paid more to stick on. And so if you look at people who are actually successful in their jobs at a senior level, they will all appear to be underpaid relative to their peers.

And finally there is liquidity (can I ever theorise about something without bringing this up?). The more senior you go, the less liquid is the market for your job. The number of potential jobs that you want to do, and which might want you, is very very low. And as I’ve explained in the first chapter of my book, when a market is illiquid, the bid-ask spread can be rather high. This means that even holding the value of your contribution to a company constant, there can be a large variation in what you are actually paid. And that is a gain why, on average, senior employees are underpaid.

So yes, there is an element of status. But there are also considerations of variance, correlation and bid-ask. And selection bias (senior employees who are overpaid relative to the value they add don’t last very long in their jobs). And this is why, on average, you can afford to underpay senior employees.

Ronald Coase, Scott Adams and Intrapersonal Vertical Integration

I have a new HR policy. I call it “intrapersonal vertical integration”. Read on.

Back in the 193os, economist Ronald Coase wrote an article on “the nature of the firm” (the link is to Wikipedia, not to the actual paper). It was a description of why people form companies and partnerships and so on, rather than all being gig workers negotiating each piece of work.

The key concept here was one of transaction costs – if everyone were to be a freelancer, like I was between 2012 and 2020 (both included), then for every little piece of work there would need to be a piece of negotiation.

“Can you build this dashboard for me?”
“Yes. That would be $10000”
“No, I’ll only pay $2000”
“9000”
“3000 final”
“get lost”

During my long period of freelancing, I internalised this, and came up with a “minimum order value” – a reasonable amount which could account for transaction costs like the above (just as I write this, I’m changing videos on Youtube for my wife, and she’s asking me to put 30 second videos. And I’m refusing saying “too much transaction cost. I need my hands for something else (blogging)” ).

This worked out fine for the projects that I actually got, but transaction costs meant that a lot of the smaller deals never worked out. I lost out on potential revenue from those, and my potential clients lost out on work getting done.

So, instead, if I were to be part of a company, like I am now, transaction costs are far lower. Yes, we might negotiate on exact specifications, or deadlines, but price was a single negotiation at the time I joined the firm. And so a lot more work gets done – better for me and better for the company. And this is why companies exist. It might sound obvious, but Coase put it in a nice and elegant theoretical framework.

I’ve written about this several times on my blog – Scott Adams’s theory that there are two ways in which you can be really successful.

1. Become the best at one specific thing.
2. Become very good (top 25%) at two or more things.

This is advice that I have taken seriously, and I’ve followed the second path. Being the best at one specific thing is too hard, and too random as well – “the best” is a sort of a zero sum game. Instead, being very good in a few things is easier to do, and as I’d said in one of my other posts on this, being very good in uncorrelated things is a clear winner.

I will leave this here and come back later on in the post, like how Dasharatha gave some part of the mango to Sumitra (second in line), and then decided to come back to her later on in the distribution.

III

I came up with this random theory the other day on the purpose of product managers. This theory is really random and ill-formed, and I haven’t bothered discussing it with any real product managers.

The need for product managers comes from software engineers’ insistence on specific “system requirement specifications”.

I learnt software engineering in a formal course back in 2002. Back then, the default workflow for software engineering was the so-called “waterfall model”. It was a linear sequential thing where the first part of the process goes in clearly defining system requirement specifications. Then there would be an unambiguous “design document”. And only then would coding begin.

In that same decade (2000s), “agile” programming became a thing. This meant fast iterations and continuous improvements. Software would be built layer by layer. However, software engineers had traditionally worked only with precise specifications, and “ambiguous business rules” would throw them off. And so the role of the product manager was created – who would manage the software product in a way that they would interface with ambiguous business on one side, and precise software engineers on the other.

Their role was to turn ambiguity to certainty, and get work done. They would never be hands on – instead their job would be to give precise instructions to people who would be hands on.

I have never worked as either a software engineer or a product manager, but I don’t think I’d enjoy either job. On the one hand, I don’t like being given precise instructions, and instead prefer ambiguity. On the other, if I were to give precise instructions, I would rather use C++ or Python to give those instructions than English or Kannada. In other words, if I were to be precise in my communication, I would rather talk to a computer than to another human.

