Category: platforms

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!

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.

Algorithmic curation

When I got my first smartphone (a Samsung Galaxy Note 2) in 2013, one of the first apps I installed on it was Flipboard. I’d seen the app while checking out some phones at either the Apple or Samsung retail outlets close to my home, and it seemed like a rather interesting idea.

For a long time, Flipboard was my go-to app to check the day’s news, as it conveniently categorised news into “tech”, “business” and “sport” and learnt about my preferences and fed me stuff I wanted. And then after some update, it suddenly stopped working – somehow it started serving too much stuff I didn’t want to read about, and when I tuned (by “following” and “unfollowing” topics) my feed, it progressively got worse.

I stopped using it some 2 years back, but out of curiosity started using it again recently. While it did throw up some nice articles, there is too much unwanted stuff in the app. More precisely, there’s a lot of “clickbaity” stuff (“10 things about Narendra Modi you would never want to know” and the like) in my feed, meaning I have to wade through a lot of such articles to find the occasional good ones.

(Aside: I dedicate about half a chapter to this phenomenon in my book. The technical term is “congestion”. I talk about it in the context of markets in relationships and real estate)

Flipboard is not the only one. I use this app called Pocket to bookmark long articles and read later. A couple of years back, Pocket started giving “recommendations” based on what I’d read and liked. Initially it was good, and mostly curated from what my “friends” on Pocket recommended. Now, increasingly I’m getting clickbaity stuff again.

I stopped using Facebook a long time before they recently redesigned their newsfeed (to give more weight to friends’ stuff than third party news), but I suspect that one of the reasons they made the change was the same – the feed was getting overwhelmed with clickbaity stuff, which people liked but didn’t really read.

Basically, there seems to be a widespread problem in a lot of automatically curated news feeds. To put it another way, the clickbaity websites seem to have done too well in terms of gaming whatever algorithms the likes of Facebook, Flipboard and Pocket use to build their automated recommendations.

And more worryingly, with all these curators starting to do badly around the same time (ok this is my empirical observation. Given few data points I might be wrong), it suggests that all automated curation algorithms use a very similar algorithm! And that can’t be a good thing.

Waiting for Kumaraswamy’s Tiger

Finally, last week Softbank announced that it has closed its $9.3 Billion investment in Uber. Since the deal was in the making for a long time, the deal itself is not news. What is news is what Softbank’s Rajeev Misra told Uber – to “focus on its core markets in US, Europe and Latin America”.

One way of reading this message is to see it as “keep off from competing with our other investments in Didi, Grab and Ola“. If Uber takes Misra’s words seriously (they better do, since Softbank is now probably Uber’s second biggest shareholder, after Travis Kalanick), it is likely that they’ll go less aggressive in Asian markets, including India. This is not going to be good for customers (both drivers and passengers) of taxi marketplaces in India.

Until 2014, the Indian market had three vibrant cab marketplaces – Uber, Ola and TaxiForSure. Then in early 2015, TaxiForSure was unable to raise further funding and sold itself to Ola, turning the market into a duopoly. Back then I’d written about why it was a bad deal for Indian customers, and hoped that another company would take TaxiForSure’s place.

Three years later, that has not come to be and the Indian market continues to be a duopoly. When I visited Bangalore in December, I noticed service levels in both Uber and Ola being significantly inferior to what I’d seen a year earlier when I was living there. Now, if Uber were to cede ground to Ola in India (as Softbank implicitly wishes), things will get further worse.

Back in 2015, when TaxiForSure was shutting down, I had assumed that another corporate entity, perhaps Meru (which runs call taxis) would take its place. And for a really long time now there have been rumours of Reliance entering into the cab marketplace business. Neither has come to be.

So this time my hopes have moved from corporates to politicians. The word on the street in Bangalore when I visited in December was that former Karnataka Chief Minister HD Kumaraswamy had partnered with cab driver associations to start a new cab marketplace, supposedly called “Tygr” (sic). The point of this marketplace, I was informed during my book launch event in Bangalore in December, was that it was going to be a “driver oriented app”.

This marketplace, too, has been coming for a long time now, but with the Softbank deal, it can’t come sooner. Yes, it is likely that it will not be a great app (if it is “too driver oriented”, it won’t get passengers and the drivers will also subsequently disappear), but at least it will bring in a sense of competition into the market and keep Ola honest. And hopefully there will also similar competition in other cities in India, though it is unlikely that it will be Kumaraswamy who will disrupt those markets.

A lot is made of the fact that investors like Warren Buffett own stocks in all major airlines in the US. Now, Softbank seems to be occupying that space in the cab marketplace market. It can’t be good either for drivers or passengers.

Patanjali going online

Mint has a piece on Baba Ramdev-led FMCG company Patanjali going online to further its sales.

Some may have seen the irony in Patanjali Ayurved Ltd tying up with foreign-owned/funded e-commerce companies, even as it swears to end the reign of foreign-owned consumer brands in the market.

