## 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:

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.

## 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!

## Revenue management and transaction costs

So I just sent off a letter to India. To be precise, it is a document I had to sign and send to my accountant there – who sends regular “letters” any more?

The process at the post office (which, in my suburb, is located inside a large bookstore) was simple. In the first screen of the touch screen kiosk, there was an option for “worldwide < 20 grams”. A conveniently placed scale told me my letter weighed 18 grams, and one touch and one touch of my debit card later, I had my stamp. Within a minute, my letter was in the letterbox.

The story of how we pay the same amount for sending mail over large areas (“worldwide” in my case today) is interesting. Earlier, mail rates were based on distance, but as new roads kept being built in the 19th century America, and distances kept changing, figuring out how much to charge for a letter became “expensive”. A bright fellow figured out that the cost (in terms of time) of figuring out how much to charge for mail was of the same order of magnitude as the cost of the mail itself. And so the flat rate scheme for mail, that is prevalent worldwide today, was born.

Putting it in technical terms, transaction costs trumped price discrimination in this case. Price discrimination is the art (yes, it’s an art) of charging different amounts to different people based on their differential willingness to pay. Uber surge pricing is one example (I have a chapter in my book on this). Airline fares are another common example.

Until the late 18th century (well after mail prices had gone “flat”), price discrimination was rather common everywhere, a concept I have devoted a chapter to in the book. In fact, the initial motivation for fixed price retail was religious – Quakers, who owned many departmental stores in the US North-East, thought “all men are created equal before God” and so it was incorrect to charge different amounts to different people.

Soon other benefits of fixed prices became apparent (faster billing; less training for staff; in fact it was fixed prices that permitted the now prevalent supermarket format), and it took off. The concept is the same as stamps – the transaction cost of figuring out how much to charge whom is higher than the additional revenue you can make with such price differentiation (not counting possible loss of reputation, and fairness issues). Price discrimination at the shop is now confined to high value high margin businesses such as cars.

And it works in other high gross margin businesses such as airlines, hotels and telecom. These are all businesses with high fixed costs and low marginal costs for the suppliers. Low marginal costs has meant that price discrimination ha been termed as “revenue management” in the airline industry.

During the launch function of my book last year, I got asked if Uber’s practice of personalising fares for passengers is fair (I had given a long lecture on how Uber’s surge pricing is a necessary component of keeping average prices low and boosting liquidity in the taxi market). I had answered that a marketplace needs to ensure that its pricing is perceived as being “fair”, else they might lose customers to competitors. But what if all players in a market practice extreme price discrimination?

Thinking about it, transaction costs will take care of price discrimination before businesses and marketplaces start thinking of fairness. Beyond a point (the point varies by industry), the marginal revenues from price discrimination will fall below the transaction cost of executing this discrimination. And that poses a natural limit to how much price discrimination a business can practice.

## Vacation Shopping

This is yet another of those questions whose answer seems rather obvious to everyone, and to me in full hindsight, but which has taken me a long time to appreciate

For a long time I never understood why people shop during vacations, when both time and luggage space are precious commodities. With global trade, I reasoned that most clothes should be available at reasonably comparable prices worldwide, and barring some special needs (such as a certain kind of shoes, for example), there was no real need to shop on vacations.

The last day of our trip to Munich in June convinced me otherwise. That was the only day on the trip that the wife was free from work, and we could go out together before our afternoon flight. The only place we ended up going out to turned out to be a clothing store, where the wife freaked out shopping.

It didn’t make sense to me – she was shopping at a chain store which I was pretty certain that I had seen in London as well. So why did she shop while travelling? And she shopped far more than she does in a normal shopping trip in London.

In hindsight, the answer is rather simple – diversity. While the same stores might exist in various countries or cities, each is adapted to local tastes and prevailing fashions. And while everyone watches the same “runways” in Milan and Los Angeles, there is always a subtle difference in prevailing styles in different places. And clothes in the stores in the respective places are tailored (no pun intended) to these styles.