It possibly has to do with my work history. I spent a little over two years as a quant at a top tier investment bank. As part of the job, I was asked to write production code. I used to protest, saying writing C++ code wasn’t the best use of my time or effort. “But think about the effort involved in explaining your model to someone else”, the higher ups in the company would tell me. “Wouldn’t it be far easier to just code it yourself?”

Coase reasoned that transaction costs are the reason why we need a firm. We don’t need frequent negotiations and transaction costs, so if people were to get together in the form of a firm, they could coordinate much better and get a lot more work done, with more value accruing to every party involve.

However, I don’t think Coase went far enough. Just putting people in one firm only eliminates one level of transaction costs – of negotiating conditions and prices. Even when you are in the same firm, coordinating with colleagues implies communication, and unless precise, the communication links can end up being the weak links in how much the firm can achieve.

Henry Ford’s genius was to recognise the assembly line (a literal conveyor belt) as a precise form of communication. The workers in his factories were pretty much automatons, doing their precise job, in the knowledge that everyone else was doing their own. The assembly line made communication simpler, and that allowed greater specialisation to unlock value in the firm – to the extent that each worker could get at least five dollars a day and the firm would still be profitable.

It doesn’t work so neatly in what can be classified as “knowledge industries”. Like with the product manager and the software engineer, there is a communication layer which, if it fails, can bring down the entire process.

And there are other transaction costs implied in this communication – let’s say you are building stuff that I need to build on to make the final product. Every time I think you need to build something slightly different, it involves a process of communication and negotiation. It involves the product manager to write a new section in the document. And when working on complex problems, this can increase the complexity multifold.

So we are back to Scott Adams (finally). Building on what I’d said before – you need to be “very good” at two or more things, and it helps if these things are uncorrelated (in terms of being able to add unique value). However, it is EVEN MORE USEFUL if the supposedly uncorrelated skills you have can be stacked, in a form of vertical integration.

In other words, if you are good at several things that are uncorrelated, where the output of one thing can be the input into another, you are a clear winner.

Adams, for example, is good at understanding business, he is funny and he can draw. The combination of the first two means that he can write funny business stories, and that he can also draw means he has created a masterpiece in the form of Dilbert.

Don’t get me wrong – you can have a genius storyteller and a genius artist come together to make great art (Goscinny and Uderzo, for example). However, it takes a lot of luck for a Goscinny to find his Uderzo, or vice versa. I haven’t read much Asterix but what I’m old by friends is that the quality dropped after Uderzo was forced to be his own Goscinny (after the latter died).

At a completely different level – I have possibly uncorrelated skills in understanding business and getting insight out of data. One dovetails into the other and so I THINK I’m doing well in business intelligence. If I were only good at business, and needed to keep asking someone to churn the data on each iteration, my output would be far far slower and poorer.

So I extend this idea into “intrapersonal vertical integration”. If you are good at two or more things, and one can lead into another, you have a truly special set of skills and can be really successful.

Putting it another way – in knowledge jobs, communication can be so expensive that if you can vertically integrate yourself across multiple jobs, you can add significant value even if you are not the best at each of the individual skills.

Finish

In knowledge work, communication is the weakest link, so the fewer levels of communication you have, the better and faster you can do your job. Even if you get the best for every level in your chain, the strength (or lack of it) of communication between them can mean that they produce suboptimal output.

Instead if you can get people who are just good at two or more things in the chain (rather than being the best at any one), you can add significantly better value.

Putting it another way, yes, I’m batting for bits-and-pieces players rather than genuine batsmen or bowlers. However, the difference between what I’m saying and cricket is that in cricket batting and bowling are not vertically integrated. If they were, bits and pieces players would work far far better.

The Downside

I’ve written about this before. While being good at uncorrelated things that dovetail into one another can be a great winning strategy, liquidity can be your enemy. That you are unique means that there aren’t too many like you. And so organisations may not want to bet too much on you – since you will be hard to replace. And decide to take the slack in communication and get specialists for each position instead.

PS:

I have written a book on transaction costs and liquidity. As it happens, today it is on display at the Bangalore Literature Festival.

Cross posted on LinkedIn

People are worried about marriage market liquidity

Every time we have a sort of financial crisis that has something to do with settlement, and collaterals, and weird instruments, people start questioning why more instruments are not traded on exchanges. They cite the example of equities, which world over are exchange traded, centrally settled, and whose markets function rather efficiently.