Patanjali is only being pragmatic in doing what’s good for its own business, of being available where the consumers are. Its decision is one more pointer to the growing importance of e-commerce as a distribution channel for packaged consumer goods.

I have an entire chapter in my book dedicated to this – about the internet has revolutionised distribution and retail. In that I talk about Dollar Shave Club, pickle sellers from Sringeri and mobile manufacturers such as Xiaomi who have pioneered the “flash sale” concept. In another part of the book, I’ve written about how Amazon has revolutionised bookselling, first by selling online and then by pioneering e-books.

Whenever a new consumer goods company wants to set up shop, one of the hardest tasks is in establishing a distribution network. Conventional distribution networks are typically several layers deep, and in order to get to the customer, each layer of the distribution network needs to be adequately compensated.

Apart from the monetary cost, there is also the transaction cost of convincing each layer that it is worthwhile carrying the new seller’s goods. The other factor to be considered is that distributors at various levels are in a sense loyal to incumbent sellers (since they are responsible for a large portion of the current business), making it harder for new seller to break through.

The advantage with online retailers is that they compress the supply chain, with one entity replacing a whole network of distributors. This may not necessarily be cost-effective from the money perspective, since the online retailers will seek to capture all the value that all the layers of the current distribution chain are capturing. However, in terms of transaction costs it is significantly easier since there is only one layer to get past, and online retailers seldom have loyalty or exclusive relationships.

In fact, the size and bargaining power of online retailers (vis-a-vis offline distributors) means that if there is an exclusive relationship, it is the retailer who holds the exclusive rights and not the seller.

In Patanjali’s case, they have already established a wide offline network with exclusive stores and partnerships, but my sense is that they seem to be hitting the limits of distribution. Thanks to Baba Ramdev’s popularity as a yoga guru, Patanjali enjoys strong brand recall, and it appears as if their distribution is unable to keep pace with their brand.

From this perspective, going online (through Amazon/Flipkart) is a rational strategy for them since with one deal they get significantly higher distribution power. Moreover, being a new brand, they don’t have legacy distributors who might get pissed off if they go online (this is a problem that the Unilevers of the world face).

So it is indeed a pragmatic decision by Patanjali to take the online route. And after all, in the end, sheer commerce can trump nationalist tendencies and xenophobia.

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!

Ratings revisited

Sometimes I get a bit narcissistic, and check how my book is doing. I log on to the seller portal to see how many copies have been sold. I go to the Amazon page and see what are the other books that people who have bought my book are buying (on the US store it’s Ray Dalio’s Principles, as of now. On the UK and India stores, Sidin’s Bombay Fever is the beer to my book’s diapers).

And then I check if there are new reviews of my book. When friends write them, they notify me, so it’s easy to track. What I discover when I visit my Amazon page are the reviews written by people I don’t know. And so far, most of them have been good.

So today was one of those narcissistic days, and I was initially a bit disappointed to see a new four-star review. I started wondering what this person found wrong with my book. And then I read through the review and found it to be wholly positive.

A quick conversation with the wife followed, and she pointed out that this reviewer perhaps reserves five stars for the exceptional. And then my mind went back to this topic that I’d blogged about way back in 2015 – about rating systems.

The “4.8” score that Amazon gives as an average of all the ratings on my book so far is a rather crude measure – since one reviewer’s 4* rating might differ significantly from another reviewer’s.

For example, my “default rating” for a book might be 5/5, with 4/5 reserved for books I don’t like and 3/5 for atrocious books. On the other hand, you might use the “full scale” and use 3/5 as your average rating, giving 4 for books you really like and very rarely giving a 5.

By simply taking an arithmetic average of ratings, it is possible to overstate the quality of a product that has for whatever reason been rated mostly by people with high default ratings (such a correlation is plausible). Similarly a low average rating for a product might mask the fact that it was rated by people who inherently give low ratings.

As I argue in the penultimate chapter of my book (or maybe the chapter before that – it’s been a while since I finished it), one way that platforms foster transactions is by increasing information flow between the buyer and the seller (this is one thing I’ve gotten good at – plugging my book’s name in random sentences), and one way to do this is by sharing reviews and ratings.

From this perspective, for a platform’s judgment on a product or seller (usually it’s the seller, but for products such as AirBnb, information about buyers also matters) to be credible, it is important that they be aggregated in the right manner.

One way to do this is to use some kind of a Z-score (relative to other ratings that the rater has given) and then come up with a normalised rating. But then this needs to be readjusted for the quality of the other items that this rater has rated. So you can think of some kind of a Singular Value Decomposition you can perform on ratings to find out the “true value” of a product (ok this is an achievement – using a linear algebra reference given how badly I suck in the topic).

I mean – it need not be THAT complicated, but the basic point is that it is important that platforms aggregate ratings in the right manner in order to convey accurate information about counterparties.

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,