And it can happen that the local prevailing styles are not something that you particularly agree with. For example, for years together in Bangalore I struggled to find plain “non-faded” jeans – most people there seemed to demand faced or torn jeans, and stores responded to serve that demand (interestingly, jeans shopping in my last Bangalore trip was brilliantly simple, so I guess things have changed).

Similarly, the wife finds it hard to appreciate most dresses in the shops in London (and I appreciate why she doesn’t appreciate them – most of the dresses are a bit weird to put it mildly), and as a result hasn’t been able to shop as much in recent times. She had taken to claim that “they don’t seem to be making normal clothes any more”.

But the styles in London aren’t correlated with the styles in Munich (or elsewhere), with the result that in that one chain store in Munich, she found more nice dresses than she had in some 20 shopping trips over a year in London.

Fashion suffers from the “tyranny of the majority“. It makes eminent sense for retailers to only stock those styles and models that have a reasonably high demand (or be compensated for stocking low-demand items with a high enough margin – I have a chapter on this in my book). So if your styles don’t match with those of people around you, you are out of luck.  But when you travel, you have the chance to align yourself to another majority. And if that alignment happens, you’re in luck!

PS: On a separate note, I’m quite disappointed with the quality of clothes in London. Across brands, they seem to wear much faster than those bought in continental Europe or even in India.

## The utility of utility functions

That is the title of a webinar I delivered this morning on behalf of Kristal.AI, a company that I’ve been working with for a while now. I spoke about utility functions, and how they can be used in portfolio optimisation.

This is related to the work that I’ve been doing for Kristal, and lies at the boundaries between quantitative finance and behavioural finance, and in fact I spoke about utility functions (combined with Monte Carlo methods) as being a great method to unify quantitative and behavioural finance.

Interactive Brokers (who organised the webinar) recorded the thing, and you can find the recording here.

I think the webinar went well, though I’m not very sure since there was no feedback. This was by design – the webinar was a speaker-only broadcast, and audience weren’t allowed to participate except in terms of questions that were directly sent to me.

In the first place, webinars are hard to do since it feels like talking to an empty room – there is no feedback, not even nods or smiles, and you don’t know if people are listening. In most “normal” webinars, the audience can interject by raising their hands, and you can try make it interactive. The format used here didn’t permit such interaction which made it seem like I was talking into thin air.

Also, the Mac app of the webinar tool used didn’t seem particularly well optimised. I couldn’t share a particular screen from my laptop (like I couldn’t say “share only my PDF, nothing else” which is normal in most online chat tools), and there are times where I’ve inadvertently exposed my desktop to the full audience (you can see it on the recording).

Anyways, I think I’ve spoken about something remotely interesting, so give it a listen. My “main speech” only takes around 20-25 minutes. And if you want to know more about utility functions and behavioural economics, i recommend this piece by John Cochrane to you.

## Why AI will always be biased

Out on Marginal Revolution, Alex Tabarrok has an excellent post on why “sexism and racism will never diminish“, even when people on the whole become less sexist and racist. The basic idea is that there is always a frontier – even when we all become less sexist or racist, there will be people who will  be more sexist or racist than the others and they will get called out as extremists.

To quote a paper that Tabarrok has quoted (I would’ve used a double block-quote for this if WordPress allowed it):

…When blue dots became rare, purple dots began to look blue; when threatening faces became rare, neutral faces began to appear threatening; and when unethical research proposals became rare, ambiguous research proposals began to seem unethical. This happened even when the change in the prevalence of instances was abrupt, even when participants were explicitly told that the prevalence of instances would change, and even when participants were instructed and paid to ignore these changes.

Elsewhere, Kaiser Fung has a nice post on some of his learnings from a recent conference on Artificial Intelligence that he attended. The entire post is good, and I’ll probably comment on it in detail in my next newsletter, but there is one part that reminded me of Tabarrok’s post – on bias in AI.

Quoting Fung (no, this is not a two-level quote. it’s from his blog post):

Another moment of the day is when one speaker turned to the conference organizer and said “It’s become obvious that we need to have a bias seminar. Have a single day focused on talking about bias in AI.” That was his reaction to yet another question from the audience about “how to eliminate bias from AI”.