After the 2008 Financial Crisis, for example, there was a move to take Credit Default Swaps (CDS) to exchanges, rather than letting the market go over the counter (OTC). Every few years, ideas are floated about trading bonds on exchanges (rather than OTC, like they are now), and the blame falls on “greedy bankers who don’t want to let go of control”.

There is an excellent podcast by Bloomberg Odd Lots where Chris White, a former Goldman Sachs banker, talks about how the equity markets went electronic in the 1970s with NASDAQ, and how the “big bang” in the UK markets propelled equities into electronic trading everywhere.

A lot of these ideas have also been discussed in my book on market design.

In any case, I think I have the perfect explanation of why bond trading on exchanges hasn’t really taken off. To understand this, let’s look at another market that I discussed extensively in my book – the market for relationships (that chapter has been extracted in Mint).

The market for relationships is in the news thanks to this Netflix documentary called Indian Matchmaking. I started watching it on a whim on Saturday night, and I got so addicted to it that yesterday I postponed my work to late night so that I could finish the show instead.

Marriage can be thought of as a sale of “50% of the rest of your life“, paid for by 50% of the rest of someone else’s life.

There are two ways you can go about it – either “over the counter” (finding a partner by yourself) or “exchange traded” (said exchange could be anything from newspaper classifieds to Tinder to Shaadi.com). Brokers are frequently used in the OTC market – either parents or friends (who set you up) or priests.

The general rule of markets is that the more bespoke (or “weird” or “unusual”) an instrument is, the better the likelihood of finding a match in the OTC markets than on exchanges. The reason is simple – for an exchange to exist, the commodity being traded needs to be a commodity.

Read any literature on agricultural markets, for example, and they all talk about “assaying” and “grading” the commodities. The basic idea is that all goods being traded on a marketplace are close enough substitutes of each other that they can be interchanged for each other.

Equity shares, by definition, are commodities. Equity and index derivatives are commodities as well, easy enough to define. Commodities are, by definition, commodities. Bond futures are commodities, since they can be standardised on a small number of axes. We’ll come to bonds in a bit.

Coming back to relationship markets, the “exchanges” work best if you have very few idiosyncrasies, and can be defined fairly well in terms of a small number of variables. It also helps you to find a partner quicker in case many others in the market have similar attributes as you, which means that the market for “your type of people” becomes “liquid” (this is a recurring theme in my book).

However, in case you are either not easily describable by commonly used variables, or in case there are few others like you in the market, exchanges are likely to work less well for you. Either of these conditions makes you “illiquid”, and it is not a great idea to list an illiquid asset on an exchange.

When you list an illiquid asset on an exchange, unless you are extremely lucky, it is likely to sit there for a long time without being traded (think about “bespoke exchanges” like eBay here, where commodification is not necessary). The longer the asset sits on an exchange, the greater the likelihood that people who come across the asset on the exchange think that “something is wrong with it”.

So if you’re listing it on an exchange, its value will decay exponentially, and unless you are able to trade soon after you have listed it, you are unlikely to get much value for it.

In that sense, if you are “illiquid” for whatever reason (can’t be easily described, or belong to a type that few others in the market belong to), exchanges are not for you. And if you think about each of the characters in Indian Matchmaking who come to Sima aunty, they are illiquid in one way or another.

Aparna has entered the market at 34, and few other women of her age are in the market. Hence illiquid.
Nadia belongs to a small ethnicity, Indian-Guyanese-American, which makes her illiquid.
Pradhyuman has quirky interests (jewelry and fashion), which his parents are trying to suppress as they pass him off a liquid “rich Maadu boy”. Quirky interests mean he’s not easily describable. Hence illiquid.
Vyasar, by Indian-American standards, doesn’t have a great job. So not too many others like him. Illiquid, even before you take his family situation into account.
Ankita is professionally ambitious. Few of those women in the Indian arranged marriage market. Illiquid.
Rupam is divorced with a child. Might be liquid by conventional American markets, but illiquid in an Indian context. And she is, rather inexplicably, going the Indian way despite being American.
Akshay is possibly the most liquid (characterless except for an overly-dominating mom), and maybe that’s why he’s shown getting engaged.

All of these people will be wasting themselves listing themselves on exchanges. And so they come to a matchmaker. Now, Sima Auntie is both a broker and a clearinghouse (refer to Chapter 3 of my book 😛). She helps find matches for people, but only matches within her own inventory (though she decided Ankita has no matches at all in her own inventory, so connected her with another broker-clearinghouse).

