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,

Regulating HFT in India

The Securities and Exchange Board of India (SEBI) has set a cat among the HFT (High Frequency Trading) pigeons by proposing seven measures to curb the impact of HFT and improve “real liquidity” in the stock markets.

The big problem with HFT is that algorithms tend to cancel lots of orders – there might be a signal to place an order, and even before the market has digested that order, the order might get cancelled. This results in an illusion of liquidity, while the constant placing and removal of liquidity fucks with the minds of the other algorithms and market participants.

There has been a fair amount of research worldwide, and SEBI seems to have drawn from all of them to propose as many as seven measures – a minimum resting time between HFT orders, matching orders through frequent batch auctions rather than through the order book, introducing random delays (IEX style) for orders, randomising the order queue periodically, capping order-to-trade ratio, creating separate queues for orders from co-located servers (used by HFT algorithms) and review provision of the tick-by-tick data feed.

While the proposal seems sound and well researched (in fact, too well researched, picking up just about any proposal to regulate stock markets), the problem is that there are so many proposals, which are all pairwise mutually incompatible.

As the inimitable Matt Levine commented,

If you run batch auctions and introduce random delays and reshuffle the queue constantly, you are basically replacing your matching engine with a randomizer. You might as well just hold a lottery for who gets which stocks, instead of a market.

My opinion this is that SEBI shouldn’t mandate how each exchange should match its orders. Instead, SEBI should simply enable individual exchanges to regulate the markets in a way they see fit. So in my opinion, it is possible that all the above proposals go through (though I’m personally uncomfortable with some of them such as queue randomisation), but rather than mandating exchanges pick all of them, SEBI simply allows them to use zero or more of them.

This way, different stock exchanges in India can pick and choose their favoured form of regulation, and the market (and market participants) can decide which form of regulation they prefer. So you might have the Bombay Stock Exchange (BSE) going with order randomisation, while the National Stock Exchange (NSE) might use batch auctions. And individual participants might migrate to the platform of their choice.

The problem with this, of course, is that there are only two stock exchanges of note in India, and it is unclear if the depth in the Indian equities market will permit too many more. This might lead to limited competition between bad methods (the worst case scenario), leading to horrible market inefficiencies and the scaremongers’ pet threat of trading shifting to exchanges in Singapore or Dubai actually coming true!

The other problem with different exchanges having different mechanisms is that large institutions and banks might find it difficult to build systems that can trade accurately on all exchanges, and arbitrage opportunities across exchanges might exist for longer than they do now, leading to market inefficiency.

Then again, it’s interesting to see how a “let exchanges do what they want” approach might work. In the United States, there is a new exchange called the Intercontinental Exchange (IEX) that places “speed bumps” over incoming orders, thus reducing the advantage of HFTs. IEX started only recently, after major objections from incumbents who alleged they were making markets less fair.

With IEX having started, however, other exchanges are responding in their own ways to make the markets “fairer” to investors. NASDAQ, which had vehemently opposed IEX’s application, has now filed a proposal to reward orders by investors who wait for at least once second before cancelling them.

Surely, large institutions won’t like it if this proposal goes through, but this gives you a flavour of what competition can do! We’ll have to wait and see what SEBI does now.

Continuous and barrier regulation

One of the most important pieces of financial regulation in the US and Europe following the 2008 financial crisis is the designation of certain large institutions as “systemically important”, or in other words “too big to fail”. Institutions thus designated have greater regulatory and capital requirements, thus rendering them at a disadvantage compared to smaller competitors.

This is by design – one of the intentions of the “SiFi” (systemically important financial regulations) is to provide incentives to companies to become smaller so that the systemic risk is reduced. American insurer Metlife, for example, decided to hive off certain divisions so that it’s not a SiFi any more.

AIG, another major American insurer (which had to be bailed out during the 2008 financial crisis), is under pressure from its activist investors led by Carl Icahn to similarly break up so that it can avoid being a SiFi. The FT reports that there were celebrations in Italy when insurer Generali managed to get itself off the global SiFi list. Based on all this, the SiFi regulation seems to be working in spirit.

The problem, however, is with the method in which companies are designated SiFis, or rather, with that SiFi is a binary definition. A company is either a SiFi or it isn’t –  there is no continuum. This can lead to perverse incentives for companies to escape the SiFi tag, which might undermine the regulation.