As a statistician, I was curious to hear of the earnest belief that bias can be eliminated from AI. Food for thought: let’s say an algorithm is found to use race as a predictor and therefore it is racially biased. On discovering this bias, you remove the race data from the equation. But if you look at the differential impact on racial groups, it will still exhibit bias. That’s because most useful variables – like income, education, occupation, religion, what you do, who you know – are correlated with race.

This is exactly like what Tabarrok mentioned about humans being extremist in whatever way. You take out the most obvious biases, and the next level of biases will stand out. And so on ad infinatum.

## Relative pricing revisited

Yesterday I bought a pair of jeans. Normally it wouldn’t be a spectacular event (though one of my first blogposts was about a pair of jeans), but regular squatting has meant that I’ve been tearing through jeans well-at-a-faster-rate, and also that it’s been hard to find jeans that fit me well.

Basically, I have a well-above-average thigh and a well-below-average arse for my waist size, and that makes it hard to find readymade pants that fit well. As a consequence I’ve hardly bought trousers in the last 2-3 years, though I’ve been losing many pairs to the tear in this period of time.

And so when I found a pair of jeans that fit me comfortably yesterday I wasn’t too concerned about paying a record price for it (about 1.8 times the maximum I’d ever paid for a pair in the past). In fact, I’d seen another pair that fit well a few minutes earlier (and it was a much fancier brand), but it was well above budget (3 times as expensive as my historically costliest ever pair), and so I moved on (more importantly, it came with a button fly, and I’d find that rather inconvenient).

Jeans having been bought, we went off to a restaurant at the mall for lunch, at the end of which the wife pointed out that the money we paid for the lunch was more than the difference in prices between the two pairs of jeans. And that if only we would avoid eating out when it’s avoidable, we could spend on getting ourselves much more fancier clothes without feeling guilty.

I’ve written about relative prices in the past, especially about the Big Mac Index, and how it doesn’t make sense because of differential liquidity. After moving to London, I’m yet to come to terms with the fact that relative prices of goods here is vastly different from that back home; and that I haven’t adjusted my lifestyle accordingly leading to inefficient spending and a possible strain on lifestyle.

Food, for example, is much more expensive here than in India (we’ll use official exchange rates for the purpose of this post). The average coffee costs £2.5 (INR 225), which is about 10 times the price of an average coffee in Bangalore (I’m talking about a good quick cup of coffee here, so ignoring the chains which are basically table rentals). The average weekday takeaway lunch costs £6 (INR 540), which is again 10X what it costs in Bangalore.

Semi-fancy meals (a leisurely meal at a sit down restaurant with a drink, perhaps) are relatively less costly here, costing about £25-30 per head compared to INR 1200-1500 in Bangalore, a ratio of about 2X. A beer at a pub costs about the same, though cocktails here are much more expensive.

The alternative to eating out is, of course, eating in, and most “regular” ingredients such as vegetables and rice cost more here, though cheeses (which are relatively less liquid in India) are actually cheaper here. Milk costs about the same.

Controlling for quality, clothes cost about the same (or might even be less costly here when you go for slightly more fancy stuff). Electronics again cost about the same (they come through the same global supply chain). Contact lenses are more expensive here (though the ones I buy in India are manufactured in the UK!).

In my book, I have a chapter called “if you want to live like a Roman, live in Rome”. It’s about how different cities have different relative liquidity of goods. Similarly, different cities and countries have different relative prices, and long-term residents of these places evolve their spending to optimise for their given set of relative prices.

And when you move cities or countries, if you don’t change your lifestyle accordingly you might end up spending suboptimally, and get less welfare from life.

Once again this points out problems with international price indices being constructed based on a particular commodity, or set of commodities. For not only are different commodities differentially liquid (as I pointed out in my Mint piece linked above) in different places, but also the “standard consumption basket” also varies from city to city!

And if a Delhi-ite consumes lots of apples, and a Bangalorean consumes lots of oranges, you can’t make an apples-to-apples comparison in cost of living in these cities!

## Profit and politics

Earlier today I came across this article about data scientists on LinkedIn that I agreed with so much that I started wondering if it was simply a case of confirmation bias.