This makes it hard – first of all you have illiquid assets, and you are trying to fulfil them within limited inventory. This is why she is repeatedly showing saying that her candidates need to “compromise” (something that seems to have triggered a lot of viewers). By compromise, she is saying that these people are so illiquid that in case they need to get a deal in her little exchange, they need to be willing to accept an “illiquidity discount” in order to get a trade.

Back to bonds, why is trading them on an exchange so difficult? Because each bond is so idiosyncratic. There is the issuer, the exact date of expiry and the coupon, and occasionally some weird derivatives tacked on. The likelihood that you might find someone quickly enough to take the other side of such a deal is minuscule, so if you were to list your bond on an exchange, its value would drop significantly (by being continuously listed) before you could find a counterparty.

Hence, people trade this uncertain discount to a certain discount, by trading their bonds with market makers (investment banks) who are willing to take the other side of the deal immediately.

Unfortunately, market making is not a viable strategy when it comes to relationship markets. So what do you do if you can either be not defined easily in a few parameters, or if there are few others like you in the arranged marriage market? You basically go Over The Counter. Ditch the market and find someone for yourself, or ask people you know to set you up. Or hire a matrimonial advisor who will tell you what to do.

If this doesn’t convince you on why matchmakers are important, then may be you should read what my other half has to say. If she’s the better half or not, you figure.

Uber in Mumbai

I’m writing this from the Terminal 2 Lounge of Mumbai International Airport. I was in the city for a day of meetings today, and I’m glad I stuck to my policy of booking outgoing flights only from Terminal 2. I just can’t imagine spending an hour and a half (the length of my flight delay) waiting in the bus stand that is Terminal 1.

It was one of those visits where I’d bunched together several different meetings with several agendas (or should it be agendae?), which meant that I took a lot of cabs. All my cab rides here were through Uber. Some pertinent observations.

Whoever decided that the WagonR is a good car to be a taxi? It may be a great own-drive vehicle, but the back seat is significantly inferior to the kind of back seats you’re generally used to in cabs.
Except for the first and last trips of the day, I was forced to take the aforementioned WagonRs. in Bangalore, I instinctively book Uber Premium, and am usually rewarded with sedans (Etios or Swift DZire) driven by drivers with high ratings. In fact, in Bangalore, Uber sedans are so liquid that you sometimes get them even when you book an UberGo.
Not the case in Mumbai. Liquidity of sedans is far far inferior to WagonRs. Once today, the sedan waiting time was 15 minutes (and only one was nearby) while hatchbacks were plentiful around, and one materialised in two minutes. The other occasions I checked and simply booked WagonRs.
On the one occasion when I waited for a long time for a sedan to appear, and then cancelled and booked a WagonR, I was thankful I did so since the route involved some impossibly narrow roads (this was after Uber had failed to recognise a one way road)
In general, all the drivers I encountered today (I did five trips in total) were rather professional. Arrived and drove quietly. Air-conditioners always switched on. No calls either to me or anyone else. Occasional polite conversation. This was very different from my experience with Ubers in Mumbai on my earlier visits this year, when I encountered paan-stained cars, nonstop chattering on mobile phones and a driver who gave me a virus.
Both in Bangalore (on the way to the airport this morning) and in Mumbai (on the sea link), the taxi drivers hadn’t installed FASTAG. The former resulted in significant delays, and my reaching the gate just in time to board my flight.

Active aggression and passive aggression

For the record I’m most often actively aggressive. I believe passive aggression is a waste of energy since not only do you end up fighting but you also end up trying to second guess the other party, which leads to suboptimal outcomes. This post is a justification of that.

Let’s say you and I are trying to decide the price of something I want to sell you. There are two ways we can go about it. One way is for us to have a negotiation. I can name my asking price. You call your bid. And if the two meet, well and good. Most often they won’t meet. So one of us will have to budge. We start budging slowly, in steps, until a time when the bid and ask are close together. And then we have a deal.

In most situations (except exceptional cases where there are very few buyers and sellers – read the first chapter of my book. This is within the Kindle sample), this will lead to an efficient outcome. Even if the final price were a little too close to the bid or to the ask, both parties know that under the circumstances they couldn’t get better. And the transaction takes place and the parties move on.