Let’s say that the minimum market capitalisation for a company to be defined a SiFi is $10 billion (pulling this number out of thin air, and assuming that market cap is the only consideration for an entity to be classified as a SiFi). Does this mean that a company that is worth $10 Bn is “systemically important” but one that is worth $9.9 Bn is not? This might lead to regulatory arbitrage that might lead to a revision of the benchmark, but it still remains a binary thing.

A better method for regulation would be for the definition of SiFi to be continuous, or fuzzy, so that as the company’s size increases, its “SiFiness” also increases proportionally, and the amount of additional regulations it has to face goes up “continuously” rather than being hit by a “barrier”. This way, the chances of regulatory arbitrage remain small, and the regulation will indeed serve its purpose.

SiFi is just one example – there are several other cases which are much better served by regulating companies (or individuals) as a continuum and not classifying them into discrete buckets. When you regulate companies as parts of discrete buckets, there is always the temptation to change just enough to move from one bucket to the other, and that might result in gaming. Continuous regulation, on the other hand, leaves no room for such marginal gaming – marginal changes aer only giong to have a marginal impact.

Perhaps for something like SiFi, where the requirements of being a SiFi are binary (compliance, etc.) there may not be a choice but to keep the definition discrete (if there are 10 different compliance measures, they can kick in at 10 different points, to simulate a continuous definition).

However, when the classification results in monetary benefits or costs (let’s say something like SiFis paying additional regulatory costs) it can be managed via non-linear funding. Let’s say that you pay 10% fees (for whatever) in category A and 12% in category B (which you get to once you cross a benchmark). A simply way to regulate would be to have the fees as a superlinear function of your market cap (if that’s what the benchmark is based on).


Uber’s anchoring problem

The Karnataka transport department has come out with a proposal to regulate cab aggregators such as Uber and Ola. The proposal is hare-brained on most  counts, such as limiting drivers’ working hours, limiting the number of aggregators a driver can attach himself to and having a “digital meter”. The most bizarre regulation, however, states that the regulator will decide the fares and that dynamic pricing will not be permitted.

While these regulations have been proposed “in the interest of the customer” it is unlikely to fly as it will not bring much joy to the customers – apart from increasing the number of auto rickshaws and taxis in the city through the back door. I’m confident the aggregators will find a way to flout these regulations until a time they become more sensible.

Dynamic pricing is an integral aspect of the value that cab aggregators such as Uber or Ola add. By adjusting prices in a dynamic fashion, these aggregators push information to drivers and passengers regarding demand and supply. Passengers can use the surge price, for example, to know what the demand-supply pattern is (I’ve used Uber surge as a proxy to determine what is a fair price to pay for an auto rickshaw, for example).

Drivers get information on the surge pricing pattern, and are encouraged to move to areas of high demand, which will help clear markets more efficiently. Thus, surge pricing is not only a method to match demand and supply, but is also an important measure of information to a cab aggregator’s operations. Doing away with dynamic pricing will thus stem this flow of information, thus reducing the value that these aggregators can add. Hopefully the transport department will see greater sense and permit dynamic pricing (Disclosure: One of my lines of business is in helping companies implement dynamic pricing, so I have a vested interest here. I haven’t advised any cab aggregators though).

That said, Uber has a massive anchoring problem, because dynamic pricing works only in one way. Anchoring is a concept from behavioural economics where people’s expectations of something are defined by something similar they have seen (there is an excellent NED Talk on this topic (by Prithwiraj Mukherjee of IIMB) which I hope to upload in its entirety soon). There are certain associations that are wired in our heads thanks to past information, and these associations bias our view of the world.

A paper by economists at NorthEastern University on Uber’s surge pricing showed that demand for rides is highly elastic to price (a small increase in price leads to a large drop in demand), while the supply of rides (on behalf of drivers) is less elastic, which makes determination of the surge price hard. Based on anecdotal information (friends, family and self), elasticity of demand for Uber in India is likely to be much higher.

Uber’s anchoring problem stems from the fact that the “base prices” (prices when there is no surge) is anchored in people’s minds. Uber’s big break in India happened in late 2014 when they increased their discounts to a level where travelling by Uber became comparable in terms of cost to travelling by auto rickshaw (the then prevalent anchor for local for-hire public transport).

Over the last year, Uber’s base price (which is cheaper than an auto rickshaw fare for rides of a certain length) have become the new anchor in the minds of people, especially Uber regulars. Thus, whenever there is a demand-supply mismatch and there is a surge, comparison to the anchor price means that demand is likely to drop even if the new price is by itself fairly competitive (compared to other options at that point in time).