A few sentences (possibly taken out of context) from there that I agree with:

• Many large companies have fallen into the trap that you need a PhD to do data science, you don’t.
• There are some smart people who know a lot about a very narrow field, but data science is a very broad discipline. When these PhD’s are put in charge, they quickly find they are out of their league.
• Often companies put a strong technical person in charge when they really need a strong business person in charge.
•  I always found the academic world more political than the corporate world and when your drive is profits and customer satisfaction, that academic mindset is more of a liability than an asset.

Back to the topic, which is the last of these sentences. This is something I’ve intended to write for 5-6 years now, since the time I started off as an independent management consultant.

During the early days I took on assignments from both for-profit and not-for-profit organisations, and soon it was very clear that I enjoyed working with for-profit organisations a lot more. It wasn’t about money – I was fairly careful in my negotiations to never underprice myself. It was more to do with processes, and interactions.

The thing in for-profit companies is that objectives are clear. While not everyone in the company has an incentive to increase the bottom-line, it is not hard to understand what they want based on what they do.

For example, in most cases a sales manager optimises for maximum sales. Financial controllers want to keep a check on costs. And so on. So as part of a consulting assignment, it’s rather easy to know who wants what, and how you should pitch your solution to different people in order to get buy-in.

With a not-for-profit it’s not that clear. While each person may have their own metrics and objectives, because the company is not for profit, these objectives and metrics need not be everything they’re optimising for.

Moreover, in the not for profit world, the lack of money or profit as an objective means you cannot differentiate yourself with efficiency or quantity. Take the example of an organisation which, for whatever reason, gets to advice a ministry on a particular subject, and does so without a fee or only for a nominal fee.

How can a competitor who possibly has a better solution to the same problem “displace” the original organisation? In the business world, this can be done by showing superior metrics and efficiency and offering to do the job at a lower cost and stuff like that. In the not-for-profit setup, you can’t differentiate on things like cost or efficiency, so the only thing you can do is to somehow provide your services in parallel and hope that the client gets it.

And then there is access. If you’re a not-for-profit consultant who has a juicy project, it is in your interest to become a gatekeeper and prevent other potential consultants from getting the same kind of access you have – for you never know if someone else who might get access through you might end up elbowing you out.

## Freelancing and transaction costs

In the six years of running my own consulting business, I’d forgotten about an essential part that you need to endure as part of a job – piecemeal work. It is fairly often when you’re working for someone else that you get work that is so tiny or insignificant that you can hardly take ownership of it. The best strategy for dealing with it is to quietly get it over with and hope you won’t get such stuff again.

However, sometimes you can get caught in a rut of continuously getting this kind of work, and start wondering what you actually signed up for. And this is one thing I hadn’t expected to encounter when I got back to full time working earlier this year.

Thinking about why I never had to encounter such stuff during my consulting life, I realised there’s a fairly simple explanation – transaction costs.

Being a consultant is high transaction cost business. Every time you need to take on a new piece of work, you need to go through the charade of negotiating specifics with the client, pricing and drawing up a contract. All put together, the effort is not insignificant.

Moreover, in the line of work that I used to do, there was this massive overhead cost of understanding, cleaning and getting comfortable with the client’s data  – the effort involved in that meant that after a particular point in time I stopped taking work that wasn’t chunky enough. For a while I started refusing such work, but then got smarter and started pricing myself out of such work (though some clients were generous enough to meet that price to get their little tasks done – effectively I’d passed on the transaction costs to them).

The downside of this, of course, was that there was a fair amount of money I could have made taking up small works which I didn’t since the transaction cost was too high – this can be thought of as potential lost revenues. The upside was that whatever work I did was of high quality and (hopefully) made a big impact on the client’s business.

In the nature of the firm, Ronald Coase wrote that the purpose of the corporation was that transaction cost of dealing with co-workers can be eliminated. But then, I realise that sometimes this transaction cost can also be a good thing!

Oh, and obligatory plug here – my book Between the buyer and the seller deals with transaction costs, among other things. It’s available for sale (both in print and digital) on Amazon.

## 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,