The other situation is where one party publicly states that they are unwilling to negotiate and will do the deal if and only if the counterparty comes up with a good enough offer. If the offer is not good enough, there is no deal. This is similar to the ultimatum game popular in behavioural economics. In this case you are also required to guess (and you have exactly one guess) what the counterparty’s hurdle rate is.

When there is a liquid market, there is no issue with this kind of a game – you simply have your own hurdle rate and you bid that. And irrespective of whether it gets accepted or not, you get the optimal outcome – since the market is liquid, it is likely that your quote will get accepted somewhere.

In a highly illiquid market, with only one buyer and one seller, the ultimatum setup can lead to highly suboptimal outcomes. I mean if you’re desperate to do the sale, you might bring your price “all the way to zero” to ensure you do the deal, but the thing is that irrespective of whether you get a deal or not, you are bound to feel disappointed.

If your ask got accepted, you start wondering if you could’ve charged more. If you didn’t get your deal, you start wondering if reducing a price “just a little” would have gotten the deal done. It is endless headache, something that’s not there when there is an active negotiation process.

Now to build the analogy – instead of a sale, think of the situation when you have a disagreement with someone and need to resolve it. You can either confront them about it and solve it “using negotiation” or you can be passive aggressive, letting them know you’re “not happy”. Notice that in this case the disagreement is with one specific party, the market is as illiquid as it can get – no negotiations with any third party will have any impact (ignore snitching here).

When you express your disagreement and you talk/fight it out, you know that irrespective of the outcome (whether it was resolved or not), you have done what you could. Either it has been resolved, which has happened with you telling what exactly your position is, or you have given it all to explain yourself and things remain bad (in this case, whatever happened there would have been “no deal” or an “unhappy deal”).

And that is why active aggression is always better than passive aggression. By expressing your disagreement, even if that means you’re being aggressive, you are stating the exact extent of the problem and the solution will be to your satisfaction. When you’re passive aggressive, nobody is the winner.

PS: I realise that by writing this post I’m violating this own advice, since this post itself can be seen as a form of passive aggression! Mea culpa.

Pertinent observations on liquidity in startup markets

“Liquidity” was one of those words Wall Street people threw around when they wanted the conversation to end, and for brains to go dead, and for all questioning to cease

– Michael Lewis in Flash Boys

The quote that begins this blog post is also the quote that begins my book, which was released exactly a year ago. Despite its utility in everyday markets and economics, the concept of liquidity has not been explored too much outside of financial markets. In fact, one reason I wrote my book was that it appeared as if there was a gap in the market for material using the concept of liquidity to analyse everyday markets.

From this perspective, I was pleasantly surprised to come across a bunch of blog posts written by investors and tech analysts and startup fellows about the concept of “liquidity”. Most of these posts I came across by way of this excellent blog post by Andrew Chen of Andreessen Horowitz. It is always good to see others analysing topics in the same way as you are, so I thought I’ll share some insights from these posts here – some quotes, some pertinent observations. This is best done in bullet points. If you want to know more, I urge you to click through and read the blog posts in full. They’re all excellent.

You wonder why some startups make a big deal of how many cities they are in. This is because they usually function as within-city marketplaces, and so they need to be launched one city at a time. Uber famously started operations in San Francisco and remained there for a while.
“The best way to measure liquidity in the marketplace is to track the % of items or services that get sold/booked, and within what period of time. The higher the % and shorter period of time, the more sellers are making money and buyers are becoming loyal customers” – from here
“Where absolute pricing management makes most sense (i.e., where the marketplace operator sets prices) is where there isn’t a proper barometer for what the supply side should be charging and when the software can leverage systems should to optimize for liquidity” – from this excellent post
“In a zero sum game there, it’s most likely the marketplace with the most demand wins”. This was in the context of delivery marketplaces, and why Uber was likely to win that game (though it’s not clear if they’ve “won” it yet)
Trust is critical in building marketplaces. Both sides of the market need to trust the intermediary, and this can make marketplaces fragile. I had a recent incident where I appreciated the value of AirBnB landlord insurance (a lamp at a property I stayed at broke just after my stay, and the landlord wanted compensation). This post talks about how this insurance was critical to AirBnB’s growth
The same post talks about why even early stage businesses often make acquisitions – usually earlier stage businesses. “Marketplaces are normally winner-take-all markets. If we had lost ground to European competitors in 2012, we may have never gotten it back”
Ratings are a critical measure to build trust in a marketplace. And two-way ratings can help establish trust on both sides of the market
During the book launch function last year, there was a question on how marketplaces should build liquidity. I had given an example of the Practo/OpenTable model where you first sell a standalone service to one side of the market and then develop a marketplace. Another method (something I helped put in place for one of my current clients) is for the marketplace itself to become a “proprietary supplier”. The third, as this blog post describes, is about building markets where buyers are also sellers and the other way round (classic financial markets, for example).