The way Uber has implemented its dynamic pricing is that it has set the “base price” at one end of the distribution, and moves price in only one direction (upwards). While there are several good reasons for doing this, the problem is that the resultant anchoring can lead to much higher elasticity than desired. Also, Uber’s pricing model (more on this in a book on Liquidity that I’m writing) relies upon a certain minimum proportion of rides taking place at a surge (the “base price” is to ensure minimum utilisation during off-peak hours), and anchoring-driven elasticity can’t do this model too much good.

A possible solution to this would be to keep the base fare marginally higher, and adjust prices both ways – this will mean that during off-peak hours a discount might be offered to maintain liquidity. The problem with this might be that the new higher base fare might be anchored in people’s minds, leading to diminished demand in off-peak hours (when a discount is offered). Another problem might be that drivers might be highly elastic to drop in fares killing the discounted market. Still, it is an idea worth exploring – in my opinion there’s a sweet spot in terms of the maximum possible discount (maybe as low as 10%, but I think it’s strictly greater than zero)  where the elasticities of drivers and passengers are balanced out, maximising overall revenues for the firm.

We are in for interesting days, as long as stupid regulation doesn’t get in the way, that is.

Finally some sensible Uber regulation

Ever since Uber launched, regulators worldwide haven’t had a clue as to how to regulate it – it has been such a big disruptor in the taxicab market. Some countries and cities have taken to banning it outright (the list is too long to post links here). Others (such as some states in India) have tried to get Uber to register itself as a “taxicab company”.

The problem with all these regulations is that the Uber model (being replicated by firms such as Ola and TaxiForSure in India) is a fundamental gamechanger. As I have written in this earlier post, the on-demand model propagated by Uber implies that a number of the inefficiencies in the taxicab market don’t exist any more. In this context, trying to regulate it by moving it back to the earlier (extremely inefficient) model is extremely regressive. The right way to regulate is to create a level playing field for taxicab aggregators (which includes Uber) and move the market to a regime where the new technology-enabled efficiencies are made good use of.

And that is precisely what Los Angeles has done. In a rather progressive move (which ought to be copied by other states and cities and countries), the city has decreed that all city-based cab operators need to offer app-based booking services. The interesting bit in the regulation (see link above) is that drivers who fail to install the e-hail app are actually going to be fined.

What this will lead to is that the local taxi market is itself going to become more efficient which should definitely increase both profitability for the local cab industry and also availability of local cabs to the people of LA. What this will also do is to give people of LA a choice between using Uber and the traditional taxi app, which will lead to an improvement in Uber’s service levels. As things stand now I don’t see any downside from this LA regulation.

I hope the model succeeds in LA and other cities see the brilliance of the model and accept the efficiencies brought into the market thanks to this model and adopt similar regulation. I see this kind of regulation coming into the Bangalore market though the backdoor though. Ola already helps match auto rickshaws to customers and now TaxiForSure is also getting into that market. Will this mean that autos won’t have to line up for hours together in front of Lalbagh gate for passengers arriving in the city by bus?

Oh, and LA is not the first city to implement regulations requiring taxis to be “hailable” via an app. When I visited Singapore in November 2013, I found that cabs in the city worked the same way. Locals had an app which they would use to call taxis. The problem there though was that the app was only available to locals (your android/iOS had to be registered in Singapore for you to be able to even install the app), which made it a nightmare for us tourists to move around.

Oh, and while on the topic, a good revenue source for companies such as Ola or TaxiForSure would be to provide the technology backbone to cities that are seeking to use app-based hailing services for their cabs.


Inefficiencies in the auto rickshaw market and Uber

Taxi marketplaces such as Uber and Ola address inefficiencies and failures in the auto rickshaw / taxi market

Weary after a long cold night journey you get off the overnight bus from Chennai at Lalbagh’s Double Road gate, and look around for auto rickshaws. There are some ten of them around. The drivers are equally weary, having woken up early and left their homes to stand in the cold, hoping to find passengers alighting from buses. They want to get compensated for this, and quote you a fare that includes such compensation. All of them quote similar fares. You grudgingly bargain and agree, and conclude that Bangalore’s auto drivers are bastards.