For more on liquidity, and how it affects just about every market that you participate in on a daily basis, read my book!

We’ll miss sushi

One food item that my daughter and I will really miss when we move back to India is sushi. It is not that it is not available in Bangalore – restaurants such as Matsuri and Harima make excellent quality sushi, just that the transaction cost of procuring it will be far higher.

I grew up vegetarian, and didn’t eat meat until I was twenty eight. The decision to try meat was ad hoc – at a restaurant in Monastiraki square in Athens, the meat looked fantastic and the vegetables looked sad. And I decided that if I were losing my religion, I would lose it all the way and started my meat-eating career by eating beef souvlaki.

It wasn’t until a year later that I tasted fish, though – from childhood the smell of fish had put me off. As it happened, I first ate fish at a restaurant in Karwar, en route to Goa. Then, a consulting project in Mumbai happened, with a fish-loving client who took me to the best fish restaurants in that city (sometime during this time, I discovered I’m allergic to prawns).

It would take another year or two before I would have raw fish, though, in the form of sushi and sashimi. The first time was a trip to Matsuri, where my wife was treating me. I quickly grew fond of it, and would have a Japanese meal (at either Harima or Matsuri) at least once in six months (these are easily the best and most authentic Japanese restaurants in Bangalore. Edo is good but overpriced).

My love for sushi really took off during the three months I spent in Barcelona in 2016. That city has loads of sushi shops (it helped we were living in a dense district), mostly run by Korean immigrants. it is not too expensive either, which meant I would have it once a week at least (I might have eaten more often, but the wife was pregnant then, and hence off raw fish).

London doesn’t have the same density of sushi shops as Barcelona, but there are some chains that make pretty good sushi (Wasabi and Itsu, though I prefer the latter). Like other things London, it is not cheap, but we end up eating it reasonably often (it helps that the daughter loves sushi as well, though she only eats salmon nigiri – which also happens to be my favourite kind of sushi).

While craving sushi and planning a sushi run for dinner earlier this evening (finally we ended up eating at a Korean restaurant), it hit me that I won’t be able to have sushi so regularly in Bangalore. I started wondering what it would take for the likes of Freshmenu to be offering sushi on their menu. And I remembered a chapter in my book on specialty food.

The problem with low demand products is that the volatility of demand is high relative to the average demand. This means that for a retailer to stock items with low demand, either the margin needs to be high, or the inventory levels will be so low that customers might be disappointed rather often – neither of which is sustainable.

Making matters worse is the fact that fresh fish is an integral part of sushi, and it has an incredibly short shelf life. So unless demand can be aggregated to a high level (which Harima and Matsuri do, by being located in the middle of town and especially catering to the Japanese population in the city. In fact, I’m told the Chancery (where Matsuri is located) is the hotel of choice for Japanese visitors to Bangalore), it is not feasible to run a sushi restaurant in Bangalore.

Oh, and in the same chapter in the book, I discuss why people like to live with other people like themselves – others demanding the same thing you demand is the only way you can ensure that there is supply to meet your demand.

Information Technology and Large Cities

In my book Between the buyer and the seller, officially released exactly a year ago, I have a chapter on cities. In that I explain why industry clusters form, and certain cities or regions become hubs for certain types of industries.

In that, I spoke about the software industry in California’s Silicon Valley, and in Bangalore. I also mentioned how the Industrial Revolution wasn’t evenly distributed around England, and how it was clustered around textile hubs such as Birmingham and Manchester. I also used that chapter to talk about the problem with government-mandated special economic zones (this podcast with Amit Varma can help you understand the last point).

Back when Silicon Valley was still silicon valley (basically a semiconductor and hardware hub), it wasn’t as concentrated a hub as it is today. It was still fairly common for semiconductor companies to base themselves away from the valley. With the “new silicon valley” and the tech startup scene, though, there is no escaping the valley. It is almost an unwritten rule in US Tech startup circles that if you want to be successful with a tech startup, you better be in the valley.

And this is for good reason, as I explain in the book – Silicon Valley is where the ecosystem for successfully running a tech startup already exists, including access to skilled employees, subcontractors and investors, not to speak of a captive market. This, however, has meant that Silicon Valley is now overcrowded in many respects, with rents being sky high (reflected in high salaries), freeways jammed and other infrastructure under stress.

In fact, it is not just the silicon valley that has got crushed under the weight of being a tech hub – other “secondary hubs” such as Seattle (which also have a few tech majors, and where startups put off by the cost of the valley set up) are seeing their quality of life go down. The traffic and infrastructure woes in Bangalore are also rather similar.

So why is it that information technology has led to hubs that are much larger than historical hubs (based on other industries)? The simple answer lies in investment, or the lack of it.

Setting up an information technology company is “cheap” in terms of the investment in capital expenditure. No land needs to be bought, no plants need to be constructed and no machinery needs to be bought. All one needs is an office space (for which rent is paid monthly), and a set of employees (who again get paid a monthly salary). Even IT infrastructure (such as computing power and storage and communication) can be leased, and paid for periodically.

This implies that there is nothing that stops a startup company from locating itself in one of the existing hubs. This way, the company can avail all the benefits of being in the hub (supplier and customer infrastructure, employee pool, quality of life for employees and investors) without a high upfront cost.

Contrast this to “hard” industries that require manufacturing, where the benefits of being located in hubs is similar but the costs are far higher. As a hub develops, land gets expensive, which puts off further investors from locating themselves in the hub. This puts a natural limit on the size of the hubs, and if you think about it, large cities from earlier era were all “multi-purpose cities”, serving as hubs for several unrelated industries.

With information technology, though, the only impediment to the growth of a hub is the decreasing quality of life, information regarding which gets transmitted in indirect means such as higher rentals and commute times, and poor health. This indirect transmission of costs to investors results in friction, which means information technology hubs will grow larger before they stop growing. And as they go through this process, the quality of life of the hub’s residents suffers!

Bond Market Liquidity and Selection Bias

I’ve long been a fan of Matt Levine’s excellent Money Stuff newsletter. I’ve mentioned this newsletter here several times in the past, and on one such occasion, I got a link back.

One of my favourite sections in Levine’s newsletter is called “people are worried about bond market liquidity”. One reason I got interested in it was that I was writing a book on Liquidity (speaking of which, there’s a formal launch function in Bangalore on the 15th). More importantly, it was rather entertainingly written, and informative as well.

I appreciated the section so much that I ended up calling one of the sections of one of the chapters of my book “people are worried about bond market liquidity”.

In any case, the Levine has outdone himself several times over in his latest instalment of worries about bond market liquidity. This one is from Friday’s newsletter. I strongly encourage you to read fully the section on people being worried about bond market liquidity.

To summarise, the basic idea is that while people are generally worried about bond market liquidity, a lot of studies about such liquidity by academics and regulators have concluded that bond market liquidity is just fine. This is based on the finding that the bid-ask spread (gap between prices at which a dealer is willing to buy or sell a security) still remains tight, and so liquidity is just fine.

But the problem is that, as Levine beautifully describes the idea, there is a strong case of selection bias. While the bid-ask spread has indeed narrowed, what this data point misses out is that many trades that could have otherwise happened are not happening, and so the data comes from a very biased sample.

Levine does a much better job of describing this than me, but there are two ways in which a banker can facilitate bond trading – by either taking possession of the bonds (in other words, being a “market maker” (PS: I have a chapter on this in my book) ), or by simply helping find a counterparty to the trade, thus acting like a broker (I have a chapter on brokers as well in my book).

A new paper by economists at the Federal Reserve Board confirms that the general finding that bond market liquidity is okay is affected by selection bias. The authors find that spreads are tighter (and sometimes negative) when bankers are playing the role of brokers than when they are playing the role of market makers.

In the very first chapter of my book (dealing with football transfer markets), I had mentioned that the bid-ask spread of a market is a good indicator of market liquidity. That the higher the bid-ask spread, the less liquid a market.

Later on in the book, I’d also mentioned that the money that an intermediary can make is again a function of how inherent the market is.