Alternate scenario: as the bus reached Madivala, ten minutes away from Lalbagh Double Road gate at that time of the morning, you pull out your app and ask for a taxi to pick you up from Double Road gate in ten minutes’ time. The driver has been up, but resting at home. He leaves home now, just in time to be there at Double Road gate by the time you get off there. Off you get into the car and go.

You have to get to work and try catching an auto rickshaw. The guy asks for extra money for he has to take you through traffic-laden roads, which are a tax on his time, which the regulated fare doesn’t compensate him for. You bargain, get in, and conclude that auto drivers are bastards.

In an alternate scenario, you use an app-based taxi which calculates the fare as a linear combination of distance travelled and time taken, which means that the driver gets compensated for getting stuck in traffic without having to bargain for it. And without you having to think that the driver is a bastard.

In the evening you are trying to get an auto rickshaw from MG Road, and the guy asks for a premium. This premium is not reflective of costs, but the fact that demand for auto rickshaws in that area at that time is high, and that there will be customers willing to pay that premium. You conclude that the auto rickshaw driver is a bastard. Uber’s surge pricing (which can be steep at times) doesn’t evoke the same reaction from you. Uber has centralised knowledge of demand and supply so they can clear prices better, while the auto driver, lacking that knowledge, quotes a price that reflects his lack of market knowledge. And not having a good idea of what to charge, he might try to charge above market price.

What I’m trying to say here is that the local taxi/auto rickshaw market is inefficient, and ridden with failures. There is lack of information flow between demand and supply, which leads to inferior price negotiation, and the transaction cost of time and effort wasted on negotiation as opposed to using that time to travel! And when a market fails, the classic economic response is regulation, but in the case of taxi markets regulation is so poor (regulated prices do not reflect costs) that it enhances the market failure. The (badly) regulated prices anchor into people’s minds unrealistic expectations, and when auto drivers nudge them towards more realistic market prices, passengers assume that they (drivers) are bastards.

It is in this context that players like Uber and Ola (I’m not a fan of Ola’s pricing model, though) step in and try to resolve the market failure by improving flow of demand-supply information and setting “clearing prices” that compensate the driver in line with his costs. If you look closely, these companies are actually rescuing the local taxi market from its inherent inefficiencies and failures and bad regulation!

It is important, however, that no one market place ends up becoming a monopoly. As long as we have two or three different marketplaces, both customers and drivers have the choice of moving between one and the other, and this will ensure that these market places face market pressures from the two sides of the market, and if they “regulate” in an unfair manner, their participants will move to a competing marketplace, resulting in loss of business for the marketplace.

But then, considering the inherent network effects of the marketplace model, I don’t know how we can ensure that competition exists!


Other airlines to bail out Spice Jet?

In a rather bizarre move, the Directorate General of Civil Aviation (DGCA) has directed airlines to not charge “exorbitant fares” for passengers stood up upon cancellation of Spice Jet flights. This is a rather bizarre idea and effectively amounts to asking other airlines to partially bail out Spice Jet.

Essentially when an airline is in trouble, passengers are loathe to book tickets on it, for they know that the chances of their flight getting cancelled is high. A cancelled flight usually means either cancelling the trip itself or rebooking on another airline (sometimes airlines have arrangements with each other for taking on passengers on cancelled flights, but currently no other airline in India will give credit to Spice Jet). Either ways, it is a costly affair for the passengers.

By directing airlines to not charge “exorbitant fares”, and assuming that such a directive will be followed (very likely that this directive is meaningless for this is the busy season and other airlines are likely to be booked out), the total cost of booking a ticket on Spice Jet actually comes down, for the charge a customer will have to incur for re booking on another airline for a cancelled Spice Jet flight is likely to be reduced. And thus passengers will not abandon Spice Jet at the rate at which they normally would. And since other airlines are taking a hit on the spot fares they could potentially charge (in the absence of this directive) they are effectively subsidising and “bailing out” Spice Jet!

The other problem is that in the absence of market mechanisms (which the price cap effectively curb), how will other airlines allocate their remaining capacity among all the passengers who have been stood up by Spice Jet? Some arbitrariness is likely to ensue and passengers are likely to be left more disappointed!

The government had started off by handling the Spice Jet case rather well, as Devika Kher has argued here. However, of late, the wheels of the DGCA seem to have come off in his aspect, and there seems to be a concerted attempt to let Spice Jet stay afloat against the wishes of the market. The Airports Authority of India and oil companies have been asked to extend credit for fifteen days.

It seems Devika spoke too soon!