This story about bond market liquidity puts both these assertions into question. Bond markets see tight bid-ask spreads and bankers make little or no money (as the paper linked to above says, spreads are frequently negative). Based on my book, both of these should indicate that the market is quite liquid.

However, it turns out that both the bid-ask spread and fees made by intermediaries are biased estimates, since they don’t take into account the trades that were not done.

With bankers cutting down on market making activity (see Levine’s post or the paper for more details), there is many a time when a customer will not be able to trade at all since the bankers are unable to find them a counterparty (in the pre Volcker Rule days, bankers would’ve simply stepped in themselves and taken the other side of the trade). In such cases, the effective bid-ask spread is infinity, since the market has disappeared.

Technically this needs to be included while calculating the overall bid-ask spread. How this can actually be achieve is yet another question!

Thaler and Uber and surge pricing

I’m writing about Uber after a really long time on this blog. Basically I’d gotten tired of writing about the company and its ideas, and once I wrote a chapter about dynamic pricing in cabs in my book, there was simply nothing more to say.

Now, the Nobel Prize to Richard Thaler and his comments sometime back about Uber’s surge pricing has given me reason to revisit this topic, though I’ll keep it short.

Basically Thaler makes the point that when businesses are greedy and seen to be gouging customers in times of high demand, they might lose future demand from the same customers. In his 2015 book Misbehaving (which I borrowed from the local library a few months ago but never got down to reading), he talks specifically about Uber, and about how price gouging isn’t a great idea.

This has been reported across both mainstream and social media over the last couple of days as if Thaler is completely against the concept of surge pricing itself. For example, in this piece about Thaler, Pramit Bhattacharya of Mint introduces the concept of surge pricing and says:

Thaler was an early critic of this model. In his 2015 book Misbehaving: The Making of Behavioral Economics, Thaler argues that temporary spikes in demand, “from blizzards to rock star deaths, are an especially bad time for any business to appear greedy”. He argues that to build long-term relationships with customers, firms must be seen as “fair” and not just efficient, and that this often involves giving up on short-term profits even if customers may be willing to pay more at that point to avail themselves of its product or service.

At first sight, it is puzzling that an economist would be against the principle of dynamic pricing, since it helps the marketplace allocate resources more effectively and more importantly, use price as an information mechanism to massively improve liquidity in the system. But Thaler’s views on the topic are more nuanced. To continue to quote from Pramit’s piece:

“I love Uber as a service,” writes Thaler. “But if I were their consultant, or a shareholder, I would suggest that they simply cap surges to something like a multiple of three times the usual fare. You might wonder where the number three came from. That is my vague impression of the range of prices that one normally sees for products such as hotel rooms and plane tickets that have prices dependent on supply and demand. Furthermore, these services sell out at the most popular times, meaning that the owners are intentionally setting the prices too low during the peak season.

Thaler is NOT suggesting that Uber not use dynamic pricing – the information and liquidity effects of that are too massive to compensate for occasionally pissing off passengers. What he suggests, however, is that the surge be CAPPED, perhaps at a multiple of three.

There is a point after which dynamic pricing ceases to serve any value in terms of information and liquidity, and its sole purpose is to ensure efficient allocation of resources at that particular instant in time. At such levels, though, the cost of pissing off customers is also rather high. And Thaler suggests that 3 is the multiple at which the benefits of allocation start getting weighed down by the costs of pissing off passengers.

This is exactly what I’ve been proposing in terms of cab regulation for a couple of years now, though I don’t think I’ve put it down in writing anywhere. That rather than banning these services from not using dynamic pricing at all, a second best solution for a regulator who wants to prevent “price gouging” is to have a fare cap, and to set the cap high enough that there is enough room for the marketplaces to manoeuvre and use price as a mechanism to exchange information and boost liquidity.

Also, the price cap should be set in a way that marketplaces have flexibility in how they will arrive at the final price as long as it is within the cap – regulators might say that the total fare may not exceed a certain multiple of the distance and time or whatever, but they should not dictate how the marketplace precisely arrives at the price – since calculation of transaction cost in taxi pricing has historically been a hard problem and one of the main ways in which marketplaces such as Uber bring efficiency is in solving this problem in an innovative manner using technology.

For more on this topic, listen to my podcast with Amit Varma about how taxi marketplaces such as Uber use surge pricing to improve liquidity.

For even more on the topic, read my book Between the buyer and the seller which has a long chapter dedicated to the topic,